<reponame>awesome-archive/Dragon<gh_stars>0
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
#      <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------

"""The Implementation of the data layers."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import dragon
from ..layer import Layer


class DataLayer(Layer):
    """
    The implementation of ``DataLayer``.

    Different from ``Caffe``, we force to use `LMDB`_ backend.

    Parameters
    ----------
    source : str
        The path of database. Refer `DataParameter.source`_.
    prefetch: int
        The prefetch count. Refer `DataParameter.prefetch`_.
    batch_size : int
        The size of a mini-batch. Refer `DataParameter.batch_size`_.
    phase : caffe_pb2.Phase
        The phase of layer. Refer `LayerParameter.phase`_.
    mirrow : boolean
        Whether to randomly mirror. Refer `TransformationParameter.mirror`_.
    crop_size : int
        The crop size. Refer `TransformationParameter.crop_size`_.
    force_color : boolean
        Force to have 3 channels. Refer `TransformationParameter.force_color`_.
    color_augmentation : boolean
        Whether to distort colors. Extension of `TransformationParameter`_.
    padding : int
        The padding size. Extension of `TransformationParameter`_.
    min_random_scale : float
        The min scale of the images. Extension of `TransformationParameter`_.
    max_random_scale : float
        The max scale of the images. Extension of `TransformationParameter`_.
    dtype : caffe_pb2.MemoryDataParameter.DataType
        The output data type. ``FLOAT32`` or ``FLOAT16``.
    mean_value : list of float
        The mean of each channel. Refer `TransformationParameter.mean_value`_.
    scale : float
        The scaling factor. Refer `TransformationParameter.scale`_.

    """
    def __init__(self, LayerParameter):
        super(DataLayer, self).__init__(LayerParameter)

        param = LayerParameter.data_param
        memory_param = LayerParameter.memory_data_param
        transform_param = LayerParameter.transform_param
        parallel_param = LayerParameter.parallel_param

        self.arguments = {
            'source': param.source,
            'prefetch': param.prefetch,
            'batch_size': param.batch_size,
            'phase': {0: 'TRAIN', 1: 'TEST'}[int(LayerParameter.phase)],
            'mirror': transform_param.mirror,
            'crop_size': transform_param.crop_size,
            'force_color': transform_param.force_color,
            'color_augmentation': transform_param.color_augmentation,
            'padding': transform_param.padding,
            'min_random_scale': transform_param.min_random_scale,
            'max_random_scale': transform_param.max_random_scale,
            'shuffle': parallel_param.shuffle,
            'multiple_nodes': parallel_param.multiple_nodes,
            'partition': parallel_param.partition,
            'dtype': {0: 'float32', 1: 'float16'}[memory_param.dtype],
            'data_format': 'NCHW',
        }

        if len(transform_param.mean_value) > 0:
            self.arguments['mean_values'] = [float(element)
                for element in transform_param.mean_value]

        if transform_param.scale != 1:
            self.arguments['mean_values'] = \
                [1. / transform_param.scale] * 3

    def LayerSetup(self, bottom):
        data, label = dragon.ops.LMDBData(**self.arguments)
        return dragon.ops.ImageData(data, **self.arguments), label


class MemoryDataLayer(Layer):
    """The implementation of ``MemoryDataLayer``.

    We extend it with ``FP16`` and ``NHWC => NCHW``.

    Parameters
    ----------
    dtype : caffe_pb2.MemoryDataParameter.DataType
        The output data type. ``FLOAT32`` or ``FLOAT16``.
    mean_value : list of float
        The mean of each channel. Refer `TransformationParameter.mean_value`_.
    scale : float
        The scaling factor. Refer `TransformationParameter.scale`_.

    """
    def __init__(self, LayerParameter):
        super(MemoryDataLayer, self).__init__(LayerParameter)
        param = LayerParameter.memory_data_param
        transform_param = LayerParameter.transform_param

        self.arguments = {
            'dtype': {0: 'float32', 1: 'float16'}[param.dtype],
            'data_format': 'NCHW',
        }

        if len(transform_param.mean_value) > 0:
            self.arguments['mean_values'] = \
                [float(element) for element in transform_param.mean_value]

        if transform_param.scale != 1:
            self.arguments['mean_values'] = \
                [1. / transform_param.scale] * 3

    def LayerSetup(self, bottom):
        return dragon.ops.ImageData(bottom, **self.arguments)
# ------------------------------------------------------------
# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.
#
# Licensed under the BSD 2-Clause License.
# You should have received a copy of the BSD 2-Clause License
# along with the software. If not, See,
#
#      <https://opensource.org/licenses/BSD-2-Clause>
#
# ------------------------------------------------------------

"""The Implementation of the data layers."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import dragon
from ..layer import Layer


class DataLayer(Layer):
    """
    The implementation of ``DataLayer``.

    Different from ``Caffe``, we force to use `LMDB`_ backend.

    Parameters
    ----------
    source : str
        The path of database. Refer `DataParameter.source`_.
    prefetch: int
        The prefetch count. Refer `DataParameter.prefetch`_.
    batch_size : int
        The size of a mini-batch. Refer `DataParameter.batch_size`_.
    phase : caffe_pb2.Phase
        The phase of layer. Refer `LayerParameter.phase`_.
    mirrow : boolean
        Whether to randomly mirror. Refer `TransformationParameter.mirror`_.
    crop_size : int
        The crop size. Refer `TransformationParameter.crop_size`_.
    force_color : boolean
        Force to have 3 channels. Refer `TransformationParameter.force_color`_.
    color_augmentation : boolean
        Whether to distort colors. Extension of `TransformationParameter`_.
    padding : int
        The padding size. Extension of `TransformationParameter`_.
    min_random_scale : float
        The min scale of the images. Extension of `TransformationParameter`_.
    max_random_scale : float
        The max scale of the images. Extension of `TransformationParameter`_.
    dtype : caffe_pb2.MemoryDataParameter.DataType
        The output data type. ``FLOAT32`` or ``FLOAT16``.
    mean_value : list of float
        The mean of each channel. Refer `TransformationParameter.mean_value`_.
    scale : float
        The scaling factor. Refer `TransformationParameter.scale`_.

    """
    def __init__(self, LayerParameter):
        super(DataLayer, self).__init__(LayerParameter)

        param = LayerParameter.data_param
        memory_param = LayerParameter.memory_data_param
        transform_param = LayerParameter.transform_param
        parallel_param = LayerParameter.parallel_param

        self.arguments = {
            'source': param.source,
            'prefetch': param.prefetch,
            'batch_size': param.batch_size,
            'phase': {0: 'TRAIN', 1: 'TEST'}[int(LayerParameter.phase)],
            'mirror': transform_param.mirror,
            'crop_size': transform_param.crop_size,
            'force_color': transform_param.force_color,
            'color_augmentation': transform_param.color_augmentation,
            'padding': transform_param.padding,
            'min_random_scale': transform_param.min_random_scale,
            'max_random_scale': transform_param.max_random_scale,
            'shuffle': parallel_param.shuffle,
            'multiple_nodes': parallel_param.multiple_nodes,
            'partition': parallel_param.partition,
            'dtype': {0: 'float32', 1: 'float16'}[memory_param.dtype],
            'data_format': 'NCHW',
        }

        if len(transform_param.mean_value) > 0:
            self.arguments['mean_values'] = [float(element)
                for element in transform_param.mean_value]

        if transform_param.scale != 1:
            self.arguments['mean_values'] = \
                [1. / transform_param.scale] * 3

    def LayerSetup(self, bottom):
        data, label = dragon.ops.LMDBData(**self.arguments)
        return dragon.ops.ImageData(data, **self.arguments), label


class MemoryDataLayer(Layer):
    """The implementation of ``MemoryDataLayer``.

    We extend it with ``FP16`` and ``NHWC => NCHW``.

    Parameters
    ----------
    dtype : caffe_pb2.MemoryDataParameter.DataType
        The output data type. ``FLOAT32`` or ``FLOAT16``.
    mean_value : list of float
        The mean of each channel. Refer `TransformationParameter.mean_value`_.
    scale : float
        The scaling factor. Refer `TransformationParameter.scale`_.

    """
    def __init__(self, LayerParameter):
        super(MemoryDataLayer, self).__init__(LayerParameter)
        param = LayerParameter.memory_data_param
        transform_param = LayerParameter.transform_param

        self.arguments = {
            'dtype': {0: 'float32', 1: 'float16'}[param.dtype],
            'data_format': 'NCHW',
        }

        if len(transform_param.mean_value) > 0:
            self.arguments['mean_values'] = \
                [float(element) for element in transform_param.mean_value]

        if transform_param.scale != 1:
            self.arguments['mean_values'] = \
                [1. / transform_param.scale] * 3

    def LayerSetup(self, bottom):
        return dragon.ops.ImageData(bottom, **self.arguments)
# ------------------------------------------------------------ # Copyright (c) 2017-present, SeetaTech, Co.,Ltd. # # Licensed under the BSD 2-Clause License. # You should have received a copy of the BSD 2-Clause License # along with the software. If not, See, # #      <https://opensource.org/licenses/BSD-2-Clause> # # ------------------------------------------------------------ The Implementation of the data layers. The implementation of ``DataLayer``.      Different from ``Caffe``, we force to use `LMDB`_ backend.      Parameters     ----------     source : str         The path of database. Refer `DataParameter.source`_.     prefetch: int         The prefetch count. Refer `DataParameter.prefetch`_.     batch_size : int         The size of a mini-batch. Refer `DataParameter.batch_size`_.     phase : caffe_pb2.Phase         The phase of layer. Refer `LayerParameter.phase`_.     mirrow : boolean         Whether to randomly mirror. Refer `TransformationParameter.mirror`_.     crop_size : int         The crop size. Refer `TransformationParameter.crop_size`_.     force_color : boolean         Force to have 3 channels. Refer `TransformationParameter.force_color`_.     color_augmentation : boolean         Whether to distort colors. Extension of `TransformationParameter`_.     padding : int         The padding size. Extension of `TransformationParameter`_.     min_random_scale : float         The min scale of the images. Extension of `TransformationParameter`_.     max_random_scale : float         The max scale of the images. Extension of `TransformationParameter`_.     dtype : caffe_pb2.MemoryDataParameter.DataType         The output data type. ``FLOAT32`` or ``FLOAT16``.     mean_value : list of float         The mean of each channel. Refer `TransformationParameter.mean_value`_.     scale : float         The scaling factor. Refer `TransformationParameter.scale`_. The implementation of ``MemoryDataLayer``.      We extend it with ``FP16`` and ``NHWC => NCHW``.      Parameters     ----------     dtype : caffe_pb2.MemoryDataParameter.DataType         The output data type. ``FLOAT32`` or ``FLOAT16``.     mean_value : list of float         The mean of each channel. Refer `TransformationParameter.mean_value`_.     scale : float         The scaling factor. Refer `TransformationParameter.scale`_.