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delete image meta in eval process

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pull/13159/head
qujianwei 4 years ago
parent d6ac315c85
commit a456bb2829
4 changed files with 31 additions and 78 deletions
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2
model_zoo/official/cv/ctpn/eval.py
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@ -65,7 +65,7 @@ def ctpn_infer_test(dataset_path='', ckpt_path='', img_dir=''):
start = time.time()
# run net
output = net(img_data, img_metas, gt_bboxes, gt_labels, gt_num)
output = net(img_data, gt_bboxes, gt_labels, gt_num)
gt_bboxes = gt_bboxes.asnumpy()
gt_labels = gt_labels.asnumpy()
gt_num = gt_num.asnumpy().astype(bool)

7
model_zoo/official/cv/ctpn/src/ctpn.py
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@ -119,19 +119,14 @@ class CTPN(nn.Cell):
config.activate_num_classes,
config.use_sigmoid_cls)
self.proposal_generator_test.set_train_local(config, False)
def construct(self, img_data, img_metas, gt_bboxes, gt_labels, gt_valids):
# (1,3,600,900)
def construct(self, img_data, gt_bboxes, gt_labels, gt_valids, img_metas=None):
x = self.vgg16_feature_extractor(img_data)
x = self.conv(x)
x = self.cast(x, mstype.float16)
# (1, 512, 38, 57)
x = self.transpose(x, (0, 2, 1, 3))
x = self.reshape(x, (-1, self.input_size, self.num_step))
x = self.transpose(x, (2, 0, 1))
# (57, 38, 512)
x = self.rnn(x)
# (57, 38, 256)
#x = self.cast(x, mstype.float32)
rpn_loss, cls_score, bbox_pred, rpn_cls_loss, rpn_reg_loss = self.rpn_with_loss(x,
img_metas,
self.anchor_list,

