Error in Google Colaboratory - AttributeError: module 'PIL.Image' has no attribute 'register_decoder'
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I am running this code on Google Colaboratory and I am getting error of register decoder
image_data = dset.ImageFolder(root="drive/SemanticDataset/train/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
enter code herelabel_data = dset.ImageFolder(root="drive/SemanticDataset/label/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
image_batch = data.DataLoader(image_data, batch_size=batch_size, shuffle=False, num_workers=2)
label_batch = data.DataLoader(label_data, batch_size=batch_size, shuffle=False, num_workers=2)
for i in range(epoch):
for _, (image, label) in enumerate(zip(image_batch, label_batch)):
optimizer.zero_grad()
x = Variable(image, requires_grad=True).cuda()
y = Variable(label).cuda()
out = model.forward(x)
loss = loss_func(out, y)
loss.backward()
optimizer.step()
if _ % 100 == 0:
print("Epoch: "+i+"| Loss: " , loss)
here is the screenshot of error
python dataset python-imaging-library pytorch
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up vote
2
down vote
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I am running this code on Google Colaboratory and I am getting error of register decoder
image_data = dset.ImageFolder(root="drive/SemanticDataset/train/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
enter code herelabel_data = dset.ImageFolder(root="drive/SemanticDataset/label/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
image_batch = data.DataLoader(image_data, batch_size=batch_size, shuffle=False, num_workers=2)
label_batch = data.DataLoader(label_data, batch_size=batch_size, shuffle=False, num_workers=2)
for i in range(epoch):
for _, (image, label) in enumerate(zip(image_batch, label_batch)):
optimizer.zero_grad()
x = Variable(image, requires_grad=True).cuda()
y = Variable(label).cuda()
out = model.forward(x)
loss = loss_func(out, y)
loss.backward()
optimizer.step()
if _ % 100 == 0:
print("Epoch: "+i+"| Loss: " , loss)
here is the screenshot of error
python dataset python-imaging-library pytorch
New contributor
add a comment |
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I am running this code on Google Colaboratory and I am getting error of register decoder
image_data = dset.ImageFolder(root="drive/SemanticDataset/train/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
enter code herelabel_data = dset.ImageFolder(root="drive/SemanticDataset/label/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
image_batch = data.DataLoader(image_data, batch_size=batch_size, shuffle=False, num_workers=2)
label_batch = data.DataLoader(label_data, batch_size=batch_size, shuffle=False, num_workers=2)
for i in range(epoch):
for _, (image, label) in enumerate(zip(image_batch, label_batch)):
optimizer.zero_grad()
x = Variable(image, requires_grad=True).cuda()
y = Variable(label).cuda()
out = model.forward(x)
loss = loss_func(out, y)
loss.backward()
optimizer.step()
if _ % 100 == 0:
print("Epoch: "+i+"| Loss: " , loss)
here is the screenshot of error
python dataset python-imaging-library pytorch
New contributor
I am running this code on Google Colaboratory and I am getting error of register decoder
image_data = dset.ImageFolder(root="drive/SemanticDataset/train/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
enter code herelabel_data = dset.ImageFolder(root="drive/SemanticDataset/label/", transform = transforms.Compose([
transforms.Scale(size=img_size),
transforms.CenterCrop(size=(img_size,img_size*2)),
transforms.ToTensor(),
]))
image_batch = data.DataLoader(image_data, batch_size=batch_size, shuffle=False, num_workers=2)
label_batch = data.DataLoader(label_data, batch_size=batch_size, shuffle=False, num_workers=2)
for i in range(epoch):
for _, (image, label) in enumerate(zip(image_batch, label_batch)):
optimizer.zero_grad()
x = Variable(image, requires_grad=True).cuda()
y = Variable(label).cuda()
out = model.forward(x)
loss = loss_func(out, y)
loss.backward()
optimizer.step()
if _ % 100 == 0:
print("Epoch: "+i+"| Loss: " , loss)
here is the screenshot of error
python dataset python-imaging-library pytorch
python dataset python-imaging-library pytorch
New contributor
New contributor
edited 2 days ago
blue-phoenox
2,77471435
2,77471435
New contributor
asked 2 days ago
Aditya Kumar
112
112
New contributor
New contributor
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1 Answer
1
active
oldest
votes
up vote
1
down vote
First, check the version of pillow you have by using:
import PIL
print(PIL.PILLOW_VERSION)
and make sure you have the newest version, the one I am using right now is 5.3.0
If you have like 4.0.0
, install a new version by using:
!pip install Pillow==5.3.0
in the Colab environment.
Second, restart your Google colab environment, and check the version again, it should be updated.
I had the same problem, and I spent some time trying to solve it.
Note: Make sure you are using PyTorch 0.4.
I hope this will solve your problem.
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
First, check the version of pillow you have by using:
import PIL
print(PIL.PILLOW_VERSION)
and make sure you have the newest version, the one I am using right now is 5.3.0
If you have like 4.0.0
, install a new version by using:
!pip install Pillow==5.3.0
in the Colab environment.
Second, restart your Google colab environment, and check the version again, it should be updated.
I had the same problem, and I spent some time trying to solve it.
Note: Make sure you are using PyTorch 0.4.
I hope this will solve your problem.
add a comment |
up vote
1
down vote
First, check the version of pillow you have by using:
import PIL
print(PIL.PILLOW_VERSION)
and make sure you have the newest version, the one I am using right now is 5.3.0
If you have like 4.0.0
, install a new version by using:
!pip install Pillow==5.3.0
in the Colab environment.
Second, restart your Google colab environment, and check the version again, it should be updated.
I had the same problem, and I spent some time trying to solve it.
Note: Make sure you are using PyTorch 0.4.
I hope this will solve your problem.
add a comment |
up vote
1
down vote
up vote
1
down vote
First, check the version of pillow you have by using:
import PIL
print(PIL.PILLOW_VERSION)
and make sure you have the newest version, the one I am using right now is 5.3.0
If you have like 4.0.0
, install a new version by using:
!pip install Pillow==5.3.0
in the Colab environment.
Second, restart your Google colab environment, and check the version again, it should be updated.
I had the same problem, and I spent some time trying to solve it.
Note: Make sure you are using PyTorch 0.4.
I hope this will solve your problem.
First, check the version of pillow you have by using:
import PIL
print(PIL.PILLOW_VERSION)
and make sure you have the newest version, the one I am using right now is 5.3.0
If you have like 4.0.0
, install a new version by using:
!pip install Pillow==5.3.0
in the Colab environment.
Second, restart your Google colab environment, and check the version again, it should be updated.
I had the same problem, and I spent some time trying to solve it.
Note: Make sure you are using PyTorch 0.4.
I hope this will solve your problem.
edited yesterday
answered yesterday
Michael Heidelberg
306313
306313
add a comment |
add a comment |
Aditya Kumar is a new contributor. Be nice, and check out our Code of Conduct.
Aditya Kumar is a new contributor. Be nice, and check out our Code of Conduct.
Aditya Kumar is a new contributor. Be nice, and check out our Code of Conduct.
Aditya Kumar is a new contributor. Be nice, and check out our Code of Conduct.
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