dbg

ppcls/engine/trainer.py

@@ -335,37 +335,37 @@ class Trainer(object):
             name='gallery')
         query_feas, query_img_id, query_camera_id = self._cal_feature(
             name='query')
-        gallery_img_id = gallery_img_id.t()
-        if gallery_camera_id is not None:
-            gallery_camera_id = gallery_camera_id.t()
+        gallery_img_id = gallery_img_id
+        # if gallery_camera_id is not None:
+        #     gallery_camera_id = gallery_camera_id
         # step2. do evaluation
-        sim_block_size = self.config["Global"].get("sim_block_size", 1)
+        sim_block_size = self.config["Global"].get("sim_block_size", 64)
         sections = [sim_block_size] * (len(query_feas) // sim_block_size)
-        if not len(query_feas) % sim_block_size:
+        if len(query_feas) % sim_block_size:
             sections.append(len(query_feas) % sim_block_size)
         fea_blocks = paddle.split(query_feas, num_or_sections=sections)
-        if query_camera_id is not None:
-            camera_id_blocks = paddle.split(
-                query_camera_id, num_or_sections=sections)
-        image_id_blocks = paddle.split(query_img_id, num_or_sections=sections)
+        # if query_camera_id is not None:
+        #     camera_id_blocks = paddle.split(
+        #         query_camera_id, num_or_sections=sections)
+        # image_id_blocks = paddle.split(query_img_id, num_or_sections=sections)
         metric_key = None
 
         for block_idx, block_fea in enumerate(fea_blocks):
             similarity_matrix = paddle.matmul(
                 block_fea, gallery_feas, transpose_y=True)
-            image_id_block = image_id_blocks[block_idx]
-            image_id_mask = (image_id_block != gallery_img_id)
-            similarity_matrix = similarity_matrix.masked_select(image_id_mask)
-            if query_camera_id is not None:
-                camera_id_block = camera_id_blocks[block_idx]
-                camera_id_mask = (camera_id_block != gallery_camera_id)
-                similarity_matrix = similarity_matrix.masked_select(
-                    camera_id_mask)
+            # image_id_block = image_id_blocks[block_idx]
+            # image_id_mask = (image_id_block != gallery_img_id)
+            # similarity_matrix = similarity_matrix.masked_select(image_id_mask)
+            # if query_camera_id is not None:
+            #     camera_id_block = camera_id_blocks[block_idx]
+            #     camera_id_mask = (camera_id_block != gallery_camera_id)
+            #     similarity_matrix = similarity_matrix.masked_select(
+            #         camera_id_mask)
             if similarity_matrix is None:
                 cum_similarity_matrix = similarity_matrix
             else:
-                cum_similarity_matrix = paddle.concat(cum_similarity_matrix,
-                                                      similarity_matrix)
+                cum_similarity_matrix = paddle.concat(
+                    [cum_similarity_matrix, similarity_matrix], axis=0)
 
         # calc metric
         if self.eval_metric_func is not None: