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import PIL
from captum.attr import GradientShap, Occlusion, LayerGradCam, LayerAttribution, IntegratedGradients
from captum.attr import visualization as viz
import torch
from torchvision import transforms
from matplotlib.colors import LinearSegmentedColormap
import torch.nn.functional as F
import gradio as gr
from torchvision.models import resnet50
import torch.nn as nn
import torch
import numpy as np
class Explainer:
def __init__(self, model, img, class_names):
self.model = model
self.default_cmap = LinearSegmentedColormap.from_list('custom blue',
[(0, '#ffffff'),
(0.25, '#000000'),
(1, '#000000')], N=256)
self.class_names = class_names
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor()
])
transform_normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
self.transformed_img = transform(img)
self.input = transform_normalize(self.transformed_img)
self.input = self.input.unsqueeze(0)
with torch.no_grad():
self.output = self.model(self.input)
self.output = F.softmax(self.output, dim=1)
self.confidences = {class_names[i]: float(self.output[0, i]) for i in range(3)}
self.pred_score, self.pred_label_idx = torch.topk(self.output, 1)
self.pred_label = self.class_names[self.pred_label_idx]
self.fig_title = 'Predicted: ' + self.pred_label + ' (' + str(round(self.pred_score.squeeze().item(), 2)) + ')'
def convert_fig_to_pil(self, fig):
fig.canvas.draw()
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return PIL.Image.fromarray(data)
def shap(self, n_samples, stdevs):
gradient_shap = GradientShap(self.model)
rand_img_dist = torch.cat([self.input * 0, self.input * 1])
attributions_gs = gradient_shap.attribute(self.input, n_samples=int(n_samples), stdevs=stdevs, baselines=rand_img_dist, target=self.pred_label_idx)
fig, _ = viz.visualize_image_attr_multiple(np.transpose(attributions_gs.squeeze().cpu().detach().numpy(), (1,2,0)),
np.transpose(self.transformed_img.squeeze().cpu().detach().numpy(), (1,2,0)),
["original_image", "heat_map"],
show_colorbar=True,
titles=["Original", "Positive Attribution", "Masked"],
fig_size=(18, 6))
fig.suptitle("GradCAM layer3[1].conv2 | " + self.fig_title, fontsize=12)
return self.convert_fig_to_pil(fig)
def create_model_from_checkpoint():
# Loads a model from a checkpoint
model = resnet50()
model.fc = nn.Linear(model.fc.in_features, 3)
model.load_state_dict(torch.load("best_model", map_location=torch.device('cpu')))
model.eval()
return model
model = create_model_from_checkpoint()
labels = [ "benign", "malignant", "normal" ]
def predict(img, shap_samples, shap_stdevs, occlusion_stride, occlusion_window):
explainer = Explainer(model, img, labels)
return [explainer.confidences,
explainer.shap(shap_samples, shap_stdevs),
explainer.occlusion(occlusion_stride, occlusion_window),
explainer.gradcam()]
ui = gr.Interface(fn=predict,
inputs=[
gr.Image(type="pil"),
gr.Slider(minimum=10, maximum=100, default=50, label="SHAP Samples", step=1),
gr.Slider(minimum=0.0001, maximum=0.01, default=0.0001, label="SHAP Stdevs", step=0.0001),
gr.Slider(minimum=4, maximum=80, default=8, label="Occlusion Stride", step=1),
gr.Slider(minimum=4, maximum=80, default=15, label="Occlusion Window", step=1)
],
outputs=[gr.Label(num_top_classes=3), gr.Image(type="pil"), gr.Image(type="pil"), gr.Image(type="pil")],
examples=[["benign (52).png", 50, 0.0001, 8, 15],
["benign (243).png", 50, 0.0001, 8, 15],
["malignant (127).png", 50, 0.0001, 8, 15],
["malignant (201).png", 50, 0.0001, 8, 15],
["normal (81).png", 50, 0.0001, 8, 15],
["normal (101).png", 50, 0.0001, 8, 15]]).launch()
ui.launch(share=True) |