def __init__(self,
batch_size,
output_type,
input_type,
use_input,
num_threads=3,
device_id=0,
num_gpus=1):
super(CVPipeline, self).__init__(batch_size,
num_threads,
device_id,
seed=7865,
exec_async=False,
exec_pipelined=False)
self.use_input = use_input
self.name = "cv"
self.input = ops.CaffeReader(path=caffe_db_folder,
shard_id=device_id,
num_shards=num_gpus)
self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB)
if self.use_input:
self.transform_source = ops.ExternalSource()
self.warp = ops.PythonFunction(
function=CVWarp(output_type, input_type))
else:
self.warp = ops.PythonFunction(function=CVWarp(
output_type, input_type, [[0.1, 0.9, 10], [0.8, -0.2, -20]]))
self.iter = 0
def __init__(self, device, batch_size, dims, axes, axis_names, batch=False,
out_type=None, in_type=None, shift=None, scale=None,
num_threads=3, device_id=0, num_gpus=1):
super(NormalizePipeline, self).__init__(
batch_size, num_threads, device_id, seed=7865,
exec_async=False, exec_pipelined=False)
common_args = {
"device": device,
"axes": axes,
"axis_names": axis_names,
"batch": batch,
"dtype": dali_type(out_type),
"shift": shift,
"scale": scale
}
self.in_type = in_type
self.out_type = out_type
self.device = device
self.input = ops.ExternalSource()
self.add_layout = None
if axis_names is not None:
layout = ''
for i in range(dims):
layout += chr(ord('a') + i)
self.add_layout = ops.Reshape(layout=layout)
self.batch = batch
self.dims = dims
self.has_axes = axes is not None or axis_names is not None
self.scale = scale
self.shift = shift
self.is_integral = out_type is not None and out_type is not np.float32
if axis_names is not None:
axes = []
for a in axis_names:
axes.append(ord(a) - ord('a'))
self.axes = axes
self.axis_names = axis_names
self.ddof = 2 if axes is not None and len(axes) > 0 else 0
self.eps = 0.25
self.mean = ops.PythonFunction(function=custom_mean(batch, axes), batch_processing=True)
self.stddev = ops.PythonFunction(function=custom_stddev(batch, axes), batch_processing=True)
self.normalize = ops.Normalize(**common_args, ddof=self.ddof)
self.scalar_mean = ops.Normalize(**common_args, mean=1, ddof=self.ddof, epsilon=self.eps)
self.scalar_stddev = ops.Normalize(**common_args, stddev=2, epsilon=self.eps)
self.scalar_params = ops.Normalize(**common_args, mean=1, stddev=2)
def __init__(self, batch_size, num_threads, device_id, data_dir, crop):
super(HybridTrainPipe, self).__init__(batch_size, num_threads,
device_id, data_dir, crop)
self.pad = ops.Paste(device="gpu",
fill_value=0,
ratio=1.1,
min_canvas_size=crop)
self.res = ops.RandomResizedCrop(device="gpu",
size=crop,
random_area=[0.9, 1.1],
random_aspect_ratio=1.33333)
self.cutmix = ops.PythonFunction(function=cut_mixe_image,
num_outputs=2,
device='gpu')
self.cmnp = ops.CropMirrorNormalize(
device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
image_type=types.RGB,
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
self.coin = ops.CoinFlip(probability=0.5)
self.rotated = ops.Rotate(device="gpu", keep_size=True)
self.rotated_rng = ops.Uniform(range=(-5.0, 5.0))
self.brightness = ops.Brightness(device="gpu")
self.brightness_rng = ops.Uniform(range=(0.8, 1.2))
self.reshape = ops.Reshape(device="gpu", layout="HWC")
self.one_hot = ops.OneHot(num_classes=3,
dtype=types.INT32,
device="cpu")
self.jitter_rng = ops.CoinFlip(probability=0.3)
self.jittered = ops.Jitter(device="gpu")
def __init__(self,
batch_size,
layout,
iterator,
pos_size_iter,
num_threads=1,
device_id=0,
num_gpus=1,
axes=None,
axis_names=None,
normalized_anchor=True,
normalized_shape=True):
super(SliceSynthDataPipelinePythonOp,
self).__init__(batch_size,
num_threads,
device_id,
seed=12345,
exec_async=False,
exec_pipelined=False)
self.device = "cpu"
self.layout = layout
self.iterator = iterator
self.pos_size_iter = pos_size_iter
self.inputs = ops.ExternalSource()
self.input_crop_pos = ops.ExternalSource()
self.input_crop_size = ops.ExternalSource()
function = partial(slice_func_helper, axes, axis_names, self.layout,
normalized_anchor, normalized_shape)
self.slice = ops.PythonFunction(function=function)
def __init__(self,
batch_size,
layout,
iterator,
pos_size_iter,
num_threads=1,
device_id=0,
num_gpus=1,
axes=None,
