def make_tuning_loader(manifest_file, manifest_root, backend_obj):
aeon_config = common_config(manifest_file,
manifest_root,
backend_obj.bsz,
subset_pct=20)
aeon_config['shuffle_manifest'] = True
return wrap_dataloader(AeonDataLoader(aeon_config))
def make_validation_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz,
subset_pct)
return wrap_dataloader(AeonDataLoader(aeon_config))
def build_dataloader(config, frcn_rois_per_img):
"""
Builds the dataloader for the Faster-RCNN network using our aeon loader.
Besides, the base loader, we add several operations:
1. Cast the image data into float32 format
2. Subtract the BGRMean from the image. We used pre-defined means from training
the VGG network.
3. Repack the data for Faster-RCNN model. This model has several nested branches, so
The buffers have to repacked into nested tuples to match the branch leafs. Additionally,
buffers for training the RCNN portion of the model are also allocated and provisioned
to the model.
Arguments:
config (dict): dataloader configuration
be (backend): compute backend
frcn_rois_per_img (int): Number of ROIs to use for training the RCNN portion of the
model. This is used to create the target buffers for RCNN.
Returns:
dataloader object.
"""
dl = AeonDataLoader(config)
dl = TypeCast(dl, index=0, dtype=np.float32) # cast image to float
dl = BGRMeanSubtract(dl, index=0,
pixel_mean=util.FRCN_PIXEL_MEANS) # subtract means
dl = ObjectLocalization(
dl, frcn_rois_per_img=frcn_rois_per_img) # repack faster-rcnn
return dl
def make_test_loader(manifest_file, manifest_root, backend_obj):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
aeon_config['type'] = 'audio' # No labels provided
aeon_config.pop('label', None)
dl = AeonDataLoader(aeon_config, backend_obj)
dl = TypeCast(dl, index=0, dtype=np.float32)
return dl
def make_inference_loader(manifest_file, backend_obj):
manifest_root = "" # This is used for demo script which generates abs path manifest
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
aeon_config['type'] = 'video' # No labels provided
aeon_config.pop('label', None)
dl = AeonDataLoader(aeon_config, backend_obj)
dl = TypeCast(dl, index=0, dtype=np.float32)
return dl
def make_loader(manifest_file, manifest_root, backend_obj, subset_pct=100, random_seed=0):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz, subset_pct)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_enable'] = True
aeon_config['random_seed'] = random_seed
aeon_config['augmentation'][0]['center'] = True
aeon_config['augmentation'][0]['flip_enable'] = False
return wrap_dataloader(AeonDataLoader(aeon_config))
def make_test_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
aeon_config['subset_fraction'] = float(subset_pct / 100.0)
dl = AeonDataLoader(aeon_config, backend_obj)
dl = OneHot(dl, index=1, nclasses=101)
dl = TypeCast(dl, index=0, dtype=np.float32)
return dl
def make_tuning_loader(manifest_file,
manifest_root,
backend_obj,
dtype=np.float32):
aeon_config = common_config(manifest_file,
manifest_root,
backend_obj.bsz,
subset_pct=10)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_every_epoch'] = True
return wrap_dataloader(AeonDataLoader(aeon_config, backend_obj), dtype)
def validation_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100,
h=96,
w=96,
scale=[1., 1.],
ncls=10):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz,
subset_pct, h, w, scale)
aeon_config['image']['center'] = True
return wrap_dataloader(AeonDataLoader(aeon_config, backend_obj), ncls=ncls)
def make_train_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100,
random_seed=0):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz,
subset_pct)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_every_epoch'] = True
aeon_config['random_seed'] = random_seed
aeon_config['image']['center'] = False
aeon_config['image']['flip_enable'] = True
return wrap_dataloader(AeonDataLoader(aeon_config, backend_obj))
def make_inference_loader(manifest_file, backend_obj):
manifest_root = "" # This is used for demo script which generates abs path manifest
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
video_config = {
"type": "video",
"max_frame_count": 16,
"frame": {
"height": 112,
"width": 112
}
}
aeon_config['etl'] = [video_config]
dl = AeonDataLoader(aeon_config)
