179 lines
6.4 KiB
Python
179 lines
6.4 KiB
Python
import os
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F # Add this line
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import torchvision
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import torchvision.transforms as transforms
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sys.path.append("..")
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from tools import show
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seed = 4321
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# device = torch.device("mps")
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num_epochs = 1
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batch_size = 64
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transform = transforms.Compose([transforms.ToTensor()])
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train_dataset = torchvision.datasets.MNIST(root="./data", train=True, download=True, transform=transform)
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test_dataset = torchvision.datasets.MNIST(root="./data", train=False, download=True, transform=transform)
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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class ConvNet(nn.Module):
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def __init__(self):
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super(ConvNet, self).__init__()
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self.conv1 = nn.Conv2d(1, 8, 5, 1, 0)
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self.pool = nn.MaxPool2d(2, 2)
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self.conv2 = nn.Conv2d(8, 1, 5, 1, 0)
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self.fc1 = nn.Linear(1 * 4 * 4, 10)
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def forward(self, x):
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x = self.pool(self.conv1(x))
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x = self.pool(self.conv2(x))
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x = x.view(x.shape[0], -1)
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x = self.fc1(x)
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return x
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def forward_unsuper(self, x):
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x = self.pool(self.conv1(x))
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return x
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def forward_finetune(self, x):
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x = self.pool(self.conv1(x))
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x = self.pool(self.conv2(x))
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x = x.view(x.shape[0], -1)
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x = self.fc1(x)
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return x
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def printFector(self, x, label, dir=""):
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show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/input_image.png", Contrast=[0, 1.0])
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# show.DumpTensorToLog(x, "input_image.log")
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x = self.conv1(x)
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w = self.conv1.weight
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show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), dir + "/conv1_weight.png", Contrast=[-1.0, 1.0])
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# show.DumpTensorToLog(w, "conv1_weight.log")
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show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]), dir + "/conv1_output.png", Contrast=[-1.0, 1.0])
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# show.DumpTensorToLog(x, "conv1_output.png")
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x = self.pool(F.relu(x))
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x = self.conv2(x)
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w = self.conv2.weight
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show.DumpTensorToImage(
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w.view(-1, w.shape[2], w.shape[3]).cpu(), dir + "/conv2_weight.png", Contrast=[-1.0, 1.0]
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)
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show.DumpTensorToImage(
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x.view(-1, x.shape[2], x.shape[3]).cpu(), dir + "/conv2_output.png", Contrast=[-1.0, 1.0]
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)
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x = self.pool(F.relu(x))
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show.DumpTensorToImage(x.view(-1, x.shape[2], x.shape[3]).cpu(), dir + "/pool_output.png", Contrast=[-1.0, 1.0])
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pool_shape = x.shape
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x = x.view(x.shape[0], -1)
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x = self.fc1(x)
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show.DumpTensorToImage(
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self.fc1.weight.view(-1, pool_shape[2], pool_shape[3]), dir + "/fc_weight.png", Contrast=[-1.0, 1.0]
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)
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show.DumpTensorToImage(x.view(-1).cpu(), dir + "/fc_output.png")
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criterion = nn.CrossEntropyLoss()
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loss = criterion(x, label)
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loss.backward()
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if self.conv1.weight.requires_grad:
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w = self.conv1.weight.grad
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show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]).cpu(), dir + "/conv1_weight_grad.png")
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if self.conv2.weight.requires_grad:
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w = self.conv2.weight.grad
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show.DumpTensorToImage(w.view(-1, w.shape[2], w.shape[3]), dir + "/conv2_weight_grad.png")
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if self.fc1.weight.requires_grad:
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show.DumpTensorToImage(
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self.fc1.weight.grad.view(-1, pool_shape[2], pool_shape[3]), dir + "/fc_weight_grad.png"
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)
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model = ConvNet().to(device)
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model.train()
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# Train the model unsuper
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epochs = 10
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model.conv1.weight.requires_grad = True
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model.conv2.weight.requires_grad = False
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model.fc1.weight.requires_grad = False
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optimizer_unsuper = torch.optim.SGD(model.parameters(), lr=0.1)
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n_total_steps = len(train_loader)
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for epoch in range(epochs):
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for i, (images, labels) in enumerate(train_loader):
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images = images.to(device)
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outputs = model.forward_unsuper(images)
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sample = outputs.view(outputs.shape[0], -1)
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sample_mean = torch.mean(sample, dim=1, keepdim=True)
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diff_mean = torch.mean(torch.abs(sample - sample_mean), dim=1, keepdim=True)
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diff_ratio = (sample - sample_mean) / diff_mean
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diff_ratio_mean = torch.mean(diff_ratio * diff_ratio, dim=1)
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label = diff_ratio_mean * 0.5
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loss = F.l1_loss(diff_ratio_mean, label)
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optimizer_unsuper.zero_grad()
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loss.backward()
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optimizer_unsuper.step()
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if (i + 1) % 100 == 0:
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print(f"Epoch [{epoch+1}/{epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.8f}")
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# Train the model
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model.conv1.weight.requires_grad = False
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model.conv2.weight.requires_grad = True
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model.fc1.weight.requires_grad = True
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=0.2)
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n_total_steps = len(train_loader)
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for epoch in range(num_epochs):
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for i, (images, labels) in enumerate(train_loader):
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images = images.to(device)
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labels = labels.to(device)
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outputs = model.forward_finetune(images)
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loss = criterion(outputs, labels)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if (i + 1) % 100 == 0:
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print(f"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}")
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print("Finished Training")
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test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False)
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test_loader = iter(test_loader)
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images, labels = next(test_loader)
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images = images.to(device)
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labels = labels.to(device)
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model.printFector(images, labels, "dump1")
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images, labels = next(test_loader)
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images = images.to(device)
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labels = labels.to(device)
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model.printFector(images, labels, "dump2")
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# Test the model
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with torch.no_grad():
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n_correct = 0
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n_samples = 0
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for images, labels in test_loader:
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images = images.to(device)
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labels = labels.to(device)
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outputs = model(images)
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# max returns (value ,index)
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_, predicted = torch.max(outputs.data, 1)
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n_samples += labels.size(0)
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n_correct += (predicted == labels).sum().item()
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acc = 100.0 * n_correct / n_samples
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print(f"Accuracy of the network on the 10000 test images: {acc} %")
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