90
model_zoo/official/cv/ctpn/src/dataset.py
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@ -15,7 +15,6 @@
"""CTPN dataset"""
from __future__ import division
import os
import numpy as np
from numpy import random
import mmcv
@ -23,7 +22,6 @@ 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
class PhotoMetricDistortion:
@ -98,7 +96,7 @@ class Expand:
boxes += np.tile((left, top), 2)
return img, boxes, labels
def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num):
def rescale_column(img, gt_bboxes, gt_label, gt_num, img_shape):
"""rescale operation for image"""
img_data, scale_factor = mmcv.imrescale(img, (config.img_width, config.img_height), return_scale=True)
if img_data.shape[0] > config.img_height:
@ -112,10 +110,10 @@ def rescale_column(img, img_shape, gt_bboxes, gt_label, gt_num):
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
return (img_data, gt_bboxes, gt_label, gt_num, img_shape)
def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
def resize_column(img, gt_bboxes, gt_label, gt_num, img_shape):
"""resize operation for image"""
img_data = img
img_data, w_scale, h_scale = mmcv.imresize(
@ -129,10 +127,10 @@ def resize_column(img, img_shape, gt_bboxes, gt_label, gt_num):
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
return (img_data, gt_bboxes, gt_label, gt_num, img_shape)
def resize_column_test(img, img_shape, gt_bboxes, gt_label, gt_num):
def resize_column_test(img, gt_bboxes, gt_label, gt_num, img_shape):
"""resize operation for image of eval"""
img_data = img
img_data, w_scale, h_scale = mmcv.imresize(
@ -149,34 +147,34 @@ def resize_column_test(img, img_shape, gt_bboxes, gt_label, gt_num):
gt_bboxes[:, 0::2] = np.clip(gt_bboxes[:, 0::2], 0, img_shape[1] - 1)
gt_bboxes[:, 1::2] = np.clip(gt_bboxes[:, 1::2], 0, img_shape[0] - 1)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
return (img_data, gt_bboxes, gt_label, gt_num, img_shape)
def flipped_generation(img, img_shape, gt_bboxes, gt_label, gt_num):
def flipped_generation(img, gt_bboxes, gt_label, gt_num, img_shape):
"""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)
return (img_data, flipped, gt_label, gt_num, img_shape)
def image_bgr_rgb(img, img_shape, gt_bboxes, gt_label, gt_num):
def image_bgr_rgb(img, gt_bboxes, gt_label, gt_num, img_shape):
img_data = img[:, :, ::-1]
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
return (img_data, gt_bboxes, gt_label, gt_num, img_shape)
def photo_crop_column(img, img_shape, gt_bboxes, gt_label, gt_num):
def photo_crop_column(img, gt_bboxes, gt_label, gt_num, img_shape):
"""photo crop operation for image"""
random_photo = PhotoMetricDistortion()
img_data, gt_bboxes, gt_label = random_photo(img, gt_bboxes, gt_label)
return (img_data, img_shape, gt_bboxes, gt_label, gt_num)
return (img_data, gt_bboxes, gt_label, gt_num, img_shape)
def expand_column(img, img_shape, gt_bboxes, gt_label, gt_num):
def expand_column(img, gt_bboxes, gt_label, gt_num, img_shape):
"""expand operation for image"""
expand = Expand()
img, gt_bboxes, gt_label = expand(img, gt_bboxes, gt_label)
return (img, img_shape, gt_bboxes, gt_label, gt_num)
return (img, gt_bboxes, gt_label, gt_num, img_shape)
def split_gtbox_label(gt_bbox_total):
"""split ground truth box label"""
@ -193,7 +191,7 @@ def split_gtbox_label(gt_bbox_total):
gtbox_list.append([x0, gt_bbox[1], x0+15, gt_bbox[3], 1])
return np.array(gtbox_list)
def pad_label(img, img_shape, gt_bboxes, gt_label, gt_valid):
def pad_label(img, gt_bboxes, gt_label, gt_valid, img_shape):
"""pad ground truth label"""
pad_max_number = 256
gt_label = gt_bboxes[:, 4]
@ -208,13 +206,13 @@ def pad_label(img, img_shape, gt_bboxes, gt_label, gt_valid):
gt_box = gt_bboxes[0:pad_max_number]
gt_label = gt_label[0:pad_max_number]
gt_valid = gt_valid[0:pad_max_number]
return (img, img_shape, gt_box[:, :4], gt_label, gt_valid)
return (img, gt_box[:, :4], gt_label, gt_valid, img_shape)
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_valid):
def _infer_data(image_bgr, gt_box_new, gt_label_new, gt_valid, image_shape):
image_shape = image_shape[:2]
input_data = image_bgr, image_shape, gt_box_new, gt_label_new, gt_valid
input_data = image_bgr, gt_box_new, gt_label_new, gt_valid, image_shape
if config.keep_ratio:
input_data = rescale_column(*input_data)
else:
@ -234,9 +232,9 @@ def preprocess_fn(image, box, is_training):
gt_box = box[:, :4]
gt_label = box[:, 4]
gt_valid = box[:, 4]
input_data = image_bgr, image_shape, gt_box, gt_label, gt_valid
input_data = image_bgr, gt_box, gt_label, gt_valid, image_shape
if not is_training:
return _infer_data(image_bgr, image_shape, gt_box, gt_label, gt_valid)
return _infer_data(image_bgr, gt_box, gt_label, gt_valid, image_shape)
expand = (np.random.rand() < config.expand_ratio)
if expand:
input_data = expand_column(*input_data)
@ -260,46 +258,6 @@ 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 = []
image_anno_dict = {}
if not os.path.isdir(image_dir):
raise RuntimeError("Path given is not valid.")
if not os.path.isfile(anno_path):
raise RuntimeError("Annotation file is not valid.")
with open(anno_path, "rb") as f:
lines = f.readlines()
for line in lines:
line_str = line.decode("utf-8").strip()
line_split = str(line_str).split(' ')
file_name = line_split[0]
image_path = os.path.join(image_dir, file_name)
if os.path.isfile(image_path):
image_anno_dict[image_path] = anno_parser(line_split[1:])
image_files.append(image_path)
return image_files, image_anno_dict
def data_to_mindrecord_byte_image(is_training=True, prefix="cptn_mlt.mindrecord", file_num=8):
"""Create MindRecord file."""
mindrecord_dir = config.mindrecord_dir
mindrecord_path = os.path.join(mindrecord_dir, prefix)
writer = FileWriter(mindrecord_path, file_num)
image_files, image_anno_dict = create_icdar_test_label()
ctpn_json = {
"image": {"type": "bytes"},
"annotation": {"type": "int32", "shape": [-1, 6]},
}
writer.add_schema(ctpn_json, "ctpn_json")
for image_name in image_files:
with open(image_name, 'rb') as f:
img = f.read()
annos = np.array(image_anno_dict[image_name], dtype=np.int32)
row = {"image": img, "annotation": annos}
writer.write_raw_data([row])
writer.commit()
def create_ctpn_dataset(mindrecord_file, batch_size=1, repeat_num=1, device_num=1, rank_id=0,
is_training=True, num_parallel_workers=12):
"""Creatr ctpn dataset with MindDataset."""
@ -316,8 +274,8 @@ def create_ctpn_dataset(mindrecord_file, batch_size=1, repeat_num=1, device_num=
type_cast3 = CC.TypeCast(mstype.bool_)
if is_training:
ds = ds.map(operations=compose_map_func, input_columns=["image", "annotation"],
output_columns=["image", "image_shape", "box", "label", "valid_num"],
column_order=["image", "image_shape", "box", "label", "valid_num"],
output_columns=["image", "box", "label", "valid_num", "image_shape"],
column_order=["image", "box", "label", "valid_num", "image_shape"],
num_parallel_workers=num_parallel_workers,
python_multiprocessing=True)
ds = ds.map(operations=[normalize_op, type_cast0], input_columns=["image"],
@ -329,8 +287,8 @@ def create_ctpn_dataset(mindrecord_file, batch_size=1, repeat_num=1, device_num=
else:
ds = ds.map(operations=compose_map_func,
input_columns=["image", "annotation"],
output_columns=["image", "image_shape", "box", "label", "valid_num"],
column_order=["image", "image_shape", "box", "label", "valid_num"],
output_columns=["image", "box", "label", "valid_num", "image_shape"],
column_order=["image", "box", "label", "valid_num", "image_shape"],
num_parallel_workers=num_parallel_workers,
python_multiprocessing=True)