axis_names=None,
normalized_anchor=True,
normalized_shape=True,
input_type=types.FLOAT,
output_type=None):
super().__init__(batch_size,
num_threads,
device_id,
seed=12345,
exec_async=False,
exec_pipelined=False)
self.device = "cpu"
self.layout = layout
self.iterator = iterator
self.pos_size_iter = pos_size_iter
self.inputs = ops.ExternalSource()
self.input_crop_pos = ops.ExternalSource()
self.input_crop_size = ops.ExternalSource()
self.cast_in = ops.Cast(dtype=input_type)
function = partial(slice_func_helper, axes, axis_names, self.layout,
normalized_anchor, normalized_shape)
self.slice = ops.PythonFunction(function=function,
output_layouts=layout)
self.output_type = output_type
if self.output_type is not None:
self.cast_out = ops.Cast(dtype=output_type)
def __init__(self,
batch_size,
pos_size_iter,
num_threads=1,
device_id=0,
num_gpus=1,
axes=None,
axis_names=None,
normalized_anchor=True,
normalized_shape=True):
super(SlicePythonOp, self).__init__(batch_size,
num_threads,
device_id,
seed=12345,
exec_async=False,
exec_pipelined=False)
self.device = "cpu"
self.layout = "HWC"
self.pos_size_iter = pos_size_iter
self.input = ops.CaffeReader(path=caffe_db_folder,
random_shuffle=False)
self.decode = ops.ImageDecoder(device='cpu', output_type=types.RGB)
self.input_crop_pos = ops.ExternalSource()
self.input_crop_size = ops.ExternalSource()
function = partial(slice_func_helper, axes, axis_names, self.layout,
normalized_anchor, normalized_shape)
self.slice = ops.PythonFunction(function=function)
self.set_layout = ops.Reshape(layout="HWC")
def __init__(self,
batch_size,
output_type,
input_type,
fixed_size,
num_threads=3,
device_id=0,
num_gpus=1):
super(CVPipeline, self).__init__(batch_size,
num_threads,
device_id,
seed=7865,
exec_async=False,
exec_pipelined=False)
self.name = "cv"
self.input = ops.CaffeReader(path=caffe_db_folder,
shard_id=device_id,
num_shards=num_gpus)
self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB)
self.rotate = ops.PythonFunction(
function=CVRotate(output_type, input_type, 30, fixed_size))
# TODO(michalz): When we move from Support to CPU operators, replace hardcoded angle
# with one taken from the distribution below
# self.uniform = ops.Uniform(range = (-180.0, 180.0), seed = 42);
self.iter = 0
def __init__(self, batch_size, num_threads, device_id, seed, image_dir,
function):
super(TwoOutputsPythonOperatorPipeline,
self).__init__(batch_size, num_threads, device_id, seed,
image_dir)
self.python_function = ops.PythonFunction(function=function,
num_outputs=2)
def __init__(self,
function,
batch_size,
iterator,
data_shape,
data_layout,
num_threads=1,
device_id=0):
super(Crop3dPythonOpPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False)
self.iterator = iterator
self.inputs = ops.ExternalSource()
self.data_shape = data_shape
self.data_layout = data_layout
def crop_func(image):
return function(image,
layout=self.data_layout,
shape=self.data_shape)
self.crop = ops.PythonFunction(function=crop_func,
output_layouts=data_layout)
def __init__(self,
function,
batch_size,
data_layout,
iterator,
anchor,
shape,
axis_names,
axes,
fill_value,
erase_func=erase_func,
num_threads=1,
device_id=0):
super(ErasePythonPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False)
self.iterator = iterator
self.inputs = ops.ExternalSource()
self.data_layout = data_layout
function = partial(erase_func, anchor, shape, axis_names, axes,
data_layout, fill_value)
self.erase = ops.PythonFunction(function=function,
output_layouts=data_layout)
def __init__(self, batch_size, num_threads, device_id, seed, image_dir,
function):
super(MultiInputMultiOutputPipeline,
self).__init__(batch_size, num_threads, device_id, seed,
image_dir)
self.python_function = ops.PythonFunction(function=function,
num_outputs=3)
def __init__(self,
device,
batch_size,
iterator,
nfft,
window_length,
window_step,
window=None,
center=None,
num_threads=1,
device_id=0,
spectrogram_func=spectrogram_func_librosa):
super(SpectrogramPythonPipeline, self).__init__(batch_size,
num_threads,
device_id,
seed=12345,
exec_async=False,
exec_pipelined=False)
self.device = "cpu"
self.iterator = iterator
self.inputs = ops.ExternalSource()
