def test(net, test_loader, epoch):
net.eval()
test_step = len(test_loader)
print('\nEvaluating...')
with torch.no_grad():
evaluator = Evaluator()
for i, sample in enumerate(test_loader):
for key in sample:
sample[key] = sample[key].cuda()
output = net(sample)
evaluator.add(output, sample)
if (i + 1) % 100 == 0:
print('Val Step [{}/{}]'.format(i + 1, test_step))
results = evaluator.evaluate()
print('Epoch {}/{}'.format(epoch + 1, args.epoch))
print(
'| L.Collar | R.Collar | L.Sleeve | R.Sleeve | L.Waist | R.Waist | L.Hem | R.Hem | ALL |'
)
print(
'| {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} |'
.format(results['lm_dist'][0], results['lm_dist'][1],
results['lm_dist'][2], results['lm_dist'][3],
results['lm_dist'][4], results['lm_dist'][5],
results['lm_dist'][6], results['lm_dist'][7],
results['lm_dist_all']))
def test(net, test_loader, epoch):
net.eval()
test_step = len(test_loader)
print('\nEvaluating...')
with torch.no_grad():
evaluator = Evaluator()
for i, sample in enumerate(test_loader):
for key in sample:
if key[0:2] != 'D_':
sample[key] = sample[key].cuda()
output = net(sample)
evaluator.add(output, sample)
if (i + 1) % 100 == 0:
print('Val Step [{}/{}]'.format(i + 1, test_step)) # j + 1 ?
results = evaluator.evaluate()
print('Epoch {}/{}'.format(epoch + 1, args.epoch))
print(
'| L.Collar | R.Collar | L.Sleeve | R.Sleeve | L.Waist | R.Waist | L.Hem | R.Hem | ALL |'
)
print(
'| {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} |'
.format(results['lm_dist'][0], results['lm_dist'][1],
results['lm_dist'][2], results['lm_dist'][3],
results['lm_dist'][4], results['lm_dist'][5],
results['lm_dist'][6], results['lm_dist'][7],
results['lm_dist_all']))
file = open('results_lr_%.4f_base_%d_de_%d_g_%.2f.txt'% \
(args.learning_rate, \
args.base_epoch, \
args.decay_epoch, \
args.gamma), 'a')
file.write('Epoch {}\n'.format(args.base_epoch + epoch + 1))
file.write(
'| L.Collar | R.Collar | L.Sleeve | R.Sleeve | L.Waist | R.Waist | L.Hem | R.Hem | ALL |\n'
)
file.write(
'| {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} |\n\n'
.format(results['lm_dist'][0], results['lm_dist'][1],
results['lm_dist'][2], results['lm_dist'][3],
results['lm_dist'][4], results['lm_dist'][5],
results['lm_dist'][6], results['lm_dist'][7],
results['lm_dist_all']))
file.close()
def test(net, test_loader, epoch):
net.eval()
test_step = len(test_loader)
print('\nEvaluating...')
with torch.no_grad():
evaluator = Evaluator()
for i, (sample, img) in enumerate(test_loader):
#import matplotlib.pyplot as plt
#print(len(sample['image_original']))
#plt.imshow(sample['image_original'].numpy().swapaxes(0, 2))
#plt.show()
#image_name = sample['image_name']
#print(image_name)
for key in sample:
if key != 'image_name':
sample[key] = sample[key].cuda()
output = net(sample)
#print(type(output))
pos_map = output['lm_pos_map']
#print(pos_map.size())
#resh = pos_map.reshape(50, 8, -1)
#print(resh.size())
evaluator.add(output, sample, img)
if (i + 1) % 100 == 0:
print('Val Step [{}/{}]'.format(i + 1, test_step))
results = evaluator.evaluate()
print('Epoch {}/{}'.format(epoch + 1, args.epoch))
print(
'| L.Collar | R.Collar | L.Sleeve | R.Sleeve | L.Waist | R.Waist | L.Hem | R.Hem | ALL |'
)
print(
'| {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} | {:.5f} |'
.format(results['lm_dist'][0], results['lm_dist'][1],
results['lm_dist'][2], results['lm_dist'][3],
results['lm_dist'][4], results['lm_dist'][5],
results['lm_dist'][6], results['lm_dist'][7],
results['lm_dist_all']))
def test(net, test_loader, epoch):
net.eval()
test_step = len(test_loader)
print('\nEvaluating...')
with torch.no_grad():
evaluator = Evaluator()
pbar2 = tqdm(test_loader)
for i, sample in enumerate(pbar2):
iter_start_time = time.time()
for key in sample:
sample[key] = sample[key].cuda()
output = net(sample)
evaluator.add(output, sample)
t = time.time() - iter_start_time
if (i + 1) % 100 == 0:
tqdm.write('Val Step [{}/{}], Time:{:.3f}'.format(
i + 1, test_step, t))
results = evaluator.evaluate()
print('Epoch {}/{}'.format(epoch + 1, args.epoch))
print('lm_dist_all: {:.5f} '.format(results['lm_dist_all']))
