lstm.py

# -*- coding: utf-8 -*-
"""lstm

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/19aiQICbRQ30rL60eOVsID47o_yUPSurH
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset

# Define a model for sequence-to-sequence mapping
class FrameGenerationModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(FrameGenerationModel, self).__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        output = self.fc(lstm_out)
        return output

# Assuming you have your frame shift sequences and corresponding image sequences loaded as tensors
frame_shifts, images = ...

# Define the model
input_size = 2  # Assuming one-dimensional frame shifts
hidden_size = 64  # Adjust based on your needs
output_size = 3 * 512 * 512  # Assuming images are RGB with dimensions 64x64
model = FrameGenerationModel(input_size, hidden_size, output_size)

# Define the loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Create DataLoader
dataset = TensorDataset(frame_shifts, images)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# Training loop
num_epochs = 10
for epoch in range(num_epochs):
    model.train()
    for frame_shifts_batch, images_batch in dataloader:
        optimizer.zero_grad()

        # Expand dimensions to match the expected input shape for the LSTM
        frame_shifts_batch = frame_shifts_batch.unsqueeze(-1)

        # Forward pass
        outputs = model(frame_shifts_batch)

        # Reshape outputs and targets to compute the loss
        outputs = outputs.view(-1, output_size)
        images_batch = images_batch.view(-1, output_size)

        loss = criterion(outputs, images_batch)
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# After training, you can use the model to generate image sequences
model.eval()
with torch.no_grad():
    # Example: Generate images for a new sequence of frame shifts
    new_frame_shifts = torch.randn(1, sequence_length, input_size)  # Adjust based on your needs
    new_frame_shifts = new_frame_shifts.unsqueeze(-1)
    generated_images = model(new_frame_shifts)

# Make sure to post-process the generated_images tensor as needed for visualization or further use