Create app.py

app.py

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+import torch
+import torch.nn as nn
+import torchvision
+from torchvision import models, transforms, utils
+from torch.autograd import Variable
+import numpy as np
+import matplotlib.pyplot as plt
+#import scipy.misc
+from PIL import Image
+import json
+import gradio as gr
+import os
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=0., std=1.)
+])
+
+def generate_feature_maps(im):
+    image = Image.fromarray(im, 'RGB')
+    plt.imshow(image)
+
+    model = models.resnet18(pretrained=True)
+    print(model)
+
+    # we will save the conv layer weights in this list
+    model_weights =[]
+    #we will save the 49 conv layers in this list
+    conv_layers = []
+    # get all the model children as list
+    model_children = list(model.children())
+    #counter to keep count of the conv layers
+    counter = 0
+    #append all the conv layers and their respective wights to the list
+    for i in range(len(model_children)):
+        if type(model_children[i]) == nn.Conv2d:
+            counter+=1
+            model_weights.append(model_children[i].weight)
+            conv_layers.append(model_children[i])
+        elif type(model_children[i]) == nn.Sequential:
+            for j in range(len(model_children[i])):
+                for child in model_children[i][j].children():
+                    if type(child) == nn.Conv2d:
+                        counter+=1
+                        model_weights.append(child.weight)
+                        conv_layers.append(child)
+    print(f"Total convolution layers: {counter}")
+    print("conv_layers")
+
+    device = torch.device('cpu')
+    model = model.to(device)
+
+    image = transform(image)
+    print(f"Image shape before: {image.shape}")
+    image = image.unsqueeze(0)
+    print(f"Image shape after: {image.shape}")
+    image = image.to(device)
+
+    outputs = []
+    names = []
+    for layer in conv_layers[0:]:
+        image = layer(image)
+        outputs.append(image)
+        names.append(str(layer))
+    print(len(outputs))
+    #print feature_maps
+    for feature_map in outputs:
+        print(feature_map.shape)
+
+    processed = []
+    for feature_map in outputs:
+        feature_map = feature_map.squeeze(0)
+        gray_scale = torch.sum(feature_map,0)
+        gray_scale = gray_scale / feature_map.shape[0]
+        processed.append(gray_scale.data.cpu().numpy())
+    for fm in processed:
+        print(fm.shape)
+
+    # Plot and save feature maps for each layer
+    for i, (fm, name) in enumerate(zip(processed, names)):
+        fig = plt.figure(figsize=(10, 10))
+        a = fig.add_subplot(1, 1, 1)  # You should adjust the layout as needed
+        imgplot = plt.imshow(fm, cmap='viridis')  # Adjust the colormap if needed
+        a.axis("off")
+        filename = f'layor{i}.jpg'
+        plt.savefig('C:\\Users\\cdrok\\Documents\\JuniorClinic\\AiMl\\layors\\' + filename, bbox_inches='tight')
+        plt.close(fig)  # Close the figure after saving
+
+# Gradio interface
+with gr.Blocks() as demo:
+    
+    layorNumber = gr.Slider(0, 16, value=4, label="Layor Number", info="Choose between 0 and 16", step=1)
+
+    with gr.Row():
+        im = gr.Image()
+        im2 = gr.Image(type= 'filepath',)
+
+    def show_feature_maps(im, layorNumber):
+        # Future if check for if all layors exist to run faster
+        generate_feature_maps(im)
+        this_path = 'C:\\Users\\cdrok\\Documents\\JuniorClinic\\AiMl\\layors\\layor' + str(int(layorNumber)) + '.jpg'
+        return this_path
+
+    btn = gr.Button(value="Generate Feature Maps")
+    btn.click(show_feature_maps, inputs=[im, layorNumber], outputs=[im2])
+
+if __name__ == "__main__":
+    demo.launch()