def __init__(self, batch_size, num_threads, path, training, annotations, world, device_id, mean, std, resize,
max_size, stride, rotate_augment=False,
augment_brightness=0.0,
augment_contrast=0.0, augment_hue=0.0,
augment_saturation=0.0):
super().__init__(batch_size=batch_size, num_threads=num_threads, device_id=device_id,
prefetch_queue_depth=num_threads, seed=42)
self.path = path
self.training = training
self.stride = stride
self.iter = 0
self.rotate_augment = rotate_augment
self.augment_brightness = augment_brightness
self.augment_contrast = augment_contrast
self.augment_hue = augment_hue
self.augment_saturation = augment_saturation
self.reader = ops.COCOReader(annotations_file=annotations, file_root=path, num_shards=world,
shard_id=torch.cuda.current_device(),
ltrb=True, ratio=True, shuffle_after_epoch=True, save_img_ids=True)
self.decode_train = ops.ImageDecoderSlice(device="mixed", output_type=types.RGB)
self.decode_infer = ops.ImageDecoder(device="mixed", output_type=types.RGB)
self.bbox_crop = ops.RandomBBoxCrop(device='cpu', ltrb=True, scaling=[0.3, 1.0],
thresholds=[0.1, 0.3, 0.5, 0.7, 0.9])
self.bbox_flip = ops.BbFlip(device='cpu', ltrb=True)
self.img_flip = ops.Flip(device='gpu')
self.coin_flip = ops.CoinFlip(probability=0.5)
self.bc = ops.BrightnessContrast(device='gpu')
self.hsv = ops.Hsv(device='gpu')
# Random number generation for augmentation
self.brightness_dist = ops.NormalDistribution(mean=1.0, stddev=augment_brightness)
self.contrast_dist = ops.NormalDistribution(mean=1.0, stddev=augment_contrast)
self.hue_dist = ops.NormalDistribution(mean=0.0, stddev=augment_hue)
self.saturation_dist = ops.NormalDistribution(mean=1.0, stddev=augment_saturation)
if rotate_augment:
raise RuntimeWarning("--augment-rotate current has no effect when using the DALI data loader.")
if isinstance(resize, list): resize = max(resize)
self.rand_resize = ops.Uniform(range=[resize, float(max_size)])
self.resize_train = ops.Resize(device='gpu', interp_type=types.DALIInterpType.INTERP_CUBIC, save_attrs=True)
self.resize_infer = ops.Resize(device='gpu', interp_type=types.DALIInterpType.INTERP_CUBIC,
resize_longer=max_size, save_attrs=True)
padded_size = max_size + ((self.stride - max_size % self.stride) % self.stride)
self.pad = ops.Paste(device='gpu', fill_value=0, ratio=1.1, min_canvas_size=padded_size, paste_x=0, paste_y=0)
self.normalize = ops.CropMirrorNormalize(device='gpu', mean=mean, std=std, crop=(padded_size, padded_size),
crop_pos_x=0, crop_pos_y=0)
def __init__(self,
device_id,
n_devices,
file_root,
file_list,
batch_size,
sample_rate=16000,
window_size=.02,
window_stride=.01,
nfeatures=64,
nfft=512,
frame_splicing_factor=3,
silence_threshold=-80,
dither=.00001,
preemph_coeff=.97,
lowfreq=0.0,
highfreq=0.0,
num_threads=1):
super().__init__(batch_size, num_threads, device_id, seed=42)
self.dither = dither
self.frame_splicing_factor = frame_splicing_factor
self.read = ops.FileReader(file_root=file_root,
file_list=file_list,
device="cpu",
shard_id=device_id,
num_shards=n_devices)
self.decode = ops.AudioDecoder(device="cpu",
dtype=types.FLOAT,
downmix=True)
self.normal_distribution = ops.NormalDistribution(device="cpu")
self.preemph = ops.PreemphasisFilter(preemph_coeff=preemph_coeff)
self.spectrogram = ops.Spectrogram(
device="cpu",
nfft=nfft,
window_length=window_size * sample_rate,
window_step=window_stride * sample_rate)
self.mel_fbank = ops.MelFilterBank(device="cpu",
sample_rate=sample_rate,
nfilter=nfeatures,
normalize=True,
freq_low=lowfreq,
freq_high=highfreq)
self.log_features = ops.ToDecibels(device="cpu",
multiplier=np.log(10),
reference=1.0,
cutoff_db=-80)
self.get_shape = ops.Shapes(device="cpu")
self.normalize = ops.Normalize(axes=[0], device="cpu")
self.splicing_transpose = ops.Transpose(device="cpu", perm=[1, 0])
self.splicing_reshape = ops.Reshape(
