Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
et_count = density_map.sum().item()
if save_output:
image = data.mul_(torch.Tensor([0.229,0.224,0.225]).view(3,1,1))\
.add_(torch.Tensor([0.485,0.456,0.406]).view(3,1,1))
if args.test_fixed_size != -1:
H, W = mask.shape
_, _, fixed_size = data[0].shape
assert args.test_fixed_size == fixed_size
inital_img = torch.zeros((3, H, W))
for img_slice, (x, y) in zip(image, itertools.product(range(W / fixed_size), range(H / fixed_size))):
inital_img[:, y * fixed_size:(y + 1) * fixed_size, x * fixed_size:(x + 1) * fixed_size] = img_slice
H = mask.sum(dim=0).max().item()
W = mask.sum(dim=1).max().item()
inital_img = inital_img.masked_select(mask).view(3, H, W)
image = inital_img
image = image.data.cpu().numpy()
dgen.save_image(image.transpose((1,2,0))*255.0, save_path, fname[0].split('.')[0] + "_0_img.png")
gt_dens = gt_dens.data.cpu().numpy()
density_map = density_map.data.cpu().numpy()
dgen.save_density_map(gt_dens.squeeze(), save_path, fname[0].split('.')[0] + "_1_gt.png")
dgen.save_density_map(density_map.squeeze(), save_path, fname[0].split('.')[0] + "_2_et.png")
del gt_dens
del data, dens
detail += "index: {}; fname: {}; gt: {}; et: {};\n".format(i, fname[0].split('.')[0], gt_count, et_count)
mae += abs(gt_count-et_count)
mse += ((gt_count-et_count)*(gt_count-et_count))
mae = mae/len(data_loader)
mse = np.sqrt(mse/len(data_loader))
duration = timer.toc(average=False)
print "testing time: %d" % duration
return mae,mse,detail
if __name__ == '__main__':
args = parser.parse_args()
# set gpu ids
str_ids = args.gpus.split(',')
args.gpus = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
args.gpus.append(id)
if len(args.gpus) > 0:
torch.cuda.set_device(args.gpus[0])
args.loss = None
args.test_crop_type = 'Adap'
args.pretrain = None
data_loader_test = CreateDataLoader(args, phase='test')
optimizer = lambda x: torch.optim.Adam(filter(lambda p: p.requires_grad, x.parameters()))
net = CrowdCounter(optimizer=optimizer, opt=args)
if args.model_path.endswith('.h5'):
output_path = args.model_path[:-3] + '/output/'
if not os.path.exists(args.model_path[:-3]):
os.mkdir(args.model_path[:-3])
test_once = True
else:
output_path = args.model_path + '/output/'
test_once = False
if not os.path.exists(output_path):
os.mkdir(output_path)
if test_once:
model_files = [args.model_path]
elif args.epoch is not None:
model_files = ['%06d.h5' % args.epoch]
assert args.save_output
elif not args.is_wait:
def list_dir(watch_path):
return itertools.chain(*[[filename] if (os.path.isfile(os.path.join(watch_path,filename)) and '.h5' in filename)\
else []\
for filename in os.listdir(watch_path)])
model_files = list(list_dir(args.model_path))
model_files.sort()
model_files = model_files[::-1]
assert not args.save_output
else:
model_files = ['%06d.h5' % epoch for epoch in range(0, 301)]
assert not args.save_output
if args.split is not None:
model_files = ['%06d.h5' % epoch for epoch in map(int, args.split[:-1].split(','))]
print model_files