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!8732 [MD][PERFORMANCE] Fix Faster RCNN x86 performance issue 

From: @xiefangqi
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2020-11-30 20:26:29 +08:00 committed by Gitee
parent 339f34a08c 8d0f80a79d
commit 0a9899e3a1
3 changed files with 24 additions and 93 deletions
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3
model_zoo/official/cv/faster_rcnn/eval.py
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@ -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): def FasterRcnn_eval(dataset_path, ckpt_path, ann_file):
"""FasterRcnn evaluation.""" """FasterRcnn evaluation."""
ds = create_fasterrcnn_dataset(dataset_path, batch_size=config.test_batch_size, ds = create_fasterrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
repeat_num=1, is_training=False)
net = Faster_Rcnn_Resnet50(config) net = Faster_Rcnn_Resnet50(config)
param_dict = load_checkpoint(ckpt_path) param_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, param_dict) load_param_into_net(net, param_dict)

110
model_zoo/official/cv/faster_rcnn/src/dataset.py
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@ -23,11 +23,9 @@ from numpy import random
import mmcv import mmcv
import mindspore.dataset as de import mindspore.dataset as de
import mindspore.dataset.vision.c_transforms as C 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 mindspore.mindrecord import FileWriter
from src.config import config from src.config import config
import cv2
def bbox_overlaps(bboxes1, bboxes2, mode='iou'): def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
"""Calculate the ious between each bbox of bboxes1 and bboxes2. """Calculate the ious between each bbox of bboxes1 and bboxes2.
@ -74,10 +72,8 @@ def bbox_overlaps(bboxes1, bboxes2, mode='iou'):
ious = ious.T ious = ious.T
return ious return ious
class PhotoMetricDistortion: class PhotoMetricDistortion:
"""Photo Metric Distortion""" """Photo Metric Distortion"""
def __init__(self, def __init__(self,
brightness_delta=32, brightness_delta=32,
contrast_range=(0.5, 1.5), contrast_range=(0.5, 1.5),
@ -136,10 +132,8 @@ class PhotoMetricDistortion:
return img, boxes, labels return img, boxes, labels
class Expand: class Expand:
"""expand image""" """expand image"""
def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)): def __init__(self, mean=(0, 0, 0), to_rgb=True, ratio_range=(1, 4)):
if to_rgb: if to_rgb:
self.mean = mean[::-1] self.mean = mean[::-1]
@ -162,7 +156,6 @@ class Expand:
boxes += np.tile((left, top), 2) boxes += np.tile((left, top), 2)
return img, boxes, labels return img, boxes, labels
def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num): def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""rescale operation for image""" """rescale operation for image"""
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True) 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) return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num): def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""resize operation for image""" """resize operation for image"""
img_data = img 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) return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def resize_column_test(img, 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""" """resize operation for image of eval"""
img_data = img 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) 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): def impad_to_multiple_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""impad operation for image""" """impad operation for image"""
img_data = mmcv.impad(img, (config.img_height, config.img_width)) img_data = mmcv.impad(img, (config.img_height, config.img_width))
img_data = img_data.astype(np.float32) img_data = img_data.astype(np.float32)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num) return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def imnormalize_column(img, img_shape, gt_bboxes, gt_label, gt_num): def imnormalize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""imnormalize operation for image""" """imnormalize operation for image"""
img_data = mmcv.imnormalize(img, [123.675, 116.28, 103.53], [58.395, 57.12, 57.375], True) 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) img_data = img_data.astype(np.float32)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num) return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def flip_column(img, img_shape, gt_bboxes, gt_label, gt_num): def flip_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""flip operation for image""" """flip operation for image"""
img_data = img 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) 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): def transpose_column(img, img_shape, gt_bboxes, gt_label, gt_num):
"""transpose operation for image""" """transpose operation for image"""
img_data = img.transpose(2, 0, 1).copy() 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) return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def photo_crop_column(img, 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""" """photo crop operation for image"""
random_photo = PhotoMetricDistortion() 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) return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
def expand_column(img, 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 operation for image"""
