Visualizing intermediate convnet outputs

readme/viz_convnets_.md

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+#
+
+<br/>
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+#
+
+:star_struck:
+
+> Visualizing intermediate convnet outputs ("intermediate activations").
+- Original Source --> #FRANCOIS_CHOLLET - #fchollet - Original Code from the Book from the Commit -- 04f410656e213969b4378e9e1f48963b481cdaff
+- https://github.com/fchollet/deep-learning-with-python-notebooks/blob/586d5ab0bbfcb441bae9ca390bdacb6c2b70884c/5.4-visualizing-what-convnets-learn.ipynb
+- 
+
+
+#
+
+<br/>
+
+#
+
+##### ORIGINAL SOURCE -Pytorch - Vizualise Feature Maps 
+- https://github.com/kadirnar/Pytorch-LDC/blob/2199aa2bbe5a4ac882b1ca716548ce77ca090e8c/backbone/feature_map.py#L42
+

src/basic_foo/a_test.py

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-import pandas as pd
 
+## ORIGINAL SOURCE -- https://github.com/kadirnar/Pytorch-LDC/blob/2199aa2bbe5a4ac882b1ca716548ce77ca090e8c/backbone/feature_map.py#L42
+
+
+
+
+
+
+
+
+
+
+# Script for the visualization of ResNet-50 kernels and their effects
+
+# Libraries
+import os
+import cv2 as cv
+import numpy as np
+import torch.nn as nn
+import matplotlib.pyplot as plt
+from torchvision import models, transforms
+
+
+# Load the model
+model = models.resnet50(pretrained=True)
+print(model)
+model_weights = [] # we will save the conv layer weights in this list
+conv_layers = [] # we will save the 49 conv layers in this list
+# Get all the model children as list
+model_children = list(model.children())
+
+
+# # Define current directory
+# cur_dir = 'X:/thesis/Task1'
+
+
+# Counter to keep count of the convolutional layers
+counter = 0 
+# append all the conv layers and their respective weights 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 convolutional layers: {counter}") #49
+print("---len(conv_layers----",len(conv_layers)) #49
+
+
+# Take a look at the conv layers and the respective weights
+for weight, conv in zip(model_weights, conv_layers):
+    # print(f"WEIGHT: {weight} \nSHAPE: {weight.shape}")
+    print(f"CONV: {conv} ====> SHAPE: {weight.shape}")
+
+
+# # Visualize the first convolutional layer's filters
+#TODO # FOOBAR -- https://github.com/NVlabs/instant-ngp/discussions/300
+# To get over this issue - conda activate env2_det2
+# - pip install opencv-python-headless 
+
+# plt.figure(figsize=(20, 17))
+# for i, filter in enumerate(model_weights[0]):
+#     plt.subplot(8, 8, i+1) # (8, 8) because in conv0 we have 7x7 filters and total of 64 (see printed shapes)
+#     plt.imshow(filter[0, :, :].detach(), cmap='gray')
+#     plt.axis('off')
+#     plt.savefig('filter_1.png')
+# plt.show()
+
+
+# # Read and visualize an image
+#TODO - Comment Code Bloack Above 
+# # FOOBAR -- TO READ image with cv2 -->  conda activate pytorch_venv
+img = cv.imread("./val/not_glass/image_0007.jpg")
+img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+plt.imshow(img)
+plt.axis('off')
+plt.show()
+# define the transforms
+transform = transforms.Compose([
+    transforms.ToPILImage(),
+    transforms.Resize((512, 512)),
+    transforms.ToTensor(),
+])
+img = np.array(img)
+# Apply the transforms
+img = transform(img)
+print(img.size())
+# Unsqueeze to add a batch dimension
+img = img.unsqueeze(0)
+print(img.size())
+
+"""
+torch.Size([3, 512, 512])
+torch.Size([1, 3, 512, 512])
+
+"""
+# Pass the image through all the layers
+results = [conv_layers[0](img)]
+
+for i in range(1, len(conv_layers)):
+    # pass the result from the last layer to the next layer
+    results.append(conv_layers[i](results[-1]))
+# make a copy of the `results`
+outputs = results
+
+print(img.shape)
+step = conv_layers[0](img)
+print(step.shape)
+step = conv_layers[1](step)
+print(step.shape)
+
+"""
+torch.Size([1, 3, 512, 512])
+torch.Size([1, 64, 256, 256])
+torch.Size([1, 64, 256, 256])
+"""
+
+# # Visualize 64 features from each layer 
+# # (although there are more feature maps in the upper layers)
+#TODO # FOOBAR -- https://github.com/NVlabs/instant-ngp/discussions/300
+# To get over this issue - conda activate env2_det2
+# - pip install opencv-python-headless 
+
+for num_layer in range(len(outputs)):
+    plt.figure(figsize=(30, 30))
+    layer_viz = outputs[num_layer][0, :, :, :]
+    layer_viz = layer_viz.data
+    print(layer_viz.size())
+    for i, filter in enumerate(layer_viz):
+        if i == 12: # 64 # we will visualize only 8x8 blocks from each layer
+            break
+        plt.subplot(8, 8, i + 1)
+        plt.imshow(filter, cmap='hsv_r') # viridis_r , hsv , hsv_r
+        plt.axis("off")
+    print(f"Saving layer {num_layer} feature maps...")
+    plt.savefig(f"layer_{num_layer}_12_.png")
+    # plt.show()
+    plt.close()
+
+
+# ORIGINAL SOURCE -- https://github.com/kadirnar/Pytorch-LDC/blob/2199aa2bbe5a4ac882b1ca716548ce77ca090e8c/backbone/feature_map.py#L42