def train(prepared_train_labels, train_images_folder, num_refinement_stages,
base_lr, batch_size, batches_per_iter, num_workers, checkpoint_path,
weights_only, from_mobilenet, checkpoints_folder, log_after,
val_labels, val_images_folder, val_output_name, checkpoint_after,
val_after):
net = PoseEstimationWithMobileNet(num_refinement_stages)
stride = 8
sigma = 7
path_thickness = 1
dataset = CocoTrainDataset(prepared_train_labels,
train_images_folder,
stride,
sigma,
path_thickness,
transform=transforms.Compose([
ConvertKeypoints(),
Scale(),
Rotate(pad=(128, 128, 128)),
CropPad(pad=(128, 128, 128)),
Flip()
]))
train_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
optimizer = optim.Adam([
{
'params': get_parameters_conv(net.model, 'weight')
},
{
'params': get_parameters_conv_depthwise(net.model, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.model, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.model, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv(net.cpm, 'weight'),
'lr': base_lr
},
{
'params': get_parameters_conv(net.cpm, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv_depthwise(net.cpm, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_conv(net.initial_stage, 'weight'),
'lr': base_lr
},
{
'params': get_parameters_conv(net.initial_stage, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv(net.refinement_stages, 'weight'),
'lr': base_lr * 4
},
{
'params': get_parameters_conv(net.refinement_stages, 'bias'),
'lr': base_lr * 8,
'weight_decay': 0
},
{
'params': get_parameters_bn(net.refinement_stages, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.refinement_stages, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
],
lr=base_lr,
weight_decay=5e-4)
num_iter = 0
current_epoch = 0
drop_after_epoch = [100, 200, 260]
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=drop_after_epoch,
gamma=0.333)
if checkpoint_path:
checkpoint = torch.load(checkpoint_path)
if from_mobilenet:
load_from_mobilenet(net, checkpoint)
else:
load_state(net, checkpoint)
if not weights_only:
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
num_iter = checkpoint['iter']
current_epoch = checkpoint['current_epoch']
net = DataParallel(net).cuda()
net.train()
for epochId in range(current_epoch, 280):
scheduler.step()
total_losses = [0, 0] * (num_refinement_stages + 1
) # heatmaps loss, paf loss per stage
batch_per_iter_idx = 0
for batch_data in train_loader:
if batch_per_iter_idx == 0:
optimizer.zero_grad()
# print("show imgs"
# , batch_data['keypoint_maps'].shape, batch_data['paf_maps'].shape
# , batch_data['keypoint_mask'].shape, batch_data['paf_mask'].shape
# , batch_data['mask'].shape, batch_data['image'].shape
# )
# print("seg", batch_data['label']['segmentations'])
print("batched images size", batch_data['image'].shape)
vis.images(batch_data['image'][:, [2, 1, 0], ...] + 0.5,
4,
2,
"1",
opts=dict(title="img"))
vis.images(batch_data['keypoint_mask'].permute(1, 0, 2, 3),
4,
2,
"2",
opts=dict(title="kp_mask"))
vis.images(batch_data['paf_mask'].permute(1, 0, 2, 3),
4,
2,
"3",
opts=dict(title="paf_mask"))
vis.images(batch_data['keypoint_maps'].permute(1, 0, 2, 3),
4,
2,
"4",
opts=dict(title="keypoint_maps"))
vis.images(batch_data['paf_maps'].permute(1, 0, 2, 3),
4,
2,
"5",
opts=dict(title="paf_maps"))
vis.images(batch_data['mask'].unsqueeze(0),
4,
2,
"6",
opts=dict(title="MASK"))
images = batch_data['image'].cuda()
keypoint_masks = batch_data['keypoint_mask'].cuda()
paf_masks = batch_data['paf_mask'].cuda()
keypoint_maps = batch_data['keypoint_maps'].cuda()
paf_maps = batch_data['paf_maps'].cuda()
pafs = batch_data['paf_maps'][0].permute(1, 2, 0).numpy()
scale = 4
img_p = np.zeros((pafs.shape[1] * 8, pafs.shape[0] * 8, 3),
dtype=np.uint8)
# pafs[pafs < 0.07] = 0
for idx in range(len(BODY_PARTS_PAF_IDS)):
# print(pp, pafs.shape)
pp = BODY_PARTS_PAF_IDS[idx]
k_idx = BODY_PARTS_KPT_IDS[idx]
cc = BODY_CONN_COLOR[idx]
vx = pafs[:, :, pp[0]]
vy = pafs[:, :, pp[1]]
for i in range(pafs.shape[1]):
for j in range(pafs.shape[0]):
a = (i * 2 * scale, j * 2 * scale)
b = (2 * int((i + vx[j, i] * 3) * scale), 2 * int(
(j + vy[j, i] * 3) * scale))
if a[0] == b[0] and a[1] == b[1]:
continue
cv2.line(img_p, a, b, cc, 1)
# break
cv2.imshow("paf", img_p)
key = cv2.waitKey(0)
if key == 27: # esc
exit(0)
stages_output = net(images)
losses = []
for loss_idx in range(len(total_losses) // 2):
losses.append(
l2_loss(stages_output[loss_idx * 2], keypoint_maps,
keypoint_masks, images.shape[0]))
losses.append(
l2_loss(stages_output[loss_idx * 2 + 1], paf_maps,
paf_masks, images.shape[0]))
total_losses[loss_idx *
2] += losses[-2].item() / batches_per_iter
total_losses[loss_idx * 2 +
1] += losses[-1].item() / batches_per_iter
loss = losses[0]
for loss_idx in range(1, len(losses)):
loss += losses[loss_idx]
loss /= batches_per_iter
loss.backward()
batch_per_iter_idx += 1
if batch_per_iter_idx == batches_per_iter:
optimizer.step()
batch_per_iter_idx = 0
num_iter += 1
else:
continue
if num_iter % log_after == 0:
print('Iter: {}'.format(num_iter))
for loss_idx in range(len(total_losses) // 2):
print('\n'.join([
'stage{}_pafs_loss: {}',
'stage{}_heatmaps_loss: {}'
]).format(loss_idx + 1,
total_losses[loss_idx * 2 + 1] / log_after,
loss_idx + 1,
total_losses[loss_idx * 2] / log_after))
for loss_idx in range(len(total_losses)):
total_losses[loss_idx] = 0
if num_iter % checkpoint_after == 0:
snapshot_name = '{}/checkpoint_iter_{}.pth'.format(
checkpoints_folder, num_iter)
torch.save(
{
'state_dict': net.module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iter': num_iter,
'current_epoch': epochId
}, snapshot_name)
if num_iter % val_after == 0:
print('Validation...')
evaluate(val_labels, val_output_name, val_images_folder, net)
net.train()
def train(prepared_train_labels, train_images_folder, num_refinement_stages,
base_lr, batch_size, batches_per_iter, num_workers, checkpoint_path,
weights_only, from_mobilenet, checkpoints_folder, log_after,
val_labels, val_images_folder, val_output_name, checkpoint_after,
val_after):
net = PoseEstimationWithMobileNet(num_refinement_stages)
stride = 8
sigma = 7
path_thickness = 1
dataset = CocoTrainDataset(prepared_train_labels,
train_images_folder,
stride,
sigma,
path_thickness,
transform=transforms.Compose([
ConvertKeypoints(),
Scale(),
Rotate(pad=(128, 128, 128)),
CropPad(pad=(128, 128, 128)),
Flip()
]))
train_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
optimizer = optim.Adam([
{
'params': get_parameters_conv(net.model, 'weight')
},
{
'params': get_parameters_conv_depthwise(net.model, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.model, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.model, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv(net.cpm, 'weight'),
'lr': base_lr
},
{
'params': get_parameters_conv(net.cpm, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv_depthwise(net.cpm, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_conv(net.initial_stage, 'weight'),
'lr': base_lr
},
{
'params': get_parameters_conv(net.initial_stage, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv(net.refinement_stages, 'weight'),
'lr': base_lr * 4
},
{
'params': get_parameters_conv(net.refinement_stages, 'bias'),
'lr': base_lr * 8,
'weight_decay': 0
},
{
'params': get_parameters_bn(net.refinement_stages, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.refinement_stages, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
],
lr=base_lr,
weight_decay=5e-4)
num_iter = 0
current_epoch = 0
drop_after_epoch = [100, 200, 260]
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=drop_after_epoch,
gamma=0.333)
if checkpoint_path:
checkpoint = torch.load(checkpoint_path)
if from_mobilenet:
load_from_mobilenet(net, checkpoint)
else:
load_state(net, checkpoint)
if not weights_only:
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
num_iter = checkpoint['iter']
current_epoch = checkpoint['current_epoch']
net = DataParallel(net).cuda()
net.train()
for epochId in range(current_epoch, 280):
scheduler.step()
total_losses = [0, 0] * (num_refinement_stages + 1
) # heatmaps loss, paf loss per stage
batch_per_iter_idx = 0
for batch_data in train_loader:
if batch_per_iter_idx == 0:
optimizer.zero_grad()
images = batch_data['image'].cuda()
keypoint_masks = batch_data['keypoint_mask'].cuda()
paf_masks = batch_data['paf_mask'].cuda()
keypoint_maps = batch_data['keypoint_maps'].cuda()
paf_maps = batch_data['paf_maps'].cuda()
stages_output = net(images)
losses = []
for loss_idx in range(len(total_losses) // 2):
losses.append(
l2_loss(stages_output[loss_idx * 2], keypoint_maps,
keypoint_masks, images.shape[0]))
losses.append(
l2_loss(stages_output[loss_idx * 2 + 1], paf_maps,
paf_masks, images.shape[0]))
total_losses[loss_idx *
2] += losses[-2].item() / batches_per_iter
total_losses[loss_idx * 2 +
1] += losses[-1].item() / batches_per_iter
loss = losses[0]
for loss_idx in range(1, len(losses)):
loss += losses[loss_idx]
loss /= batches_per_iter
loss.backward()
batch_per_iter_idx += 1
if batch_per_iter_idx == batches_per_iter:
optimizer.step()
batch_per_iter_idx = 0
num_iter += 1
else:
continue
if num_iter % log_after == 0:
print('Iter: {}'.format(num_iter))
for loss_idx in range(len(total_losses) // 2):
print('\n'.join([
'stage{}_pafs_loss: {}',
'stage{}_heatmaps_loss: {}'
]).format(loss_idx + 1,
total_losses[loss_idx * 2 + 1] / log_after,
loss_idx + 1,
total_losses[loss_idx * 2] / log_after))
for loss_idx in range(len(total_losses)):
total_losses[loss_idx] = 0
if num_iter % checkpoint_after == 0:
snapshot_name = '{}/checkpoint_iter_{}.pth'.format(
checkpoints_folder, num_iter)
torch.save(
{
'state_dict': net.module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iter': num_iter,
'current_epoch': epochId
}, snapshot_name)
if num_iter % val_after == 0:
print('Validation...')
evaluate(val_labels, val_output_name, val_images_folder, net)
net.train()
def train(prepared_train_labels, train_images_folder, num_refinement_stages,
base_lr, batch_size, batches_per_iter, num_workers, checkpoint_path,
weights_only, from_mobilenet, checkpoints_folder, log_after,
val_labels, val_images_folder, val_output_name, checkpoint_after,
val_after):
net = PoseEstimationWithMobileNet(num_refinement_stages)
stride = 8
sigma = 7
path_thickness = 1
dataset = CocoTrainDataset(prepared_train_labels,
train_images_folder,
stride,
sigma,
path_thickness,
transform=transforms.Compose([
ConvertKeypoints(),
Scale(),
Rotate(pad=(128, 128, 128)),
CropPad(pad=(128, 128, 128)),
Flip()
]))
train_loader = DataLoader(dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
optimizer = optim.Adam([
{
'params': get_parameters_conv(net.model, 'weight')
},
{
'params': get_parameters_conv_depthwise(net.model, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.model, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.model, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv(net.cpm, 'weight'),
'lr': base_lr
},
{
'params': get_parameters_conv(net.cpm, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv_depthwise(net.cpm, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_conv(net.initial_stage, 'weight'),
'lr': base_lr
},
{
'params': get_parameters_conv(net.initial_stage, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
