Python cntk.FreeDimension() Examples

The following are 30 code examples of cntk.FreeDimension(). 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 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #2
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #3
Source File: proposal_target_layer.py    From cntk-hotel-pictures-classificator with MIT License 6 votes vote down vote up 
def infer_outputs(self):
        # sampled rois (0, x1, y1, x2, y2)
        # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python
        rois_shape = (FreeDimension, 4)
        labels_shape = (FreeDimension, self._num_classes)
        bbox_targets_shape = (FreeDimension, self._num_classes * 4)
        bbox_inside_weights_shape = (FreeDimension, self._num_classes * 4)

        return [output_variable(rois_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="rpn_target_rois_raw", needs_gradient=False),
                output_variable(labels_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="label_targets_raw", needs_gradient=False),
                output_variable(bbox_targets_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="bbox_targets_raw", needs_gradient=False),
                output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="bbox_inside_w_raw", needs_gradient=False)]
Example #4
Source File: cntk_backend.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #5
Source File: cntk_backend.py    From GraphicDesignPatternByPython with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #6
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index)
Example #7
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #8
Source File: proposal_target_layer.py    From raster-deep-learning with Apache License 2.0 6 votes vote down vote up 
def infer_outputs(self):
        # sampled rois (0, x1, y1, x2, y2)
        # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python
        rois_shape = (FreeDimension, 4)
        labels_shape = (FreeDimension, self._num_classes)
        bbox_targets_shape = (FreeDimension, self._num_classes * 4)
        bbox_inside_weights_shape = (FreeDimension, self._num_classes * 4)

        return [output_variable(rois_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="rpn_target_rois_raw", needs_gradient=False),
                output_variable(labels_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="label_targets_raw", needs_gradient=False),
                output_variable(bbox_targets_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="bbox_targets_raw", needs_gradient=False),
                output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="bbox_inside_w_raw", needs_gradient=False)]
Example #9
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #10
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #11
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #12
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #13
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index)
Example #14
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #15
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #16
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index)
Example #17
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #18
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #19
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #20
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index)
Example #21
Source File: proposal_target_layer.py    From cntk-python-web-service-on-azure with MIT License 6 votes vote down vote up 
def infer_outputs(self):
        # sampled rois (0, x1, y1, x2, y2)
        # for CNTK the proposal shape is [4 x roisPerImage], and mirrored in Python
        rois_shape = (FreeDimension, 4)
        labels_shape = (FreeDimension, self._num_classes)
        bbox_targets_shape = (FreeDimension, self._num_classes * 4)
        bbox_inside_weights_shape = (FreeDimension, self._num_classes * 4)

        return [output_variable(rois_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="rpn_target_rois_raw", needs_gradient=False),
                output_variable(labels_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="label_targets_raw", needs_gradient=False),
                output_variable(bbox_targets_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="bbox_targets_raw", needs_gradient=False),
                output_variable(bbox_inside_weights_shape, self.inputs[0].dtype, self.inputs[0].dynamic_axes,
                                name="bbox_inside_w_raw", needs_gradient=False)]
Example #22
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #23
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #24
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index)
Example #25
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def repeat(x, n):
    # this is a workaround for recurrent layer
    # if n is inferred dimension,
    # we can't figure out how to repeat it in cntk now
    # return the same x to take cntk broadcast feature
    # to make the recurrent layer work.
    # need to be fixed in GA.
    if n is C.InferredDimension or n is C.FreeDimension:
        return x
    index = 1 - _get_dynamic_axis_num(x)
    if index < 0 or index > 1:
        raise NotImplementedError

    new_shape = list(x.shape)
    new_shape.insert(index, 1)
    new_shape = tuple(new_shape)
    x = C.reshape(x, new_shape)
    temp = [x] * n
    return C.splice(*temp, axis=index)
Example #26
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def squeeze(x, axis):
    if isinstance(axis, tuple):
        axis = list(axis)
    if not isinstance(axis, list):
        axis = [axis]

    shape = list(int_shape(x))

    _axis = []
    for _ in axis:
        if isinstance(_, int):
            _axis.append(_ if _ >= 0 else _ + len(shape))

    if len(_axis) == 0:
        return x

    nones = _get_dynamic_axis_num(x)
    for _ in sorted(_axis, reverse=True):
        del shape[_]

    new_shape = shape[nones:]
    new_shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in new_shape])
    return C.reshape(x, new_shape)
Example #27
Source File: cntk_backend.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def _reshape_dummy_dim(x, axis):
    shape = list(x.shape)

    _axis = [_ + len(shape) if _ < 0 else _ for _ in axis]

    if shape.count(C.InferredDimension) > 1 or shape.count(C.FreeDimension) > 1:
        result = x
        for index in sorted(_axis, reverse=True):
            result = C.reshape(result,
                               shape=(),
                               begin_axis=index,
                               end_axis=index + 1)
        return result
    else:
        for index in sorted(_axis, reverse=True):
            del shape[index]

        shape = [C.InferredDimension if _ == C.FreeDimension else _ for _ in shape]
        return C.reshape(x, shape)
Example #28
Source File: cntk_backend.py    From deepQuest with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def reshape(x, shape):
    shape = tuple([C.InferredDimension if _ == C.FreeDimension else _ for _ in shape])
    if isinstance(x, C.variables.Parameter):
        return C.reshape(x, shape)
    else:
        num_dynamic_axis = _get_dynamic_axis_num(x)

        if num_dynamic_axis == 1 and len(shape) > 0 and shape[0] == -1:
            # collapse axis with batch axis
            if b_any(_ == C.InferredDimension for _ in x.shape) or b_any(
                    _ == C.FreeDimension for _ in x.shape):
                warnings.warn(
                    'Warning: CNTK backend does not support '
                    'collapse of batch axis with inferred dimension. '
                    'The reshape did not take place.')
                return x
            return _reshape_batch(x, shape)
        else:
            # no collapse, then first need to padding the shape
            if num_dynamic_axis >= len(shape):
                i = 0
                while i < len(shape):
                    if shape[i] is None or shape[i] == -1:
                        i += 1
                    else:
                        break
                shape = tuple([-1 for _ in range(num_dynamic_axis - i)]) + shape

            new_shape = list(shape)
            new_shape = new_shape[num_dynamic_axis:]
            new_shape = [C.InferredDimension if _ is None else _ for _ in new_shape]
            return C.reshape(x, new_shape)
Example #29
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def count_params(x):
    for _ in x.shape:
        if _ == C.InferredDimension or _ == C.FreeDimension:
            raise ValueError('CNTK backend: `count_params` with dynamic '
                             'shape is not supported. Please provide '
                             'fixed dimension instead of `None`.')

    return np.prod([x.shape[i] for i in range(len(x.shape))])
Example #30
Source File: cntk_backend.py    From keras-lambda with MIT License 5 votes vote down vote up 
def reshape(x, shape):
    if isinstance(x, C.variables.Parameter):
        return C.reshape(x, shape)
    else:
        num_dynamic_axis = _get_dynamic_axis_num(x)

        if num_dynamic_axis == 1 and len(shape) > 0 and shape[0] == -1:
            # collapse axis with batch axis
            if b_any(_ == C.InferredDimension for _ in x.shape) or b_any(
                    _ == C.FreeDimension for _ in x.shape):
                warnings.warn(
                    'Warning: CNTK backend does not support '
                    'collapse of batch axis with inferred dimension. '
                    'The reshape did not take place.')
                return x
            return C.user_function(ReshapeBatch(x, shape[1:]))
        else:
            # no collaps, then first need to padding the shape
            if num_dynamic_axis >= len(shape):
                i = 0
                while i < len(shape):
                    if shape[i] is None or shape[i] == -1:
                        i += 1
                    else:
                        break
                shape = tuple([-1 for _ in range(num_dynamic_axis - i)]) + shape

            new_shape = list(shape)
            new_shape = new_shape[num_dynamic_axis:]
            new_shape = [C.InferredDimension if _ is None else _ for _ in new_shape]
            return C.reshape(x, new_shape)