def check_crop_with_out_of_bounds_policy_support(device,
batch_size,
input_shape=(100, 200, 3),
out_of_bounds_policy=None,
fill_values=(0x76, 0xb9,
0x00)):
# This test case is written with HWC layout in mind and "HW" axes in slice arguments
layout = "HWC"
assert (len(input_shape) == 3)
if fill_values is not None and len(fill_values) > 1:
assert (input_shape[2] == len(fill_values))
eii = RandomDataIterator(batch_size, shape=input_shape)
crop_shape = tuple(extent * 2 for extent in input_shape[:2])
crop_y = 0.5
crop_x = 0.5
pipe = CropSynthPipe(device,
batch_size,
iter(eii),
layout=layout,
crop_shape=crop_shape,
crop_x=crop_y,
crop_y=crop_x,
out_of_bounds_policy=out_of_bounds_policy,
fill_values=fill_values,
extra_outputs=True)
if fill_values is None:
fill_values = 0
pipe.build()
for k in range(3):
outs = pipe.run()
out = outs[0]
in_data = outs[1]
if isinstance(out, dali.backend_impl.TensorListGPU):
out = out.as_cpu()
if isinstance(in_data, dali.backend_impl.TensorListGPU):
in_data = in_data.as_cpu()
assert (batch_size == len(out))
for idx in range(batch_size):
sample_in = in_data.at(idx)
sample_out = out.at(idx)
in_shape = list(sample_in.shape)
out_shape = list(sample_out.shape)
crop_anchor_norm = [crop_y, crop_x]
crop_anchor_abs = [
crop_anchor_norm[k] * (input_shape[k] - crop_shape[k])
for k in range(2)
]
abs_start, abs_end, abs_slice_shape = abs_slice_start_and_end(
in_shape[:2], crop_anchor_abs, crop_shape, False, False)
check_slice_output(sample_in, sample_out, crop_anchor_abs,
abs_slice_shape, abs_start, abs_end,
out_of_bounds_policy, fill_values)
def check_cmn_with_out_of_bounds_policy_support(device, batch_size, dtype, input_layout, input_shape, output_layout,
mirror_probability, mean, std, should_pad,
out_of_bounds_policy=None, fill_values=(0x76, 0xb9, 0x00)):
# This test case is written with HWC layout in mind and "HW" axes in slice arguments
assert(input_layout == "HWC")
assert(len(input_shape) == 3)
if fill_values is not None and len(fill_values) > 1:
assert(input_shape[2] == len(fill_values))
eii = RandomDataIterator(batch_size, shape=input_shape)
crop_y, crop_x = 0.5, 0.5
crop_h, crop_w = input_shape[0] * 2, input_shape[1] * 2
pipe = CMNRandomDataPipeline(device, batch_size, input_layout, iter(eii),
dtype = dtype, output_layout = output_layout,
mirror_probability = mirror_probability, mean = mean, std = std, pad_output = should_pad,
crop_w = crop_w, crop_h = crop_h,
crop_pos_x = crop_x, crop_pos_y = crop_y,
out_of_bounds_policy = out_of_bounds_policy, fill_values = fill_values, extra_outputs = True)
permute = None
if output_layout != input_layout:
permute = []
for d in range(len(input_layout)):
perm_d = input_layout.find(output_layout[d])
permute.append(perm_d)
if fill_values is None:
fill_values = 0
pipe.build()
for k in range(3):
outs = pipe.run()
out = outs[0]
in_data = outs[1]
mirror_data = outs[2]
if isinstance(out, dali.backend_impl.TensorListGPU):
out = out.as_cpu()
if isinstance(in_data, dali.backend_impl.TensorListGPU):
in_data = in_data.as_cpu()
assert(batch_size == len(out))
for idx in range(batch_size):
sample_in = in_data.at(idx)
sample_out = out.at(idx)
mirror = mirror_data.at(idx)
flip = [0, mirror[0]]
in_shape = list(sample_in.shape)
out_shape = list(sample_out.shape)
crop_anchor_norm = [crop_y, crop_x]
crop_shape = [crop_h, crop_w]
crop_anchor_abs = [crop_anchor_norm[k] * (input_shape[k] - crop_shape[k]) for k in range(2)]
abs_start, abs_end, abs_slice_shape = abs_slice_start_and_end(in_shape[:2], crop_anchor_abs, crop_shape, False, False)
check_slice_output(sample_in, sample_out, crop_anchor_abs, abs_slice_shape, abs_start, abs_end,
out_of_bounds_policy, fill_values, mean=mean, std=std, flip=flip, permute=permute)
