study/torch-tc.py at master · kjh1997/study · GitHub

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import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.core.lightning import LightningModule
from torchmetrics import functional as FM
import matplotlib.pyplot as plt

training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)
train_dataset, val_dataset = random_split(training_data, [55000, 5000])

learning_rate = 1e-3
batch_size = 64
epochs = 10

train_dataloader = DataLoader(train_dataset, batch_size=batch_size)
val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

class LitModel(LightningModule):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Flatten(),
            nn.Linear(28*28, 512),
            torch.nn.BatchNorm1d(512),
            nn.ReLU(inplace=True),
            nn.Linear(512, 256),
            torch.nn.BatchNorm1d(256),
            nn.ReLU(inplace=True),
            nn.Linear(256, 64),
            torch.nn.BatchNorm1d(64),
            nn.ReLU(inplace=True),
            nn.Linear(64, 10)
        )
    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        return loss

    def validation_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        acc = FM.accuracy(logits, y)
        loss = F.cross_entropy(logits, y)
        metrics = {'val_acc': acc, 'val_loss': loss}
        self.log_dict(metrics)

    def test_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        acc = FM.accuracy(logits, y)
        loss = F.cross_entropy(logits, y)
        metrics = {'test_acc': acc, 'test_loss': loss}
        self.log_dict(metrics)

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=learning_rate)

model = LitModel()
trainer = Trainer(max_epochs=epochs, gpus=0)
trainer.fit(model, train_dataloader, val_dataloader)

trainer.test(test_dataloaders=test_dataloader)

label_tags = {
    0: 'T-Shirt',
    1: 'Trouser',
    2: 'Pullover',
    3: 'Dress',
    4: 'Coat',
    5: 'Sandal',
    6: 'Shirt',
    7: 'Sneaker',
    8: 'Bag',
    9: 'Ankle Boot'
}
columns = 6
rows = 6
fig = plt.figure(figsize=(10,10))

model.eval()
for i in range(1, columns*rows+1):
    data_idx = torch.randint(len(test_dataloader),(1,)).item()
    input_img = test_data[data_idx][0].unsqueeze(dim=0)

    output = model(input_img)
    _, argmax = torch.max(output, 1)
    pred = label_tags[argmax.item()]
    label = label_tags[test_data[data_idx][1]]

    fig.add_subplot(rows, columns, i)
    if pred == label:
        plt.title(pred)
        cmap = 'Blues'
    else:
        plt.title(pred + '=>' +  label)
        cmap = 'Reds'
    plot_img = test_data[data_idx][0][0,:,:]
    plt.imshow(plot_img, cmap=cmap)
    plt.axis('off')

plt.show()