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!6356 improve maskrcnn precision and performance 

Merge pull request !6356 from gengdongjie/master
This commit is contained in:
mindspore-ci-bot 2020-09-17 14:47:22 +08:00 committed by Gitee
parent 65c960d3e2 33d8fd8602
commit 74255d9faf
7 changed files with 38 additions and 22 deletions
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5
mindspore/ops/_op_impl/tbe/tile.py
View File

@ -26,9 +26,8 @@ tile_op_info = TBERegOp("Tile") \
.attr("multiples", "optional", "listInt", "all")\
.input(0, "x1", False, "required", "all") \
.output(0, "y", False, "required", "all") \
.dtype_format(DataType.I32_Default, DataType.I32_Default) \
.dtype_format(DataType.F16_Default, DataType.F16_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default) \
.op_pattern("dynamicFormat") \
.dtype_format(DataType.None_None, DataType.None_None) \
.get_op_info()

5
mindspore/ops/operations/image_ops.py
View File

@ -30,7 +30,8 @@ class CropAndResize(PrimitiveWithInfer):
Args:
method (str): An optional string that specifies the sampling method for resizing.
It can be either "bilinear" or "nearest". Default: "bilinear"
It can be "bilinear", "nearest" or "bilinear_v2". The option "bilinear" stands for standard bilinear
interpolation algorithm, while "bilinear_v2" may result in better result in some cases. Default: "bilinear"
extrapolation_value (float): An optional float value used extrapolation, if applicable. Default: 0.
Inputs:
@ -81,7 +82,7 @@ class CropAndResize(PrimitiveWithInfer):
"""init CropAndResize"""
self.init_prim_io_names(inputs=['x', 'boxes', 'box_index', 'crop_size'], outputs=['y'])
validator.check_value_type("method", method, [str], self.name)
validator.check_string("method", method, ["bilinear", "nearest"], self.name)
validator.check_string("method", method, ["bilinear", "nearest", "bilinear_v2"], self.name)
self.method = method
validator.check_value_type("extrapolation_value", extrapolation_value, [float], self.name)
self.extrapolation_value = extrapolation_value

15
model_zoo/official/cv/maskrcnn/src/dataset.py
View File

@ -406,9 +406,14 @@ def create_coco_label(is_training):
image_anno_dict = {}
masks = {}
masks_shape = {}
for img_id in image_ids:
images_num = len(image_ids)
for ind, img_id in enumerate(image_ids):
image_info = coco.loadImgs(img_id)
file_name = image_info[0]["file_name"]
image_path = os.path.join(coco_root, data_type, file_name)
if not os.path.isfile(image_path):
print("{}/{}: {} is in annotations but not exist".format(ind + 1, images_num, image_path))
continue
anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)
anno = coco.loadAnns(anno_ids)
image_path = os.path.join(coco_root, data_type, file_name)
@ -416,7 +421,8 @@ def create_coco_label(is_training):
instance_masks = []
image_height = coco.imgs[img_id]["height"]
image_width = coco.imgs[img_id]["width"]
print("image file name: ", file_name)
if (ind + 1) % 10 == 0:
print("{}/{}: parsing annotation for image={}".format(ind + 1, images_num, file_name))
if not is_training:
image_files.append(image_path)
image_anno_dict[image_path] = np.array([0, 0, 0, 0, 0, 1])
@ -478,13 +484,16 @@ def data_to_mindrecord_byte_image(dataset="coco", is_training=True, prefix="mask
}
writer.add_schema(maskrcnn_json, "maskrcnn_json")
for image_name in image_files:
image_files_num = len(image_files)
for ind, image_name in enumerate(image_files):
with open(image_name, 'rb') as f:
img = f.read()
annos = np.array(image_anno_dict[image_name], dtype=np.int32)
mask = masks[image_name]
mask_shape = masks_shape[image_name]
row = {"image": img, "annotation": annos, "mask": mask, "mask_shape": mask_shape}
if (ind + 1) % 10 == 0:
print("writing {}/{} into mindrecord".format(ind + 1, image_files_num))
writer.write_raw_data([row])
writer.commit()

2
model_zoo/official/cv/maskrcnn/src/maskrcnn/bbox_assign_sample_stage2.py
View File

@ -108,7 +108,7 @@ class BboxAssignSampleForRcnn(nn.Cell):
self.round = P.Round()
self.image_h_w = Tensor([cfg.img_height, cfg.img_width, cfg.img_height, cfg.img_width], dtype=mstype.float16)
self.range = nn.Range(start=0, limit=cfg.num_expected_pos_stage2)
self.crop_and_resize = P.CropAndResize()
self.crop_and_resize = P.CropAndResize(method="bilinear_v2")
self.mask_shape = (cfg.mask_shape[0], cfg.mask_shape[1])
self.squeeze_mask_last = P.Squeeze(axis=-1)
def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids, gt_masks_i):

