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train_jornet_orig.py
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import torch
from torch.autograd import Variable
import synthhands_handler
import trainer
import time
from magic import display_est_time_loop
import losses as my_losses
from debugger import print_verbose
from JORNet import JORNet
import numpy as np
def get_loss_weights(curr_iter):
weights_heatmaps_loss = [0.5, 0.5, 0.5, 1.0]
weights_joints_loss = [1250, 1250, 1250, 2500]
if curr_iter > 45000:
weights_heatmaps_loss = [0.1, 0.1, 0.1, 1.0]
weights_joints_loss = [250, 250, 250, 2500]
return weights_heatmaps_loss, weights_joints_loss
def train(train_loader, model, optimizer, train_vars, control_vars, verbose=True):
curr_epoch_iter = 1
for batch_idx, (data, target) in enumerate(train_loader):
control_vars['batch_idx'] = batch_idx
if batch_idx < control_vars['iter_size']:
print_verbose("\rPerforming first iteration; current mini-batch: " +
str(batch_idx+1) + "/" + str(control_vars['iter_size']), verbose, n_tabs=0, erase_line=True)
# check if arrived at iter to start
if control_vars['curr_epoch_iter'] < control_vars['start_iter_mod']:
control_vars['curr_epoch_iter'] = control_vars['start_iter_mod']
msg = ''
if batch_idx % control_vars['iter_size'] == 0:
msg += print_verbose("\rGoing through iterations to arrive at last one saved... " +
str(int(control_vars['curr_epoch_iter']*100.0/control_vars['start_iter_mod'])) + "% of " +
str(control_vars['start_iter_mod']) + " iterations (" +
str(control_vars['curr_epoch_iter']) + "/" + str(control_vars['start_iter_mod']) + ")",
verbose, n_tabs=0, erase_line=True)
control_vars['curr_epoch_iter'] += 1
control_vars['curr_iter'] += 1
curr_epoch_iter += 1
if not control_vars['output_filepath'] == '':
with open(control_vars['output_filepath'], 'a') as f:
f.write(msg + '\n')
continue
# save checkpoint after final iteration
if control_vars['curr_iter'] == control_vars['num_iter']:
print_verbose("\nReached final number of iterations: " + str(control_vars['num_iter']), verbose)
print_verbose("\tSaving final model checkpoint...", verbose)
final_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
trainer.save_checkpoint(final_model_dict,
filename=train_vars['checkpoint_filenamebase'] +
'final' + str(control_vars['num_iter']) + '.pth.tar')
control_vars['done_training'] = True
break
# start time counter
start = time.time()
# get data and targetas cuda variables
target_heatmaps, target_joints, target_roothand = target
data, target_heatmaps, target_joints, target_roothand = Variable(data), Variable(target_heatmaps),\
Variable(target_joints), Variable(target_roothand)
if train_vars['use_cuda']:
data = data.cuda()
target_heatmaps = target_heatmaps.cuda()
target_joints = target_joints.cuda()
# get model output
output = model(data)
'''
visualize.plot_joints_from_heatmaps(target_heatmaps[0, :, :, :].cpu().data.numpy(),
title='', data=data[0].cpu().data.numpy())
visualize.show()
visualize.plot_image_and_heatmap(target_heatmaps[0][4].cpu().data.numpy(),
data=data[0].cpu().data.numpy(),
title='')
visualize.show()
visualize.plot_image_and_heatmap(output[3][0][4].cpu().data.numpy(),
data=data[0].cpu().data.numpy(),
title='')
visualize.show()
'''
# accumulate loss for sub-mini-batch
if train_vars['cross_entropy']:
loss_func = my_losses.cross_entropy_loss_p_logq
else:
loss_func = my_losses.euclidean_loss
weights_heatmaps_loss, weights_joints_loss = get_loss_weights(control_vars['curr_iter'])
loss, loss_heatmaps, loss_joints = my_losses.calculate_loss_JORNet(
loss_func, output, target_heatmaps, target_joints, train_vars['joint_ixs'],
weights_heatmaps_loss, weights_joints_loss, control_vars['iter_size'])
loss.backward()
train_vars['total_loss'] += loss.data[0]
train_vars['total_joints_loss'] += loss_joints.data[0]
train_vars['total_heatmaps_loss'] += loss_heatmaps.data[0]
# accumulate pixel dist loss for sub-mini-batch
train_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
train_vars['total_pixel_loss'], output[3], target_heatmaps, control_vars['batch_size'])
train_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
train_vars['total_pixel_loss_sample'], output[3], target_heatmaps, control_vars['batch_size'])
# get boolean variable stating whether a mini-batch has been completed
minibatch_completed = (batch_idx+1) % control_vars['iter_size'] == 0
if minibatch_completed:
# optimise for mini-batch
optimizer.step()
# clear optimiser
optimizer.zero_grad()
# append total loss
train_vars['losses'].append(train_vars['total_loss'])
# erase total loss
total_loss = train_vars['total_loss']
train_vars['total_loss'] = 0
# append total joints loss
train_vars['losses_joints'].append(train_vars['total_joints_loss'])
# erase total joints loss
train_vars['total_joints_loss'] = 0
# append total joints loss
train_vars['losses_heatmaps'].append(train_vars['total_heatmaps_loss'])
# erase total joints loss
train_vars['total_heatmaps_loss'] = 0
# append dist loss
train_vars['pixel_losses'].append(train_vars['total_pixel_loss'])
# erase pixel dist loss
train_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
train_vars['pixel_losses_sample'].append(train_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
train_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
if train_vars['losses'][-1] < train_vars['best_loss']:
train_vars['best_loss'] = train_vars['losses'][-1]
print_verbose(" This is a best loss found so far: " + str(train_vars['losses'][-1]), verbose)
train_vars['best_model_dict'] = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
if train_vars['losses_joints'][-1] < train_vars['best_loss_joints']:
train_vars['best_loss_joints'] = train_vars['losses_joints'][-1]
if train_vars['losses_heatmaps'][-1] < train_vars['best_loss_heatmaps']:
train_vars['best_loss_heatmaps'] = train_vars['losses_heatmaps'][-1]
# log checkpoint
if control_vars['curr_iter'] % control_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, epoch, total_loss, train_vars, control_vars)
aa1 = target_joints[0].data.cpu().numpy()
aa2 = output[7][0].data.cpu().numpy()
output_joint_loss = np.sum(np.abs(aa1 - aa2)) / 63
msg = ''
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
msg += print_verbose('\tJoint Coord Avg Loss for first image of current mini-batch: ' +
str(output_joint_loss) + '\n', control_vars['verbose'])
msg += print_verbose(
"-------------------------------------------------------------------------------------------",
verbose) + "\n"
if not control_vars['output_filepath'] == '':
with open(control_vars['output_filepath'], 'a') as f:
f.write(msg + '\n')
if control_vars['curr_iter'] % control_vars['log_interval_valid'] == 0:
print_verbose("\nSaving model and checkpoint model for validation", verbose)
checkpoint_model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': train_vars,
}
trainer.save_checkpoint(checkpoint_model_dict,
filename=train_vars['checkpoint_filenamebase'] + 'for_valid_' +
str(control_vars['curr_iter']) + '.pth.tar')
# print time lapse
prefix = 'Training (Epoch #' + str(epoch) + ' ' + str(control_vars['curr_epoch_iter']) + '/' +\
str(control_vars['tot_iter']) + ')' + ', (Batch ' + str(control_vars['batch_idx']+1) +\
'(' + str(control_vars['iter_size']) + ')' + '/' +\
str(control_vars['num_batches']) + ')' + ', (Iter #' + str(control_vars['curr_iter']) +\
'(' + str(control_vars['batch_size']) + ')' +\
' - log every ' + str(control_vars['log_interval']) + ' iter): '
control_vars['tot_toc'] = display_est_time_loop(control_vars['tot_toc'] + time.time() - start,
control_vars['curr_iter'], control_vars['num_iter'],
prefix=prefix)
control_vars['curr_iter'] += 1
control_vars['start_iter'] = control_vars['curr_iter'] + 1
control_vars['curr_epoch_iter'] += 1
return train_vars, control_vars
model, optimizer, control_vars, train_vars = trainer.get_vars(model_class=JORNet)
if train_vars['use_cuda']:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
train_loader = synthhands_handler.get_SynthHands_trainloader(root_folder=train_vars['root_folder'],
joint_ixs=model.joint_ixs,
heatmap_res=(128, 128),
batch_size=control_vars['max_mem_batch'],
verbose=control_vars['verbose'],
crop_hand=True)
control_vars['num_batches'] = len(train_loader)
control_vars['n_iter_per_epoch'] = int(len(train_loader) / control_vars['iter_size'])
control_vars['tot_iter'] = int(len(train_loader) / control_vars['iter_size'])
control_vars['start_iter_mod'] = control_vars['start_iter'] % control_vars['tot_iter']
trainer.print_header_info(model, train_loader, control_vars)
model.train()
control_vars['curr_iter'] = 1
for epoch in range(control_vars['num_epochs']):
control_vars['curr_epoch_iter'] = 1
if epoch + 1 < control_vars['start_epoch']:
print_verbose("Advancing through epochs: " + str(epoch + 1), control_vars['verbose'], erase_line=True)
control_vars['curr_iter'] += control_vars['n_iter_per_epoch']
continue
train_vars['total_loss'] = 0
train_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
train_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
optimizer.zero_grad()
# train model
train_vars, control_vars = train(train_loader, model, optimizer, train_vars, control_vars, control_vars['verbose'])
if control_vars['done_training']:
print_verbose("Done training.", control_vars['verbose'])
break