id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
3,466 | import importlib
import math
from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch.cuda.amp import autocast
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
from transformers.generation.logits_process import LogitsProcessorList
from transformers.generation.utils import GenerateOutput
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from torch import nn
from .configuration_qwen import QWenConfig
from .qwen_generation_utils import (
HistoryType,
make_context,
decode_tokens,
get_stop_words_ids,
StopWordsLogitsProcessor,
)
from .visual import VisionTransformer
apply_rotary_emb_func = None
def _rotate_half(x):
from einops import rearrange
x = rearrange(x, "... (j d) -> ... j d", j=2)
x1, x2 = x.unbind(dim=-2)
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(t, freqs):
cos, sin = freqs
if apply_rotary_emb_func is not None and t.is_cuda:
t_ = t.float()
cos = cos.squeeze(0).squeeze(1)[:, : cos.shape[-1] // 2]
sin = sin.squeeze(0).squeeze(1)[:, : sin.shape[-1] // 2]
output = apply_rotary_emb_func(t_, cos, sin).type_as(t)
return output
else:
rot_dim = freqs[0].shape[-1]
cos, sin = freqs
t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
t_ = t_.float()
t_pass_ = t_pass_.float()
t_ = (t_ * cos) + (_rotate_half(t_) * sin)
return torch.cat((t_, t_pass_), dim=-1).type_as(t) | null |
3,467 | import base64
import logging
import os
import requests
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional
import tiktoken
import numpy as np
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from transformers import PreTrainedTokenizer, AddedToken
from transformers.utils import try_to_load_from_cache
import matplotlib.colors as mcolors
from matplotlib.font_manager import FontProperties
import colorsys
import logging
import math
import numpy as np
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg
from PIL import Image
import random
def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
with open(tiktoken_bpe_file, "rb") as f:
contents = f.read()
return {
base64.b64decode(token): int(rank)
for token, rank in (line.split() for line in contents.splitlines() if line)
} | null |
3,468 | import base64
import logging
import os
import requests
import unicodedata
from typing import Collection, Dict, List, Set, Tuple, Union, Any, Callable, Optional
import tiktoken
import numpy as np
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
from transformers import PreTrainedTokenizer, AddedToken
from transformers.utils import try_to_load_from_cache
import matplotlib.colors as mcolors
from matplotlib.font_manager import FontProperties
def _list_find(
input_list: List[Any],
candidates: Tuple[Any],
start: int = 0,
):
for i in range(start, len(input_list)):
if input_list[i] in candidates:
return i
return -1
import colorsys
import logging
import math
import numpy as np
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg
from PIL import Image
import random
def _replace_closed_tag(
input_tokens: List[Any],
start_tags: Union[Any, Tuple[Any]],
end_tags: Union[Any, Tuple[Any]],
inclusive_replace_func: Callable,
exclusive_replace_func: Callable = lambda x: x,
):
if isinstance(start_tags, (str, int)):
start_tags = (start_tags,)
if isinstance(end_tags, (str, int)):
end_tags = (end_tags,)
assert len(start_tags) == len(end_tags)
output_tokens = []
end = 0
while True:
start = _list_find(input_tokens, start_tags, end)
if start == -1:
break
output_tokens.extend(exclusive_replace_func(input_tokens[end : start]))
tag_idx = start_tags.index(input_tokens[start])
end = _list_find(input_tokens, (end_tags[tag_idx],), start)
if end == -1:
raise ValueError("Unclosed image token")
output_tokens.extend(inclusive_replace_func(input_tokens[start : end + 1]))
end += 1
output_tokens.extend(exclusive_replace_func(input_tokens[end : ]))
return output_tokens | null |
3,472 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
def reconstruct_matrix(windows):
temp =[]
for col in windows:
temp.append(torch.cat((col),dim=3))
all_img = torch.cat(temp,dim=2)
return all_img | null |
3,473 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
def sliding_window(matrix, window_size, stride):
b,c,height, width = matrix.shape
window_rows = (height - window_size[0]) // stride + 1
window_cols = (width - window_size[1]) // stride + 1
windows = []
for i in range(window_rows):
windows_col = []
for j in range(window_cols):
window = matrix[:,:, i*stride:i*stride+window_size[0], j*stride:j*stride+window_size[1]]
windows_col.append(window)
windows.append(windows_col)
return windows | null |
3,474 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
def get_abs_pos(abs_pos, tgt_size):
# abs_pos: L, C
# tgt_size: M
# return: M, C
src_size = int(math.sqrt(abs_pos.size(0)))
tgt_size = int(math.sqrt(tgt_size))
dtype = abs_pos.dtype
if src_size != tgt_size:
return F.interpolate(
abs_pos.float().reshape(1, src_size, src_size, -1).permute(0, 3, 1, 2),
size=(tgt_size, tgt_size),
mode="bicubic",
align_corners=False,
).permute(0, 2, 3, 1).flatten(0, 2).to(dtype=dtype)
else:
return abs_pos | null |
3,475 | from collections import OrderedDict
import math
import requests
from io import BytesIO
from functools import partial
from PIL import Image
from typing import Callable, Optional, Sequence, Tuple, List
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import trunc_normal_
from torchvision import transforms
from torchvision.transforms import InterpolationMode
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
The provided code snippet includes necessary dependencies for implementing the `get_2d_sincos_pos_embed` function. Write a Python function `def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False)` to solve the following problem:
grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
Here is the function:
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed | grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) |
3,476 | import json
from argparse import ArgumentParser
def open_json(path):
with open(path,"r") as f:
data=json.load(f)
return data | null |
3,477 | import json
from argparse import ArgumentParser
def save_json(json_list,save_path):
with open(save_path, 'w') as file:
json.dump(json_list, file, indent=4) | null |
3,478 | import json
from argparse import ArgumentParser
def caculate_IOU(box1,box2):
Ax1=box1[0]
Ay1=box1[1]
Ax2=box1[2]
Ay2=box1[3]
Bx1=box2[0]
By1=box2[1]
Bx2=box2[2]
By2=box2[3]
Ix1 = max(Ax1, Bx1)
Iy1 = max(Ay1, By1)
Ix2 = min(Ax2, Bx2)
Iy2 = min(Ay2, By2)
IntersectionArea = max(0, Ix2 - Ix1 + 1) * max(0, Iy2 - Iy1 + 1)
BoxAArea = (Ax2 - Ax1 + 1) * (Ay2 - Ay1 + 1)
BoxBArea = (Bx2 - Bx1 + 1) * (By2 - By1 + 1)
UnionArea = BoxAArea + BoxBArea - IntersectionArea
IOU = IntersectionArea / UnionArea
return IOU | null |
3,479 | import json
from argparse import ArgumentParser
def _get_args():
parser = ArgumentParser()
parser.add_argument("--blip2_caption", type=str, default="./outputs/blip2_cap.json")
parser.add_argument("--ori_caption", type=str, default=None)
parser.add_argument("--grit", type=str, default="./outputs/grit_score.json")
parser.add_argument("--ppocr", type=str, default="./outputs/ppocr.json")
parser.add_argument("--sam_blip2", type=str, default="./outputs/sam_blip2_score.json")
parser.add_argument("--output", type=str, default="./outputs/ann_all.json")
args = parser.parse_args()
return args | null |
3,480 | from typing import Any
import torch
from PIL import Image
from argparse import ArgumentParser
from lavis.models import load_model_and_preprocess
import os
import json
from tqdm import tqdm
import re
def save_json(json_list,save_path):
with open(save_path, 'w') as file:
json.dump(json_list, file, indent=4) | null |
3,481 | from typing import Any
import torch
from PIL import Image
from argparse import ArgumentParser
from lavis.models import load_model_and_preprocess
import os
import json
from tqdm import tqdm
import re
def _get_args():
parser = ArgumentParser()
parser.add_argument("--image_folder", type=str, default="./images")
parser.add_argument("--ann_path", type=str, default="./outputs/sam_blip2.json")
parser.add_argument("--output_path", type=str, default="./outputs/sam_blip2_score.json")
parser.add_argument("--device", type=str, default="cuda:0")
args = parser.parse_args()
return args | null |
3,482 | import cv2
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
import json
import os
from typing import Any, Dict, List
from tqdm import tqdm
from PIL import Image
def write_masks_to_folder(masks: List[Dict[str, Any]], path: str) -> None:
header = "id,area,bbox_x0,bbox_y0,bbox_w,bbox_h,point_input_x,point_input_y,predicted_iou,stability_score,crop_box_x0,crop_box_y0,crop_box_w,crop_box_h" # noqa
metadata = [header]
for i, mask_data in enumerate(masks):
mask = mask_data["segmentation"]
filename = f"{i}.png"
cv2.imwrite(os.path.join(path, filename), mask * 255)
mask_metadata = [
str(i),
str(mask_data["area"]),
*[str(x) for x in mask_data["bbox"]],
*[str(x) for x in mask_data["point_coords"][0]],
str(mask_data["predicted_iou"]),
str(mask_data["stability_score"]),
*[str(x) for x in mask_data["crop_box"]],
]
row = ",".join(mask_metadata)
metadata.append(row)
metadata_path = os.path.join(path, "metadata.csv")
with open(metadata_path, "w") as f:
f.write("\n".join(metadata))
return | null |
3,483 | import cv2
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
import json
import os
from typing import Any, Dict, List
from tqdm import tqdm
from PIL import Image
def get_amg_kwargs(args):
amg_kwargs = {
"points_per_side": args.points_per_side,
"points_per_batch": args.points_per_batch,
"pred_iou_thresh": args.pred_iou_thresh,
"stability_score_thresh": args.stability_score_thresh,
"stability_score_offset": args.stability_score_offset,
"box_nms_thresh": args.box_nms_thresh,
"crop_n_layers": args.crop_n_layers,
"crop_nms_thresh": args.crop_nms_thresh,
"crop_overlap_ratio": args.crop_overlap_ratio,
"crop_n_points_downscale_factor": args.crop_n_points_downscale_factor,
"min_mask_region_area": args.min_mask_region_area,
}
amg_kwargs = {k: v for k, v in amg_kwargs.items() if v is not None}
return amg_kwargs | null |
3,484 | import cv2
from segment_anything import SamAutomaticMaskGenerator, sam_model_registry
import argparse
import json
import os
from typing import Any, Dict, List
from tqdm import tqdm
from PIL import Image
def get_image_files(folder_path):
image_files = []
for root, dirs, files in os.walk(folder_path):
for file in files:
if file.endswith('.jpg') or file.endswith('.png'):
image_files.append(os.path.join(root, file))
return image_files | null |
3,485 | from detectron2.data import transforms as T
from .transforms.custom_augmentation_impl import EfficientDetResizeCrop
class EfficientDetResizeCrop(Augmentation):
"""
Scale the shorter edge to the given size, with a limit of `max_size` on the longer edge.
If `max_size` is reached, then downscale so that the longer edge does not exceed max_size.
"""
def __init__(
self, size, scale, interp=Image.BILINEAR
):
"""
"""
super().__init__()
self.target_size = (size, size)
self.scale = scale
self.interp = interp
def get_transform(self, img):
# Select a random scale factor.
scale_factor = np.random.uniform(*self.scale)
scaled_target_height = scale_factor * self.target_size[0]
scaled_target_width = scale_factor * self.target_size[1]
# Recompute the accurate scale_factor using rounded scaled image size.
width, height = img.shape[1], img.shape[0]
img_scale_y = scaled_target_height / height
img_scale_x = scaled_target_width / width
img_scale = min(img_scale_y, img_scale_x)
# Select non-zero random offset (x, y) if scaled image is larger than target size
scaled_h = int(height * img_scale)
scaled_w = int(width * img_scale)
offset_y = scaled_h - self.target_size[0]
offset_x = scaled_w - self.target_size[1]
offset_y = int(max(0.0, float(offset_y)) * np.random.uniform(0, 1))
offset_x = int(max(0.0, float(offset_x)) * np.random.uniform(0, 1))
return EfficientDetResizeCropTransform(
scaled_h, scaled_w, offset_y, offset_x, img_scale, self.target_size, self.interp)
The provided code snippet includes necessary dependencies for implementing the `build_custom_augmentation` function. Write a Python function `def build_custom_augmentation(cfg, is_train, scale=None, size=None, min_size=None, max_size=None)` to solve the following problem:
Create a list of default :class:`Augmentation` from config. Now it includes resizing and flipping. Returns: list[Augmentation]
Here is the function:
def build_custom_augmentation(cfg, is_train, scale=None, size=None, \
min_size=None, max_size=None):
"""
Create a list of default :class:`Augmentation` from config.
Now it includes resizing and flipping.
Returns:
list[Augmentation]
"""
if cfg.INPUT.CUSTOM_AUG == 'ResizeShortestEdge':
if is_train:
min_size = cfg.INPUT.MIN_SIZE_TRAIN if min_size is None else min_size
max_size = cfg.INPUT.MAX_SIZE_TRAIN if max_size is None else max_size
sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
else:
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
sample_style = "choice"
augmentation = [T.ResizeShortestEdge(min_size, max_size, sample_style)]
elif cfg.INPUT.CUSTOM_AUG == 'EfficientDetResizeCrop':
if is_train:
scale = cfg.INPUT.SCALE_RANGE if scale is None else scale
size = cfg.INPUT.TRAIN_SIZE if size is None else size
else:
scale = (1, 1)
size = cfg.INPUT.TEST_SIZE
augmentation = [EfficientDetResizeCrop(size, scale)]
else:
assert 0, cfg.INPUT.CUSTOM_AUG
if is_train:
augmentation.append(T.RandomFlip())
return augmentation | Create a list of default :class:`Augmentation` from config. Now it includes resizing and flipping. Returns: list[Augmentation] |
3,486 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
def load_GRiTcoco_json(json_file, image_root, dataset_name=None):
'''
Load COCO class name text for object description for GRiT
'''
json_file = PathManager.get_local_path(json_file)
timer = Timer()
lvis_api = LVIS(json_file)
if timer.seconds() > 1:
logger.info("Loading {} takes {:.2f} seconds.".format(
json_file, timer.seconds()))
class_names = {}
sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id'])
for x in sort_cat:
class_names[x['id']] = x['name']
img_ids = sorted(lvis_api.imgs.keys())
imgs = lvis_api.load_imgs(img_ids)
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
assert len(set(ann_ids)) == len(ann_ids), \
"Annotation ids in '{}' are not unique".format(json_file)
imgs_anns = list(zip(imgs, anns))
logger.info("Loaded {} images in the LVIS v1 format from {}".format(
len(imgs_anns), json_file))
dataset_dicts = []
for (img_dict, anno_dict_list) in imgs_anns:
record = {}
if "file_name" in img_dict:
file_name = img_dict["file_name"]
record["file_name"] = os.path.join(image_root, file_name)
record["height"] = int(img_dict["height"])
record["width"] = int(img_dict["width"])
image_id = record["image_id"] = img_dict["id"]
objs = []
for anno in anno_dict_list:
assert anno["image_id"] == image_id
if anno.get('iscrowd', 0) > 0:
continue
obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
obj["category_id"] = 0
obj["object_description"] = class_names[anno['category_id']]
if 'segmentation' in anno:
segm = anno["segmentation"]
valid_segm = [poly for poly in segm \
if len(poly) % 2 == 0 and len(poly) >= 6]
if not len(segm) == len(valid_segm):
print('Annotation contains an invalid polygon with < 3 points')
assert len(segm) > 0
obj["segmentation"] = segm
objs.append(obj)
record["annotations"] = objs
if len(record["annotations"]) == 0:
continue
record["task"] = "ObjectDet"
dataset_dicts.append(record)
return dataset_dicts
def register_GRiTcoco_instances(name, metadata, json_file, image_root):
"""
"""
DatasetCatalog.register(name, lambda: load_GRiTcoco_json(
json_file, image_root, name))
MetadataCatalog.get(name).set(
json_file=json_file, image_root=image_root,
evaluator_type="coco", **metadata
) | null |
3,487 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_GRiTcoco_instances(
key,
get_GRiTcoco_meta(),
os.path.join("datasets", json_file) if "://" not in json_file else json_file,
os.path.join("datasets", image_root),
)
def get_GRiTcoco_meta():
categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
categories = sorted(categories, key=lambda x: x["id"])
thing_classes = [k["name"] for k in categories]
meta = {"thing_classes": thing_classes}
return meta | null |
3,488 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
def load_vg_json(json_file, image_root, dataset_name=None):
json_file = PathManager.get_local_path(json_file)
timer = Timer()
lvis_api = LVIS(json_file)
if timer.seconds() > 1:
logger.info("Loading {} takes {:.2f} seconds.".format(
json_file, timer.seconds()))
img_ids = sorted(lvis_api.imgs.keys())
imgs = lvis_api.load_imgs(img_ids)
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
assert len(set(ann_ids)) == len(ann_ids), \
"Annotation ids in '{}' are not unique".format(json_file)
imgs_anns = list(zip(imgs, anns))
logger.info("Loaded {} images in the LVIS v1 format from {}".format(
len(imgs_anns), json_file))
dataset_dicts = []
for (img_dict, anno_dict_list) in imgs_anns:
record = {}
if "file_name" in img_dict:
file_name = img_dict["file_name"]
record["file_name"] = os.path.join(image_root, file_name)
record["height"] = int(img_dict["height"])
record["width"] = int(img_dict["width"])
image_id = record["image_id"] = img_dict["id"]
objs = []
for anno in anno_dict_list:
assert anno["image_id"] == image_id
if anno.get('iscrowd', 0) > 0:
continue
obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
obj["category_id"] = 0
obj["object_description"] = anno["caption"]
objs.append(obj)
record["annotations"] = objs
if len(record["annotations"]) == 0:
continue
record["task"] = "DenseCap"
dataset_dicts.append(record)
return dataset_dicts
def register_vg_instances(name, metadata, json_file, image_root):
"""
"""
DatasetCatalog.register(name, lambda: load_vg_json(
json_file, image_root, name))
MetadataCatalog.get(name).set(
json_file=json_file, image_root=image_root,
evaluator_type="vg", **metadata
) | null |
3,489 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_vg_instances(
key,
get_vg_meta(),
os.path.join("datasets", json_file) if "://" not in json_file else json_file,
os.path.join("datasets", image_root),
)
def get_vg_meta():
categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
vg_categories = sorted(categories, key=lambda x: x["id"])
thing_classes = [k["name"] for k in vg_categories]
meta = {"thing_classes": thing_classes}
return meta | null |
3,490 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
def load_o365_json(json_file, image_root, dataset_name=None):
def register_o365_instances(name, metadata, json_file, image_root):
DatasetCatalog.register(name, lambda: load_o365_json(
json_file, image_root, name))
MetadataCatalog.get(name).set(
json_file=json_file, image_root=image_root,
evaluator_type="lvis", **metadata
) | null |
3,491 | import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_o365_instances(
key,
get_o365_meta(),
os.path.join("datasets", json_file) if "://" not in json_file else json_file,
os.path.join("datasets", image_root),
)
def get_o365_meta():
categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
o365_categories = sorted(categories, key=lambda x: x["id"])
thing_classes = [k["name"] for k in o365_categories]
meta = {"thing_classes": thing_classes}
return meta | null |
3,492 | import operator
import torch
import torch.utils.data
from detectron2.utils.comm import get_world_size
from detectron2.config import configurable
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader
from detectron2.data.samplers import TrainingSampler
from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram
from detectron2.data.build import filter_images_with_only_crowd_annotations
from detectron2.data.build import filter_images_with_few_keypoints
from detectron2.data.build import check_metadata_consistency
from detectron2.data.catalog import MetadataCatalog, DatasetCatalog
from detectron2.utils import comm
import itertools
from typing import Optional
def get_detection_dataset_dicts_with_source(
dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None
):
assert len(dataset_names)
dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
for dataset_name, dicts in zip(dataset_names, dataset_dicts):
assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
for source_id, (dataset_name, dicts) in \
enumerate(zip(dataset_names, dataset_dicts)):
assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
for d in dicts:
d['dataset_source'] = source_id
if "annotations" in dicts[0]:
try:
class_names = MetadataCatalog.get(dataset_name).thing_classes
check_metadata_consistency("thing_classes", dataset_name)
print_instances_class_histogram(dicts, class_names)
except AttributeError: # class names are not available for this dataset
pass
assert proposal_files is None
dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
has_instances = "annotations" in dataset_dicts[0]
if filter_empty and has_instances:
dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
if min_keypoints > 0 and has_instances:
dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
return dataset_dicts
class MultiDatasetSampler(Sampler):
def __init__(
self,
dataset_dicts,
dataset_ratio,
seed: Optional[int] = None,
):
sizes = [0 for _ in range(len(dataset_ratio))]
for d in dataset_dicts:
sizes[d['dataset_source']] += 1
print('dataset sizes', sizes)
self.sizes = sizes
assert len(dataset_ratio) == len(sizes), \
'length of dataset ratio {} should be equal to number if dataset {}'.format(
len(dataset_ratio), len(sizes)
)
if seed is None:
seed = comm.shared_random_seed()
self._seed = int(seed)
self._rank = comm.get_rank()
self._world_size = comm.get_world_size()
self.dataset_ids = torch.tensor(
[d['dataset_source'] for d in dataset_dicts], dtype=torch.long)
self.dataset_ratio = dataset_ratio
dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \
for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]
dataset_weight = torch.cat(dataset_weight)
self.weights = dataset_weight
self.sample_epoch_size = len(self.weights)
def __iter__(self):
start = self._rank
yield from itertools.islice(
self._infinite_indices(), start, None, self._world_size)
def _infinite_indices(self):
g = torch.Generator()
g.manual_seed(self._seed)
while True:
if len(self.dataset_ratio) > 1:
# multiple datasets
ids = torch.multinomial(
self.weights, self.sample_epoch_size, generator=g,
replacement=True)
nums = [(self.dataset_ids[ids] == i).sum().int().item() \
for i in range(len(self.sizes))]
yield from ids
else:
# single dataset
yield from torch.randperm(self.sizes[0], generator=g).tolist()
class DatasetMapper:
"""
A callable which takes a dataset dict in Detectron2 Dataset format,
and map it into a format used by the model.
This is the default callable to be used to map your dataset dict into training data.
You may need to follow it to implement your own one for customized logic,
such as a different way to read or transform images.
See :doc:`/tutorials/data_loading` for details.
The callable currently does the following:
1. Read the image from "file_name"
2. Applies cropping/geometric transforms to the image and annotations
3. Prepare data and annotations to Tensor and :class:`Instances`
"""
def __init__(
self,
is_train: bool,
*,
augmentations: List[Union[T.Augmentation, T.Transform]],
image_format: str,
use_instance_mask: bool = False,
use_keypoint: bool = False,
instance_mask_format: str = "polygon",
keypoint_hflip_indices: Optional[np.ndarray] = None,
precomputed_proposal_topk: Optional[int] = None,
recompute_boxes: bool = False,
):
"""
NOTE: this interface is experimental.
Args:
is_train: whether it's used in training or inference
augmentations: a list of augmentations or deterministic transforms to apply
image_format: an image format supported by :func:`detection_utils.read_image`.
use_instance_mask: whether to process instance segmentation annotations, if available
use_keypoint: whether to process keypoint annotations if available
instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation
masks into this format.
keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`
precomputed_proposal_topk: if given, will load pre-computed
proposals from dataset_dict and keep the top k proposals for each image.
recompute_boxes: whether to overwrite bounding box annotations
by computing tight bounding boxes from instance mask annotations.
"""
if recompute_boxes:
assert use_instance_mask, "recompute_boxes requires instance masks"
# fmt: off
self.is_train = is_train
self.augmentations = T.AugmentationList(augmentations)
self.image_format = image_format
self.use_instance_mask = use_instance_mask
self.instance_mask_format = instance_mask_format
self.use_keypoint = use_keypoint
self.keypoint_hflip_indices = keypoint_hflip_indices
self.proposal_topk = precomputed_proposal_topk
self.recompute_boxes = recompute_boxes
# fmt: on
logger = logging.getLogger(__name__)
mode = "training" if is_train else "inference"
logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}")
def from_config(cls, cfg, is_train: bool = True):
augs = utils.build_augmentation(cfg, is_train)
if cfg.INPUT.CROP.ENABLED and is_train:
augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
recompute_boxes = cfg.MODEL.MASK_ON
else:
recompute_boxes = False
ret = {
"is_train": is_train,
"augmentations": augs,
"image_format": cfg.INPUT.FORMAT,
"use_instance_mask": cfg.MODEL.MASK_ON,
"instance_mask_format": cfg.INPUT.MASK_FORMAT,
"use_keypoint": cfg.MODEL.KEYPOINT_ON,
"recompute_boxes": recompute_boxes,
}
if cfg.MODEL.KEYPOINT_ON:
ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
if cfg.MODEL.LOAD_PROPOSALS:
ret["precomputed_proposal_topk"] = (
cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
if is_train
else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
)
return ret
def _transform_annotations(self, dataset_dict, transforms, image_shape):
# USER: Modify this if you want to keep them for some reason.
for anno in dataset_dict["annotations"]:
if not self.use_instance_mask:
anno.pop("segmentation", None)
if not self.use_keypoint:
anno.pop("keypoints", None)
# USER: Implement additional transformations if you have other types of data
annos = [
utils.transform_instance_annotations(
obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(
annos, image_shape, mask_format=self.instance_mask_format
)
# After transforms such as cropping are applied, the bounding box may no longer
# tightly bound the object. As an example, imagine a triangle object
# [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
# bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
# the intersection of original bounding box and the cropping box.
if self.recompute_boxes:
instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
dataset_dict["instances"] = utils.filter_empty_instances(instances)
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
# USER: Write your own image loading if it's not from a file
image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
utils.check_image_size(dataset_dict, image)
# USER: Remove if you don't do semantic/panoptic segmentation.
if "sem_seg_file_name" in dataset_dict:
sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
else:
sem_seg_gt = None
aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
transforms = self.augmentations(aug_input)
image, sem_seg_gt = aug_input.image, aug_input.sem_seg
image_shape = image.shape[:2] # h, w
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
if sem_seg_gt is not None:
dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
# USER: Remove if you don't use pre-computed proposals.
# Most users would not need this feature.
if self.proposal_topk is not None:
utils.transform_proposals(
dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
)
if not self.is_train:
# USER: Modify this if you want to keep them for some reason.
dataset_dict.pop("annotations", None)
dataset_dict.pop("sem_seg_file_name", None)
return dataset_dict
if "annotations" in dataset_dict:
self._transform_annotations(dataset_dict, transforms, image_shape)
return dataset_dict
def get_detection_dataset_dicts(
names,
filter_empty=True,
min_keypoints=0,
proposal_files=None,
check_consistency=True,
):
"""
Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
Args:
names (str or list[str]): a dataset name or a list of dataset names
filter_empty (bool): whether to filter out images without instance annotations
min_keypoints (int): filter out images with fewer keypoints than
`min_keypoints`. Set to 0 to do nothing.
proposal_files (list[str]): if given, a list of object proposal files
that match each dataset in `names`.
check_consistency (bool): whether to check if datasets have consistent metadata.
Returns:
list[dict]: a list of dicts following the standard dataset dict format.
"""
if isinstance(names, str):
names = [names]
assert len(names), names
dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
for dataset_name, dicts in zip(names, dataset_dicts):
assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
if proposal_files is not None:
assert len(names) == len(proposal_files)
# load precomputed proposals from proposal files
dataset_dicts = [
load_proposals_into_dataset(dataset_i_dicts, proposal_file)
for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files)
]
if isinstance(dataset_dicts[0], torchdata.Dataset):
return torchdata.ConcatDataset(dataset_dicts)
dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
has_instances = "annotations" in dataset_dicts[0]
if filter_empty and has_instances:
dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
if min_keypoints > 0 and has_instances:
dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
if check_consistency and has_instances:
try:
class_names = MetadataCatalog.get(names[0]).thing_classes
check_metadata_consistency("thing_classes", names)
print_instances_class_histogram(dataset_dicts, class_names)
except AttributeError: # class names are not available for this dataset
pass
assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
return dataset_dicts
def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
if 'MultiDataset' in sampler_name:
dataset_dicts = get_detection_dataset_dicts_with_source(
cfg.DATASETS.TRAIN,
filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if cfg.MODEL.KEYPOINT_ON else 0,
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
)
else:
dataset_dicts = get_detection_dataset_dicts(
cfg.DATASETS.TRAIN,
filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if cfg.MODEL.KEYPOINT_ON else 0,
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
)
if mapper is None:
mapper = DatasetMapper(cfg, True)
if sampler is not None:
pass
elif sampler_name == "TrainingSampler":
sampler = TrainingSampler(len(dataset))
elif sampler_name == "MultiDatasetSampler":
sampler = MultiDatasetSampler(
dataset_dicts,
dataset_ratio=cfg.DATALOADER.DATASET_RATIO,
)
else:
raise ValueError("Unknown training sampler: {}".format(sampler_name))
return {
"dataset": dataset_dicts,
"sampler": sampler,
"mapper": mapper,
"total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
"num_workers": cfg.DATALOADER.NUM_WORKERS,
'dataset_bs': cfg.DATALOADER.DATASET_BS,
'num_datasets': len(cfg.DATASETS.TRAIN)
} | null |
3,493 | import operator
import torch
import torch.utils.data
from detectron2.utils.comm import get_world_size
from detectron2.config import configurable
from torch.utils.data.sampler import BatchSampler, Sampler
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader
from detectron2.data.samplers import TrainingSampler
from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram
from detectron2.data.build import filter_images_with_only_crowd_annotations
from detectron2.data.build import filter_images_with_few_keypoints
from detectron2.data.build import check_metadata_consistency
from detectron2.data.catalog import MetadataCatalog, DatasetCatalog
from detectron2.utils import comm
import itertools
from typing import Optional
def build_dataset_batch_data_loader(
dataset_bs, dataset, sampler, total_batch_size, num_datasets, num_workers=0
):
world_size = get_world_size()
assert (
total_batch_size > 0 and total_batch_size % world_size == 0
), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
total_batch_size, world_size
)
data_loader = torch.utils.data.DataLoader(
dataset,
sampler=sampler,
num_workers=num_workers,
batch_sampler=None,
collate_fn=operator.itemgetter(0), # don't batch, but yield individual elements
worker_init_fn=worker_init_reset_seed,
)
if num_datasets > 1:
return MultiDatasets(data_loader, dataset_bs, num_datasets)
else:
return SingleDataset(data_loader, dataset_bs)
class MapDataset(data.Dataset):
"""
Map a function over the elements in a dataset.
"""
def __init__(self, dataset, map_func):
"""
Args:
dataset: a dataset where map function is applied. Can be either
map-style or iterable dataset. When given an iterable dataset,
the returned object will also be an iterable dataset.
map_func: a callable which maps the element in dataset. map_func can
return None to skip the data (e.g. in case of errors).
How None is handled depends on the style of `dataset`.
If `dataset` is map-style, it randomly tries other elements.
If `dataset` is iterable, it skips the data and tries the next.
"""
self._dataset = dataset
self._map_func = PicklableWrapper(map_func) # wrap so that a lambda will work
self._rng = random.Random(42)
self._fallback_candidates = set(range(len(dataset)))
def __new__(cls, dataset, map_func):
is_iterable = isinstance(dataset, data.IterableDataset)
if is_iterable:
return _MapIterableDataset(dataset, map_func)
else:
return super().__new__(cls)
def __getnewargs__(self):
return self._dataset, self._map_func
def __len__(self):
return len(self._dataset)
def __getitem__(self, idx):
retry_count = 0
cur_idx = int(idx)
while True:
data = self._map_func(self._dataset[cur_idx])
if data is not None:
self._fallback_candidates.add(cur_idx)
return data
# _map_func fails for this idx, use a random new index from the pool
retry_count += 1
self._fallback_candidates.discard(cur_idx)
cur_idx = self._rng.sample(self._fallback_candidates, k=1)[0]
if retry_count >= 3:
logger = logging.getLogger(__name__)
logger.warning(
"Failed to apply `_map_func` for idx: {}, retry count: {}".format(
idx, retry_count
)
)
class DatasetFromList(data.Dataset):
"""
Wrap a list to a torch Dataset. It produces elements of the list as data.
"""
def __init__(self, lst: list, copy: bool = True, serialize: bool = True):
"""
Args:
lst (list): a list which contains elements to produce.
copy (bool): whether to deepcopy the element when producing it,
so that the result can be modified in place without affecting the
source in the list.
serialize (bool): whether to hold memory using serialized objects, when
enabled, data loader workers can use shared RAM from master
process instead of making a copy.
"""
self._lst = lst
self._copy = copy
self._serialize = serialize
def _serialize(data):
buffer = pickle.dumps(data, protocol=-1)
return np.frombuffer(buffer, dtype=np.uint8)
if self._serialize:
logger = logging.getLogger(__name__)
logger.info(
"Serializing {} elements to byte tensors and concatenating them all ...".format(
len(self._lst)
)
)
self._lst = [_serialize(x) for x in self._lst]
self._addr = np.asarray([len(x) for x in self._lst], dtype=np.int64)
self._addr = np.cumsum(self._addr)
self._lst = np.concatenate(self._lst)
logger.info("Serialized dataset takes {:.2f} MiB".format(len(self._lst) / 1024 ** 2))
def __len__(self):
if self._serialize:
return len(self._addr)
else:
return len(self._lst)
def __getitem__(self, idx):
if self._serialize:
start_addr = 0 if idx == 0 else self._addr[idx - 1].item()
end_addr = self._addr[idx].item()
bytes = memoryview(self._lst[start_addr:end_addr])
return pickle.loads(bytes)
elif self._copy:
return copy.deepcopy(self._lst[idx])
else:
return self._lst[idx]
def build_custom_train_loader(
dataset, *, mapper, sampler,
total_batch_size=16,
num_workers=0,
num_datasets=1,
dataset_bs=1
):
if isinstance(dataset, list):
dataset = DatasetFromList(dataset, copy=False)
if mapper is not None:
dataset = MapDataset(dataset, mapper)
if sampler is None:
sampler = TrainingSampler(len(dataset))
assert isinstance(sampler, torch.utils.data.sampler.Sampler)
return build_dataset_batch_data_loader(
dataset_bs,
dataset,
sampler,
total_batch_size,
num_datasets=num_datasets,
num_workers=num_workers,
) | null |
3,494 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `window_partition` function. Write a Python function `def window_partition(x, window_size)` to solve the following problem:
Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition
Here is the function:
def window_partition(x, window_size):
"""
Partition into non-overlapping windows with padding if needed.
Args:
x (tensor): input tokens with [B, H, W, C].
window_size (int): window size.
Returns:
windows: windows after partition with [B * num_windows, window_size, window_size, C].
