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import gradio as gr
import timm
import torch
from PIL import Image
import requests
from io import BytesIO
import numpy as np
from pytorch_grad_cam import GradCAM, HiResCAM, ScoreCAM, GradCAMPlusPlus, AblationCAM, XGradCAM, EigenCAM, FullGrad
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget, get_target_layer
from pytorch_grad_cam.utils.image import show_cam_on_image
from timm.data import create_transform
# List of available timm models
MODELS = timm.list_models()
# List of available GradCAM methods
CAM_METHODS = {
"GradCAM": GradCAM,
"HiResCAM": HiResCAM,
"ScoreCAM": ScoreCAM,
"GradCAM++": GradCAMPlusPlus,
"AblationCAM": AblationCAM,
"XGradCAM": XGradCAM,
"EigenCAM": EigenCAM,
"FullGrad": FullGrad
}
def load_model(model_name):
model = timm.create_model(model_name, pretrained=True)
model.eval()
return model
def process_image(image_path, model):
if image_path.startswith('http'):
response = requests.get(image_path)
image = Image.open(BytesIO(response.content))
else:
image = Image.open(image_path)
config = model.pretrained_cfg
transform = create_transform(
input_size=config['input_size'],
crop_pct=config['crop_pct'],
mean=config['mean'],
std=config['std'],
interpolation=config['interpolation'],
is_training=False
)
tensor = transform(image).unsqueeze(0)
return tensor
def get_cam_image(model, image, target_layer, cam_method):
cam = CAM_METHODS[cam_method](model=model, target_layers=[target_layer], use_cuda=torch.cuda.is_available())
grayscale_cam = cam(input_tensor=image)
config = model.pretrained_cfg
mean = torch.tensor(config['mean']).view(3, 1, 1)
std = torch.tensor(config['std']).view(3, 1, 1)
rgb_img = (image.squeeze(0) * std + mean).permute(1, 2, 0).cpu().numpy()
rgb_img = np.clip(rgb_img, 0, 1)
cam_image = show_cam_on_image(rgb_img, grayscale_cam[0, :], use_rgb=True)
return Image.fromarray(cam_image)
def get_feature_info(model):
if hasattr(model, 'feature_info'):
return [f['module'] for f in model.feature_info]
else:
return []
def explain_image(model_name, image_path, cam_method, feature_module):
model = load_model(model_name)
image = process_image(image_path, model)
if feature_module:
target_layer = get_target_layer(model, feature_module)
print(f"Using feature module: {feature_module}")
else:
# Fallback to the last feature module or last convolutional layer
feature_info = get_feature_info(model)
if feature_info:
target_layer = get_target_layer(model, feature_info[-1])
print(f"Using last feature module: {feature_info[-1]}")
else:
# Fallback to finding last convolutional layer
for name, module in reversed(list(model.named_modules())):
if isinstance(module, torch.nn.Conv2d):
target_layer = module
print(f"Fallback: Using last convolutional layer: {name}")
break
if target_layer is None:
raise ValueError("Could not find a suitable target layer.")
cam_image = get_cam_image(model, image, target_layer, cam_method)
return cam_image
def update_feature_modules(model_name):
model = load_model(model_name)
feature_modules = get_feature_info(model)
return gr.Dropdown.update(choices=feature_modules, value=feature_modules[-1] if feature_modules else None)
iface = gr.Interface(
fn=explain_image,
inputs=[
gr.Dropdown(choices=MODELS, label="Select Model"),
gr.Image(type="filepath", label="Upload Image"),
gr.Dropdown(choices=list(CAM_METHODS.keys()), label="Select CAM Method"),
gr.Dropdown(label="Select Feature Module (optional)")
],
outputs=gr.Image(type="pil", label="Explained Image"),
title="Explainable AI with timm models",
description="Upload an image, select a model, CAM method, and optionally a specific feature module to visualize the explanation.",
allow_flagging="never"
)
iface.load(update_feature_modules, inputs=[iface.inputs[0]], outputs=[iface.inputs[3]])
iface.launch() |