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engine.py
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engine.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Train and eval functions used in main.py
Mostly copy-paste from DETR (https://github.com/facebookresearch/detr).
"""
import math
import os
import sys
from typing import Iterable
import torch
import util.misc as utils
from util.misc import NestedTensor
import numpy as np
import time
import torchvision.transforms as standard_transforms
import cv2
class DeNormalize(object):
def __init__(self, mean, std):
self.mean = mean
self.std = std
def __call__(self, tensor):
for t, m, s in zip(tensor, self.mean, self.std):
t.mul_(s).add_(m)
return tensor
def vis(samples, targets, pred, vis_dir, des=None):
'''
samples -> tensor: [batch, 3, H, W]
targets -> list of dict: [{'points':[], 'image_id': str}]
pred -> list: [num_preds, 2]
'''
gts = [t['point'].tolist() for t in targets]
pil_to_tensor = standard_transforms.ToTensor()
restore_transform = standard_transforms.Compose([
DeNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
standard_transforms.ToPILImage()
])
# draw one by one
for idx in range(samples.shape[0]):
sample = restore_transform(samples[idx])
sample = pil_to_tensor(sample.convert('RGB')).numpy() * 255
sample_gt = sample.transpose([1, 2, 0])[:, :, ::-1].astype(np.uint8).copy()
sample_pred = sample.transpose([1, 2, 0])[:, :, ::-1].astype(np.uint8).copy()
max_len = np.max(sample_gt.shape)
size = 2
# draw gt
for t in gts[idx]:
sample_gt = cv2.circle(sample_gt, (int(t[0]), int(t[1])), size, (0, 255, 0), -1)
# draw predictions
for p in pred[idx]:
sample_pred = cv2.circle(sample_pred, (int(p[0]), int(p[1])), size, (0, 0, 255), -1)
name = targets[idx]['image_id']
# save the visualized images
if des is not None:
cv2.imwrite(os.path.join(vis_dir, '{}_{}_gt_{}_pred_{}_gt.jpg'.format(int(name),
des, len(gts[idx]), len(pred[idx]))), sample_gt)
cv2.imwrite(os.path.join(vis_dir, '{}_{}_gt_{}_pred_{}_pred.jpg'.format(int(name),
des, len(gts[idx]), len(pred[idx]))), sample_pred)
else:
cv2.imwrite(
os.path.join(vis_dir, '{}_gt_{}_pred_{}_gt.jpg'.format(int(name), len(gts[idx]), len(pred[idx]))),
sample_gt)
cv2.imwrite(
os.path.join(vis_dir, '{}_gt_{}_pred_{}_pred.jpg'.format(int(name), len(gts[idx]), len(pred[idx]))),
sample_pred)
# the training routine
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):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
# iterate all training samples
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]
# forward
outputs = model(samples)
# calc the losses
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 all losses
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)
# backward
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
# update logger
metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# 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()}
# the inference routine
@torch.no_grad()
def evaluate_crowd_no_overlap(model, data_loader, device, vis_dir=None):
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
# run inference on all images to calc MAE
maes = []
mses = []
for samples, targets in data_loader:
samples = samples.to(device)
outputs = model(samples)
outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]
outputs_points = outputs['pred_points'][0]
gt_cnt = targets[0]['point'].shape[0]
# 0.5 is used by default
threshold = 0.5
points = outputs_points[outputs_scores > threshold].detach().cpu().numpy().tolist()
predict_cnt = int((outputs_scores > threshold).sum())
# if specified, save the visualized images
if vis_dir is not None:
vis(samples, targets, [points], vis_dir)
# accumulate MAE, MSE
mae = abs(predict_cnt - gt_cnt)
mse = (predict_cnt - gt_cnt) * (predict_cnt - gt_cnt)
maes.append(float(mae))
mses.append(float(mse))
# calc MAE, MSE
mae = np.mean(maes)
mse = np.sqrt(np.mean(mses))
return mae, mse