(Hp, Wp): padded height and width before partition
"""
B, H, W, C = x.shape
pad_h = (window_size - H % window_size) % window_size
pad_w = (window_size - W % window_size) % window_size
if pad_h > 0 or pad_w > 0:
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows, (Hp, Wp) | Partition into non-overlapping windows with padding if needed. Args: x (tensor): input tokens with [B, H, W, C]. window_size (int): window size. Returns: windows: windows after partition with [B * num_windows, window_size, window_size, C]. (Hp, Wp): padded height and width before partition |
3,495 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `window_unpartition` function. Write a Python function `def window_unpartition(windows, window_size, pad_hw, hw)` to solve the following problem:
Window unpartition into original sequences and removing padding. Args: x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C].
Here is the function:
def window_unpartition(windows, window_size, pad_hw, hw):
"""
Window unpartition into original sequences and removing padding.
Args:
x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
window_size (int): window size.
pad_hw (Tuple): padded height and width (Hp, Wp).
hw (Tuple): original height and width (H, W) before padding.
Returns:
x: unpartitioned sequences with [B, H, W, C].
"""
Hp, Wp = pad_hw
H, W = hw
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
if Hp > H or Wp > W:
x = x[:, :H, :W, :].contiguous()
return x | Window unpartition into original sequences and removing padding. Args: x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. window_size (int): window size. pad_hw (Tuple): padded height and width (Hp, Wp). hw (Tuple): original height and width (H, W) before padding. Returns: x: unpartitioned sequences with [B, H, W, C]. |
3,496 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_rel_pos(q_size, k_size, rel_pos):
"""
Get relative positional embeddings according to the relative positions of
query and key sizes.
Args:
q_size (int): size of query q.
k_size (int): size of key k.
rel_pos (Tensor): relative position embeddings (L, C).
Returns:
Extracted positional embeddings according to relative positions.
"""
max_rel_dist = int(2 * max(q_size, k_size) - 1)
# Interpolate rel pos if needed.
if rel_pos.shape[0] != max_rel_dist:
# Interpolate rel pos.
rel_pos_resized = F.interpolate(
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
size=max_rel_dist,
mode="linear",
)
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
else:
rel_pos_resized = rel_pos
# Scale the coords with short length if shapes for q and k are different.
q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
return rel_pos_resized[relative_coords.long()]
The provided code snippet includes necessary dependencies for implementing the `add_decomposed_rel_pos` function. Write a Python function `def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size)` to solve the following problem:
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 Args: attn (Tensor): attention map. q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. q_size (Tuple): spatial sequence size of query q with (q_h, q_w). k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: attn (Tensor): attention map with added relative positional embeddings.
Here is the function:
def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
"""
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
Args:
attn (Tensor): attention map.
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
Returns:
attn (Tensor): attention map with added relative positional embeddings.
"""
q_h, q_w = q_size
k_h, k_w = k_size
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
B, _, dim = q.shape
r_q = q.reshape(B, q_h, q_w, dim)
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
attn = (
attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
).view(B, q_h * q_w, k_h * k_w)
return attn | Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950 Args: attn (Tensor): attention map. q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C). rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis. rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis. q_size (Tuple): spatial sequence size of query q with (q_h, q_w). k_size (Tuple): spatial sequence size of key k with (k_h, k_w). Returns: attn (Tensor): attention map with added relative positional embeddings. |
3,497 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
The provided code snippet includes necessary dependencies for implementing the `get_abs_pos` function. Write a Python function `def get_abs_pos(abs_pos, has_cls_token, hw)` to solve the following problem:
Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Args: abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. hw (Tuple): size of input image tokens. Returns: Absolute positional embeddings after processing with shape (1, H, W, C)
Here is the function:
def get_abs_pos(abs_pos, has_cls_token, hw):
"""
Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token
dimension for the original embeddings.
Args:
abs_pos (Tensor): absolute positional embeddings with (1, num_position, C).
has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token.
hw (Tuple): size of input image tokens.
Returns:
Absolute positional embeddings after processing with shape (1, H, W, C)
"""
h, w = hw
if has_cls_token:
abs_pos = abs_pos[:, 1:]
xy_num = abs_pos.shape[1]
size = int(math.sqrt(xy_num))
assert size * size == xy_num
if size != h or size != w:
new_abs_pos = F.interpolate(
abs_pos.reshape(1, size, size, -1).permute(0, 3, 1, 2),
size=(h, w),
mode="bicubic",
align_corners=False,
)
return new_abs_pos.permute(0, 2, 3, 1)
else:
return abs_pos.reshape(1, h, w, -1) | Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Args: abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. hw (Tuple): size of input image tokens. Returns: Absolute positional embeddings after processing with shape (1, H, W, C) |
3,498 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
import sys
from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, Mlp, trunc_normal_
from detectron2.modeling.backbone.backbone import Backbone
from .utils import (
PatchEmbed,
add_decomposed_rel_pos,
get_abs_pos,
window_partition,
window_unpartition,
)
class ViT(Backbone):
"""
This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.
"Exploring Plain Vision Transformer Backbones for Object Detection",
https://arxiv.org/abs/2203.16527
"""
def __init__(
self,
img_size=1024,
patch_size=16,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
use_abs_pos=True,
use_rel_pos=False,
rel_pos_zero_init=True,
window_size=0,
window_block_indexes=(),
residual_block_indexes=(),
use_act_checkpoint=True,
pretrain_img_size=224,
pretrain_use_cls_token=True,
out_feature="last_feat",
):
"""
Args:
img_size (int): Input image size.
patch_size (int): Patch size.
in_chans (int): Number of input image channels.
embed_dim (int): Patch embedding dimension.
depth (int): Depth of ViT.
num_heads (int): Number of attention heads in each ViT block.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool): If True, add a learnable bias to query, key, value.
drop_path_rate (float): Stochastic depth rate.
norm_layer (nn.Module): Normalization layer.
act_layer (nn.Module): Activation layer.
use_abs_pos (bool): If True, use absolute positional embeddings.
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
window_size (int): Window size for window attention blocks.
window_block_indexes (list): Indexes for blocks using window attention.
residual_block_indexes (list): Indexes for blocks using conv propagation.
use_act_checkpoint (bool): If True, use activation checkpointing.
pretrain_img_size (int): input image size for pretraining models.
pretrain_use_cls_token (bool): If True, pretrainig models use class token.
out_feature (str): name of the feature from the last block.
"""
super().__init__()
self.pretrain_use_cls_token = pretrain_use_cls_token
self.use_act_checkpoint = use_act_checkpoint
self.patch_embed = PatchEmbed(
kernel_size=(patch_size, patch_size),
stride=(patch_size, patch_size),
in_chans=in_chans,
embed_dim=embed_dim,
)
if use_abs_pos:
# Initialize absolute positional embedding with pretrain image size.
num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size)
num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
else:
self.pos_embed = None
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
self.blocks = nn.ModuleList()
for i in range(depth):
block = Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
use_rel_pos=use_rel_pos,
rel_pos_zero_init=rel_pos_zero_init,
window_size=window_size if i in window_block_indexes else 0,
use_residual_block=i in residual_block_indexes,
input_size=(img_size // patch_size, img_size // patch_size),
)
self.blocks.append(block)
self._out_feature_channels = {out_feature: embed_dim}
self._out_feature_strides = {out_feature: patch_size}
self._out_features = [out_feature]
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=0.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x):
x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + get_abs_pos(
self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])
)
for blk in self.blocks:
if self.use_act_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
return x.permute(0, 3, 1, 2)
class ViT_FPN(Backbone):
def __init__(self, bottom_up=None, top_block=None, out_channels=None, strides=None, vit_out_dim=None):
super(ViT_FPN, self).__init__()
assert isinstance(bottom_up, Backbone)
self.bottom_up = bottom_up
self.top_block = top_block
self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
self._out_features = list(self._out_feature_strides.keys())
self._out_feature_channels = {k: out_channels for k in self._out_features}
self._size_divisibility = strides[2]
self.maxpool = nn.MaxPool2d(2, stride=2)
self.fpn_stride_16_8 = nn.ConvTranspose2d(vit_out_dim, vit_out_dim, 2, stride=2, bias=False)
self.fpn_stride8_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride8_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride8_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride8_norm2 = nn.LayerNorm(out_channels)
self.fpn_stride16_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride16_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride16_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride16_norm2 = nn.LayerNorm(out_channels)
self.fpn_stride32_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride32_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride32_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride32_norm2 = nn.LayerNorm(out_channels)
def forward(self, x):
vit_output_featuremap = self.bottom_up(x)
stride8_feature = self.fpn_stride_16_8(vit_output_featuremap)
stride8_feature = self.fpn_stride8_norm1(self.fpn_stride8_conv1(stride8_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride8_feature = self.fpn_stride8_norm2(self.fpn_stride8_conv2(stride8_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride32_feature = self.maxpool(vit_output_featuremap)
stride32_feature = self.fpn_stride32_norm1(self.fpn_stride32_conv1(stride32_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride32_feature = self.fpn_stride32_norm2(self.fpn_stride32_conv2(stride32_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride16_feature = self.fpn_stride16_norm1(self.fpn_stride16_conv1(vit_output_featuremap).
permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride16_feature = self.fpn_stride16_norm2(self.fpn_stride16_conv2(stride16_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
results = [stride8_feature, stride16_feature, stride32_feature]
results.extend(self.top_block(stride32_feature))
assert len(self._out_features) == len(results)
fpn_out = {f: res for f, res in zip(self._out_features, results)}
return fpn_out
def size_divisibility(self):
return self._size_divisibility
def output_shape(self):
return {
name: ShapeSpec(
channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
)
for name in self._out_features
}
class LastLevelP6P7_P5(nn.Module):
"""
This module is used in RetinaNet to generate extra layers, P6 and P7 from
C5 feature.
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.num_levels = 2
self.in_feature = "p5"
self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
for module in [self.p6, self.p7]:
weight_init.c2_xavier_fill(module)
def forward(self, c5):
p6 = self.p6(c5)
p7 = self.p7(F.relu(p6))
return [p6, p7]
def build_vit_fpn_backbone(cfg, input_shape: ShapeSpec):
embed_dim = 768
vit_out_dim = embed_dim
bottom_up = ViT( # Single-scale ViT backbone
img_size=1024,
patch_size=16,
embed_dim=embed_dim,
depth=12,
num_heads=12,
drop_path_rate=0.1,
window_size=14,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
window_block_indexes=[
# 2, 5, 8 11 for global attention
0,
1,
3,
4,
6,
7,
9,
10,
],
residual_block_indexes=[],
use_act_checkpoint=cfg.USE_ACT_CHECKPOINT,
use_rel_pos=True,
out_feature="last_feat",)
out_channels = cfg.MODEL.FPN.OUT_CHANNELS
assert out_channels == 256 or out_channels == 768 or out_channels == 1024
backbone = ViT_FPN(bottom_up=bottom_up,
top_block=LastLevelP6P7_P5(out_channels, out_channels),
out_channels=out_channels,
strides=[8, 16, 32, 64, 128],
vit_out_dim=vit_out_dim)
return backbone | null |
3,499 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
import sys
from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, Mlp, trunc_normal_
from detectron2.modeling.backbone.backbone import Backbone
from .utils import (
PatchEmbed,
add_decomposed_rel_pos,
get_abs_pos,
window_partition,
window_unpartition,
)
class ViT(Backbone):
"""
This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.
"Exploring Plain Vision Transformer Backbones for Object Detection",
https://arxiv.org/abs/2203.16527
"""
def __init__(
self,
img_size=1024,
patch_size=16,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
use_abs_pos=True,
use_rel_pos=False,
rel_pos_zero_init=True,
window_size=0,
window_block_indexes=(),
residual_block_indexes=(),
use_act_checkpoint=True,
pretrain_img_size=224,
pretrain_use_cls_token=True,
out_feature="last_feat",
):
"""
Args:
img_size (int): Input image size.
patch_size (int): Patch size.
in_chans (int): Number of input image channels.
embed_dim (int): Patch embedding dimension.
depth (int): Depth of ViT.
num_heads (int): Number of attention heads in each ViT block.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool): If True, add a learnable bias to query, key, value.
drop_path_rate (float): Stochastic depth rate.
norm_layer (nn.Module): Normalization layer.
act_layer (nn.Module): Activation layer.
use_abs_pos (bool): If True, use absolute positional embeddings.
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
window_size (int): Window size for window attention blocks.
window_block_indexes (list): Indexes for blocks using window attention.
residual_block_indexes (list): Indexes for blocks using conv propagation.
use_act_checkpoint (bool): If True, use activation checkpointing.
pretrain_img_size (int): input image size for pretraining models.
pretrain_use_cls_token (bool): If True, pretrainig models use class token.
out_feature (str): name of the feature from the last block.
"""
super().__init__()
self.pretrain_use_cls_token = pretrain_use_cls_token
self.use_act_checkpoint = use_act_checkpoint
self.patch_embed = PatchEmbed(
kernel_size=(patch_size, patch_size),
stride=(patch_size, patch_size),
in_chans=in_chans,
embed_dim=embed_dim,
)
if use_abs_pos:
# Initialize absolute positional embedding with pretrain image size.
num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size)
num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
else:
self.pos_embed = None
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
self.blocks = nn.ModuleList()
for i in range(depth):
block = Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
use_rel_pos=use_rel_pos,
rel_pos_zero_init=rel_pos_zero_init,
window_size=window_size if i in window_block_indexes else 0,
use_residual_block=i in residual_block_indexes,
input_size=(img_size // patch_size, img_size // patch_size),
)
self.blocks.append(block)
self._out_feature_channels = {out_feature: embed_dim}
self._out_feature_strides = {out_feature: patch_size}
self._out_features = [out_feature]
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=0.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x):
x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + get_abs_pos(
self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])
)
for blk in self.blocks:
if self.use_act_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
return x.permute(0, 3, 1, 2)
class ViT_FPN(Backbone):
def __init__(self, bottom_up=None, top_block=None, out_channels=None, strides=None, vit_out_dim=None):
super(ViT_FPN, self).__init__()
assert isinstance(bottom_up, Backbone)
self.bottom_up = bottom_up
self.top_block = top_block
self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
self._out_features = list(self._out_feature_strides.keys())
self._out_feature_channels = {k: out_channels for k in self._out_features}
self._size_divisibility = strides[2]
self.maxpool = nn.MaxPool2d(2, stride=2)
self.fpn_stride_16_8 = nn.ConvTranspose2d(vit_out_dim, vit_out_dim, 2, stride=2, bias=False)
self.fpn_stride8_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride8_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride8_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride8_norm2 = nn.LayerNorm(out_channels)
self.fpn_stride16_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride16_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride16_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride16_norm2 = nn.LayerNorm(out_channels)
self.fpn_stride32_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride32_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride32_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride32_norm2 = nn.LayerNorm(out_channels)
def forward(self, x):
vit_output_featuremap = self.bottom_up(x)
stride8_feature = self.fpn_stride_16_8(vit_output_featuremap)
stride8_feature = self.fpn_stride8_norm1(self.fpn_stride8_conv1(stride8_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride8_feature = self.fpn_stride8_norm2(self.fpn_stride8_conv2(stride8_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride32_feature = self.maxpool(vit_output_featuremap)
stride32_feature = self.fpn_stride32_norm1(self.fpn_stride32_conv1(stride32_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride32_feature = self.fpn_stride32_norm2(self.fpn_stride32_conv2(stride32_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride16_feature = self.fpn_stride16_norm1(self.fpn_stride16_conv1(vit_output_featuremap).
permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride16_feature = self.fpn_stride16_norm2(self.fpn_stride16_conv2(stride16_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
results = [stride8_feature, stride16_feature, stride32_feature]
results.extend(self.top_block(stride32_feature))
assert len(self._out_features) == len(results)
fpn_out = {f: res for f, res in zip(self._out_features, results)}
return fpn_out
def size_divisibility(self):
return self._size_divisibility
def output_shape(self):
return {
name: ShapeSpec(
channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
)
for name in self._out_features
}
class LastLevelP6P7_P5(nn.Module):
"""
This module is used in RetinaNet to generate extra layers, P6 and P7 from
C5 feature.
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.num_levels = 2
self.in_feature = "p5"
self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
for module in [self.p6, self.p7]:
weight_init.c2_xavier_fill(module)
def forward(self, c5):
p6 = self.p6(c5)
p7 = self.p7(F.relu(p6))
return [p6, p7]
def build_vit_fpn_backbone_large(cfg, input_shape: ShapeSpec):
window_block_indexes = (list(range(0, 5)) + list(range(6, 11)) + list(range(12, 17)) + list(range(18, 23)))
embed_dim = 1024
vit_out_dim = embed_dim
bottom_up = ViT( # Single-scale ViT backbone
img_size=1024,
patch_size=16,
embed_dim=embed_dim,
depth=24,
num_heads=16,
drop_path_rate=0.4,
window_size=14,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
window_block_indexes=window_block_indexes,
residual_block_indexes=[],
use_act_checkpoint=cfg.USE_ACT_CHECKPOINT,
use_rel_pos=True,
out_feature="last_feat",)
out_channels = cfg.MODEL.FPN.OUT_CHANNELS
assert out_channels == 256 or out_channels == 768 or out_channels == 1024
backbone = ViT_FPN(bottom_up=bottom_up,
top_block=LastLevelP6P7_P5(out_channels, out_channels),
out_channels=out_channels,
strides=[8, 16, 32, 64, 128],
vit_out_dim=vit_out_dim)
return backbone | null |
3,500 | import logging
import math
import fvcore.nn.weight_init as weight_init
import torch
import torch.nn as nn
from functools import partial
from detectron2.layers import CNNBlockBase, Conv2d, get_norm
from detectron2.modeling.backbone.build import BACKBONE_REGISTRY
from detectron2.layers import ShapeSpec
import sys
from centernet.modeling.backbone.fpn_p5 import LastLevelP6P7_P5
import torch.utils.checkpoint as checkpoint
from timm.models.layers import DropPath, Mlp, trunc_normal_
from detectron2.modeling.backbone.backbone import Backbone
from .utils import (
PatchEmbed,
add_decomposed_rel_pos,
get_abs_pos,
window_partition,
window_unpartition,
)
class ViT(Backbone):
"""
This module implements Vision Transformer (ViT) backbone in :paper:`vitdet`.
"Exploring Plain Vision Transformer Backbones for Object Detection",
https://arxiv.org/abs/2203.16527
"""
def __init__(
self,
img_size=1024,
patch_size=16,
in_chans=3,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
qkv_bias=True,
drop_path_rate=0.0,
norm_layer=nn.LayerNorm,
act_layer=nn.GELU,
use_abs_pos=True,
use_rel_pos=False,
rel_pos_zero_init=True,
window_size=0,
window_block_indexes=(),
residual_block_indexes=(),
use_act_checkpoint=True,
pretrain_img_size=224,
pretrain_use_cls_token=True,
out_feature="last_feat",
):
"""
Args:
img_size (int): Input image size.
patch_size (int): Patch size.
in_chans (int): Number of input image channels.
embed_dim (int): Patch embedding dimension.
depth (int): Depth of ViT.
num_heads (int): Number of attention heads in each ViT block.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool): If True, add a learnable bias to query, key, value.
drop_path_rate (float): Stochastic depth rate.
norm_layer (nn.Module): Normalization layer.
act_layer (nn.Module): Activation layer.
use_abs_pos (bool): If True, use absolute positional embeddings.
use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
window_size (int): Window size for window attention blocks.
window_block_indexes (list): Indexes for blocks using window attention.
residual_block_indexes (list): Indexes for blocks using conv propagation.
use_act_checkpoint (bool): If True, use activation checkpointing.
pretrain_img_size (int): input image size for pretraining models.
pretrain_use_cls_token (bool): If True, pretrainig models use class token.
out_feature (str): name of the feature from the last block.
"""
super().__init__()
self.pretrain_use_cls_token = pretrain_use_cls_token
self.use_act_checkpoint = use_act_checkpoint
self.patch_embed = PatchEmbed(
kernel_size=(patch_size, patch_size),
stride=(patch_size, patch_size),
in_chans=in_chans,
embed_dim=embed_dim,
)
if use_abs_pos:
# Initialize absolute positional embedding with pretrain image size.
num_patches = (pretrain_img_size // patch_size) * (pretrain_img_size // patch_size)
num_positions = (num_patches + 1) if pretrain_use_cls_token else num_patches
self.pos_embed = nn.Parameter(torch.zeros(1, num_positions, embed_dim))
else:
self.pos_embed = None
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]
self.blocks = nn.ModuleList()
for i in range(depth):
block = Block(
dim=embed_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop_path=dpr[i],
norm_layer=norm_layer,
act_layer=act_layer,
use_rel_pos=use_rel_pos,
rel_pos_zero_init=rel_pos_zero_init,
window_size=window_size if i in window_block_indexes else 0,
use_residual_block=i in residual_block_indexes,
input_size=(img_size // patch_size, img_size // patch_size),
)
self.blocks.append(block)
self._out_feature_channels = {out_feature: embed_dim}
self._out_feature_strides = {out_feature: patch_size}
self._out_features = [out_feature]
if self.pos_embed is not None:
trunc_normal_(self.pos_embed, std=0.02)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward(self, x):
x = self.patch_embed(x)
if self.pos_embed is not None:
x = x + get_abs_pos(
self.pos_embed, self.pretrain_use_cls_token, (x.shape[1], x.shape[2])
)
for blk in self.blocks:
if self.use_act_checkpoint:
x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
return x.permute(0, 3, 1, 2)
class ViT_FPN(Backbone):
def __init__(self, bottom_up=None, top_block=None, out_channels=None, strides=None, vit_out_dim=None):
super(ViT_FPN, self).__init__()
assert isinstance(bottom_up, Backbone)
self.bottom_up = bottom_up
self.top_block = top_block
self._out_feature_strides = {"p{}".format(int(math.log2(s))): s for s in strides}
self._out_features = list(self._out_feature_strides.keys())
self._out_feature_channels = {k: out_channels for k in self._out_features}
self._size_divisibility = strides[2]
self.maxpool = nn.MaxPool2d(2, stride=2)
self.fpn_stride_16_8 = nn.ConvTranspose2d(vit_out_dim, vit_out_dim, 2, stride=2, bias=False)
self.fpn_stride8_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride8_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride8_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride8_norm2 = nn.LayerNorm(out_channels)
self.fpn_stride16_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride16_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride16_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride16_norm2 = nn.LayerNorm(out_channels)
self.fpn_stride32_conv1 = nn.Conv2d(in_channels=vit_out_dim, out_channels=out_channels, kernel_size=1, bias=False)
self.fpn_stride32_norm1 = nn.LayerNorm(out_channels)
self.fpn_stride32_conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.fpn_stride32_norm2 = nn.LayerNorm(out_channels)
def forward(self, x):
vit_output_featuremap = self.bottom_up(x)
stride8_feature = self.fpn_stride_16_8(vit_output_featuremap)
stride8_feature = self.fpn_stride8_norm1(self.fpn_stride8_conv1(stride8_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride8_feature = self.fpn_stride8_norm2(self.fpn_stride8_conv2(stride8_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride32_feature = self.maxpool(vit_output_featuremap)
stride32_feature = self.fpn_stride32_norm1(self.fpn_stride32_conv1(stride32_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride32_feature = self.fpn_stride32_norm2(self.fpn_stride32_conv2(stride32_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride16_feature = self.fpn_stride16_norm1(self.fpn_stride16_conv1(vit_output_featuremap).
permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
stride16_feature = self.fpn_stride16_norm2(self.fpn_stride16_conv2(stride16_feature)
.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
results = [stride8_feature, stride16_feature, stride32_feature]
results.extend(self.top_block(stride32_feature))
assert len(self._out_features) == len(results)
fpn_out = {f: res for f, res in zip(self._out_features, results)}
return fpn_out
def size_divisibility(self):
return self._size_divisibility
def output_shape(self):
return {
name: ShapeSpec(
channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
)
for name in self._out_features
}
class LastLevelP6P7_P5(nn.Module):
"""
This module is used in RetinaNet to generate extra layers, P6 and P7 from
C5 feature.
"""
def __init__(self, in_channels, out_channels):
super().__init__()
self.num_levels = 2
self.in_feature = "p5"
self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
for module in [self.p6, self.p7]:
weight_init.c2_xavier_fill(module)
def forward(self, c5):
p6 = self.p6(c5)
p7 = self.p7(F.relu(p6))
return [p6, p7]
def build_vit_fpn_backbone_huge(cfg, input_shape: ShapeSpec):
window_block_indexes = (list(range(0, 7)) + list(range(8, 15)) + list(range(16, 23)) + list(range(24, 31)))
embed_dim = 1280
vit_out_dim = embed_dim
bottom_up = ViT( # Single-scale ViT backbone
img_size=1024,
patch_size=16,
embed_dim=embed_dim,
depth=32,
num_heads=16,
drop_path_rate=0.5,
window_size=14,
mlp_ratio=4,
qkv_bias=True,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
window_block_indexes=window_block_indexes,
residual_block_indexes=[],
use_act_checkpoint=cfg.USE_ACT_CHECKPOINT,
use_rel_pos=True,
out_feature="last_feat",)
out_channels = cfg.MODEL.FPN.OUT_CHANNELS
assert out_channels == 256 or out_channels == 768 or out_channels == 1024
backbone = ViT_FPN(bottom_up=bottom_up,
top_block=LastLevelP6P7_P5(out_channels, out_channels),
out_channels=out_channels,
strides=[8, 16, 32, 64, 128],
vit_out_dim=vit_out_dim)
return backbone | null |
3,501 | from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import json
import logging
import os
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `filename_to_url` function. Write a Python function `def filename_to_url(filename, cache_dir=None)` to solve the following problem:
Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
Here is the function:
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + '.json'
if not os.path.exists(meta_path):
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata['url']
etag = metadata['etag']
return url, etag | Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. |
3,502 | from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import json
import logging
import os
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
def get_from_cache(url, cache_dir=None):
"""
Given a URL, look for the corresponding dataset in the local cache.
If it's not there, download it. Then return the path to the cached file.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
if sys.version_info[0] == 2 and not isinstance(cache_dir, str):
cache_dir = str(cache_dir)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
etag = s3_etag(url)
else:
try:
response = requests.head(url, allow_redirects=True)
if response.status_code != 200:
etag = None
else:
etag = response.headers.get("ETag")
except EnvironmentError:
etag = None
if sys.version_info[0] == 2 and etag is not None:
etag = etag.decode('utf-8')
filename = url_to_filename(url, etag)
# get cache path to put the file
cache_path = os.path.join(cache_dir, filename)
# If we don't have a connection (etag is None) and can't identify the file
# try to get the last downloaded one
if not os.path.exists(cache_path) and etag is None:
matching_files = fnmatch.filter(os.listdir(cache_dir), filename + '.*')
matching_files = list(filter(lambda s: not s.endswith('.json'), matching_files))
if matching_files:
cache_path = os.path.join(cache_dir, matching_files[-1])
if not os.path.exists(cache_path):
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with tempfile.NamedTemporaryFile() as temp_file:
logger.info("%s not found in cache, downloading to %s", url, temp_file.name)
# GET file object
if url.startswith("s3://"):
s3_get(url, temp_file)
else:
http_get(url, temp_file)
# we are copying the file before closing it, so flush to avoid truncation
temp_file.flush()
# shutil.copyfileobj() starts at the current position, so go to the start
temp_file.seek(0)
logger.info("copying %s to cache at %s", temp_file.name, cache_path)
with open(cache_path, 'wb') as cache_file:
shutil.copyfileobj(temp_file, cache_file)
logger.info("creating metadata file for %s", cache_path)
meta = {'url': url, 'etag': etag}
meta_path = cache_path + '.json'
with open(meta_path, 'w') as meta_file:
output_string = json.dumps(meta)
meta_file.write(output_string)
logger.info("removing temp file %s", temp_file.name)
return cache_path
The provided code snippet includes necessary dependencies for implementing the `cached_path` function. Write a Python function `def cached_path(url_or_filename, cache_dir=None)` to solve the following problem:
Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path.
Here is the function:
def cached_path(url_or_filename, cache_dir=None):
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
return the path to the cached file. If it's already a local path,
make sure the file exists and then return the path.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if sys.version_info[0] == 3 and isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if sys.version_info[0] == 3 and isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
parsed = urlparse(url_or_filename)
if parsed.scheme in ('http', 'https', 's3'):
# URL, so get it from the cache (downloading if necessary)
return get_from_cache(url_or_filename, cache_dir)
elif os.path.exists(url_or_filename):
# File, and it exists.
return url_or_filename
elif parsed.scheme == '':
# File, but it doesn't exist.
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) | Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. |
3,503 | from __future__ import absolute_import, division, print_function, unicode_literals
import sys
import json
import logging
import os
import shutil
import tempfile
import fnmatch
from functools import wraps
from hashlib import sha256
from io import open
import boto3
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `s3_request` function. Write a Python function `def s3_request(func)` to solve the following problem:
Wrapper function for s3 requests in order to create more helpful error messages.
Here is the function:
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise EnvironmentError("file {} not found".format(url))
else:
raise
return wrapper | Wrapper function for s3 requests in order to create more helpful error messages. |
3,504 | from torch import nn
import torch
import functools
from torch.nn import functional as F
import warnings
class BertEncoderAsDecoder(nn.Module):
def __init__(self, encoder):
super().__init__()
self.encoder = encoder
def forward(self, tgt, memory,
tgt_mask=None,
tgt_key_padding_mask=None,
memory_key_padding_mask=None,
tgt_bi_valid_mask=None,
encoder_history_states=None,
):
assert tgt_key_padding_mask is None, 'not supported'
assert tgt_mask.dim() == 2
assert tgt_mask.shape[0] == tgt_mask.shape[1]
# tgt_mask should always be 0/negative infinity
tgt = tgt.transpose(0, 1)
memory = memory.transpose(0, 1)
hidden_states = torch.cat((memory, tgt), dim=1)
num_tgt = tgt.shape[1]
num_memory = memory.shape[1]
device = tgt.device
dtype = tgt.dtype
top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype)
top_right = torch.full((num_memory, num_tgt), float('-inf'), device=tgt.device, dtype=dtype,)
bottom_left = torch.zeros((num_tgt, num_memory), dtype=dtype, device=tgt_mask.device,)
left = torch.cat((top_left, bottom_left), dim=0)
right = torch.cat((top_right, tgt_mask.to(dtype)), dim=0)
full_attention_mask = torch.cat((left, right), dim=1)[None, :]
if memory_key_padding_mask is None:
memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device)
# if it is False, it means valid. That is, it is not a padding
assert memory_key_padding_mask.dtype == torch.bool
zero_negative_infinity = torch.zeros_like(memory_key_padding_mask, dtype=tgt.dtype)
zero_negative_infinity[memory_key_padding_mask] = float('-inf')
full_attention_mask = full_attention_mask.expand((memory_key_padding_mask.shape[0], num_memory + num_tgt, num_memory + num_tgt))
full_attention_mask = full_attention_mask.clone()
origin_left = full_attention_mask[:, :, :num_memory]
update = zero_negative_infinity[:, None, :]
full_attention_mask[:, :, :num_memory] = origin_left + update
if tgt_bi_valid_mask is not None:
# verify the correctness
bs = full_attention_mask.shape[0]
# during inference, tgt_bi_valid_mask's length is not changed, but
# num_tgt can be increased
max_valid_target = tgt_bi_valid_mask.shape[1]
mask = tgt_bi_valid_mask[:, None, :].expand((bs, num_memory+num_tgt, max_valid_target))
full_attention_mask[:, :, num_memory:(num_memory+max_valid_target)][mask] = 0
# add axis for multi-head
full_attention_mask = full_attention_mask[:, None, :, :]
if encoder_history_states is None:
result = self.encoder(
hidden_states=hidden_states,
attention_mask=full_attention_mask,
encoder_history_states=encoder_history_states,
)
result = list(result)
result[0] = result[0][:, num_memory:].transpose(0, 1)
if self.encoder.output_hidden_states:
return result[0], result[1]
else:
# make it back-compatible
return result[0]
else:
encoder_out = self.encoder(
hidden_states=hidden_states[:, -1:],
attention_mask=full_attention_mask[:, :, -1:],
encoder_history_states=encoder_history_states,
)
result = encoder_out[0].transpose(0, 1)
if self.encoder.output_hidden_states:
return result, encoder_out[1]
else:
return result
class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
tgt_key_padding_mask=None, memory_key_padding_mask=None):
# fmt: off
# We use the members (modules) from super-class, just the order of
# operations is changed here. First layernorm, then attention.
tgt2 = self.norm1(tgt)
tgt2, _ = self.self_attn(
tgt2, tgt2, tgt2, attn_mask=tgt_mask,
key_padding_mask=tgt_key_padding_mask
)
tgt = tgt + self.dropout1(tgt2)
# Layernorm first, then decoder attention.
tgt2 = self.norm2(tgt)
tgt2, _ = self.multihead_attn(
tgt2, memory, memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask
)
tgt = tgt + self.dropout2(tgt2)
# Layernorm first, then transformation through feedforward network.
tgt2 = self.norm3(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
tgt = tgt + self.dropout3(tgt2)
return tgt
class BertEncoder(nn.Module):
def __init__(self, config, use_act_checkpoint=True):
super(BertEncoder, self).__init__()
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = nn.ModuleList([BertLayer(config, use_act_checkpoint=use_act_checkpoint) for _ in range(config.num_hidden_layers)])
self.pre_norm = hasattr(config, 'pre_norm') and config.pre_norm
if self.pre_norm:
self.LayerNorm = LayerNormClass(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states, attention_mask, head_mask=None,
encoder_history_states=None):
all_hidden_states = ()
all_attentions = ()
for i, layer_module in enumerate(self.layer):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
history_state = None if encoder_history_states is None else encoder_history_states[i]
layer_outputs = layer_module(
hidden_states, attention_mask,
(None if head_mask is None else head_mask[i]),
history_state,
)
hidden_states = layer_outputs[0]
if self.output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if self.pre_norm:
hidden_states = self.LayerNorm(hidden_states)
outputs = (hidden_states,)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states,)
if self.output_attentions:
outputs = outputs + (all_attentions,)
return outputs
class BertConfig(PretrainedConfig):
r"""
:class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
`BertModel`.
Arguments:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_attention_heads: Number of attention heads for each attention layer in
the Transformer encoder.
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
hidden_dropout_prob: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
probabilities.
max_position_embeddings: The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
type_vocab_size: The vocabulary size of the `token_type_ids` passed into
`BertModel`.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
layer_norm_eps: The epsilon used by LayerNorm.
"""
pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self,
vocab_size_or_config_json_file=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
**kwargs):
super(BertConfig, self).__init__(**kwargs)
if isinstance(vocab_size_or_config_json_file, str):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
else:
raise ValueError("First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)")
def create_transformer(decoder_type, norm_type,
textual_feature_size,
attention_heads,
feedforward_size,
dropout,
num_layers,
output_hidden_states=False,
use_mlp_wrapper=None,
use_act_checkpoint=True,
):
assert norm_type in ['post', 'pre']
if decoder_type is None:
LayerClass = (
nn.TransformerDecoderLayer
if norm_type == "post"
else PreNormTransformerDecoderLayer
)
_layer = LayerClass(
textual_feature_size,
attention_heads,
dim_feedforward=feedforward_size,
dropout=dropout,
activation="gelu",
)
return nn.TransformerDecoder(_layer, num_layers)
elif decoder_type == 'bert_en':
from .modeling_bert import BertConfig, BertEncoder
config = BertConfig(
vocab_size_or_config_json_file=30522,
hidden_size=textual_feature_size,
num_hidden_layers=num_layers,
num_attention_heads=attention_heads,
intermediate_size=feedforward_size,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
layer_norm_eps=1e-12,
)
config.pre_norm = (norm_type == 'pre')
config.use_mlp_wrapper = use_mlp_wrapper
config.output_hidden_states = output_hidden_states
encoder = BertEncoder(config, use_act_checkpoint=use_act_checkpoint)
return BertEncoderAsDecoder(encoder) | null |
3,505 | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import os
import json
import logging
import math
import sys
from io import open
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .file_utils import cached_path
def qk2attn(query, key, attention_mask, gamma):
query = query / gamma
attention_scores = torch.matmul(query, key.transpose(-1, -2))
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
return attention_scores.softmax(dim=-1) | null |
3,506 | from __future__ import absolute_import, division, print_function, unicode_literals
import copy
import os
import json
import logging
import math
import sys
from io import open
import torch
from torch import nn
import torch.utils.checkpoint as checkpoint
from .file_utils import cached_path
def _gelu_python(x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0))) | null |
3,507 | from typing import Dict, List, Optional, Tuple
import torch
from detectron2.config import configurable
from detectron2.structures import ImageList, Instances, Boxes
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN
from detectron2.structures import Instances, ROIMasks
def detector_postprocess(
results: Instances, output_height: int, output_width: int, mask_threshold: float = 0.5
):
"""
Resize the output instances.
The input images are often resized when entering an object detector.
As a result, we often need the outputs of the detector in a different
resolution from its inputs.
This function will resize the raw outputs of an R-CNN detector
to produce outputs according to the desired output resolution.
Args:
results (Instances): the raw outputs from the detector.
`results.image_size` contains the input image resolution the detector sees.
This object might be modified in-place.
output_height, output_width: the desired output resolution.
Returns:
Instances: the resized output from the model, based on the output resolution
"""
if isinstance(output_width, torch.Tensor):
# This shape might (but not necessarily) be tensors during tracing.
# Converts integer tensors to float temporaries to ensure true
# division is performed when computing scale_x and scale_y.
output_width_tmp = output_width.float()
output_height_tmp = output_height.float()
new_size = torch.stack([output_height, output_width])
else:
new_size = (output_height, output_width)
output_width_tmp = output_width
output_height_tmp = output_height
scale_x, scale_y = (
output_width_tmp / results.image_size[1],
output_height_tmp / results.image_size[0],
)
results = Instances(new_size, **results.get_fields())
if results.has("pred_boxes"):
output_boxes = results.pred_boxes
elif results.has("proposal_boxes"):
output_boxes = results.proposal_boxes
else:
output_boxes = None
assert output_boxes is not None, "Predictions must contain boxes!"
output_boxes.scale(scale_x, scale_y)
output_boxes.clip(results.image_size)
results = results[output_boxes.nonempty()]
if results.has("pred_masks"):
if isinstance(results.pred_masks, ROIMasks):
roi_masks = results.pred_masks
else:
# pred_masks is a tensor of shape (N, 1, M, M)
roi_masks = ROIMasks(results.pred_masks[:, 0, :, :])
results.pred_masks = roi_masks.to_bitmasks(
results.pred_boxes, output_height, output_width, mask_threshold
).tensor # TODO return ROIMasks/BitMask object in the future
if results.has("pred_keypoints"):
results.pred_keypoints[:, :, 0] *= scale_x
results.pred_keypoints[:, :, 1] *= scale_y
return results
The provided code snippet includes necessary dependencies for implementing the `_postprocess` function. Write a Python function `def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes)` to solve the following problem:
Rescale the output instances to the target size.
Here is the function:
def _postprocess(instances, batched_inputs: List[Dict[str, torch.Tensor]], image_sizes):
"""
Rescale the output instances to the target size.
"""
# note: private function; subject to changes
processed_results = []
for results_per_image, input_per_image, image_size in zip(
instances, batched_inputs, image_sizes
):
height = input_per_image.get("height", image_size[0])
width = input_per_image.get("width", image_size[1])
# r = detector_postprocess(results_per_image, height, width)
if type(results_per_image)==list:
r=detector_postprocess(results_per_image[0], height, width)
else:
r=detector_postprocess(results_per_image, height, width)
processed_results.append({"instances": r})
return processed_results | Rescale the output instances to the target size. |
3,508 | from typing import Dict, List, Optional, Tuple
import torch
from detectron2.config import configurable
from detectron2.structures import ImageList, Instances, Boxes
from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN
from detectron2.structures import Instances, ROIMasks
def get_feature(i, features):
new_features={}
for key, value in features.items():
new_features[key]=value[i].unsqueeze(0)
return new_features | null |
3,509 | import torch
from detectron2.structures import Boxes, RotatedBoxes, pairwise_iou, pairwise_iou_rotated
def soft_nms(boxes, scores, method, gaussian_sigma, linear_threshold, prune_threshold):
"""
Performs soft non-maximum suppression algorithm on axis aligned boxes
Args:
boxes (Tensor[N, 5]):
boxes where NMS will be performed. They
are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format
scores (Tensor[N]):
scores for each one of the boxes
method (str):
one of ['gaussian', 'linear', 'hard']
see paper for details. users encouraged not to use "hard", as this is the
same nms available elsewhere in detectron2
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept
"""
return _soft_nms(
Boxes,
pairwise_iou,
boxes,
scores,
method,
gaussian_sigma,
linear_threshold,
prune_threshold,
)
The provided code snippet includes necessary dependencies for implementing the `batched_soft_nms` function. Write a Python function `def batched_soft_nms( boxes, scores, idxs, method, gaussian_sigma, linear_threshold, prune_threshold )` to solve the following problem:
Performs soft non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 4]): boxes where NMS will be performed. They are expected to be in (x1, y1, x2, y2) format scores (Tensor[N]): scores for each one of the boxes idxs (Tensor[N]): indices of the categories for each one of the boxes. method (str): one of ['gaussian', 'linear', 'hard'] see paper for details. users encouraged not to use "hard", as this is the same nms available elsewhere in detectron2 gaussian_sigma (float): parameter for Gaussian penalty function linear_threshold (float): iou threshold for applying linear decay. Nt from the paper re-used as threshold for standard "hard" nms prune_threshold (float): boxes with scores below this threshold are pruned at each iteration. Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] Returns: tuple(Tensor, Tensor): [0]: int64 tensor with the indices of the elements that have been kept by Soft NMS, sorted in decreasing order of scores [1]: float tensor with the re-scored scores of the elements that were kept
Here is the function:
def batched_soft_nms(
boxes, scores, idxs, method, gaussian_sigma, linear_threshold, prune_threshold
):
"""
Performs soft non-maximum suppression in a batched fashion.
Each index value correspond to a category, and NMS
will not be applied between elements of different categories.
Args:
boxes (Tensor[N, 4]):
boxes where NMS will be performed. They
are expected to be in (x1, y1, x2, y2) format
scores (Tensor[N]):
scores for each one of the boxes
idxs (Tensor[N]):
indices of the categories for each one of the boxes.
method (str):
one of ['gaussian', 'linear', 'hard']
see paper for details. users encouraged not to use "hard", as this is the
same nms available elsewhere in detectron2
gaussian_sigma (float):
parameter for Gaussian penalty function
linear_threshold (float):
iou threshold for applying linear decay. Nt from the paper
re-used as threshold for standard "hard" nms
prune_threshold (float):
boxes with scores below this threshold are pruned at each iteration.
Dramatically reduces computation time. Authors use values in [10e-4, 10e-2]
Returns:
tuple(Tensor, Tensor):
[0]: int64 tensor with the indices of the elements that have been kept
by Soft NMS, sorted in decreasing order of scores
[1]: float tensor with the re-scored scores of the elements that were kept
"""
if boxes.numel() == 0:
return (
torch.empty((0,), dtype=torch.int64, device=boxes.device),
torch.empty((0,), dtype=torch.float32, device=scores.device),
)
# strategy: in order to perform NMS independently per class.
# we add an offset to all the boxes. The offset is dependent
# only on the class idx, and is large enough so that boxes
# from different classes do not overlap
max_coordinate = boxes.max()
offsets = idxs.to(boxes) * (max_coordinate + 1)
boxes_for_nms = boxes + offsets[:, None]
return soft_nms(
boxes_for_nms, scores, method, gaussian_sigma, linear_threshold, prune_threshold
) | Performs soft non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 4]): boxes where NMS will be performed. They are expected to be in (x1, y1, x2, y2) format scores (Tensor[N]): scores for each one of the boxes idxs (Tensor[N]): indices of the categories for each one of the boxes. method (str): one of ['gaussian', 'linear', 'hard'] see paper for details. users encouraged not to use "hard", as this is the same nms available elsewhere in detectron2 gaussian_sigma (float): parameter for Gaussian penalty function linear_threshold (float): iou threshold for applying linear decay. Nt from the paper re-used as threshold for standard "hard" nms prune_threshold (float): boxes with scores below this threshold are pruned at each iteration. Dramatically reduces computation time. Authors use values in [10e-4, 10e-2] Returns: tuple(Tensor, Tensor): [0]: int64 tensor with the indices of the elements that have been kept by Soft NMS, sorted in decreasing order of scores [1]: float tensor with the re-scored scores of the elements that were kept |
3,510 | from detectron2.config import CfgNode as CN
def add_grit_config(cfg):
_C = cfg
_C.MODEL.BEAM_SIZE = 1
_C.MODEL.TRAIN_TASK = ["ObjectDet", "DenseCap"]
_C.MODEL.TEST_TASK = "DenseCap" # This can be varied if the model is jointly trained on multiple tasks
_C.MODEL.ROI_BOX_HEAD.USE_BIAS = 0.0 # >= 0: not use
_C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False
_C.MODEL.ROI_HEADS.MASK_WEIGHT = 1.0
_C.MODEL.ROI_HEADS.OBJECT_FEAT_POOLER_RES = 14
_C.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
# Backbones
_C.MODEL.VIT_LAYERS = 12
# Text Decoder
_C.TEXT_DECODER = CN()
_C.TEXT_DECODER.VOCAB_SIZE = 30522
_C.TEXT_DECODER.HIDDEN_SIZE = 768
_C.TEXT_DECODER.NUM_LAYERS = 6
_C.TEXT_DECODER.ATTENTION_HEADS = 12
_C.TEXT_DECODER.FEEDFORWARD_SIZE = 768 * 4
# Multi-dataset dataloader
_C.DATALOADER.DATASET_RATIO = [1, 1] # sample ratio
_C.DATALOADER.DATASET_BS = 1
_C.DATALOADER.DATASET_INPUT_SIZE = [1024, 1024]
_C.DATALOADER.DATASET_INPUT_SCALE = [(0.1, 2.0), (0.1, 2.0)]
_C.DATALOADER.DATASET_MIN_SIZES = [(640, 800), (640, 800)]
_C.DATALOADER.DATASET_MAX_SIZES = [1333, 1333]
_C.SOLVER.USE_CUSTOM_SOLVER = True
_C.SOLVER.OPTIMIZER = 'ADAMW'
_C.SOLVER.VIT_LAYER_DECAY = True
_C.SOLVER.VIT_LAYER_DECAY_RATE = 0.7
_C.INPUT.CUSTOM_AUG = 'EfficientDetResizeCrop'
_C.INPUT.TRAIN_SIZE = 1024
_C.INPUT.TEST_SIZE = 1024
_C.INPUT.SCALE_RANGE = (0.1, 2.)
# 'default' for fixed short / long edge
_C.INPUT.TEST_INPUT_TYPE = 'default'
_C.FIND_UNUSED_PARAM = True
_C.USE_ACT_CHECKPOINT = True | null |
3,511 | import itertools
from typing import Any, Callable, Dict, Iterable, List, Set, Type, Union
import torch
from detectron2.config import CfgNode
from detectron2.solver.build import maybe_add_gradient_clipping
def get_vit_lr_decay_rate(name, lr_decay_rate=1.0, num_layers=12):
"""
Calculate lr decay rate for different ViT blocks.
Args:
name (string): parameter name.
lr_decay_rate (float): base lr decay rate.
num_layers (int): number of ViT blocks.
Returns:
lr decay rate for the given parameter.
"""
layer_id = num_layers + 1
if name.startswith("backbone"):
if ".pos_embed" in name or ".patch_embed" in name:
layer_id = 0
elif ".blocks." in name and ".residual." not in name:
layer_id = int(name[name.find(".blocks.") :].split(".")[2]) + 1
return lr_decay_rate ** (num_layers + 1 - layer_id)
def maybe_add_gradient_clipping(
cfg: CfgNode, optimizer: Type[torch.optim.Optimizer]
) -> Type[torch.optim.Optimizer]:
"""
If gradient clipping is enabled through config options, wraps the existing
optimizer type to become a new dynamically created class OptimizerWithGradientClip
that inherits the given optimizer and overrides the `step` method to
include gradient clipping.
Args:
cfg: CfgNode, configuration options
optimizer: type. A subclass of torch.optim.Optimizer
Return:
type: either the input `optimizer` (if gradient clipping is disabled), or
a subclass of it with gradient clipping included in the `step` method.
"""
if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED:
return optimizer
if isinstance(optimizer, torch.optim.Optimizer):
optimizer_type = type(optimizer)
else:
assert issubclass(optimizer, torch.optim.Optimizer), optimizer
optimizer_type = optimizer
grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS)
OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping(
optimizer_type, per_param_clipper=grad_clipper
)
if isinstance(optimizer, torch.optim.Optimizer):
optimizer.__class__ = OptimizerWithGradientClip # a bit hacky, not recommended
return optimizer
else:
return OptimizerWithGradientClip
def build_custom_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
params: List[Dict[str, Any]] = []
memo: Set[torch.nn.parameter.Parameter] = set()
optimizer_type = cfg.SOLVER.OPTIMIZER
for key, value in model.named_parameters(recurse=True):
if not value.requires_grad:
continue
# Avoid duplicating parameters
if value in memo:
continue
memo.add(value)
lr = cfg.SOLVER.BASE_LR
weight_decay = cfg.SOLVER.WEIGHT_DECAY
if cfg.SOLVER.VIT_LAYER_DECAY:
lr = lr * get_vit_lr_decay_rate(key, cfg.SOLVER.VIT_LAYER_DECAY_RATE, cfg.MODEL.VIT_LAYERS)
param = {"params": [value], "lr": lr}
if optimizer_type != 'ADAMW':
param['weight_decay'] = weight_decay
params += [param]
def maybe_add_full_model_gradient_clipping(optim): # optim: the optimizer class
# detectron2 doesn't have full model gradient clipping now
clip_norm_val = cfg.SOLVER.CLIP_GRADIENTS.CLIP_VALUE
enable = (
cfg.SOLVER.CLIP_GRADIENTS.ENABLED
and cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model"
and clip_norm_val > 0.0
)
class FullModelGradientClippingOptimizer(optim):
def step(self, closure=None):
all_params = itertools.chain(*[x["params"] for x in self.param_groups])
torch.nn.utils.clip_grad_norm_(all_params, clip_norm_val)
super().step(closure=closure)
return FullModelGradientClippingOptimizer if enable else optim
if optimizer_type == 'SGD':
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.SGD)(
params, cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM,
nesterov=cfg.SOLVER.NESTEROV
)
elif optimizer_type == 'ADAMW':
optimizer = maybe_add_full_model_gradient_clipping(torch.optim.AdamW)(
params, cfg.SOLVER.BASE_LR,
weight_decay=cfg.SOLVER.WEIGHT_DECAY
)
else:
raise NotImplementedError(f"no optimizer type {optimizer_type}")
if not cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE == "full_model":
optimizer = maybe_add_gradient_clipping(cfg, optimizer)
return optimizer | null |
3,512 | import itertools
import json
import os
from detectron2.structures import Boxes, BoxMode, pairwise_iou
from detectron2.utils.file_io import PathManager
import numpy as np
import pycocotools.mask as mask_util
from detectron2.evaluation.coco_evaluation import COCOEvaluator
from detectron2.evaluation.coco_evaluation import _evaluate_predictions_on_coco
The provided code snippet includes necessary dependencies for implementing the `instances_to_coco_json` function. Write a Python function `def instances_to_coco_json(instances, img_id, output_logits=False)` to solve the following problem:
Add object_descriptions and logit (if applicable) to detectron2's instances_to_coco_json
Here is the function:
def instances_to_coco_json(instances, img_id, output_logits=False):
"""
Add object_descriptions and logit (if applicable) to
detectron2's instances_to_coco_json
"""
num_instance = len(instances)
if num_instance == 0:
return []
boxes = instances.pred_boxes.tensor.numpy()
boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
boxes = boxes.tolist()
scores = instances.scores.tolist()
classes = instances.pred_classes.tolist()
object_descriptions = instances.pred_object_descriptions.data
if output_logits:
logits = instances.logits.tolist()
results = []
for k in range(num_instance):
result = {
"image_id": img_id,
"category_id": classes[k],
"bbox": boxes[k],
"score": scores[k],
'object_descriptions': object_descriptions[k],
}
if output_logits:
result["logit"] = logits[k]
results.append(result)
return results | Add object_descriptions and logit (if applicable) to detectron2's instances_to_coco_json |
3,513 | import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
def get_version():
init_py_path = path.join(path.abspath(path.dirname(__file__)), "detectron2", "__init__.py")
init_py = open(init_py_path, "r").readlines()
version_line = [l.strip() for l in init_py if l.startswith("__version__")][0]
version = version_line.split("=")[-1].strip().strip("'\"")
# The following is used to build release packages.
# Users should never use it.
suffix = os.getenv("D2_VERSION_SUFFIX", "")
version = version + suffix
if os.getenv("BUILD_NIGHTLY", "0") == "1":
from datetime import datetime
date_str = datetime.today().strftime("%y%m%d")
version = version + ".dev" + date_str
new_init_py = [l for l in init_py if not l.startswith("__version__")]
new_init_py.append('__version__ = "{}"\n'.format(version))
with open(init_py_path, "w") as f:
f.write("".join(new_init_py))
return version | null |
3,514 | import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
torch_ver = [int(x) for x in torch.__version__.split(".")[:2]]
assert torch_ver >= [1, 8], "Requires PyTorch >= 1.8"
def get_extensions():
this_dir = path.dirname(path.abspath(__file__))
extensions_dir = path.join(this_dir, "detectron2", "layers", "csrc")
main_source = path.join(extensions_dir, "vision.cpp")
sources = glob.glob(path.join(extensions_dir, "**", "*.cpp"))
from torch.utils.cpp_extension import ROCM_HOME
is_rocm_pytorch = (
True if ((torch.version.hip is not None) and (ROCM_HOME is not None)) else False
)
if is_rocm_pytorch:
assert torch_ver >= [1, 8], "ROCM support requires PyTorch >= 1.8!"
# common code between cuda and rocm platforms, for hipify version [1,0,0] and later.
source_cuda = glob.glob(path.join(extensions_dir, "**", "*.cu")) + glob.glob(
path.join(extensions_dir, "*.cu")
)
sources = [main_source] + sources
extension = CppExtension
extra_compile_args = {"cxx": []}
define_macros = []
if (torch.cuda.is_available() and ((CUDA_HOME is not None) or is_rocm_pytorch)) or os.getenv(
"FORCE_CUDA", "0"
) == "1":
extension = CUDAExtension
sources += source_cuda
if not is_rocm_pytorch:
define_macros += [("WITH_CUDA", None)]
extra_compile_args["nvcc"] = [
"-O3",
"-DCUDA_HAS_FP16=1",
"-D__CUDA_NO_HALF_OPERATORS__",
"-D__CUDA_NO_HALF_CONVERSIONS__",
"-D__CUDA_NO_HALF2_OPERATORS__",
]
else:
define_macros += [("WITH_HIP", None)]
extra_compile_args["nvcc"] = []
if torch_ver < [1, 7]:
# supported by https://github.com/pytorch/pytorch/pull/43931
CC = os.environ.get("CC", None)
if CC is not None:
extra_compile_args["nvcc"].append("-ccbin={}".format(CC))
include_dirs = [extensions_dir]
ext_modules = [
extension(
"detectron2._C",
sources,
include_dirs=include_dirs,
define_macros=define_macros,
extra_compile_args=extra_compile_args,
)
]
return ext_modules | null |
3,515 | import glob
import os
import shutil
from os import path
from setuptools import find_packages, setup
from typing import List
import torch
from torch.utils.cpp_extension import CUDA_HOME, CppExtension, CUDAExtension
The provided code snippet includes necessary dependencies for implementing the `get_model_zoo_configs` function. Write a Python function `def get_model_zoo_configs() -> List[str]` to solve the following problem:
Return a list of configs to include in package for model zoo. Copy over these configs inside detectron2/model_zoo.
Here is the function:
def get_model_zoo_configs() -> List[str]:
"""
Return a list of configs to include in package for model zoo. Copy over these configs inside
detectron2/model_zoo.
"""
# Use absolute paths while symlinking.
source_configs_dir = path.join(path.dirname(path.realpath(__file__)), "configs")
destination = path.join(
path.dirname(path.realpath(__file__)), "detectron2", "model_zoo", "configs"
)
# Symlink the config directory inside package to have a cleaner pip install.
# Remove stale symlink/directory from a previous build.
if path.exists(source_configs_dir):
if path.islink(destination):
os.unlink(destination)
elif path.isdir(destination):
shutil.rmtree(destination)
if not path.exists(destination):
try:
os.symlink(source_configs_dir, destination)
except OSError:
# Fall back to copying if symlink fails: ex. on Windows.
shutil.copytree(source_configs_dir, destination)
config_paths = glob.glob("configs/**/*.yaml", recursive=True) + glob.glob(
"configs/**/*.py", recursive=True
)
return config_paths | Return a list of configs to include in package for model zoo. Copy over these configs inside detectron2/model_zoo. |
3,516 | import os
import sys
from unittest import mock
from sphinx.domains import Domain
from typing import Dict, List, Tuple
import sphinx_rtd_theme
class GithubURLDomain(Domain):
"""
Resolve certain links in markdown files to github source.
"""
name = "githuburl"
ROOT = "https://github.com/facebookresearch/detectron2/blob/main/"
LINKED_DOC = ["tutorials/install", "tutorials/getting_started"]
def resolve_any_xref(self, env, fromdocname, builder, target, node, contnode):
github_url = None
if not target.endswith("html") and target.startswith("../../"):
url = target.replace("../", "")
github_url = url
if fromdocname in self.LINKED_DOC:
# unresolved links in these docs are all github links
github_url = target
if github_url is not None:
if github_url.endswith("MODEL_ZOO") or github_url.endswith("README"):
# bug of recommonmark.
# https://github.com/readthedocs/recommonmark/blob/ddd56e7717e9745f11300059e4268e204138a6b1/recommonmark/parser.py#L152-L155
github_url += ".md"
print("Ref {} resolved to github:{}".format(target, github_url))
contnode["refuri"] = self.ROOT + github_url
return [("githuburl:any", contnode)]
else:
return []
from recommonmark.parser import CommonMarkParser
import detectron2
def autodoc_skip_member(app, what, name, obj, skip, options):
# we hide something deliberately
if getattr(obj, "__HIDE_SPHINX_DOC__", False):
return True
# Hide some that are deprecated or not intended to be used
HIDDEN = {
"ResNetBlockBase",
"GroupedBatchSampler",
"build_transform_gen",
"apply_transform_gens",
"TransformGen",
"apply_augmentations",
"StandardAugInput",
"build_batch_data_loader",
"draw_panoptic_seg_predictions",
"WarmupCosineLR",
"WarmupMultiStepLR",
"downgrade_config",
"upgrade_config",
"add_export_config",
}
try:
if name in HIDDEN or (
hasattr(obj, "__doc__") and obj.__doc__.lower().strip().startswith("deprecated")
):
print("Skipping deprecated object: {}".format(name))
return True
except:
pass
return skip
def paper_ref_role(
typ: str,
rawtext: str,
text: str,
lineno: int,
inliner,
options: Dict = {},
content: List[str] = [],
):
"""
Parse :paper:`xxx`. Similar to the "extlinks" sphinx extension.
"""
from docutils import nodes, utils
from sphinx.util.nodes import split_explicit_title
text = utils.unescape(text)
has_explicit_title, title, link = split_explicit_title(text)
link = link.lower()
if link not in _PAPER_DATA:
inliner.reporter.warning("Cannot find paper " + link)
paper_url, paper_title = "#", link
else:
paper_url, paper_title = _PAPER_DATA[link]
if "/" not in paper_url:
paper_url = "https://arxiv.org/abs/" + paper_url
if not has_explicit_title:
title = paper_title
pnode = nodes.reference(title, title, internal=False, refuri=paper_url)
return [pnode], []
def setup(app):
from recommonmark.transform import AutoStructify
app.add_domain(GithubURLDomain)
app.connect("autodoc-skip-member", autodoc_skip_member)
app.add_role("paper", paper_ref_role)
app.add_config_value(
"recommonmark_config",
{"enable_math": True, "enable_inline_math": True, "enable_eval_rst": True},
True,
)
app.add_transform(AutoStructify) | null |
3,517 | import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_c2_detectron_names(weights):
"""
Map Caffe2 Detectron weight names to Detectron2 names.
Args:
weights (dict): name -> tensor
Returns:
dict: detectron2 names -> tensor
dict: detectron2 names -> C2 names
"""
logger = logging.getLogger(__name__)
logger.info("Renaming Caffe2 weights ......")
original_keys = sorted(weights.keys())
layer_keys = copy.deepcopy(original_keys)
layer_keys = convert_basic_c2_names(layer_keys)
# --------------------------------------------------------------------------
# RPN hidden representation conv
# --------------------------------------------------------------------------
# FPN case
# In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then
# shared for all other levels, hence the appearance of "fpn2"
layer_keys = [
k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys
]
# Non-FPN case
layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys]
# --------------------------------------------------------------------------
# RPN box transformation conv
# --------------------------------------------------------------------------
# FPN case (see note above about "fpn2")
layer_keys = [
k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas")
for k in layer_keys
]
layer_keys = [
k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits")
for k in layer_keys
]
# Non-FPN case
layer_keys = [
k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys
]
layer_keys = [
k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits")
for k in layer_keys
]
# --------------------------------------------------------------------------
# Fast R-CNN box head
# --------------------------------------------------------------------------
layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys]
layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys]
layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys]
layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys]
# 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s
layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys]
# --------------------------------------------------------------------------
# FPN lateral and output convolutions
# --------------------------------------------------------------------------
def fpn_map(name):
"""
Look for keys with the following patterns:
1) Starts with "fpn.inner."
Example: "fpn.inner.res2.2.sum.lateral.weight"
Meaning: These are lateral pathway convolutions
2) Starts with "fpn.res"
Example: "fpn.res2.2.sum.weight"
Meaning: These are FPN output convolutions
"""
splits = name.split(".")
norm = ".norm" if "norm" in splits else ""
if name.startswith("fpn.inner."):
# splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight']
stage = int(splits[2][len("res") :])
return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1])
elif name.startswith("fpn.res"):
# splits example: ['fpn', 'res2', '2', 'sum', 'weight']
stage = int(splits[1][len("res") :])
return "fpn_output{}{}.{}".format(stage, norm, splits[-1])
return name
layer_keys = [fpn_map(k) for k in layer_keys]
# --------------------------------------------------------------------------
# Mask R-CNN mask head
# --------------------------------------------------------------------------
# roi_heads.StandardROIHeads case
layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys]
layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys]
layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys]
# roi_heads.Res5ROIHeads case
layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys]
# --------------------------------------------------------------------------
# Keypoint R-CNN head
# --------------------------------------------------------------------------
# interestingly, the keypoint head convs have blob names that are simply "conv_fcnX"
layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys]
layer_keys = [
k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys
]
layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys]
# --------------------------------------------------------------------------
# Done with replacements
# --------------------------------------------------------------------------
assert len(set(layer_keys)) == len(layer_keys)
assert len(original_keys) == len(layer_keys)
new_weights = {}
new_keys_to_original_keys = {}
for orig, renamed in zip(original_keys, layer_keys):
new_keys_to_original_keys[renamed] = orig
if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."):
# remove the meaningless prediction weight for background class
new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1
new_weights[renamed] = weights[orig][new_start_idx:]
logger.info(
"Remove prediction weight for background class in {}. The shape changes from "
"{} to {}.".format(
renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape)
)
)
elif renamed.startswith("cls_score."):
# move weights of bg class from original index 0 to last index
logger.info(
"Move classification weights for background class in {} from index 0 to "
"index {}.".format(renamed, weights[orig].shape[0] - 1)
)
new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]])
else:
new_weights[renamed] = weights[orig]
return new_weights, new_keys_to_original_keys
def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]):
"""
Params in the same submodule are grouped together.
Args:
keys: names of all parameters
original_names: mapping from parameter name to their name in the checkpoint
Returns:
dict[name -> all other names in the same group]
"""
def _submodule_name(key):
pos = key.rfind(".")
if pos < 0:
return None
prefix = key[: pos + 1]
return prefix
all_submodules = [_submodule_name(k) for k in keys]
all_submodules = [x for x in all_submodules if x]
all_submodules = sorted(all_submodules, key=len)
ret = {}
for prefix in all_submodules:
group = [k for k in keys if k.startswith(prefix)]
if len(group) <= 1:
continue
original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group])
if len(original_name_lcp) == 0:
# don't group weights if original names don't share prefix
continue
for k in group:
if k in ret:
continue
ret[k] = group
return ret
def _longest_common_prefix(names: List[str]) -> str:
"""
["abc.zfg", "abc.zef"] -> "abc."
"""
names = [n.split(".") for n in names]
m1, m2 = min(names), max(names)
ret = [a for a, b in zip(m1, m2) if a == b]
ret = ".".join(ret) + "." if len(ret) else ""
return ret
def _group_str(names: List[str]) -> str:
"""
Turn "common1", "common2", "common3" into "common{1,2,3}"
"""
lcp = _longest_common_prefix_str(names)
rest = [x[len(lcp) :] for x in names]
rest = "{" + ",".join(rest) + "}"
ret = lcp + rest
# add some simplification for BN specifically
ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*")
ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*")
return ret
The provided code snippet includes necessary dependencies for implementing the `align_and_update_state_dicts` function. Write a Python function `def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True)` to solve the following problem:
Match names between the two state-dict, and returns a new chkpt_state_dict with names converted to match model_state_dict with heuristics. The returned dict can be later loaded with fvcore checkpointer. If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2 model and will be renamed at first. Strategy: suppose that the models that we will create will have prefixes appended to each of its keys, for example due to an extra level of nesting that the original pre-trained weights from ImageNet won't contain. For example, model.state_dict() might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains res2.conv1.weight. We thus want to match both parameters together. For that, we look for each model weight, look among all loaded keys if there is one that is a suffix of the current weight name, and use it if that's the case. If multiple matches exist, take the one with longest size of the corresponding name. For example, for the same model as before, the pretrained weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case, we want to match backbone[0].body.conv1.weight to conv1.weight, and backbone[0].body.res2.conv1.weight to res2.conv1.weight.
Here is the function:
def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True):
"""
Match names between the two state-dict, and returns a new chkpt_state_dict with names
converted to match model_state_dict with heuristics. The returned dict can be later
loaded with fvcore checkpointer.
If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2
model and will be renamed at first.
Strategy: suppose that the models that we will create will have prefixes appended
to each of its keys, for example due to an extra level of nesting that the original
pre-trained weights from ImageNet won't contain. For example, model.state_dict()
might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
res2.conv1.weight. We thus want to match both parameters together.
For that, we look for each model weight, look among all loaded keys if there is one
that is a suffix of the current weight name, and use it if that's the case.
If multiple matches exist, take the one with longest size
of the corresponding name. For example, for the same model as before, the pretrained
weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
we want to match backbone[0].body.conv1.weight to conv1.weight, and
backbone[0].body.res2.conv1.weight to res2.conv1.weight.
"""
model_keys = sorted(model_state_dict.keys())
if c2_conversion:
ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict)
# original_keys: the name in the original dict (before renaming)
else:
original_keys = {x: x for x in ckpt_state_dict.keys()}
ckpt_keys = sorted(ckpt_state_dict.keys())
def match(a, b):
# Matched ckpt_key should be a complete (starts with '.') suffix.
# For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
# but matches whatever_conv1 or mesh_head.whatever_conv1.
return a == b or a.endswith("." + b)
# get a matrix of string matches, where each (i, j) entry correspond to the size of the
# ckpt_key string, if it matches
match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys]
match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys))
# use the matched one with longest size in case of multiple matches
max_match_size, idxs = match_matrix.max(1)
# remove indices that correspond to no-match
idxs[max_match_size == 0] = -1
logger = logging.getLogger(__name__)
# matched_pairs (matched checkpoint key --> matched model key)
matched_keys = {}
result_state_dict = {}
for idx_model, idx_ckpt in enumerate(idxs.tolist()):
if idx_ckpt == -1:
continue
key_model = model_keys[idx_model]
key_ckpt = ckpt_keys[idx_ckpt]
value_ckpt = ckpt_state_dict[key_ckpt]
shape_in_model = model_state_dict[key_model].shape
if shape_in_model != value_ckpt.shape:
logger.warning(
"Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format(
key_ckpt, value_ckpt.shape, key_model, shape_in_model
)
)
logger.warning(
"{} will not be loaded. Please double check and see if this is desired.".format(
key_ckpt
)
)
continue
assert key_model not in result_state_dict
result_state_dict[key_model] = value_ckpt
if key_ckpt in matched_keys: # already added to matched_keys
logger.error(
"Ambiguity found for {} in checkpoint!"
