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app.py

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+import gradio as gr
+import requests
+import torch
+import torch.nn as nn
+from PIL import Image
+from torchvision.models import resnet18
+from torchvision.transforms import functional as F
+
+def main():
+    # モデルの準備
+    model_ft = resnet18(num_classes = 11,pretrained=False)
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model_ft = model_ft.to(device)
+    if torch.cuda.is_available():
+        model_ft.load_state_dict(torch.load('cucumber_resnet18_last_model.pth'))
+    else:
+        model_ft.load_state_dict(
+            torch.load('cucumber_resnet18_last_model.pth', map_location=torch.device("cpu"))
+        )
+    model_ft.eval()
+
+    # 学種済みモデルのラベルの取得
+    # response = requests.get("https://git.io/JJkYN")
+    # labels = response.text.split("\n")
+    labels = [
+        "健全",
+        "うどんこ病",
+        "灰色かび病",
+        "炭疽病",
+        "べと病",
+        "褐斑病",
+        "つる枯病",
+        "斑点細菌病",
+        "CCYV",
+        "モザイク病",
+        "MYSV",
+    ]
+
+    # 画像分類を行う関数を定義
+    @torch.no_grad()
+    def inference(gr_input):
+        img = Image.fromarray(gr_input.astype("uint8"), "RGB")
+
+        # 前処理
+        img = F.resize(img, (224, 224))
+        img = F.to_tensor(img)
+        img = img.unsqueeze(0)
+        # img = F.normalize(
+        #     img,
+        #     mean=[0.485, 0.456, 0.406],
+        #     std=[0.229, 0.224, 0.225],
+        # )
+
+        # 推論
+        output = model_ft(img).squeeze(0)
+        probs = nn.functional.softmax(output, dim=0).numpy()
+
+        # ラベルごとの確率をdictとして返す
+        return {labels[i]: float(probs[i]) for i in range(11)}
+
+    # 入力の形式を画像とする
+    inputs = gr.inputs.Image()
+    # 出力はラベル形式で，top5まで表示する
+    outputs = gr.outputs.Label(num_top_classes=5)
+
+    # サーバーの立ち上げ
+    interface = gr.Interface(fn=inference, inputs=inputs, outputs=outputs)
+    interface.launch()
+
+
+if __name__ == "__main__":
+    main()

cucumber_resnet18_last_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:46ea5b3332a072a019bc4f78fb86e172ad39f3e38b463cb194170501f903eba1
+size 44806605