def check_roi_random_crop_error(shape_like_in=None,
in_shape=None,
crop_shape=None,
roi_start=None,
roi_shape=None,
roi_end=None):
ndim = 2
batch_size = 3
niter = 3
pipe = dali.pipeline.Pipeline(batch_size=batch_size,
num_threads=4,
device_id=0,
seed=1234)
with pipe:
inputs = [] if shape_like_in is None else [shape_like_in]
out = fn.roi_random_crop(*inputs,
in_shape=in_shape,
crop_shape=crop_shape,
roi_start=roi_start,
roi_shape=roi_shape,
roi_end=roi_end,
device='cpu')
pipe.set_outputs(out)
with assert_raises(RuntimeError):
pipe.build()
for _ in range(niter):
outputs = pipe.run()
def biased_crop_fn(self, img, label):
roi_start, roi_end = fn.segmentation.random_object_bbox(
label,
format="start_end",
foreground_prob=self.oversampling,
background=0,
seed=self.internal_seed,
device="cpu",
cache_objects=True,
)
anchor = fn.roi_random_crop(label, roi_start=roi_start, roi_end=roi_end, crop_shape=[1, *self.patch_size])
anchor = fn.slice(anchor, 1, 3, axes=[0]) # drop channels from anchor
img, label = fn.slice(
[img, label], anchor, self.crop_shape, axis_names="DHW", out_of_bounds_policy="pad", device="cpu"
)
return img.gpu(), label.gpu()
def check_roi_random_crop(ndim=2, max_batch_size=16,
roi_min_start = 0, roi_max_start = 100,
roi_min_extent = 20, roi_max_extent = 50,
crop_min_extent = 20, crop_max_extent = 50,
in_shape_min = 400, in_shape_max = 500,
niter=3):
pipe = dali.pipeline.Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0, seed=1234)
with pipe:
assert in_shape_min < in_shape_max
shape_gen_fn = lambda: random_shape(in_shape_min, in_shape_max, ndim)
data_gen_f = lambda: batch_gen(max_batch_size, shape_gen_fn)
shape_like_in = dali.fn.external_source(data_gen_f, device='cpu')
in_shape = dali.fn.shapes(shape_like_in, dtype=types.INT32)
crop_shape = [(crop_min_extent + crop_max_extent) // 2] * ndim if random.choice([True, False]) \
else fn.random.uniform(range=(crop_min_extent, crop_max_extent + 1),
shape=(ndim,), dtype=types.INT32, device='cpu')
if random.choice([True, False]):
roi_shape = [(roi_min_extent + roi_max_extent) // 2] * ndim
roi_start = [(roi_min_start + roi_max_start) // 2] * ndim
roi_end = [roi_start[d] + roi_shape[d] for d in range(ndim)]
else:
roi_shape = fn.random.uniform(range=(roi_min_extent, roi_max_extent + 1),
shape=(ndim,), dtype=types.INT32, device='cpu')
roi_start = fn.random.uniform(range=(roi_min_start, roi_max_start + 1),
shape=(ndim,), dtype=types.INT32, device='cpu')
roi_end = roi_start + roi_shape
outs = [
fn.roi_random_crop(crop_shape=crop_shape,
roi_start=roi_start, roi_shape=roi_shape,
device='cpu'),
fn.roi_random_crop(crop_shape=crop_shape,
roi_start=roi_start, roi_end=roi_end,
device='cpu'),
fn.roi_random_crop(shape_like_in, crop_shape=crop_shape,
roi_start=roi_start, roi_shape=roi_shape,
device='cpu'),
fn.roi_random_crop(shape_like_in, crop_shape=crop_shape,
roi_start=roi_start, roi_end=roi_end,
device='cpu'),
fn.roi_random_crop(in_shape=in_shape, crop_shape=crop_shape,
roi_start=roi_start, roi_shape=roi_shape,
device='cpu'),
fn.roi_random_crop(in_shape=in_shape, crop_shape=crop_shape,
roi_start=roi_start, roi_end=roi_end,
device='cpu'),
]
outputs = [in_shape, roi_start, roi_shape, crop_shape, *outs]
pipe.set_outputs(*outputs)
pipe.build()
for _ in range(niter):
outputs = pipe.run()
batch_size = len(outputs[0])
for s in range(batch_size):
in_shape = np.array(outputs[0][s]).tolist()
roi_start = np.array(outputs[1][s]).tolist()
roi_shape = np.array(outputs[2][s]).tolist()
crop_shape = np.array(outputs[3][s]).tolist()
def check_crop_start(crop_start, roi_start, roi_shape, crop_shape, in_shape=None):
ndim = len(crop_start)
roi_end = [roi_start[d] + roi_shape[d] for d in range(ndim)]
crop_end = [crop_start[d] + crop_shape[d] for d in range(ndim)]
for d in range(ndim):
if in_shape is not None:
assert crop_start[d] >= 0
assert crop_end[d] <= in_shape[d]
if crop_shape[d] >= roi_shape[d]:
assert crop_start[d] <= roi_start[d]
assert crop_end[d] >= roi_end[d]
else:
assert crop_start[d] >= roi_start[d]
assert crop_end[d] <= roi_end[d]
for idx in range(4, 6):
check_crop_start(np.array(outputs[idx][s]).tolist(), roi_start, roi_shape, crop_shape)
for idx in range(6, 10):
check_crop_start(np.array(outputs[idx][s]).tolist(), roi_start, roi_shape, crop_shape, in_shape)
