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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("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 inference(input_img, transparency = 0.5, target_layer_number = -1):
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.layer2[target_layer_number]]
cam = GradCAM(model=model, target_layers=target_layers, 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)
return confidences, visualization
def inference_confidences(input_img, transparency = 0.5, target_layer_number = -1):
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)}
return confidences
def inference_visualization(input_img, transparency = 0.5, target_layer_number = -1):
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.layer2[target_layer_number]]
cam = GradCAM(model=model, target_layers=target_layers, 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)
return visualization
title = "CIFAR10 trained on ResNet18 Model with GradCAM"
description = "Gradio interface to infer on ResNet18 model, and get GradCAM results"
examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
demo = gr.Interface(
# inference,
# inputs = [gr.Image(shape=(32, 32), label="Input Image"), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")],
# outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
# title = title,
# description = description,
# examples = examples,
fn=inference, # We'll add the function later after defining all functions
inputs=[
gr.Radio(["Yes", "No"], label="View GradCAM images?"),
gr.Number(label="Number of GradCAM images to view", default=5, max=10),
gr.Textbox(label="Layer name for GradCAM visualization", default="layer4", lines=1),
gr.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.5, label="Opacity"),
gr.Radio(["Yes", "No"], label="View misclassified images?"),
gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
gr.Radio(["Upload New Images", "Example Images"], label="Select images source"),
gr.Image(label="Uploaded image" if "Interface" in gr.__file__ else "Image"),
gr.Button(label="Submit", type="boolean")
],
outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
examples = examples,
live=True
)
# Callback function for the Gradio interface
def gradio_callback(view_gradcam, num_gradcam_images, layer_name, opacity,
view_misclassified, num_misclassified_images,
image_source, uploaded_image, submit,input_img, transparency = 0.5, target_layer_number = -1):
confidence = inference_confidences(input_img, transparency = 0.5, target_layer_number = -1)
visualization = inference_visualization(input_img, transparency = 0.5, target_layer_number = -1)
return confidence, visualization
# Set the callback function to the Gradio interface
demo.fn = gradio_callback
demo.launch() |