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engine.py
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engine.py
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"""
Train and eval functions used in main.py
Modified from DETR (https://github.com/facebookresearch/detr)
"""
import math
import os
import sys
from typing import Iterable
import pickle
import collections
import torch
import util.misc as utils
from eval_utils import eval_pose3d, transform_pts_torch
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,
):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
count = 0
print_freq = 10
for samples, _targets in metric_logger.log_every(data_loader, print_freq, header):
samples = samples.to(device=device)
# targets = [{k: v.to(device=device) for k, v in t.items() if not isinstance(v, list)} for t in targets]
targets = []
for t in _targets:
tmp = {}
for k, v in t.items():
if k in ['kpts2d', 'depth', 'bbxes', 'track_ids', 'traj_ids', 'input_size', 'inv_trans']:
tmp[k] = v.to(device=device)
else:
tmp[k] = v
targets.append(tmp)
optimizer.zero_grad()
# float32 backward
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()}
# print(loss_dict_reduced_unscaled)
loss_dict_reduced_scaled = {k: v * weight_dict[k] for k, v in loss_dict_reduced.items() if k in weight_dict}
# print(loss_dict_reduced_scaled)
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
# print(losses_reduced_scaled)
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)
losses.backward()
if max_norm > 0:
torch.nn.utils.clip_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(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()}
@torch.no_grad()
def evaluate(model, criterion, postprocessors, data_loader, device, output_dir, save_vis, epoch,
seq_l, future_seq_l, final_evaluation=False):
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
save_data = collections.defaultdict(list)
save_data_coco = collections.defaultdict(list)
pose3d = {'mpjpe_root': list(), 'mpjpe_joint': list(), 'pel_mpjpe_joint': list(), '3dpck': list()}
pose3d_future = {'mpjpe_root': list(), 'mpjpe_joint': list(), 'pel_mpjpe_joint': list(), '3dpck': list()}
# test
count = 0
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() if not isinstance(v, list)} for t in targets]
targets = []
for t in _targets:
tmp = {}
for k, v in t.items():
if k in ['kpts2d', 'depth', 'bbxes', 'track_ids', 'traj_ids', 'input_size', 'inv_trans']:
tmp[k] = v.to(device=device)
else:
tmp[k] = v
targets.append(tmp)
outputs, att_vis_data = model(samples)
loss_dict, indices = 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)
results = postprocessors(outputs, targets, indices)
count += 1
if save_vis and count < 10 and output_dir:
visualize_eval_kepts_pred(samples, targets, results,
seq_l, 0, output_dir='{}/vis_results_{:03d}'.format(output_dir, epoch),
epoch=epoch)
# save_decoder_att_data(samples, targets, results, att_vis_data, output_dir, epoch)
if save_vis and output_dir:
save_results_for_evaluation(save_data, results, targets, 0, seq_l)
save_results_for_evaluation_coco(save_data_coco, results, targets, 0, seq_l)
if output_dir and final_evaluation:
for i in range(len(results)):
dataset_name = results[i]['dataset']
if dataset_name == 'posetrack':
tmp = targets[i]['filenames'][0].split('/')
filename, frame_idx = tmp[-2], tmp[-1].split('.')[0]
