add app.py

app.py

@@ -0,0 +1,170 @@
+
+import torch
+import matplotlib.pyplot as plt
+
+from monai.networks.nets import SegResNet
+from monai.inferers import sliding_window_inference
+
+from monai.transforms import (
+    Activations,
+    AsDiscrete,
+    Compose,
+)
+
+model = SegResNet(
+    blocks_down=[1, 2, 2, 4],
+    blocks_up=[1, 1, 1],
+    init_filters=16,
+    in_channels=4,
+    out_channels=3,
+    dropout_prob=0.2,
+)
+
+model.load_state_dict(
+    torch.load("model.pt", map_location=torch.device('cpu'))
+)
+
+# define inference method
+VAL_AMP = True
+
+def inference(input):
+
+    def _compute(input):
+        return sliding_window_inference(
+            inputs=input,
+            roi_size=(240, 240, 160),
+            sw_batch_size=1,
+            predictor=model,
+            overlap=0.5,
+        )
+
+    if VAL_AMP:
+        with torch.cuda.amp.autocast():
+            return _compute(input)
+    else:
+        return _compute(input)
+
+
+post_trans = Compose(
+    [Activations(sigmoid=True), AsDiscrete(threshold=0.5)]
+)
+
+import gradio as gr
+
+def load_sample1():
+    return load_sample(1)
+
+def load_sample2():
+    return load_sample(2)
+
+def load_sample3():
+    return load_sample(3)
+
+def load_sample4():
+    return load_sample(4)
+
+import torchvision
+
+def load_sample(index):
+    #sample_index = index
+
+    sample = torch.load(f"val{index-1}.pt")
+    imgs = []
+    for i in range(4):
+        imgs.append(sample["image"][i, :, :, 70])
+
+    pil_images = []
+    for i in range(4):
+        pil_images.append(torchvision.transforms.functional.to_pil_image(imgs[i]))
+
+    imgs_label = []
+    for i in range(3):
+        imgs_label.append(sample["label"][i, :, :, 70])
+
+    pil_images_label = []
+    for i in range(3):
+        pil_images_label.append(torchvision.transforms.functional.to_pil_image(imgs_label[i]))
+
+    return [index, pil_images[0], pil_images[1], pil_images[2], pil_images[3],
+            pil_images_label[0], pil_images_label[1], pil_images_label[2]]
+
+
+def predict(sample_index):
+    print(sample_index)
+    sample = torch.load(f"val{sample_index-1}.pt")
+    model.eval()
+    with torch.no_grad():
+        # select one image to evaluate and visualize the model output
+        val_input = sample["image"].unsqueeze(0)
+        roi_size = (128, 128, 64)
+        sw_batch_size = 4
+        val_output = inference(val_input)
+        val_output = post_trans(val_output[0])
+
+    imgs_output = []
+    for i in range(3):
+        imgs_output.append(val_output[i, :, :, 70])
+
+    pil_images_output = []
+    for i in range(3):
+        pil_images_output.append(torchvision.transforms.functional.to_pil_image(imgs_output[i]))
+
+    return [pil_images_output[0], pil_images_output[1], pil_images_output[2]]
+
+with gr.Blocks(css=".gradio-container {background:lightyellow;color:red;}", title="テスト"
+               ) as demo:
+    sample_index = gr.State([])
+
+    gr.HTML('<div style="font-size:12pt; text-align:center; color:yellow;">MNIST 分類器</div>')
+
+    with gr.Row():
+        input_image0 = gr.Image(label="image channel 0", type="pil", shape=(240, 240))
+        input_image1 = gr.Image(label="image channel 1", type="pil", shape=(240, 240))
+        input_image2 = gr.Image(label="image channel 2", type="pil", shape=(240, 240))
+        input_image3 = gr.Image(label="image channel 3", type="pil", shape=(240, 240))
+
+    #input_image = gr.Image(label="画像入力", type="pil", image_mode="RGB", shape=(240, 240))
+
+    with gr.Row():
+        label_image0 = gr.Image(label="label channel 0", type="pil")
+        label_image1 = gr.Image(label="label channel 1", type="pil")
+        label_image2 = gr.Image(label="label channel 2", type="pil")
+
+    with gr.Row():
+        example1_btn = gr.Button("Example 1")
+        example2_btn = gr.Button("Example 2")
+        example3_btn = gr.Button("Example 3")
+        example4_btn = gr.Button("Example 4")
+
+        example1_btn.click(fn=load_sample1, inputs=None, 
+                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
+                                    label_image0, label_image1, label_image2])
+        example2_btn.click(fn=load_sample2, inputs=None, 
+                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
+                                    label_image0, label_image1, label_image2])
+        example3_btn.click(fn=load_sample3, inputs=None, 
+                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
+                                    label_image0, label_image1, label_image2])
+        example4_btn.click(fn=load_sample4, inputs=None, 
+                           outputs=[sample_index, input_image0, input_image1, input_image2, input_image3,
+                                    label_image0, label_image1, label_image2])
+
+    with gr.Row():
+        output_image0 = gr.Image(label="output channel 0", type="pil")
+        output_image1 = gr.Image(label="output channel 1", type="pil")
+        output_image2 = gr.Image(label="output channel 2", type="pil")
+
+    #output_label=gr.Label(label="予測確率", num_top_classes=3)
+    
+    send_btn = gr.Button("予測する")
+
+    #gr.Examples(['2.png', '4.png'], inputs=input_image2)
+
+    send_btn.click(fn=predict, inputs=[sample_index], outputs=[output_image0, output_image1, output_image2])
+
+#demo.queue()
+demo.launch(debug=True)
+
+
+
+### EOF  ###