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# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------
"""
Train and eval functions used in main.py
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator, convert_to_xywh
from datasets.data_prefetcher import data_prefetcher
from datasets.panoptic_eval import PanopticEvaluator
from models.utils_softnms import batched_soft_nms
from util.misc import NestedTensor
# Make sure this is consistent with datasets/coco.py
# TODO: make it configurable
SCALE_RANGES_DICT = {
1728: [[0, 10000], [32, 10000], [32, 10000],],
1824: [[0, 10000], [0, 10000], [64, 10000], [64, 10000],],
}
def filter_boxes(boxes, min_scale, max_scale):
"""
boxes: (N, 4) shape
"""
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]
keep = (w * h > min_scale * min_scale) & (w * h < max_scale * max_scale)
return keep
@torch.no_grad()
def evaluate_tta(
model_no_ema,
criterion,
postprocessors,
data_loader,
base_ds,
device,
output_dir,
test_hflip_aug,
tta,
soft_nms,
ema=None,
save_result=False,
save_result_dir="",
soft_nms_method="quad",
nms_thresh=0.7,
quad_scale=0.5,
lsj_img_size=1824,
):
model = model_no_ema if ema is None else ema
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
"class_error", utils.SmoothedValue(window_size=1, fmt="{value:.2f}")
)
header = "Test:"
iou_types = tuple(k for k in ("segm", "bbox") if k in postprocessors.keys())
coco_evaluator = CocoEvaluator(base_ds, iou_types)
# coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75]
SCALE_RANGES = SCALE_RANGES_DICT[lsj_img_size]
IMAGE_SIZE = [lsj_img_size for _ in range(len(SCALE_RANGES))]
prediction_list = []
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
metric_logger.update(loss=0, class_error=0, loss_bbox=0, loss_ce=0)
########################### Begin of inference_one_image ###########################
if tta:
assert samples.tensors.shape[0] == 1, "tta only supports batch size 1"
assert (
samples.tensors.shape[1] % 3 == 0
), "tta requires dimensions of samples.tensors to be divisible by 3"
all_boxes = []
all_scores = []
all_classes = []
num_scales = samples.tensors.shape[1] // 3
for scale_ind in range(num_scales):
first_samples = NestedTensor(
samples.tensors[
:,
scale_ind * 3 : (scale_ind + 1) * 3,
: IMAGE_SIZE[scale_ind // 2],
: IMAGE_SIZE[scale_ind // 2],
],
samples.mask[
:,
scale_ind,
: IMAGE_SIZE[scale_ind // 2],
: IMAGE_SIZE[scale_ind // 2],
],
)
if scale_ind % 2 == 0:
######## no flip #######
outputs = model(first_samples)
noaug_results = postprocessors["bbox"](
outputs,
orig_target_sizes,
soft_nms=soft_nms,
method=soft_nms_method,
nms_thresh=nms_thresh,
quad_scale=quad_scale,
)
keep = filter_boxes(
noaug_results[0]["boxes"], *SCALE_RANGES[scale_ind // 2]
)
all_boxes.append(noaug_results[0]["boxes"][keep])
all_scores.append(noaug_results[0]["scores"][keep])
all_classes.append(noaug_results[0]["labels"][keep])
else:
######## flipped #######
flipped_outputs = model(first_samples)
flipped_pred_logits = flipped_outputs["pred_logits"]
flipped_pred_boxes = flipped_outputs["pred_boxes"]
reflipped_pred_boxes = flipped_pred_boxes[
:, :, [0, 1, 2, 3]
] * torch.as_tensor([-1, 1, 1, 1]).to(
flipped_pred_boxes.device
) + torch.as_tensor(
[1, 0, 0, 0]
).to(
flipped_pred_boxes.device
)
new_outputs = {}
new_outputs["pred_logits"] = flipped_pred_logits
new_outputs["pred_boxes"] = reflipped_pred_boxes
new_results = postprocessors["bbox"](
new_outputs,
orig_target_sizes,
soft_nms=soft_nms,
method=soft_nms_method,
nms_thresh=nms_thresh,
quad_scale=quad_scale,
)
keep = filter_boxes(
new_results[0]["boxes"], *SCALE_RANGES[scale_ind // 2]
)
all_boxes.append(new_results[0]["boxes"][keep])
all_scores.append(new_results[0]["scores"][keep])
all_classes.append(new_results[0]["labels"][keep])
######## merge #######
all_boxes = torch.cat(all_boxes, dim=0)
all_scores = torch.cat(all_scores, dim=0)
all_classes = torch.cat(all_classes, dim=0)
keep_inds, updated_scores = batched_soft_nms(
all_boxes,
all_scores,
all_classes,
method=soft_nms_method,
threshold=nms_thresh,
quad_scale=quad_scale,
)
merged_scores = updated_scores
merged_classes = all_classes[keep_inds]
merged_boxes = all_boxes[keep_inds]
results = [
{
"boxes": merged_boxes,
"scores": merged_scores,
"labels": merged_classes,
}
]
else:
outputs = model(samples)
results = postprocessors["bbox"](outputs, orig_target_sizes)
########################### End of inference_one_image ###########################
res = {
target["image_id"].item(): output
for target, output in zip(targets, results)
}
if coco_evaluator is not None:
coco_evaluator.update(res)
for target, output in zip(targets, results):
res_cpu = {
target["image_id"].item(): {
"boxes": output["boxes"].cpu(),
"labels": output["labels"].cpu(),
"scores": output["scores"].cpu(),
}
}
prediction_list.append(res_cpu)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
if save_result:
from torch import distributed as dist
os.makedirs(save_result_dir, exist_ok=True)
rank = dist.get_rank()
torch.save(
prediction_list,
os.path.join(save_result_dir, f"val2017_prediction_{rank}.pth"),
)
if coco_evaluator is not None:
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
if coco_evaluator is not None:
coco_evaluator.accumulate()
coco_evaluator.summarize()
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
if coco_evaluator is not None:
if "bbox" in postprocessors.keys():
stats["coco_eval_bbox"] = coco_evaluator.coco_eval["bbox"].stats.tolist()
return stats, coco_evaluator
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