{
'params': get_parameters_conv(net.refinement_stages, 'weight'),
'lr': base_lr * 4
},
{
'params': get_parameters_conv(net.refinement_stages, 'bias'),
'lr': base_lr * 8,
'weight_decay': 0
},
{
'params': get_parameters_bn(net.refinement_stages, 'weight'),
'weight_decay': 0
},
{
'params': get_parameters_bn(net.refinement_stages, 'bias'),
'lr': base_lr * 2,
'weight_decay': 0
},
],
lr=base_lr,
weight_decay=5e-4)
num_iter = 0
current_epoch = 0
drop_after_epoch = [100, 200, 260]
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=drop_after_epoch,
gamma=0.333)
if checkpoint_path:
checkpoint = torch.load(checkpoint_path)
if from_mobilenet:
load_from_mobilenet(net, checkpoint)
else:
load_state(net, checkpoint)
if not weights_only:
optimizer.load_state_dict(checkpoint['optimizer'])
scheduler.load_state_dict(checkpoint['scheduler'])
num_iter = checkpoint['iter']
current_epoch = checkpoint['current_epoch']
print("optimizer LR")
for param_group in optimizer.param_groups:
print(param_group['lr'])
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
net = DataParallel(net).cuda()
net.train()
from DGPT.Visualize.Viz import Viz
viz = Viz(dict(env="refine"))
for epochId in range(current_epoch, 280):
# scheduler.step()
total_losses = [0, 0] * (num_refinement_stages + 1
) # heatmaps loss, paf loss per stage
batch_per_iter_idx = 0
for batch_data in train_loader:
if batch_per_iter_idx == 0:
optimizer.zero_grad()
images = batch_data['image'].cuda()
keypoint_masks = batch_data['keypoint_mask'].cuda()
paf_masks = batch_data['paf_mask'].cuda()
keypoint_maps = batch_data['keypoint_maps'].cuda()
paf_maps = batch_data['paf_maps'].cuda()
images = preprocess(images)
stages_output = net(images)
losses = []
for loss_idx in range(len(total_losses) // 2):
losses.append(
l2_loss(stages_output[loss_idx * 2], keypoint_maps,
keypoint_masks, images.shape[0]))
losses.append(
l2_loss(stages_output[loss_idx * 2 + 1], paf_maps,
paf_masks, images.shape[0]))
total_losses[loss_idx *
2] += losses[-2].item() / batches_per_iter
total_losses[loss_idx * 2 +
1] += losses[-1].item() / batches_per_iter
loss = losses[0]
for loss_idx in range(1, len(losses)):
loss += losses[loss_idx]
loss /= batches_per_iter
loss.backward()
viz.draw_line(num_iter, loss.item(), "Loss")
batch_per_iter_idx += 1
if batch_per_iter_idx == batches_per_iter:
optimizer.step()
batch_per_iter_idx = 0
num_iter += 1
scheduler.step()
else:
continue
if num_iter % log_after == 0:
print('Iter: {}'.format(num_iter))
for loss_idx in range(len(total_losses) // 2):
print('\n'.join([
'stage{}_pafs_loss: {}',
'stage{}_heatmaps_loss: {}'
]).format(loss_idx + 1,
total_losses[loss_idx * 2 + 1] / log_after,
loss_idx + 1,
total_losses[loss_idx * 2] / log_after))
for loss_idx in range(len(total_losses)):
total_losses[loss_idx] = 0
xx = images[:1, ...].detach() #.clone()
hh = keypoint_maps[:1, ...].detach() #.clone()
mm = keypoint_masks[:1, ...].detach() #.clone()
print(xx.shape, hh.shape, mm.shape)
hh = hh.squeeze(0).reshape(19, 1, hh.shape[2], hh.shape[3])
mm = mm.squeeze(0).reshape(19, 1, hh.shape[2], hh.shape[3])
viz.draw_images(xx, "input1")
viz.draw_images(hh, "input1_heatmap")
viz.draw_images(mm, "input1_mask")
oh = stages_output[-2].detach()[:1, :-1, ...]
oh = oh.reshape(oh.shape[1], 1, oh.shape[2], oh.shape[3])
viz.draw_images(oh, "output1_heatmap")
if num_iter % checkpoint_after == 0:
snapshot_name = '{}/checkpoint_iter_{}.pth'.format(
checkpoints_folder, num_iter)
torch.save(
{
'state_dict': net.module.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iter': num_iter,
'current_epoch': epochId
}, snapshot_name)
if num_iter % val_after == 0:
print('Validation...')
evaluate(val_labels, val_output_name, val_images_folder, net)
net.train()