device="cpu", rel_shape=[-1, frame_splicing_factor])
self.splicing_pad = ops.Pad(axes=[0],
fill_value=0,
align=frame_splicing_factor,
shape=[1],
device="cpu")
self.get_nonsilent_region = ops.NonsilentRegion(
device="cpu", cutoff_db=silence_threshold)
self.trim_silence = ops.Slice(device="cpu", axes=[0])
self.to_float = ops.Cast(dtype=types.FLOAT)
def __init__(
self,
*,
train_pipeline:
bool, # True if train pipeline, False if validation pipeline
device_id,
num_threads,
batch_size,
file_root: str,
file_list: str,
sample_rate,
discrete_resample_range: bool,
resample_range: list,
window_size,
window_stride,
nfeatures,
nfft,
frame_splicing_factor,
dither_coeff,
silence_threshold,
preemph_coeff,
pad_align,
max_duration,
mask_time_num_regions,
mask_time_min,
mask_time_max,
mask_freq_num_regions,
mask_freq_min,
mask_freq_max,
mask_both_num_regions,
mask_both_min_time,
mask_both_max_time,
mask_both_min_freq,
mask_both_max_freq,
preprocessing_device="gpu"):
super().__init__(batch_size, num_threads, device_id)
self._dali_init_log(locals())
if torch.distributed.is_initialized():
shard_id = torch.distributed.get_rank()
n_shards = torch.distributed.get_world_size()
else:
shard_id = 0
n_shards = 1
self.preprocessing_device = preprocessing_device.lower()
assert self.preprocessing_device == "cpu" or self.preprocessing_device == "gpu", \
"Incorrect preprocessing device. Please choose either 'cpu' or 'gpu'"
self.frame_splicing_factor = frame_splicing_factor
assert frame_splicing_factor == 1, "DALI doesn't support frame splicing operation"
self.resample_range = resample_range
self.discrete_resample_range = discrete_resample_range
self.train = train_pipeline
self.sample_rate = sample_rate
self.dither_coeff = dither_coeff
self.nfeatures = nfeatures
self.max_duration = max_duration
self.mask_params = {
'time_num_regions': mask_time_num_regions,
'time_min': mask_time_min,
'time_max': mask_time_max,
'freq_num_regions': mask_freq_num_regions,
'freq_min': mask_freq_min,
'freq_max': mask_freq_max,
'both_num_regions': mask_both_num_regions,
'both_min_time': mask_both_min_time,
'both_max_time': mask_both_max_time,
'both_min_freq': mask_both_min_freq,
'both_max_freq': mask_both_max_freq,
}
self.do_remove_silence = True if silence_threshold is not None else False
self.read = ops.FileReader(device="cpu",
file_root=file_root,
file_list=file_list,
shard_id=shard_id,
num_shards=n_shards,
shuffle_after_epoch=train_pipeline)
# TODO change ExternalSource to Uniform for new DALI release
if discrete_resample_range and resample_range is not None:
self.speed_perturbation_coeffs = ops.ExternalSource(
device="cpu",
cycle=True,
source=self._discrete_resample_coeffs_generator)
elif resample_range is not None:
self.speed_perturbation_coeffs = ops.Uniform(device="cpu",
range=resample_range)
else:
self.speed_perturbation_coeffs = None
self.decode = ops.AudioDecoder(
device="cpu",
sample_rate=self.sample_rate if resample_range is None else None,
dtype=types.FLOAT,
downmix=True)
self.normal_distribution = ops.NormalDistribution(
device=preprocessing_device)
self.preemph = ops.PreemphasisFilter(device=preprocessing_device,
preemph_coeff=preemph_coeff)
self.spectrogram = ops.Spectrogram(
device=preprocessing_device,
nfft=nfft,
window_length=window_size * sample_rate,
window_step=window_stride * sample_rate)
self.mel_fbank = ops.MelFilterBank(device=preprocessing_device,
sample_rate=sample_rate,
nfilter=self.nfeatures,
normalize=True)
self.log_features = ops.ToDecibels(device=preprocessing_device,
multiplier=np.log(10),
reference=1.0,
cutoff_db=math.log(1e-20))
self.get_shape = ops.Shapes(device=preprocessing_device)
self.normalize = ops.Normalize(device=preprocessing_device, axes=[1])
self.pad = ops.Pad(device=preprocessing_device,
axes=[1],
fill_value=0,
align=pad_align)
