回答編集履歴
3
修正
answer
CHANGED
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@@ -53,7 +53,7 @@
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53
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self.transform = transform
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self.data = []
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self.labels = []
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-
name_to_label = {"
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name_to_label = {"cat": 0, "dog": 1}
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target_path_list = []
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target_dir = os.path.join(root_dir, key, "**/*")
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2
修正
answer
CHANGED
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@@ -35,4 +35,54 @@
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```
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参考リンク ↓ ImageFolder を使った学習の例
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-
[Pytorch - 事前学習モデルを使ってクラス分類モデルを学習する方法 - pystyle](https://pystyle.info/pytorch-train-classification-problem-using-a-pretrained-model/)
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[Pytorch - 事前学習モデルを使ってクラス分類モデルを学習する方法 - pystyle](https://pystyle.info/pytorch-train-classification-problem-using-a-pretrained-model/)
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## 追記
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```python
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import glob
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import os
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from PIL import Image
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from torch.utils import data as data
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from torchvision import transforms as transforms
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class MyDatasets(data.Dataset):
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def __init__(self, root_dir, key, transform):
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self.transform = transform
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self.data = []
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self.labels = []
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name_to_label = {"dog": 0, "cat": 1}
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target_path_list = []
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target_dir = os.path.join(root_dir, key, "**/*")
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for path in glob.glob(target_dir):
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name = os.path.basename(os.path.dirname(path))
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label = name_to_label[name]
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self.data.append(path)
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self.labels.append(label)
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def __len__(self):
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return len(self.data)
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def __getitem__(self, index):
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img_path = self.data[index]
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label = self.labels[index]
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img = Image.open(img_path).convert("RGB")
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img = self.transform(img)
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return img, label
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transform = transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor()])
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train_dataset = MyDatasets("/data/mydataset", "train", transform)
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train_dataloader = data.DataLoader(train_dataset, batch_size=32)
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for data, labels in train_dataloader:
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print(data.shape, labels.shape)
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```
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1
修正
answer
CHANGED
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@@ -1,4 +1,18 @@
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1
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## 質問のコードでエラーが起こっている原因
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`train_dataset = MyDatasets()` で引数を指定していないので、`self.path` が None になっています。
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それを以下で文字列と結合しようとして、None と str の結合はできないとエラーになっています。
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```python
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target_path = os.path.join(self.path + self.key + '/**/*.jpg')
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```
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## 提案
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14
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```
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2
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animal_dataset
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3
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├─train
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│ ├─cat（70枚くらい）
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