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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 util.ema import requires_grad, update_ema
from util.misc import NestedTensor
def train_one_epoch(
model: torch.nn.Module,
criterion: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
max_norm: float = 0,
ema: torch.nn.Module = None,
ema_decay: float = 0.999,
):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value:.6f}"))
metric_logger.add_meter(
"class_error", utils.SmoothedValue(window_size=1, fmt="{value:.2f}")
)
metric_logger.add_meter(
"grad_norm", utils.SmoothedValue(window_size=1, fmt="{value:.2f}")
)
header = "Epoch: [{}]".format(epoch)
print_freq = 10
prefetcher = data_prefetcher(data_loader, device, prefetch=True)
samples, targets = prefetcher.next()
# for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
for _ in metric_logger.log_every(range(len(data_loader)), print_freq, header):
outputs = model(samples)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
losses = sum(
loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict
)
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = {
f"{k}_unscaled": v for k, v in loss_dict_reduced.items()
}
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items()
if k in weight_dict
}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
grad_total_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), max_norm
)
else:
grad_total_norm = utils.get_total_grad_norm(model.parameters(), max_norm)
optimizer.step()
if ema is not None:
update_ema(ema, model.module, ema_decay)
# torch.cuda.empty_cache()
metric_logger.update(
loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled
)
metric_logger.update(class_error=loss_dict_reduced["class_error"])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(grad_norm=grad_total_norm)
samples, targets = prefetcher.next()
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(
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]
panoptic_evaluator = None
if "panoptic" in postprocessors.keys():
panoptic_evaluator = PanopticEvaluator(
data_loader.dataset.ann_file,
data_loader.dataset.ann_folder,
output_dir=os.path.join(output_dir, "panoptic_eval"),
)
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]
if test_hflip_aug:
assert (
samples.tensors.shape[0] == 1
), "test_hflip_aug only supports batch size 1"
assert (
samples.tensors.shape[1] == 6
), "test_hflip_aug requires two images in a batch"
first_samples = NestedTensor(samples.tensors[:, :3], samples.mask)
outputs = model(first_samples)
flipped_samples = NestedTensor(samples.tensors[:, 3:], samples.mask)
flipped_outputs = model(flipped_samples)
else:
outputs = model(samples)
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items()
if k in weight_dict
}
loss_dict_reduced_unscaled = {
f"{k}_unscaled": v for k, v in loss_dict_reduced.items()
}
metric_logger.update(
loss=sum(loss_dict_reduced_scaled.values()),
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled,
)
metric_logger.update(class_error=loss_dict_reduced["class_error"])
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
if test_hflip_aug:
new_outputs = {}
pred_logits = outputs["pred_logits"]
pred_boxes = outputs["pred_boxes"]
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_pred_logits = torch.cat([pred_logits, flipped_pred_logits], dim=1)
new_pred_boxes = torch.cat([pred_boxes, reflipped_pred_boxes], dim=1)
new_outputs["pred_logits"] = new_pred_logits
new_outputs["pred_boxes"] = new_pred_boxes
results = postprocessors["bbox"](
new_outputs,
orig_target_sizes,
soft_nms=soft_nms,
method=soft_nms_method,
nms_thresh=nms_thresh,
quad_scale=quad_scale,
)
else:
results = postprocessors["bbox"](
outputs,
orig_target_sizes,
soft_nms=soft_nms,
method=soft_nms_method,
nms_thresh=nms_thresh,
quad_scale=quad_scale,
)
if "segm" in postprocessors.keys():
target_sizes = torch.stack([t["size"] for t in targets], dim=0)
results = postprocessors["segm"](
results, outputs, orig_target_sizes, target_sizes
)
res = {
target["image_id"].item(): output
for target, output in zip(targets, results)
}
if coco_evaluator is not None:
coco_evaluator.update(res)
if panoptic_evaluator is not None:
res_pano = postprocessors["panoptic"](
outputs, target_sizes, orig_target_sizes
)
for i, target in enumerate(targets):
image_id = target["image_id"].item()
file_name = f"{image_id:012d}.png"
res_pano[i]["image_id"] = image_id
res_pano[i]["file_name"] = file_name
panoptic_evaluator.update(res_pano)
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()
if panoptic_evaluator is not None:
panoptic_evaluator.synchronize_between_processes()
# accumulate predictions from all images
if coco_evaluator is not None:
coco_evaluator.accumulate()
coco_evaluator.summarize()
panoptic_res = None
if panoptic_evaluator is not None:
panoptic_res = panoptic_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()
if "segm" in postprocessors.keys():
stats["coco_eval_masks"] = coco_evaluator.coco_eval["segm"].stats.tolist()
if panoptic_res is not None:
stats["PQ_all"] = panoptic_res["All"]
stats["PQ_th"] = panoptic_res["Things"]
stats["PQ_st"] = panoptic_res["Stuff"]
return stats, coco_evaluator
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