(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)
(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)