質問編集履歴

修正・追加依頼に基づく改変

2022/01/03 20:18

投稿

k_0fall

スコア7

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@@ -14,338 +14,266 @@
 Traceback (most recent call last):
-  File "/Users/○○○○○/Desktop/●●_data/cnn.py", line 150, in <module>
+  File "/Users/○○○/Desktop/△△△_data/cnn.py", line 125, in <module>
+    train_loss_list, test_loss_list = run(30, optimizer, criterion, device)
+  File "/Users/○○○/Desktop/△△△_data/cnn.py", line 73, in run
+    train_loss = train_epoch(model, optimizer, criterion, train_loader, device)
+  File "/Users/○○○/Desktop/△△△_data/cnn.py", line 46, in train_epoch
+    outputs = model(images)
+  File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
+    return forward_call(*input, **kwargs)
+  File "/Users/○○○/Desktop/△△△_data/cnn.py", line 33, in forward
+    x = x.view(-1, 16*5*5)
+RuntimeError: shape '[-1, 400]' is invalid for input of size 2080832
+```
+### 該当のソースコード
+```python3
+from pathlib import Path
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+from PIL import Image
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision.transforms as transforms
+import matplotlib.pyplot as plt
+from torchvision.datasets import ImageFolder
+class Net(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)  # 畳み込み層:(入力チャンネル数, フィルタ数、フィルタサイズ)
+        # 出力画像サイズ28
+        self.pool = nn.MaxPool2d(2, 2)  # プーリング層:（領域のサイズ, ストライド）
+        # 出力画像サイズ14
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        # 出力画像サイズ5
+        self.fc1 = nn.Linear(16*5*5, 256)  # 全結合層
+        self.dropout = nn.Dropout(p=0.5)  # ドロップアウト:(p=ドロップアウト率)
+        self.fc2 = nn.Linear(256, 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, 16*5*5)
+        x = F.relu(self.fc1(x))
+        x = self.dropout(x)
+        x = self.fc2(x)
+        return x
+def train_epoch(model, optimizer, criterion, dataloader, device):
+    train_loss = 0
+    model.train()
+    for i, (images, labels) in enumerate(dataloader):
+        images, labels = images.to(device), labels.to(device)
+        optimizer.zero_grad()
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        loss.backward()
+        optimizer.step()
+        train_loss += loss.item()
+    train_loss = train_loss / len(train_loader.dataset)
+    return train_loss
+def inference(model, optimizer, criterion, dataloader, device):
+    model.eval()
+    test_loss=0
+    with torch.no_grad():
+        for i, (images, labels) in enumerate(test_loader):
+            images, labels = images.to(device), labels.to(device)
+            outputs = model(images)
+            loss = criterion(outputs, labels)
+            test_loss += loss.item()
+        test_loss = test_loss / len(test_loader.dataset)
+    return test_loss
+def run(num_epochs, optimizer, criterion, device):
+    train_loss_list = []
+    test_loss_list = []
+    for epoch in range(num_epochs):
+        train_loss = train_epoch(model, optimizer, criterion, train_loader, device)
+        test_loss = inference(model, optimizer, criterion, test_loader, device)
+        print(f'Epoch [{epoch+1}], train_Loss : {train_loss:.4f}, test_Loss : {test_loss:.4f}')
+        train_loss_list.append(train_loss)
+        test_loss_list.append(test_loss)
+    return train_loss_list, test_loss_list
+if __name__ == '__main__':
+    # Transform を作成する。
+    transform = transforms.Compose([transforms.Resize(256), transforms.ToTensor()])
+    # Dataset を作成する。
+    dataset = ImageFolder('/Users/○○○/Desktop/△△△_data/□□□_dataset', transform)
+    # DataLoader を作成する。
+    dataloader = DataLoader(dataset, batch_size=3)
+    #for batch in dataloader:
+    #   print(batch.shape)
+    # グラフのスタイルを指定
+    plt.style.use('seaborn-darkgrid')
+    # 正規化
+    normalize = transforms.Normalize(mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0))
+    #  Tensor化
+    to_tensor = transforms.ToTensor()
+    train_ratio = 0.8
+    train_size = int(train_ratio * len(dataset))
+    val_size  = len(dataset) - train_size
+    data_size  = {"train":train_size, "val":val_size}
+    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
+    transform_train = transforms.Compose([to_tensor, normalize])
+    transform_test = transforms.Compose([to_tensor, normalize])
+    batch_size = 64
     train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-  File "/usr/local/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 268, in __init__
-    sampler = RandomSampler(dataset, generator=generator)
-  File "/usr/local/lib/python3.9/site-packages/torch/utils/data/sampler.py", line 102, in __init__
-    raise ValueError("num_samples should be a positive integer "
-ValueError: num_samples should be a positive integer value, but got num_samples=0
-```
-### 該当のソースコード
-```python3
-from pathlib import Path
-from torch.utils.data import DataLoader, Dataset
-from torchvision import transforms
-from PIL import Image
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-import torchvision
-import torchvision.transforms as transforms
-import numpy as np
-import matplotlib.pyplot as plt
-class ImageFolder(Dataset):
-    IMG_EXTENSIONS = [".jpg", ".jpeg", ".png", ".bmp"]
-    def __init__(self, img_dir, transform=None):
-        # 画像ファイルのパス一覧を取得する。
-        self.img_paths = self._get_img_paths(img_dir)
-        self.transform = transform
-    def __getitem__(self, index):
-        path = self.img_paths[index]
-        # 画像を読み込む。
-        img = Image.open(path)
-        if self.transform is not None:
-            # 前処理がある場合は行う。
-            img = self.transform(img)
-        return img
-    def _get_img_paths(self, img_dir):
-        """指定したディレクトリ内の画像ファイルのパス一覧を取得する。
-        """
-        img_dir = Path(img_dir)
-        img_paths = [
-            p for p in img_dir.iterdir() if p.suffix in ImageFolder.IMG_EXTENSIONS
-        ]
-        return img_paths
-    def __len__(self):
-        """ディレクトリ内の画像ファイルの数を返す。
-        """
-        return len(self.img_paths)
-class Net(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)  # 畳み込み層:(入力チャンネル数, フィルタ数、フィルタサイズ)
-        # 出力画像サイズ28
-        self.pool = nn.MaxPool2d(2, 2)  # プーリング層:（領域のサイズ, ストライド）
-        # 出力画像サイズ14
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        # 出力画像サイズ5
-        self.fc1 = nn.Linear(16*5*5, 256)  # 全結合層
-        self.dropout = nn.Dropout(p=0.5)  # ドロップアウト:(p=ドロップアウト率)
-        self.fc2 = nn.Linear(256, 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, 16*5*5)
-        x = F.relu(self.fc1(x))
-        x = self.dropout(x)
-        x = self.fc2(x)
-        return x
-def train_epoch(model, optimizer, criterion, dataloader, device):
-    train_loss = 0
-    model.train()
-    for i, (images, labels) in enumerate(dataloader):
-        images, labels = images.to(device), labels.to(device)
-        optimizer.zero_grad()
-        outputs = model(images)
-        loss = criterion(outputs, labels)
-        loss.backward()
-        optimizer.step()
-        train_loss += loss.item()
-    train_loss = train_loss / len(train_loader.dataset)
-    return train_loss
-def inference(model, optimizer, criterion, dataloader, device):
-    model.eval()
-    test_loss=0
-    with torch.no_grad():
-        for i, (images, labels) in enumerate(test_loader):
-            images, labels = images.to(device), labels.to(device)
-            outputs = model(images)
-            loss = criterion(outputs, labels)
-            test_loss += loss.item()
-        test_loss = test_loss / len(test_loader.dataset)
-    return test_loss
-def run(num_epochs, optimizer, criterion, device):
-    train_loss_list = []
-    test_loss_list = []
-    for epoch in range(num_epochs):
-        train_loss = train_epoch(model, optimizer, criterion, train_loader, device)
-        test_loss = inference(model, optimizer, criterion, test_loader, device)
-        print(f'Epoch [{epoch+1}], train_Loss : {train_loss:.4f}, test_Loss : {test_loss:.4f}')
-        train_loss_list.append(train_loss)
-        test_loss_list.append(test_loss)
-    return train_loss_list, test_loss_list
-if __name__ == '__main__':
-    # Transform を作成する。
-    transform = transforms.Compose([transforms.Resize(256), transforms.ToTensor()])
-    # Dataset を作成する。
-    dataset = ImageFolder('/Users/○○○○○/Desktop/●●_data/△△△△△_dataset', transform)
-    # DataLoader を作成する。
-    dataloader = DataLoader(dataset, batch_size=3)
-    for batch in dataloader:
-        print(batch.shape)
-    # グラフのスタイルを指定
-    plt.style.use('seaborn-darkgrid')
-    # 正規化
-    normalize = transforms.Normalize(mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0))
-    #  Tensor化
-    to_tensor = transforms.ToTensor()
-    train_ratio = 0.8
-    train_size = int(train_ratio * len(dataset))
-    # int()で整数に。
-    val_size  = len(dataset) - train_size
-    data_size  = {"train":train_size, "val":val_size}
-    train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])
-    transform_train = transforms.Compose([to_tensor, normalize])
-    transform_test = transforms.Compose([to_tensor, normalize])
-    batch_size = 64
-    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)
     dataloaders  = {"train":train_loader, "val":test_loader}
     model = Net()
@@ -412,6 +340,46 @@
-```
+```
+### 該当のデータセット
+```
+./□□□_dataset
+├── 0
+│   ├── 0_0.png
+│   ├── 0_1.png
+│   ├── ・・・
+│   ├── 0_10.png
+├── 1
+│   ├── 1_0.png
+│   ├── 1_1.png
+│   ├── ・・・
+│   ├── 1_10.png
+├── 2
+│   ├── 2_0.png
+│   ├── 2_1.png
+│   ├── ・・・
+│   ├── 2_10.png
+```