def test_random_bbox_crop_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
test_box_shape = [200, 4]
def get_boxes():
out = [
(np.random.randint(0, 255, size=test_box_shape, dtype=np.uint8) /
255).astype(dtype=np.float32) for _ in range(batch_size)
]
return out
test_lables_shape = [200, 1]
def get_lables():
out = [
np.random.randint(0, 255, size=test_lables_shape, dtype=np.int32)
for _ in range(batch_size)
]
return out
boxes = fn.external_source(source=get_boxes)
lables = fn.external_source(source=get_lables)
processed, _, _, _ = fn.random_bbox_crop(boxes,
lables,
aspect_ratio=[0.5, 2.0],
thresholds=[0.1, 0.3, 0.5],
scaling=[0.8, 1.0],
bbox_layout="xyXY")
pipe.set_outputs(processed)
pipe.build()
for _ in range(3):
pipe.run()
def random_bbox_crop_pipeline(get_boxes, get_labels, seed):
boxes = fn.external_source(source=get_boxes)
labels = fn.external_source(source=get_labels)
out = fn.random_bbox_crop(boxes, labels, aspect_ratio=[0.5, 2.0], thresholds=[
0.1, 0.3, 0.5], scaling=[0.8, 1.0], bbox_layout="xyXY", seed=seed)
return tuple(out)
def define_graph(self):
inputs, bboxes, labels, polygons, vertices = fn.readers.coco(
file_root=self.file_root,
annotations_file=self.annotation_file,
skip_empty=True,
shard_id=self.share_id,
num_shards=self.num_gpus,
ratio=True,
ltrb=True,
polygon_masks = True,
random_shuffle=self.random_shuffle,
shuffle_after_epoch=self.shuffle_after_epoch,
name="Reader")
input_shape = fn.slice(fn.cast(fn.peek_image_shape(inputs), dtype=types.INT32), 0, 2, axes=[0])
h = fn.slice(input_shape, 0, 1, axes = [0], dtype=types.FLOAT)
w = fn.slice(input_shape, 1, 1, axes = [0], dtype=types.FLOAT)
short_side = math.min(w, h)
scale = fn.random.uniform(range=[0.3, 1.])
crop_side = fn.cast(math.ceil(scale * short_side), dtype=types.INT32)
crop_shape = fn.cat(crop_side, crop_side)
anchor_rel, shape_rel, bboxes, labels, bbox_indices = fn.random_bbox_crop(
bboxes,
labels,
input_shape=input_shape,
crop_shape=crop_shape,
shape_layout="HW",
thresholds=[0.], # No minimum intersection-over-union, for demo purposes
allow_no_crop=False, # No-crop is disallowed, for demo purposes
seed=-1, # Fixed random seed for deterministic results
bbox_layout="xyXY", # left, top, right, back
output_bbox_indices=True, # Output indices of the filtered bounding boxes
total_num_attempts=1024,
)
polygons, vertices = fn.segmentation.select_masks(
bbox_indices, polygons, vertices
)
images = fn.decoders.image_slice(
inputs, anchor_rel, shape_rel, normalized_anchor=False, normalized_shape=False, device='mixed'
)
images = fn.color_space_conversion(images, image_type=types.RGB, output_type=types.BGR)
MT_1_vertices = fn.transforms.crop(
to_start=(0.0, 0.0), to_end=fn.cat(w, h)
)
MT_2_vertices = fn.transforms.crop(
from_start=anchor_rel, from_end=(anchor_rel + shape_rel),
to_start=(0.0, 0.0), to_end=(1., 1.)
