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engine.py
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engine.py
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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
from datasets.open_world_eval import OWEvaluator
from datasets.panoptic_eval import PanopticEvaluator
from datasets.data_prefetcher import data_prefetcher
from util.box_ops import box_xyxy_to_cxcywh, box_cxcywh_to_xyxy
from util.plot_utils import plot_prediction
import matplotlib.pyplot as plt
from copy import deepcopy
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, nc_epoch: int, max_norm: float = 0):
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 _ in metric_logger.log_every(range(len(data_loader)), print_freq, header):
outputs = model(samples)
loss_dict = criterion(samples, outputs, targets, epoch) ## samples variable needed for feature selection
weight_dict = deepcopy(criterion.weight_dict)
## condition for starting nc loss computation after certain epoch so that the F_cls branch has the time
## to learn the within classes seperation.
if epoch < nc_epoch:
for k,v in weight_dict.items():
if 'NC' in k:
weight_dict[k] = 0
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)
## Just printing NOt affectin gin loss function
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()
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()}
## ORIGINAL FUNCTION
@torch.no_grad()
def evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir, args):
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessors.keys())
coco_evaluator = OWEvaluator(base_ds, iou_types)
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"),
)
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]
outputs = model(samples)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = postprocessors['bbox'](outputs, orig_target_sizes)
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)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
# print("Averaged stats:", metric_logger)
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
@torch.no_grad()
def viz(model, criterion, postprocessors, data_loader, base_ds, device, output_dir):
import numpy as np
os.makedirs(output_dir, exist_ok=True)
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
for samples, targets in data_loader:
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
top_k = len(targets[0]['boxes'])
outputs = model(samples)
indices = outputs['pred_logits'][0].softmax(-1)[..., 1].sort(descending=True)[1][:top_k]
predictied_boxes = torch.stack([outputs['pred_boxes'][0][i] for i in indices]).unsqueeze(0)
logits = torch.stack([outputs['pred_logits'][0][i] for i in indices]).unsqueeze(0)
fig, ax = plt.subplots(1, 3, figsize=(10,3), dpi=200)
img = samples.tensors[0].cpu().permute(1,2,0).numpy()
img = img * np.array([0.229, 0.224, 0.225]) + np.array([0.485, 0.456, 0.406])
img = (img * 255)
img = img.astype('uint8')
h, w = img.shape[:-1]
# Pred results
plot_prediction(samples.tensors[0:1], predictied_boxes, logits, ax[1], plot_prob=False)
ax[1].set_title('Prediction (Ours)')
# GT Results
plot_prediction(samples.tensors[0:1], targets[0]['boxes'].unsqueeze(0), torch.zeros(1, targets[0]['boxes'].shape[0], 4).to(logits), ax[2], plot_prob=False)
ax[2].set_title('GT')
for i in range(3):
ax[i].set_aspect('equal')
ax[i].set_axis_off()
plt.savefig(os.path.join(output_dir, f'img_{int(targets[0]["image_id"][0])}.jpg'))