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mit-han-lab / torchquantum

A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
https://torchquantum.org
MIT License
1.35k stars 206 forks source link

size error #273

Open badreddinemerabet opened 6 months ago

badreddinemerabet commented 6 months ago

when i tried to run the example from the youtube video "https://www.youtube.com/watch?v=-Grfxkg3-DI" I got this error when I try to run the training cell Epoch 1:

RuntimeError Traceback (most recent call last) in <cell line: 46>() 47 # train 48 print(f"Epoch {epoch}:") ---> 49 train(dataflow, model, device, optimizer) 50 print(optimizer.param_groups[0]['lr']) 51

13 frames /content/torchquantum/torchquantum/functional/gate_wrapper.py in apply_unitary_bmm(state, mat, wires) 129 if len(mat.shape) > 2: 130 # both matrix and state are in batch mode --> 131 new_state = mat.bmm(permuted) 132 else: 133 # matrix no batch, state in batch mode

RuntimeError: Expected size for first two dimensions of batch2 tensor to be: [10, 2] but got: [1, 2].

i didn't change anything

01110011011101010110010001101111 commented 6 months ago

Try https://github.com/mit-han-lab/torchquantum/tree/main/examples/quanvolution (with #147 to increase accuracy); I’ll ask about updating the link!

badreddinemerabet commented 6 months ago

thank you, it worked

badreddinemerabet commented 6 months ago

Define transformations for the dataset

transform = transforms.Compose([ transforms.Grayscale(num_output_channels=1), transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ])

Load your dataset

data_dir = 'OAM28' # Replace with the path to your dataset full_dataset = datasets.ImageFolder(root=data_dir, transform=transform)

Split dataset into train and test

train_size = int(0.9 * len(full_dataset)) test_size = len(full_dataset) - train_size train_dataset, test_dataset = random_split(full_dataset, [train_size, test_size])

Create data loaders

batch_size = 16 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=8, pin_memory=True) test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=8, pin_memory=True)

dataflow = {'train': train_loader, 'test': test_loader}

01110011011101010110010001101111 commented 5 days ago

Hello, do you have a follow up? Happy to help but am a bit unsure what you are asking!