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some examples are edited

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yunjey
2017-03-18 15:53:08 +09:00
parent a7f2f7fd53
commit a29ce5472e
1 changed files with 14 additions and 7 deletions
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tutorials/00 - PyTorch Basics/main.py
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@ -79,9 +79,8 @@ print('loss after 1 step optimization: ', loss.data[0])
#======================== Loading data from numpy ========================#
a = np.array([[1,2], [3,4]])
b = torch.from_numpy(a)
print (b)
b = torch.from_numpy(a) # convert numpy array to torch tensor
c = b.numpy() # convert torch tensor to numpy array
#===================== Implementing the input pipline =====================#
@ -113,6 +112,7 @@ for images, labels in train_loader:
# Your training code will be written here
pass
#===================== Input pipline for custom dataset =====================#
# You should build custom dataset as below.
class CustomDataset(data.Dataset):
@ -123,14 +123,16 @@ class CustomDataset(data.Dataset):
def __getitem__(self, index):
# TODO
# 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
# 2. Return a data pair (e.g. image and label).
# 2. Preprocess the data (e.g. torchvision.Transform).
# 3. Return a data pair (e.g. image and label).
pass
def __len__(self):
# You should change 0 to the total size of your dataset.
return 0
# Then, you can just use prebuilt torch's data loader.
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
custom_dataset = CustomDataset()
train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
batch_size=100,
shuffle=True,
num_workers=2)
@ -153,6 +155,11 @@ outputs = resnet(images)
print (outputs.size()) # (10, 100)
#============================ Save and load model ============================#
#============================ Save and load the model ============================#
# Save and load the entire model.
torch.save(resnet, 'model.pkl')
model = torch.load('model.pkl')
# Save and load only the model parameters(recommended).
torch.save(resnet.state_dict(), 'params.pkl')
resnet.load_state_dict(torch.load('params.pkl'))