repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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deep-person-reid | deep-person-reid-master/torchreid/losses/cross_entropy_loss.py | from __future__ import division, absolute_import
import torch
import torch.nn as nn
class CrossEntropyLoss(nn.Module):
r"""Cross entropy loss with label smoothing regularizer.
Reference:
Szegedy et al. Rethinking the Inception Architecture for Computer Vision. CVPR 2016.
With label smoothing... | 1,923 | 36.72549 | 92 | py |
deep-person-reid | deep-person-reid-master/projects/OSNet_AIN/main.py | import os
import sys
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, compute_model_complexity
)
import osnet_search as osnet_models
from softmax_... | 4,193 | 27.726027 | 79 | py |
deep-person-reid | deep-person-reid-master/projects/OSNet_AIN/softmax_nas.py | from __future__ import division, print_function, absolute_import
from torchreid import metrics
from torchreid.engine import Engine
from torchreid.losses import CrossEntropyLoss
class ImageSoftmaxNASEngine(Engine):
def __init__(
self,
datamanager,
model,
optimizer,
schedul... | 2,108 | 27.5 | 73 | py |
deep-person-reid | deep-person-reid-master/projects/OSNet_AIN/osnet_search.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
EPS = 1e-12
NORM_AFFINE = False # enable affine transformations for normalization layer
##########
# Basic layers
##########
class IBN(nn.Module):
"""Instance + Batch Normalization."""
def... | 18,586 | 30.77265 | 91 | py |
deep-person-reid | deep-person-reid-master/projects/OSNet_AIN/osnet_child.py | from __future__ import division, absolute_import
from torch import nn
from torch.nn import functional as F
##########
# Basic layers
##########
class ConvLayer(nn.Module):
"""Convolution layer (conv + bn + relu)."""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
... | 16,436 | 29.666045 | 91 | py |
deep-person-reid | deep-person-reid-master/projects/attribute_recognition/main.py | from __future__ import division, print_function
import sys
import copy
import time
import numpy as np
import os.path as osp
import datetime
import warnings
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, AverageMeter, check_isfile, open_all_layers, save_checkpoint,
set... | 12,430 | 30.0775 | 79 | py |
deep-person-reid | deep-person-reid-master/projects/attribute_recognition/models/osnet.py | from __future__ import division, absolute_import
import torch
from torch import nn
from torch.nn import functional as F
__all__ = ['osnet_avgpool', 'osnet_maxpool']
##########
# Basic layers
##########
class ConvLayer(nn.Module):
"""Convolution layer."""
def __init__(
self,
in_channels,
... | 11,484 | 26.674699 | 80 | py |
deep-person-reid | deep-person-reid-master/projects/attribute_recognition/datasets/dataset.py | from __future__ import division, print_function, absolute_import
import os.path as osp
from torchreid.utils import read_image
class Dataset(object):
def __init__(
self,
train,
val,
test,
attr_dict,
transform=None,
mode='train',
verbose=True,
... | 2,563 | 28.136364 | 69 | py |
deep-person-reid | deep-person-reid-master/projects/DML/main.py | import sys
import copy
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
)
from dml import ImageD... | 4,789 | 27.682635 | 79 | py |
deep-person-reid | deep-person-reid-master/projects/DML/dml.py | from __future__ import division, print_function, absolute_import
import torch
from torch.nn import functional as F
from torchreid.utils import open_all_layers, open_specified_layers
from torchreid.engine import Engine
from torchreid.losses import TripletLoss, CrossEntropyLoss
class ImageDMLEngine(Engine):
def _... | 4,430 | 28.54 | 72 | py |
deep-person-reid | deep-person-reid-master/scripts/main.py | import sys
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
)
from default_config import (
i... | 5,871 | 29.583333 | 81 | py |
deep-person-reid | deep-person-reid-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,646 | 30.027473 | 79 | py |
VEXAS-DR2 | VEXAS-DR2-main/classification/models/ann.py | import torch
from torch import nn
from torch.nn import functional as F
class NeuralNetworkClassifier(nn.Module):
def __init__(self, **kwargs):
super(NeuralNetworkClassifier, self).__init__()
self.head = nn.Sequential(
nn.Linear(in_features=kwargs["input_shape"], out_features=kwargs["e... | 1,461 | 36.487179 | 99 | py |