# Silence trimming
self.get_nonsilent_region = ops.NonsilentRegion(
device="cpu", cutoff_db=silence_threshold)
self.trim_silence = ops.Slice(device="cpu",
normalized_anchor=False,
normalized_shape=False,
axes=[0])
self.to_float = ops.Cast(device="cpu", dtype=types.FLOAT)
# Spectrogram masking
self.spectrogram_cutouts = ops.ExternalSource(
source=self._cutouts_generator, num_outputs=2, cycle=True)
self.mask_spectrogram = ops.Erase(device=preprocessing_device,
axes=[0, 1],
fill_value=0,
normalized_anchor=True)
def __init__(self, batch_size, dtype):
super(NormalDistributionPipelineDefault, self).__init__(batch_size)
self.norm = ops.NormalDistribution(device="cpu", dtype=dtype)
def __init__(self, shape, dtype):
super(NormalDistributionPipelineWithArgument, self).__init__(1)
self.norm = ops.NormalDistribution(device="cpu",
shape=shape,
dtype=dtype)
def __init__(self, premade_batch, dtype):
super(NormalDistributionPipelineWithInput,
self).__init__(len(premade_batch))
self.premade_batch = premade_batch
self.ext_src = ops.ExternalSource()
self.norm = ops.NormalDistribution(device="cpu", dtype=dtype)
def __init__(self,
*,
pipeline_type,
device_id,
num_threads,
batch_size,
file_root: str,
sampler,
sample_rate,
resample_range: list,
window_size,
window_stride,
nfeatures,
nfft,
dither_coeff,
silence_threshold,
preemph_coeff,
max_duration,
preprocessing_device="gpu"):
super().__init__(batch_size, num_threads, device_id)
self._dali_init_log(locals())
if torch.distributed.is_initialized():
shard_id = torch.distributed.get_rank()
n_shards = torch.distributed.get_world_size()
else:
shard_id = 0
n_shards = 1
self.preprocessing_device = preprocessing_device.lower()
assert self.preprocessing_device == "cpu" or self.preprocessing_device == "gpu", \
"Incorrect preprocessing device. Please choose either 'cpu' or 'gpu'"
self.resample_range = resample_range
train_pipeline = pipeline_type == 'train'
self.train = train_pipeline
self.sample_rate = sample_rate
self.dither_coeff = dither_coeff
self.nfeatures = nfeatures
self.max_duration = max_duration
self.do_remove_silence = True if silence_threshold is not None else False
shuffle = train_pipeline and not sampler.is_sampler_random()
self.read = ops.FileReader(name="Reader",
pad_last_batch=(pipeline_type == 'val'),
device="cpu",
file_root=file_root,
file_list=sampler.get_file_list_path(),
shard_id=shard_id,
num_shards=n_shards,
shuffle_after_epoch=shuffle)
# TODO change ExternalSource to Uniform for new DALI release
if resample_range is not None:
self.speed_perturbation_coeffs = ops.Uniform(device="cpu",
range=resample_range)
else:
self.speed_perturbation_coeffs = None
self.decode = ops.AudioDecoder(
device="cpu",
sample_rate=self.sample_rate if resample_range is None else None,
dtype=types.FLOAT,
downmix=True)
self.normal_distribution = ops.NormalDistribution(
device=preprocessing_device)
self.preemph = ops.PreemphasisFilter(device=preprocessing_device,
preemph_coeff=preemph_coeff)
self.spectrogram = ops.Spectrogram(
device=preprocessing_device,
nfft=nfft,
window_length=window_size * sample_rate,
window_step=window_stride * sample_rate)
self.mel_fbank = ops.MelFilterBank(device=preprocessing_device,
sample_rate=sample_rate,
nfilter=self.nfeatures,
normalize=True)
self.log_features = ops.ToDecibels(device=preprocessing_device,
multiplier=np.log(10),
reference=1.0,
cutoff_db=math.log(1e-20))
self.get_shape = ops.Shapes(device=preprocessing_device)
self.normalize = ops.Normalize(device=preprocessing_device, axes=[1])
self.pad = ops.Pad(device=preprocessing_device, fill_value=0)
# Silence trimming
self.get_nonsilent_region = ops.NonsilentRegion(
device="cpu", cutoff_db=silence_threshold)
self.trim_silence = ops.Slice(device="cpu",
normalized_anchor=False,
normalized_shape=False,
axes=[0])
self.to_float = ops.Cast(device="cpu", dtype=types.FLOAT)