a = np.zeros_like(hm_pred)
a[:, :, :img_w2 // config.hm_stride] = np.flip(
hm_pred[:, :, :img_w2 // config.hm_stride], 2)
for conj in config.conjug:
a[conj] = a[conj[::-1]]
hm_pred = a
return hm_pred
# x, y = encoder.decode_np(hm_pred, scale, config.hm_stride, method='maxoffset')
# keypoints = np.stack([x, y, np.ones(x.shape)], axis=1).astype(np.int16)
# return keypoints
if __name__ == '__main__':
config = Config('trousers')
n_gpu = pytorch_utils.setgpu('3')
test_kpda = KPDA(config, db_path, 'test')
print('Test sample number: %d' % test_kpda.size())
df = data_frame_template()
net = CascadePyramidNet(config)
checkpoint = torch.load(
root_path +
'checkpoints/trousers_027_posneu_lgtrain.ckpt') # must before cuda
net.load_state_dict(checkpoint['state_dict'])
net = net.cuda()
cudnn.benchmark = True
net = DataParallel(net)
net.eval()
encoder = KeypointEncoder()
for idx in tqdm(range(test_kpda.size())):
img_path = test_kpda.get_image_path(idx)
bboxes2, scores2 = encoder.decode(
reg_target, cls_target,
[config.img_max_size, config.img_max_size])
img = np.transpose(img.numpy(), (1, 2, 0))
img = ((img * config.sigma + config.mu) * 255).astype(np.uint8)
draw_bbox(
img, bboxes.numpy(), scores.numpy(),
'/home/storage/lsy/fashion/predictions/' + config.clothes +
'/%d-%d.png' % (i, j), bboxes2.numpy())
if __name__ == '__main__':
batch_size = 24
workers = 16
config = Config('outwear')
n_gpu = pytorch_utils.setgpu('all')
test_kpda = KPDA(config, db_path, 'val')
print('Val sample number: %d' % test_kpda.size())
net = RetinaNet(config, num_classes=2)
checkpoint = torch.load(root_path +
'checkpoints/rpn_053.ckpt') # must before cuda
net.load_state_dict(checkpoint['state_dict'])
net = net.cuda()
cudnn.benchmark = True
net = DataParallel(net)
test_dataset = DataGenerator(config, test_kpda, phase='test')
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
:param output: matrix nx14
:param target: matrix nx14
:return: list of 14 elements
"""
aucs = []
num_classes = output.shape[1]
for i in range(num_classes):
aucs.append(roc_auc_score(target[:, i], output[:, i]))
return aucs
if __name__ == '__main__':
config = Config()
resume = False
workers = config.test_workers
n_gpu = pytorch_utils.setgpu(config.test_gpus)
batch_size = config.test_batch_size_per_gpu * n_gpu
test_data = CXR2(config.data_dir, 'test')
print('Test sample number: %d' % test_data.size())
net = DenseNet121(num_classes=len(test_data.labels))
checkpoint = torch.load(os.path.join(config.proj_dir, 'checkpoints', 'densenet_024.ckpt')) # must before cuda
net.load_state_dict(checkpoint['state_dict'])
net = net.cuda()
cudnn.benchmark = True
net = DataParallel(net).cuda()
test_generator = DataGenerator(config, test_data, phase='test')
test_loader = DataLoader(test_generator,
batch_size=batch_size,
loss_lst.append(loss_output.data.cpu().item())
target_lst.append(target.data.cpu().numpy())
output_lst.append((output.data.cpu().numpy()))
target_lst = np.concatenate(target_lst, axis=0)
output_lst = np.concatenate(output_lst, axis=0)
aucs = compute_AUCs(output_lst, target_lst)
end_time = time.time()
metrics = [np.mean(loss_lst), aucs]
return metrics, end_time - start_time
if __name__ == '__main__':
config = Config()
resume = False
workers = config.train_workers
n_gpu = pytorch_utils.setgpu(config.train_gpus)
batch_size = config.train_batch_size_per_gpu * n_gpu
epochs = config.epochs
base_lr = config.base_lr
save_dir = config.proj_dir + 'checkpoints/'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
train_data = CXR2(config.data_dir, 'train')
val_data = CXR2(config.data_dir, 'val')
print('Train sample number: %d' % train_data.size())
print('Val sample number: %d' % val_data.size())
pos = train_data.get_occurrences()
neg = [train_data.size() - x for x in pos]
net = DenseNet121(num_classes=len(train_data.labels))
hp = print_heatmap(hp, img, config)
hp2 = print_heatmap(hp2, img, config)
hps.append(np.concatenate([hp, hp2], axis=0))
cv2.imwrite('/home/storage/lsy/fashion/tmp/%d-%d-hm.png' % (i, j), np.concatenate(hps, axis=1))
cv2.imwrite('/home/storage/lsy/fashion/tmp/%d-%d-kp.png' % (i, j), kp_img)
print(np.nanmean(np.array(nes)))
if __name__ == '__main__':
batch_size = 32
workers = 16
config = Config('outwear')
n_gpu = pytorch_utils.setgpu('0,1')
test_kpda = KPDA(config, db_path, 'val')
print('Test sample number: %d' % test_kpda.size())
net = CascadePyramidNet(config) # UNet(config) #VGG_FCN(config, layer_num=8) #ResidualUNet2D(config) #
checkpoint = torch.load(root_path + 'checkpoints/outwear_063_cascade.ckpt') # must before cuda
net.load_state_dict(checkpoint['state_dict'])
net = net.cuda()
cudnn.benchmark = True
net = DataParallel(net)
test_dataset = DataGenerator(config, test_kpda, phase='test')
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=workers,
lbls = Variable(lbls.cuda(async=True), volatile=True).squeeze()
outputs, _ = net(imgs)
loss_output = loss(outputs, lbls)
pred = outputs.data.max(1, keepdim=True)[1]
correct += pred.eq(lbls.data.view_as(pred)).long().cpu().sum()
total += lbls.size(0)
metrics.append(loss_output.data[0])
end_time = time.time()
metrics = np.asarray(metrics, np.float32)
return metrics, float(correct) / total, end_time - start_time
if __name__ == '__main__':
batch_size = 64
workers = 32
n_gpu = pytorch_utils.setgpu('5,6')
epochs = 100
base_lr = 1e-3
save_dir = root_path + 'checkpoints/'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
resume = False
config = Config()
net = ResNet_CAM()
start_epoch = 1
lr = base_lr
best_val_loss = float('inf')
log_mode = 'w'
if resume:
metrics = np.asarray(metrics, np.float32)
return metrics, end_time - start_time
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument('-c',
'--clothes',
help='one clothes type',
default='outwear')
args = parser.parse_args(sys.argv[1:])
print('Training ' + args.clothes)
batch_size = 24
workers = 10
n_gpu = pytorch_utils.setgpu('4,5,6,7')
epochs = 1000
# 256 pixels: SGD L1 loss starts from 1e-2, L2 loss starts from 1e-3
# 512 pixels: SGD L1 loss starts from 1e-3, L2 loss starts from 1e-4
base_lr = 1e-3
save_dir = root_path + 'checkpoints/'
if not os.path.exists(save_dir):
os.mkdir(save_dir)
resume = False
config = Config(args.clothes)
net = CascadePyramidNetV8(config)
loss = VisErrorLoss()
train_data = KPDA(config, db_path, 'train')
val_data = KPDA(config, db_path, 'val')
print('Train sample number: %d' % train_data.size())