dl = TypeCast(dl, index=0, dtype=np.float32)
return dl
def make_alexnet_train_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100,
random_seed=0,
dtype=np.float32):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz,
subset_pct)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_enable'] = True
aeon_config['random_seed'] = random_seed
aeon_config['augmentation'][0]["center"] = False
aeon_config['augmentation'][0]["flip_enable"] = True
return wrap_dataloader(AeonDataLoader(aeon_config))
def make_train_loader(manifest_file, manifest_root, backend_obj, noise_file=None, random_seed=0):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_enable'] = True
aeon_config['random_seed'] = random_seed
if noise_file is not None:
aeon_config['augmentation'] = []
aeon_config['augmentation'].append(dict())
aeon_config['augmentation'][0]['type'] = "audio"
aeon_config['augmentation'][0]['noise_index_file'] = noise_file
aeon_config['augmentation'][0]['noise_root'] = os.path.dirname(noise_file)
aeon_config['augmentation'][0]['add_noise_probability'] = 0.5
aeon_config['augmentation'][0]['noise_level'] = (0.0, 0.5)
return wrap_dataloader(AeonDataLoader(aeon_config))
def make_train_loader(manifest_file,
manifest_root,
backend_obj,
noise_file=None,
random_seed=0):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_every_epoch'] = True
aeon_config['random_seed'] = random_seed
if noise_file is not None:
aeon_config['audio']['noise_index_file'] = noise_file
aeon_config['audio']['noise_root'] = manifest_root
aeon_config['audio']['add_noise_probability'] = 0.5
aeon_config['audio']['noise_level'] = [0.0, 0.5]
return wrap_dataloader(AeonDataLoader(aeon_config, backend_obj))
def make_train_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100,
random_seed=0):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
aeon_config['subset_fraction'] = float(subset_pct / 100.0)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_every_epoch'] = True
aeon_config['random_seed'] = random_seed
aeon_config['video']['frame']['center'] = False
aeon_config['video']['frame']['flip_enable'] = True
dl = AeonDataLoader(aeon_config, backend_obj)
dl = OneHot(dl, index=1, nclasses=101)
dl = TypeCast(dl, index=0, dtype=np.float32)
return dl
def train_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100,
random_seed=0,
h=96,
w=96,
scale=[1., 1.],
binary=False):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz,
subset_pct, h, w, scale)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_every_epoch'] = True
aeon_config['random_seed'] = random_seed
aeon_config['image']['center'] = False
aeon_config['image']['flip_enable'] = True
aeon_config['label']['binary'] = binary
# return wrap_dataloader(AeonDataLoader(aeon_config, backend_obj))
return AeonDataLoader(aeon_config, backend_obj)
def make_msra_train_loader(manifest_file,
manifest_root,
backend_obj,
subset_pct=100,
random_seed=0,
dtype=np.float32):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz,
subset_pct)
aeon_config['shuffle_manifest'] = True
aeon_config['shuffle_enable'] = True
aeon_config['random_seed'] = random_seed
aeon_config['augmentation'][0]["center"] = False
aeon_config['augmentation'][0]["flip_enable"] = True
aeon_config['augmentation'][0]['scale'] = [0.08, 1.0]
aeon_config['augmentation'][0]['do_area_scale'] = True
aeon_config['augmentation'][0]['horizontal_distortion'] = [0.75, 1.33]
aeon_config['augmentation'][0]['lighting'] = [0.0, 0.01]
aeon_config['augmentation'][0]['contrast'] = [0.9, 1.1]
aeon_config['augmentation'][0]['brightness'] = [0.9, 1.1]
aeon_config['augmentation'][0]['saturation'] = [0.9, 1.1]
return wrap_dataloader(AeonDataLoader(aeon_config))
def make_val_loader(manifest_file, manifest_root, backend_obj):
aeon_config = common_config(manifest_file, manifest_root, backend_obj.bsz)
return wrap_dataloader(AeonDataLoader(aeon_config, backend_obj))
def make_loader(manifest_file, alphabet, nbands, max_tscrpt_len, max_utt_len,
backend_obj):
aeon_config = common_config(manifest_file, backend_obj.bsz, alphabet,
nbands, max_tscrpt_len, max_utt_len)
return wrap_dataloader(AeonDataLoader(aeon_config))
def wrap_dataloader(aeon_config):
dl = AeonDataLoader(aeon_config)
dl = OneHot(dl, index=1, nclasses=101)
dl = TypeCast(dl, index=0, dtype=np.float32)
return dl
