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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 custom_resnet import Net
from PIL import Image
import io
model = Net('batch')
model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
classes = ('plane', 'car', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck')
def inference(input_img_files, transparency = 0.5, target_layer_number = -1):
confidences_list = []
visualizations_list = []
for input_img_file in input_img_files:
# Convert the temporary file wrapper to a PIL Image
with open(input_img_file.name, "rb") as f:
input_img = Image.open(io.BytesIO(f.read())).convert("RGB")
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)}
confidences_list.append(confidences)
_, 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)
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)
visualizations_list.append(visualization)
return confidences_list, visualizations_list
title = "CIFAR10 trained on ResNet18 Model with GradCAM"
description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
examples = [["cat.png", 0.5, -1],["dog.png", 0.5, -1]]
demo = gr.Interface(
inference,
inputs=[
gr.inputs.File(file_count="multiple"),
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": "128px", "height": "128px"})],
title=title,
description=description,
examples=examples,
)
demo.launch()
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