update

app.py

@@ -3,22 +3,56 @@ import numpy as np
 import gradio as gr
 from lavis.models import load_model_and_preprocess
 from PIL import Image
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import torchvision
+
+def create_heatmap(activation_map):
+    # アクティベーションマップをnumpy配列に変換
+    activation_map_np = activation_map.squeeze().detach().cpu().numpy()
+
+    # アクティベーションマップの最小値と最大値を取得
+    min_value = np.min(activation_map_np)
+    max_value = np.max(activation_map_np)
+
+    # アクティベーションマップを0-1の範囲に正規化
+    normalized_map = (activation_map_np - min_value) / (max_value - min_value)
+
+    # 正規化されたアクティベーションマップをヒートマップに変換
+    heatmap = cm.jet(normalized_map)
+
+    # ヒートマップを [0, 255] の範囲にスケーリングし、uint8型に変換
+    heatmap = np.uint8(255 * heatmap)
+
+    return heatmap
 
 def process(input_image, prompt):
     device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-    model, vis_processors, txt_processors = load_model_and_preprocess(name="blip_feature_extractor", model_type="base", is_eval=True, device=device)
-
-    input_image = input_image.resize((256, 256), Image.LANCZOS)
+    model, vis_processors, txt_processors = load_model_and_preprocess(name="pnp_vqa", model_type="base", is_eval=True, device=device)
+    input_image = input_image.resize((256, 256))
     image = vis_processors["eval"](input_image).unsqueeze(0).to(device)
     text_input = txt_processors["eval"](prompt)
     sample = {"image": image, "text_input": [text_input]}
 
-    features_multimodal = model.extract_features(sample, mode="multimodal")
-    preds = features_multimodal.multimodal_embeds.squeeze().detach().cpu().numpy()
-    preds = np.where(preds > 0.3, 255, 0).astype(np.uint8)
-    preds = Image.fromarray(preds.astype(np.uint8))
-    preds = np.array(preds.resize((input_image.width, input_image.height)))
+    output = model.forward_itm(samples=sample)
+    activation_map = output['gradcams'].reshape(24, 24)
+
+    relu = torch.nn.ReLU()
+    # ヒートマップを計算
+    heatmap = create_heatmap(activation_map)
+
+    heatmap = Image.fromarray(heatmap)
+    heatmap = torchvision.transforms.functional.to_tensor(heatmap)
+    heatmap = relu(heatmap)
+    heatmap = torchvision.transforms.functional.to_pil_image(heatmap)
+    heatmap = heatmap.resize((256, 256))
+    heatmap = np.array(heatmap)
+    heatmap = torch.sigmoid(torch.from_numpy(heatmap)).numpy()
+    preds = heatmap.reshape(256, 256, -1)
+    preds = Image.fromarray(preds.astype(np.uint8)).convert('L')
+    preds = np.array(preds)
+    preds = np.where(preds > 0.5, 255, 0)
     
     return preds
 
@@ -29,11 +63,7 @@ if __name__ == '__main__':
             input_image, prompt
         ]
     outputs = "image"
-    input_size = (256, 256)
-    output_size = (256, 256)
     iface = gr.Interface(fn=process,
                          inputs=ips,
-                         outputs=outputs,
-                         input_size=input_size, 
-                         output_size=output_size)
-    iface.launch()
+                         outputs=outputs)
+    iface.launch()

requirements.txt

@@ -3,4 +3,6 @@ transformers
 torch
 pillow
 salesforce-lavis==1.0.2
-numpy
+numpy
+torchvision
+matplotlib