def main():
"""Create the model and start the evaluation process."""
for i in range(1, 50):
model_path = './snapshots/GTA2Cityscapes/GTA5_{0:d}.pth'.format(i *
2000)
save_path = './result/GTA2Cityscapes_{0:d}'.format(i * 2000)
args = get_arguments()
gpu0 = args.gpu
if not os.path.exists(save_path):
os.makedirs(save_path)
model = Res_Deeplab(num_classes=args.num_classes)
saved_state_dict = torch.load(model_path)
model.load_state_dict(saved_state_dict)
model.eval()
model.cuda(gpu0)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir,
args.data_list,
crop_size=(1024, 512),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=1,
shuffle=False,
pin_memory=True)
interp = nn.Upsample(size=(1024, 2048),
mode='bilinear',
align_corners=True)
with torch.no_grad():
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processd' % index)
image, _, _, name = batch
output1, output2 = model(Variable(image).cuda(gpu0))
output = interp(output1 + output2).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
output_col = colorize_mask(output)
output = Image.fromarray(output)
name = name[0].split('/')[-1]
output.save('%s/%s' % (save_path, name))
output_col.save('%s/%s_color.png' %
(save_path, name.split('.')[0]))
print(save_path)
def main():
"""Create the model and start the evaluation process."""
args = get_arguments()
os.makedirs(args.save, exist_ok=True)
device = torch.device("cuda" if not args.cpu else "cpu")
model = Res_Deeplab(num_classes=args.num_classes)
saved_state_dict = torch.load(args.restore_from)
model.load_state_dict(saved_state_dict)
model.eval()
model.to(device)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir, args.data_list, crop_size=(1024, 512), mean=IMG_MEAN, scale=False, mirror=False, set=args.set),
batch_size=1, shuffle=False, pin_memory=True)
interp = nn.Upsample(size=(1024, 2048), mode='bilinear', align_corners=True)
print('### STARTING EVALUATING ###')
print('total to process: %d' % len(testloader))
with torch.no_grad():
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processed' % index)
image, _, name, = batch
output1, output2 = model(image.to(device))
output = interp(output1 + output2).cpu().data[0].numpy()
output = output.transpose(1,2,0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
output_col = colorize_mask(output)
output = Image.fromarray(output)
name = name[0].split('/')[-1]
output.save('%s/%s' % (args.save, name))
output_col.save('%s/%s_color.png' % (args.save, name.split('.')[0]))
print('### EVALUATING FINISHED ###')
def main(args):
"""Create the model and start the evaluation process."""
save_dir = os.path.join(args.save, args.experiment)
model_dir = os.path.join(args.restore_from, args.experiment)
os.makedirs(save_dir, exist_ok=True)
device = torch.device("cuda" if not args.cpu else "cpu")
start = time.time()
n_files = len([name for name in os.listdir(model_dir)])
if args.d:
n_files = int(n_files / 2)
if args.a:
n_files = int(n_files / 3)
for i in range(1, n_files + 1):
model_path = os.path.join(model_dir,
'GTA5_{0:d}.pth'.format(i * args.save_step))
save_path = os.path.join(save_dir, '{0:d}'.format(i * args.save_step))
os.makedirs(save_path, exist_ok=True)
print('#### Evaluating model: ' + str(i) + '/' + str(n_files) +
' ####')
model = Res_Deeplab(num_classes=args.num_classes)
saved_state_dict = torch.load(model_path)
model.load_state_dict(saved_state_dict)
model.eval()
model.to(device)
testloader = data.DataLoader(cityscapesDataSet(args.data_dir,
args.data_list,
crop_size=(1024, 512),
mean=IMG_MEAN,
scale=False,
mirror=False,
set=args.set),
batch_size=1,
shuffle=False,
pin_memory=True)
interp = nn.Upsample(size=(1024, 2048),
mode='bilinear',
align_corners=True)
with torch.no_grad():
for index, batch in enumerate(testloader):
if index % 100 == 0:
print('%d processed' % index)
image, _, name = batch
output1, output2 = model(image.to(device))
output = interp(output1 + output2).cpu().data[0].numpy()
output = output.transpose(1, 2, 0)
output = np.asarray(np.argmax(output, axis=2), dtype=np.uint8)
output_col = colorize_mask(output)
output = Image.fromarray(output)
name = name[0].split('/')[-1]
output.save('%s/%s' % (save_path, name))
output_col.save('%s/%s_color.png' %
(save_path, name.split('.')[0]))
print(save_path)
end = time.time()
print('Total time: {} min, {} sec '.format(int((end - start) / 60 % 60),
int((end - start) % 60)))