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import torch, torchvision
from torchvision import transforms
import numpy as np
import gradio as gr
from PIL import Image
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from custom_resnet import Net
import gradio as gr
from io import BytesIO
import os, re
import matplotlib.pyplot as plt
model = Net()
model.load_state_dict(torch.load("custom_resnet_model.pt", map_location=torch.device('cpu')), strict=False)
inv_normalize = transforms.Normalize(
mean=[0.4914, 0.4822, 0.4471],
std=[0.2469, 0.2433, 0.2615]
)
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
def inference(input_img, transparency = 0.5, target_layer_number = -1, top_predictions=3, miss_classified_images_count=3):
transform = transforms.ToTensor()
org_img = input_img
input_img = transform(input_img)
input_img = input_img
input_img = input_img.unsqueeze(0)
outputs = model(input_img)
softmax = torch.nn.Softmax(dim=0)
o = softmax(outputs.flatten())
confidences = {classes[i]: float(o[i]) for i in range(10)}
_, prediction = torch.max(outputs, 1)
target_layers = [[model.X3],[model.R3]]
cam = GradCAM(model=model, target_layers=target_layers[target_layer_number], use_cuda=False)
grayscale_cam = cam(input_tensor=input_img, targets=None)
grayscale_cam = grayscale_cam[0, :]
img = input_img.squeeze(0)
img = inv_normalize(img)
rgb_img = np.transpose(img, (1, 2, 0))
rgb_img = rgb_img.numpy()
visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
# Sort the confidences dictionary based on confidence values
sorted_confidences = dict(sorted(confidences.items(), key=lambda item: item[1], reverse=True))
# Pick the top n predictions
top_n_confidences = dict(list(sorted_confidences.items())[:top_predictions])
files = os.listdir('./misclassified_images/')
# Plot the misclassified images
fig = plt.figure(figsize=(12, 5))
for i in range(miss_classified_images_count):
sub = fig.add_subplot(2, 5, i+1)
npimg = Image.open('./misclassified_images/' + files[i])
# Use regex to extract target and predicted classes
match = re.search(r'Target_(\w+)_Pred_(\w+)_\d+.jpeg', files[i])
target_class = match.group(1)
predicted_class = match.group(2)
plt.imshow(npimg, cmap='gray', interpolation='none')
sub.set_title("Actual: {}, Pred: {}".format(target_class, predicted_class),color='red')
plt.tight_layout()
buffer = BytesIO()
plt.savefig(buffer,format='png')
visualization_missclassified = Image.open(buffer)
return top_n_confidences, visualization, visualization_missclassified
title = "Jaiyesh's ResNet18 Model with GradCAM"
description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
examples = [["cat.jpg", 0.8, -1,3,3],
["dog.jpeg", 0.8, -1,3,3],
["plane.jpeg", 0.8, -1,3,3],
["deer.jpeg", 0.8, -1,3,3],
["horse.jpeg", 0.8, -1,3,3],
["bird.jpeg", 0.8, -1,3,3],
["frog.jpeg", 0.8, -1,3,3],
["ship.jpeg", 0.8, -1,3,3],
["truck.jpeg", 0.8, -1,3,3],
["car.jpeg", 0.8, -1,3,3]]
demo = gr.Interface(
inference,
inputs = [gr.Image(shape=(32, 32), label="Input Image"),
gr.Slider(0, 1, value = 0.8, label="Opacity of GradCAM"),
gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"),
gr.Slider(2, 10, value = 3, step=1, label="Top Predictions"),
gr.Slider(1, 10, value = 3, step=1, label="Misclassified Images"),],
outputs = [gr.Label(label="Top Predictions"),
gr.Image(shape=(32, 32), label="Output").style(width=128, height=128),
gr.Image(shape=(640, 360), label="Misclassified Images").style(width=640, height=360)],
title = title,
description = description,
examples = examples,
)
demo.launch() |