elif dataset_name == 'coco':
filename, frame_idx = '{:06d}'.format(targets[i]['image_id']), 0
elif dataset_name == 'mupots':
tmp = targets[i]['filenames'][0].split('/')
filename = tmp[0]
frame_idx = tmp[1].split('_')[-1].split('.')[0]
elif dataset_name == 'jta':
tmp = targets[i]['filenames'][0].split('/')
filename = tmp[0]
frame_idx = tmp[1].split('.')[0]
elif dataset_name == 'panoptic':
filename = targets[i]['filenames'][0]
frame_idx = '{:08d}'.format(targets[i]['frame_indices'][0])
else:
print('cannot find {}-{}'.format(dataset_name, targets[i]['filenames'][0]))
filename, frame_idx = 'missing', '0000'
results_np = {}
for k, v in results[i].items():
if k == 'indices':
results_np[k] = [v[0].cpu().numpy(), v[1].cpu().numpy()]
elif k == 'filenames':
results_np[k] = v
elif k in ['heatmaps', 'video_name', 'frame_indices', 'dataset', 'image_id']:
continue
else:
results_np[k] = v.cpu().numpy()
# '{}/eval_results_{:03d}'.format(output_dir, epoch)
save_dir = "{}/eval_results_{:03d}/{}_{}.pkl".format(output_dir, epoch, filename, frame_idx)
with open(save_dir, 'wb') as f:
pickle.dump(results_np, f)
for key in pose3d.keys():
if 'mpjpe' in key:
mpjpe = eval_pose3d(key, results, 0, seq_l)
pose3d[key].append(mpjpe)
if future_seq_l > 0:
mpjpe = eval_pose3d(key, results, seq_l, seq_l + future_seq_l)
pose3d_future[key].append(mpjpe)
if '3dpck' in key:
pel_mpjpe = eval_pose3d('pel_mpjpe_joint', results, 0, seq_l)
pose3d[key].append((pel_mpjpe < 0.15).float())
if future_seq_l > 0:
pel_mpjpe = eval_pose3d('pel_mpjpe_joint', results, seq_l, seq_l + future_seq_l)
pose3d_future[key].append((pel_mpjpe < 0.15).float())
stat = {}
for k in pose3d.keys():
if 'mpjpe' in k:
stat[k + '_current'] = torch.mean(1000 * torch.cat(pose3d[k], dim=0)).item()
elif 'pck' in k:
stat[k + '_current'] = torch.mean(torch.cat(pose3d[k], dim=0)).item()
else:
pass
for k in pose3d_future.keys():
if len(pose3d_future[k]) == 0:
continue
if 'mpjpe' in k:
stat[k + '_future'] = torch.mean(1000 * torch.cat(pose3d_future[k], dim=0)).item()
elif 'pck' in k:
stat[k + '_future'] = torch.mean(torch.cat(pose3d_future[k], dim=0)).item()
else:
pass
return stat, save_data, save_data_coco
def visualize_eval_kepts_pred(samples, targets, results, seq_l, future_seq_l, output_dir, epoch):
# 'human_score': human_prob, # [n]
# 'pred_kpt_scores': out_score, # [n, T, num_joints, 1]
# 'pred_kpts': out_kepts2d, # [n, T, num_kpts, 2]
# 'gt_kpts': tgt_kpts2d, # [m, T, num_kpts, 2]
# 'gt_kpts_vis': tgt_kpts2d_vis, # [m, T, num_kpts, 1]
# 'bbxes': tgt_bbxes, # [m, T, 4]
# 'gt_bbxes_head': tgt_bbxes_head, # [m, T, 4]
# 'gt_track_ids': tgt_track_ids, # [m, T]
# 'indices': indices[i], # [src_idx, tgt_idx]
# 'inv_trans': targets[i]['inv_tran'],
import numpy as np
from datasets.data_preprocess.dataset_util import posetrack_visualization
from datasets.hybrid_dataloader import SKELETONS
_, num_joints = results[0]['pred_kpts'].shape[1:3]
imgs, _ = samples.decompose()
b, c, h, w = imgs.shape
imgs = imgs.reshape(b // seq_l, seq_l, c, h, w)
imgs = imgs.cpu().numpy()
imgs = (imgs * 255).astype(np.uint8)
bs = len(results)
for i in range(bs):
if i > 0:
break
# print(targets[i]['filenames'][0])
gt_track_ids = results[i]['gt_track_ids']
gt_traj_ids = results[i]['gt_traj_ids']
dataset_name = results[i]['dataset']
if gt_traj_ids.shape[0] == 0:
# no annotations
continue
# src_idx, tgt_idx = match_pose2d(results[i]['gt_kpts'], results[i]['gt_kpts_vis'],
# results[i]['gt_bbxes_head'], results[i]['pred_kpts'],
# results[i]['pred_kpt_scores'])
src_idx, tgt_idx = results[i]['indices']
# exist_person = (results[i]['human_score'] > 0.5).cpu().numpy()
# n = np.sum(exist_person).astype(np.int32)
# exist_pid = np.where(exist_person == 1)[0]
# print('epoch {:3d}: {}_{}.jpg - number of persons {}'.format(epoch, filename, frame_idx, n))
for j in range(seq_l):
exist_gt_person = (gt_track_ids[:, j] > 0) & \
(results[i]['gt_kpts_vis'][:, j].sum(dim=(-1, -2)) > 0)
# print(exist_gt_person)
if exist_gt_person.sum() == 0:
# print(t, 'skip')
continue
exist_person = src_idx[exist_gt_person].cpu().numpy()
exist_pid = gt_traj_ids[exist_gt_person].cpu().numpy()
# exist_person = (results[i]['human_score'][:, j]).cpu().numpy() > 0.5
# exist_pid = np.arange(exist_person.shape[0])[exist_person]
if dataset_name == 'posetrack':
tmp = targets[i]['filenames'][j].split('/')
filename, frame_idx = tmp[-2], tmp[-1].split('.')[0]
elif dataset_name == 'coco':
filename, frame_idx = '{:06d}'.format(targets[i]['image_id']), 0
elif dataset_name == 'mupots':
tmp = targets[i]['filenames'][j].split('/')
filename = tmp[0]
frame_idx = tmp[1].split('_')[-1].split('.')[0]
elif dataset_name == 'jta':
tmp = targets[i]['filenames'][j].split('/')
filename = tmp[0]
frame_idx = tmp[1].split('.')[0]
elif dataset_name == 'panoptic':
filename = targets[i]['filenames'][j]
frame_idx = '{:08d}'.format(targets[i]['frame_indices'][j])
else:
print('cannot find {}-{}'.format(dataset_name, targets[i]['filenames'][j]))
filename, frame_idx = 'missing', '0000'
kpts2d = results[i]['pred_kpts'][exist_person, j].cpu().numpy()
kpt_scores = results[i]['pred_kpt_scores'][exist_person, j].cpu().numpy()
vis = kpt_scores > 0.1
kpts = np.concatenate([kpts2d, vis], axis=-1) # [m, num_joints, 3]
# save_dir = "{}/{}_{}_epoch{:03d}_kpts.npy".format(output_dir, filename, frame_idx, epoch)
# np.save(save_dir, np.concatenate([kpts, kpt_scores], axis=-1))
# if j == 0:
# with open('{}/{}_{}_epoch{:03d}.pkl'.format(output_dir, filename, frame_idx, epoch), 'wb') as f:
# pickle.dump(results[i], f)
img = np.transpose(imgs[i, j], [1, 2, 0]) # [h, w, c]
save_dir = "{}/{}_{}_epoch{:03d}.jpg".format(output_dir, filename, frame_idx, epoch)
print(save_dir)
posetrack_visualization(img[np.newaxis], kpts[np.newaxis], [exist_pid], 'eval', SKELETONS, save_dir)
# future frame data
for j in range(seq_l, seq_l + future_seq_l):
exist_gt_person = (gt_track_ids[:, j] > 0) & \
(results[i]['gt_kpts_vis'][:, j].sum(dim=(-1, -2)) > 0)
# print(exist_gt_person)
if exist_gt_person.sum() == 0:
# print(t, 'skip')
continue
exist_person = src_idx[exist_gt_person].cpu().numpy()
# exist_pid = gt_traj_ids[exist_gt_person].cpu().numpy()
if dataset_name == 'posetrack':
tmp = targets[i]['filenames'][j].split('/')
filename, frame_idx = tmp[-2], tmp[-1].split('.')[0]
elif dataset_name == 'coco':
filename, frame_idx = '{:06d}'.format(targets[i]['image_id']), 0
elif dataset_name == 'mupots':
tmp = targets[i]['filenames'][j].split('/')
filename = tmp[0]
frame_idx = tmp[1].split('_')[-1].split('.')[0]
elif dataset_name == 'jta':
tmp = targets[i]['filenames'][j].split('/')
filename = tmp[0]
frame_idx = tmp[1].split('.')[0]
