TypeError: Cannot handle this data type
TypeError: Cannot handle this data type
正在尝试将显着性图放到图像上并制作新的数据集
trainloader = utilsxai.load_data_cifar10(batch_size=1,test=False)
testloader = utilsxai.load_data_cifar10(batch_size=128, test=True)
这个load_cifar10是torchvision
data = trainloader.dataset.data
trainloader.dataset.data = (data * sal_maps_hf).reshape(data.shape)
sal_maps_hf shape with (50000,32,32,3)
and trainloader shape
with (50000,32,32,3)
但是当我 运行 这个
for idx,img in enumerate(trainloader):
--------------------------------------------------------------------------- KeyError Traceback (most recent call
last) ~/venv/lib/python3.7/site-packages/PIL/Image.py in
fromarray(obj, mode) 2644 typekey = (1, 1) + shape[2:],
arr["typestr"]
-> 2645 mode, rawmode = _fromarray_typemap[typekey] 2646 except KeyError:
KeyError: ((1, 1, 3), '
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call
last) in
----> 1 show_images(trainloader)
in show_images(trainloader)
1 def show_images(trainloader):
----> 2 for idx,(img,target) in enumerate(trainloader):
3 img = img.squeeze()
4 #pritn(img)
5 img = torch.tensor(img)
~/venv/lib/python3.7/site-packages/torch/utils/data/dataloader.py in
next(self)
344 def next(self):
345 index = self._next_index() # may raise StopIteration
--> 346 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
347 if self._pin_memory:
348 data = _utils.pin_memory.pin_memory(data)
~/venv/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in
fetch(self, possibly_batched_index)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
~/venv/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in
(.0)
42 def fetch(self, possibly_batched_index):
43 if self.auto_collation:
---> 44 data = [self.dataset[idx] for idx in possibly_batched_index]
45 else:
46 data = self.dataset[possibly_batched_index]
~/venv/lib/python3.7/site-packages/torchvision/datasets/cifar.py in
getitem(self, index)
120 # doing this so that it is consistent with all other datasets
121 # to return a PIL Image
--> 122 img = Image.fromarray(img)
123
124 if self.transform is not None:
~/venv/lib/python3.7/site-packages/PIL/Image.py in fromarray(obj,
mode) 2645 mode, rawmode = _fromarray_typemap[typekey]
2646 except KeyError:
-> 2647 raise TypeError("Cannot handle this data type") 2648 else: 2649 rawmode = mode
TypeError: Cannot handle this data type
trainloader.dataset.__getitem__
getitem of Dataset CIFAR10
Number of datapoints: 50000
Root location: /mnt/3CE35B99003D727B/input/pytorch/data
Split: Train
StandardTransform Transform: Compose(
Resize(size=32, interpolation=PIL.Image.BILINEAR)
ToTensor()
)
你的 sal_maps_hf 不是 np.uint8。
根据问题和评论中的部分信息，我猜你的面具是 dtype np.float （或类似的），乘以 data * sal_maps_hf 你的数据是强制转换为 dtype 而不是 np.uint8，后者会导致 PIL.Image 抛出异常。
尝试：
trainloader.dataset.data = (data * sal_maps_hf).reshape(data.shape).astype(np.uint8)
正在尝试将显着性图放到图像上并制作新的数据集
trainloader = utilsxai.load_data_cifar10(batch_size=1,test=False)
testloader = utilsxai.load_data_cifar10(batch_size=128, test=True)
这个load_cifar10是torchvision
data = trainloader.dataset.data
trainloader.dataset.data = (data * sal_maps_hf).reshape(data.shape)
sal_maps_hf shape with (50000,32,32,3)
and trainloader shape with (50000,32,32,3)
但是当我 运行 这个
for idx,img in enumerate(trainloader):
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) ~/venv/lib/python3.7/site-packages/PIL/Image.py in fromarray(obj, mode) 2644 typekey = (1, 1) + shape[2:], arr["typestr"] -> 2645 mode, rawmode = _fromarray_typemap[typekey] 2646 except KeyError:
KeyError: ((1, 1, 3), '
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last) in ----> 1 show_images(trainloader)
in show_images(trainloader) 1 def show_images(trainloader): ----> 2 for idx,(img,target) in enumerate(trainloader): 3 img = img.squeeze() 4 #pritn(img) 5 img = torch.tensor(img)
~/venv/lib/python3.7/site-packages/torch/utils/data/dataloader.py in next(self) 344 def next(self): 345 index = self._next_index() # may raise StopIteration --> 346 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 347 if self._pin_memory: 348 data = _utils.pin_memory.pin_memory(data)
~/venv/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index) 42 def fetch(self, possibly_batched_index): 43 if self.auto_collation: ---> 44 data = [self.dataset[idx] for idx in possibly_batched_index] 45 else: 46 data = self.dataset[possibly_batched_index]
~/venv/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py in (.0) 42 def fetch(self, possibly_batched_index): 43 if self.auto_collation: ---> 44 data = [self.dataset[idx] for idx in possibly_batched_index] 45 else: 46 data = self.dataset[possibly_batched_index]
~/venv/lib/python3.7/site-packages/torchvision/datasets/cifar.py in getitem(self, index) 120 # doing this so that it is consistent with all other datasets 121 # to return a PIL Image --> 122 img = Image.fromarray(img) 123 124 if self.transform is not None:
~/venv/lib/python3.7/site-packages/PIL/Image.py in fromarray(obj, mode) 2645 mode, rawmode = _fromarray_typemap[typekey] 2646 except KeyError: -> 2647 raise TypeError("Cannot handle this data type") 2648 else: 2649 rawmode = mode
TypeError: Cannot handle this data type
trainloader.dataset.__getitem__
getitem of Dataset CIFAR10 Number of datapoints: 50000 Root location: /mnt/3CE35B99003D727B/input/pytorch/data Split: Train StandardTransform Transform: Compose( Resize(size=32, interpolation=PIL.Image.BILINEAR) ToTensor() )
你的 sal_maps_hf 不是 np.uint8。
根据问题和评论中的部分信息，我猜你的面具是 dtype np.float （或类似的），乘以 data * sal_maps_hf 你的数据是强制转换为 dtype 而不是 np.uint8，后者会导致 PIL.Image 抛出异常。
尝试：
trainloader.dataset.data = (data * sal_maps_hf).reshape(data.shape).astype(np.uint8)