VEXAS-DR2 | VEXAS-DR2-main/classification/models/models.py | import warnings; warnings.filterwarnings('ignore')
import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import logging; logging.getLogger('tensorflow').setLevel(logging.ERROR)
import torch
import pickle
from torch.optim import Adam
from torch.nn import BCELoss
from poutyne.framework import Model
from catalyst.dl.utils... | 6,990 | 33.269608 | 104 | py |
VEXAS-DR2 | VEXAS-DR2-main/imputation/inference.py | from autoencoder import set_seed
set_seed()
import warnings
warnings.filterwarnings("ignore")
import os
import argparse
import logging
import numpy as np
from tf.keras.optimizers import Nadam
from dataset.dataset import VexasDataset
from model.autoencoder import Autoencoder, callbacks
from settings import BATCH_SIZ... | 1,403 | 28.25 | 83 | py |
VEXAS-DR2 | VEXAS-DR2-main/imputation/imputer.py | import warnings
warnings.filterwarnings("ignore")
from model.autoencoder import set_seed
set_seed()
import os
import logging
import argparse
import numpy as np
from tf.keras.optimizers import Nadam
from dataset.dataset import VexasDataset
from model.autoencoder import Autoencoder, callbacks
from utility.plot import... | 2,642 | 32.884615 | 88 | py |
VEXAS-DR2 | VEXAS-DR2-main/imputation/model/autoencoder.py | import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import logging
logging.getLogger('tensorflow').setLevel(logging.ERROR)
import sys
import math
import numbers
import numpy as np
import random as rd
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import ModelCheckpoint, Ear... | 9,487 | 39.374468 | 111 | py |
TerrainMesh | TerrainMesh-master/loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import matplotlib.pyplot as plt
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch3d
from pytorch3d.loss import chamfer_distance, mesh_edge_loss, mesh_laplacian_smoothing
from pytorch3d.ops import sample... | 14,394 | 41.970149 | 143 | py |
TerrainMesh | TerrainMesh-master/train_deeplab.py | # Load a PyTorch deeplab and train
import numpy as np
import os
import shutil
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from config import get_sensat_cfg
from dataset.build_data_loader import build_data_loader
from loss import FocalLoss
from model.deep... | 7,790 | 45.933735 | 148 | py |
TerrainMesh | TerrainMesh-master/predict.py | # THe training script
import os
import shutil
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.tensorboard import SummaryWriter
from config import get_sensat_cfg
from dataset.build_data_l... | 7,964 | 45.578947 | 149 | py |
TerrainMesh | TerrainMesh-master/train.py | # THe training script
import os
import shutil
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import ReduceLROnPlateau, StepLR
from torch.utils.tensorboard import SummaryWriter
from config import get_sensat_cfg
from dataset.build_data_loader import build_data_... | 14,339 | 52.707865 | 158 | py |
TerrainMesh | TerrainMesh-master/predict_deeplab.py | # Load a PyTorch deeplab and train
import numpy as np
import os
import shutil
from tqdm import tqdm
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
from config import get_sensat_cfg
from dataset.build_data_loader import build_data_loader
from loss ... | 5,752 | 43.596899 | 141 | py |
TerrainMesh | TerrainMesh-master/dataset/build_data_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from .terrain_dataset import TerrainDataset
from .sensat_dataset import Sensat... | 3,811 | 36.742574 | 81 | py |
TerrainMesh | TerrainMesh-master/dataset/terrain_dataset.py | import json
import logging
import os
import numpy as np
from imageio import imread
import time
import torch
from pytorch3d.ops import sample_points_from_meshes
from pytorch3d.structures import Meshes
from pytorch3d.io import load_obj
from torch.utils.data import Dataset
import torchvision.transforms as T
from PIL impo... | 11,263 | 41.992366 | 131 | py |
TerrainMesh | TerrainMesh-master/dataset/sensat_dataset.py | import logging
import os
import numpy as np
from imageio import imread
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torchvision import transforms
from pytorch3d.io import load_obj
from pytorch3d.structures import Meshes
logger = logging.getLogger(__name__)
# - dat... | 15,155 | 46.810726 | 169 | py |
TerrainMesh | TerrainMesh-master/mesh_init/run_mesh_init_sparse_depth.py | # This script is used to generate some meshes initialized by sparse depths measurment. We solve a linear equation.