elif dataset_name == 'panoptic':
filename = targets[i]['filenames'][j]
frame_idx = '{:08d}'.format(targets[i]['frame_indices'][j])
else:
print('cannot find {}-{}'.format(dataset_name, targets[i]['filenames'][j]))
filename, frame_idx = 'missing', '0000'
kpts2d = results[i]['pred_kpts'][exist_person, j].cpu().numpy()
kpt_scores = results[i]['pred_kpt_scores'][exist_person, j].cpu().numpy()
vis = kpt_scores > 0.1
kpts = np.concatenate([kpts2d, vis], axis=-1) # [m, num_joints, 3]
save_dir = "{}/{}_{}_epoch{:03d}_kpts_future.npy".format(output_dir, filename, frame_idx, epoch)
np.save(save_dir, np.concatenate([kpts, kpt_scores], axis=-1))
print(save_dir)
def save_results_for_evaluation(save_data, results, targets, start_t, end_t, post_processing=False):
# 'human_score': human_prob, # [n]
# 'pred_kpt_scores': out_score, # [n, T, num_joints, 1]
# 'pred_kpts': out_kepts2d, # [n, T, num_kpts, 2]
# 'gt_kpts': tgt_kpts2d, # [m, T, num_kpts, 2]
# 'gt_kpts_vis': tgt_kpts2d_vis, # [m, T, num_kpts, 1]
# 'bbxes': tgt_bbxes, # [m, T, 4]
# 'gt_bbxes_head': tgt_bbxes_head, # [m, T, 4]
# 'gt_track_ids': tgt_track_ids, # [m, T]
# 'gt_traj_ids': traj_ids,
# 'indices': indices[i], # [src_idx, tgt_idx]
# 'inv_trans': inv_trans, # [2, 3]
# 'filenames': targets[i]['filenames'],
# 'video_name': targets[i]['video_name'],
# 'frame_indices': targets[i]['frame_indices']
bs = len(results)
for i in range(bs):
if results[i]['dataset'] != 'posetrack':
continue
# print(results[i]['filenames'][0])
gt_track_ids = results[i]['gt_track_ids'] # [n, T]
gt_traj_ids = results[i]['gt_traj_ids'] # [n]
if gt_traj_ids.shape[0] == 0:
# no annotations
continue
# src_idx, tgt_idx = match_pose2d(results[i]['gt_kpts'], results[i]['gt_kpts_vis'],
# results[i]['gt_bbxes_head'], results[i]['pred_kpts'],
# results[i]['pred_kpt_scores'])
src_idx, tgt_idx = results[i]['indices']
# print(src_idx, tgt_idx)
# print(results[i]['indices'])
inv_trans = results[i]['inv_trans']
for t in range(start_t, end_t):
exist_gt_person = (gt_track_ids[:, t] > 0) & (results[i]['gt_kpts_vis'][:, t].sum(dim=(-1, -2)) > 0)
# print(exist_gt_person)
if exist_gt_person.sum() == 0:
# print(t, 'skip')
continue
# print(t, 'compute')
_gt_kpts = results[i]['gt_kpts'][tgt_idx[exist_gt_person], t] # [m, num_kpts, 2]
_gt_kpts_vis = results[i]['gt_kpts_vis'][tgt_idx[exist_gt_person], t] # [m, num_kpts, 1]
_gt_bbxes_head = results[i]['gt_bbxes_head'][tgt_idx[exist_gt_person], t] # [m, 4]
_pred_kpts = results[i]['pred_kpts'][src_idx[exist_gt_person], t] # [m, num_kpts, 2]
_pred_kpt_scores = results[i]['pred_kpt_scores'][src_idx[exist_gt_person], t] # [m, num_kpts, 1]
# src_idx, tgt_idx = match_pckh(results[i]['gt_kpts'][exist_gt_person, t:t+1],
# results[i]['gt_kpts_vis'][exist_gt_person, t:t+1],
# results[i]['gt_bbxes_head'][exist_gt_person, t:t+1],
# results[i]['pred_kpts'])
# # print(results[i]['indices'])
# # print(src_idx, tgt_idx)
# _gt_kpts = results[i]['gt_kpts'][exist_gt_person, t] # [m, num_kpts, 2]
# _gt_kpts_vis = results[i]['gt_kpts_vis'][exist_gt_person, t] # [m, num_kpts, 1]
# _gt_bbxes_head = results[i]['gt_bbxes_head'][exist_gt_person, t] # [m, 4]
# _pred_kpts = results[i]['pred_kpts'][src_idx, t] # [m, num_kpts, 2]
# _pred_kpt_scores = results[i]['pred_kpt_scores'][src_idx, t] # [m, num_kpts, 1]
# transform
_gt_kpts = transform_pts_torch(_gt_kpts, inv_trans)