)
vertices = fn.coord_transform(fn.coord_transform(vertices, MT=MT_1_vertices), MT=MT_2_vertices)
targets = fn.cat( bboxes, fn.reshape(vertices, shape=[-1, 10]), axis=1)
interp_methods = [types.INTERP_LINEAR, types.INTERP_CUBIC, types.INTERP_LANCZOS3, types.INTERP_GAUSSIAN, types.INTERP_NN, types.INTERP_TRIANGULAR]
interp_method = fn.random.uniform(values=[int(x) for x in interp_methods], dtype=types.INT32)
interp_method = fn.reinterpret(interp_method, dtype=types.INTERP_TYPE)
images = fn.resize(images, dtype=types.FLOAT, size=self.input_dim, interp_type=interp_method)
labels = labels.gpu()
targets = targets.gpu()
return (images, targets, labels)
def create_coco_pipeline(file_root,
annotations_file,
batch_size=1,
device_id=0,
num_threads=4,
local_rank=0,
world_size=1):
pipeline = Pipeline(batch_size, num_threads,
local_rank, seed=42 + device_id)
with pipeline:
images, bboxes, labels = fn.coco_reader(file_root=file_root,
annotations_file=annotations_file,
skip_empty=True,
shard_id=local_rank,
num_shards=world_size,
ratio=True,
ltrb=True,
random_shuffle=False,
shuffle_after_epoch=True,
name="Reader")
crop_begin, crop_size, bboxes, labels = fn.random_bbox_crop(bboxes, labels,
device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0],
bbox_layout="xyXY",
allow_no_crop=True,
num_attempts=50)
images = fn.image_decoder_slice(images, crop_begin, crop_size, device="mixed", output_type=types.RGB)
flip_coin = fn.coin_flip(probability=0.5)
images = fn.resize(images,
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
# use float to avoid clipping and quantizing the intermediate result
images = fn.hsv(images, dtype=types.FLOAT, hue=fn.uniform(range=[-0.5, 0.5]),
saturation=fn.uniform(range=[0.5, 1.5]))
images = fn.brightness_contrast(images,
contrast_center = 128, # input is in float, but in 0..255 range
dtype = types.UINT8,
brightness = fn.uniform(range=[0.875, 1.125]),
contrast = fn.uniform(range=[0.5, 1.5]))
bboxes = fn.bb_flip(bboxes, ltrb=True, horizontal=flip_coin)
images = fn.crop_mirror_normalize(images,
mean=[104., 117., 123.],
std=[1., 1., 1.],
mirror=flip_coin,
dtype=types.FLOAT,
output_layout="CHW",
pad_output=False)
pipeline.set_outputs(images, bboxes, labels)
return pipeline
def random_bbox_crop_fixed_aspect_ratio():
in_sh = fn.random.uniform(range=(400, 600), shape=(2,), dtype=types.INT32)
inputs = fn.external_source(source=bbox_source, num_outputs=bbox_source.num_outputs)
outputs = fn.random_bbox_crop(
*inputs,
device='cpu',
aspect_ratio=(1.0, 1.0),
scaling=(0.5, 0.8),
thresholds=[0.0],
threshold_type='iou',
bbox_layout="xyXY",
total_num_attempts=100,
allow_no_crop=False,
input_shape=in_sh if use_input_shape else None,
)
return in_sh, outputs[1]
def create_coco_pipeline(default_boxes, args):
try:
shard_id = torch.distributed.get_rank()
num_shards = torch.distributed.get_world_size()
except RuntimeError:
shard_id = 0
num_shards = 1
images, bboxes, labels = fn.readers.coco(
file_root=args.train_coco_root,
annotations_file=args.train_annotate,
skip_empty=True,
shard_id=shard_id,
num_shards=num_shards,
ratio=True,
ltrb=True,
random_shuffle=False,
shuffle_after_epoch=True,
name="Reader")
crop_begin, crop_size, bboxes, labels = fn.random_bbox_crop(
bboxes,
labels,
device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0],
bbox_layout="xyXY",
allow_no_crop=True,
num_attempts=50)
images = fn.image_decoder_slice(images,
crop_begin,
crop_size,
device="mixed",
output_type=types.RGB)
flip_coin = fn.random.coin_flip(probability=0.5)
images = fn.resize(images,
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
saturation = fn.uniform(range=[0.5, 1.5])
contrast = fn.uniform(range=[0.5, 1.5])
brightness = fn.uniform(range=[0.875, 1.125])
hue = fn.uniform(range=[-0.5, 0.5])
images = fn.hsv(images, dtype=types.FLOAT, hue=hue,
saturation=saturation) # use float to avoid clipping and
# quantizing the intermediate result
images = fn.brightness_contrast(
images,
contrast_center=128, # input is in float, but in 0..255 range
dtype=types.UINT8,
brightness=brightness,
contrast=contrast)
dtype = types.FLOAT16 if args.fp16 else types.FLOAT
bboxes = fn.bb_flip(bboxes, ltrb=True, horizontal=flip_coin)
images = fn.crop_mirror_normalize(
images,
crop=(300, 300),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
mirror=flip_coin,
dtype=dtype,
output_layout="CHW",
pad_output=False)
bboxes, labels = fn.box_encoder(bboxes,
labels,
criteria=0.5,
anchors=default_boxes.as_ltrb_list())
labels = labels.gpu()
bboxes = bboxes.gpu()
return images, bboxes, labels
def check_bbox_random_crop_adjust_polygons(file_root,
annotations_file,
batch_size=3,
num_iters=4,
num_threads=4,
device_id=0,
seed=1234):
pipe = Pipeline(batch_size=batch_size,
num_threads=num_threads,
device_id=device_id,
seed=seed)
with pipe:
# Read data from COCO
# ratio=True means both bboxes and masks coordinates will be
# relative to the image dimensions (range [0.0, 1.0])
inputs, in_bboxes, labels, in_polygons, in_vertices = \
fn.readers.coco(
file_root=file_root, annotations_file=annotations_file, shard_id=0, num_shards=1,
ratio=True, ltrb=True, polygon_masks=True
)
# Generate a random crop. out_bboxes are adjusted to the crop window
slice_anchor, slice_shape, out_bboxes, labels, bbox_indices = \
fn.random_bbox_crop(
in_bboxes, labels,
aspect_ratio=[0.5, 2.0], thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0], bbox_layout='xyXY', output_bbox_indices=True
)
# Crop the image
_ = fn.decoders.image_slice(inputs,
slice_anchor,
slice_shape,
device='mixed',
axis_names='WH')
sel_polygons, sel_vertices = fn.segmentation.select_masks(
bbox_indices, in_polygons, in_vertices)
# Adjust masks coordinates to the coordinate space of the cropped image
MT = fn.transforms.crop(from_start=slice_anchor,
from_end=(slice_anchor + slice_shape))
out_vertices = fn.coord_transform(sel_vertices, MT=MT)
# Converting to absolute coordinates (demo purposes)
image_shape = fn.peek_image_shape(inputs, dtype=types.FLOAT)
h = fn.slice(image_shape, 0, 1, axes=[0])
w = fn.slice(image_shape, 1, 1, axes=[0])
# Original bboxes
bbox_x = fn.slice(in_bboxes, 0, 1, axes=[1])
bbox_y = fn.slice(in_bboxes, 1, 1, axes=[1])
bbox_X = fn.slice(in_bboxes, 2, 1, axes=[1])
bbox_Y = fn.slice(in_bboxes, 3, 1, axes=[1])
in_bboxes_abs = fn.cat(bbox_x * w,
bbox_y * h,
bbox_X * w,
bbox_Y * h,
axis=1)
# Transform to convert relative coordinates to absolute
scale_rel_to_abs = fn.transforms.scale(scale=fn.cat(w, h))
# Selected vertices (relative coordinates)
sel_vertices_abs = fn.coord_transform(out_vertices,
MT=scale_rel_to_abs)
# Output bboxes
bbox2_x = fn.slice(out_bboxes, 0, 1, axes=[1])
bbox2_y = fn.slice(out_bboxes, 1, 1, axes=[1])
bbox2_X = fn.slice(out_bboxes, 2, 1, axes=[1])
bbox2_Y = fn.slice(out_bboxes, 3, 1, axes=[1])
out_bboxes_abs = fn.cat(bbox2_x * w,
bbox2_y * h,
bbox2_X * w,
bbox2_Y * h,
axis=1)
# Output vertices (absolute coordinates)
out_vertices_abs = fn.coord_transform(out_vertices,
MT=scale_rel_to_abs)
# Clamped coordinates
out_vertices_clamped = math.clamp(out_vertices, 0.0, 1.0)
out_vertices_clamped_abs = fn.coord_transform(out_vertices_clamped,
MT=scale_rel_to_abs)
pipe.set_outputs(in_vertices, sel_vertices, sel_vertices_abs, out_vertices,
out_vertices_clamped, out_vertices_abs,
out_vertices_clamped_abs, in_bboxes, in_bboxes_abs,
out_bboxes, out_bboxes_abs, in_polygons, sel_polygons,
image_shape, slice_anchor, slice_shape, bbox_indices)
pipe.build()
# Enough iterations to see an example with more than one bounding box
for i in range(num_iters):
outs = pipe.run()
for j in range(batch_size):
(in_vertices, sel_vertices, sel_vertices_abs, out_vertices,
out_vertices_clamped, out_vertices_abs, out_vertices_clamped_abs,
in_bboxes, in_bboxes_abs, out_bboxes, out_bboxes_abs, in_polygons,
sel_polygons, image_shape, slice_anchor, slice_shape,
bbox_indices) = (outs[k].at(j) for k in range(len(outs)))
# Checking that the output polygon descriptors are the ones associated with the
# selected bounding boxes
expected_polygons_list = []
expected_vertices_list = []
ver_count = 0
for k in range(in_polygons.shape[0]):
mask_id = in_polygons[k][0]
in_ver_start_idx = in_polygons[k][1]
in_ver_end_idx = in_polygons[k][2]
pol_nver = in_ver_end_idx - in_ver_start_idx
if mask_id in bbox_indices:
expected_polygons_list.append(
[mask_id, ver_count, ver_count + pol_nver])
for j in range(in_ver_start_idx, in_ver_end_idx):
expected_vertices_list.append(in_vertices[j])
ver_count = ver_count + pol_nver
expected_sel_polygons = np.array(expected_polygons_list)
np.testing.assert_equal(expected_sel_polygons, sel_polygons)
# Checking the selected vertices correspond to the selected masks
expected_sel_vertices = np.array(expected_vertices_list)
np.testing.assert_equal(expected_sel_vertices, sel_vertices)
# Chekc that the vertices are correctly mapped to the cropping window
expected_out_vertices = np.copy(expected_sel_vertices)
crop_x, crop_y = slice_anchor
crop_w, crop_h = slice_shape
for v in range(expected_out_vertices.shape[0]):
expected_out_vertices[v, 0] = (expected_out_vertices[v, 0] -
crop_x) / crop_w
expected_out_vertices[v, 1] = (expected_out_vertices[v, 1] -
crop_y) / crop_h
np.testing.assert_allclose(expected_out_vertices,
out_vertices,
rtol=1e-4)
# Checking the conversion to absolute coordinates
h, w, _ = image_shape
wh = np.array([w, h])
whwh = np.array([w, h, w, h])
expected_out_vertices_abs = expected_out_vertices * wh
np.testing.assert_allclose(expected_out_vertices_abs,
out_vertices_abs,
rtol=1e-4)
# Checking clamping of the relative coordinates
expected_out_vertices_clamped = np.clip(expected_out_vertices,
a_min=0.0,
a_max=1.0)
np.testing.assert_allclose(expected_out_vertices_clamped,
out_vertices_clamped,
rtol=1e-4)
# Checking clamping of the absolute coordinates
expected_out_vertices_clamped_abs = np.clip(
expected_out_vertices_abs, 0, wh)
np.testing.assert_allclose(expected_out_vertices_clamped_abs,
out_vertices_clamped_abs,
rtol=1e-4)
# Checking scaling of the bounding boxes
expected_in_bboxes_abs = in_bboxes * whwh
np.testing.assert_allclose(expected_in_bboxes_abs,
in_bboxes_abs,
rtol=1e-4)
# Check box selection and mapping to the cropping window
expected_out_bboxes = np.copy(in_bboxes[bbox_indices, :])
for k in range(expected_out_bboxes.shape[0]):
expected_out_bboxes[k, 0] = (expected_out_bboxes[k, 0] -
crop_x) / crop_w
expected_out_bboxes[k, 1] = (expected_out_bboxes[k, 1] -
crop_y) / crop_h
expected_out_bboxes[k, 2] = (expected_out_bboxes[k, 2] -
crop_x) / crop_w
expected_out_bboxes[k, 3] = (expected_out_bboxes[k, 3] -
crop_y) / crop_h
expected_out_bboxes = np.clip(expected_out_bboxes,
a_min=0.0,
a_max=1.0)
np.testing.assert_allclose(expected_out_bboxes,
out_bboxes,
rtol=1e-4)
expected_out_bboxes_abs = expected_out_bboxes * whwh
np.testing.assert_allclose(expected_out_bboxes_abs,
out_bboxes_abs,
rtol=1e-4)