function = partial(spectrogram_func, nfft, window_length, window_step,
window, center)
self.spectrogram = ops.PythonFunction(function=function,
output_layouts=["ft"])
def __init__(self,
batch_size,
nfft,
window_length,
window_step,
center,
layout="ft",
num_threads=1,
device_id=0,
spectrogram_func=spectrogram_func_librosa):
super(AudioSpectrogramPythonPipeline,
self).__init__(batch_size,
num_threads,
device_id,
seed=12345,
exec_async=False,
exec_pipelined=False)
self.input = ops.readers.File(device="cpu", files=audio_files)
self.decode = ops.decoders.Audio(device="cpu",
dtype=types.FLOAT,
downmix=True)
function = partial(spectrogram_func, nfft, window_length, window_step,
None, center)
self.spectrogram = ops.PythonFunction(function=function,
output_layouts=["ft"])
self.layout = layout
def __init__(self, iterator, batch_size):
super().__init__(iterator,
batch_size,
exec_async=False,
exec_pipelined=False)
function = partial(log_tensor)
self.log = ops.PythonFunction(function=function)
def __init__(self,
function,
batch_size,
iterator,
data_shape,
data_layout,
num_threads=1,
device_id=0,
dictionary={},
default_value=0.0):
super(LookupTablePythonOpPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False)
self.iterator = iterator
self.inputs = ops.ExternalSource()
self.data_shape = data_shape
self.data_layout = data_layout
def lookup_table_func(input_data):
return function(input_data,
shape=data_shape,
dictionary=dictionary,
default_value=default_value)
self.lookup = ops.PythonFunction(function=lookup_table_func)
self.set_layout = ops.Reshape(layout=data_layout)
def __init__(self,
device,
batch_size,
iterator,
nfilter,
sample_rate,
freq_low,
freq_high,
normalize,
mel_formula,
num_threads=1,
device_id=0,
func=mel_fbank_func):
super(MelFilterBankPythonPipeline, self).__init__(batch_size,
num_threads,
device_id,
seed=12345,
exec_async=False,
exec_pipelined=False)
self.device = "cpu"
self.iterator = iterator
self.inputs = ops.ExternalSource()
function = partial(func, nfilter, sample_rate, freq_low, freq_high,
normalize, mel_formula)
self.mel_fbank = ops.PythonFunction(function=function)
def __init__(self, batch_size, num_threads, device_id, _seed, image_dir):
super(CommonPipeline, self).__init__(batch_size, num_threads, device_id, seed=_seed, exec_async=False,
exec_pipelined=False)
self.input = ops.FileReader(file_root=image_dir)
self.decode = ops.ImageDecoder(device = 'cpu', output_type=types.RGB)
self.resize = ops.PythonFunction(function=resize)
self.set_layout = ops.Reshape(layout="HWC")
def __init__(self, batch_size, cutoff_value, window_size, reference_power, reset_interval):
super(NonsilenceRosaPipeline, self).__init__(batch_size, num_threads=1,
exec_async=False, exec_pipelined=False)
hop_length = 1
function = partial(trim_ref, cutoff_value,
np.max if not reference_power else reference_power,
window_size, hop_length)
self.nonsilence = ops.PythonFunction(function=function, num_outputs=2)
def __init__(self, device, batch_size, use_wildcard, num_threads=3, device_id=0, num_gpus=1):
super(ReshapeWithInput, self).__init__(batch_size, num_threads, device_id, seed=7865, exec_async=False, exec_pipelined=False)
self.device = device
self.input = ops.CaffeReader(path = caffe_db_folder, shard_id = device_id, num_shards = num_gpus)
self.decode = ops.ImageDecoder(device = "cpu", output_type = types.RGB)
fn = CollapseChannelsWildcard if use_wildcard else CollapseChannels
self.gen_shapes = ops.PythonFunction(function=fn)
self.reshape = ops.Reshape(device = device, layout = "ab");
def __init__(self, batch_size, output_type, input_type, fixed_size, num_threads=3, device_id=0, num_gpus=1):
super(CVPipeline, self).__init__(batch_size, num_threads, device_id, seed=7865, exec_async=False, exec_pipelined=False)
self.name = "cv"
self.input = ops.CaffeReader(path = caffe_db_folder, shard_id = device_id, num_shards = num_gpus)
self.decode = ops.ImageDecoder(device = "cpu", output_type = types.RGB)
self.rotate = ops.PythonFunction(function=CVRotate(output_type, input_type, fixed_size))
self.uniform = ops.Uniform(range = (-180.0, 180.0), seed = 42);
self.iter = 0
def __init__(self, function, batch_size, num_threads=1, device_id=0):
super(PythonOperatorPipeline, self).__init__(batch_size, num_threads, device_id,