"It matches at least two keys in the model ({} and {}).".format(
key_ckpt, key_model, matched_keys[key_ckpt]
)
)
raise ValueError("Cannot match one checkpoint key to multiple keys in the model.")
matched_keys[key_ckpt] = key_model
# logging:
matched_model_keys = sorted(matched_keys.values())
if len(matched_model_keys) == 0:
logger.warning("No weights in checkpoint matched with model.")
return ckpt_state_dict
common_prefix = _longest_common_prefix(matched_model_keys)
rev_matched_keys = {v: k for k, v in matched_keys.items()}
original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys}
model_key_groups = _group_keys_by_module(matched_model_keys, original_keys)
table = []
memo = set()
for key_model in matched_model_keys:
if key_model in memo:
continue
if key_model in model_key_groups:
group = model_key_groups[key_model]
memo |= set(group)
shapes = [tuple(model_state_dict[k].shape) for k in group]
table.append(
(
_longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*",
_group_str([original_keys[k] for k in group]),
" ".join([str(x).replace(" ", "") for x in shapes]),
)
)
else:
key_checkpoint = original_keys[key_model]
shape = str(tuple(model_state_dict[key_model].shape))
table.append((key_model[len(common_prefix) :], key_checkpoint, shape))
table_str = tabulate(
table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"]
)
logger.info(
"Following weights matched with "
+ (f"submodule {common_prefix[:-1]}" if common_prefix else "model")
+ ":\n"
+ table_str
)
unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())]
for k in unmatched_ckpt_keys:
result_state_dict[k] = ckpt_state_dict[k]
return result_state_dict | Match names between the two state-dict, and returns a new chkpt_state_dict with names converted to match model_state_dict with heuristics. The returned dict can be later loaded with fvcore checkpointer. If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2 model and will be renamed at first. Strategy: suppose that the models that we will create will have prefixes appended to each of its keys, for example due to an extra level of nesting that the original pre-trained weights from ImageNet won't contain. For example, model.state_dict() might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains res2.conv1.weight. We thus want to match both parameters together. For that, we look for each model weight, look among all loaded keys if there is one that is a suffix of the current weight name, and use it if that's the case. If multiple matches exist, take the one with longest size of the corresponding name. For example, for the same model as before, the pretrained weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case, we want to match backbone[0].body.conv1.weight to conv1.weight, and backbone[0].body.res2.conv1.weight to res2.conv1.weight. |
3,518 | import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `_keypoints_to_heatmap` function. Write a Python function `def _keypoints_to_heatmap( keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int ) -> Tuple[torch.Tensor, torch.Tensor]` to solve the following problem:
Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"): d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. Arguments: keypoints: tensor of keypoint locations in of shape (N, K, 3). rois: Nx4 tensor of rois in xyxy format heatmap_size: integer side length of square heatmap. Returns: heatmaps: A tensor of shape (N, K) containing an integer spatial label in the range [0, heatmap_size**2 - 1] for each keypoint in the input. valid: A tensor of shape (N, K) containing whether each keypoint is in the roi or not.
Here is the function:
def _keypoints_to_heatmap(
keypoints: torch.Tensor, rois: torch.Tensor, heatmap_size: int
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space.
Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the
closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the
continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"):
d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
Arguments:
keypoints: tensor of keypoint locations in of shape (N, K, 3).
rois: Nx4 tensor of rois in xyxy format
heatmap_size: integer side length of square heatmap.
Returns:
heatmaps: A tensor of shape (N, K) containing an integer spatial label
in the range [0, heatmap_size**2 - 1] for each keypoint in the input.
valid: A tensor of shape (N, K) containing whether each keypoint is in
the roi or not.
"""
if rois.numel() == 0:
return rois.new().long(), rois.new().long()
offset_x = rois[:, 0]
offset_y = rois[:, 1]
scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])
offset_x = offset_x[:, None]
offset_y = offset_y[:, None]
scale_x = scale_x[:, None]
scale_y = scale_y[:, None]
x = keypoints[..., 0]
y = keypoints[..., 1]
x_boundary_inds = x == rois[:, 2][:, None]
y_boundary_inds = y == rois[:, 3][:, None]
x = (x - offset_x) * scale_x
x = x.floor().long()
y = (y - offset_y) * scale_y
y = y.floor().long()
x[x_boundary_inds] = heatmap_size - 1
y[y_boundary_inds] = heatmap_size - 1
valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
vis = keypoints[..., 2] > 0
valid = (valid_loc & vis).long()
lin_ind = y * heatmap_size + x
heatmaps = lin_ind * valid
return heatmaps, valid | Encode keypoint locations into a target heatmap for use in SoftmaxWithLoss across space. Maps keypoints from the half-open interval [x1, x2) on continuous image coordinates to the closed interval [0, heatmap_size - 1] on discrete image coordinates. We use the continuous-discrete conversion from Heckbert 1990 ("What is the coordinate of a pixel?"): d = floor(c) and c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. Arguments: keypoints: tensor of keypoint locations in of shape (N, K, 3). rois: Nx4 tensor of rois in xyxy format heatmap_size: integer side length of square heatmap. Returns: heatmaps: A tensor of shape (N, K) containing an integer spatial label in the range [0, heatmap_size**2 - 1] for each keypoint in the input. valid: A tensor of shape (N, K) containing whether each keypoint is in the roi or not. |
3,519 | import numpy as np
from typing import Any, List, Tuple, Union
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `heatmaps_to_keypoints` function. Write a Python function `def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor` to solve the following problem:
Extract predicted keypoint locations from heatmaps. Args: maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for each ROI and each keypoint. rois (Tensor): (#ROIs, 4). The box of each ROI. Returns: Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to (x, y, logit, score) for each keypoint. When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate, we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
Here is the function:
def heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) -> torch.Tensor:
"""
Extract predicted keypoint locations from heatmaps.
Args:
maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for
each ROI and each keypoint.
rois (Tensor): (#ROIs, 4). The box of each ROI.
Returns:
Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to
(x, y, logit, score) for each keypoint.
When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate,
we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from
Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.
"""
# The decorator use of torch.no_grad() was not supported by torchscript.
# https://github.com/pytorch/pytorch/issues/44768
maps = maps.detach()
rois = rois.detach()
offset_x = rois[:, 0]
offset_y = rois[:, 1]
widths = (rois[:, 2] - rois[:, 0]).clamp(min=1)
heights = (rois[:, 3] - rois[:, 1]).clamp(min=1)
widths_ceil = widths.ceil()
heights_ceil = heights.ceil()
num_rois, num_keypoints = maps.shape[:2]
xy_preds = maps.new_zeros(rois.shape[0], num_keypoints, 4)
width_corrections = widths / widths_ceil
height_corrections = heights / heights_ceil
keypoints_idx = torch.arange(num_keypoints, device=maps.device)
for i in range(num_rois):
outsize = (int(heights_ceil[i]), int(widths_ceil[i]))
roi_map = F.interpolate(
maps[[i]], size=outsize, mode="bicubic", align_corners=False
).squeeze(
0
) # #keypoints x H x W
# softmax over the spatial region
max_score, _ = roi_map.view(num_keypoints, -1).max(1)
max_score = max_score.view(num_keypoints, 1, 1)
tmp_full_resolution = (roi_map - max_score).exp_()
tmp_pool_resolution = (maps[i] - max_score).exp_()
# Produce scores over the region H x W, but normalize with POOL_H x POOL_W,
# so that the scores of objects of different absolute sizes will be more comparable
roi_map_scores = tmp_full_resolution / tmp_pool_resolution.sum((1, 2), keepdim=True)
w = roi_map.shape[2]
pos = roi_map.view(num_keypoints, -1).argmax(1)
x_int = pos % w
y_int = (pos - x_int) // w
assert (
roi_map_scores[keypoints_idx, y_int, x_int]
== roi_map_scores.view(num_keypoints, -1).max(1)[0]
).all()
x = (x_int.float() + 0.5) * width_corrections[i]
y = (y_int.float() + 0.5) * height_corrections[i]
xy_preds[i, :, 0] = x + offset_x[i]
xy_preds[i, :, 1] = y + offset_y[i]
xy_preds[i, :, 2] = roi_map[keypoints_idx, y_int, x_int]
xy_preds[i, :, 3] = roi_map_scores[keypoints_idx, y_int, x_int]
return xy_preds | Extract predicted keypoint locations from heatmaps. Args: maps (Tensor): (#ROIs, #keypoints, POOL_H, POOL_W). The predicted heatmap of logits for each ROI and each keypoint. rois (Tensor): (#ROIs, 4). The box of each ROI. Returns: Tensor of shape (#ROIs, #keypoints, 4) with the last dimension corresponding to (x, y, logit, score) for each keypoint. When converting discrete pixel indices in an NxN image to a continuous keypoint coordinate, we maintain consistency with :meth:`Keypoints.to_heatmap` by using the conversion from Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate. |
3,520 | import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ROIAlign
from detectron2.utils.memory import retry_if_cuda_oom
from .boxes import Boxes
def polygon_area(x, y):
# Using the shoelace formula
# https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) | null |
3,521 | import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ROIAlign
from detectron2.utils.memory import retry_if_cuda_oom
from .boxes import Boxes
def polygons_to_bitmask(polygons: List[np.ndarray], height: int, width: int) -> np.ndarray:
"""
Args:
polygons (list[ndarray]): each array has shape (Nx2,)
height, width (int)
Returns:
ndarray: a bool mask of shape (height, width)
"""
if len(polygons) == 0:
# COCOAPI does not support empty polygons
return np.zeros((height, width)).astype(np.bool)
rles = mask_util.frPyObjects(polygons, height, width)
rle = mask_util.merge(rles)
return mask_util.decode(rle).astype(np.bool)
The provided code snippet includes necessary dependencies for implementing the `rasterize_polygons_within_box` function. Write a Python function `def rasterize_polygons_within_box( polygons: List[np.ndarray], box: np.ndarray, mask_size: int ) -> torch.Tensor` to solve the following problem:
Rasterize the polygons into a mask image and crop the mask content in the given box. The cropped mask is resized to (mask_size, mask_size). This function is used when generating training targets for mask head in Mask R-CNN. Given original ground-truth masks for an image, new ground-truth mask training targets in the size of `mask_size x mask_size` must be provided for each predicted box. This function will be called to produce such targets. Args: polygons (list[ndarray[float]]): a list of polygons, which represents an instance. box: 4-element numpy array mask_size (int): Returns: Tensor: BoolTensor of shape (mask_size, mask_size)
Here is the function:
def rasterize_polygons_within_box(
polygons: List[np.ndarray], box: np.ndarray, mask_size: int
) -> torch.Tensor:
"""
Rasterize the polygons into a mask image and
crop the mask content in the given box.
The cropped mask is resized to (mask_size, mask_size).
This function is used when generating training targets for mask head in Mask R-CNN.
Given original ground-truth masks for an image, new ground-truth mask
training targets in the size of `mask_size x mask_size`
must be provided for each predicted box. This function will be called to
produce such targets.
Args:
polygons (list[ndarray[float]]): a list of polygons, which represents an instance.
box: 4-element numpy array
mask_size (int):
Returns:
Tensor: BoolTensor of shape (mask_size, mask_size)
"""
# 1. Shift the polygons w.r.t the boxes
w, h = box[2] - box[0], box[3] - box[1]
polygons = copy.deepcopy(polygons)
for p in polygons:
p[0::2] = p[0::2] - box[0]
p[1::2] = p[1::2] - box[1]
# 2. Rescale the polygons to the new box size
# max() to avoid division by small number
ratio_h = mask_size / max(h, 0.1)
ratio_w = mask_size / max(w, 0.1)
if ratio_h == ratio_w:
for p in polygons:
p *= ratio_h
else:
for p in polygons:
p[0::2] *= ratio_w
p[1::2] *= ratio_h
# 3. Rasterize the polygons with coco api
mask = polygons_to_bitmask(polygons, mask_size, mask_size)
mask = torch.from_numpy(mask)
return mask | Rasterize the polygons into a mask image and crop the mask content in the given box. The cropped mask is resized to (mask_size, mask_size). This function is used when generating training targets for mask head in Mask R-CNN. Given original ground-truth masks for an image, new ground-truth mask training targets in the size of `mask_size x mask_size` must be provided for each predicted box. This function will be called to produce such targets. Args: polygons (list[ndarray[float]]): a list of polygons, which represents an instance. box: 4-element numpy array mask_size (int): Returns: Tensor: BoolTensor of shape (mask_size, mask_size) |
3,522 | import math
from typing import List, Tuple
import torch
from detectron2.layers.rotated_boxes import pairwise_iou_rotated
from .boxes import Boxes
class RotatedBoxes(Boxes):
"""
This structure stores a list of rotated boxes as a Nx5 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and also behaves like a Tensor
(support indexing, `to(device)`, `.device`, and iteration over all boxes)
"""
def __init__(self, tensor: torch.Tensor):
"""
Args:
tensor (Tensor[float]): a Nx5 matrix. Each row is
(x_center, y_center, width, height, angle),
in which angle is represented in degrees.
While there's no strict range restriction for it,
the recommended principal range is between [-180, 180) degrees.
Assume we have a horizontal box B = (x_center, y_center, width, height),
where width is along the x-axis and height is along the y-axis.
The rotated box B_rot (x_center, y_center, width, height, angle)
can be seen as:
1. When angle == 0:
B_rot == B
2. When angle > 0:
B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CCW;
3. When angle < 0:
B_rot is obtained by rotating B w.r.t its center by :math:`|angle|` degrees CW.
Mathematically, since the right-handed coordinate system for image space
is (y, x), where y is top->down and x is left->right, the 4 vertices of the
rotated rectangle :math:`(yr_i, xr_i)` (i = 1, 2, 3, 4) can be obtained from
the vertices of the horizontal rectangle :math:`(y_i, x_i)` (i = 1, 2, 3, 4)
in the following way (:math:`\\theta = angle*\\pi/180` is the angle in radians,
:math:`(y_c, x_c)` is the center of the rectangle):
.. math::
yr_i = \\cos(\\theta) (y_i - y_c) - \\sin(\\theta) (x_i - x_c) + y_c,
xr_i = \\sin(\\theta) (y_i - y_c) + \\cos(\\theta) (x_i - x_c) + x_c,
which is the standard rigid-body rotation transformation.
Intuitively, the angle is
(1) the rotation angle from y-axis in image space
to the height vector (top->down in the box's local coordinate system)
of the box in CCW, and
(2) the rotation angle from x-axis in image space
to the width vector (left->right in the box's local coordinate system)
of the box in CCW.
More intuitively, consider the following horizontal box ABCD represented
in (x1, y1, x2, y2): (3, 2, 7, 4),
covering the [3, 7] x [2, 4] region of the continuous coordinate system
which looks like this:
.. code:: none
O--------> x
|
| A---B
| | |
| D---C
|
v y
Note that each capital letter represents one 0-dimensional geometric point
instead of a 'square pixel' here.
In the example above, using (x, y) to represent a point we have:
.. math::
O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)
We name vector AB = vector DC as the width vector in box's local coordinate system, and
vector AD = vector BC as the height vector in box's local coordinate system. Initially,
when angle = 0 degree, they're aligned with the positive directions of x-axis and y-axis
in the image space, respectively.
For better illustration, we denote the center of the box as E,
.. code:: none
O--------> x
|
| A---B
| | E |
| D---C
|
v y
where the center E = ((3+7)/2, (2+4)/2) = (5, 3).
Also,
.. math::
width = |AB| = |CD| = 7 - 3 = 4,
height = |AD| = |BC| = 4 - 2 = 2.
Therefore, the corresponding representation for the same shape in rotated box in
(x_center, y_center, width, height, angle) format is:
(5, 3, 4, 2, 0),
Now, let's consider (5, 3, 4, 2, 90), which is rotated by 90 degrees
CCW (counter-clockwise) by definition. It looks like this:
.. code:: none
O--------> x
| B-C
| | |
| |E|
| | |
| A-D
v y
The center E is still located at the same point (5, 3), while the vertices
ABCD are rotated by 90 degrees CCW with regard to E:
A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)
Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to
vector AD or vector BC (the top->down height vector in box's local coordinate system),
or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right
width vector in box's local coordinate system).
.. math::
width = |AB| = |CD| = 5 - 1 = 4,
height = |AD| = |BC| = 6 - 4 = 2.
Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise)
by definition? It looks like this:
.. code:: none
O--------> x
| D-A
| | |
| |E|
| | |
| C-B
v y
The center E is still located at the same point (5, 3), while the vertices
ABCD are rotated by 90 degrees CW with regard to E:
A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)
.. math::
width = |AB| = |CD| = 5 - 1 = 4,
height = |AD| = |BC| = 6 - 4 = 2.
This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU
will be 1. However, these two will generate different RoI Pooling results and
should not be treated as an identical box.
On the other hand, it's easy to see that (X, Y, W, H, A) is identical to
(X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be
identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is
equivalent to rotating the same shape 90 degrees CW.
We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):
.. code:: none
O--------> x
|
| C---D
| | E |
| B---A
|
v y
.. math::
A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),
width = |AB| = |CD| = 7 - 3 = 4,
height = |AD| = |BC| = 4 - 2 = 2.
Finally, this is a very inaccurate (heavily quantized) illustration of
how (5, 3, 4, 2, 60) looks like in case anyone wonders:
.. code:: none
O--------> x
| B\
| / C
| /E /
| A /
| `D
v y
It's still a rectangle with center of (5, 3), width of 4 and height of 2,
but its angle (and thus orientation) is somewhere between
(5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).
"""
device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
if tensor.numel() == 0:
# Use reshape, so we don't end up creating a new tensor that does not depend on
# the inputs (and consequently confuses jit)
tensor = tensor.reshape((0, 5)).to(dtype=torch.float32, device=device)
assert tensor.dim() == 2 and tensor.size(-1) == 5, tensor.size()
self.tensor = tensor
def clone(self) -> "RotatedBoxes":
"""
Clone the RotatedBoxes.
Returns:
RotatedBoxes
"""
return RotatedBoxes(self.tensor.clone())
def to(self, device: torch.device):
# Boxes are assumed float32 and does not support to(dtype)
return RotatedBoxes(self.tensor.to(device=device))
def area(self) -> torch.Tensor:
"""
Computes the area of all the boxes.
Returns:
torch.Tensor: a vector with areas of each box.
"""
box = self.tensor
area = box[:, 2] * box[:, 3]
return area
def normalize_angles(self) -> None:
"""
Restrict angles to the range of [-180, 180) degrees
"""
self.tensor[:, 4] = (self.tensor[:, 4] + 180.0) % 360.0 - 180.0
def clip(self, box_size: Tuple[int, int], clip_angle_threshold: float = 1.0) -> None:
"""
Clip (in place) the boxes by limiting x coordinates to the range [0, width]
and y coordinates to the range [0, height].
For RRPN:
Only clip boxes that are almost horizontal with a tolerance of
clip_angle_threshold to maintain backward compatibility.
Rotated boxes beyond this threshold are not clipped for two reasons:
1. There are potentially multiple ways to clip a rotated box to make it
fit within the image.
2. It's tricky to make the entire rectangular box fit within the image
and still be able to not leave out pixels of interest.
Therefore we rely on ops like RoIAlignRotated to safely handle this.
Args:
box_size (height, width): The clipping box's size.
clip_angle_threshold:
Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees),
we do the clipping as horizontal boxes.
"""
h, w = box_size
# normalize angles to be within (-180, 180] degrees
self.normalize_angles()
idx = torch.where(torch.abs(self.tensor[:, 4]) <= clip_angle_threshold)[0]
# convert to (x1, y1, x2, y2)
x1 = self.tensor[idx, 0] - self.tensor[idx, 2] / 2.0
y1 = self.tensor[idx, 1] - self.tensor[idx, 3] / 2.0
x2 = self.tensor[idx, 0] + self.tensor[idx, 2] / 2.0
y2 = self.tensor[idx, 1] + self.tensor[idx, 3] / 2.0
# clip
x1.clamp_(min=0, max=w)
y1.clamp_(min=0, max=h)
x2.clamp_(min=0, max=w)
y2.clamp_(min=0, max=h)
# convert back to (xc, yc, w, h)
self.tensor[idx, 0] = (x1 + x2) / 2.0
self.tensor[idx, 1] = (y1 + y2) / 2.0
# make sure widths and heights do not increase due to numerical errors
self.tensor[idx, 2] = torch.min(self.tensor[idx, 2], x2 - x1)
self.tensor[idx, 3] = torch.min(self.tensor[idx, 3], y2 - y1)
def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
"""
Find boxes that are non-empty.
A box is considered empty, if either of its side is no larger than threshold.
Returns:
Tensor: a binary vector which represents
whether each box is empty (False) or non-empty (True).
"""
box = self.tensor
widths = box[:, 2]
heights = box[:, 3]
keep = (widths > threshold) & (heights > threshold)
return keep
def __getitem__(self, item) -> "RotatedBoxes":
"""
Returns:
RotatedBoxes: Create a new :class:`RotatedBoxes` by indexing.
The following usage are allowed:
1. `new_boxes = boxes[3]`: return a `RotatedBoxes` which contains only one box.
2. `new_boxes = boxes[2:10]`: return a slice of boxes.
3. `new_boxes = boxes[vector]`, where vector is a torch.ByteTensor
with `length = len(boxes)`. Nonzero elements in the vector will be selected.
Note that the returned RotatedBoxes might share storage with this RotatedBoxes,
subject to Pytorch's indexing semantics.
"""
if isinstance(item, int):
return RotatedBoxes(self.tensor[item].view(1, -1))
b = self.tensor[item]
assert b.dim() == 2, "Indexing on RotatedBoxes with {} failed to return a matrix!".format(
item
)
return RotatedBoxes(b)
def __len__(self) -> int:
return self.tensor.shape[0]
def __repr__(self) -> str:
return "RotatedBoxes(" + str(self.tensor) + ")"
def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
"""
Args:
box_size (height, width): Size of the reference box covering
[0, width] x [0, height]
boundary_threshold (int): Boxes that extend beyond the reference box
boundary by more than boundary_threshold are considered "outside".
For RRPN, it might not be necessary to call this function since it's common
for rotated box to extend to outside of the image boundaries
(the clip function only clips the near-horizontal boxes)
Returns:
a binary vector, indicating whether each box is inside the reference box.
"""
height, width = box_size
cnt_x = self.tensor[..., 0]
cnt_y = self.tensor[..., 1]
half_w = self.tensor[..., 2] / 2.0
half_h = self.tensor[..., 3] / 2.0
a = self.tensor[..., 4]
c = torch.abs(torch.cos(a * math.pi / 180.0))
s = torch.abs(torch.sin(a * math.pi / 180.0))
# This basically computes the horizontal bounding rectangle of the rotated box
max_rect_dx = c * half_w + s * half_h
max_rect_dy = c * half_h + s * half_w
inds_inside = (
(cnt_x - max_rect_dx >= -boundary_threshold)
& (cnt_y - max_rect_dy >= -boundary_threshold)
& (cnt_x + max_rect_dx < width + boundary_threshold)
& (cnt_y + max_rect_dy < height + boundary_threshold)
)
return inds_inside
def get_centers(self) -> torch.Tensor:
"""
Returns:
The box centers in a Nx2 array of (x, y).
"""
return self.tensor[:, :2]
def scale(self, scale_x: float, scale_y: float) -> None:
"""
Scale the rotated box with horizontal and vertical scaling factors
Note: when scale_factor_x != scale_factor_y,
the rotated box does not preserve the rectangular shape when the angle
is not a multiple of 90 degrees under resize transformation.
Instead, the shape is a parallelogram (that has skew)
Here we make an approximation by fitting a rotated rectangle to the parallelogram.
"""
self.tensor[:, 0] *= scale_x
self.tensor[:, 1] *= scale_y
theta = self.tensor[:, 4] * math.pi / 180.0
c = torch.cos(theta)
s = torch.sin(theta)
# In image space, y is top->down and x is left->right
# Consider the local coordintate system for the rotated box,
# where the box center is located at (0, 0), and the four vertices ABCD are
# A(-w / 2, -h / 2), B(w / 2, -h / 2), C(w / 2, h / 2), D(-w / 2, h / 2)
# the midpoint of the left edge AD of the rotated box E is:
# E = (A+D)/2 = (-w / 2, 0)
# the midpoint of the top edge AB of the rotated box F is:
# F(0, -h / 2)
# To get the old coordinates in the global system, apply the rotation transformation
# (Note: the right-handed coordinate system for image space is yOx):
# (old_x, old_y) = (s * y + c * x, c * y - s * x)
# E(old) = (s * 0 + c * (-w/2), c * 0 - s * (-w/2)) = (-c * w / 2, s * w / 2)
# F(old) = (s * (-h / 2) + c * 0, c * (-h / 2) - s * 0) = (-s * h / 2, -c * h / 2)
# After applying the scaling factor (sfx, sfy):
# E(new) = (-sfx * c * w / 2, sfy * s * w / 2)
# F(new) = (-sfx * s * h / 2, -sfy * c * h / 2)
# The new width after scaling tranformation becomes:
# w(new) = |E(new) - O| * 2
# = sqrt[(sfx * c * w / 2)^2 + (sfy * s * w / 2)^2] * 2
# = sqrt[(sfx * c)^2 + (sfy * s)^2] * w
# i.e., scale_factor_w = sqrt[(sfx * c)^2 + (sfy * s)^2]
#
# For example,
# when angle = 0 or 180, |c| = 1, s = 0, scale_factor_w == scale_factor_x;
# when |angle| = 90, c = 0, |s| = 1, scale_factor_w == scale_factor_y
self.tensor[:, 2] *= torch.sqrt((scale_x * c) ** 2 + (scale_y * s) ** 2)
# h(new) = |F(new) - O| * 2
# = sqrt[(sfx * s * h / 2)^2 + (sfy * c * h / 2)^2] * 2
# = sqrt[(sfx * s)^2 + (sfy * c)^2] * h
# i.e., scale_factor_h = sqrt[(sfx * s)^2 + (sfy * c)^2]
#
# For example,
# when angle = 0 or 180, |c| = 1, s = 0, scale_factor_h == scale_factor_y;
# when |angle| = 90, c = 0, |s| = 1, scale_factor_h == scale_factor_x
self.tensor[:, 3] *= torch.sqrt((scale_x * s) ** 2 + (scale_y * c) ** 2)
# The angle is the rotation angle from y-axis in image space to the height
# vector (top->down in the box's local coordinate system) of the box in CCW.
#
# angle(new) = angle_yOx(O - F(new))
# = angle_yOx( (sfx * s * h / 2, sfy * c * h / 2) )
# = atan2(sfx * s * h / 2, sfy * c * h / 2)
# = atan2(sfx * s, sfy * c)
#
# For example,
# when sfx == sfy, angle(new) == atan2(s, c) == angle(old)
self.tensor[:, 4] = torch.atan2(scale_x * s, scale_y * c) * 180 / math.pi
def cat(cls, boxes_list: List["RotatedBoxes"]) -> "RotatedBoxes":
"""
Concatenates a list of RotatedBoxes into a single RotatedBoxes
Arguments:
boxes_list (list[RotatedBoxes])
Returns:
RotatedBoxes: the concatenated RotatedBoxes
"""
assert isinstance(boxes_list, (list, tuple))
if len(boxes_list) == 0:
return cls(torch.empty(0))
assert all([isinstance(box, RotatedBoxes) for box in boxes_list])
# use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
return cat_boxes
def device(self) -> torch.device:
return self.tensor.device
def __iter__(self):
"""
Yield a box as a Tensor of shape (5,) at a time.
"""
yield from self.tensor
def pairwise_iou_rotated(boxes1, boxes2):
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in
(x_center, y_center, width, height, angle) format.
Arguments:
boxes1 (Tensor[N, 5])
boxes2 (Tensor[M, 5])
Returns:
iou (Tensor[N, M]): the NxM matrix containing the pairwise
IoU values for every element in boxes1 and boxes2
"""
return torch.ops.detectron2.box_iou_rotated(boxes1, boxes2)
The provided code snippet includes necessary dependencies for implementing the `pairwise_iou` function. Write a Python function `def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None` to solve the following problem:
Given two lists of rotated boxes of size N and M, compute the IoU (intersection over union) between **all** N x M pairs of boxes. The box order must be (x_center, y_center, width, height, angle). Args: boxes1, boxes2 (RotatedBoxes): two `RotatedBoxes`. Contains N & M rotated boxes, respectively. Returns: Tensor: IoU, sized [N,M].
Here is the function:
def pairwise_iou(boxes1: RotatedBoxes, boxes2: RotatedBoxes) -> None:
"""
Given two lists of rotated boxes of size N and M,
compute the IoU (intersection over union)
between **all** N x M pairs of boxes.
The box order must be (x_center, y_center, width, height, angle).
Args:
boxes1, boxes2 (RotatedBoxes):
two `RotatedBoxes`. Contains N & M rotated boxes, respectively.
Returns:
Tensor: IoU, sized [N,M].
"""
return pairwise_iou_rotated(boxes1.tensor, boxes2.tensor) | Given two lists of rotated boxes of size N and M, compute the IoU (intersection over union) between **all** N x M pairs of boxes. The box order must be (x_center, y_center, width, height, angle). Args: boxes1, boxes2 (RotatedBoxes): two `RotatedBoxes`. Contains N & M rotated boxes, respectively. Returns: Tensor: IoU, sized [N,M]. |
3,523 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and also behaves like a Tensor
(support indexing, `to(device)`, `.device`, and iteration over all boxes)
Attributes:
tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
"""
def __init__(self, tensor: torch.Tensor):
"""
Args:
tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).
"""
device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
if tensor.numel() == 0:
# Use reshape, so we don't end up creating a new tensor that does not depend on
# the inputs (and consequently confuses jit)
tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device)
assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()
self.tensor = tensor
def clone(self) -> "Boxes":
"""
Clone the Boxes.
Returns:
Boxes
"""
return Boxes(self.tensor.clone())
def to(self, device: torch.device):
# Boxes are assumed float32 and does not support to(dtype)
return Boxes(self.tensor.to(device=device))
def area(self) -> torch.Tensor:
"""
Computes the area of all the boxes.
Returns:
torch.Tensor: a vector with areas of each box.
"""
box = self.tensor
area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
return area
def clip(self, box_size: Tuple[int, int]) -> None:
"""
Clip (in place) the boxes by limiting x coordinates to the range [0, width]
and y coordinates to the range [0, height].
Args:
box_size (height, width): The clipping box's size.
"""
assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!"
h, w = box_size
x1 = self.tensor[:, 0].clamp(min=0, max=w)
y1 = self.tensor[:, 1].clamp(min=0, max=h)
x2 = self.tensor[:, 2].clamp(min=0, max=w)
y2 = self.tensor[:, 3].clamp(min=0, max=h)
self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)
def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
"""
Find boxes that are non-empty.
A box is considered empty, if either of its side is no larger than threshold.
Returns:
Tensor:
a binary vector which represents whether each box is empty
(False) or non-empty (True).
"""
box = self.tensor
widths = box[:, 2] - box[:, 0]
heights = box[:, 3] - box[:, 1]
keep = (widths > threshold) & (heights > threshold)
return keep
def __getitem__(self, item) -> "Boxes":
"""
Args:
item: int, slice, or a BoolTensor
Returns:
Boxes: Create a new :class:`Boxes` by indexing.
The following usage are allowed:
1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.
2. `new_boxes = boxes[2:10]`: return a slice of boxes.
3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor
with `length = len(boxes)`. Nonzero elements in the vector will be selected.
Note that the returned Boxes might share storage with this Boxes,
subject to Pytorch's indexing semantics.
"""
if isinstance(item, int):
return Boxes(self.tensor[item].view(1, -1))
b = self.tensor[item]
assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item)
return Boxes(b)
def __len__(self) -> int:
return self.tensor.shape[0]
def __repr__(self) -> str:
return "Boxes(" + str(self.tensor) + ")"
def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
"""
Args:
box_size (height, width): Size of the reference box.
boundary_threshold (int): Boxes that extend beyond the reference box
boundary by more than boundary_threshold are considered "outside".
Returns:
a binary vector, indicating whether each box is inside the reference box.
"""
height, width = box_size
inds_inside = (
(self.tensor[..., 0] >= -boundary_threshold)
& (self.tensor[..., 1] >= -boundary_threshold)
& (self.tensor[..., 2] < width + boundary_threshold)
& (self.tensor[..., 3] < height + boundary_threshold)
)
return inds_inside
def get_centers(self) -> torch.Tensor:
"""
Returns:
The box centers in a Nx2 array of (x, y).
"""
return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2
def scale(self, scale_x: float, scale_y: float) -> None:
"""
Scale the box with horizontal and vertical scaling factors
"""
self.tensor[:, 0::2] *= scale_x
self.tensor[:, 1::2] *= scale_y
def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
"""
Concatenates a list of Boxes into a single Boxes
Arguments:
boxes_list (list[Boxes])
Returns:
Boxes: the concatenated Boxes
"""
assert isinstance(boxes_list, (list, tuple))
if len(boxes_list) == 0:
return cls(torch.empty(0))
assert all([isinstance(box, Boxes) for box in boxes_list])
# use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
return cat_boxes
def device(self) -> device:
return self.tensor.device
# type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript
# https://github.com/pytorch/pytorch/issues/18627
def __iter__(self):
"""
Yield a box as a Tensor of shape (4,) at a time.
"""
yield from self.tensor
def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
"""
Given two lists of boxes of size N and M,
compute the intersection area between __all__ N x M pairs of boxes.
The box order must be (xmin, ymin, xmax, ymax)
Args:
boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.
Returns:
Tensor: intersection, sized [N,M].
"""
boxes1, boxes2 = boxes1.tensor, boxes2.tensor
width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(
boxes1[:, None, :2], boxes2[:, :2]
) # [N,M,2]
width_height.clamp_(min=0) # [N,M,2]
intersection = width_height.prod(dim=2) # [N,M]
return intersection
The provided code snippet includes necessary dependencies for implementing the `pairwise_iou` function. Write a Python function `def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor` to solve the following problem:
Given two lists of boxes of size N and M, compute the IoU (intersection over union) between **all** N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M].
Here is the function:
def pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
"""
Given two lists of boxes of size N and M, compute the IoU
(intersection over union) between **all** N x M pairs of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Args:
boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.
Returns:
Tensor: IoU, sized [N,M].
"""
area1 = boxes1.area() # [N]
area2 = boxes2.area() # [M]
inter = pairwise_intersection(boxes1, boxes2)
# handle empty boxes
iou = torch.where(
inter > 0,
inter / (area1[:, None] + area2 - inter),
torch.zeros(1, dtype=inter.dtype, device=inter.device),
)
return iou | Given two lists of boxes of size N and M, compute the IoU (intersection over union) between **all** N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoU, sized [N,M]. |
3,524 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and also behaves like a Tensor
(support indexing, `to(device)`, `.device`, and iteration over all boxes)
Attributes:
tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
"""
def __init__(self, tensor: torch.Tensor):
"""
Args:
tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).
"""
device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
if tensor.numel() == 0:
# Use reshape, so we don't end up creating a new tensor that does not depend on
# the inputs (and consequently confuses jit)
tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device)
assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()
self.tensor = tensor
def clone(self) -> "Boxes":
"""
Clone the Boxes.
Returns:
Boxes
"""
return Boxes(self.tensor.clone())
def to(self, device: torch.device):
# Boxes are assumed float32 and does not support to(dtype)
return Boxes(self.tensor.to(device=device))
def area(self) -> torch.Tensor:
"""
Computes the area of all the boxes.
Returns:
torch.Tensor: a vector with areas of each box.
"""
box = self.tensor
area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
return area
def clip(self, box_size: Tuple[int, int]) -> None:
"""
Clip (in place) the boxes by limiting x coordinates to the range [0, width]
and y coordinates to the range [0, height].
Args:
box_size (height, width): The clipping box's size.
"""
assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!"
h, w = box_size
x1 = self.tensor[:, 0].clamp(min=0, max=w)
y1 = self.tensor[:, 1].clamp(min=0, max=h)
x2 = self.tensor[:, 2].clamp(min=0, max=w)
y2 = self.tensor[:, 3].clamp(min=0, max=h)
self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)
def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
"""
Find boxes that are non-empty.
A box is considered empty, if either of its side is no larger than threshold.
Returns:
Tensor:
a binary vector which represents whether each box is empty
(False) or non-empty (True).
"""
box = self.tensor
widths = box[:, 2] - box[:, 0]
heights = box[:, 3] - box[:, 1]
keep = (widths > threshold) & (heights > threshold)
return keep
def __getitem__(self, item) -> "Boxes":
"""
Args:
item: int, slice, or a BoolTensor
Returns:
Boxes: Create a new :class:`Boxes` by indexing.
The following usage are allowed:
1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.
2. `new_boxes = boxes[2:10]`: return a slice of boxes.
3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor
with `length = len(boxes)`. Nonzero elements in the vector will be selected.
Note that the returned Boxes might share storage with this Boxes,
subject to Pytorch's indexing semantics.
"""
if isinstance(item, int):
return Boxes(self.tensor[item].view(1, -1))
b = self.tensor[item]
assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item)
return Boxes(b)
def __len__(self) -> int:
return self.tensor.shape[0]
def __repr__(self) -> str:
return "Boxes(" + str(self.tensor) + ")"
def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
"""
Args:
box_size (height, width): Size of the reference box.
boundary_threshold (int): Boxes that extend beyond the reference box
boundary by more than boundary_threshold are considered "outside".
Returns:
a binary vector, indicating whether each box is inside the reference box.
"""
height, width = box_size
inds_inside = (
(self.tensor[..., 0] >= -boundary_threshold)
& (self.tensor[..., 1] >= -boundary_threshold)
& (self.tensor[..., 2] < width + boundary_threshold)
& (self.tensor[..., 3] < height + boundary_threshold)
)
return inds_inside
def get_centers(self) -> torch.Tensor:
"""
Returns:
The box centers in a Nx2 array of (x, y).
"""
return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2
def scale(self, scale_x: float, scale_y: float) -> None:
"""
Scale the box with horizontal and vertical scaling factors
"""
self.tensor[:, 0::2] *= scale_x
self.tensor[:, 1::2] *= scale_y
def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
"""
Concatenates a list of Boxes into a single Boxes
Arguments:
boxes_list (list[Boxes])
Returns:
Boxes: the concatenated Boxes
"""
assert isinstance(boxes_list, (list, tuple))
if len(boxes_list) == 0:
return cls(torch.empty(0))
assert all([isinstance(box, Boxes) for box in boxes_list])
# use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
return cat_boxes
def device(self) -> device:
return self.tensor.device
# type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript
# https://github.com/pytorch/pytorch/issues/18627
def __iter__(self):
"""
Yield a box as a Tensor of shape (4,) at a time.
"""
yield from self.tensor
def pairwise_intersection(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
"""
Given two lists of boxes of size N and M,
compute the intersection area between __all__ N x M pairs of boxes.
The box order must be (xmin, ymin, xmax, ymax)
Args:
boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.
Returns:
Tensor: intersection, sized [N,M].
"""
boxes1, boxes2 = boxes1.tensor, boxes2.tensor
width_height = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) - torch.max(
boxes1[:, None, :2], boxes2[:, :2]
) # [N,M,2]
width_height.clamp_(min=0) # [N,M,2]
intersection = width_height.prod(dim=2) # [N,M]
return intersection
The provided code snippet includes necessary dependencies for implementing the `pairwise_ioa` function. Write a Python function `def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor` to solve the following problem:
Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoA, sized [N,M].
Here is the function:
def pairwise_ioa(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
"""
Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area).
Args:
boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively.
Returns:
Tensor: IoA, sized [N,M].
"""
area2 = boxes2.area() # [M]
inter = pairwise_intersection(boxes1, boxes2)
# handle empty boxes
ioa = torch.where(
inter > 0, inter / area2, torch.zeros(1, dtype=inter.dtype, device=inter.device)
)
return ioa | Similar to :func:`pariwise_iou` but compute the IoA (intersection over boxes2 area). Args: boxes1,boxes2 (Boxes): two `Boxes`. Contains N & M boxes, respectively. Returns: Tensor: IoA, sized [N,M]. |
3,525 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and also behaves like a Tensor
(support indexing, `to(device)`, `.device`, and iteration over all boxes)
Attributes:
tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
"""
def __init__(self, tensor: torch.Tensor):
"""
Args:
tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).
"""
device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
if tensor.numel() == 0:
# Use reshape, so we don't end up creating a new tensor that does not depend on
# the inputs (and consequently confuses jit)
tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device)
assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()
self.tensor = tensor
def clone(self) -> "Boxes":
"""
Clone the Boxes.
Returns:
Boxes
"""
return Boxes(self.tensor.clone())
def to(self, device: torch.device):
# Boxes are assumed float32 and does not support to(dtype)
return Boxes(self.tensor.to(device=device))
def area(self) -> torch.Tensor:
"""
Computes the area of all the boxes.
Returns:
torch.Tensor: a vector with areas of each box.
"""
box = self.tensor
area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
return area
def clip(self, box_size: Tuple[int, int]) -> None:
"""
Clip (in place) the boxes by limiting x coordinates to the range [0, width]
and y coordinates to the range [0, height].
Args:
box_size (height, width): The clipping box's size.
"""
assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!"
h, w = box_size
x1 = self.tensor[:, 0].clamp(min=0, max=w)
y1 = self.tensor[:, 1].clamp(min=0, max=h)
x2 = self.tensor[:, 2].clamp(min=0, max=w)
y2 = self.tensor[:, 3].clamp(min=0, max=h)
self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)
def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
"""
Find boxes that are non-empty.
A box is considered empty, if either of its side is no larger than threshold.
Returns:
Tensor:
a binary vector which represents whether each box is empty
(False) or non-empty (True).
"""
box = self.tensor
widths = box[:, 2] - box[:, 0]
heights = box[:, 3] - box[:, 1]
keep = (widths > threshold) & (heights > threshold)
return keep
def __getitem__(self, item) -> "Boxes":
"""
Args:
item: int, slice, or a BoolTensor
Returns:
Boxes: Create a new :class:`Boxes` by indexing.
The following usage are allowed:
1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.
2. `new_boxes = boxes[2:10]`: return a slice of boxes.
3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor
with `length = len(boxes)`. Nonzero elements in the vector will be selected.
Note that the returned Boxes might share storage with this Boxes,
subject to Pytorch's indexing semantics.
"""
if isinstance(item, int):
return Boxes(self.tensor[item].view(1, -1))
b = self.tensor[item]
assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item)
return Boxes(b)
def __len__(self) -> int:
return self.tensor.shape[0]
def __repr__(self) -> str:
return "Boxes(" + str(self.tensor) + ")"
def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
"""
Args:
box_size (height, width): Size of the reference box.
boundary_threshold (int): Boxes that extend beyond the reference box
boundary by more than boundary_threshold are considered "outside".
Returns:
a binary vector, indicating whether each box is inside the reference box.
"""
height, width = box_size
inds_inside = (
(self.tensor[..., 0] >= -boundary_threshold)
& (self.tensor[..., 1] >= -boundary_threshold)
& (self.tensor[..., 2] < width + boundary_threshold)
& (self.tensor[..., 3] < height + boundary_threshold)
)
return inds_inside
def get_centers(self) -> torch.Tensor:
"""
Returns:
The box centers in a Nx2 array of (x, y).
"""
return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2
def scale(self, scale_x: float, scale_y: float) -> None:
"""
Scale the box with horizontal and vertical scaling factors
"""
self.tensor[:, 0::2] *= scale_x
self.tensor[:, 1::2] *= scale_y
def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
"""
Concatenates a list of Boxes into a single Boxes
Arguments:
boxes_list (list[Boxes])
Returns:
Boxes: the concatenated Boxes
"""
assert isinstance(boxes_list, (list, tuple))
if len(boxes_list) == 0:
return cls(torch.empty(0))
assert all([isinstance(box, Boxes) for box in boxes_list])
# use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
return cat_boxes
def device(self) -> device:
return self.tensor.device
# type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript
# https://github.com/pytorch/pytorch/issues/18627
def __iter__(self):
"""
Yield a box as a Tensor of shape (4,) at a time.
"""
yield from self.tensor
The provided code snippet includes necessary dependencies for implementing the `pairwise_point_box_distance` function. Write a Python function `def pairwise_point_box_distance(points: torch.Tensor, boxes: Boxes)` to solve the following problem:
Pairwise distance between N points and M boxes. The distance between a point and a box is represented by the distance from the point to 4 edges of the box. Distances are all positive when the point is inside the box. Args: points: Nx2 coordinates. Each row is (x, y) boxes: M boxes Returns: Tensor: distances of size (N, M, 4). The 4 values are distances from the point to the left, top, right, bottom of the box.
Here is the function:
def pairwise_point_box_distance(points: torch.Tensor, boxes: Boxes):
"""
Pairwise distance between N points and M boxes. The distance between a
point and a box is represented by the distance from the point to 4 edges
of the box. Distances are all positive when the point is inside the box.
Args:
points: Nx2 coordinates. Each row is (x, y)
boxes: M boxes
Returns:
Tensor: distances of size (N, M, 4). The 4 values are distances from
the point to the left, top, right, bottom of the box.
"""
x, y = points.unsqueeze(dim=2).unbind(dim=1) # (N, 1)
x0, y0, x1, y1 = boxes.tensor.unsqueeze(dim=0).unbind(dim=2) # (1, M)
return torch.stack([x - x0, y - y0, x1 - x, y1 - y], dim=2) | Pairwise distance between N points and M boxes. The distance between a point and a box is represented by the distance from the point to 4 edges of the box. Distances are all positive when the point is inside the box. Args: points: Nx2 coordinates. Each row is (x, y) boxes: M boxes Returns: Tensor: distances of size (N, M, 4). The 4 values are distances from the point to the left, top, right, bottom of the box. |
3,526 | import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
class Boxes:
"""
This structure stores a list of boxes as a Nx4 torch.Tensor.
It supports some common methods about boxes
(`area`, `clip`, `nonempty`, etc),
and also behaves like a Tensor
(support indexing, `to(device)`, `.device`, and iteration over all boxes)
Attributes:
tensor (torch.Tensor): float matrix of Nx4. Each row is (x1, y1, x2, y2).
"""
def __init__(self, tensor: torch.Tensor):
"""
Args:
tensor (Tensor[float]): a Nx4 matrix. Each row is (x1, y1, x2, y2).
"""
device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
tensor = torch.as_tensor(tensor, dtype=torch.float32, device=device)
if tensor.numel() == 0:
# Use reshape, so we don't end up creating a new tensor that does not depend on
# the inputs (and consequently confuses jit)
tensor = tensor.reshape((-1, 4)).to(dtype=torch.float32, device=device)
assert tensor.dim() == 2 and tensor.size(-1) == 4, tensor.size()
self.tensor = tensor
def clone(self) -> "Boxes":
"""
Clone the Boxes.
Returns:
Boxes
"""
return Boxes(self.tensor.clone())
def to(self, device: torch.device):
# Boxes are assumed float32 and does not support to(dtype)
return Boxes(self.tensor.to(device=device))
def area(self) -> torch.Tensor:
"""
Computes the area of all the boxes.
Returns:
torch.Tensor: a vector with areas of each box.
"""
box = self.tensor
area = (box[:, 2] - box[:, 0]) * (box[:, 3] - box[:, 1])
return area
def clip(self, box_size: Tuple[int, int]) -> None:
"""
Clip (in place) the boxes by limiting x coordinates to the range [0, width]
and y coordinates to the range [0, height].
Args:
box_size (height, width): The clipping box's size.
"""
assert torch.isfinite(self.tensor).all(), "Box tensor contains infinite or NaN!"
h, w = box_size
x1 = self.tensor[:, 0].clamp(min=0, max=w)
y1 = self.tensor[:, 1].clamp(min=0, max=h)
x2 = self.tensor[:, 2].clamp(min=0, max=w)
y2 = self.tensor[:, 3].clamp(min=0, max=h)
self.tensor = torch.stack((x1, y1, x2, y2), dim=-1)
def nonempty(self, threshold: float = 0.0) -> torch.Tensor:
"""
Find boxes that are non-empty.
A box is considered empty, if either of its side is no larger than threshold.
Returns:
Tensor:
a binary vector which represents whether each box is empty
(False) or non-empty (True).
"""
box = self.tensor
widths = box[:, 2] - box[:, 0]
heights = box[:, 3] - box[:, 1]
keep = (widths > threshold) & (heights > threshold)
return keep
def __getitem__(self, item) -> "Boxes":
"""
Args:
item: int, slice, or a BoolTensor
Returns:
Boxes: Create a new :class:`Boxes` by indexing.
The following usage are allowed:
1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.
2. `new_boxes = boxes[2:10]`: return a slice of boxes.
3. `new_boxes = boxes[vector]`, where vector is a torch.BoolTensor
with `length = len(boxes)`. Nonzero elements in the vector will be selected.
Note that the returned Boxes might share storage with this Boxes,
subject to Pytorch's indexing semantics.
"""
if isinstance(item, int):
return Boxes(self.tensor[item].view(1, -1))
b = self.tensor[item]
assert b.dim() == 2, "Indexing on Boxes with {} failed to return a matrix!".format(item)
return Boxes(b)
def __len__(self) -> int:
return self.tensor.shape[0]
def __repr__(self) -> str:
return "Boxes(" + str(self.tensor) + ")"
def inside_box(self, box_size: Tuple[int, int], boundary_threshold: int = 0) -> torch.Tensor:
"""
Args:
box_size (height, width): Size of the reference box.
boundary_threshold (int): Boxes that extend beyond the reference box
boundary by more than boundary_threshold are considered "outside".
Returns:
a binary vector, indicating whether each box is inside the reference box.
"""
height, width = box_size
inds_inside = (
(self.tensor[..., 0] >= -boundary_threshold)
& (self.tensor[..., 1] >= -boundary_threshold)
& (self.tensor[..., 2] < width + boundary_threshold)
& (self.tensor[..., 3] < height + boundary_threshold)
)
return inds_inside
def get_centers(self) -> torch.Tensor:
"""
Returns:
The box centers in a Nx2 array of (x, y).
"""
return (self.tensor[:, :2] + self.tensor[:, 2:]) / 2
def scale(self, scale_x: float, scale_y: float) -> None:
"""
Scale the box with horizontal and vertical scaling factors
"""
self.tensor[:, 0::2] *= scale_x
self.tensor[:, 1::2] *= scale_y
def cat(cls, boxes_list: List["Boxes"]) -> "Boxes":
"""
Concatenates a list of Boxes into a single Boxes
Arguments:
boxes_list (list[Boxes])
Returns:
Boxes: the concatenated Boxes
"""
assert isinstance(boxes_list, (list, tuple))
if len(boxes_list) == 0:
return cls(torch.empty(0))
assert all([isinstance(box, Boxes) for box in boxes_list])
# use torch.cat (v.s. layers.cat) so the returned boxes never share storage with input
cat_boxes = cls(torch.cat([b.tensor for b in boxes_list], dim=0))
return cat_boxes
def device(self) -> device:
return self.tensor.device
# type "Iterator[torch.Tensor]", yield, and iter() not supported by torchscript
# https://github.com/pytorch/pytorch/issues/18627
def __iter__(self):
"""
Yield a box as a Tensor of shape (4,) at a time.
"""
yield from self.tensor
The provided code snippet includes necessary dependencies for implementing the `matched_pairwise_iou` function. Write a Python function `def matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor` to solve the following problem:
Compute pairwise intersection over union (IOU) of two sets of matched boxes that have the same number of boxes. Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix. Args: boxes1 (Boxes): bounding boxes, sized [N,4]. boxes2 (Boxes): same length as boxes1 Returns: Tensor: iou, sized [N].
Here is the function:
def matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
"""
Compute pairwise intersection over union (IOU) of two sets of matched
boxes that have the same number of boxes.
Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix.
Args:
boxes1 (Boxes): bounding boxes, sized [N,4].
boxes2 (Boxes): same length as boxes1
Returns:
Tensor: iou, sized [N].
"""
assert len(boxes1) == len(
boxes2
), "boxlists should have the same" "number of entries, got {}, {}".format(
len(boxes1), len(boxes2)
)
area1 = boxes1.area() # [N]
area2 = boxes2.area() # [N]
box1, box2 = boxes1.tensor, boxes2.tensor
lt = torch.max(box1[:, :2], box2[:, :2]) # [N,2]
rb = torch.min(box1[:, 2:], box2[:, 2:]) # [N,2]
wh = (rb - lt).clamp(min=0) # [N,2]
inter = wh[:, 0] * wh[:, 1] # [N]
iou = inter / (area1 + area2 - inter) # [N]
return iou | Compute pairwise intersection over union (IOU) of two sets of matched boxes that have the same number of boxes. Similar to :func:`pairwise_iou`, but computes only diagonal elements of the matrix. Args: boxes1 (Boxes): bounding boxes, sized [N,4]. boxes2 (Boxes): same length as boxes1 Returns: Tensor: iou, sized [N]. |
3,527 | import copy
import logging
import os
import torch
from caffe2.proto import caffe2_pb2
from torch import nn
from detectron2.config import CfgNode
from detectron2.utils.file_io import PathManager
from .caffe2_inference import ProtobufDetectionModel
from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
from .shared import get_pb_arg_vali, get_pb_arg_vals, save_graph
def add_export_config(cfg):
return cfg | null |
3,528 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe2Compatible,
Caffe2FastRCNNOutputsInference,
Caffe2KeypointRCNNInference,
Caffe2MaskRCNNInference,
Caffe2ROIPooler,
Caffe2RPN,
)
class Caffe2CompatibleConverter(object):
"""
A GenericUpdater which implements the `create_from` interface, by modifying
module object and assign it with another class replaceCls.
"""
def __init__(self, replaceCls):
self.replaceCls = replaceCls
def create_from(self, module):
# update module's class to the new class
assert isinstance(module, torch.nn.Module)
if issubclass(self.replaceCls, GenericMixin):
# replaceCls should act as mixin, create a new class on-the-fly
new_class = type(
"{}MixedWith{}".format(self.replaceCls.__name__, module.__class__.__name__),
(self.replaceCls, module.__class__),
{}, # {"new_method": lambda self: ...},
)
module.__class__ = new_class
else:
# replaceCls is complete class, this allow arbitrary class swap
module.__class__ = self.replaceCls
# initialize Caffe2Compatible
if isinstance(module, Caffe2Compatible):
module.tensor_mode = False
return module
def patch(model, target, updater, *args, **kwargs):
"""
recursively (post-order) update all modules with the target type and its
subclasses, make a initialization/composition/inheritance/... via the
updater.create_from.
"""
for name, module in model.named_children():
model._modules[name] = patch(module, target, updater, *args, **kwargs)
if isinstance(model, target):
return updater.create_from(model, *args, **kwargs)
return model
class Caffe2RPN(Caffe2Compatible, rpn.RPN):
def _generate_proposals(
self, images, objectness_logits_pred, anchor_deltas_pred, gt_instances=None
):
assert isinstance(images, ImageList)
if self.tensor_mode:
im_info = images.image_sizes
else:
im_info = torch.tensor([[im_sz[0], im_sz[1], 1.0] for im_sz in images.image_sizes]).to(
images.tensor.device
)
assert isinstance(im_info, torch.Tensor)
rpn_rois_list = []
rpn_roi_probs_list = []
for scores, bbox_deltas, cell_anchors_tensor, feat_stride in zip(
objectness_logits_pred,
anchor_deltas_pred,
iter(self.anchor_generator.cell_anchors),
self.anchor_generator.strides,
):
scores = scores.detach()
bbox_deltas = bbox_deltas.detach()
rpn_rois, rpn_roi_probs = torch.ops._caffe2.GenerateProposals(
scores,
bbox_deltas,
im_info,
cell_anchors_tensor,
spatial_scale=1.0 / feat_stride,
pre_nms_topN=self.pre_nms_topk[self.training],
post_nms_topN=self.post_nms_topk[self.training],
nms_thresh=self.nms_thresh,
min_size=self.min_box_size,
# correct_transform_coords=True, # deprecated argument
angle_bound_on=True, # Default
angle_bound_lo=-180,
angle_bound_hi=180,
clip_angle_thresh=1.0, # Default
legacy_plus_one=False,
)
rpn_rois_list.append(rpn_rois)
rpn_roi_probs_list.append(rpn_roi_probs)
# For FPN in D2, in RPN all proposals from different levels are concated
# together, ranked and picked by top post_nms_topk. Then in ROIPooler
# it calculates level_assignments and calls the RoIAlign from
# the corresponding level.
if len(objectness_logits_pred) == 1:
rpn_rois = rpn_rois_list[0]
rpn_roi_probs = rpn_roi_probs_list[0]
else:
assert len(rpn_rois_list) == len(rpn_roi_probs_list)
rpn_post_nms_topN = self.post_nms_topk[self.training]
device = rpn_rois_list[0].device
input_list = [to_device(x, "cpu") for x in (rpn_rois_list + rpn_roi_probs_list)]
# TODO remove this after confirming rpn_max_level/rpn_min_level
# is not needed in CollectRpnProposals.
feature_strides = list(self.anchor_generator.strides)
rpn_min_level = int(math.log2(feature_strides[0]))
rpn_max_level = int(math.log2(feature_strides[-1]))
assert (rpn_max_level - rpn_min_level + 1) == len(
rpn_rois_list
), "CollectRpnProposals requires continuous levels"
rpn_rois = torch.ops._caffe2.CollectRpnProposals(
input_list,
# NOTE: in current implementation, rpn_max_level and rpn_min_level
# are not needed, only the subtraction of two matters and it
# can be infer from the number of inputs. Keep them now for
# consistency.
rpn_max_level=2 + len(rpn_rois_list) - 1,
rpn_min_level=2,
rpn_post_nms_topN=rpn_post_nms_topN,
)
rpn_rois = to_device(rpn_rois, device)
rpn_roi_probs = []
proposals = self.c2_postprocess(im_info, rpn_rois, rpn_roi_probs, self.tensor_mode)
return proposals, {}
def forward(self, images, features, gt_instances=None):
assert not self.training
features = [features[f] for f in self.in_features]
objectness_logits_pred, anchor_deltas_pred = self.rpn_head(features)
return self._generate_proposals(
images,
objectness_logits_pred,
anchor_deltas_pred,
gt_instances,
)
def c2_postprocess(im_info, rpn_rois, rpn_roi_probs, tensor_mode):
proposals = InstancesList(
im_info=im_info,
indices=rpn_rois[:, 0],
extra_fields={
"proposal_boxes": Caffe2Boxes(rpn_rois),
"objectness_logits": (torch.Tensor, rpn_roi_probs),
},
)
if not tensor_mode:
proposals = InstancesList.to_d2_instances_list(proposals)
else:
proposals = [proposals]
return proposals
class Caffe2ROIPooler(Caffe2Compatible, poolers.ROIPooler):
def c2_preprocess(box_lists):
assert all(isinstance(x, Boxes) for x in box_lists)
if all(isinstance(x, Caffe2Boxes) for x in box_lists):
# input is pure-tensor based
assert len(box_lists) == 1
pooler_fmt_boxes = box_lists[0].tensor
else:
pooler_fmt_boxes = poolers.convert_boxes_to_pooler_format(box_lists)
return pooler_fmt_boxes
def forward(self, x, box_lists):
assert not self.training
pooler_fmt_boxes = self.c2_preprocess(box_lists)
num_level_assignments = len(self.level_poolers)
if num_level_assignments == 1:
if isinstance(self.level_poolers[0], ROIAlignRotated):
c2_roi_align = torch.ops._caffe2.RoIAlignRotated
aligned = True
else:
c2_roi_align = torch.ops._caffe2.RoIAlign
aligned = self.level_poolers[0].aligned
x0 = x[0]
if x0.is_quantized:
x0 = x0.dequantize()
out = c2_roi_align(
x0,
pooler_fmt_boxes,
order="NCHW",
spatial_scale=float(self.level_poolers[0].spatial_scale),
pooled_h=int(self.output_size[0]),
pooled_w=int(self.output_size[1]),
sampling_ratio=int(self.level_poolers[0].sampling_ratio),
aligned=aligned,
)
return out
device = pooler_fmt_boxes.device
assert (
self.max_level - self.min_level + 1 == 4
), "Currently DistributeFpnProposals only support 4 levels"
fpn_outputs = torch.ops._caffe2.DistributeFpnProposals(
to_device(pooler_fmt_boxes, "cpu"),
roi_canonical_scale=self.canonical_box_size,
roi_canonical_level=self.canonical_level,
roi_max_level=self.max_level,
roi_min_level=self.min_level,
legacy_plus_one=False,
)
fpn_outputs = [to_device(x, device) for x in fpn_outputs]
rois_fpn_list = fpn_outputs[:-1]
rois_idx_restore_int32 = fpn_outputs[-1]
roi_feat_fpn_list = []
for roi_fpn, x_level, pooler in zip(rois_fpn_list, x, self.level_poolers):
if isinstance(pooler, ROIAlignRotated):
c2_roi_align = torch.ops._caffe2.RoIAlignRotated
aligned = True
else:
c2_roi_align = torch.ops._caffe2.RoIAlign
aligned = bool(pooler.aligned)
if x_level.is_quantized:
x_level = x_level.dequantize()
roi_feat_fpn = c2_roi_align(
x_level,
roi_fpn,
order="NCHW",
spatial_scale=float(pooler.spatial_scale),
pooled_h=int(self.output_size[0]),
pooled_w=int(self.output_size[1]),
sampling_ratio=int(pooler.sampling_ratio),
aligned=aligned,
)
roi_feat_fpn_list.append(roi_feat_fpn)
roi_feat_shuffled = cat(roi_feat_fpn_list, dim=0)
assert roi_feat_shuffled.numel() > 0 and rois_idx_restore_int32.numel() > 0, (
"Caffe2 export requires tracing with a model checkpoint + input that can produce valid"
" detections. But no detections were obtained with the given checkpoint and input!"
)
roi_feat = torch.ops._caffe2.BatchPermutation(roi_feat_shuffled, rois_idx_restore_int32)
return roi_feat
def patch_generalized_rcnn(model):
ccc = Caffe2CompatibleConverter
model = patch(model, rpn.RPN, ccc(Caffe2RPN))
model = patch(model, poolers.ROIPooler, ccc(Caffe2ROIPooler))
return model | null |
3,529 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe2Compatible,
Caffe2FastRCNNOutputsInference,
Caffe2KeypointRCNNInference,
Caffe2MaskRCNNInference,
Caffe2ROIPooler,
Caffe2RPN,
)
def patch(model, target, updater, *args, **kwargs):
"""
recursively (post-order) update all modules with the target type and its
subclasses, make a initialization/composition/inheritance/... via the
updater.create_from.
"""
for name, module in model.named_children():
model._modules[name] = patch(module, target, updater, *args, **kwargs)
if isinstance(model, target):
return updater.create_from(model, *args, **kwargs)
return model
class FastRCNNOutputLayers(nn.Module):
"""
Two linear layers for predicting Fast R-CNN outputs:
1. proposal-to-detection box regression deltas
2. classification scores
"""
def __init__(
self,
input_shape: ShapeSpec,
*,
box2box_transform,
num_classes: int,
test_score_thresh: float = 0.0,
test_nms_thresh: float = 0.5,
test_topk_per_image: int = 100,
cls_agnostic_bbox_reg: bool = False,
smooth_l1_beta: float = 0.0,
box_reg_loss_type: str = "smooth_l1",
loss_weight: Union[float, Dict[str, float]] = 1.0,
):
"""
NOTE: this interface is experimental.
Args:
input_shape (ShapeSpec): shape of the input feature to this module
box2box_transform (Box2BoxTransform or Box2BoxTransformRotated):
num_classes (int): number of foreground classes
test_score_thresh (float): threshold to filter predictions results.
test_nms_thresh (float): NMS threshold for prediction results.
test_topk_per_image (int): number of top predictions to produce per image.
cls_agnostic_bbox_reg (bool): whether to use class agnostic for bbox regression
smooth_l1_beta (float): transition point from L1 to L2 loss. Only used if
`box_reg_loss_type` is "smooth_l1"
box_reg_loss_type (str): Box regression loss type. One of: "smooth_l1", "giou",
"diou", "ciou"
loss_weight (float|dict): weights to use for losses. Can be single float for weighting
all losses, or a dict of individual weightings. Valid dict keys are:
* "loss_cls": applied to classification loss
* "loss_box_reg": applied to box regression loss
"""
super().__init__()
if isinstance(input_shape, int): # some backward compatibility
input_shape = ShapeSpec(channels=input_shape)
self.num_classes = num_classes
input_size = input_shape.channels * (input_shape.width or 1) * (input_shape.height or 1)
# prediction layer for num_classes foreground classes and one background class (hence + 1)
self.cls_score = nn.Linear(input_size, num_classes + 1)
num_bbox_reg_classes = 1 if cls_agnostic_bbox_reg else num_classes
box_dim = len(box2box_transform.weights)
self.bbox_pred = nn.Linear(input_size, num_bbox_reg_classes * box_dim)
nn.init.normal_(self.cls_score.weight, std=0.01)
nn.init.normal_(self.bbox_pred.weight, std=0.001)
for l in [self.cls_score, self.bbox_pred]:
nn.init.constant_(l.bias, 0)
self.box2box_transform = box2box_transform
self.smooth_l1_beta = smooth_l1_beta
self.test_score_thresh = test_score_thresh
self.test_nms_thresh = test_nms_thresh
self.test_topk_per_image = test_topk_per_image
self.box_reg_loss_type = box_reg_loss_type
if isinstance(loss_weight, float):
loss_weight = {"loss_cls": loss_weight, "loss_box_reg": loss_weight}
self.loss_weight = loss_weight
def from_config(cls, cfg, input_shape):
return {
"input_shape": input_shape,
"box2box_transform": Box2BoxTransform(weights=cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS),
# fmt: off
"num_classes" : cfg.MODEL.ROI_HEADS.NUM_CLASSES,
"cls_agnostic_bbox_reg" : cfg.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG,
"smooth_l1_beta" : cfg.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA,
"test_score_thresh" : cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST,
"test_nms_thresh" : cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST,
"test_topk_per_image" : cfg.TEST.DETECTIONS_PER_IMAGE,
"box_reg_loss_type" : cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE,
"loss_weight" : {"loss_box_reg": cfg.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT},
# fmt: on
}
def forward(self, x):
"""
Args:
x: per-region features of shape (N, ...) for N bounding boxes to predict.
Returns:
(Tensor, Tensor):
First tensor: shape (N,K+1), scores for each of the N box. Each row contains the
scores for K object categories and 1 background class.
Second tensor: bounding box regression deltas for each box. Shape is shape (N,Kx4),
or (N,4) for class-agnostic regression.
"""
if x.dim() > 2:
x = torch.flatten(x, start_dim=1)
scores = self.cls_score(x)
proposal_deltas = self.bbox_pred(x)
return scores, proposal_deltas
def losses(self, predictions, proposals):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were used
to compute predictions. The fields ``proposal_boxes``, ``gt_boxes``,
``gt_classes`` are expected.
Returns:
Dict[str, Tensor]: dict of losses
"""
scores, proposal_deltas = predictions
# parse classification outputs
gt_classes = (
cat([p.gt_classes for p in proposals], dim=0) if len(proposals) else torch.empty(0)
)
_log_classification_stats(scores, gt_classes)
# parse box regression outputs
if len(proposals):
proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0) # Nx4
assert not proposal_boxes.requires_grad, "Proposals should not require gradients!"
# If "gt_boxes" does not exist, the proposals must be all negative and
# should not be included in regression loss computation.
# Here we just use proposal_boxes as an arbitrary placeholder because its
# value won't be used in self.box_reg_loss().
gt_boxes = cat(
[(p.gt_boxes if p.has("gt_boxes") else p.proposal_boxes).tensor for p in proposals],
dim=0,
)
else:
proposal_boxes = gt_boxes = torch.empty((0, 4), device=proposal_deltas.device)
losses = {
"loss_cls": cross_entropy(scores, gt_classes, reduction="mean"),
"loss_box_reg": self.box_reg_loss(
proposal_boxes, gt_boxes, proposal_deltas, gt_classes
),
}
return {k: v * self.loss_weight.get(k, 1.0) for k, v in losses.items()}
def box_reg_loss(self, proposal_boxes, gt_boxes, pred_deltas, gt_classes):
"""
Args:
proposal_boxes/gt_boxes are tensors with the same shape (R, 4 or 5).
pred_deltas has shape (R, 4 or 5), or (R, num_classes * (4 or 5)).
gt_classes is a long tensor of shape R, the gt class label of each proposal.
R shall be the number of proposals.
"""
box_dim = proposal_boxes.shape[1] # 4 or 5
# Regression loss is only computed for foreground proposals (those matched to a GT)
fg_inds = nonzero_tuple((gt_classes >= 0) & (gt_classes < self.num_classes))[0]
if pred_deltas.shape[1] == box_dim: # cls-agnostic regression
fg_pred_deltas = pred_deltas[fg_inds]
else:
fg_pred_deltas = pred_deltas.view(-1, self.num_classes, box_dim)[
fg_inds, gt_classes[fg_inds]
]
loss_box_reg = _dense_box_regression_loss(
[proposal_boxes[fg_inds]],
self.box2box_transform,
[fg_pred_deltas.unsqueeze(0)],
[gt_boxes[fg_inds]],
...,
self.box_reg_loss_type,
self.smooth_l1_beta,
)
# The reg loss is normalized using the total number of regions (R), not the number
# of foreground regions even though the box regression loss is only defined on
# foreground regions. Why? Because doing so gives equal training influence to
# each foreground example. To see how, consider two different minibatches:
# (1) Contains a single foreground region
# (2) Contains 100 foreground regions
# If we normalize by the number of foreground regions, the single example in
# minibatch (1) will be given 100 times as much influence as each foreground
# example in minibatch (2). Normalizing by the total number of regions, R,
# means that the single example in minibatch (1) and each of the 100 examples
# in minibatch (2) are given equal influence.
return loss_box_reg / max(gt_classes.numel(), 1.0) # return 0 if empty
def inference(self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were
used to compute predictions. The ``proposal_boxes`` field is expected.