import os
import numpy as np
from imageio import imread, imwrite
import torch
from pytorch3d.io import save_obj
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (
SfMPerspectiveCa... | 7,005 | 49.768116 | 150 | py |
TerrainMesh | TerrainMesh-master/mesh_init/mesh_renderer.py | # render the mesh using a customized naive shader that preserve the vertex color without any other effects.
import os
import open3d as o3d
import numpy as np
import matplotlib.pyplot as plt
from imageio import imread
import time
import torch
import torch.nn as nn
from pytorch3d.loss import (
point_mesh_face_distan... | 6,293 | 35.807018 | 142 | py |
TerrainMesh | TerrainMesh-master/mesh_init/mesh_opt.py | ## A few different ways of meshing
## Maining including the regular-grid like meshing
import numpy as np
import os
from imageio import imread
import matplotlib.pyplot as plt
import cv2
import open3d as o3d
from tqdm import tqdm
import torch
import torch.nn as nn
from pytorch3d.ops import sample_points_from_meshes
from... | 6,147 | 40.261745 | 203 | py |
TerrainMesh | TerrainMesh-master/mesh_init/meshing.py | # Include some mesh building functions.
# Build a flat regular mesh on 2D.
import numpy as np
from scipy.spatial import Delaunay
import torch
from pytorch3d.structures import Meshes
def regular_512_576():
x = np.linspace(-2,513,24)
y = np.linspace(-2,513,24)
xx, yy = np.meshgrid(x, y)
vertices = np.c... | 2,547 | 37.029851 | 80 | py |
TerrainMesh | TerrainMesh-master/mesh_init/mesh_init_linear_solver.py | import numpy as np
import open3d as o3d
from imageio import imread
import os
import time
import torch
import torch.nn as nn
from pytorch3d.structures import Meshes
from pytorch3d.renderer import (
SfMPerspectiveCameras,
RasterizationSettings,
MeshRasterizer,
)
from .meshing import regular_512_576, regular_5... | 8,775 | 42.88 | 154 | py |
TerrainMesh | TerrainMesh-master/mesh_init/run_calc_edt.py | import os
from imageio import imread, imwrite
from scipy import ndimage
import torch
import matplotlib.pyplot as plt
import numpy as np
dataset_dir = "/mnt/NVMe-2TB/qiaojun/SensatUrban/"
dataset_name_list = ["birmingham_2", "birmingham_3", "birmingham_4", "birmingham_5", "birmingham_6", "cambridge_4",
... | 1,167 | 45.72 | 130 | py |
TerrainMesh | TerrainMesh-master/mesh_init/run_mesh_gt_depth.py | # This script is used to generate some meshes initialized by sparse depths measurment. We solve a linear equation.
import os
import numpy as np
from imageio import imread, imwrite
import time
import matplotlib.pyplot as plt
import torch
from pytorch3d.io import save_obj
from pytorch3d.ops import sample_points_from_mesh... | 3,585 | 46.813333 | 216 | py |
TerrainMesh | TerrainMesh-master/utils/project_verts.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
""" Utilities for working with different 3D coordinate systems """
import torch
from pytorch3d.structures import Meshes
def project_verts(verts, P, eps=1e-1):
"""
Project verticies using a 4x4 transformation matrix
Inputs:
- verts... | 1,707 | 35.340426 | 81 | py |
TerrainMesh | TerrainMesh-master/utils/run_sem_2d.py | # Use a pretrained 2D segmentation model to derive the segmentation mask
import os
import numpy as np
from imageio import imread
import matplotlib.pyplot as plt
from PIL import Image
import torch
import torch.nn as nn
from torchvision import transforms
from config import get_sensat_cfg
from model.deeplab import deep... | 2,782 | 35.618421 | 155 | py |
TerrainMesh | TerrainMesh-master/utils/optimizer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
def build_optimizer(cfg, model):
# TODO add weight decay?
name = cfg.SOLVER.OPTIMIZER
lr = cfg.SOLVER.BASE_LR
momentum = cfg.SOLVER.MOMENTUM
if name == "sgd":
return torch.optim.SGD(model.parameters(), lr=lr... | 616 | 35.294118 | 76 | py |
TerrainMesh | TerrainMesh-master/utils/deprecated/test_predict_sdtri.py | import numpy as np
from scipy.spatial import Delaunay
import open3d as o3d
import matplotlib.pyplot as plt
from imageio import imread, imwrite
import os
from tqdm import tqdm
import torch
from pytorch3d.structures import Meshes
from pytorch3d.io import save_obj,load_objs_as_meshes
from pytorch3d.loss import chamfer... | 4,662 | 41.390909 | 260 | py |
TerrainMesh | TerrainMesh-master/utils/deprecated/predict_single.py | # THe training script
import os
import shutil
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from imageio import imwrite
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.tensorboard import SummaryWriter
from pytorch3d.io import save_ob... | 9,568 | 45.906863 | 260 | py |
TerrainMesh | TerrainMesh-master/model/mesh_head.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
from pytorch3d.ops import GraphConv, SubdivideMeshes, vert_align
from pytorch3d.renderer import TexturesVertex
from torch.nn import functional as F
def project_verts(verts, P, eps=1e-1):
"""
Project vertic... | 21,530 | 47.167785 | 201 | py |
TerrainMesh | TerrainMesh-master/model/image_backbone.py | import torch.nn as nn
import torchvision
class ResNetBackbone(nn.Module):
def __init__(self, net, in_channels=3):
super(ResNetBackbone, self).__init__()
if in_channels == 3:
self.conv1 = net.conv1
else:
self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2,... | 2,863 | 34.8 | 155 | py |
TerrainMesh | TerrainMesh-master/model/deeplab.py | # Some Deeplab models
import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision
from torchvision.models.resnet import resnet18, resnet34
from torchvision.models.segmentation.deeplabv3 import DeepLabHead, ASPP
from .image_backbone import ResNetBackbone
class DeepLabHeadNew(nn.Sequentia... | 2,406 | 41.982143 | 121 | py |
TerrainMesh | TerrainMesh-master/model/models.py | import torch
import torch.nn as nn
from collections import OrderedDict
from pytorch3d.ops import vert_align
from pytorch3d.renderer import TexturesVertex
from .deeplab import deeplabv3_resnet50, deeplabv3_resnet34, deeplabv3_resnet18
from .image_backbone import build_backbone
from .mesh_head import MeshRefinementHead
... | 3,908 | 36.228571 | 134 | py |
serverless-bert-huggingface-aws-lambda-docker | serverless-bert-huggingface-aws-lambda-docker-main/handler.py | import json
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, AutoConfig
def encode(tokenizer, question, context):
"""encodes the question and context with a given tokenizer"""
encoded = tokenizer.encode_plus(question, context)
return encoded["input_ids"], encoded["attenti... | 2,502 | 36.924242 | 108 | py |
OBBDetection | OBBDetection-master/setup.py | #!/usr/bin/env python
import os
import subprocess
import time
from setuptools import find_packages, setup
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
def readme():
with open('README.md', encoding='utf-8') as f:
co... | 11,146 | 33.943574 | 125 | py |
OBBDetection | OBBDetection-master/tools/test.py | import argparse
import os
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from tools.fuse_conv_bn import fuse_module
from mmdet.apis import multi_gpu_test, single_gpu_test... | 5,668 | 35.811688 | 79 | py |
OBBDetection | OBBDetection-master/tools/benchmark.py | import argparse
import time
import torch
from mmcv import Config
from mmcv.parallel import MMDataParallel
from mmcv.runner import load_checkpoint
from tools.fuse_conv_bn import fuse_module
from mmdet.core import wrap_fp16_model
from mmdet.datasets import build_dataloader, build_dataset
from mmdet.models import build_... | 2,802 | 28.819149 | 79 | py |
OBBDetection | OBBDetection-master/tools/fuse_conv_bn.py | import argparse
import torch
import torch.nn as nn
