You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
(pytorch_hyperseg_master) F:\22-lx\code\hyperseg-main>python hyperseg/test.py checkpoints/vocsbd/vocsbd_efficientnet_b3_hyperseg-l -td "hyperseg.datasets.voc_sbd.VOCSBDDataset('data/vocsbd','data/vocsbd/VOCdevkit/VOC2012/val.txt')"
-it "seg_transforms.LargerEdgeResize([512,1024])"
=> using GPU devices: 0
=> Loading segmentation model: "model_best.pth"...
Loading pretrained weights for efficientnet-b3...
0%| | 0/25 [00:03<?, ?batches/s]
Traceback (most recent call last):
File "F:\22-lx\code\hyperseg-main\hyperseg\test.py", line 296, in
main(**vars(parser.parse_args()))
File "F:\22-lx\code\hyperseg-main\hyperseg\test.py", line 156, in main
for i, (input, target) in enumerate(tqdm(test_loader, unit='batches', file=sys.stdout)):
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\tqdm\std.py", line 1195, in iter
for obj in iterable:
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data\dataloader.py", line 681, in next
data = self._next_data()
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data\dataloader.py", line 1376, in _next_data
return self._process_data(data)
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data\dataloader.py", line 1402, in _process_data
data.reraise()
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch_utils.py", line 461, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data_utils\worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\voc_sbd.py", line 97, in getitem
img, target = self.transforms(img, target)
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 52, in call
input = list(t(*input))
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 173, in call
img = larger_edge_resize(img, self.size, self.interpolation)
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 148, in larger_edge_resize
return img.resize(size[::-1], interpolation)
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\PIL\Image.py", line 2130, in resize
raise ValueError(msg)
ValueError: Unknown resampling filter (InterpolationMode.BICUBIC). Use Image.Resampling.NEAREST (0), Image.Resampling.LANCZOS (1), Image.Resampling.BILINEAR (2), Image.Resampling.BICUBIC (3), Image.Resampling.BOX (4) or Image.Resamp
ling.HAMMING (5)
The text was updated successfully, but these errors were encountered:
(pytorch_hyperseg_master) F:\22-lx\code\hyperseg-main>python hyperseg/test.py checkpoints/vocsbd/vocsbd_efficientnet_b3_hyperseg-l -td "hyperseg.datasets.voc_sbd.VOCSBDDataset('data/vocsbd','data/vocsbd/VOCdevkit/VOC2012/val.txt')"
-it "seg_transforms.LargerEdgeResize([512,1024])"
=> using GPU devices: 0
=> Loading segmentation model: "model_best.pth"...
Loading pretrained weights for efficientnet-b3...
0%| | 0/25 [00:03<?, ?batches/s]
Traceback (most recent call last):
File "F:\22-lx\code\hyperseg-main\hyperseg\test.py", line 296, in
main(**vars(parser.parse_args()))
File "F:\22-lx\code\hyperseg-main\hyperseg\test.py", line 156, in main
for i, (input, target) in enumerate(tqdm(test_loader, unit='batches', file=sys.stdout)):
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\tqdm\std.py", line 1195, in iter
for obj in iterable:
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data\dataloader.py", line 681, in next
data = self._next_data()
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data\dataloader.py", line 1376, in _next_data
return self._process_data(data)
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data\dataloader.py", line 1402, in _process_data
data.reraise()
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch_utils.py", line 461, in reraise
raise exception
ValueError: Caught ValueError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data_utils\worker.py", line 302, in _worker_loop
data = fetcher.fetch(index)
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\torch\utils\data_utils\fetch.py", line 49, in
data = [self.dataset[idx] for idx in possibly_batched_index]
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\voc_sbd.py", line 97, in getitem
img, target = self.transforms(img, target)
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 52, in call
input = list(t(*input))
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 173, in call
img = larger_edge_resize(img, self.size, self.interpolation)
File "f:\22-lx\code\hyperseg-main\hyperseg\datasets\seg_transforms.py", line 148, in larger_edge_resize
return img.resize(size[::-1], interpolation)
File "D:\Anaconda\envs\pytorch_hyperseg_master\lib\site-packages\PIL\Image.py", line 2130, in resize
raise ValueError(msg)
ValueError: Unknown resampling filter (InterpolationMode.BICUBIC). Use Image.Resampling.NEAREST (0), Image.Resampling.LANCZOS (1), Image.Resampling.BILINEAR (2), Image.Resampling.BICUBIC (3), Image.Resampling.BOX (4) or Image.Resamp
ling.HAMMING (5)
The text was updated successfully, but these errors were encountered: