删除cnn_hands_on_transfer.ipynb

%% Cell type:code id:a139b79a-05ea-4e35-aa93-5061e7285d40 tags:

``` python

# NOTE:

# You may choose to use ChatGPT (or any AI-based tool) to assist with your assignment,

# but you must ensure that you fully understand the entire code.

# You are solely responsible for the work you submit.

# Please keep in mind: ChatGPT will not be available during the exam.

```

%% Cell type:code id:551d437a tags:

``` python

import pytorch_lightning as pl

import torch

import torch.nn as nn

import torchvision as torchvision

import torchvision.models as models

from pytorch_lightning.callbacks import EarlyStopping

from sklearn.metrics import accuracy_score

from torch.utils.data import DataLoader, random_split

```

%% Cell type:code id:9ae7f21b tags:

``` python

class TransferCNN(pl.LightningModule):

    def __init__(self):

        super(TransferCNN, self).__init__()

        self.model_ft = models.resnet18(pretrained=True)

        for param in self.model_ft.parameters():

            param.requires_grad = False

        num_ftrs = self.model_ft.fc.in_features

        self.model_ft.fc = nn.Linear(num_ftrs, 5)

        self.cross_entropy_loss = nn.CrossEntropyLoss()

    def forward(self, x):

        x = self.model_ft(x)

        return x

    def configure_optimizers(self):

        optimizer = torch.optim.Adam(self.parameters(), lr=0.01)

        return optimizer

    def training_step(self, batch, batch_idx):

        x, y = batch

        predictions = self.forward(x)

        loss = self.cross_entropy_loss(predictions, y)

        self.log("train_loss", loss)

        return loss

    def validation_step(self, batch, batch_idx):

        x, y = batch

        y_hat = self.forward(x)

        loss = self.cross_entropy_loss(y_hat, y)

        self.log("val_loss", loss)

    def test_step(self, batch, batch_idx):

        x, y = batch

        logits = self.forward(x)

        loss = self.cross_entropy_loss(logits, y)

        a, y_hat = torch.max(logits, dim=1)

        test_acc = accuracy_score(y_hat.cpu(), y.cpu())

        metrics = {"test_loss": loss, "test_acc": torch.tensor(test_acc)}

        self.log_dict(metrics)

        return metrics

    def test_epoch_end(self, outputs):

        avg_loss = torch.stack([x["test_loss"] for x in outputs]).mean()

        avg_test_acc = torch.stack([x["test_acc"] for x in outputs]).mean()

        logs = {"test_loss": avg_loss, "test_acc": avg_test_acc}

        results = {

            "avg_test_loss": avg_loss,

            "avg_test_acc": avg_test_acc,

            "log": logs,

            "progress_bar": logs,

        }

        self.log_dict(results)

        return results

```

%% Cell type:code id:a024efa3 tags:

``` python

if __name__ == "__main__":

    # Main function of script

    batch_size = 16

    train_set = torchvision.datasets.ImageFolder(root="/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/train_crop_images",

                                                 transform=torchvision.transforms.Compose(

                                                     [torchvision.transforms.ToTensor(),

                                                      torchvision.transforms.RandomResizedCrop(224),

                                                      torchvision.transforms.Normalize((0.5, 0.5, 0.5),

                                                                                       (0.5, 0.5, 0.5))]))

    test_set = torchvision.datasets.ImageFolder(root="/home/jovyan/data/IntroML/Chapter8_data/Assignment_4/test_crop_images",

                                                transform=torchvision.transforms.Compose(

                                                    [torchvision.transforms.ToTensor(),

                                                     torchvision.transforms.RandomResizedCrop(224),

                                                     torchvision.transforms.Normalize((0.5, 0.5, 0.5),

                                                                                      (0.5, 0.5, 0.5))]))

    train_set, val_set = random_split(train_set, [770, 84])

    train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size,

                                               shuffle=True, num_workers=12)

    val_loader = torch.utils.data.DataLoader(val_set, batch_size=batch_size,

                                             shuffle=False, num_workers=12)

    test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size,

                                              shuffle=False, num_workers=12)

    model = TransferCNN()

    trainer = pl.Trainer(max_epochs=100, gpus=0, default_root_dir="./hands_on_transfer",

                         callbacks=EarlyStopping(monitor="val_loss", min_delta=0.00, patience=15, verbose=True))

    trainer.fit(model, train_dataloader=train_loader, val_dataloaders=val_loader)

    trainer.test(model=model, test_dataloaders=test_loader)

```

%% Output

    Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/jovyan/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth

    GPU available: False, used: False

    TPU available: False, using: 0 TPU cores

    IPU available: False, using: 0 IPUs

    /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:730: LightningDeprecationWarning: `trainer.fit(train_dataloader)` is deprecated in v1.4 and will be removed in v1.6. Use `trainer.fit(train_dataloaders)` instead. HINT: added 's'

      rank_zero_deprecation(

      | Name               | Type             | Params

    --------------------------------------------------------

    0 | model_ft           | ResNet           | 11.2 M

    1 | cross_entropy_loss | CrossEntropyLoss | 0

    --------------------------------------------------------

    2.6 K     Trainable params

    11.2 M    Non-trainable params

    11.2 M    Total params

    44.716    Total estimated model params size (MB)

    /opt/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:393: UserWarning: The number of training samples (49) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.

      rank_zero_warn(

%% Cell type:code id:64c5b677-9db8-4d23-97bd-e2c4ec177421 tags:

``` python

```