from mmcv.runner import save_checkpoint
from mmdet.apis import init_detector
def fuse_conv_bn(conv, bn):
""" During inference, the functionary of batch norm layers is turned off
but only the mean and var alone channels are used, which exposes the
chance... | 2,200 | 30.898551 | 77 | py |
OBBDetection | OBBDetection-master/tools/get_flops.py | import argparse
import torch
from mmcv import Config
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a detector')
... | 1,732 | 26.507937 | 79 | py |
OBBDetection | OBBDetection-master/tools/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,072 | 27.236842 | 77 | py |
OBBDetection | OBBDetection-master/tools/regnet2mmdet.py | import argparse
from collections import OrderedDict
import torch
def convert_stem(model_key, model_weight, state_dict, converted_names):
new_key = model_key.replace('stem.conv', 'conv1')
new_key = new_key.replace('stem.bn', 'bn1')
state_dict[new_key] = model_weight
converted_names.add(model_key)
... | 3,015 | 32.511111 | 77 | py |
OBBDetection | OBBDetection-master/tools/pytorch2onnx.py | import argparse
import io
import mmcv
import onnx
import torch
from mmcv.runner import load_checkpoint
from onnx import optimizer
from torch.onnx import OperatorExportTypes
from mmdet.models import build_detector
from mmdet.ops import RoIAlign, RoIPool
def export_onnx_model(model, inputs, passes):
"""
Trace... | 3,996 | 30.722222 | 76 | py |
OBBDetection | OBBDetection-master/tools/upgrade_model_version.py | import argparse
import re
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def is_head(key):
valid_head_list = [
'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
]
return any(key.startswith(h) for h in valid_head_list)
def parse_co... | 6,215 | 31.041237 | 79 | py |
OBBDetection | OBBDetection-master/tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from pycocotools.coco import COCO
fro... | 17,153 | 36.372549 | 79 | py |
OBBDetection | OBBDetection-master/tools/train.py | import argparse
import copy
import os
import os.path as osp
import time
import mmcv
import torch
from mmcv import Config, DictAction
from mmcv.runner import init_dist
from mmdet import __version__
from mmdet.apis import set_random_seed, train_detector
from mmdet.datasets import build_dataset
from mmdet.models import ... | 5,345 | 33.714286 | 79 | py |
OBBDetection | OBBDetection-master/tools/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,530 | 41.542169 | 78 | py |
OBBDetection | OBBDetection-master/tests/async_benchmark.py | import asyncio
import os
import shutil
import urllib
import mmcv
import torch
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result)
from mmdet.utils.contextmanagers import concurrent
from mmdet.utils.profiling import profile_time
async def main():
... | 3,124 | 29.048077 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_roi_extractor.py | import pytest
import torch
from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor
def test_groie():
# test with pre/post
cfg = dict(
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
out_channels=256,
featmap_strides=[4, 8, 16, 32],
pre_cfg=dict(
... | 3,174 | 26.850877 | 74 | py |
OBBDetection | OBBDetection-master/tests/test_anchor.py | """
CommandLine:
pytest tests/test_anchor.py
xdoctest tests/test_anchor.py zero
"""
import torch
def test_standard_anchor_generator():
from mmdet.core.anchor import build_anchor_generator
anchor_generator_cfg = dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
... | 16,127 | 42.826087 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_forward.py | """
pytest tests/test_forward.py
"""
import copy
from os.path import dirname, exists, join
import numpy as np
import pytest
import torch
def _get_config_directory():
""" Find the predefined detector config directory """
try:
# Assume we are running in the source mmdetection repo
repo_dpath = ... | 10,828 | 30.388406 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_async.py | """Tests for async interface."""
