add stack_outputs

model.py

@@ -1083,7 +1083,7 @@ class Model(fluid.dygraph.Layer):
 
         return eval_result
 
-    def predict(self, test_data, batch_size=1, num_workers=0):
+    def predict(self, test_data, batch_size=1, num_workers=0, stack_outputs=True):
         """
         FIXME: add more comments and usage
         Args:
@@ -1096,6 +1096,12 @@ class Model(fluid.dygraph.Layer):
             num_workers (int): the number of subprocess to load data, 0 for no subprocess 
                 used and loading data in main process. When train_data and eval_data are
                 both the instance of Dataloader, this parameter will be ignored.
+            stack_output (bool): whether stack output field like a batch, as for an output
+                filed of a sample is in shape [X, Y], test_data contains N samples, predict
+                output field will be in shape [N, X, Y] if stack_output is True, and will
+                be a length N list in shape [[X, Y], [X, Y], ....[X, Y]] if stack_outputs
+                is False. stack_outputs as False is used for LoDTensor output situation,
+                it is recommended set as True if outputs contains no LoDTensor. Default False
         """
 
         if fluid.in_dygraph_mode():
@@ -1127,10 +1133,11 @@ class Model(fluid.dygraph.Layer):
             data = flatten(data)
             outputs.append(self.test(data[:len(self._inputs)]))
 
-        # NOTE: we do not stack or concanate here for output
-        # lod tensor may loss its detail info, just pack sample
-        # list data to batch data
+        # NOTE: for lod tensor output, we should not stack outputs
+        # for stacking may loss its detail info
         outputs = list(zip(*outputs))
+        if stack_outputs:
+            outputs = [np.stack(outs, axis=0) for outs in outputs]
 
         self._test_dataloader = None
         if test_loader is not None and self._adapter._nranks > 1 \

yolov3/README.md

@@ -78,7 +78,7 @@ YOLOv3 的网络结构由基础特征提取网络、multi-scale特征融合层
 模型目前支持COCO数据集格式的数据读入和精度评估，我们同时提供了将转换为COCO数据集的格式的Pascal VOC数据集下载，可通过如下命令下载。
 
   ```bash
-  python dataset/voc/download.py
+  python dataset/download_voc.py
   ```
 
 数据目录结构如下：

yolov3/darknet.py

-#  Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
+#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
 #
 #Licensed under the Apache License, Version 2.0 (the "License");
 #you may not use this file except in compliance with the License.

yolov3/dataset/voc/download.py → yolov3/dataset/download_voc.py

-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,9 +13,14 @@
 # limitations under the License.
 
 import os
+import os.path as osp
+import sys
 import tarfile
 
-from paddle.dataset.common import download
+from download import _download
+
+import logging
+logger = logging.getLogger(__name__)
 
 DATASETS = {
     'voc': [
@@ -26,7 +31,7 @@ DATASETS = {
 
 def download_decompress_file(data_dir, url, md5):
     logger.info("Downloading from {}".format(url))
-    tar_file = download(url, data_dir, md5)
+    tar_file = _download(url, data_dir, md5)
     logger.info("Decompressing {}".format(tar_file))
     with tarfile.open(tar_file) as tf:
         tf.extractall(path=data_dir)

yolov3/main.py

-# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -147,7 +147,7 @@ def main():
     if FLAGS.eval_only:
         if FLAGS.weights is not None:
             model.load(FLAGS.weights, reset_optimizer=True)
-        preds = model.predict(loader)
+        preds = model.predict(loader, stack_outputs=False)
         _, _, _, img_ids, bboxes = preds
 
         anno_path = os.path.join(FLAGS.data, 'annotations/instances_val2017.json')

yolov3/visualizer.py

@@ -18,14 +18,27 @@ from __future__ import print_function
 import numpy as np
 from PIL import Image, ImageDraw
 
-from colormap import colormap
-
 import logging
 logger = logging.getLogger(__name__)
 
 __all__ = ['draw_bbox']
 
 
+def color_map(num_classes):
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = np.array(color_map).reshape(-1, 3)
+    return color_map
+
+
 def draw_bbox(image, catid2name, bboxes, threshold):
     """
     Draw bbox on image
@@ -38,7 +51,7 @@ def draw_bbox(image, catid2name, bboxes, threshold):
     draw = ImageDraw.Draw(image)
 
     catid2color = {}
-    color_list = colormap(rgb=True)[:40]
+    color_list = color_map(len(catid2name))
     for bbox in bboxes:
         catid, score, xmin, ymin, xmax, ymax = bbox