exec_async=False,
exec_pipelined=False,
seed=1234)
self.input = ops.CaffeReader(path = caffe_db_folder, random_shuffle=False)
self.decode = ops.HostDecoder(device = 'cpu', output_type = types.RGB)
self.python_function = ops.PythonFunction(function=function)
def __init__(self, batch_size, num_threads, device_id, _seed):
super(AsyncPipeline, self).__init__(batch_size,
num_threads,
device_id,
seed=_seed,
exec_async=True,
exec_pipelined=True)
self.op = ops.PythonFunction(function=lambda: numpy.zeros([2, 2, 2]))
def __init__(self, function, batch_size, num_threads=1, device_id=0):
super(MultichannelPythonOpPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False)
self.reader = ops.readers.File(file_root=multichannel_tiff_root)
self.decoder = ops.decoders.Image(device = 'cpu', output_type = types.ANY_DATA)
self.oper = ops.PythonFunction(function=function, output_layouts="HWC")
def __init__(self, batch_size,function, num_threads=1, device_id=0, num_gpus=1 ):
super(WaterPythonPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False)
self.input = ops.CaffeReader(path = caffe_db_folder, shard_id = device_id, num_shards = num_gpus)
self.decode = ops.HostDecoder(device = "cpu", output_type = types.RGB)
self.water = ops.PythonFunction(function=function)
def __init__(self, device, batch_size, relative, use_wildcard, num_threads=3, device_id=0, num_gpus=1):
super(ReshapeWithArgInput, self).__init__(batch_size, num_threads, device_id, seed=7865, exec_async=False, exec_pipelined=False)
self.device = device
self.input = ops.CaffeReader(path = caffe_db_folder, shard_id = device_id, num_shards = num_gpus)
self.resize = ops.Resize(device = "cpu");
self.decode = ops.ImageDecoder(device = "cpu", output_type = types.RGB)
self.gen_shapes = ops.PythonFunction(function=MakeTallFunc(relative, use_wildcard))
self.reshape = ops.Reshape(device = device);
self.relative = relative
def __init__(self, function, batch_size, layout, iterator, num_threads=1, device_id=0):
super(PythonOpPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False)
self.layout = layout
self.iterator = iterator
self.inputs = ops.ExternalSource()
self.oper = ops.PythonFunction(function=function)
def __init__(self, function, device, num_outputs=1):
super(PythonFunctionPipeline, self).__init__(BATCH_SIZE, NUM_WORKERS, DEVICE_ID,
seed=SEED,
exec_async=False, exec_pipelined=False)
self.device = device
self.reader = ops.readers.File(file_root=images_dir)
self.decode = ops.decoders.Image(device='cpu',
output_type=types.RGB)
self.norm = ops.CropMirrorNormalize(std=255., mean=0., device=device, output_layout="HWC")
self.func = ops.PythonFunction(device=device, function=function, num_outputs=num_outputs)
def __init__(self, device, batch_size, iterator, axis=0, dct_type=2, lifter=1.0, n_mfcc=20,
norm=None, num_threads=1, device_id=0, func=mfcc_func):
super(MFCCPythonPipeline, self).__init__(
batch_size, num_threads, device_id,
seed=12345, exec_async=False, exec_pipelined=False)
self.device = "cpu"
self.iterator = iterator
self.inputs = ops.ExternalSource()
function = partial(func, axis, dct_type, lifter, n_mfcc, norm)
self.mfcc = ops.PythonFunction(function=function)
def __init__(self, batch_size, nfft, window_length, window_step,
num_threads=1, device_id=0, spectrogram_func=spectrogram_func_librosa):
super(AudioSpectrogramPythonPipeline, self).__init__(
batch_size, num_threads, device_id,
seed=12345, exec_async=False, exec_pipelined=False)
self.input = ops.FileReader(device="cpu", file_root=audio_files)
self.decode = ops.AudioDecoder(device="cpu", dtype=types.FLOAT, downmix=True)
function = partial(spectrogram_func, nfft, window_length, window_step)
self.spectrogram = ops.PythonFunction(function=function)
def __init__(self, device, batch_size, iterator, multiplier, reference, cutoff_db,
num_threads=1, device_id=0, func=to_db_func):
super(ToDecibelsPythonPipeline, self).__init__(
batch_size, num_threads, device_id,
seed=12345, exec_async=False, exec_pipelined=False)
self.device = "cpu"
self.iterator = iterator
self.inputs = ops.ExternalSource()
function = partial(func, multiplier, reference, cutoff_db)
self.dB = ops.PythonFunction(function=function)