Returns:
list[Instances]: same as `fast_rcnn_inference`.
list[Tensor]: same as `fast_rcnn_inference`.
"""
boxes = self.predict_boxes(predictions, proposals)
scores = self.predict_probs(predictions, proposals)
image_shapes = [x.image_size for x in proposals]
return fast_rcnn_inference(
boxes,
scores,
image_shapes,
self.test_score_thresh,
self.test_nms_thresh,
self.test_topk_per_image,
)
def predict_boxes_for_gt_classes(self, predictions, proposals):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were used
to compute predictions. The fields ``proposal_boxes``, ``gt_classes`` are expected.
Returns:
list[Tensor]:
A list of Tensors of predicted boxes for GT classes in case of
class-specific box head. Element i of the list has shape (Ri, B), where Ri is
the number of proposals for image i and B is the box dimension (4 or 5)
"""
if not len(proposals):
return []
scores, proposal_deltas = predictions
proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)
N, B = proposal_boxes.shape
predict_boxes = self.box2box_transform.apply_deltas(
proposal_deltas, proposal_boxes
) # Nx(KxB)
K = predict_boxes.shape[1] // B
if K > 1:
gt_classes = torch.cat([p.gt_classes for p in proposals], dim=0)
# Some proposals are ignored or have a background class. Their gt_classes
# cannot be used as index.
gt_classes = gt_classes.clamp_(0, K - 1)
predict_boxes = predict_boxes.view(N, K, B)[
torch.arange(N, dtype=torch.long, device=predict_boxes.device), gt_classes
]
num_prop_per_image = [len(p) for p in proposals]
return predict_boxes.split(num_prop_per_image)
def predict_boxes(
self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]
):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were
used to compute predictions. The ``proposal_boxes`` field is expected.
Returns:
list[Tensor]:
A list of Tensors of predicted class-specific or class-agnostic boxes
for each image. Element i has shape (Ri, K * B) or (Ri, B), where Ri is
the number of proposals for image i and B is the box dimension (4 or 5)
"""
if not len(proposals):
return []
_, proposal_deltas = predictions
num_prop_per_image = [len(p) for p in proposals]
proposal_boxes = cat([p.proposal_boxes.tensor for p in proposals], dim=0)
predict_boxes = self.box2box_transform.apply_deltas(
proposal_deltas,
proposal_boxes,
) # Nx(KxB)
return predict_boxes.split(num_prop_per_image)
def predict_probs(
self, predictions: Tuple[torch.Tensor, torch.Tensor], proposals: List[Instances]
):
"""
Args:
predictions: return values of :meth:`forward()`.
proposals (list[Instances]): proposals that match the features that were
used to compute predictions.
Returns:
list[Tensor]:
A list of Tensors of predicted class probabilities for each image.
Element i has shape (Ri, K + 1), where Ri is the number of proposals for image i.
"""
scores, _ = predictions
num_inst_per_image = [len(p) for p in proposals]
probs = F.softmax(scores, dim=-1)
return probs.split(num_inst_per_image, dim=0)
class Caffe2FastRCNNOutputsInference:
def __init__(self, tensor_mode):
self.tensor_mode = tensor_mode # whether the output is caffe2 tensor mode
def __call__(self, box_predictor, predictions, proposals):
"""equivalent to FastRCNNOutputLayers.inference"""
num_classes = box_predictor.num_classes
score_thresh = box_predictor.test_score_thresh
nms_thresh = box_predictor.test_nms_thresh
topk_per_image = box_predictor.test_topk_per_image
is_rotated = len(box_predictor.box2box_transform.weights) == 5
if is_rotated:
box_dim = 5
assert box_predictor.box2box_transform.weights[4] == 1, (
"The weights for Rotated BBoxTransform in C2 have only 4 dimensions,"
+ " thus enforcing the angle weight to be 1 for now"
)
box2box_transform_weights = box_predictor.box2box_transform.weights[:4]
else:
box_dim = 4
box2box_transform_weights = box_predictor.box2box_transform.weights
class_logits, box_regression = predictions
if num_classes + 1 == class_logits.shape[1]:
class_prob = F.softmax(class_logits, -1)
else:
assert num_classes == class_logits.shape[1]
class_prob = F.sigmoid(class_logits)
# BoxWithNMSLimit will infer num_classes from the shape of the class_prob
# So append a zero column as placeholder for the background class
class_prob = torch.cat((class_prob, torch.zeros(class_prob.shape[0], 1)), dim=1)
assert box_regression.shape[1] % box_dim == 0
cls_agnostic_bbox_reg = box_regression.shape[1] // box_dim == 1
input_tensor_mode = proposals[0].proposal_boxes.tensor.shape[1] == box_dim + 1
rois = type(proposals[0].proposal_boxes).cat([p.proposal_boxes for p in proposals])
device, dtype = rois.tensor.device, rois.tensor.dtype
if input_tensor_mode:
im_info = proposals[0].image_size
rois = rois.tensor
else:
im_info = torch.tensor(
[[sz[0], sz[1], 1.0] for sz in [x.image_size for x in proposals]]
)
batch_ids = cat(
[
torch.full((b, 1), i, dtype=dtype, device=device)
for i, b in enumerate(len(p) for p in proposals)
],
dim=0,
)
rois = torch.cat([batch_ids, rois.tensor], dim=1)
roi_pred_bbox, roi_batch_splits = torch.ops._caffe2.BBoxTransform(
to_device(rois, "cpu"),
to_device(box_regression, "cpu"),
to_device(im_info, "cpu"),
weights=box2box_transform_weights,
apply_scale=True,
rotated=is_rotated,
angle_bound_on=True,
angle_bound_lo=-180,
angle_bound_hi=180,
clip_angle_thresh=1.0,
legacy_plus_one=False,
)
roi_pred_bbox = to_device(roi_pred_bbox, device)
roi_batch_splits = to_device(roi_batch_splits, device)
nms_outputs = torch.ops._caffe2.BoxWithNMSLimit(
to_device(class_prob, "cpu"),
to_device(roi_pred_bbox, "cpu"),
to_device(roi_batch_splits, "cpu"),
score_thresh=float(score_thresh),
nms=float(nms_thresh),
detections_per_im=int(topk_per_image),
soft_nms_enabled=False,
soft_nms_method="linear",
soft_nms_sigma=0.5,
soft_nms_min_score_thres=0.001,
rotated=is_rotated,
cls_agnostic_bbox_reg=cls_agnostic_bbox_reg,
input_boxes_include_bg_cls=False,
output_classes_include_bg_cls=False,
legacy_plus_one=False,
)
roi_score_nms = to_device(nms_outputs[0], device)
roi_bbox_nms = to_device(nms_outputs[1], device)
roi_class_nms = to_device(nms_outputs[2], device)
roi_batch_splits_nms = to_device(nms_outputs[3], device)
roi_keeps_nms = to_device(nms_outputs[4], device)
roi_keeps_size_nms = to_device(nms_outputs[5], device)
if not self.tensor_mode:
roi_class_nms = roi_class_nms.to(torch.int64)
roi_batch_ids = cat(
[
torch.full((b, 1), i, dtype=dtype, device=device)
for i, b in enumerate(int(x.item()) for x in roi_batch_splits_nms)
],
dim=0,
)
roi_class_nms = alias(roi_class_nms, "class_nms")
roi_score_nms = alias(roi_score_nms, "score_nms")
roi_bbox_nms = alias(roi_bbox_nms, "bbox_nms")
roi_batch_splits_nms = alias(roi_batch_splits_nms, "batch_splits_nms")
roi_keeps_nms = alias(roi_keeps_nms, "keeps_nms")
roi_keeps_size_nms = alias(roi_keeps_size_nms, "keeps_size_nms")
results = InstancesList(
im_info=im_info,
indices=roi_batch_ids[:, 0],
extra_fields={
"pred_boxes": Caffe2Boxes(roi_bbox_nms),
"scores": roi_score_nms,
"pred_classes": roi_class_nms,
},
)
if not self.tensor_mode:
results = InstancesList.to_d2_instances_list(results)
batch_splits = roi_batch_splits_nms.int().tolist()
kept_indices = list(roi_keeps_nms.to(torch.int64).split(batch_splits))
else:
results = [results]
kept_indices = [roi_keeps_nms]
return results, kept_indices
def mock_fastrcnn_outputs_inference(
tensor_mode, check=True, box_predictor_type=FastRCNNOutputLayers
):
with mock.patch.object(
box_predictor_type,
"inference",
autospec=True,
side_effect=Caffe2FastRCNNOutputsInference(tensor_mode),
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0 | null |
3,530 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe2Compatible,
Caffe2FastRCNNOutputsInference,
Caffe2KeypointRCNNInference,
Caffe2MaskRCNNInference,
Caffe2ROIPooler,
Caffe2RPN,
)
def patch(model, target, updater, *args, **kwargs):
"""
recursively (post-order) update all modules with the target type and its
subclasses, make a initialization/composition/inheritance/... via the
updater.create_from.
"""
for name, module in model.named_children():
model._modules[name] = patch(module, target, updater, *args, **kwargs)
if isinstance(model, target):
return updater.create_from(model, *args, **kwargs)
return model
class Caffe2MaskRCNNInference:
def __call__(self, pred_mask_logits, pred_instances):
"""equivalent to mask_head.mask_rcnn_inference"""
if all(isinstance(x, InstancesList) for x in pred_instances):
assert len(pred_instances) == 1
mask_probs_pred = pred_mask_logits.sigmoid()
mask_probs_pred = alias(mask_probs_pred, "mask_fcn_probs")
pred_instances[0].pred_masks = mask_probs_pred
else:
mask_rcnn_inference(pred_mask_logits, pred_instances)
def mock_mask_rcnn_inference(tensor_mode, patched_module, check=True):
with mock.patch(
"{}.mask_rcnn_inference".format(patched_module), side_effect=Caffe2MaskRCNNInference()
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0 | null |
3,531 | import contextlib
from unittest import mock
import torch
from detectron2.modeling import poolers
from detectron2.modeling.proposal_generator import rpn
from detectron2.modeling.roi_heads import keypoint_head, mask_head
from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
from .c10 import (
Caffe2Compatible,
Caffe2FastRCNNOutputsInference,
Caffe2KeypointRCNNInference,
Caffe2MaskRCNNInference,
Caffe2ROIPooler,
Caffe2RPN,
)
def patch(model, target, updater, *args, **kwargs):
"""
recursively (post-order) update all modules with the target type and its
subclasses, make a initialization/composition/inheritance/... via the
updater.create_from.
"""
for name, module in model.named_children():
model._modules[name] = patch(module, target, updater, *args, **kwargs)
if isinstance(model, target):
return updater.create_from(model, *args, **kwargs)
return model
class Caffe2KeypointRCNNInference:
def __init__(self, use_heatmap_max_keypoint):
self.use_heatmap_max_keypoint = use_heatmap_max_keypoint
def __call__(self, pred_keypoint_logits, pred_instances):
# just return the keypoint heatmap for now,
# there will be option to call HeatmapMaxKeypointOp
output = alias(pred_keypoint_logits, "kps_score")
if all(isinstance(x, InstancesList) for x in pred_instances):
assert len(pred_instances) == 1
if self.use_heatmap_max_keypoint:
device = output.device
output = torch.ops._caffe2.HeatmapMaxKeypoint(
to_device(output, "cpu"),
pred_instances[0].pred_boxes.tensor,
should_output_softmax=True, # worth make it configerable?
)
output = to_device(output, device)
output = alias(output, "keypoints_out")
pred_instances[0].pred_keypoints = output
return pred_keypoint_logits
def mock_keypoint_rcnn_inference(tensor_mode, patched_module, use_heatmap_max_keypoint, check=True):
with mock.patch(
"{}.keypoint_rcnn_inference".format(patched_module),
side_effect=Caffe2KeypointRCNNInference(use_heatmap_max_keypoint),
) as mocked_func:
yield
if check:
assert mocked_func.call_count > 0 | null |
3,532 | import collections
from dataclasses import dataclass
from typing import Callable, List, Optional, Tuple
import torch
from torch import nn
from detectron2.structures import Boxes, Instances, ROIMasks
from detectron2.utils.registry import _convert_target_to_string, locate
from .torchscript_patch import patch_builtin_len
class ListSchema(Schema):
schemas: List[Schema] # the schemas that define how to flatten each element in the list
sizes: List[int] # the flattened length of each element
def __call__(self, values):
values = self._split(values, self.sizes)
if len(values) != len(self.schemas):
raise ValueError(
f"Values has length {len(values)} but schemas " f"has length {len(self.schemas)}!"
)
values = [m(v) for m, v in zip(self.schemas, values)]
return list(values)
def flatten(cls, obj):
res = [flatten_to_tuple(k) for k in obj]
values, sizes = cls._concat([k[0] for k in res])
return values, cls([k[1] for k in res], sizes)
class TupleSchema(ListSchema):
def __call__(self, values):
return tuple(super().__call__(values))
class IdentitySchema(Schema):
def __call__(self, values):
return values[0]
def flatten(cls, obj):
return (obj,), cls()
class DictSchema(ListSchema):
keys: List[str]
def __call__(self, values):
values = super().__call__(values)
return dict(zip(self.keys, values))
def flatten(cls, obj):
for k in obj.keys():
if not isinstance(k, str):
raise KeyError("Only support flattening dictionaries if keys are str.")
keys = sorted(obj.keys())
values = [obj[k] for k in keys]
ret, schema = ListSchema.flatten(values)
return ret, cls(schema.schemas, schema.sizes, keys)
class InstancesSchema(DictSchema):
def __call__(self, values):
image_size, fields = values[-1], values[:-1]
fields = super().__call__(fields)
return Instances(image_size, **fields)
def flatten(cls, obj):
ret, schema = super().flatten(obj.get_fields())
size = obj.image_size
if not isinstance(size, torch.Tensor):
size = torch.tensor(size)
return ret + (size,), schema
class TensorWrapSchema(Schema):
"""
For classes that are simple wrapper of tensors, e.g.
Boxes, RotatedBoxes, BitMasks
"""
class_name: str
def __call__(self, values):
return locate(self.class_name)(values[0])
def flatten(cls, obj):
return (obj.tensor,), cls(_convert_target_to_string(type(obj)))
The provided code snippet includes necessary dependencies for implementing the `flatten_to_tuple` function. Write a Python function `def flatten_to_tuple(obj)` to solve the following problem:
Flatten an object so it can be used for PyTorch tracing. Also returns how to rebuild the original object from the flattened outputs. Returns: res (tuple): the flattened results that can be used as tracing outputs schema: an object with a ``__call__`` method such that ``schema(res) == obj``. It is a pure dataclass that can be serialized.
Here is the function:
def flatten_to_tuple(obj):
"""
Flatten an object so it can be used for PyTorch tracing.
Also returns how to rebuild the original object from the flattened outputs.
Returns:
res (tuple): the flattened results that can be used as tracing outputs
schema: an object with a ``__call__`` method such that ``schema(res) == obj``.
It is a pure dataclass that can be serialized.
"""
schemas = [
((str, bytes), IdentitySchema),
(list, ListSchema),
(tuple, TupleSchema),
(collections.abc.Mapping, DictSchema),
(Instances, InstancesSchema),
((Boxes, ROIMasks), TensorWrapSchema),
]
for klass, schema in schemas:
if isinstance(obj, klass):
F = schema
break
else:
F = IdentitySchema
return F.flatten(obj) | Flatten an object so it can be used for PyTorch tracing. Also returns how to rebuild the original object from the flattened outputs. Returns: res (tuple): the flattened results that can be used as tracing outputs schema: an object with a ``__call__`` method such that ``schema(res) == obj``. It is a pure dataclass that can be serialized. |
3,533 | import os
import sys
import tempfile
from contextlib import ExitStack, contextmanager
from copy import deepcopy
from unittest import mock
import torch
from torch import nn
import detectron2
from detectron2.structures import Boxes, Instances
from detectron2.utils.env import _import_file
The provided code snippet includes necessary dependencies for implementing the `patch_builtin_len` function. Write a Python function `def patch_builtin_len(modules=())` to solve the following problem:
Patch the builtin len() function of a few detectron2 modules to use __len__ instead, because __len__ does not convert values to integers and therefore is friendly to tracing. Args: modules (list[stsr]): names of extra modules to patch len(), in addition to those in detectron2.
Here is the function:
def patch_builtin_len(modules=()):
"""
Patch the builtin len() function of a few detectron2 modules
to use __len__ instead, because __len__ does not convert values to
integers and therefore is friendly to tracing.
Args:
modules (list[stsr]): names of extra modules to patch len(), in
addition to those in detectron2.
"""
def _new_len(obj):
return obj.__len__()
with ExitStack() as stack:
MODULES = [
"detectron2.modeling.roi_heads.fast_rcnn",
"detectron2.modeling.roi_heads.mask_head",
"detectron2.modeling.roi_heads.keypoint_head",
] + list(modules)
ctxs = [stack.enter_context(mock.patch(mod + ".len")) for mod in MODULES]
for m in ctxs:
m.side_effect = _new_len
yield | Patch the builtin len() function of a few detectron2 modules to use __len__ instead, because __len__ does not convert values to integers and therefore is friendly to tracing. Args: modules (list[stsr]): names of extra modules to patch len(), in addition to those in detectron2. |
3,534 | import os
import sys
import tempfile
from contextlib import ExitStack, contextmanager
from copy import deepcopy
from unittest import mock
import torch
from torch import nn
import detectron2
from detectron2.structures import Boxes, Instances
from detectron2.utils.env import _import_file
The provided code snippet includes necessary dependencies for implementing the `patch_nonscriptable_classes` function. Write a Python function `def patch_nonscriptable_classes()` to solve the following problem:
Apply patches on a few nonscriptable detectron2 classes. Should not have side-effects on eager usage.
Here is the function:
def patch_nonscriptable_classes():
"""
Apply patches on a few nonscriptable detectron2 classes.
Should not have side-effects on eager usage.
"""
# __prepare_scriptable__ can also be added to models for easier maintenance.
# But it complicates the clean model code.
from detectron2.modeling.backbone import ResNet, FPN
# Due to https://github.com/pytorch/pytorch/issues/36061,
# we change backbone to use ModuleList for scripting.
# (note: this changes param names in state_dict)
def prepare_resnet(self):
ret = deepcopy(self)
ret.stages = nn.ModuleList(ret.stages)
for k in self.stage_names:
delattr(ret, k)
return ret
ResNet.__prepare_scriptable__ = prepare_resnet
def prepare_fpn(self):
ret = deepcopy(self)
ret.lateral_convs = nn.ModuleList(ret.lateral_convs)
ret.output_convs = nn.ModuleList(ret.output_convs)
for name, _ in self.named_children():
if name.startswith("fpn_"):
delattr(ret, name)
return ret
FPN.__prepare_scriptable__ = prepare_fpn
# Annotate some attributes to be constants for the purpose of scripting,
# even though they are not constants in eager mode.
from detectron2.modeling.roi_heads import StandardROIHeads
if hasattr(StandardROIHeads, "__annotations__"):
# copy first to avoid editing annotations of base class
StandardROIHeads.__annotations__ = deepcopy(StandardROIHeads.__annotations__)
StandardROIHeads.__annotations__["mask_on"] = torch.jit.Final[bool]
StandardROIHeads.__annotations__["keypoint_on"] = torch.jit.Final[bool] | Apply patches on a few nonscriptable detectron2 classes. Should not have side-effects on eager usage. |
3,535 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 import Caffe2Compatible
from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
from .shared import (
alias,
check_set_pb_arg,
get_pb_arg_floats,
get_pb_arg_valf,
get_pb_arg_vali,
get_pb_arg_vals,
mock_torch_nn_functional_interpolate,
)
The provided code snippet includes necessary dependencies for implementing the `assemble_rcnn_outputs_by_name` function. Write a Python function `def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False)` to solve the following problem:
A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) to detectron2's format (i.e. list of Instances instance). This only works when the model follows the Caffe2 detectron's naming convention. Args: image_sizes (List[List[int, int]]): [H, W] of every image. tensor_outputs (Dict[str, Tensor]): external_output to its tensor. force_mask_on (Bool): if true, the it make sure there'll be pred_masks even if the mask is not found from tensor_outputs (usually due to model crash)
Here is the function:
def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False):
"""
A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor])
to detectron2's format (i.e. list of Instances instance).
This only works when the model follows the Caffe2 detectron's naming convention.
Args:
image_sizes (List[List[int, int]]): [H, W] of every image.
tensor_outputs (Dict[str, Tensor]): external_output to its tensor.
force_mask_on (Bool): if true, the it make sure there'll be pred_masks even
if the mask is not found from tensor_outputs (usually due to model crash)
"""
results = [Instances(image_size) for image_size in image_sizes]
batch_splits = tensor_outputs.get("batch_splits", None)
if batch_splits:
raise NotImplementedError()
assert len(image_sizes) == 1
result = results[0]
bbox_nms = tensor_outputs["bbox_nms"]
score_nms = tensor_outputs["score_nms"]
class_nms = tensor_outputs["class_nms"]
# Detection will always success because Conv support 0-batch
assert bbox_nms is not None
assert score_nms is not None
assert class_nms is not None
if bbox_nms.shape[1] == 5:
result.pred_boxes = RotatedBoxes(bbox_nms)
else:
result.pred_boxes = Boxes(bbox_nms)
result.scores = score_nms
result.pred_classes = class_nms.to(torch.int64)
mask_fcn_probs = tensor_outputs.get("mask_fcn_probs", None)
if mask_fcn_probs is not None:
# finish the mask pred
mask_probs_pred = mask_fcn_probs
num_masks = mask_probs_pred.shape[0]
class_pred = result.pred_classes
indices = torch.arange(num_masks, device=class_pred.device)
mask_probs_pred = mask_probs_pred[indices, class_pred][:, None]
result.pred_masks = mask_probs_pred
elif force_mask_on:
# NOTE: there's no way to know the height/width of mask here, it won't be
# used anyway when batch size is 0, so just set them to 0.
result.pred_masks = torch.zeros([0, 1, 0, 0], dtype=torch.uint8)
keypoints_out = tensor_outputs.get("keypoints_out", None)
kps_score = tensor_outputs.get("kps_score", None)
if keypoints_out is not None:
# keypoints_out: [N, 4, #kypoints], where 4 is in order of (x, y, score, prob)
keypoints_tensor = keypoints_out
# NOTE: it's possible that prob is not calculated if "should_output_softmax"
# is set to False in HeatmapMaxKeypoint, so just using raw score, seems
# it doesn't affect mAP. TODO: check more carefully.
keypoint_xyp = keypoints_tensor.transpose(1, 2)[:, :, [0, 1, 2]]
result.pred_keypoints = keypoint_xyp
elif kps_score is not None:
# keypoint heatmap to sparse data structure
pred_keypoint_logits = kps_score
keypoint_head.keypoint_rcnn_inference(pred_keypoint_logits, [result])
return results | A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor]) to detectron2's format (i.e. list of Instances instance). This only works when the model follows the Caffe2 detectron's naming convention. Args: image_sizes (List[List[int, int]]): [H, W] of every image. tensor_outputs (Dict[str, Tensor]): external_output to its tensor. force_mask_on (Bool): if true, the it make sure there'll be pred_masks even if the mask is not found from tensor_outputs (usually due to model crash) |
3,536 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 import Caffe2Compatible
from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
from .shared import (
alias,
check_set_pb_arg,
get_pb_arg_floats,
get_pb_arg_valf,
get_pb_arg_vali,
get_pb_arg_vals,
mock_torch_nn_functional_interpolate,
)
def _cast_to_f32(f64):
return struct.unpack("f", struct.pack("f", f64))[0] | null |
3,537 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 import Caffe2Compatible
from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
from .shared import (
alias,
check_set_pb_arg,
get_pb_arg_floats,
get_pb_arg_valf,
get_pb_arg_vali,
get_pb_arg_vals,
mock_torch_nn_functional_interpolate,
)
class Caffe2Compatible(object):
"""
A model can inherit this class to indicate that it can be traced and deployed with caffe2.
"""
def _get_tensor_mode(self):
return self._tensor_mode
def _set_tensor_mode(self, v):
self._tensor_mode = v
tensor_mode = property(_get_tensor_mode, _set_tensor_mode)
"""
If true, the model expects C2-style tensor only inputs/outputs format.
"""
def set_caffe2_compatible_tensor_mode(model, enable=True):
def _fn(m):
if isinstance(m, Caffe2Compatible):
m.tensor_mode = enable
model.apply(_fn) | null |
3,538 | import functools
import io
import struct
import types
import torch
from detectron2.modeling import meta_arch
from detectron2.modeling.box_regression import Box2BoxTransform
from detectron2.modeling.roi_heads import keypoint_head
from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
from .c10 import Caffe2Compatible
from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
from .shared import (
alias,
check_set_pb_arg,
get_pb_arg_floats,
get_pb_arg_valf,
get_pb_arg_vali,
get_pb_arg_vals,
mock_torch_nn_functional_interpolate,
)
The provided code snippet includes necessary dependencies for implementing the `convert_batched_inputs_to_c2_format` function. Write a Python function `def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device)` to solve the following problem:
See get_caffe2_inputs() below.
Here is the function:
def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device):
"""
See get_caffe2_inputs() below.
"""
assert all(isinstance(x, dict) for x in batched_inputs)
assert all(x["image"].dim() == 3 for x in batched_inputs)
images = [x["image"] for x in batched_inputs]
images = ImageList.from_tensors(images, size_divisibility)
im_info = []
for input_per_image, image_size in zip(batched_inputs, images.image_sizes):
target_height = input_per_image.get("height", image_size[0])
target_width = input_per_image.get("width", image_size[1]) # noqa
# NOTE: The scale inside im_info is kept as convention and for providing
# post-processing information if further processing is needed. For
# current Caffe2 model definitions that don't include post-processing inside
# the model, this number is not used.
# NOTE: There can be a slight difference between width and height
# scales, using a single number can results in numerical difference
# compared with D2's post-processing.
scale = target_height / image_size[0]
im_info.append([image_size[0], image_size[1], scale])
im_info = torch.Tensor(im_info)
return images.tensor.to(device), im_info.to(device) | See get_caffe2_inputs() below. |
3,539 | import copy
import io
import logging
import numpy as np
from typing import List
import onnx
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python.onnx.backend import Caffe2Backend
from tabulate import tabulate
from termcolor import colored
from torch.onnx import OperatorExportTypes
from .shared import (
ScopedWS,
construct_init_net_from_params,
fuse_alias_placeholder,
fuse_copy_between_cpu_and_gpu,
get_params_from_init_net,
group_norm_replace_aten_with_caffe2,
infer_device_type,
remove_dead_end_ops,
remove_reshape_for_fc,
save_graph,
)
logger = logging.getLogger(__name__)
def export_onnx_model(model, inputs):
"""
Trace and export a model to onnx format.
Args:
model (nn.Module):
inputs (tuple[args]): the model will be called by `model(*inputs)`
Returns:
an onnx model
"""
assert isinstance(model, torch.nn.Module)
# make sure all modules are in eval mode, onnx may change the training state
# of the module if the states are not consistent
def _check_eval(module):
assert not module.training
model.apply(_check_eval)
# Export the model to ONNX
with torch.no_grad():
with io.BytesIO() as f:
torch.onnx.export(
model,
inputs,
f,
operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
# verbose=True, # NOTE: uncomment this for debugging
# export_params=True,
)
onnx_model = onnx.load_from_string(f.getvalue())
# Apply ONNX's Optimization
all_passes = onnx.optimizer.get_available_passes()
passes = ["fuse_bn_into_conv"]
assert all(p in all_passes for p in passes)
onnx_model = onnx.optimizer.optimize(onnx_model, passes)
return onnx_model
def _op_stats(net_def):
type_count = {}
for t in [op.type for op in net_def.op]:
type_count[t] = type_count.get(t, 0) + 1
type_count_list = sorted(type_count.items(), key=lambda kv: kv[0]) # alphabet
type_count_list = sorted(type_count_list, key=lambda kv: -kv[1]) # count
return "\n".join("{:>4}x {}".format(count, name) for name, count in type_count_list)
def _assign_device_option(
predict_net: caffe2_pb2.NetDef, init_net: caffe2_pb2.NetDef, tensor_inputs: List[torch.Tensor]
):
"""
ONNX exported network doesn't have concept of device, assign necessary
device option for each op in order to make it runable on GPU runtime.
"""
def _get_device_type(torch_tensor):
assert torch_tensor.device.type in ["cpu", "cuda"]
assert torch_tensor.device.index == 0
return torch_tensor.device.type
def _assign_op_device_option(net_proto, net_ssa, blob_device_types):
for op, ssa_i in zip(net_proto.op, net_ssa):
if op.type in ["CopyCPUToGPU", "CopyGPUToCPU"]:
op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0))
else:
devices = [blob_device_types[b] for b in ssa_i[0] + ssa_i[1]]
assert all(d == devices[0] for d in devices)
if devices[0] == "cuda":
op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0))
# update ops in predict_net
predict_net_input_device_types = {
(name, 0): _get_device_type(tensor)
for name, tensor in zip(predict_net.external_input, tensor_inputs)
}
predict_net_device_types = infer_device_type(
predict_net, known_status=predict_net_input_device_types, device_name_style="pytorch"
)
predict_net_ssa, _ = core.get_ssa(predict_net)
_assign_op_device_option(predict_net, predict_net_ssa, predict_net_device_types)
# update ops in init_net
init_net_ssa, versions = core.get_ssa(init_net)
init_net_output_device_types = {
(name, versions[name]): predict_net_device_types[(name, 0)]
for name in init_net.external_output
}
init_net_device_types = infer_device_type(
init_net, known_status=init_net_output_device_types, device_name_style="pytorch"
)
_assign_op_device_option(init_net, init_net_ssa, init_net_device_types)
def construct_init_net_from_params(
params: Dict[str, Any], device_options: Optional[Dict[str, caffe2_pb2.DeviceOption]] = None
) -> caffe2_pb2.NetDef:
"""
Construct the init_net from params dictionary
"""
init_net = caffe2_pb2.NetDef()
device_options = device_options or {}
for name, blob in params.items():
if isinstance(blob, str):
logger.warning(
(
"Blob {} with type {} is not supported in generating init net,"
" skipped.".format(name, type(blob))
)
)
continue
init_net.op.extend(
[create_const_fill_op(name, blob, device_option=device_options.get(name, None))]
)
init_net.external_output.append(name)
return init_net
def get_params_from_init_net(
init_net: caffe2_pb2.NetDef,
) -> [Dict[str, Any], Dict[str, caffe2_pb2.DeviceOption]]:
"""
Take the output blobs from init_net by running it.
Outputs:
params: dict from blob name to numpy array
device_options: dict from blob name to the device option of its creating op
"""
# NOTE: this assumes that the params is determined by producer op with the
# only exception be CopyGPUToCPU which is CUDA op but returns CPU tensor.
def _get_device_option(producer_op):
if producer_op.type == "CopyGPUToCPU":
return caffe2_pb2.DeviceOption()
else:
return producer_op.device_option
with ScopedWS("__get_params_from_init_net__", is_reset=True, is_cleanup=True) as ws:
ws.RunNetOnce(init_net)
params = {b: fetch_any_blob(b) for b in init_net.external_output}
ssa, versions = core.get_ssa(init_net)
producer_map = get_producer_map(ssa)
device_options = {
b: _get_device_option(init_net.op[producer_map[(b, versions[b])][0]])
for b in init_net.external_output
}
return params, device_options
def group_norm_replace_aten_with_caffe2(predict_net: caffe2_pb2.NetDef):
"""
For ONNX exported model, GroupNorm will be represented as ATen op,
this can be a drop in replacement from ATen to GroupNorm
"""
count = 0
for op in predict_net.op:
if op.type == "ATen":
op_name = get_pb_arg_vals(op, "operator", None) # return byte in py3
if op_name and op_name.decode() == "group_norm":
op.arg.remove(get_pb_arg(op, "operator"))
if get_pb_arg_vali(op, "cudnn_enabled", None):
op.arg.remove(get_pb_arg(op, "cudnn_enabled"))
num_groups = get_pb_arg_vali(op, "num_groups", None)
if num_groups is not None:
op.arg.remove(get_pb_arg(op, "num_groups"))
check_set_pb_arg(op, "group", "i", num_groups)
op.type = "GroupNorm"
count += 1
if count > 1:
logger.info("Replaced {} ATen operator to GroupNormOp".format(count))
def fuse_alias_placeholder(predict_net, init_net):
"""Remove AliasWithName placeholder and rename the input/output of it"""
# First we finish all the re-naming
for i, op in enumerate(predict_net.op):
if op.type == "AliasWithName":
assert len(op.input) == 1
assert len(op.output) == 1
name = get_pb_arg_vals(op, "name", None).decode()
is_backward = bool(get_pb_arg_vali(op, "is_backward", 0))
rename_op_input(predict_net, init_net, i, 0, name, from_producer=is_backward)
rename_op_output(predict_net, i, 0, name)
# Remove AliasWithName, should be very safe since it's a non-op
new_ops = []
for op in predict_net.op:
if op.type != "AliasWithName":
new_ops.append(op)
else:
# safety check
assert op.input == op.output
assert op.input[0] == op.arg[0].s.decode()
del predict_net.op[:]
predict_net.op.extend(new_ops)
def remove_reshape_for_fc(predict_net, params):
"""
In PyTorch nn.Linear has to take 2D tensor, this often leads to reshape
a 4D tensor to 2D by calling .view(). However this (dynamic) reshaping
doesn't work well with ONNX and Int8 tools, and cause using extra
ops (eg. ExpandDims) that might not be available on mobile.
Luckily Caffe2 supports 4D tensor for FC, so we can remove those reshape
after exporting ONNX model.
