From 8d0f80a79d2732b3319ca4272c7a13ba22c81e3f Mon Sep 17 00:00:00 2001
From: xiefangqi <xiefangqi2@huawei.com>
Date: Wed, 18 Nov 2020 14:22:17 +0800
Subject: [PATCH] fix fasterrcnn x86 performance problem
---
model_zoo/official/cv/faster_rcnn/eval.py | 3 +-
.../official/cv/faster_rcnn/src/dataset.py | 110 ++++--------------
model_zoo/official/cv/faster_rcnn/train.py | 4 +-
3 files changed, 24 insertions(+), 93 deletions(-)
diff --git a/model_zoo/official/cv/faster_rcnn/eval.py b/model_zoo/official/cv/faster_rcnn/eval.py
index a7831bec9f..866d9ba7d5 100644
--- a/model_zoo/official/cv/faster_rcnn/eval.py
+++ b/model_zoo/official/cv/faster_rcnn/eval.py
@@ -41,8 +41,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=a
def FasterRcnn_eval(dataset_path, ckpt_path, ann_file):
"""FasterRcnn evaluation."""
- ds = create_fasterrcnn_dataset(dataset_path, batch_size=config.test_batch_size,
- repeat_num=1, is_training=False)
+ ds = create_fasterrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
net = Faster_Rcnn_Resnet50(config)
param_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, param_dict)
diff --git a/model_zoo/official/cv/faster_rcnn/src/dataset.py b/model_zoo/official/cv/faster_rcnn/src/dataset.py
index 150c5a15bd..2c4fd20714 100644
--- a/model_zoo/official/cv/faster_rcnn/src/dataset.py
+++ b/model_zoo/official/cv/faster_rcnn/src/dataset.py
@@ -23,11 +23,9 @@ from numpy import random
import mmcv
import mindspore.dataset as de
import mindspore.dataset.vision.c_transforms as C
-import mindspore.dataset.transforms.c_transforms as CC
-import mindspore.common.dtype as mstype
from mindspore.mindrecord import FileWriter
from src.config import config
-
+import cv2
def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
"""Calculate the ious between each bbox of bboxes1 and bboxes2.
@@ -74,10 +72,8 @@ def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
ious = ious.T
return ious
-
class PhotoMetricDistortion:
"""Photo Metric Distortion"""
-
def __init__(self,
brightness_delta=32,
contrast_range=(0.5, 1.5),
@@ -136,10 +132,8 @@ class PhotoMetricDistortion:
return img, boxes, labels
-
class Expand:
"""expand image"""
-
def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)):
if to_rgb:
self.mean = mean[::-1]
@@ -162,7 +156,6 @@ class Expand:
boxes += np.tile((left, top), 2)
return img, boxes, labels
-
def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""rescale operation for image"""
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True)
@@ -178,7 +171,6 @@ def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""resize operation for image"""
img_data = img
@@ -196,7 +188,6 @@ def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def resize_column_test(img, img_shape, gt_bboxes, gt_label, gt_num):
"""resize operation for image of eval"""
img_data = img
@@ -214,21 +205,18 @@ def resize_column_test(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def impad_to_multiple_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""impad operation for image"""
img_data = mmcv.impad(img, (config.img_height, config.img_width))
img_data = img_data.astype(np.float32)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def imnormalize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""imnormalize operation for image"""
img_data = mmcv.imnormalize(img, [123.675, 116.28, 103.53], [58.395, 57.12, 57.375], True)
img_data = img_data.astype(np.float32)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def flip_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""flip operation for image"""
img_data = img
@@ -241,24 +229,6 @@ def flip_column(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img_data, img_shape, flipped, gt_label, gt_num)
-
-def flipped_generation(img, img_shape, gt_bboxes, gt_label, gt_num):
- """flipped generation"""
- img_data = img
- flipped = gt_bboxes.copy()
- _, w, _ = img_data.shape
-
- flipped[..., 0::4] = w - gt_bboxes[..., 2::4] - 1
- flipped[..., 2::4] = w - gt_bboxes[..., 0::4] - 1
-
- return (img_data, img_shape, flipped, gt_label, gt_num)
-
-
-def image_bgr_rgb(img, img_shape, gt_bboxes, gt_label, gt_num):
- img_data = img[:, :, ::-1]
- return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
-
def transpose_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""transpose operation for image"""
img_data = img.transpose(2, 0, 1).copy()
@@ -270,7 +240,6 @@ def transpose_column(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def photo_crop_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""photo crop operation for image"""
random_photo = PhotoMetricDistortion()
@@ -278,7 +247,6 @@ def photo_crop_column(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
-
def expand_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""expand operation for image"""
expand = Expand()
@@ -286,10 +254,8 @@ def expand_column(img, img_shape, gt_bboxes, gt_label, gt_num):
return (img, img_shape, gt_bboxes, gt_label, gt_num)
-
def preprocess_fn(image, box, is_training):
"""Preprocess function for dataset."""
