126 lines
3.7 KiB
Python
126 lines
3.7 KiB
Python
import time
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import numpy as np
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import torch
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from torch.nn.functional import *
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from torch.utils.data import Dataset, DataLoader
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from torch import nn
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from torchvision import datasets, transforms
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from tqdm import tqdm
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from utils import *
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import ipdb
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class Model_1_3:
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def __init__(self, num_classes):
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self.flatten = My_Flatten()
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self.linear = My_Linear(in_features=28 * 28, out_features=num_classes)
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self.params = self.linear.params
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def __call__(self, x: torch.Tensor):
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return self.forward(x)
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def forward(self, x: torch.Tensor):
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x = self.flatten(x)
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x = self.linear(x)
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return x
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def to(self, device: str):
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for param in self.params:
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param.data = param.data.to(device=device)
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return self
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def parameters(self):
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return self.params
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def train(self):
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for param in self.params:
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param.requires_grad = True
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def eval(self):
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for param in self.params:
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param.requires_grad = False
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if __name__ == "__main__":
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learning_rate = 1e-1
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num_epochs = 10
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batch_size = 512
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num_classes = 10
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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transform = transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize((0.5,), (0.5,)),
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]
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)
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train_mnist_dataset = datasets.MNIST(root="../dataset", train=True, transform=transform, download=True)
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test_mnist_dataset = datasets.MNIST(root="../dataset", train=False, transform=transform, download=True)
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train_loader = DataLoader(
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dataset=train_mnist_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=14,
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pin_memory=True,
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)
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test_loader = DataLoader(
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dataset=test_mnist_dataset,
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batch_size=batch_size,
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shuffle=True,
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num_workers=14,
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pin_memory=True,
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)
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model = Model_1_3(num_classes).to(device)
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criterion = My_CrossEntropyLoss()
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optimizer = My_optimizer(model.parameters(), lr=learning_rate)
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for epoch in range(num_epochs):
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model.train()
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total_epoch_loss = 0
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start_time = time.time()
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for index, (images, targets) in tqdm(
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enumerate(train_loader), total=len(train_loader)
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):
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optimizer.zero_grad()
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images = images.to(device)
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targets = targets.to(device)
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one_hot_targets = my_one_hot(targets, num_classes=num_classes).to(dtype=torch.float)
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outputs = model(images)
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loss = criterion(outputs, one_hot_targets)
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total_epoch_loss += loss.item()
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loss.backward()
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optimizer.step()
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end_time = time.time()
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train_time = end_time - start_time
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model.eval()
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with torch.no_grad():
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total_epoch_acc = 0
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start_time = time.time()
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for index, (image, targets) in tqdm(
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enumerate(test_loader), total=len(test_loader)
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):
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image = image.to(device)
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targets = targets.to(device)
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outputs = model(image)
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pred = my_softmax(outputs, dim=1)
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total_epoch_acc += (pred.argmax(1) == targets).sum().item()
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end_time = time.time()
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test_time = end_time - start_time
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avg_epoch_acc = total_epoch_acc / len(test_mnist_dataset)
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print(
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f"Epoch [{epoch + 1}/{num_epochs}],",
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f"Train Loss: {total_epoch_loss:.10f},",
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f"Used Time: {train_time * 1000:.3f}ms,",
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f"Test Acc: {avg_epoch_acc * 100:.3f}%,",
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f"Used Time: {test_time * 1000:.3f}ms",
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)
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