"""
from caffe2.python import core
# find all reshape sub-graph that can be removed, which is now all Reshape
# sub-graph whose output is only consumed by FC.
# TODO: to make it safer, we may need the actually value to better determine
# if a Reshape before FC is removable.
reshape_sub_graphs = identify_reshape_sub_graph(predict_net)
sub_graphs_to_remove = []
for reshape_sub_graph in reshape_sub_graphs:
reshape_op_id = reshape_sub_graph[-1]
assert predict_net.op[reshape_op_id].type == "Reshape"
ssa, _ = core.get_ssa(predict_net)
reshape_output = ssa[reshape_op_id][1][0]
consumers = [i for i in range(len(ssa)) if reshape_output in ssa[i][0]]
if all(predict_net.op[consumer].type == "FC" for consumer in consumers):
# safety check if the sub-graph is isolated, for this reshape sub-graph,
# it means it has one non-param external input and one external output.
ext_inputs, ext_outputs = get_sub_graph_external_input_output(
predict_net, reshape_sub_graph
)
non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0]
if len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1:
sub_graphs_to_remove.append(reshape_sub_graph)
# perform removing subgraph by:
# 1: rename the Reshape's output to its input, then the graph can be
# seen as in-place itentify, meaning whose external input/output are the same.
# 2: simply remove those ops.
remove_op_ids = []
params_to_remove = []
for sub_graph in sub_graphs_to_remove:
logger.info(
"Remove Reshape sub-graph:\n{}".format(
"".join(["(#{:>4})\n{}".format(i, predict_net.op[i]) for i in sub_graph])
)
)
reshape_op_id = sub_graph[-1]
new_reshap_output = predict_net.op[reshape_op_id].input[0]
rename_op_output(predict_net, reshape_op_id, 0, new_reshap_output)
ext_inputs, ext_outputs = get_sub_graph_external_input_output(predict_net, sub_graph)
non_params_ext_inputs = [inp for inp in ext_inputs if inp[1] != 0]
params_ext_inputs = [inp for inp in ext_inputs if inp[1] == 0]
assert len(non_params_ext_inputs) == 1 and len(ext_outputs) == 1
assert ext_outputs[0][0] == non_params_ext_inputs[0][0]
assert ext_outputs[0][1] == non_params_ext_inputs[0][1] + 1
remove_op_ids.extend(sub_graph)
params_to_remove.extend(params_ext_inputs)
predict_net = copy.deepcopy(predict_net)
new_ops = [op for i, op in enumerate(predict_net.op) if i not in remove_op_ids]
del predict_net.op[:]
predict_net.op.extend(new_ops)
for versioned_params in params_to_remove:
name = versioned_params[0]
logger.info("Remove params: {} from init_net and predict_net.external_input".format(name))
del params[name]
predict_net.external_input.remove(name)
return predict_net, params
def fuse_copy_between_cpu_and_gpu(predict_net: caffe2_pb2.NetDef):
"""
In-place fuse extra copy ops between cpu/gpu for the following case:
a -CopyAToB-> b -CopyBToA> c1 -NextOp1-> d1
-CopyBToA> c2 -NextOp2-> d2
The fused network will look like:
a -NextOp1-> d1
-NextOp2-> d2
"""
_COPY_OPS = ["CopyCPUToGPU", "CopyGPUToCPU"]
def _fuse_once(predict_net):
ssa, blob_versions = core.get_ssa(predict_net)
consumer_map = get_consumer_map(ssa)
versioned_external_output = [
(name, blob_versions[name]) for name in predict_net.external_output
]
for op_id, op in enumerate(predict_net.op):
if op.type in _COPY_OPS:
fw_copy_versioned_output = ssa[op_id][1][0]
consumer_ids = [x[0] for x in consumer_map[fw_copy_versioned_output]]
reverse_op_type = _COPY_OPS[1 - _COPY_OPS.index(op.type)]
is_fusable = (
len(consumer_ids) > 0
and fw_copy_versioned_output not in versioned_external_output
and all(
predict_net.op[_op_id].type == reverse_op_type
and ssa[_op_id][1][0] not in versioned_external_output
for _op_id in consumer_ids
)
)
if is_fusable:
for rv_copy_op_id in consumer_ids:
# making each NextOp uses "a" directly and removing Copy ops
rs_copy_versioned_output = ssa[rv_copy_op_id][1][0]
next_op_id, inp_id = consumer_map[rs_copy_versioned_output][0]
predict_net.op[next_op_id].input[inp_id] = op.input[0]
# remove CopyOps
new_ops = [
op
for i, op in enumerate(predict_net.op)
if i != op_id and i not in consumer_ids
]
del predict_net.op[:]
predict_net.op.extend(new_ops)
return True
return False
# _fuse_once returns False is nothing can be fused
while _fuse_once(predict_net):
pass
def remove_dead_end_ops(net_def: caffe2_pb2.NetDef):
"""remove ops if its output is not used or not in external_output"""
ssa, versions = core.get_ssa(net_def)
versioned_external_output = [(name, versions[name]) for name in net_def.external_output]
consumer_map = get_consumer_map(ssa)
removed_op_ids = set()
def _is_dead_end(versioned_blob):
return not (
versioned_blob in versioned_external_output
or (
len(consumer_map[versioned_blob]) > 0
and all(x[0] not in removed_op_ids for x in consumer_map[versioned_blob])
)
)
for i, ssa_i in reversed(list(enumerate(ssa))):
versioned_outputs = ssa_i[1]
if all(_is_dead_end(outp) for outp in versioned_outputs):
removed_op_ids.add(i)
# simply removing those deadend ops should have no effect to external_output
new_ops = [op for i, op in enumerate(net_def.op) if i not in removed_op_ids]
del net_def.op[:]
net_def.op.extend(new_ops)
The provided code snippet includes necessary dependencies for implementing the `export_caffe2_detection_model` function. Write a Python function `def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor])` to solve the following problem:
Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. Arg: model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py tensor_inputs: a list of tensors that caffe2 model takes as input.
Here is the function:
def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor]):
"""
Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX.
Arg:
model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py
tensor_inputs: a list of tensors that caffe2 model takes as input.
"""
model = copy.deepcopy(model)
assert isinstance(model, torch.nn.Module)
assert hasattr(model, "encode_additional_info")
# Export via ONNX
logger.info(
"Exporting a {} model via ONNX ...".format(type(model).__name__)
+ " Some warnings from ONNX are expected and are usually not to worry about."
)
onnx_model = export_onnx_model(model, (tensor_inputs,))
# Convert ONNX model to Caffe2 protobuf
init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model)
ops_table = [[op.type, op.input, op.output] for op in predict_net.op]
table = tabulate(ops_table, headers=["type", "input", "output"], tablefmt="pipe")
logger.info(
"ONNX export Done. Exported predict_net (before optimizations):\n" + colored(table, "cyan")
)
# Apply protobuf optimization
fuse_alias_placeholder(predict_net, init_net)
if any(t.device.type != "cpu" for t in tensor_inputs):
fuse_copy_between_cpu_and_gpu(predict_net)
remove_dead_end_ops(init_net)
_assign_device_option(predict_net, init_net, tensor_inputs)
params, device_options = get_params_from_init_net(init_net)
predict_net, params = remove_reshape_for_fc(predict_net, params)
init_net = construct_init_net_from_params(params, device_options)
group_norm_replace_aten_with_caffe2(predict_net)
# Record necessary information for running the pb model in Detectron2 system.
model.encode_additional_info(predict_net, init_net)
logger.info("Operators used in predict_net: \n{}".format(_op_stats(predict_net)))
logger.info("Operators used in init_net: \n{}".format(_op_stats(init_net)))
return predict_net, init_net | Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. Arg: model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py tensor_inputs: a list of tensors that caffe2 model takes as input. |
3,540 | import copy
import io
import logging
import numpy as np
from typing import List
import onnx
import torch
from caffe2.proto import caffe2_pb2
from caffe2.python import core
from caffe2.python.onnx.backend import Caffe2Backend
from tabulate import tabulate
from termcolor import colored
from torch.onnx import OperatorExportTypes
from .shared import (
ScopedWS,
construct_init_net_from_params,
fuse_alias_placeholder,
fuse_copy_between_cpu_and_gpu,
get_params_from_init_net,
group_norm_replace_aten_with_caffe2,
infer_device_type,
remove_dead_end_ops,
remove_reshape_for_fc,
save_graph,
)
logger = logging.getLogger(__name__)
class ScopedWS(object):
def __init__(self, ws_name, is_reset, is_cleanup=False):
self.ws_name = ws_name
self.is_reset = is_reset
self.is_cleanup = is_cleanup
self.org_ws = ""
def __enter__(self):
self.org_ws = workspace.CurrentWorkspace()
if self.ws_name is not None:
workspace.SwitchWorkspace(self.ws_name, True)
if self.is_reset:
workspace.ResetWorkspace()
return workspace
def __exit__(self, *args):
if self.is_cleanup:
workspace.ResetWorkspace()
if self.ws_name is not None:
workspace.SwitchWorkspace(self.org_ws)
def save_graph(net, file_name, graph_name="net", op_only=True, blob_sizes=None, blob_ranges=None):
blob_rename_f = functools.partial(_rename_blob, blob_sizes=blob_sizes, blob_ranges=blob_ranges)
return save_graph_base(net, file_name, graph_name, op_only, blob_rename_f)
The provided code snippet includes necessary dependencies for implementing the `run_and_save_graph` function. Write a Python function `def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path)` to solve the following problem:
Run the caffe2 model on given inputs, recording the shape and draw the graph. predict_net/init_net: caffe2 model. tensor_inputs: a list of tensors that caffe2 model takes as input. graph_save_path: path for saving graph of exported model.
Here is the function:
def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path):
"""
Run the caffe2 model on given inputs, recording the shape and draw the graph.
predict_net/init_net: caffe2 model.
tensor_inputs: a list of tensors that caffe2 model takes as input.
graph_save_path: path for saving graph of exported model.
"""
logger.info("Saving graph of ONNX exported model to {} ...".format(graph_save_path))
save_graph(predict_net, graph_save_path, op_only=False)
# Run the exported Caffe2 net
logger.info("Running ONNX exported model ...")
with ScopedWS("__ws_tmp__", True) as ws:
ws.RunNetOnce(init_net)
initialized_blobs = set(ws.Blobs())
uninitialized = [inp for inp in predict_net.external_input if inp not in initialized_blobs]
for name, blob in zip(uninitialized, tensor_inputs):
ws.FeedBlob(name, blob)
try:
ws.RunNetOnce(predict_net)
except RuntimeError as e:
logger.warning("Encountered RuntimeError: \n{}".format(str(e)))
ws_blobs = {b: ws.FetchBlob(b) for b in ws.Blobs()}
blob_sizes = {b: ws_blobs[b].shape for b in ws_blobs if isinstance(ws_blobs[b], np.ndarray)}
logger.info("Saving graph with blob shapes to {} ...".format(graph_save_path))
save_graph(predict_net, graph_save_path, op_only=False, blob_sizes=blob_sizes)
return ws_blobs | Run the caffe2 model on given inputs, recording the shape and draw the graph. predict_net/init_net: caffe2 model. tensor_inputs: a list of tensors that caffe2 model takes as input. graph_save_path: path for saving graph of exported model. |
3,541 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_pb2
from caffe2.python import core, net_drawer, workspace
from torch.nn.functional import interpolate as interp
def onnx_compatibale_interpolate(
input, size=None, scale_factor=None, mode="nearest", align_corners=None
):
def mock_torch_nn_functional_interpolate():
if torch.onnx.is_in_onnx_export():
with mock.patch(
"torch.nn.functional.interpolate", side_effect=onnx_compatibale_interpolate
):
yield
else:
yield | null |
3,542 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_pb2
from caffe2.python import core, net_drawer, workspace
from torch.nn.functional import interpolate as interp
def get_pb_arg(pb, arg_name):
for x in pb.arg:
if x.name == arg_name:
return x
return None
def get_pb_arg_valf(pb, arg_name, default_val):
arg = get_pb_arg(pb, arg_name)
return arg.f if arg is not None else default_val | null |
3,543 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_pb2
from caffe2.python import core, net_drawer, workspace
from torch.nn.functional import interpolate as interp
def get_pb_arg(pb, arg_name):
for x in pb.arg:
if x.name == arg_name:
return x
return None
def get_pb_arg_floats(pb, arg_name, default_val):
arg = get_pb_arg(pb, arg_name)
return list(map(float, arg.floats)) if arg is not None else default_val | null |
3,544 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_pb2
from caffe2.python import core, net_drawer, workspace
from torch.nn.functional import interpolate as interp
def get_pb_arg(pb, arg_name):
for x in pb.arg:
if x.name == arg_name:
return x
return None
def get_pb_arg_ints(pb, arg_name, default_val):
arg = get_pb_arg(pb, arg_name)
return list(map(int, arg.ints)) if arg is not None else default_val | null |
3,545 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_pb2
from caffe2.python import core, net_drawer, workspace
from torch.nn.functional import interpolate as interp
def get_pb_arg(pb, arg_name):
for x in pb.arg:
if x.name == arg_name:
return x
return None
def get_pb_arg_valstrings(pb, arg_name, default_val):
arg = get_pb_arg(pb, arg_name)
return list(arg.strings) if arg is not None else default_val | null |
3,546 | import collections
import contextlib
import copy
import functools
import logging
import numpy as np
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
from unittest import mock
import caffe2.python.utils as putils
import torch
import torch.nn.functional as F
from caffe2.proto import caffe2_pb2
from caffe2.python import core, net_drawer, workspace
from torch.nn.functional import interpolate as interp
def alias(x, name, is_backward=False):
if not torch.onnx.is_in_onnx_export():
return x
assert isinstance(x, torch.Tensor)
return torch.ops._caffe2.AliasWithName(x, name, is_backward=is_backward) | null |
3,547 | import os
import torch
from detectron2.utils.file_io import PathManager
from .torchscript_patch import freeze_training_mode, patch_instances
def patch_instances(fields):
"""
A contextmanager, under which the Instances class in detectron2 is replaced
by a statically-typed scriptable class, defined by `fields`.
See more in `scripting_with_instances`.
"""
with tempfile.TemporaryDirectory(prefix="detectron2") as dir, tempfile.NamedTemporaryFile(
mode="w", encoding="utf-8", suffix=".py", dir=dir, delete=False
) as f:
try:
# Objects that use Instances should not reuse previously-compiled
# results in cache, because `Instances` could be a new class each time.
_clear_jit_cache()
cls_name, s = _gen_instance_module(fields)
f.write(s)
f.flush()
f.close()
module = _import(f.name)
new_instances = getattr(module, cls_name)
_ = torch.jit.script(new_instances)
# let torchscript think Instances was scripted already
Instances.__torch_script_class__ = True
# let torchscript find new_instances when looking for the jit type of Instances
Instances._jit_override_qualname = torch._jit_internal._qualified_name(new_instances)
_add_instances_conversion_methods(new_instances)
yield new_instances
finally:
try:
del Instances.__torch_script_class__
del Instances._jit_override_qualname
except AttributeError:
pass
sys.modules.pop(module.__name__)
def freeze_training_mode(model):
"""
A context manager that annotates the "training" attribute of every submodule
to constant, so that the training codepath in these modules can be
meta-compiled away. Upon exiting, the annotations are reverted.
"""
classes = {type(x) for x in model.modules()}
# __constants__ is the old way to annotate constants and not compatible
# with __annotations__ .
classes = {x for x in classes if not hasattr(x, "__constants__")}
for cls in classes:
cls.__annotations__["training"] = torch.jit.Final[bool]
yield
for cls in classes:
cls.__annotations__["training"] = bool
The provided code snippet includes necessary dependencies for implementing the `scripting_with_instances` function. Write a Python function `def scripting_with_instances(model, fields)` to solve the following problem:
Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult for scripting to support it out of the box. This function is made to support scripting a model that uses :class:`Instances`. It does the following: 1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``, but with all attributes been "static". The attributes need to be statically declared in the ``fields`` argument. 2. Register ``new_Instances``, and force scripting compiler to use it when trying to compile ``Instances``. After this function, the process will be reverted. User should be able to script another model using different fields. Example: Assume that ``Instances`` in the model consist of two attributes named ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and :class:`Tensor` respectively during inference. You can call this function like: :: fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} torchscipt_model = scripting_with_instances(model, fields) Note: It only support models in evaluation mode. Args: model (nn.Module): The input model to be exported by scripting. fields (Dict[str, type]): Attribute names and corresponding type that ``Instances`` will use in the model. Note that all attributes used in ``Instances`` need to be added, regardless of whether they are inputs/outputs of the model. Data type not defined in detectron2 is not supported for now. Returns: torch.jit.ScriptModule: the model in torchscript format
Here is the function:
def scripting_with_instances(model, fields):
"""
Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since
attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult
for scripting to support it out of the box. This function is made to support scripting
a model that uses :class:`Instances`. It does the following:
1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``,
but with all attributes been "static".
The attributes need to be statically declared in the ``fields`` argument.
2. Register ``new_Instances``, and force scripting compiler to
use it when trying to compile ``Instances``.
After this function, the process will be reverted. User should be able to script another model
using different fields.
Example:
Assume that ``Instances`` in the model consist of two attributes named
``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and
:class:`Tensor` respectively during inference. You can call this function like:
::
fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor}
torchscipt_model = scripting_with_instances(model, fields)
Note:
It only support models in evaluation mode.
Args:
model (nn.Module): The input model to be exported by scripting.
fields (Dict[str, type]): Attribute names and corresponding type that
``Instances`` will use in the model. Note that all attributes used in ``Instances``
need to be added, regardless of whether they are inputs/outputs of the model.
Data type not defined in detectron2 is not supported for now.
Returns:
torch.jit.ScriptModule: the model in torchscript format
"""
assert (
not model.training
), "Currently we only support exporting models in evaluation mode to torchscript"
with freeze_training_mode(model), patch_instances(fields):
scripted_model = torch.jit.script(model)
return scripted_model | Run :func:`torch.jit.script` on a model that uses the :class:`Instances` class. Since attributes of :class:`Instances` are "dynamically" added in eager mode，it is difficult for scripting to support it out of the box. This function is made to support scripting a model that uses :class:`Instances`. It does the following: 1. Create a scriptable ``new_Instances`` class which behaves similarly to ``Instances``, but with all attributes been "static". The attributes need to be statically declared in the ``fields`` argument. 2. Register ``new_Instances``, and force scripting compiler to use it when trying to compile ``Instances``. After this function, the process will be reverted. User should be able to script another model using different fields. Example: Assume that ``Instances`` in the model consist of two attributes named ``proposal_boxes`` and ``objectness_logits`` with type :class:`Boxes` and :class:`Tensor` respectively during inference. You can call this function like: :: fields = {"proposal_boxes": Boxes, "objectness_logits": torch.Tensor} torchscipt_model = scripting_with_instances(model, fields) Note: It only support models in evaluation mode. Args: model (nn.Module): The input model to be exported by scripting. fields (Dict[str, type]): Attribute names and corresponding type that ``Instances`` will use in the model. Note that all attributes used in ``Instances`` need to be added, regardless of whether they are inputs/outputs of the model. Data type not defined in detectron2 is not supported for now. Returns: torch.jit.ScriptModule: the model in torchscript format |
3,548 | import os
import torch
from detectron2.utils.file_io import PathManager
from .torchscript_patch import freeze_training_mode, patch_instances
PathManager = PathManagerBase()
PathManager.register_handler(HTTPURLHandler())
PathManager.register_handler(OneDrivePathHandler())
PathManager.register_handler(Detectron2Handler())
The provided code snippet includes necessary dependencies for implementing the `dump_torchscript_IR` function. Write a Python function `def dump_torchscript_IR(model, dir)` to solve the following problem:
Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, inlined graph). Useful for debugging. Args: model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module dir (str): output directory to dump files.
Here is the function:
def dump_torchscript_IR(model, dir):
"""
Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph,
inlined graph). Useful for debugging.
Args:
model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module
dir (str): output directory to dump files.
"""
dir = os.path.expanduser(dir)
PathManager.mkdirs(dir)
def _get_script_mod(mod):
if isinstance(mod, torch.jit.TracedModule):
return mod._actual_script_module
return mod
# Dump pretty-printed code: https://pytorch.org/docs/stable/jit.html#inspecting-code
with PathManager.open(os.path.join(dir, "model_ts_code.txt"), "w") as f:
def get_code(mod):
# Try a few ways to get code using private attributes.
try:
# This contains more information than just `mod.code`
return _get_script_mod(mod)._c.code
except AttributeError:
pass
try:
return mod.code
except AttributeError:
return None
def dump_code(prefix, mod):
code = get_code(mod)
name = prefix or "root model"
if code is None:
f.write(f"Could not found code for {name} (type={mod.original_name})\n")
f.write("\n")
else:
f.write(f"\nCode for {name}, type={mod.original_name}:\n")
f.write(code)
f.write("\n")
f.write("-" * 80)
for name, m in mod.named_children():
dump_code(prefix + "." + name, m)
if isinstance(model, torch.jit.ScriptFunction):
f.write(get_code(model))
else:
dump_code("", model)
def _get_graph(model):
try:
# Recursively dump IR of all modules
return _get_script_mod(model)._c.dump_to_str(True, False, False)
except AttributeError:
return model.graph.str()
with PathManager.open(os.path.join(dir, "model_ts_IR.txt"), "w") as f:
f.write(_get_graph(model))
# Dump IR of the entire graph (all submodules inlined)
with PathManager.open(os.path.join(dir, "model_ts_IR_inlined.txt"), "w") as f:
f.write(str(model.inlined_graph))
if not isinstance(model, torch.jit.ScriptFunction):
# Dump the model structure in pytorch style
with PathManager.open(os.path.join(dir, "model.txt"), "w") as f:
f.write(str(model)) | Dump IR of a TracedModule/ScriptModule/Function in various format (code, graph, inlined graph). Useful for debugging. Args: model (TracedModule/ScriptModule/ScriptFUnction): traced or scripted module dir (str): output directory to dump files. |
3,549 | import os
from typing import Optional
import pkg_resources
import torch
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import CfgNode, LazyConfig, get_cfg, instantiate
from detectron2.modeling import build_model
def get_config(config_path, trained: bool = False):
"""
Returns a config object for a model in model zoo.
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
trained (bool): If True, will set ``MODEL.WEIGHTS`` to trained model zoo weights.
If False, the checkpoint specified in the config file's ``MODEL.WEIGHTS`` is used
instead; this will typically (though not always) initialize a subset of weights using
an ImageNet pre-trained model, while randomly initializing the other weights.
Returns:
CfgNode or omegaconf.DictConfig: a config object
"""
cfg_file = get_config_file(config_path)
if cfg_file.endswith(".yaml"):
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
if trained:
cfg.MODEL.WEIGHTS = get_checkpoint_url(config_path)
return cfg
elif cfg_file.endswith(".py"):
cfg = LazyConfig.load(cfg_file)
if trained:
url = get_checkpoint_url(config_path)
if "train" in cfg and "init_checkpoint" in cfg.train:
cfg.train.init_checkpoint = url
else:
raise NotImplementedError
return cfg
def instantiate(cfg):
"""
Recursively instantiate objects defined in dictionaries by
"_target_" and arguments.
Args:
cfg: a dict-like object with "_target_" that defines the caller, and
other keys that define the arguments
Returns:
object instantiated by cfg
"""
from omegaconf import ListConfig
if isinstance(cfg, ListConfig):
lst = [instantiate(x) for x in cfg]
return ListConfig(lst, flags={"allow_objects": True})
if isinstance(cfg, list):
# Specialize for list, because many classes take
# list[objects] as arguments, such as ResNet, DatasetMapper
return [instantiate(x) for x in cfg]
if isinstance(cfg, abc.Mapping) and "_target_" in cfg:
# conceptually equivalent to hydra.utils.instantiate(cfg) with _convert_=all,
# but faster: https://github.com/facebookresearch/hydra/issues/1200
cfg = {k: instantiate(v) for k, v in cfg.items()}
cls = cfg.pop("_target_")
cls = instantiate(cls)
if isinstance(cls, str):
cls_name = cls
cls = locate(cls_name)
assert cls is not None, cls_name
else:
try:
cls_name = cls.__module__ + "." + cls.__qualname__
except Exception:
# target could be anything, so the above could fail
cls_name = str(cls)
assert callable(cls), f"_target_ {cls} does not define a callable object"
try:
return cls(**cfg)
except TypeError:
logger = logging.getLogger(__name__)
logger.error(f"Error when instantiating {cls_name}!")
raise
return cfg # return as-is if don't know what to do
The provided code snippet includes necessary dependencies for implementing the `get` function. Write a Python function `def get(config_path, trained: bool = False, device: Optional[str] = None)` to solve the following problem:
Get a model specified by relative path under Detectron2's official ``configs/`` directory. Args: config_path (str): config file name relative to detectron2's "configs/" directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" trained (bool): see :func:`get_config`. device (str or None): overwrite the device in config, if given. Returns: nn.Module: a detectron2 model. Will be in training mode. Example: :: from detectron2 import model_zoo model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True)
Here is the function:
def get(config_path, trained: bool = False, device: Optional[str] = None):
"""
Get a model specified by relative path under Detectron2's official ``configs/`` directory.
Args:
config_path (str): config file name relative to detectron2's "configs/"
directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml"
trained (bool): see :func:`get_config`.
device (str or None): overwrite the device in config, if given.
Returns:
nn.Module: a detectron2 model. Will be in training mode.
Example:
::
from detectron2 import model_zoo
model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True)
"""
cfg = get_config(config_path, trained)
if device is None and not torch.cuda.is_available():
device = "cpu"
if device is not None and isinstance(cfg, CfgNode):
cfg.MODEL.DEVICE = device
if isinstance(cfg, CfgNode):
model = build_model(cfg)
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
else:
model = instantiate(cfg.model)
if device is not None:
model = model.to(device)
if "train" in cfg and "init_checkpoint" in cfg.train:
DetectionCheckpointer(model).load(cfg.train.init_checkpoint)
return model | Get a model specified by relative path under Detectron2's official ``configs/`` directory. Args: config_path (str): config file name relative to detectron2's "configs/" directory, e.g., "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml" trained (bool): see :func:`get_config`. device (str or None): overwrite the device in config, if given. Returns: nn.Module: a detectron2 model. Will be in training mode. Example: :: from detectron2 import model_zoo model = model_zoo.get("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml", trained=True) |
3,550 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `shapes_to_tensor` function. Write a Python function `def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor` to solve the following problem:
Turn a list of integer scalars or integer Tensor scalars into a vector, in a way that's both traceable and scriptable. In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs. In scripting or eager, `x` should be a list of int.
Here is the function:
def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor:
"""
Turn a list of integer scalars or integer Tensor scalars into a vector,
in a way that's both traceable and scriptable.
In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs.
In scripting or eager, `x` should be a list of int.
"""
if torch.jit.is_scripting():
return torch.as_tensor(x, device=device)
if torch.jit.is_tracing():
assert all(
[isinstance(t, torch.Tensor) for t in x]
), "Shape should be tensor during tracing!"
# as_tensor should not be used in tracing because it records a constant
ret = torch.stack(x)
if ret.device != device: # avoid recording a hard-coded device if not necessary
ret = ret.to(device=device)
return ret
return torch.as_tensor(x, device=device) | Turn a list of integer scalars or integer Tensor scalars into a vector, in a way that's both traceable and scriptable. In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs. In scripting or eager, `x` should be a list of int. |
3,551 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `cat` function. Write a Python function `def cat(tensors: List[torch.Tensor], dim: int = 0)` to solve the following problem:
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
Here is the function:
def cat(tensors: List[torch.Tensor], dim: int = 0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if len(tensors) == 1:
return tensors[0]
return torch.cat(tensors, dim) | Efficient version of torch.cat that avoids a copy if there is only a single element in a list |
3,552 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `cross_entropy` function. Write a Python function `def cross_entropy(input, target, *, reduction="mean", **kwargs)` to solve the following problem:
Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan) for empty inputs.
Here is the function:
def cross_entropy(input, target, *, reduction="mean", **kwargs):
"""
Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan)
for empty inputs.
"""
if target.numel() == 0 and reduction == "mean":
return input.sum() * 0.0 # connect the gradient
return F.cross_entropy(input, target, reduction=reduction, **kwargs) | Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan) for empty inputs. |
3,553 | from typing import List, Optional
import torch
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `nonzero_tuple` function. Write a Python function `def nonzero_tuple(x)` to solve the following problem:
A 'as_tuple=True' version of torch.nonzero to support torchscript. because of https://github.com/pytorch/pytorch/issues/38718
Here is the function:
def nonzero_tuple(x):
"""
A 'as_tuple=True' version of torch.nonzero to support torchscript.
because of https://github.com/pytorch/pytorch/issues/38718
"""
if torch.jit.is_scripting():
if x.dim() == 0:
return x.unsqueeze(0).nonzero().unbind(1)
return x.nonzero().unbind(1)
else:
return x.nonzero(as_tuple=True) | A 'as_tuple=True' version of torch.nonzero to support torchscript. because of https://github.com/pytorch/pytorch/issues/38718 |
3,554 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `diou_loss` function. Write a Python function `def diou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor` to solve the following problem:
Distance Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. eps (float): small number to prevent division by zero
Here is the function:
def diou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Distance Intersection over Union Loss (Zhaohui Zheng et. al)
https://arxiv.org/abs/1911.08287
Args:
boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,).
reduction: 'none' | 'mean' | 'sum'
'none': No reduction will be applied to the output.
'mean': The output will be averaged.
'sum': The output will be summed.
eps (float): small number to prevent division by zero
"""
x1, y1, x2, y2 = boxes1.unbind(dim=-1)
x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
# TODO: use torch._assert_async() when pytorch 1.8 support is dropped
assert (x2 >= x1).all(), "bad box: x1 larger than x2"
assert (y2 >= y1).all(), "bad box: y1 larger than y2"
# Intersection keypoints
xkis1 = torch.max(x1, x1g)
ykis1 = torch.max(y1, y1g)
xkis2 = torch.min(x2, x2g)
ykis2 = torch.min(y2, y2g)
intsct = torch.zeros_like(x1)
mask = (ykis2 > ykis1) & (xkis2 > xkis1)
intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps
iou = intsct / union
# smallest enclosing box
xc1 = torch.min(x1, x1g)
yc1 = torch.min(y1, y1g)
xc2 = torch.max(x2, x2g)
yc2 = torch.max(y2, y2g)
diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps
# centers of boxes
x_p = (x2 + x1) / 2
y_p = (y2 + y1) / 2
x_g = (x1g + x2g) / 2
y_g = (y1g + y2g) / 2
distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2)
# Eqn. (7)
loss = 1 - iou + (distance / diag_len)
if reduction == "mean":
loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
elif reduction == "sum":
loss = loss.sum()
return loss | Distance Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. eps (float): small number to prevent division by zero |
3,555 | import math
import torch
The provided code snippet includes necessary dependencies for implementing the `ciou_loss` function. Write a Python function `def ciou_loss( boxes1: torch.Tensor, boxes2: torch.Tensor, reduction: str = "none", eps: float = 1e-7, ) -> torch.Tensor` to solve the following problem:
Complete Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. eps (float): small number to prevent division by zero
Here is the function:
def ciou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Complete Intersection over Union Loss (Zhaohui Zheng et. al)
https://arxiv.org/abs/1911.08287
Args:
boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,).
reduction: 'none' | 'mean' | 'sum'
'none': No reduction will be applied to the output.
'mean': The output will be averaged.
'sum': The output will be summed.
eps (float): small number to prevent division by zero
"""
x1, y1, x2, y2 = boxes1.unbind(dim=-1)
x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
# TODO: use torch._assert_async() when pytorch 1.8 support is dropped
assert (x2 >= x1).all(), "bad box: x1 larger than x2"
assert (y2 >= y1).all(), "bad box: y1 larger than y2"
# Intersection keypoints
xkis1 = torch.max(x1, x1g)
ykis1 = torch.max(y1, y1g)
xkis2 = torch.min(x2, x2g)
ykis2 = torch.min(y2, y2g)
intsct = torch.zeros_like(x1)
mask = (ykis2 > ykis1) & (xkis2 > xkis1)
intsct[mask] = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask])
union = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g) - intsct + eps
iou = intsct / union
# smallest enclosing box
xc1 = torch.min(x1, x1g)
yc1 = torch.min(y1, y1g)
xc2 = torch.max(x2, x2g)
yc2 = torch.max(y2, y2g)
diag_len = ((xc2 - xc1) ** 2) + ((yc2 - yc1) ** 2) + eps
# centers of boxes
x_p = (x2 + x1) / 2
y_p = (y2 + y1) / 2
x_g = (x1g + x2g) / 2
y_g = (y1g + y2g) / 2
distance = ((x_p - x_g) ** 2) + ((y_p - y_g) ** 2)
# width and height of boxes
w_pred = x2 - x1
h_pred = y2 - y1
w_gt = x2g - x1g
h_gt = y2g - y1g
v = (4 / (math.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
with torch.no_grad():
alpha = v / (1 - iou + v + eps)
# Eqn. (10)
loss = 1 - iou + (distance / diag_len) + alpha * v
if reduction == "mean":
loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
elif reduction == "sum":
loss = loss.sum()
return loss | Complete Intersection over Union Loss (Zhaohui Zheng et. al) https://arxiv.org/abs/1911.08287 Args: boxes1, boxes2 (Tensor): box locations in XYXY format, shape (N, 4) or (4,). reduction: 'none' | 'mean' | 'sum' 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. eps (float): small number to prevent division by zero |
3,556 | import torch
from torchvision.ops import boxes as box_ops
from torchvision.ops import nms
The provided code snippet includes necessary dependencies for implementing the `batched_nms` function. Write a Python function `def batched_nms( boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float )` to solve the following problem:
Same as torchvision.ops.boxes.batched_nms, but with float().
Here is the function:
def batched_nms(
boxes: torch.Tensor, scores: torch.Tensor, idxs: torch.Tensor, iou_threshold: float
):
"""
Same as torchvision.ops.boxes.batched_nms, but with float().
"""
assert boxes.shape[-1] == 4
# Note: Torchvision already has a strategy (https://github.com/pytorch/vision/issues/1311)
# to decide whether to use coordinate trick or for loop to implement batched_nms. So we
# just call it directly.
# Fp16 does not have enough range for batched NMS, so adding float().
return box_ops.batched_nms(boxes.float(), scores, idxs, iou_threshold) | Same as torchvision.ops.boxes.batched_nms, but with float(). |
3,557 | import torch
from torchvision.ops import boxes as box_ops
from torchvision.ops import nms
def nms_rotated(boxes, scores, iou_threshold):
"""
Performs non-maximum suppression (NMS) on the rotated boxes according
to their intersection-over-union (IoU).
Rotated NMS iteratively removes lower scoring rotated boxes which have an
IoU greater than iou_threshold with another (higher scoring) rotated box.
Note that RotatedBox (5, 3, 4, 2, -90) covers exactly the same region as
RotatedBox (5, 3, 4, 2, 90) does, and their IoU will be 1. However, they
can be representing completely different objects in certain tasks, e.g., OCR.
As for the question of whether rotated-NMS should treat them as faraway boxes
even though their IOU is 1, it depends on the application and/or ground truth annotation.
As an extreme example, consider a single character v and the square box around it.
