I have a convolutional neural network. And I wanted to train it on images from the training set but first they should be wrapped with my function change(tensor, float) that takes in a tensor/image of the form [hight,width,3] and a float. Batch size =4
loading data
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2)
Cnn architecture
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
#size of inputs [4,3,32,32]
#size of labels [4]
inputs = change(inputs,0.1) <----------------------------
# zero the parameter gradients
optimizer.zero_grad()
# forward backward optimize
outputs = net(inputs) #[4, 10]
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss = loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print(f'[{epoch 1}, {i 1:5d}] loss: {running_loss / 2000:.3f}')
running_loss = 0.0
print('Finished Training')
I am trying to apply the image function change but it gives an object error. it there a quick way to fix it?
I am using a Julia function but it works completely fine with other objects. Error message:
JULIA: MethodError: no method matching copy(::PyObject)
Closest candidates are:
copy(!Matched::T) where T<:SHA.SHA3_CTX at /opt/julia-1.7.2/share/julia/stdlib/v1.7/SHA/src/types.jl:213
copy(!Matched::T) where T<:SHA.SHA2_CTX at /opt/julia-1.7.2/share/julia/stdlib/v1.7/SHA/src/types.jl:212
copy(!Matched::Number) at /opt/julia-1.7.2/share/julia/base/number.jl:113
CodePudding user response:
I would recommend to put change
function to transforms
list, so you do data changes on transformation stage.
partial
from functools
will help you to fix number of arguments, like this:
from functools import partial
def change(input, float):
pass
# Use partial to fix number of params, such that change accepts only input
change_partial = partial(change, float=pass_float_value_here)
# Add change_partial to a list of transforms before or after converting to tensors
transforms = Compose([
RandomResizedCrop(img_size), # example
# Add change_partial here if it operates on PIL Image
change_partial,
ToTensor(), # convert to tensor
# Add change_partial here if it operates on torch tensors
change_partial,
])