-
def _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert):
image_shape = image_shape[:2]
input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert
@@ -298,10 +264,9 @@ def preprocess_fn(image, box, is_training):
input_data = rescale_column(*input_data)
else:
input_data = resize_column_test(*input_data)
+ input_data = imnormalize_column(*input_data)
- input_data = image_bgr_rgb(*input_data)
-
- output_data = input_data
+ output_data = transpose_column(*input_data)
return output_data
def _data_aug(image, box, is_training):
@@ -324,25 +289,25 @@ def preprocess_fn(image, box, is_training):
if not is_training:
return _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert)
+ flip = (np.random.rand() < config.flip_ratio)
+ expand = (np.random.rand() < config.expand_ratio)
input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert
- expand = (np.random.rand() < config.expand_ratio)
if expand:
input_data = expand_column(*input_data)
-
if config.keep_ratio:
input_data = rescale_column(*input_data)
else:
input_data = resize_column(*input_data)
+ input_data = imnormalize_column(*input_data)
+ if flip:
+ input_data = flip_column(*input_data)
- input_data = image_bgr_rgb(*input_data)
-
- output_data = input_data
+ output_data = transpose_column(*input_data)
return output_data
return _data_aug(image, box, is_training)
-
def create_coco_label(is_training):
"""Get image path and annotation from COCO."""
from pycocotools.coco import COCO
@@ -393,7 +358,6 @@ def create_coco_label(is_training):
return image_files, image_anno_dict
-
def anno_parser(annos_str):
"""Parse annotation from string to list."""
annos = []
@@ -402,7 +366,6 @@ def anno_parser(annos_str):
annos.append(anno)
return annos
-
def filter_valid_data(image_dir, anno_path):
"""Filter valid image file, which both in image_dir and anno_path."""
image_files = []
@@ -424,7 +387,6 @@ def filter_valid_data(image_dir, anno_path):
image_files.append(image_path)
return image_files, image_anno_dict
-
def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="fasterrcnn.mindrecord", file_num=8):
"""Create MindRecord file."""
mindrecord_dir = config.mindrecord_dir
@@ -449,59 +411,29 @@ def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="fast
writer.write_raw_data([row])
writer.commit()
-
-def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, device_num=1, rank_id=0,
- is_training=True, num_parallel_workers=4):
+def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, device_num=1, rank_id=0, is_training=True,
+ num_parallel_workers=8):
"""Creatr FasterRcnn dataset with MindDataset."""
+ cv2.setNumThreads(0)
+ de.config.set_prefetch_size(8)
ds = de.MindDataset(mindrecord_file, columns_list=["image", "annotation"], num_shards=device_num, shard_id=rank_id,
- num_parallel_workers=1, shuffle=is_training)
+ num_parallel_workers=4, shuffle=is_training)
decode = C.Decode()
- ds = ds.map(operations=decode, input_columns=["image"], num_parallel_workers=1)
+ ds = ds.map(input_columns=["image"], operations=decode)
compose_map_func = (lambda image, annotation: preprocess_fn(image, annotation, is_training))
- hwc_to_chw = C.HWC2CHW()
- normalize_op = C.Normalize((123.675, 116.28, 103.53), (58.395, 57.12, 57.375))
- horizontally_op = C.RandomHorizontalFlip(1)
- type_cast0 = CC.TypeCast(mstype.float32)
- type_cast1 = CC.TypeCast(mstype.float16)
- type_cast2 = CC.TypeCast(mstype.int32)
- type_cast3 = CC.TypeCast(mstype.bool_)
-
if is_training:
- ds = ds.map(operations=compose_map_func, input_columns=["image", "annotation"],
+ ds = ds.map(input_columns=["image", "annotation"],
output_columns=["image", "image_shape", "box", "label", "valid_num"],
column_order=["image", "image_shape", "box", "label", "valid_num"],
+ operations=compose_map_func, python_multiprocessing=False,
num_parallel_workers=num_parallel_workers)
-
- flip = (np.random.rand() < config.flip_ratio)
- if flip:
- ds = ds.map(operations=[normalize_op, type_cast0, horizontally_op], input_columns=["image"],
- num_parallel_workers=12)
- ds = ds.map(operations=flipped_generation,
- input_columns=["image", "image_shape", "box", "label", "valid_num"],
- num_parallel_workers=num_parallel_workers)
- else:
- ds = ds.map(operations=[normalize_op, type_cast0], input_columns=["image"],
- num_parallel_workers=12)
- ds = ds.map(operations=[hwc_to_chw, type_cast1], input_columns=["image"],
- num_parallel_workers=12)
-
+ ds = ds.batch(batch_size, drop_remainder=True)
else:
- ds = ds.map(operations=compose_map_func,
- input_columns=["image", "annotation"],
+ ds = ds.map(input_columns=["image", "annotation"],
output_columns=["image", "image_shape", "box", "label", "valid_num"],
column_order=["image", "image_shape", "box", "label", "valid_num"],
+ operations=compose_map_func,
num_parallel_workers=num_parallel_workers)
-
- ds = ds.map(operations=[normalize_op, hwc_to_chw, type_cast1], input_columns=["image"],
- num_parallel_workers=24)
-
- # transpose_column from python to c
- ds = ds.map(operations=[type_cast1], input_columns=["image_shape"])
- ds = ds.map(operations=[type_cast1], input_columns=["box"])
- ds = ds.map(operations=[type_cast2], input_columns=["label"])
- ds = ds.map(operations=[type_cast3], input_columns=["valid_num"])
- ds = ds.batch(batch_size, drop_remainder=True)
- ds = ds.repeat(repeat_num)
-
+ ds = ds.batch(batch_size, drop_remainder=True)
return ds
diff --git a/model_zoo/official/cv/faster_rcnn/train.py b/model_zoo/official/cv/faster_rcnn/train.py
index 1e08ebf4ff..bcdbbfe310 100644
--- a/model_zoo/official/cv/faster_rcnn/train.py
+++ b/model_zoo/official/cv/faster_rcnn/train.py
@@ -101,8 +101,8 @@ if __name__ == '__main__':
loss_scale = float(config.loss_scale)
# When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0.
- dataset = create_fasterrcnn_dataset(mindrecord_file, repeat_num=1,
- batch_size=config.batch_size, device_num=device_num, rank_id=rank)
+ dataset = create_fasterrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
+ device_num=device_num, rank_id=rank)
dataset_size = dataset.get_dataset_size()
print("Create dataset done!")