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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 CustomResNet import CustomResNet |
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import gradio as gr |
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import os |
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import torch |
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from pytorch_lightning import LightningModule, Trainer |
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from torch import nn |
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from torch.nn import functional as F |
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from torch.utils.data import DataLoader, random_split |
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from torchmetrics import Accuracy |
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from torchvision import transforms |
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from torchvision.datasets import CIFAR10 |
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PATH_DATASETS = os.environ.get("PATH_DATASETS", ".") |
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BATCH_SIZE = 64 |
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model = CustomResNet(input_size=32,learning_rate=0.001,num_classes=10,) |
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model.load_state_dict(torch.load("custom_resnet_model.pth", map_location=torch.device('cpu')), strict=False) |
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inv_normalize = transforms.Normalize( |
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mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23], |
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std=[1/0.23, 1/0.23, 1/0.23] |
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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): |
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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.layer_2[target_layer_number]] |
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cam = GradCAM(model=model, target_layers=target_layers, 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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return confidences, visualization |
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title = "CIFAR10 trained on Custom Rest Net 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 = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]] |
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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"), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")], |
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outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)], |
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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() |
