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import torch
import torchvision
from torchvision import transforms
import gradio as gr
import numpy as np
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
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 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(size=(int(width*scale), int(height*scale)), resample=Image.NEAREST)
# crop resized image
resized = resized.crop((0, 0, new_width, new_height))
return resized
# def inference(input_img, transparency):
# transform = transforms.ToTensor()
# input_img = transform(input_img)
# input_img = input_img.to(device)
# input_img = input_img.unsqueeze(0)
# outputs = model(input_img)
# _, prediction = torch.max(outputs, 1)
# target_layers = [model.layer2[-2]]
# cam = GradCAM(model=model, target_layers=target_layers, use_cuda=True)
# grayscale_cam = cam(input_tensor=input_img, targets=targets)
# grayscale_cam = grayscale_cam[0, :]
# img = input_img.squeeze(0).to('cpu')
# img = inv_normalize(img)
# rgb_img = np.transpose(img, (1, 2, 0))
# rgb_img = rgb_img.numpy()
# visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
# return classes[prediction[0].item()], visualization
def inference(input_img, transparency=0.5, target_layer_number=-1):
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)}
_, 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, :]
visualization = show_cam_on_image(
org_img/255,
grayscale_cam,
use_rgb=True,
image_weight=transparency
)
return classes[prediction[0].item()], visualization, confidences
demo = gr.Interface(
fn=inference,
inputs=[
gr.Image(width=256, height=256, label="Input Image"),
gr.Slider(0,1, value=0.5, label="Overall opacity value"),
gr.Slider(-2, -1, value=-2, label="Which model layer to use for GradCAM?")
],
outputs = [
"text",
gr.Image(width=256, height=256, label="Output"),
gr.Label(num_top_classes=3)
],
title="CIFAR10 trained on ResNet18 with GradCAM",
description = "A simple Gradio interface to infer on ResNet model with GradCAM results shown on top.",
examples = [
["cat.jpg", 0.5, -1],
["dog.jpg", 0.7, -2]
]
)
demo.launch() |