13
model_zoo/official/cv/maskrcnn/src/maskrcnn/fpn_neck.py
View File

@ -84,9 +84,10 @@ class FeatPyramidNeck(nn.Cell):
self.fpn_convs_.append(fpn_conv)
self.lateral_convs_list = nn.layer.CellList(self.lateral_convs_list_)
self.fpn_convs_list = nn.layer.CellList(self.fpn_convs_)
self.interpolate1 = P.ResizeNearestNeighbor((48, 80))
self.interpolate2 = P.ResizeNearestNeighbor((96, 160))
self.interpolate3 = P.ResizeNearestNeighbor((192, 320))
self.interpolate1 = P.ResizeBilinear((48, 80))
self.interpolate2 = P.ResizeBilinear((96, 160))
self.interpolate3 = P.ResizeBilinear((192, 320))
self.cast = P.Cast()
self.maxpool = P.MaxPool(ksize=1, strides=2, padding="same")
def construct(self, inputs):
@ -95,9 +96,9 @@ class FeatPyramidNeck(nn.Cell):
x += (self.lateral_convs_list[i](inputs[i]),)
y = (x[3],)
y = y + (x[2] + self.interpolate1(y[self.fpn_layer - 4]),)
y = y + (x[1] + self.interpolate2(y[self.fpn_layer - 3]),)
y = y + (x[0] + self.interpolate3(y[self.fpn_layer - 2]),)
y = y + (x[2] + self.cast(self.interpolate1(y[self.fpn_layer - 4]), mstype.float16),)
y = y + (x[1] + self.cast(self.interpolate2(y[self.fpn_layer - 3]), mstype.float16),)
y = y + (x[0] + self.cast(self.interpolate3(y[self.fpn_layer - 2]), mstype.float16),)
z = ()
for i in range(self.fpn_layer - 1, -1, -1):

5
model_zoo/official/cv/maskrcnn/src/util.py
View File

@ -247,7 +247,6 @@ def get_seg_masks(mask_pred, det_bboxes, det_labels, img_meta, rescale, num_clas
else:
img_h = np.round(ori_shape[0] * scale_factor[0]).astype(np.int32)
img_w = np.round(ori_shape[1] * scale_factor[1]).astype(np.int32)
scale_factor = 1.0
for i in range(bboxes.shape[0]):
bbox = (bboxes[i, :] / 1.0).astype(np.int32)
@ -256,6 +255,10 @@ def get_seg_masks(mask_pred, det_bboxes, det_labels, img_meta, rescale, num_clas
h = max(bbox[3] - bbox[1] + 1, 1)
w = min(w, img_w - bbox[0])
h = min(h, img_h - bbox[1])
if w <= 0 or h <= 0:
print("there is invalid proposal bbox, index={} bbox={} w={} h={}".format(i, bbox, w, h))
w = max(w, 1)
h = max(h, 1)
mask_pred_ = mask_pred[i, :, :]
im_mask = np.zeros((img_h, img_w), dtype=np.uint8)
bbox_mask = mmcv.imresize(mask_pred_, (w, h))

15
model_zoo/official/cv/maskrcnn/train.py
View File

@ -16,6 +16,7 @@
"""train MaskRcnn and get checkpoint files."""
import os
import time
import argparse
import ast
@ -26,7 +27,7 @@ from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMoni
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.nn import SGD
from mindspore.nn import Momentum
from mindspore.common import set_seed
from src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
@ -71,7 +72,7 @@ if __name__ == '__main__':
prefix = "MaskRcnn.mindrecord"
mindrecord_dir = config.mindrecord_dir
mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
if not os.path.exists(mindrecord_file):
if rank == 0 and not os.path.exists(mindrecord_file):
if not os.path.isdir(mindrecord_dir):
os.makedirs(mindrecord_dir)
if args_opt.dataset == "coco":
@ -80,14 +81,16 @@ if __name__ == '__main__':
data_to_mindrecord_byte_image("coco", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
print("coco_root not exits.")
raise Exception("coco_root not exits.")
else:
if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
print("Create Mindrecord.")
data_to_mindrecord_byte_image("other", True, prefix)
print("Create Mindrecord Done, at {}".format(mindrecord_dir))
else:
print("IMAGE_DIR or ANNO_PATH not exits.")
raise Exception("IMAGE_DIR or ANNO_PATH not exits.")
while not os.path.exists(mindrecord_file+".db"):
time.sleep(5)
if not args_opt.only_create_dataset:
loss_scale = float(config.loss_scale)
@ -115,8 +118,8 @@ if __name__ == '__main__':
loss = LossNet()
lr = Tensor(dynamic_lr(config, rank_size=device_num, start_steps=config.pretrain_epoch_size * dataset_size),
mstype.float32)
opt = SGD(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
weight_decay=config.weight_decay, loss_scale=config.loss_scale)
opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,
weight_decay=config.weight_decay, loss_scale=config.loss_scale)
net_with_loss = WithLossCell(net, loss)
if args_opt.run_distribute: