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import os |
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from typing import List |
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import gradio as gr |
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import numpy as np |
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import torch |
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import torch.nn.functional as F |
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from model import DavidPageNet |
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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 pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget |
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from torchvision import transforms |
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mean = [0.485, 0.456, 0.406] |
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std = [0.229, 0.224, 0.225] |
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inv_mean = [-mean / std for mean, std in zip(mean, std)] |
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inv_std = [1 / s for s in std] |
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transform = transforms.Compose([ |
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transforms.ToTensor(), |
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transforms.Normalize(mean=mean, std=std), |
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] |
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) |
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inv_normalize = transforms.Normalize(mean=inv_mean, std=inv_std) |
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classes = [ |
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'airplane', |
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'automobile', |
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'bird', |
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'cat', |
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'deer', |
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'dog', |
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'frog', |
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'horse', |
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'ship', |
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'truck' |
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] |
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class Gradio: |
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def __init__(self, model_path: str): |
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use_cuda = torch.cuda.is_available() |
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self.device = torch.device("cuda" if use_cuda else "cpu") |
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self.model = self.load_model(model_path) |
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self.temperature = 2 |
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def load_model(self, model_path: str): |
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model = DavidPageNet().to(self.device) |
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if os.path.isfile(model_path): |
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model.load_state_dict( |
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torch.load(model_path, map_location=self.device), strict=False |
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) |
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return model |
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def cam( |
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self, |
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input_tensor: torch.Tensor, |
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target_class_id: int, |
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layer_nums: List, |
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transparency: float = 0.7, |
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): |
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targets = [ClassifierOutputTarget(target_class_id)] |
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target_layers = [getattr(self.model, f"block{layer-1}") for layer in layer_nums] |
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with GradCAM( |
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model=self.model, |
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target_layers=target_layers, |
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use_cuda=self.device == torch.device("cuda"), |
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) as cam: |
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grayscale_cam = cam(input_tensor=input_tensor, targets=targets) |
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grayscale_cam = grayscale_cam[0, :] |
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img = inv_normalize(input_tensor) |
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rgb_img = img[0].permute(1, 2, 0).cpu().numpy() |
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visualization = show_cam_on_image( |
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rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency |
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) |
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return visualization |
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def inference( |
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self, |
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input_img: np.array, |
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transparency: float=0.7, |
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ntop_classes: int=2, |
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layer_nums: List=[3, 4], |
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cam_for_class: str="default", |
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): |
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self.model.eval() |
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input_img = transform(input_img) |
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input_img = input_img.to(self.device) |
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input_img = input_img.unsqueeze(0) |
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with torch.no_grad(): |
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outputs = self.model(input_img).squeeze(0) |
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outputs = F.softmax(outputs / self.temperature, dim=-1) |
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probability, prediction = torch.sort(outputs, descending=True) |
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prediction = list(zip(prediction.tolist(), probability.tolist())) |
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class_id = ( |
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prediction[0][0] |
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if cam_for_class in ["default", ""] |
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else classes.index(cam_for_class) |
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) |
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visualization = self.cam( |
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input_tensor=input_img, |
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target_class_id=class_id, |
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layer_nums=layer_nums, |
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transparency=transparency, |
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) |
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top_nclass_result = [ |
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(classes[class_id], round(score, 2)) |
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for class_id, score in prediction[:ntop_classes] |
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] |
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return visualization, dict(top_nclass_result) |
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method = Gradio(model_path="./checkpoint/model.pt") |
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demo = gr.Interface( |
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fn=method.inference, |
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inputs=[ |
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gr.Image(shape=(32, 32), label="Input Image", value="./samples/dog_cat.jpeg"), |
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gr.Slider( |
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minimum=0, |
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maximum=1, |
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value=0.5, |
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label="Transparency", |
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info="Transparency of the CAM-Attention Output", |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=10, |
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step=1, |
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value=2, |
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label="Top Classes", |
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info="Number of Top Predicted Classes", |
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), |
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gr.CheckboxGroup( |
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choices=[1, 2, 3, 4], |
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value=[3, 4], |
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label="Network Layers", |
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info="Network Layers for CAM-Attention Extraction", |
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), |
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gr.Dropdown( |
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choices=["default"] + classes, |
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multiselect=False, |
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value="default", |
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label="Class Activation Map (CAM) Focus Visualization", |
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info="This section showcases the specific region of interest within the input image that the Class Activation Map (CAM) algorithm emphasizes to make predictions based on the selected class from the dropdown menu. The 'default' value serves as the default choice, representing the top class predicted by the model.", |
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), |
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], |
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outputs=[ |
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gr.Image(shape=(32, 32), label="CAM Output for the Chosen Class").style(width=256, height=256), |
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gr.Label(label="Top Classes"), |
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], |
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examples=[[os.path.join("./samples/", f)] for f in os.listdir("./samples/")] |
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) |
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demo.launch() |
