revert fluid.data to fluid.layers.data (#42)

PaddlePaddle · Nov 26, 2019 · 50381ed · 50381ed
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commit 50381ed
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Showing 3 changed files with 26 additions and 28 deletions.
diff --git a/docs/EXPORT_MODEL.md b/docs/EXPORT_MODEL.md
@@ -14,14 +14,14 @@
 使用[训练/评估/推断](GETTING_STARTED_cn.md)中训练得到的模型进行试用，脚本如下
 
 ```bash
-# 导出FasterRCNN模型
+# 导出FasterRCNN模型, 模型中data层默认的shape为3x800x1333
 python tools/export_model.py -c configs/faster_rcnn_r50_1x.yml \
  --output_dir=./inference_model \
  -o weights=output/faster_rcnn_r50_1x/model_final \
 
 ```
 
-- 预测模型会导出到`inference_model/faster_rcnn_r50_1x`目录下，模型名和参数名分别为`__model__`和`__params__`。
+预测模型会导出到`inference_model/faster_rcnn_r50_1x`目录下，模型名和参数名分别为`__model__`和`__params__`。
 
 ## 设置导出模型的输入大小
 
@@ -46,5 +46,3 @@ python tools/export_model.py -c configs/ssd/ssd_mobilenet_v1_voc.yml \
  -o weights= https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar \
  SSDTestFeed.image_shape=[3,300,300]
 ```
-
-- 保存FPN系列模型时，需要保证上下采样维度一致，因此image_shape须设置为32的倍数
diff --git a/ppdet/data/data_feed.py b/ppdet/data/data_feed.py
@@ -453,7 +453,7 @@ def __init__(self,
  'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
  'is_crowd'
  ],
- image_shape=[3, None, None],
+ image_shape=[3, 800, 1333],
  sample_transforms=[
  DecodeImage(to_rgb=True),
  RandomFlipImage(prob=0.5),
@@ -505,7 +505,7 @@ def __init__(self,
  COCO_VAL_IMAGE_DIR).__dict__,
  fields=['image', 'im_info', 'im_id', 'im_shape', 'gt_box',
  'gt_label', 'is_difficult'],
- image_shape=[3, None, None],
+ image_shape=[3, 800, 1333],
  sample_transforms=[
  DecodeImage(to_rgb=True),
  NormalizeImage(mean=[0.485, 0.456, 0.406],
@@ -552,7 +552,7 @@ def __init__(self,
  dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
  COCO_VAL_IMAGE_DIR).__dict__,
  fields=['image', 'im_info', 'im_id', 'im_shape'],
- image_shape=[3, None, None],
+ image_shape=[3, 800, 1333],
  sample_transforms=[
  DecodeImage(to_rgb=True),
  NormalizeImage(mean=[0.485, 0.456, 0.406],
@@ -600,7 +600,7 @@ def __init__(self,
  'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
  'is_crowd', 'gt_mask'
  ],
- image_shape=[3, None, None],
+ image_shape=[3, 800, 1333],
  sample_transforms=[
  DecodeImage(to_rgb=True),
  RandomFlipImage(prob=0.5, is_mask_flip=True),
@@ -646,7 +646,7 @@ def __init__(self,
  dataset=CocoDataSet(COCO_VAL_ANNOTATION,
  COCO_VAL_IMAGE_DIR).__dict__,
  fields=['image', 'im_info', 'im_id', 'im_shape'],
- image_shape=[3, None, None],
+ image_shape=[3, 800, 1333],
  sample_transforms=[
  DecodeImage(to_rgb=True),
  NormalizeImage(mean=[0.485, 0.456, 0.406],
@@ -698,7 +698,7 @@ def __init__(self,
  dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
  COCO_VAL_IMAGE_DIR).__dict__,
  fields=['image', 'im_info', 'im_id', 'im_shape'],
- image_shape=[3, None, None],
+ image_shape=[3, 800, 1333],
  sample_transforms=[
  DecodeImage(to_rgb=True),
  NormalizeImage(

diff --git a/ppdet/modeling/model_input.py b/ppdet/modeling/model_input.py
@@ -25,22 +25,22 @@
 
 # yapf: disable
 feed_var_def = [
- {'name': 'im_info', 'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
- {'name': 'im_id', 'shape': [None, 1], 'dtype': 'int32', 'lod_level': 0},
- {'name': 'gt_box', 'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
- {'name': 'gt_label', 'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
- {'name': 'is_crowd', 'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
- {'name': 'gt_mask', 'shape': [None, 2], 'dtype': 'float32', 'lod_level': 3},
- {'name': 'is_difficult', 'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
- {'name': 'gt_score', 'shape': [None, 1], 'dtype': 'float32', 'lod_level': 0},
- {'name': 'im_shape', 'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
- {'name': 'im_size', 'shape': [None, 2], 'dtype': 'int32', 'lod_level': 0},
+ {'name': 'im_info', 'shape': [3], 'dtype': 'float32', 'lod_level': 0},
+ {'name': 'im_id', 'shape': [1], 'dtype': 'int32', 'lod_level': 0},
+ {'name': 'gt_box', 'shape': [4], 'dtype': 'float32', 'lod_level': 1},
+ {'name': 'gt_label', 'shape': [1], 'dtype': 'int32', 'lod_level': 1},
+ {'name': 'is_crowd', 'shape': [1], 'dtype': 'int32', 'lod_level': 1},
+ {'name': 'gt_mask', 'shape': [2], 'dtype': 'float32', 'lod_level': 3},
+ {'name': 'is_difficult', 'shape': [1], 'dtype': 'int32', 'lod_level': 1},
+ {'name': 'gt_score', 'shape': [1], 'dtype': 'float32', 'lod_level': 0},
+ {'name': 'im_shape', 'shape': [3], 'dtype': 'float32', 'lod_level': 0},
+ {'name': 'im_size', 'shape': [2], 'dtype': 'int32', 'lod_level': 0},
 ]
 # yapf: enable
 
 
 def create_feed(feed, iterable=False, sub_prog_feed=False):
- image_shape = [None] + feed.image_shape
+ image_shape = feed.image_shape
  feed_var_map = {var['name']: var for var in feed_var_def}
  feed_var_map['image'] = {
  'name': 'image',
@@ -52,10 +52,10 @@ def create_feed(feed, iterable=False, sub_prog_feed=False):
  # tensor padding with 0 is used instead of LoD tensor when 
  # num_max_boxes is set
  if getattr(feed, 'num_max_boxes', None) is not None:
- feed_var_map['gt_label']['shape'] = [None, feed.num_max_boxes]
- feed_var_map['gt_score']['shape'] = [None, feed.num_max_boxes]
- feed_var_map['gt_box']['shape'] = [None, feed.num_max_boxes, 4]
- feed_var_map['is_difficult']['shape'] = [None, feed.num_max_boxes]
+ feed_var_map['gt_label']['shape'] = [feed.num_max_boxes]
+ feed_var_map['gt_score']['shape'] = [feed.num_max_boxes]
+ feed_var_map['gt_box']['shape'] = [feed.num_max_boxes, 4]
+ feed_var_map['is_difficult']['shape'] = [feed.num_max_boxes]
  feed_var_map['gt_label']['lod_level'] = 0
  feed_var_map['gt_score']['lod_level'] = 0
  feed_var_map['gt_box']['lod_level'] = 0
@@ -98,22 +98,22 @@ def create_feed(feed, iterable=False, sub_prog_feed=False):
  'lod_level': 0
  }
  image_name_list.append(name)
- feed_var_map['im_info']['shape'] = [None, feed.num_scale * 3]
+ feed_var_map['im_info']['shape'] = [feed.num_scale * 3]
  feed.fields = image_name_list + feed.fields[1:]
  if sub_prog_feed:
  box_names = ['bbox', 'bbox_flip']
  for box_name in box_names:
  sub_prog_feed = {
  'name': box_name,
- 'shape': [None, 6],
+ 'shape': [6],
  'dtype': 'float32',
  'lod_level': 1
  }
 
  feed.fields = feed.fields + [box_name]
  feed_var_map[box_name] = sub_prog_feed
 
- feed_vars = OrderedDict([(key, fluid.data(
+ feed_vars = OrderedDict([(key, fluid.layers.data(
  name=feed_var_map[key]['name'],
  shape=feed_var_map[key]['shape'],
  dtype=feed_var_map[key]['dtype'],