问题
I encountered the following problem when I reproduce the ESRGAN related program. libpng error: Read Error
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'
I tried to find the code for the line where the error occurred.
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
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}
I feel that the picture I read in has gone wrong. One of them reads in and is None. I don't know how to deal with this problem. I am running this program with the NVIDIA Tesla P100GPU.
19-08-30 06:12:28.193 - INFO: l_g_pix: 3.9939e-03 l_g_fea: 2.3352e+00 l_g_gan: 1.0448e-01 l_d_real: 1.5721e-06 l_d_fake: 1.6599e-05 D_real: 7.0139e+00 D_fake: -1.3881e+01 19-08-30 06:14:34.038 - INFO: l_g_pix: 2.9632e-03 l_g_fea: 1.7633e+00 l_g_gan: 7.9122e-02 l_d_real: 5.6028e-06 l_d_fake: 4.7490e-05 D_real: 7.1848e+00 D_fake: -8.6396e+00 19-08-30 06:16:38.986 - INFO: l_g_pix: 3.6181e-03 l_g_fea: 2.2983e+00 l_g_gan: 3.5791e-02 l_d_real: 3.3302e-03 l_d_fake: 2.6311e-03 D_real: 1.6663e+01 D_fake: 9.5084e+00 19-08-30 06:18:42.645 - INFO: l_g_pix: 3.9908e-03 l_g_fea: 2.1037e+00 l_g_gan: 5.0026e-02 l_d_real: 2.2486e-04 l_d_fake: 7.5957e-04 D_real: 1.0516e+00 D_fake: -8.9531e+00 libpng error: Read Error Traceback (most recent call last):
………………
回答1:
1) check the image path is correct.
2) make sure that image is read as numpy ndarray e.g(using matplotlib, cv2), using PIL it reads image in another format so it becomes impossible to apply numpy array operations.
来源：https://stackoverflow.com/questions/57273464/attributeerror-nonetype-object-has-no-attribute-astype