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d31170d d82f8ec d31170d 473b629 d31170d 8f49a5b d31170d e5c1737 d31170d 59ac305 09898e2 b8cbc9e d31170d 099df44 d31170d d0c5619 d31170d e5c1737 d31170d 52c6c75 d31170d 55ede45 d31170d f7aadc0 | 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 | import torch
import 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_lightning import ResNet18Model
import gradio as gr
model = ResNet18Model.load_from_checkpoint("epoch=19-step=3920.ckpt")
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):
img = Image.fromarray(np.array(image))
width, height = img.size
width_scale = new_width/width
height_scale = new_height/height
scale = min(width_scale, height_scale)
resized = img.resize((int(width*scale), int(height*scale)), Image.NEAREST)
resized = resized.crop((0,0,new_width, new_height))
return resized
def inference(input_img, transparancy = 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)
print(outputs)
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)
grayscale_cam = grayscale_cam[0, :]
visualization = show_cam_on_image(org_img/255,grayscale_cam,use_rgb=True,
image_weight=transparancy)
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 of the overelay"),
gr.Slider(-2,-1, value =-2, step=1, label= "Which layer for Gradcam")
],
outputs = [
"text",
gr.Image(width= 256, height=256,label="Output"),
gr.Label(num_top_classes=3)
],
title = "CIFAR 10 trained on ResNet model in pytorch lightning with Gradcam",
description = " A simple gradio inference to infer on resnet18 model",
examples = [["cat.jpg", 0.5, -1],["dog.jpg",0.7,-2]]
)
if __name__ == "__main__":
demo.launch()
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