10
model_zoo/official/cv/ctpn/src/network_define.py
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@ -99,8 +99,8 @@ class WithLossCell(nn.Cell):
self._backbone = backbone
self._loss_fn = loss_fn
def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num):
rpn_loss, _, _, rpn_cls_loss, rpn_reg_loss = self._backbone(x, img_shape, gt_bboxe, gt_label, gt_num)
def construct(self, x, gt_bbox, gt_label, gt_num, img_shape=None):
rpn_loss, _, _, rpn_cls_loss, rpn_reg_loss = self._backbone(x, gt_bbox, gt_label, gt_num, img_shape)
return self._loss_fn(rpn_loss, rpn_cls_loss, rpn_reg_loss)
@property
@ -144,10 +144,10 @@ class TrainOneStepCell(nn.Cell):
if reduce_flag:
self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
def construct(self, x, img_shape, gt_bboxe, gt_label, gt_num):
def construct(self, x, gt_bbox, gt_label, gt_num, img_shape=None):
weights = self.weights
rpn_loss, _, _, rpn_cls_loss, rpn_reg_loss = self.backbone(x, img_shape, gt_bboxe, gt_label, gt_num)
grads = self.grad(self.network, weights)(x, img_shape, gt_bboxe, gt_label, gt_num, self.sens)
rpn_loss, _, _, rpn_cls_loss, rpn_reg_loss = self.backbone(x, gt_bbox, gt_label, gt_num, img_shape)
grads = self.grad(self.network, weights)(x, gt_bbox, gt_label, gt_num, img_shape, self.sens)
if self.reduce_flag:
grads = self.grad_reducer(grads)
return F.depend(rpn_loss, self.optimizer(grads)), rpn_cls_loss, rpn_reg_loss

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