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validate_jornet.py
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import torch
from torch.autograd import Variable
import synthhands_handler
import trainer
import validator
import time
from magic import display_est_time_loop
import losses as my_losses
from debugger import print_verbose
from JORNet import JORNet
import visualize
import converter as conv
import numpy as np
import camera
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 validate(valid_loader, model, optimizer, valid_vars, control_vars, verbose=True):
curr_epoch_iter = 1
for batch_idx, (data, target) in enumerate(valid_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)
# start time counter
start = time.time()
# get data and targetas cuda variables
target_heatmaps, target_joints, target_handroot = target
# make target joints be relative
target_joints = target_joints[:, 3:]
data, target_heatmaps = Variable(data), Variable(target_heatmaps)
if valid_vars['use_cuda']:
data = data.cuda()
target_joints = target_joints.cuda()
target_heatmaps = target_heatmaps.cuda()
target_handroot = target_handroot.cuda()
# visualize if debugging
# get model output
output = model(data)
# accumulate loss for sub-mini-batch
if model.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, valid_vars['joint_ixs'],
weights_heatmaps_loss, weights_joints_loss, control_vars['iter_size'])
valid_vars['total_loss'] += loss
valid_vars['total_joints_loss'] += loss_joints
valid_vars['total_heatmaps_loss'] += loss_heatmaps
# accumulate pixel dist loss for sub-mini-batch
valid_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
valid_vars['total_pixel_loss'], output[3], target_heatmaps, control_vars['batch_size'])
valid_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
valid_vars['total_pixel_loss_sample'], output[3], target_heatmaps, control_vars['batch_size'])
# get boolean variable stating whether a mini-batch has been completed
for i in range(control_vars['max_mem_batch']):
filenamebase_idx = (batch_idx * control_vars['max_mem_batch']) + i
filenamebase = valid_loader.dataset.get_filenamebase(filenamebase_idx)
print('')
print(filenamebase)
visualize.plot_image(data[i].data.numpy())
visualize.show()
output_batch_numpy = output[7][i].data.cpu().numpy()
print('\n-------------------------------')
reshaped_out = output_batch_numpy.reshape((20, 3))
for j in range(20):
print('[{}, {}, {}],'.format(reshaped_out[j, 0], reshaped_out[j, 1], reshaped_out[j, 2]))
print('-------------------------------')
fig, ax = visualize.plot_3D_joints(target_joints[i])
visualize.plot_3D_joints(output_batch_numpy, fig=fig, ax=ax, color='C6')
visualize.title(filenamebase)
visualize.show()
temp = np.zeros((21, 3))
output_batch_numpy_abs = output_batch_numpy.reshape((20, 3))
temp[1:, :] = output_batch_numpy_abs
output_batch_numpy_abs = temp
output_joints_colorspace = camera.joints_depth2color(
output_batch_numpy_abs,
depth_intr_matrix=synthhands_handler.DEPTH_INTR_MTX,
handroot=target_handroot[i].data.cpu().numpy())
visualize.plot_3D_joints(output_joints_colorspace)
visualize.show()
aa1 = target_joints[i].data.cpu().numpy().reshape((20, 3))
aa2 = output[7][i].data.cpu().numpy().reshape((20, 3))
print('\n----------------------------------')
print(np.sum(np.abs(aa1 - aa2)) / 60)
print('----------------------------------')
#loss.backward()
valid_vars['total_loss'] += loss
valid_vars['total_joints_loss'] += loss_joints
valid_vars['total_heatmaps_loss'] += loss_heatmaps
# accumulate pixel dist loss for sub-mini-batch
valid_vars['total_pixel_loss'] = my_losses.accumulate_pixel_dist_loss_multiple(
valid_vars['total_pixel_loss'], output[3], target_heatmaps, control_vars['batch_size'])
valid_vars['total_pixel_loss_sample'] = my_losses.accumulate_pixel_dist_loss_from_sample_multiple(
valid_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:
# append total loss
valid_vars['losses'].append(valid_vars['total_loss'].data[0])
# erase total loss
total_loss = valid_vars['total_loss'].data[0]
valid_vars['total_loss'] = 0
# append total joints loss
valid_vars['losses_joints'].append(valid_vars['total_joints_loss'].data[0])
# erase total joints loss
valid_vars['total_joints_loss'] = 0
# append total joints loss
valid_vars['losses_heatmaps'].append(valid_vars['total_heatmaps_loss'].data[0])
# erase total joints loss
valid_vars['total_heatmaps_loss'] = 0
# append dist loss
valid_vars['pixel_losses'].append(valid_vars['total_pixel_loss'])
# erase pixel dist loss
valid_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
# append dist loss of sample from output
valid_vars['pixel_losses_sample'].append(valid_vars['total_pixel_loss_sample'])
# erase dist loss of sample from output
valid_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
# check if loss is better
#if valid_vars['losses'][-1] < valid_vars['best_loss']:
# valid_vars['best_loss'] = valid_vars['losses'][-1]
# print_verbose(" This is a best loss found so far: " + str(valid_vars['losses'][-1]), verbose)
# log checkpoint
if control_vars['curr_iter'] % control_vars['log_interval'] == 0:
trainer.print_log_info(model, optimizer, 1, total_loss, valid_vars, control_vars)
model_dict = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'control_vars': control_vars,
'train_vars': valid_vars,
}
trainer.save_checkpoint(model_dict,
filename=valid_vars['checkpoint_filenamebase'] +
str(control_vars['num_iter']) + '.pth.tar')
# print time lapse
prefix = 'Validating (Epoch #' + str(1) + ' ' + 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 valid_vars, control_vars
model, optimizer, control_vars, valid_vars, train_control_vars = validator.parse_args(model_class=JORNet)
if valid_vars['use_cuda']:
torch.set_default_tensor_type('torch.cuda.FloatTensor')
'''
visualize.plot_line(valid_vars['losses'], 'Main loss')
visualize.show()
visualize.plot_line(valid_vars['losses_heatmaps'], 'batch_halnetHeatmap loss')
visualize.show()
visualize.plot_line(valid_vars['losses_joints'], 'Joint loss')
visualize.show()
'''
valid_loader = synthhands_handler.get_SynthHands_validloader(root_folder=valid_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(valid_loader)
control_vars['n_iter_per_epoch'] = int(len(valid_loader) / control_vars['iter_size'])
control_vars['num_iter'] = len(valid_loader)
control_vars['tot_iter'] = int(len(valid_loader) / control_vars['iter_size'])
control_vars['start_iter_mod'] = control_vars['start_iter'] % control_vars['tot_iter']
trainer.print_header_info(model, valid_loader, control_vars)
control_vars['curr_iter'] = 1
control_vars['curr_epoch_iter'] = 1
valid_vars['total_loss'] = 0
valid_vars['total_pixel_loss'] = [0] * len(model.joint_ixs)
valid_vars['total_pixel_loss_sample'] = [0] * len(model.joint_ixs)
valid_vars, control_vars = validate(valid_loader, model, optimizer, valid_vars, control_vars, control_vars['verbose'])