import asyncio
import os
import sys
import asynctest
import mmcv
import torch
from mmdet.apis import async_inference_detector, init_detector
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import concurrent
class AsyncTestCase(asynctest.TestCase):
use_defau... | 2,560 | 29.855422 | 75 | py |
OBBDetection | OBBDetection-master/tests/test_config.py | from os.path import dirname, exists, join, relpath
import torch
from mmcv.runner import build_optimizer
from mmdet.core import BitmapMasks, PolygonMasks
def _get_config_directory():
""" Find the predefined detector config directory """
try:
# Assume we are running in the source mmdetection repo
... | 14,223 | 38.62117 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_necks.py | import pytest
import torch
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.necks import FPN
def test_fpn():
"""Tests fpn """
s = 64
in_channels = [8, 16, 32, 64]
feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8]
out_channels = 8
# `num_outs` is not equal to len... | 6,570 | 31.529703 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_sampler.py | import torch
from mmdet.core.bbox.assigners import MaxIoUAssigner
from mmdet.core.bbox.samplers import (OHEMSampler, RandomSampler,
ScoreHLRSampler)
def test_random_sampler():
assigner = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
ignore_iof_thr... | 9,906 | 28.750751 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_heads.py | import mmcv
import torch
from mmdet.core import bbox2roi, build_assigner, build_sampler
from mmdet.models.dense_heads import (AnchorHead, FCOSHead, FSAFHead,
GuidedAnchorHead)
from mmdet.models.roi_heads.bbox_heads import BBoxHead
from mmdet.models.roi_heads.mask_heads import FCNM... | 21,883 | 33.51735 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_pisa_heads.py | import mmcv
import torch
from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead
from mmdet.models.roi_heads import PISARoIHead
def test_pisa_retinanet_head_loss():
"""
Tests pisa retinanet head loss when truth is empty and non-empty
"""
s = 256
img_metas = [{
'img_shape': (s, s,... | 8,777 | 33.972112 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_losses.py | import pytest
import torch
def test_ce_loss():
from mmdet.models import build_loss
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(
type='CrossEntropyLoss',
use_mask=True,
use_sigmoid=True,
... | 967 | 29.25 | 78 | py |
OBBDetection | OBBDetection-master/tests/test_masks.py | import numpy as np
import pytest
import torch
from mmdet.core import BitmapMasks, PolygonMasks
def dummy_raw_bitmap_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
Return:
ndarray: dummy mask
"""
return np.random.randint(0, 2, size, dtype=n... | 23,708 | 37.995066 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_backbone.py | import pytest
import torch
from torch.nn.modules import AvgPool2d, GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones import RegNet, Res2Net, ResNet, ResNetV1d, ResNeXt
from mmdet.models.backbones.hourglass import HourglassNet
from mmdet.models.backbones.res2net import Bottle2neck
... | 28,580 | 33.643636 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_assigner.py | """
Tests the Assigner objects.
CommandLine:
pytest tests/test_assigner.py
xdoctest tests/test_assigner.py zero
"""
import torch
from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
CenterRegionAssigner, MaxIoUAssigner,
P... | 12,014 | 28.813896 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_fp16.py | import numpy as np
import pytest
import torch
import torch.nn as nn
from mmdet.core import auto_fp16, force_fp32
from mmdet.core.fp16.utils import cast_tensor_type
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type(... | 9,713 | 31.165563 | 75 | py |
OBBDetection | OBBDetection-master/tests/test_pipelines/test_transform.py | import copy
import os.path as osp
import mmcv
import numpy as np
import pytest
import torch
from mmcv.utils import build_from_cfg
from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
from mmdet.datasets.builder import PIPELINES
def test_resize():
# test assertion if img_scale is a list
with pytest.... | 20,178 | 35.822993 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_pipelines/test_models_aug_test.py | import os.path as osp
import mmcv
import torch
from mmcv.parallel import collate
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
from mmdet.models import build_detector
def model_aug_test_template(cfg_file):
# get config
cfg = mmcv.Config.fromfile(cfg_file)
# init mode... | 1,909 | 29.806452 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_ops/test_merge_cells.py | """
CommandLine:
pytest tests/test_merge_cells.py
"""
import torch
import torch.nn.functional as F
from mmdet.ops.merge_cells import (BaseMergeCell, ConcatCell,
GlobalPoolingCell, SumCell)
def test_sum_cell():
inputs_x = torch.randn([2, 256, 32, 32])
inputs_y = torch.ra... | 2,504 | 36.954545 | 75 | py |
