Witllm/unsuper/minist.py

import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F  # Add this line
import torchvision
import torchvision.transforms as transforms

sys.path.append("..")
from tools import show

seed = 4321
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# device = torch.device("mps")

# Hyper-parameters
num_epochs = 1
batch_size = 128
learning_rate = 0.2

transform = transforms.Compose([transforms.ToTensor()])

train_dataset = torchvision.datasets.MNIST(root="./data", train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root="./data", train=False, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)


class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 8, 5, 1, 0)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(8, 8, 5, 1, 0)
        self.fc1 = nn.Linear(8 * 4 * 4, 10)
        # self.fc2 = nn.Linear(120, 84)
        # self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 8 * 4 * 4)
        # x = F.relu(self.fc1(x))
        # x = F.relu(self.fc2(x))
        # x = self.fc3(x)

        x = self.fc1(x)
        return x

    def printFector(self, x, label):
        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), "input_image.png", Contrast=[0, 1.0])
        # show.DumpTensorToLog(x, "input_image.log")
        x = self.conv1(x)
        w = self.conv1.weight
        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv1_weight.png", Contrast=[-1.0, 1.0])
        # show.DumpTensorToLog(w, "conv1_weight.log")

        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), "conv1_output.png", Contrast=[-1.0, 1.0])
        # show.DumpTensorToLog(x, "conv1_output.png")

        x = self.pool(F.relu(x))
        x = self.conv2(x)
        w = self.conv2.weight
        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]).cpu(), "conv2_weight.png")

        show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]).cpu(), "conv2_output.png")
        x = self.pool(F.relu(x))
        x = x.view(-1, 8 * 4 * 4)
        x = self.fc1(x)

        show.DumpTensorToImage(self.fc1.weight.view(-1, 16, 8).permute(2, 0, 1), "fc_weight.png")

        show.DumpTensorToImage(x.view(-1).cpu(), "fc_output.png")

        criterion = nn.CrossEntropyLoss()
        loss = criterion(x, label)
        optimizer.zero_grad()
        loss.backward()

        w = self.conv1.weight.grad
        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]).cpu(), "conv1_weight_grad.png")
        w = self.conv2.weight.grad
        show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), "conv2_weight_grad.png")
        show.DumpTensorToImage(self.fc1.weight.grad.view(-1, 16, 8).permute(2, 0, 1), "fc_weight_grad.png")


model = ConvNet().to(device)

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)


# Train the model
n_total_steps = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i + 1) % 100 == 0:
            print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}")

test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
for images, labels in test_loader:
    images = images.to(device)
    labels = labels.to(device)
    model.printFector(images, labels)
    break

print("Finished Training")

# Test the model
with torch.no_grad():
    n_correct = 0
    n_samples = 0
    for images, labels in test_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)

        # max returns (value ,index)
        _, predicted = torch.max(outputs.data, 1)
        n_samples += labels.size(0)
        n_correct += (predicted == labels).sum().item()

    acc = 100.0 * n_correct / n_samples
    print(f"Accuracy of the network on the 10000 test images: {acc} %")