Python cntk.AVG_POOLING Examples

The following are 22 code examples of cntk.AVG_POOLING(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module cntk , or try the search function .
Example #1
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #2
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #3
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #4
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #5
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #6
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #7
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #8
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #9
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #10
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #11
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #12
Source File: cntk_backend.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    data_format = normalize_data_format(data_format)

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #13
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #14
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #15
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #16
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #17
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #18
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)
Example #19
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def pool2d(x, pool_size, strides=(1, 1),
           padding='valid', data_format=None,
           pool_mode='max'):
    if data_format is None:
        data_format = image_data_format()
    if data_format not in {'channels_first', 'channels_last'}:
        raise ValueError('Unknown data_format ' + str(data_format))

    padding = _preprocess_border_mode(padding)
    strides = strides
    pool_size = pool_size
    x = _preprocess_conv2d_input(x, data_format)
    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))
    return _postprocess_conv2d_output(x, data_format)
Example #20
Source File: cntk_parser.py    From MMdnn with MIT License 5 votes vote down vote up 
def rename_Pooling(self, source_node):
        IR_node = self._convert_identity_operation(source_node, new_op='Pool')
        dim = len(IR_node.attr['_output_shapes'].list.shape[0].dim)
        kwargs = {}

        # strides
        kwargs['strides'] = list(source_node.get_attr('strides')) + [1]
        if len(kwargs['strides']) < dim:
            kwargs['strides'] = [1] + kwargs['strides']

        # window_shape
        kwargs['kernel_shape'] = list(source_node.get_attr('poolingWindowShape')) + [1]
        if len(kwargs['kernel_shape']) < dim:
            kwargs['kernel_shape'] = [1] + kwargs['kernel_shape']

        # pool type
        pool_type = source_node.get_attr('poolingType')
        if pool_type == _cntk.MAX_POOLING:
            kwargs['pooling_type'] = 'MAX'
        elif pool_type == _cntk.AVG_POOLING:
            kwargs['pooling_type'] = 'AVG'
        else:
            raise ValueError("Unknown pooling type [{}].".format(pool_type))

        # padding
        padding = source_node.get_attr('autoPadding')
        if len(padding) >= dim - 1:
            padding = padding[1:]
        elif len(padding) < dim - 2:
            padding.extend([padding[-1]] * (dim - len(padding) - 2))
        for pad in padding:
            assert pad == padding[-1]
        kwargs['auto_pad'] = 'SAME_LOWER' if padding[0] else 'VALID'
        kwargs['pads'] = self._convert_padding_to_IR(kwargs['kernel_shape'][1:-1], padding)

        assign_IRnode_values(IR_node, kwargs)
Example #21
Source File: cntk_emitter.py    From MMdnn with MIT License 5 votes vote down vote up 
def emit_Pool(self, IR_node):
        input_node = self.IR_graph.get_node(IR_node.in_edges[0]).real_variable_name
        if IR_node.layer.attr['global_pooling'].b:
            self.used_layers.add('GlobalPooling')
            code = "{:<15} = global_pooling({}, '{}', name = '{}')".format(
                IR_node.variable_name,
                input_node,
                IR_node.get_attr('pooling_type'),
                IR_node.name)
        else:
            for e in IR_node.get_attr('dilations', []):
                assert e == 1

            dim = len(IR_node.get_attr('kernel_shape')) - 2
            padding = not self.is_valid_padding(IR_node.get_attr('auto_pad'), IR_node.get_attr('pads'))
            padding = [False] + [padding] * dim
            ceil_out_dim = self.is_ceil_mode(IR_node.get_attr('pads'))

            pooling_type = IR_node.get_attr('pooling_type')
            if pooling_type == 'MAX':
                pooling_type = cntk.MAX_POOLING
            elif pooling_type == 'AVG':
                pooling_type = cntk.AVG_POOLING
            else:
                raise ValueError

            if self.weight_loaded:
                self.used_layers.add(IR_node.type)
                code = "{:<15} = pooling({}, pooling_type={}, pooling_window_shape={}, strides={}, auto_padding={}, ceil_out_dim={})".format(
                    IR_node.variable_name,
                    input_node,
                    pooling_type,
                    tuple(IR_node.get_attr('kernel_shape')[1:-1]),
                    tuple(IR_node.get_attr('strides')[1:-1]),
                    padding,
                    ceil_out_dim
                    )
            else:
                raise NotImplementedError
        return code
Example #22
Source File: cntk_backend.py    From GraphicDesignPatternByPython with MIT License 5 votes vote down vote up 
def pool3d(x, pool_size, strides=(1, 1, 1), padding='valid',
           data_format=None, pool_mode='max'):
    data_format = normalize_data_format(data_format)

    padding = _preprocess_border_mode(padding)

    x = _preprocess_conv3d_input(x, data_format)

    if pool_mode == 'max':
        x = C.pooling(
            x,
            C.MAX_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    elif pool_mode == 'avg':
        x = C.pooling(
            x,
            C.AVG_POOLING,
            pool_size,
            strides,
            auto_padding=[padding])
    else:
        raise ValueError('Invalid pooling mode: ' + str(pool_mode))

    return _postprocess_conv3d_output(x, data_format)