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5b73392 7e9e840 5b73392 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | 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() |