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import torch, torchvision |
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from torchvision import transforms |
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import numpy as np |
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import gradio as gr |
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from PIL import Image |
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from pytorch_grad_cam import GradCAM |
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from pytorch_grad_cam.utils.image import show_cam_on_image |
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from custom_resnet import Net |
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import gradio as gr |
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from io import BytesIO |
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import os, re |
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import matplotlib.pyplot as plt |
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model = Net() |
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model.load_state_dict(torch.load("custom_resnet_model.pt", map_location=torch.device('cpu')), strict=False) |
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inv_normalize = transforms.Normalize( |
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mean=[0.4914, 0.4822, 0.4471], |
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std=[0.2469, 0.2433, 0.2615] |
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) |
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classes = ('plane', 'car', 'bird', 'cat', 'deer', |
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'dog', 'frog', 'horse', 'ship', 'truck') |
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def inference(input_img, transparency = 0.5, target_layer_number = -1, top_predictions=3, miss_classified_images_count=3): |
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transform = transforms.ToTensor() |
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org_img = input_img |
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input_img = transform(input_img) |
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input_img = input_img |
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input_img = input_img.unsqueeze(0) |
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outputs = model(input_img) |
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softmax = torch.nn.Softmax(dim=0) |
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o = softmax(outputs.flatten()) |
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confidences = {classes[i]: float(o[i]) for i in range(10)} |
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_, prediction = torch.max(outputs, 1) |
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target_layers = [[model.X3],[model.R3]] |
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cam = GradCAM(model=model, target_layers=target_layers[target_layer_number], use_cuda=False) |
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grayscale_cam = cam(input_tensor=input_img, targets=None) |
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grayscale_cam = grayscale_cam[0, :] |
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img = input_img.squeeze(0) |
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img = inv_normalize(img) |
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rgb_img = np.transpose(img, (1, 2, 0)) |
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rgb_img = rgb_img.numpy() |
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visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency) |
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sorted_confidences = dict(sorted(confidences.items(), key=lambda item: item[1], reverse=True)) |
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top_n_confidences = dict(list(sorted_confidences.items())[:top_predictions]) |
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files = os.listdir('./misclassfied_images/') |
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fig = plt.figure(figsize=(12, 5)) |
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for i in range(miss_classified_images_count): |
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sub = fig.add_subplot(2, 5, i+1) |
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npimg = Image.open('./misclassfied_images/' + files[i]) |
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match = re.search(r'Target_(\w+)_Pred_(\w+)_\d+.jpeg', files[i]) |
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target_class = match.group(1) |
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predicted_class = match.group(2) |
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plt.imshow(npimg, cmap='gray', interpolation='none') |
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sub.set_title("Actual: {}, Pred: {}".format(target_class, predicted_class),color='red') |
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plt.tight_layout() |
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buffer = BytesIO() |
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plt.savefig(buffer,format='png') |
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visualization_missclassified = Image.open(buffer) |
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return top_n_confidences, visualization, visualization_missclassified |
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title = "CIFAR10 trained on ResNet18 Model with GradCAM" |
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description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results" |
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examples = [["./examples/cat.jpg", 0.8, -1,3,3], |
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["./examples/dog.jpeg", 0.8, -1,3,3], |
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["./examples/plane.jpeg", 0.8, -1,3,3], |
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["./examples/deer.jpeg", 0.8, -1,3,3], |
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["./examples/horse.jpeg", 0.8, -1,3,3], |
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["./examples/bird.jpeg", 0.8, -1,3,3], |
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["./examples/frog.jpeg", 0.8, -1,3,3], |
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["./examples/ship.jpeg", 0.8, -1,3,3], |
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["./examples/truck.jpeg", 0.8, -1,3,3], |
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["./examples/car.jpeg", 0.8, -1,3,3]] |
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demo = gr.Interface( |
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inference, |
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inputs = [gr.Image(shape=(32, 32), label="Input Image"), |
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gr.Slider(0, 1, value = 0.8, label="Opacity of GradCAM"), |
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gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"), |
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gr.Slider(2, 10, value = 3, step=1, label="Top Predictions"), |
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gr.Slider(1, 10, value = 3, step=1, label="Misclassified Images"),], |
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outputs = [gr.Label(label="Top Predictions"), |
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gr.Image(shape=(32, 32), label="Output").style(width=128, height=128), |
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gr.Image(shape=(640, 360), label="Misclassified Images").style(width=640, height=360)], |
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title = title, |
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description = description, |
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examples = examples, |
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) |
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demo.launch() |
