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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 resnet import ResNet18
import gradio as gr
model = ResNet18()
model.load_state_dict(torch.load("cifar10_saved_model.pth", map_location=torch.device('cpu')), strict=False)
inv_normalize = transforms.Normalize(
mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
std=[1/0.23, 1/0.23, 1/0.23]
)
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
def resize_image_pil(image, new_width, new_height):
# Convert to PIL image
img = Image.fromarray(np.array(image))
# Get original size
width, height = img.size
# Calculate scale
width_scale = new_width / width
height_scale = new_height / height
scale = min(width_scale, height_scale)
# Resize
resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
# Crop to exact size
resized = resized.crop((0, 0, new_width, new_height))
return resized
def inference(input_img, transparency=0.5, target_layer_number=-1, grad_cam_option="Yes",top_classes=3):
input_img = resize_image_pil(input_img, 32, 32)
input_img = np.array(input_img)
org_img = input_img
input_img = input_img.reshape((32, 32, 3))
transform = transforms.ToTensor()
input_img = transform(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)}
confidences = dict(list(confidences.items())[:top_classes])
_, prediction = torch.max(outputs, 1)
target_layers = [model.layer2[target_layer_number]]
cam = GradCAM(model=model, target_layers=target_layers)
grayscale_cam = cam(input_tensor=input_img, targets=None)
grayscale_cam = grayscale_cam[0, :]
img = input_img.squeeze(0)
img = inv_normalize(img)
print('Confidences ',confidences)
if grad_cam_option == "Yes":
visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
return classes[prediction[0].item()], visualization, confidences
else:
return classes[prediction[0].item()], None, confidences
title = "CIFAR10 trained on ResNet18 Model with GradCAM"
description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1], ["bird_1.jpg", 0.5, -1], ["cat_1.jpg", 0.5, -1], ["cat_2.jpg", 0.5, -1],
["dog_1.jpg", 0.5, -1], ["dog_2.jpg", 0.5, -1], ["dog_3.jpg", 0.5, -1], ["ship_1.jpg", 0.5, -1], ["ship_2.jpg", 0.5, -1]]
demo = gr.Interface(
inference,
inputs=[
gr.Image(width=256, height=256, label="Input Image"),
gr.Slider(0, 1, value=0.5, label="Overall Opacity of Image"),
gr.Slider(-2, -1, value=-2, step=1, label="Which Layer?"),
gr.Dropdown(["Yes", "No"], label="Want to see Grad Cam Images?"),
gr.Number(value=3, minimum=1,maximum=10)
],
outputs=[
"text",
gr.Image(width=256, height=256, label="Output"),
gr.Label()
],
title=title,
description=description,
examples=examples,
)
demo.launch(share=True)
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