expand = Expand() 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) return (img, img_shape, gt_bboxes, gt_label, gt_num)
def preprocess_fn(image, box, is_training): def preprocess_fn(image, box, is_training):
"""Preprocess function for dataset.""" """Preprocess function for dataset."""
def _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert): def _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert):
image_shape = image_shape[:2] image_shape = image_shape[:2]
input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert 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) input_data = rescale_column(*input_data)
else: else:
input_data = resize_column_test(*input_data) input_data = resize_column_test(*input_data)
input_data = imnormalize_column(*input_data)
input_data = image_bgr_rgb(*input_data) output_data = transpose_column(*input_data)
output_data = input_data
return output_data return output_data
def _data_aug(image, box, is_training): def _data_aug(image, box, is_training):
@ -324,25 +289,25 @@ def preprocess_fn(image, box, is_training):
if not is_training: if not is_training:
return _infer_data(image_bgr, image_shape, gt_box_new, gt_label_new, gt_iscrowd_new_revert) 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 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: if expand:
input_data = expand_column(*input_data) input_data = expand_column(*input_data)
if config.keep_ratio: if config.keep_ratio:
input_data = rescale_column(*input_data) input_data = rescale_column(*input_data)
else: else:
input_data = resize_column(*input_data) 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 = transpose_column(*input_data)
output_data = input_data
return output_data return output_data
return _data_aug(image, box, is_training) return _data_aug(image, box, is_training)
def create_coco_label(is_training): def create_coco_label(is_training):
"""Get image path and annotation from COCO.""" """Get image path and annotation from COCO."""
from pycocotools.coco import COCO from pycocotools.coco import COCO
@ -393,7 +358,6 @@ def create_coco_label(is_training):
return image_files, image_anno_dict return image_files, image_anno_dict
def anno_parser(annos_str): def anno_parser(annos_str):
"""Parse annotation from string to list.""" """Parse annotation from string to list."""
annos = [] annos = []
@ -402,7 +366,6 @@ def anno_parser(annos_str):
annos.append(anno) annos.append(anno)
return annos return annos
def filter_valid_data(image_dir, anno_path): def filter_valid_data(image_dir, anno_path):
"""Filter valid image file, which both in image_dir and anno_path.""" """Filter valid image file, which both in image_dir and anno_path."""
image_files = [] image_files = []
@ -424,7 +387,6 @@ def filter_valid_data(image_dir, anno_path):
image_files.append(image_path) image_files.append(image_path)
return image_files, image_anno_dict return image_files, image_anno_dict
def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="fasterrcnn.mindrecord", file_num=8): def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="fasterrcnn.mindrecord", file_num=8):
"""Create MindRecord file.""" """Create MindRecord file."""
mindrecord_dir = config.mindrecord_dir 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.write_raw_data([row])
writer.commit() writer.commit()
def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, device_num=1, rank_id=0, is_training=True,
def create_fasterrcnn_dataset(mindrecord_file, batch_size=2, repeat_num=12, device_num=1, rank_id=0, num_parallel_workers=8):
is_training=True, num_parallel_workers=4):
"""Creatr FasterRcnn dataset with MindDataset.""" """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, 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() 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)) 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: 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"], output_columns=["image", "image_shape", "box", "label", "valid_num"],
column_order=["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) num_parallel_workers=num_parallel_workers)
ds = ds.batch(batch_size, drop_remainder=True)
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)
else: else:
ds = ds.map(operations=compose_map_func, ds = ds.map(input_columns=["image", "annotation"],
input_columns=["image", "annotation"],
output_columns=["image", "image_shape", "box", "label", "valid_num"], output_columns=["image", "image_shape", "box", "label", "valid_num"],
column_order=["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) num_parallel_workers=num_parallel_workers)
ds = ds.batch(batch_size, drop_remainder=True)
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)
return ds return ds

4
model_zoo/official/cv/faster_rcnn/train.py
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@ -101,8 +101,8 @@ if __name__ == '__main__':
loss_scale = float(config.loss_scale) loss_scale = float(config.loss_scale)
# When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0. # When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0.
dataset = create_fasterrcnn_dataset(mindrecord_file, repeat_num=1, dataset = create_fasterrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
batch_size=config.batch_size, device_num=device_num, rank_id=rank) device_num=device_num, rank_id=rank)
dataset_size = dataset.get_dataset_size() dataset_size = dataset.get_dataset_size()
print("Create dataset done!") print("Create dataset done!")