Skip to content

M
models

PaddlePaddle / models
大约 1 年 前同步成功

通知 222
Star 6828
Fork 2962
M
models
未验证 提交 fba30199 编写于 作者: W wangguanzhong 提交者： GitHub
浏览文件
操作

change fluid.layers.data to fluid.data (#3966)

上级 c73b6dbd
隐藏空白更改
内联 并排
Showing with 31 addition and 29 deletion
PaddleCV/PaddleDetection/docs/EXPORT_MODEL.md
浏览文件 @ fba30199
......@@ -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, 模型中data层默认的shape为3x800x1333
# 导出FasterRCNN模型
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
......@@ -46,3 +46,5 @@ 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
PaddleCV/PaddleDetection/ppdet/data/data_feed.py
浏览文件 @ fba30199
......@@ -452,7 +452,7 @@ class FasterRCNNTrainFeed(DataFeed):
'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
'is_crowd'
],
image_shape=[3, 800, 1333],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
RandomFlipImage(prob=0.5),
......@@ -504,7 +504,7 @@ class FasterRCNNEvalFeed(DataFeed):
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape', 'gt_box',
'gt_label', 'is_difficult'],
image_shape=[3, 800, 1333],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
......@@ -551,7 +551,7 @@ class FasterRCNNTestFeed(DataFeed):
dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 800, 1333],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
......@@ -598,7 +598,7 @@ class MaskRCNNTrainFeed(DataFeed):
'image', 'im_info', 'im_id', 'gt_box', 'gt_label',
'is_crowd', 'gt_mask'
],
image_shape=[3, 800, 1333],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
RandomFlipImage(prob=0.5, is_mask_flip=True),
......@@ -644,7 +644,7 @@ class MaskRCNNEvalFeed(DataFeed):
dataset=CocoDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 800, 1333],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(mean=[0.485, 0.456, 0.406],
......@@ -696,7 +696,7 @@ class MaskRCNNTestFeed(DataFeed):
dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_info', 'im_id', 'im_shape'],
image_shape=[3, 800, 1333],
image_shape=[None, 3, None, None],
sample_transforms=[
DecodeImage(to_rgb=True),
NormalizeImage(
......@@ -740,7 +740,7 @@ class SSDTrainFeed(DataFeed):
def __init__(self,
dataset=VocDataSet().__dict__,
fields=['image', 'gt_box', 'gt_label'],
image_shape=[3, 300, 300],
image_shape=[None, 3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True, with_mixup=False),
NormalizeBox(),
......@@ -799,7 +799,7 @@ class SSDEvalFeed(DataFeed):
dataset=VocDataSet(VOC_VAL_ANNOTATION).__dict__,
fields=['image', 'im_shape', 'im_id', 'gt_box',
'gt_label', 'is_difficult'],
image_shape=[3, 300, 300],
image_shape=[None, 3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True, with_mixup=False),
NormalizeBox(),
......@@ -844,7 +844,7 @@ class SSDTestFeed(DataFeed):
def __init__(self,
dataset=SimpleDataSet(VOC_VAL_ANNOTATION).__dict__,
fields=['image', 'im_id', 'im_shape'],
image_shape=[3, 300, 300],
image_shape=[None, 3, 300, 300],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(target_size=300, use_cv2=False, interp=1),
......@@ -890,7 +890,7 @@ class YoloTrainFeed(DataFeed):
def __init__(self,
dataset=CocoDataSet().__dict__,
fields=['image', 'gt_box', 'gt_label', 'gt_score'],
image_shape=[3, 608, 608],
image_shape=[None, 3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True, with_mixup=True),
MixupImage(alpha=1.5, beta=1.5),
......@@ -962,7 +962,7 @@ class YoloEvalFeed(DataFeed):
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_size', 'im_id', 'gt_box',
'gt_label', 'is_difficult'],
image_shape=[3, 608, 608],
image_shape=[None, 3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(target_size=608, interp=2),
......@@ -1018,7 +1018,7 @@ class YoloTestFeed(DataFeed):
dataset=SimpleDataSet(COCO_VAL_ANNOTATION,
COCO_VAL_IMAGE_DIR).__dict__,
fields=['image', 'im_size', 'im_id'],
image_shape=[3, 608, 608],
image_shape=[None, 3, 608, 608],
sample_transforms=[
DecodeImage(to_rgb=True),
ResizeImage(target_size=608, interp=2),
......
PaddleCV/PaddleDetection/ppdet/modeling/model_input.py
浏览文件 @ fba30199
......@@ -25,16 +25,16 @@ __all__ = ['create_feed']
# yapf: disable
feed_var_def = [
{'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},
{'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},
]
# yapf: enable
......@@ -52,10 +52,10 @@ def create_feed(feed, use_pyreader=True, 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'] = [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']['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']['lod_level'] = 0
feed_var_map['gt_score']['lod_level'] = 0
feed_var_map['gt_box']['lod_level'] = 0
......@@ -113,7 +113,7 @@ def create_feed(feed, use_pyreader=True, sub_prog_feed=False):
feed.fields = feed.fields + [box_name]
feed_var_map[box_name] = sub_prog_feed
feed_vars = OrderedDict([(key, fluid.layers.data(
feed_vars = OrderedDict([(key, fluid.data(
name=feed_var_map[key]['name'],
shape=feed_var_map[key]['shape'],
dtype=feed_var_map[key]['dtype'],
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或