_pred_kpts = transform_pts_torch(_pred_kpts, inv_trans)
# error = torch.mean((_gt_kpts - _pred_kpts) ** 2)
# print(error)
# if error > 0.1:
# # fn, filename, frame_idx, indice, max_valid_gap
# print('error', error)
# print(_gt_kpts, _pred_kpts)
# print('\n')
sample_result = {}
sample_result['video_name'] = results[i]['video_name']
sample_result['filename'] = results[i]['filenames'][t]
sample_result['index'] = results[i]['frame_indices'][t]
sample_result['pred_kpts'] = _pred_kpts.cpu().numpy() # [n, num_joints, 2]
sample_result['pred_kpt_scores'] = _pred_kpt_scores.cpu().numpy() # [n, num_joints, 1]
sample_result['traj_ids'] = gt_traj_ids[tgt_idx[exist_gt_person]].cpu().numpy()
sample_result['gt_kpts'] = _gt_kpts.cpu().numpy()
sample_result['gt_kpt_scores'] = _gt_kpts_vis.cpu().numpy()
sample_result['gt_bbxes_head'] = _gt_bbxes_head.cpu().numpy()
save_data[sample_result['video_name']].append(sample_result)
def save_results_for_evaluation_coco(save_data, results, targets, start_t, end_t, post_processing=False):
# 'human_score': human_prob, # [n, T]
# 'pred_kpt_scores': out_score, # [n, T, num_joints, 1]
# 'pred_kpts': out_kepts2d, # [n, T, num_kpts, 2]
# 'gt_kpts': tgt_kpts2d, # [m, T, num_kpts, 2]
# 'gt_kpts_vis': tgt_kpts2d_vis, # [m, T, num_kpts, 1]
# 'bbxes': tgt_bbxes, # [m, T, 4]
# 'gt_bbxes_head': tgt_bbxes_head, # [m, T, 4]
# 'gt_track_ids': tgt_track_ids, # [m, T]
# 'gt_traj_ids': traj_ids,
# 'indices': indices[i], # [src_idx, tgt_idx]
# 'inv_trans': inv_trans, # [2, 3]
# 'filenames': targets[i]['filenames'],
# 'video_name': targets[i]['video_name'],
# 'frame_indices': targets[i]['frame_indices']
bs = len(results)
for i in range(bs):
if results[i]['dataset'] != 'coco':
continue
src_idx, tgt_idx = results[i]['indices']
image_id = results[i]['image_id']
inv_trans = results[i]['inv_trans']
human_score = results[i]['human_score'][:, 0]
_pred_kpts = results[i]['pred_kpts'][:, 0] # [m, num_kpts, 2]
_pred_kpt_scores = results[i]['pred_kpt_scores'][:, 0] # [m, num_kpts, 1]
_gt_kpts = results[i]['gt_kpts'][:, 0] # [m, num_kpts, 2]
_gt_kpts_vis = results[i]['gt_kpts_vis'][:, 0] # [m, num_kpts, 1]
exist_person = human_score > 0.5
_pred_kpts = _pred_kpts[exist_person]
_pred_kpt_scores = _pred_kpt_scores[exist_person]
human_score = human_score[exist_person]
# src_idx, tgt_idx = results[i]['indices']
# _pred_kpts = _pred_kpts[src_idx]
# _pred_kpt_scores = _pred_kpt_scores[src_idx]
# human_score = human_score[src_idx]
# _gt_kpts = _gt_kpts[tgt_idx]
# _gt_kpts_vis = _gt_kpts_vis[tgt_idx]
_pred_kpts = transform_pts_torch(_pred_kpts, inv_trans)
_gt_kpts = transform_pts_torch(_gt_kpts, inv_trans)
pred_kpts2d = torch.cat([_pred_kpts, _pred_kpt_scores], dim=-1)
gt_kpts2d = torch.cat([_gt_kpts, _gt_kpts_vis], dim=-1)
# human_score = human_score[src_idx]
# pred_kpts2d = pred_kpts2d[src_idx]
# gt_kpts2d = gt_kpts2d[tgt_idx]
# idx = torch.where(human_score > 0.5)[0]
# # print('idx', idx)
# error = torch.mean((gt_kpts2d - pred_kpts2d[idx]) ** 2)
# print(error)
# if error > 0.1:
# # fn, filename, frame_idx, indice, max_valid_gap
# print('error', error)
# print(gt_kpts2d, pred_kpts2d)
# print('\n')
save_data[image_id].append([human_score.cpu().numpy(),
pred_kpts2d.cpu().numpy(),
gt_kpts2d.cpu().numpy(),
(src_idx.cpu().numpy(), tgt_idx.cpu().numpy())])