If the angle is 0 degree, the object (text) would be read as 'v';
If the angle is 90 degrees, the object (text) would become '>';
If the angle is 180 degrees, the object (text) would become '^';
If the angle is 270/-90 degrees, the object (text) would become '<'
All of these cases have IoU of 1 to each other, and rotated NMS that only
uses IoU as criterion would only keep one of them with the highest score -
which, practically, still makes sense in most cases because typically
only one of theses orientations is the correct one. Also, it does not matter
as much if the box is only used to classify the object (instead of transcribing
them with a sequential OCR recognition model) later.
On the other hand, when we use IoU to filter proposals that are close to the
ground truth during training, we should definitely take the angle into account if
we know the ground truth is labeled with the strictly correct orientation (as in,
upside-down words are annotated with -180 degrees even though they can be covered
with a 0/90/-90 degree box, etc.)
The way the original dataset is annotated also matters. For example, if the dataset
is a 4-point polygon dataset that does not enforce ordering of vertices/orientation,
we can estimate a minimum rotated bounding box to this polygon, but there's no way
we can tell the correct angle with 100% confidence (as shown above, there could be 4 different
rotated boxes, with angles differed by 90 degrees to each other, covering the exactly
same region). In that case we have to just use IoU to determine the box
proximity (as many detection benchmarks (even for text) do) unless there're other
assumptions we can make (like width is always larger than height, or the object is not
rotated by more than 90 degrees CCW/CW, etc.)
In summary, not considering angles in rotated NMS seems to be a good option for now,
but we should be aware of its implications.
Args:
boxes (Tensor[N, 5]): Rotated boxes to perform NMS on. They are expected to be in
(x_center, y_center, width, height, angle_degrees) format.
scores (Tensor[N]): Scores for each one of the rotated boxes
iou_threshold (float): Discards all overlapping rotated boxes with IoU < iou_threshold
Returns:
keep (Tensor): int64 tensor with the indices of the elements that have been kept
by Rotated NMS, sorted in decreasing order of scores
"""
return torch.ops.detectron2.nms_rotated(boxes, scores, iou_threshold)
The provided code snippet includes necessary dependencies for implementing the `batched_nms_rotated` function. Write a Python function `def batched_nms_rotated(boxes, scores, idxs, iou_threshold)` to solve the following problem:
Performs non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 5]): boxes where NMS will be performed. They are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format scores (Tensor[N]): scores for each one of the boxes idxs (Tensor[N]): indices of the categories for each one of the boxes. iou_threshold (float): discards all overlapping boxes with IoU < iou_threshold Returns: Tensor: int64 tensor with the indices of the elements that have been kept by NMS, sorted in decreasing order of scores
Here is the function:
def batched_nms_rotated(boxes, scores, idxs, iou_threshold):
"""
Performs non-maximum suppression in a batched fashion.
Each index value correspond to a category, and NMS
will not be applied between elements of different categories.
Args:
boxes (Tensor[N, 5]):
boxes where NMS will be performed. They
are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format
scores (Tensor[N]):
scores for each one of the boxes
idxs (Tensor[N]):
indices of the categories for each one of the boxes.
iou_threshold (float):
discards all overlapping boxes
with IoU < iou_threshold
Returns:
Tensor:
int64 tensor with the indices of the elements that have been kept
by NMS, sorted in decreasing order of scores
"""
assert boxes.shape[-1] == 5
if boxes.numel() == 0:
return torch.empty((0,), dtype=torch.int64, device=boxes.device)
boxes = boxes.float() # fp16 does not have enough range for batched NMS
# Strategy: in order to perform NMS independently per class,
# we add an offset to all the boxes. The offset is dependent
# only on the class idx, and is large enough so that boxes
# from different classes do not overlap
# Note that batched_nms in torchvision/ops/boxes.py only uses max_coordinate,
# which won't handle negative coordinates correctly.
# Here by using min_coordinate we can make sure the negative coordinates are
# correctly handled.
max_coordinate = (
torch.max(boxes[:, 0], boxes[:, 1]) + torch.max(boxes[:, 2], boxes[:, 3]) / 2
).max()
min_coordinate = (
torch.min(boxes[:, 0], boxes[:, 1]) - torch.max(boxes[:, 2], boxes[:, 3]) / 2
).min()
offsets = idxs.to(boxes) * (max_coordinate - min_coordinate + 1)
boxes_for_nms = boxes.clone() # avoid modifying the original values in boxes
boxes_for_nms[:, :2] += offsets[:, None]
keep = nms_rotated(boxes_for_nms, scores, iou_threshold)
return keep | Performs non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between elements of different categories. Args: boxes (Tensor[N, 5]): boxes where NMS will be performed. They are expected to be in (x_ctr, y_ctr, width, height, angle_degrees) format scores (Tensor[N]): scores for each one of the boxes idxs (Tensor[N]): indices of the categories for each one of the boxes. iou_threshold (float): discards all overlapping boxes with IoU < iou_threshold Returns: Tensor: int64 tensor with the indices of the elements that have been kept by NMS, sorted in decreasing order of scores |
3,558 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `paste_mask_in_image_old` function. Write a Python function `def paste_mask_in_image_old(mask, box, img_h, img_w, threshold)` to solve the following problem:
Paste a single mask in an image. This is a per-box implementation of :func:`paste_masks_in_image`. This function has larger quantization error due to incorrect pixel modeling and is not used any more. Args: mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single object instance. Values are in [0, 1]. box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners of the object instance. img_h, img_w (int): Image height and width. threshold (float): Mask binarization threshold in [0, 1]. Returns: im_mask (Tensor): The resized and binarized object mask pasted into the original image plane (a tensor of shape (img_h, img_w)).
Here is the function:
def paste_mask_in_image_old(mask, box, img_h, img_w, threshold):
"""
Paste a single mask in an image.
This is a per-box implementation of :func:`paste_masks_in_image`.
This function has larger quantization error due to incorrect pixel
modeling and is not used any more.
Args:
mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
object instance. Values are in [0, 1].
box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
of the object instance.
img_h, img_w (int): Image height and width.
threshold (float): Mask binarization threshold in [0, 1].
Returns:
im_mask (Tensor):
The resized and binarized object mask pasted into the original
image plane (a tensor of shape (img_h, img_w)).
"""
# Conversion from continuous box coordinates to discrete pixel coordinates
# via truncation (cast to int32). This determines which pixels to paste the
# mask onto.
box = box.to(dtype=torch.int32) # Continuous to discrete coordinate conversion
# An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to
# a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1
# pixels (not x1 - x0 pixels).
samples_w = box[2] - box[0] + 1 # Number of pixel samples, *not* geometric width
samples_h = box[3] - box[1] + 1 # Number of pixel samples, *not* geometric height
# Resample the mask from it's original grid to the new samples_w x samples_h grid
mask = Image.fromarray(mask.cpu().numpy())
mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR)
mask = np.array(mask, copy=False)
if threshold >= 0:
mask = np.array(mask > threshold, dtype=np.uint8)
mask = torch.from_numpy(mask)
else:
# for visualization and debugging, we also
# allow it to return an unmodified mask
mask = torch.from_numpy(mask * 255).to(torch.uint8)
im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8)
x_0 = max(box[0], 0)
x_1 = min(box[2] + 1, img_w)
y_0 = max(box[1], 0)
y_1 = min(box[3] + 1, img_h)
im_mask[y_0:y_1, x_0:x_1] = mask[
(y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
]
return im_mask | Paste a single mask in an image. This is a per-box implementation of :func:`paste_masks_in_image`. This function has larger quantization error due to incorrect pixel modeling and is not used any more. Args: mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single object instance. Values are in [0, 1]. box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners of the object instance. img_h, img_w (int): Image height and width. threshold (float): Mask binarization threshold in [0, 1]. Returns: im_mask (Tensor): The resized and binarized object mask pasted into the original image plane (a tensor of shape (img_h, img_w)). |
3,559 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `pad_masks` function. Write a Python function `def pad_masks(masks, padding)` to solve the following problem:
Args: masks (tensor): A tensor of shape (B, M, M) representing B masks. padding (int): Number of cells to pad on all sides. Returns: The padded masks and the scale factor of the padding size / original size.
Here is the function:
def pad_masks(masks, padding):
"""
Args:
masks (tensor): A tensor of shape (B, M, M) representing B masks.
padding (int): Number of cells to pad on all sides.
Returns:
The padded masks and the scale factor of the padding size / original size.
"""
B = masks.shape[0]
M = masks.shape[-1]
pad2 = 2 * padding
scale = float(M + pad2) / M
padded_masks = masks.new_zeros((B, M + pad2, M + pad2))
padded_masks[:, padding:-padding, padding:-padding] = masks
return padded_masks, scale | Args: masks (tensor): A tensor of shape (B, M, M) representing B masks. padding (int): Number of cells to pad on all sides. Returns: The padded masks and the scale factor of the padding size / original size. |
3,560 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
The provided code snippet includes necessary dependencies for implementing the `scale_boxes` function. Write a Python function `def scale_boxes(boxes, scale)` to solve the following problem:
Args: boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 coords representing the corners x0, y0, x1, y1, scale (float): The box scaling factor. Returns: Scaled boxes.
Here is the function:
def scale_boxes(boxes, scale):
"""
Args:
boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4
coords representing the corners x0, y0, x1, y1,
scale (float): The box scaling factor.
Returns:
Scaled boxes.
"""
w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5
h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5
x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5
y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5
w_half *= scale
h_half *= scale
scaled_boxes = torch.zeros_like(boxes)
scaled_boxes[:, 0] = x_c - w_half
scaled_boxes[:, 2] = x_c + w_half
scaled_boxes[:, 1] = y_c - h_half
scaled_boxes[:, 3] = y_c + h_half
return scaled_boxes | Args: boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4 coords representing the corners x0, y0, x1, y1, scale (float): The box scaling factor. Returns: Scaled boxes. |
3,561 | import numpy as np
from typing import Tuple
import torch
from PIL import Image
from torch.nn import functional as F
def paste_masks_in_image(
masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5
):
"""
Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image.
The location, height, and width for pasting each mask is determined by their
corresponding bounding boxes in boxes.
Note:
This is a complicated but more accurate implementation. In actual deployment, it is
often enough to use a faster but less accurate implementation.
See :func:`paste_mask_in_image_old` in this file for an alternative implementation.
Args:
masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of
detected object instances in the image and Hmask, Wmask are the mask width and mask
height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1].
boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4).
boxes[i] and masks[i] correspond to the same object instance.
image_shape (tuple): height, width
threshold (float): A threshold in [0, 1] for converting the (soft) masks to
binary masks.
Returns:
img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the
number of detected object instances and Himage, Wimage are the image width
and height. img_masks[i] is a binary mask for object instance i.
"""
assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported"
N = len(masks)
if N == 0:
return masks.new_empty((0,) + image_shape, dtype=torch.uint8)
if not isinstance(boxes, torch.Tensor):
boxes = boxes.tensor
device = boxes.device
assert len(boxes) == N, boxes.shape
img_h, img_w = image_shape
# The actual implementation split the input into chunks,
# and paste them chunk by chunk.
if device.type == "cpu" or torch.jit.is_scripting():
# CPU is most efficient when they are pasted one by one with skip_empty=True
# so that it performs minimal number of operations.
num_chunks = N
else:
# GPU benefits from parallelism for larger chunks, but may have memory issue
# int(img_h) because shape may be tensors in tracing
num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
assert (
num_chunks <= N
), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it"
chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
img_masks = torch.zeros(
N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8
)
for inds in chunks:
masks_chunk, spatial_inds = _do_paste_mask(
masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu"
)
if threshold >= 0:
masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
else:
# for visualization and debugging
masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
if torch.jit.is_scripting(): # Scripting does not use the optimized codepath
img_masks[inds] = masks_chunk
else:
img_masks[(inds,) + spatial_inds] = masks_chunk
return img_masks
The provided code snippet includes necessary dependencies for implementing the `_paste_masks_tensor_shape` function. Write a Python function `def _paste_masks_tensor_shape( masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[torch.Tensor, torch.Tensor], threshold: float = 0.5, )` to solve the following problem:
A wrapper of paste_masks_in_image where image_shape is Tensor. During tracing, shapes might be tensors instead of ints. The Tensor->int conversion should be scripted rather than traced.
Here is the function:
def _paste_masks_tensor_shape(
masks: torch.Tensor,
boxes: torch.Tensor,
image_shape: Tuple[torch.Tensor, torch.Tensor],
threshold: float = 0.5,
):
"""
A wrapper of paste_masks_in_image where image_shape is Tensor.
During tracing, shapes might be tensors instead of ints. The Tensor->int
conversion should be scripted rather than traced.
"""
return paste_masks_in_image(masks, boxes, (int(image_shape[0]), int(image_shape[1])), threshold) | A wrapper of paste_masks_in_image where image_shape is Tensor. During tracing, shapes might be tensors instead of ints. The Tensor->int conversion should be scripted rather than traced. |
3,562 | import torch
import torch.distributed as dist
from fvcore.nn.distributed import differentiable_all_reduce
from torch import nn
from torch.nn import functional as F
from detectron2.utils import comm, env
from .wrappers import BatchNorm2d
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters are fixed.
It contains non-trainable buffers called
"weight" and "bias", "running_mean", "running_var",
initialized to perform identity transformation.
The pre-trained backbone models from Caffe2 only contain "weight" and "bias",
which are computed from the original four parameters of BN.
The affine transform `x * weight + bias` will perform the equivalent
computation of `(x - running_mean) / sqrt(running_var) * weight + bias`.
When loading a backbone model from Caffe2, "running_mean" and "running_var"
will be left unchanged as identity transformation.
Other pre-trained backbone models may contain all 4 parameters.
The forward is implemented by `F.batch_norm(..., training=False)`.
"""
_version = 3
def __init__(self, num_features, eps=1e-5):
super().__init__()
self.num_features = num_features
self.eps = eps
self.register_buffer("weight", torch.ones(num_features))
self.register_buffer("bias", torch.zeros(num_features))
self.register_buffer("running_mean", torch.zeros(num_features))
self.register_buffer("running_var", torch.ones(num_features) - eps)
def forward(self, x):
if x.requires_grad:
# When gradients are needed, F.batch_norm will use extra memory
# because its backward op computes gradients for weight/bias as well.
scale = self.weight * (self.running_var + self.eps).rsqrt()
bias = self.bias - self.running_mean * scale
scale = scale.reshape(1, -1, 1, 1)
bias = bias.reshape(1, -1, 1, 1)
out_dtype = x.dtype # may be half
return x * scale.to(out_dtype) + bias.to(out_dtype)
else:
# When gradients are not needed, F.batch_norm is a single fused op
# and provide more optimization opportunities.
return F.batch_norm(
x,
self.running_mean,
self.running_var,
self.weight,
self.bias,
training=False,
eps=self.eps,
)
def _load_from_state_dict(
self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
):
version = local_metadata.get("version", None)
if version is None or version < 2:
# No running_mean/var in early versions
# This will silent the warnings
if prefix + "running_mean" not in state_dict:
state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean)
if prefix + "running_var" not in state_dict:
state_dict[prefix + "running_var"] = torch.ones_like(self.running_var)
super()._load_from_state_dict(
state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
)
def __repr__(self):
return "FrozenBatchNorm2d(num_features={}, eps={})".format(self.num_features, self.eps)
def convert_frozen_batchnorm(cls, module):
"""
Convert all BatchNorm/SyncBatchNorm in module into FrozenBatchNorm.
Args:
module (torch.nn.Module):
Returns:
If module is BatchNorm/SyncBatchNorm, returns a new module.
Otherwise, in-place convert module and return it.
Similar to convert_sync_batchnorm in
https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py
"""
bn_module = nn.modules.batchnorm
bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm)
res = module
if isinstance(module, bn_module):
res = cls(module.num_features)
if module.affine:
res.weight.data = module.weight.data.clone().detach()
res.bias.data = module.bias.data.clone().detach()
res.running_mean.data = module.running_mean.data
res.running_var.data = module.running_var.data
res.eps = module.eps
else:
for name, child in module.named_children():
new_child = cls.convert_frozen_batchnorm(child)
if new_child is not child:
res.add_module(name, new_child)
return res
class NaiveSyncBatchNorm(BatchNorm2d):
"""
In PyTorch<=1.5, ``nn.SyncBatchNorm`` has incorrect gradient
when the batch size on each worker is different.
(e.g., when scale augmentation is used, or when it is applied to mask head).
This is a slower but correct alternative to `nn.SyncBatchNorm`.
Note:
There isn't a single definition of Sync BatchNorm.
When ``stats_mode==""``, this module computes overall statistics by using
statistics of each worker with equal weight. The result is true statistics
of all samples (as if they are all on one worker) only when all workers
have the same (N, H, W). This mode does not support inputs with zero batch size.
When ``stats_mode=="N"``, this module computes overall statistics by weighting
the statistics of each worker by their ``N``. The result is true statistics
of all samples (as if they are all on one worker) only when all workers
have the same (H, W). It is slower than ``stats_mode==""``.
Even though the result of this module may not be the true statistics of all samples,
it may still be reasonable because it might be preferrable to assign equal weights
to all workers, regardless of their (H, W) dimension, instead of putting larger weight
on larger images. From preliminary experiments, little difference is found between such
a simplified implementation and an accurate computation of overall mean & variance.
"""
def __init__(self, *args, stats_mode="", **kwargs):
super().__init__(*args, **kwargs)
assert stats_mode in ["", "N"]
self._stats_mode = stats_mode
def forward(self, input):
if comm.get_world_size() == 1 or not self.training:
return super().forward(input)
B, C = input.shape[0], input.shape[1]
half_input = input.dtype == torch.float16
if half_input:
# fp16 does not have good enough numerics for the reduction here
input = input.float()
mean = torch.mean(input, dim=[0, 2, 3])
meansqr = torch.mean(input * input, dim=[0, 2, 3])
if self._stats_mode == "":
assert B > 0, 'SyncBatchNorm(stats_mode="") does not support zero batch size.'
vec = torch.cat([mean, meansqr], dim=0)
vec = differentiable_all_reduce(vec) * (1.0 / dist.get_world_size())
mean, meansqr = torch.split(vec, C)
momentum = self.momentum
else:
if B == 0:
vec = torch.zeros([2 * C + 1], device=mean.device, dtype=mean.dtype)
vec = vec + input.sum() # make sure there is gradient w.r.t input
else:
vec = torch.cat(
[mean, meansqr, torch.ones([1], device=mean.device, dtype=mean.dtype)], dim=0
)
vec = differentiable_all_reduce(vec * B)
total_batch = vec[-1].detach()
momentum = total_batch.clamp(max=1) * self.momentum # no update if total_batch is 0
mean, meansqr, _ = torch.split(vec / total_batch.clamp(min=1), C) # avoid div-by-zero
var = meansqr - mean * mean
invstd = torch.rsqrt(var + self.eps)
scale = self.weight * invstd
bias = self.bias - mean * scale
scale = scale.reshape(1, -1, 1, 1)
bias = bias.reshape(1, -1, 1, 1)
self.running_mean += momentum * (mean.detach() - self.running_mean)
self.running_var += momentum * (var.detach() - self.running_var)
ret = input * scale + bias
if half_input:
ret = ret.half()
return ret
BatchNorm2d = torch.nn.BatchNorm2d
The provided code snippet includes necessary dependencies for implementing the `get_norm` function. Write a Python function `def get_norm(norm, out_channels)` to solve the following problem:
Args: norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; or a callable that takes a channel number and returns the normalization layer as a nn.Module. Returns: nn.Module or None: the normalization layer
Here is the function:
def get_norm(norm, out_channels):
"""
Args:
norm (str or callable): either one of BN, SyncBN, FrozenBN, GN;
or a callable that takes a channel number and returns
the normalization layer as a nn.Module.
Returns:
nn.Module or None: the normalization layer
"""
if norm is None:
return None
if isinstance(norm, str):
if len(norm) == 0:
return None
norm = {
"BN": BatchNorm2d,
# Fixed in https://github.com/pytorch/pytorch/pull/36382
"SyncBN": NaiveSyncBatchNorm if env.TORCH_VERSION <= (1, 5) else nn.SyncBatchNorm,
"FrozenBN": FrozenBatchNorm2d,
"GN": lambda channels: nn.GroupNorm(32, channels),
# for debugging:
"nnSyncBN": nn.SyncBatchNorm,
"naiveSyncBN": NaiveSyncBatchNorm,
# expose stats_mode N as an option to caller, required for zero-len inputs
"naiveSyncBN_N": lambda channels: NaiveSyncBatchNorm(channels, stats_mode="N"),
}[norm]
return norm(out_channels) | Args: norm (str or callable): either one of BN, SyncBN, FrozenBN, GN; or a callable that takes a channel number and returns the normalization layer as a nn.Module. Returns: nn.Module or None: the normalization layer |
3,563 | import logging
import numpy as np
from itertools import count
from typing import List, Tuple
import torch
import tqdm
from fvcore.common.timer import Timer
from detectron2.utils import comm
from .build import build_batch_data_loader
from .common import DatasetFromList, MapDataset
from .samplers import TrainingSampler
The provided code snippet includes necessary dependencies for implementing the `iter_benchmark` function. Write a Python function `def iter_benchmark( iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60 ) -> Tuple[float, List[float]]` to solve the following problem:
Benchmark an iterator/iterable for `num_iter` iterations with an extra `warmup` iterations of warmup. End early if `max_time_seconds` time is spent on iterations. Returns: float: average time (seconds) per iteration list[float]: time spent on each iteration. Sometimes useful for further analysis.
Here is the function:
def iter_benchmark(
iterator, num_iter: int, warmup: int = 5, max_time_seconds: float = 60
) -> Tuple[float, List[float]]:
"""
Benchmark an iterator/iterable for `num_iter` iterations with an extra
`warmup` iterations of warmup.
End early if `max_time_seconds` time is spent on iterations.
Returns:
float: average time (seconds) per iteration
list[float]: time spent on each iteration. Sometimes useful for further analysis.
"""
num_iter, warmup = int(num_iter), int(warmup)
iterator = iter(iterator)
for _ in range(warmup):
next(iterator)
timer = Timer()
all_times = []
for curr_iter in tqdm.trange(num_iter):
start = timer.seconds()
if start > max_time_seconds:
num_iter = curr_iter
break
next(iterator)
all_times.append(timer.seconds() - start)
avg = timer.seconds() / num_iter
return avg, all_times | Benchmark an iterator/iterable for `num_iter` iterations with an extra `warmup` iterations of warmup. End early if `max_time_seconds` time is spent on iterations. Returns: float: average time (seconds) per iteration list[float]: time spent on each iteration. Sometimes useful for further analysis. |
3,564 | import copy
import itertools
import logging
import numpy as np
import pickle
import random
import torch.utils.data as data
from torch.utils.data.sampler import Sampler
from detectron2.utils.serialize import PicklableWrapper
def _shard_iterator_dataloader_worker(iterable):
# Shard the iterable if we're currently inside pytorch dataloader worker.
worker_info = data.get_worker_info()
if worker_info is None or worker_info.num_workers == 1:
# do nothing
yield from iterable
else:
yield from itertools.islice(iterable, worker_info.id, None, worker_info.num_workers) | null |
3,565 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.utils.file_io import PathManager
from . import transforms as T
from .catalog import MetadataCatalog
_M_YUV2RGB = [[1.0, 0.0, 1.13983], [1.0, -0.39465, -0.58060], [1.0, 2.03211, 0.0]]
The provided code snippet includes necessary dependencies for implementing the `convert_image_to_rgb` function. Write a Python function `def convert_image_to_rgb(image, format)` to solve the following problem:
Convert an image from given format to RGB. Args: image (np.ndarray or Tensor): an HWC image format (str): the format of input image, also see `read_image` Returns: (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8
Here is the function:
def convert_image_to_rgb(image, format):
"""
Convert an image from given format to RGB.
Args:
image (np.ndarray or Tensor): an HWC image
format (str): the format of input image, also see `read_image`
Returns:
(np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8
"""
if isinstance(image, torch.Tensor):
image = image.cpu().numpy()
if format == "BGR":
image = image[:, :, [2, 1, 0]]
elif format == "YUV-BT.601":
image = np.dot(image, np.array(_M_YUV2RGB).T)
image = image * 255.0
else:
if format == "L":
image = image[:, :, 0]
image = image.astype(np.uint8)
image = np.asarray(Image.fromarray(image, mode=format).convert("RGB"))
return image | Convert an image from given format to RGB. Args: image (np.ndarray or Tensor): an HWC image format (str): the format of input image, also see `read_image` Returns: (np.ndarray): (H,W,3) RGB image in 0-255 range, can be either float or uint8 |
3,566 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.utils.file_io import PathManager
from . import transforms as T
from .catalog import MetadataCatalog
class SizeMismatchError(ValueError):
"""
When loaded image has difference width/height compared with annotation.
"""
The provided code snippet includes necessary dependencies for implementing the `check_image_size` function. Write a Python function `def check_image_size(dataset_dict, image)` to solve the following problem:
Raise an error if the image does not match the size specified in the dict.
Here is the function:
def check_image_size(dataset_dict, image):
"""
Raise an error if the image does not match the size specified in the dict.
"""
if "width" in dataset_dict or "height" in dataset_dict:
image_wh = (image.shape[1], image.shape[0])
expected_wh = (dataset_dict["width"], dataset_dict["height"])
if not image_wh == expected_wh:
raise SizeMismatchError(
"Mismatched image shape{}, got {}, expect {}.".format(
" for image " + dataset_dict["file_name"]
if "file_name" in dataset_dict
else "",
image_wh,
expected_wh,
)
+ " Please check the width/height in your annotation."
)
# To ensure bbox always remap to original image size
if "width" not in dataset_dict:
dataset_dict["width"] = image.shape[1]
if "height" not in dataset_dict:
dataset_dict["height"] = image.shape[0] | Raise an error if the image does not match the size specified in the dict. |
3,567 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.utils.file_io import PathManager
from . import transforms as T
from .catalog import MetadataCatalog
The provided code snippet includes necessary dependencies for implementing the `transform_proposals` function. Write a Python function `def transform_proposals(dataset_dict, image_shape, transforms, *, proposal_topk, min_box_size=0)` to solve the following problem:
Apply transformations to the proposals in dataset_dict, if any. Args: dataset_dict (dict): a dict read from the dataset, possibly contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" image_shape (tuple): height, width transforms (TransformList): proposal_topk (int): only keep top-K scoring proposals min_box_size (int): proposals with either side smaller than this threshold are removed The input dict is modified in-place, with abovementioned keys removed. A new key "proposals" will be added. Its value is an `Instances` object which contains the transformed proposals in its field "proposal_boxes" and "objectness_logits".
Here is the function:
def transform_proposals(dataset_dict, image_shape, transforms, *, proposal_topk, min_box_size=0):
"""
Apply transformations to the proposals in dataset_dict, if any.
Args:
dataset_dict (dict): a dict read from the dataset, possibly
contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode"
image_shape (tuple): height, width
transforms (TransformList):
proposal_topk (int): only keep top-K scoring proposals
min_box_size (int): proposals with either side smaller than this
threshold are removed
The input dict is modified in-place, with abovementioned keys removed. A new
key "proposals" will be added. Its value is an `Instances`
object which contains the transformed proposals in its field
"proposal_boxes" and "objectness_logits".
"""
if "proposal_boxes" in dataset_dict:
# Transform proposal boxes
boxes = transforms.apply_box(
BoxMode.convert(
dataset_dict.pop("proposal_boxes"),
dataset_dict.pop("proposal_bbox_mode"),
BoxMode.XYXY_ABS,
)
)
boxes = Boxes(boxes)
objectness_logits = torch.as_tensor(
dataset_dict.pop("proposal_objectness_logits").astype("float32")
)
boxes.clip(image_shape)
keep = boxes.nonempty(threshold=min_box_size)
boxes = boxes[keep]
objectness_logits = objectness_logits[keep]
proposals = Instances(image_shape)
proposals.proposal_boxes = boxes[:proposal_topk]
proposals.objectness_logits = objectness_logits[:proposal_topk]
dataset_dict["proposals"] = proposals | Apply transformations to the proposals in dataset_dict, if any. Args: dataset_dict (dict): a dict read from the dataset, possibly contains fields "proposal_boxes", "proposal_objectness_logits", "proposal_bbox_mode" image_shape (tuple): height, width transforms (TransformList): proposal_topk (int): only keep top-K scoring proposals min_box_size (int): proposals with either side smaller than this threshold are removed The input dict is modified in-place, with abovementioned keys removed. A new key "proposals" will be added. Its value is an `Instances` object which contains the transformed proposals in its field "proposal_boxes" and "objectness_logits". |
3,568 | import logging
import numpy as np
from typing import List, Union
import pycocotools.mask as mask_util
import torch
from PIL import Image
from detectron2.structures import (
BitMasks,
Boxes,
BoxMode,
Instances,
Keypoints,
PolygonMasks,
RotatedBoxes,
polygons_to_bitmask,
)
from detectron2.utils.file_io import PathManager
from . import transforms as T
from .catalog import MetadataCatalog
def transform_keypoint_annotations(keypoints, transforms, image_size, keypoint_hflip_indices=None):
"""
Transform keypoint annotations of an image.
If a keypoint is transformed out of image boundary, it will be marked "unlabeled" (visibility=0)
Args:
keypoints (list[float]): Nx3 float in Detectron2's Dataset format.
Each point is represented by (x, y, visibility).
transforms (TransformList):
image_size (tuple): the height, width of the transformed image
keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`.
When `transforms` includes horizontal flip, will use the index
mapping to flip keypoints.
"""
# (N*3,) -> (N, 3)
keypoints = np.asarray(keypoints, dtype="float64").reshape(-1, 3)
keypoints_xy = transforms.apply_coords(keypoints[:, :2])
# Set all out-of-boundary points to "unlabeled"
inside = (keypoints_xy >= np.array([0, 0])) & (keypoints_xy <= np.array(image_size[::-1]))
inside = inside.all(axis=1)
keypoints[:, :2] = keypoints_xy
keypoints[:, 2][~inside] = 0
# This assumes that HorizFlipTransform is the only one that does flip
do_hflip = sum(isinstance(t, T.HFlipTransform) for t in transforms.transforms) % 2 == 1
# Alternative way: check if probe points was horizontally flipped.
# probe = np.asarray([[0.0, 0.0], [image_width, 0.0]])
# probe_aug = transforms.apply_coords(probe.copy())
# do_hflip = np.sign(probe[1][0] - probe[0][0]) != np.sign(probe_aug[1][0] - probe_aug[0][0]) # noqa
# If flipped, swap each keypoint with its opposite-handed equivalent
if do_hflip:
if keypoint_hflip_indices is None:
raise ValueError("Cannot flip keypoints without providing flip indices!")
if len(keypoints) != len(keypoint_hflip_indices):
raise ValueError(
"Keypoint data has {} points, but metadata "
"contains {} points!".format(len(keypoints), len(keypoint_hflip_indices))
)
keypoints = keypoints[np.asarray(keypoint_hflip_indices, dtype=np.int32), :]
# Maintain COCO convention that if visibility == 0 (unlabeled), then x, y = 0
keypoints[keypoints[:, 2] == 0] = 0
return keypoints
The provided code snippet includes necessary dependencies for implementing the `transform_instance_annotations` function. Write a Python function `def transform_instance_annotations( annotation, transforms, image_size, *, keypoint_hflip_indices=None )` to solve the following problem:
Apply transforms to box, segmentation and keypoints annotations of a single instance. It will use `transforms.apply_box` for the box, and `transforms.apply_coords` for segmentation polygons & keypoints. If you need anything more specially designed for each data structure, you'll need to implement your own version of this function or the transforms. Args: annotation (dict): dict of instance annotations for a single instance. It will be modified in-place. transforms (TransformList or list[Transform]): image_size (tuple): the height, width of the transformed image keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. Returns: dict: the same input dict with fields "bbox", "segmentation", "keypoints" transformed according to `transforms`. The "bbox_mode" field will be set to XYXY_ABS.
Here is the function:
def transform_instance_annotations(
annotation, transforms, image_size, *, keypoint_hflip_indices=None
):
"""
Apply transforms to box, segmentation and keypoints annotations of a single instance.
It will use `transforms.apply_box` for the box, and
`transforms.apply_coords` for segmentation polygons & keypoints.
If you need anything more specially designed for each data structure,
you'll need to implement your own version of this function or the transforms.
Args:
annotation (dict): dict of instance annotations for a single instance.
It will be modified in-place.
transforms (TransformList or list[Transform]):
image_size (tuple): the height, width of the transformed image
keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`.
Returns:
dict:
the same input dict with fields "bbox", "segmentation", "keypoints"
transformed according to `transforms`.
The "bbox_mode" field will be set to XYXY_ABS.
"""
if isinstance(transforms, (tuple, list)):
transforms = T.TransformList(transforms)
# bbox is 1d (per-instance bounding box)
bbox = BoxMode.convert(annotation["bbox"], annotation["bbox_mode"], BoxMode.XYXY_ABS)
# clip transformed bbox to image size
bbox = transforms.apply_box(np.array([bbox]))[0].clip(min=0)
annotation["bbox"] = np.minimum(bbox, list(image_size + image_size)[::-1])
annotation["bbox_mode"] = BoxMode.XYXY_ABS
if "segmentation" in annotation:
# each instance contains 1 or more polygons
segm = annotation["segmentation"]
if isinstance(segm, list):
# polygons
polygons = [np.asarray(p).reshape(-1, 2) for p in segm]
annotation["segmentation"] = [
p.reshape(-1) for p in transforms.apply_polygons(polygons)
]
elif isinstance(segm, dict):
# RLE
mask = mask_util.decode(segm)
mask = transforms.apply_segmentation(mask)
assert tuple(mask.shape[:2]) == image_size
annotation["segmentation"] = mask
else:
raise ValueError(
"Cannot transform segmentation of type '{}'!"
"Supported types are: polygons as list[list[float] or ndarray],"
" COCO-style RLE as a dict.".format(type(segm))
)
if "keypoints" in annotation:
keypoints = transform_keypoint_annotations(
annotation["keypoints"], transforms, image_size, keypoint_hflip_indices
)
annotation["keypoints"] = keypoints
return annotation | Apply transforms to box, segmentation and keypoints annotations of a single instance. It will use `transforms.apply_box` for the box, and `transforms.apply_coords` for segmentation polygons & keypoints. If you need anything more specially designed for each data structure, you'll need to implement your own version of this function or the transforms. Args: annotation (dict): dict of instance annotations for a single instance. It will be modified in-place. transforms (TransformList or list[Transform]): image_size (tuple): the height, width of the transformed image keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. Returns: dict: the same input dict with fields "bbox", "segmentation", "keypoints" transformed according to `transforms`. The "bbox_mode" field will be set to XYXY_ABS. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.