OBBDetection | OBBDetection-master/tests/test_ops/test_soft_nms.py | """
CommandLine:
pytest tests/test_soft_nms.py
"""
import numpy as np
import torch
from mmdet.ops.nms.nms_wrapper import soft_nms
def test_soft_nms_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_soft_nms.py test_soft_nms_device_and_dtypes_cpu
"""
iou_thr = 0.7
base_d... | 1,257 | 28.952381 | 78 | py |
OBBDetection | OBBDetection-master/tests/test_ops/test_nms.py | """
CommandLine:
pytest tests/test_nms.py
"""
import numpy as np
import pytest
import torch
from mmdet.ops.nms.nms_wrapper import nms, nms_match
def test_nms_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_nms.py test_nms_device_and_dtypes_cpu
"""
iou_thr = 0.6
base_d... | 4,220 | 36.026316 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_ops/test_wrappers.py | from collections import OrderedDict
from itertools import product
from unittest.mock import patch
import torch
import torch.nn as nn
from mmdet.ops import Conv2d, ConvTranspose2d, Linear, MaxPool2d
torch.__version__ = '1.1' # force test
def test_conv2d():
"""
CommandLine:
xdoctest -m tests/test_wr... | 6,705 | 32.698492 | 79 | py |
OBBDetection | OBBDetection-master/tests/test_ops/test_corner_pool.py | """
CommandLine:
pytest tests/test_corner_pool.py
"""
import pytest
import torch
from mmdet.ops import CornerPool
def test_corner_pool_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_corner_pool.py \
test_corner_pool_device_and_dtypes_cpu
"""
with pytest.raise... | 2,301 | 38.016949 | 69 | py |
OBBDetection | OBBDetection-master/demo/webcam_demo.py | import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')... | 1,260 | 25.829787 | 78 | py |
OBBDetection | OBBDetection-master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='... | 372 | 25.642857 | 53 | py |
OBBDetection | OBBDetection-master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 123 | 40.333333 | 76 | py |
OBBDetection | OBBDetection-master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='... | 372 | 25.642857 | 53 | py |
OBBDetection | OBBDetection-master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=... | 510 | 30.9375 | 76 | py |
OBBDetection | OBBDetection-master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 504 | 25.578947 | 53 | py |
OBBDetection | OBBDetection-master/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 7,989 | 32.153527 | 79 | py |
OBBDetection | OBBDetection-master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 504 | 25.578947 | 53 | py |
OBBDetection | OBBDetection-master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 1,406 | 31.72093 | 79 | py |
OBBDetection | OBBDetection-master/configs/htc/htc_r101_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
# learning policy
lr_config = dict(step=[16, 19])
total_epochs = 20
| 181 | 29.333333 | 76 | py |
OBBDetection | OBBDetection-master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(
depth=101,
dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 284 | 34.625 | 76 | py |
OBBDetection | OBBDetection-master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 139 | 45.666667 | 76 | py |
OBBDetection | OBBDetection-master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0,... | 1,931 | 27.411765 | 79 | py |
OBBDetection | OBBDetection-master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm... | 515 | 31.25 | 76 | py |
OBBDetection | OBBDetection-master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
type='GFL',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_c... | 538 | 28.944444 | 76 | py |
OBBDetection | OBBDetection-master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
type='GFL',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_c... | 410 | 24.6875 | 53 | py |
OBBDetection | OBBDetection-master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 346 | 25.692308 | 53 | py |
OBBDetection | OBBDetection-master/configs/gfl/gfl_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='GFL',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | 1,649 | 27.448276 | 72 | py |
OBBDetection | OBBDetection-master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py | _base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
dcn=dict(type='D... | 473 | 30.6 | 76 | py |
OBBDetection | OBBDetection-master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
... | 2,407 | 28.728395 | 77 | py |
OBBDetection | OBBDetection-master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
... | 2,397 | 28.975 | 77 | py |
OBBDetection | OBBDetection-master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(_delete_=True,... | 1,371 | 31.666667 | 78 | py |
OBBDetection | OBBDetection-master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
total_epochs = 36
| 125 | 24.2 | 56 | py |
OBBDetection | OBBDetection-master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | 1,053 | 30.939394 | 72 | py |
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