Traceback (most recent call last):
File "/sda/ZTL/B/codes/
train.py", line 173, in <module>
main()
File "/sda/ZTL/B/codes/
train.py", line 97, in main
for _, train_data in enumerate(train_loader):
File "/root/anaconda3/lib/python3.6/site-packages/torch/utils/data/
dataloader.py", line 637, in __next__
return self._process_next_batch(batch)
File "/root/anaconda3/lib/python3.6/site-packages/torch/utils/data/
dataloader.py", line 658, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
AttributeError: Traceback (most recent call last):
File "/root/anaconda3/lib/python3.6/site-packages/torch/utils/data/
dataloader.py", line 138, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/root/anaconda3/lib/python3.6/site-packages/torch/utils/data/
dataloader.py", line 138, in <listcomp>
samples = collate_fn([dataset[i] for i in batch_indices])
File "/sda/ZTL/B/data/
LRHR_dataset.py", line 51, in __getitem__
img_HR = util.read_img(self.HR_env, HR_path)
File "/sda/ZTL/B/data/
util.py", line 79, in read_img
img = img.astype(np.float32) / 255.
AttributeError: 'NoneType' object has no attribute 'astype'
File "/sda/ZTL/B/data/
util.py", line 79, in read_img
def read_img(env, path):
# read image by cv2 or from lmdb
# return: Numpy float32, HWC, BGR, [0,1]
if env is None: # img
img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
else:
img = _read_lmdb_img(env, path)
img = img.astype(np.float32) / 255.
if img.ndim == 2:
img = np.expand_dims(img, axis=2)
# some images have 4 channels
if img.shape[2] > 3:
img = img[:, :, :3]
return img
找到了错误代码行所在处
File "/sda/ZTL/B/data/
LRHR_dataset.py", line 51, in __getitem__
def __getitem__(self, index):
HR_path, LR_path = None, None
scale = self.opt['scale']
HR_size = self.opt['HR_size']
# get HR image
HR_path = self.paths_HR[index]
img_HR = util.read_img(self.HR_env, HR_path)
# modcrop in the validation / test phase
if self.opt['phase'] != 'train':
img_HR = util.modcrop(img_HR, scale)
# change color space if necessary
if self.opt['color']:
img_HR = util.channel_convert(img_HR.shape[2], self.opt['color'], [img_HR])[0]
# get LR image
if self.paths_LR:
LR_path = self.paths_LR[index]
img_LR = util.read_img(self.LR_env, LR_path)
else: # down-sampling on-the-fly
# randomly scale during training
if self.opt['phase'] == 'train':
random_scale = random.choice(self.random_scale_list)
H_s, W_s, _ = img_HR.shape
def _mod(n, random_scale, scale, thres):
rlt = int(n * random_scale)
rlt = (rlt // scale) * scale
return thres if rlt < thres else rlt
H_s = _mod(H_s, random_scale, scale, HR_size)
W_s = _mod(W_s, random_scale, scale, HR_size)
img_HR = cv2.resize(np.copy(img_HR), (W_s, H_s), interpolation=cv2.INTER_LINEAR)
# force to 3 channels
if img_HR.ndim == 2:
img_HR = cv2.cvtColor(img_HR, cv2.COLOR_GRAY2BGR)
H, W, _ = img_HR.shape
# using matlab imresize
img_LR = util.imresize_np(img_HR, 1 / scale, True)
if img_LR.ndim == 2:
img_LR = np.expand_dims(img_LR, axis=2)
if self.opt['phase'] == 'train':
# if the image size is too small
H, W, _ = img_HR.shape
if H < HR_size or W < HR_size:
img_HR = cv2.resize(
np.copy(img_HR), (HR_size, HR_size), interpolation=cv2.INTER_LINEAR)
# using matlab imresize
img_LR = util.imresize_np(img_HR, 1 / scale, True)
if img_LR.ndim == 2:
img_LR = np.expand_dims(img_LR, axis=2)
print(img_LR)
H, W, C = img_LR.shape
LR_size = HR_size // scale
# randomly crop
rnd_h = random.randint(0, max(0, H - LR_size))
rnd_w = random.randint(0, max(0, W - LR_size))
img_LR = img_LR[rnd_h:rnd_h + LR_size, rnd_w:rnd_w + LR_size, :]
rnd_h_HR, rnd_w_HR = int(rnd_h * scale), int(rnd_w * scale)
img_HR = img_HR[rnd_h_HR:rnd_h_HR + HR_size, rnd_w_HR:rnd_w_HR + HR_size, :]
# augmentation - flip, rotate
img_LR, img_HR = util.augment([img_LR, img_HR], self.opt['use_flip'], \
self.opt['use_rot'])
# change color space if necessary
if self.opt['color']:
img_LR = util.channel_convert(C, self.opt['color'], [img_LR])[0] # TODO during val no definetion
# BGR to RGB, HWC to CHW, numpy to tensor
if img_HR.shape[2] == 3:
img_HR = img_HR[:, :, [2, 1, 0]]
img_LR = img_LR[:, :, [2, 1, 0]]
img_HR = torch.from_numpy(np.ascontiguousarray(np.transpose(img_HR, (2, 0, 1)))).float()
img_LR = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LR, (2, 0, 1)))).float()
if LR_path is None:
LR_path = HR_path
return {'LR': img_LR, 'HR': img_HR, 'LR_path': LR_path, 'HR_path': HR_path}
我感觉是读照片时出了问题。self.HR_env 没有图片读入，我不知道这是出了什么问题。我不知道如何处理这个问题，求大神指导。我运行这个程序是在 NVIDIA 特斯拉 P100GPU 上运行的