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from typing import Tuple, Union
import gradio as gr
import matplotlib.pyplot as plt
import torch
from PIL import Image
import bcos.models.pretrained as pretrained
from bcos.data.categories import IMAGENET_CATEGORIES
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def get_model(model_name):
model = getattr(pretrained, model_name)(pretrained=True)
model = model.to(device)
model.eval()
return model
MODEL_NAMES = pretrained.list_available()
class NormalizationMode:
# this is normalization for the explanations!
INDIVIDUAL = "individual"
WRT_PREDICTION = "wrt prediction's confidence"
INDIVIDUAL_X_CONFIDENCE = "individual×confidence"
@classmethod
def all(cls):
return [cls.WRT_PREDICTION, cls.INDIVIDUAL_X_CONFIDENCE, cls.INDIVIDUAL]
def freeze(model):
for param in model.parameters():
param.requires_grad = False
def run(
model_name: str,
input_image: Image,
do_resize: bool,
do_center_crop: bool,
normalization_mode: str,
smooth: int,
alpha_percentile: Union[int, float],
plot_dpi: int,
topk: int = 5,
) -> Tuple[dict, plt.Figure]:
# cleanup previous stuff
plt.close("all")
torch.cuda.empty_cache()
# preprocess - get model and transform input image
model = get_model(model_name)
freeze(model)
x = model.transform.transform_with_options(
input_image,
center_crop=do_center_crop,
resize=do_resize,
)
x = x.unsqueeze(0).to(device).requires_grad_()
# predict and explain
with model.explanation_mode():
out = model(x)
topk_values, topk_preds = torch.topk(out, topk, dim=1)
topk_values, topk_preds = topk_values[0], topk_preds[0]
dynamic_weights = [] # list of grad tensors of shape (C, H, W)
for i in range(topk):
topk_values[i].backward(inputs=[x], retain_graph=i < topk - 1)
dynamic_weights.append(
x.grad.detach().cpu()[0],
)
x.grad = None # reset
# prepare output labels+confidences
topk_probabilities = (
model.to_probabilities(out.detach()).topk(topk, dim=1).values[0].cpu()
)
confidences = {
IMAGENET_CATEGORIES[i]: v.item() for i, v in zip(topk_preds, topk_probabilities)
}
# output plot of images
output_fig, axs = plt.subplots(
1, topk + 1, dpi=plot_dpi, figsize=((topk + 1) * 2.1, 2)
)
# visualize input image
x = x.detach().cpu()[0]
axs[0].imshow(x[:3].permute(1, 2, 0).numpy())
axs[0].set_xlabel("Input Image")
# visualize explanations
pred_confidence = topk_probabilities[0] # first one is pred
for i, ax in enumerate(axs[1:]):
expl = model.gradient_to_image(
x,
dynamic_weights[i],
smooth=smooth,
alpha_percentile=alpha_percentile,
)
if normalization_mode == NormalizationMode.INDIVIDUAL_X_CONFIDENCE:
expl[:, :, -1] *= topk_probabilities[i].item()
elif normalization_mode == NormalizationMode.WRT_PREDICTION and i > 0:
expl[:, :, -1] *= (topk_probabilities[i] / pred_confidence).item()
else: # NormalizationMode.INDIVIDUAL
pass
ax.imshow(expl)
ax.set_xlabel(IMAGENET_CATEGORIES[topk_preds[i]])
for ax in axs:
ax.set_xticks([])
ax.set_yticks([])
output_fig.tight_layout()
return confidences, output_fig
with gr.Blocks() as demo:
# basic info
gr.Markdown(
"""# B-cos Explanation Generation Demo
[Repository](https://github.com/B-cos/B-cos-v2/)
"""
)
with gr.Row():
selected_model = gr.Dropdown(
MODEL_NAMES, value="densenet121_long", label="Select model"
)
with gr.Accordion("Options", open=False):
do_resize = gr.Checkbox(
label="Resize input image's shorter side to 256", value=True
)
do_center_crop = gr.Checkbox(
label="Center crop input image to 224x224", value=False
)
normalization_mode = gr.Radio(
NormalizationMode.all(),
value=NormalizationMode.WRT_PREDICTION,
label="Normalization Mode",
)
smooth = gr.Slider(1, 51, value=15, step=2, label="Smoothing kernel size")
alpha_percentile = gr.Number(value=99.99, label="Percentile")
plot_dpi = gr.Number(value=100, label="Plot DPI")
input_image = gr.Image(type="pil", label="Image")
run_button = gr.Button("Predict and Explain", variant="primary")
# will contain all outputs in a plot
output = gr.Plot(label="Explanations")
# labels
output_labels = gr.Label(label="Top-5 Predictions")
run_button.click(
fn=run,
inputs=[
selected_model,
input_image,
do_resize,
do_center_crop,
normalization_mode,
smooth,
alpha_percentile,
plot_dpi,
],
outputs=[output_labels, output],
scroll_to_output=True,
)
demo.launch(
queue=True,
)
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