File size: 2,782 Bytes
0fb76bb 3dd39f3 51ef4a8 0fb76bb |
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 |
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_image, transparency=0.5, target_layer_number=-1):
input_image = resize_image_pil(input_image, 32, 32)
input_image = np.array(input_image)
org_img = input_image
input_image = input_image.reshape((32, 32, 3))
transforms = transforms.ToTensor()
input_image = transforms(input_image)
input_image = input_image.unsqueeze(0)
outputs = model(input_image)
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_image, target=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(
inference,
inputs = [
gr,Image(width=256, height=256, label="Input Image"),
gr.Slider(0, 1, value=0.5, label="Overall opacity fo the overlay"),
gr.Slider(-2, -1, value=-2, step=1, label="Which GradCAM layer?")
],
outputs = [
"text",
gr.Image(width=256, height=256, label="Output"),
gr.Label(num_top_classes=3)
],
title="CIFAR10 trained on ResNet18 with GradCAM feature",
description = "A simple Gradio app for checking GradCAM outputs from results of ResNet18 model.",
examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.7, -2]]
)
demo.launch() |