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import gradio as gr
import clip
import torch
import utils
#clip_model = "RN50x4"
clip_model = "RN50x64"
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load(clip_model, device=device, jit=False)
model.eval()
def grad_cam_fn(text, img, saliency_layer):
resize = model.visual.input_resolution
img = img.resize((resize, resize))
text_input = clip.tokenize([text]).to(device)
text_feature = model.encode_text(text_input).float()
image_input = preprocess(img).unsqueeze(0).to(device)
attn_map = utils.gradCAM(
model.visual,
image_input,
text_feature,
getattr(model.visual, saliency_layer)
)
attn_map = attn_map.squeeze().detach().cpu().numpy()
attn_map = utils.getAttMap(img, attn_map)
return attn_map
interface = gr.Interface(
fn=grad_cam_fn,
inputs=[
gr.inputs.Textbox(
label="Target Text",
lines=1),
gr.inputs.Image(
label='Input Image',
image_mode="RGB",
type='pil',
shape=(512, 512)),
gr.inputs.Dropdown(
["layer4", "layer3", "layer2", "layer1"],
default="layer4",
label="Saliency Layer")
],
outputs=gr.outputs.Image(
type="pil",
label="Attention Map"),
examples=[
['a cat lying on the floor', 'assets/cat_dog.jpg', 'layer4'],
['a dog sitting', 'assets/cat_dog.jpg', 'layer4']
],
description="OpenAI CLIP Grad CAM")
interface.launch()
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