update app.py # 3

app.py

@@ -37,29 +37,32 @@ def predict(input_file, downsample_size):
     mean = np.asarray([0.3156024, 0.33569682, 0.34337464], dtype=np.float32)
     std = np.asarray([0.16568947, 0.17827448, 0.18925823], dtype=np.float32)
 
-    img_vecs = []
-    with torch.no_grad():
-        for fp_i, file_path in enumerate([input_file]):
-            for i, in_img in enumerate(video_reader(file_path,
-                                                    targetFPS=9,
-                                                    targetWidth=100,
-                                                    to_rgb=True)):
-                in_img = (in_img.astype(np.float32) / 255.)
-                in_img = (in_img - mean) / std
-                in_img = np.expand_dims(in_img, 0)
-                in_img = np.transpose(in_img, (0, 3, 1, 2))
-                in_img = torch.from_numpy(in_img).float()
-                encoded = avg_pool(model(in_img))[0, :, 0, 0].cpu().numpy()
-                img_vecs += [encoded]
-    img_vecs = np.asarray(img_vecs)
-    print("images encoded")
-    rv_indices, _ = furthest_neighbours(
-        x=img_vecs,
-        downsample_size=downsample_size,
-        seed=0)
-    indices = np.zeros((img_vecs.shape[0],))
-    indices[np.asarray(rv_indices)] = 1
-    print("images selected")
+    # img_vecs = []
+    # with torch.no_grad():
+    #     for fp_i, file_path in enumerate([input_file]):
+    #         for i, in_img in enumerate(video_reader(file_path,
+    #                                                 targetFPS=9,
+    #                                                 targetWidth=100,
+    #                                                 to_rgb=True)):
+    #             in_img = (in_img.astype(np.float32) / 255.)
+    #             in_img = (in_img - mean) / std
+    #             in_img = np.expand_dims(in_img, 0)
+    #             in_img = np.transpose(in_img, (0, 3, 1, 2))
+    #             in_img = torch.from_numpy(in_img).float()
+    #             encoded = avg_pool(model(in_img))[0, :, 0, 0].cpu().numpy()
+    #             img_vecs += [encoded]
+    # img_vecs = np.asarray(img_vecs)
+    # print("images encoded")
+    # rv_indices, _ = furthest_neighbours(
+    #     x=img_vecs,
+    #     downsample_size=downsample_size,
+    #     seed=0)
+    # indices = np.zeros((img_vecs.shape[0],))
+    # indices[np.asarray(rv_indices)] = 1
+    # print("images selected")
+
+    indices = [0] * 1000
+    indices[0] = 1
 
     global_ctr = 0
     for fp_i, file_path in enumerate([input_file]):