def get_dataflow_vgg(annot_path, img_dir, strict, x_size, y_size, include_outputs_masks=False):
"""
This function initializes the tensorpack dataflow and serves generator
for training operation.
:param annot_path: path to the annotation file
:param img_dir: path to the images
:return: dataflow object
"""
coco_crop_size = 368
# configure augmentors
augmentors = [
ScaleAug(scale_min=0.5,
scale_max=1.1,
target_dist=0.6,
interp=cv2.INTER_CUBIC),
RotateAug(rotate_max_deg=40,
interp=cv2.INTER_CUBIC,
border=cv2.BORDER_CONSTANT,
border_value=(128, 128, 128), mask_border_val=1),
CropAug(coco_crop_size, coco_crop_size, center_perterb_max=40, border_value=128,
mask_border_val=1),
FlipAug(num_parts=18, prob=0.5)
]
if x_size != coco_crop_size:
augmentors.append(ResizeAug(x_size, x_size))
# prepare augment function
augment_func = functools.partial(augment,
augmentors=augmentors)
# build the dataflow
df = CocoDataFlow((coco_crop_size, coco_crop_size), annot_path, img_dir)
df.prepare()
size = df.size()
df = MapData(df, read_img)
if include_outputs_masks:
df = MapData(df, gen_mask)
build_sample_func = functools.partial(build_sample_with_masks,
y_size=y_size)
else:
build_sample_func = functools.partial(build_sample,
y_size=y_size)
df = MapData(df, augment_func)
df = MultiProcessMapDataZMQ(df, num_proc=4, map_func=build_sample_func, buffer_size=200, strict=strict)
return df, size
def get_dataflow(annot_path, img_dir, strict, x_size=224, y_size=28):
"""
This function initializes the tensorpack dataflow and serves generator
for training operation.
:param annot_path: path to the annotation file
:param img_dir: path to the images
:return: dataflow object
"""
coco_crop_size = 368
coco_crop_size = 224 # TODO(JZ)crop_size
# configure augmentors
augmentors = [
ScaleAug(scale_min=0.5,
scale_max=1.1,
target_dist=0.6,
interp=cv2.INTER_CUBIC),
RotateAug(rotate_max_deg=40,
interp=cv2.INTER_CUBIC,
border=cv2.BORDER_CONSTANT,
border_value=(128, 128, 128),
mask_border_val=1),
CropAug(coco_crop_size,
coco_crop_size,
center_perterb_max=40,
border_value=128,
mask_border_val=1),
# CropAug(64, 48, center_perterb_max=40, border_value=128,
# mask_border_val=1),
# FlipAug(num_parts=18, prob=0.5),
FlipAug(num_parts=5, prob=0.5),
#TODO(JZ)FlipAug
ResizeAug(x_size, x_size)
]
# prepare augment function
augment_func = functools.partial(augment, augmentors=augmentors)
# prepare building sample function
build_sample_func = functools.partial(build_sample, y_size=y_size)
# build the dataflow
df = CocoDataFlow((coco_crop_size, coco_crop_size), annot_path, img_dir)
df.prepare()
size = df.size()
print(df.size())
df = MapData(df, read_img)
print(df.size())
df = MapData(df, augment_func)
print(df.size())
# df = MultiProcessMapDataZMQ(df, num_proc=4, map_func=build_sample_func,
# buffer_size=200, strict=strict)
df = MultiThreadMapData(df,
4,
build_sample_func,
buffer_size=200,
strict=strict)
# TODO(JZ)ZMQ
# df = MultiThreadMapData(df, 4, build_sample_func,
# buffer_size=200, strict=strict)
print(df.size())
return df, size