B ӻdI@sdZddlZddlZddlmZddlmZddlmZddl m Z ddZ d d Z d d d Z ddZd!ddZddZddZddZddZddZddZddZdS)"z%Utilities used by convolution layers.N)ops) tensor_shape)backend) array_opscCs~|dkr8|dkrdS|dkr dS|dkr,dStd|nB|d krp|dkrLd S|dkrXd S|dkrdd Std|n td |dS)N channels_lastZNWCZNHWCZNDHWCzInput rank not supported:channels_firstZNCWZNCHWZNCDHWzInvalid data_format:) ValueError) data_formatndimrZ/var/www/html/venv/lib/python3.7/site-packages/tensorflow/python/keras/utils/conv_utils.pyconvert_data_formats"  rc Cs t|tr|f|Sy t|}Wn8tk rXtd|dt|dt|YnXt||krtd|dt|dt|xv|D]n}y t|Wqttfk rtd|dt|dt|dt|ddtt|YqXqW|SdS)aTransforms a single integer or iterable of integers into an integer tuple. Args: value: The value to validate and convert. Could an int, or any iterable of ints. n: The size of the tuple to be returned. name: The name of the argument being validated, e.g. "strides" or "kernel_size". This is only used to format error messages. Returns: A tuple of n integers. Raises: ValueError: If something else than an int/long or iterable thereof was passed. zThe `z` argument must be a tuple of z integers. Received: z including element z of type N) isinstanceinttuple TypeErrorr strlentype)valuennameZ value_tupleZ single_valuerrrnormalize_tuple2s"   ( "  LrcCst|dkr dS|dkst||d|d}|dkr:|}n*|dkrP||d}n|dkrd||d}||d|S)a(Determines output length of a convolution given input length. Args: input_length: integer. filter_size: integer. padding: one of "same", "valid", "full", "causal" stride: integer. dilation: dilation rate, integer. Returns: The output length (integer). N>validfullcausalsamer)r!r rr)AssertionError) input_length filter_sizepaddingstridedilationZdilated_filter_size output_lengthrrrconv_output_lengthYs   r)cCs`|dkr dS|dkst|dkr*|d}n|dkr8d}n|dkrH|d}|d|d||S) zDetermines input length of a convolution given output length. Args: output_length: integer. filter_size: integer. padding: one of "same", "valid", "full". stride: integer. Returns: The input length (integer). N>rrr!r!rrrr)r")r(r$r%r&padrrrconv_input_lengthss   r,cCs|dks t|dkrdS||d|d}|dkr|dkrT||t||d}q|dkrr||||d}q|dkr||}nL|dkr|d}n|dkrd}n|dkr|d}|d||d||}|S) aDetermines output length of a transposed convolution given input length. Args: input_length: Integer. filter_size: Integer. padding: one of `"same"`, `"valid"`, `"full"`. output_padding: Integer, amount of padding along the output dimension. Can be set to `None` in which case the output length is inferred. stride: Integer. dilation: Integer. Returns: The output length (integer). >rrr!Nrrrrr*r!)r"max)r#r$r%Zoutput_paddingr&r'lengthr+rrrdeconv_output_lengths(   r/cCs4|dkrt}|}|dkr0tdt||S)N>r rzWThe `data_format` argument must be one of "channels_first", "channels_last". Received: )rZimage_data_formatlowerr r)rr rrrnormalize_data_formats r1cCs6t|ttfr|S|}|dkr2tdt||S)N>rr r!zqThe `padding` argument must be a list/tuple or one of "valid", "same" (or "causal", only for `Conv1D). Received: )rlistrr0r r)rr%rrrnormalize_paddings r3cCs|dkrtd|t|}t|tr0|f|}t|trD|f|}t|}t|}||ksd||krvtd|||ft||||}||}t|tj} dd|D} xBt j | D]4} t ||| ||} xt j | D]} d| | | <qWqW| S)a@Compute a mask representing the connectivity of a convolution operation. Assume a convolution with given parameters is applied to an input having N spatial dimensions with `input_shape = (d_in1, ..., d_inN)` to produce an output with shape `(d_out1, ..., d_outN)`. This method returns a boolean array of shape `(d_in1, ..., d_inN, d_out1, ..., d_outN)` with `True` entries indicating pairs of input and output locations that are connected by a weight. Example: >>> input_shape = (4,) >>> kernel_shape = (2,) >>> strides = (1,) >>> padding = "valid" >>> conv_kernel_mask(input_shape, kernel_shape, strides, padding) array([[ True, False, False], [ True, True, False], [False, True, True], [False, False, True]]) where rows and columns correspond to inputs and outputs respectively. Args: input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the input. kernel_shape: tuple of size N, spatial shape of the convolutional kernel / receptive field. strides: tuple of size N, strides along each spatial dimension. padding: type of padding, string `"same"` or `"valid"`. `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. Returns: A boolean 2N-D `np.ndarray` of shape `(d_in1, ..., d_inN, d_out1, ..., d_outN)`, where `(d_out1, ..., d_outN)` is the spatial shape of the output. `True` entries in the mask represent pairs of input-output locations that are connected by a weight. Raises: ValueError: if `input_shape`, `kernel_shape` and `strides` don't have the same number of dimensions. NotImplementedError: if `padding` is not in {`"same"`, `"valid"`}. >rr!zGPadding type %s not supported. Only "valid" and "same" are implemented.zTNumber of strides, input and kernel dimensions must all match. Received: %d, %d, %d.cSsg|] }t|qSr)range).0dimrrr sz$conv_kernel_mask..T) NotImplementedErrorrrrr conv_output_shapenpZzerosZbool_ itertoolsproductconv_connected_inputs) input_shape kernel_shapestridesr%in_dims kernel_dims stride_dims output_shapeZ mask_shapemaskoutput_axes_ticksoutput_positioninput_axes_ticksinput_positionrrrconv_kernel_masks..     rJc csb|dkrtd|t|}t|tr0|f|}t|trD|f|}t|}t|} ||ksd| |krvtd| ||ft||||} dd| D} |dkrdd} n|d krd d} n td |xtj| D]} t||| ||}xvtj|D]h}xbt |D]V}xPt |D]D}t j | | || | |d }t j | ||| ||d }||fVq WqWqWqWd S)aYields output-input tuples of indices in a CNN layer. The generator iterates over all `(output_idx, input_idx)` tuples, where `output_idx` is an integer index in a flattened tensor representing a single output image of a convolutional layer that is connected (via the layer weights) to the respective single input image at `input_idx` Example: >>> input_shape = (2, 2) >>> kernel_shape = (2, 1) >>> strides = (1, 1) >>> padding = "valid" >>> filters_in = 1 >>> filters_out = 1 >>> data_format = "channels_last" >>> list(conv_kernel_idxs(input_shape, kernel_shape, strides, padding, ... filters_in, filters_out, data_format)) [(0, 0), (0, 2), (1, 1), (1, 3)] Args: input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the input. kernel_shape: tuple of size N, spatial shape of the convolutional kernel / receptive field. strides: tuple of size N, strides along each spatial dimension. padding: type of padding, string `"same"` or `"valid"`. `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. filters_in: `int`, number if filters in the input to the layer. filters_out: `int', number if filters in the output of the layer. data_format: string, "channels_first" or "channels_last". Yields: The next tuple `(output_idx, input_idx)`, where `output_idx` is an integer index in a flattened tensor representing a single output image of a convolutional layer that is connected (via the layer weights) to the respective single input image at `input_idx`. Raises: ValueError: if `data_format` is neither `"channels_last"` nor `"channels_first"`, or if number of strides, input, and kernel number of dimensions do not match. NotImplementedError: if `padding` is neither `"same"` nor `"valid"`. )r!rzGPadding type %s not supported. Only "valid" and "same" are implemented.zTNumber of strides, input and kernel dimensions must all match. Received: %d, %d, %d.cSsg|] }t|qSr)r4)r5r6rrrr7esz$conv_kernel_idxs..r cSs |f|S)Nr) spatial_idx filter_idxrrrhz"conv_kernel_idxs..rcSs ||fS)Nr)rKrLrrrrMjrNzXData format %s not recognized.`data_format` must be "channels_first" or "channels_last".)Z multi_indexdimsN) r8rrrr r9r;r<r=r4r:Zravel_multi_index)r>r?r@r%Z filters_inZ filters_outr rArBrCrDrFZ concat_idxsrGrHrIZf_inZf_outZout_idxZin_idxrrrconv_kernel_idxs!sD1       rPc Csg}t|}x|t|D]p}t||d}|||} ||||} |dkrV| |7} td| |} t||| | } |t| | qW|S)aReturn locations of the input connected to an output position. Assume a convolution with given parameters is applied to an input having N spatial dimensions with `input_shape = (d_in1, ..., d_inN)`. This method returns N ranges specifying the input region that was convolved with the kernel to produce the output at position `output_position = (p_out1, ..., p_outN)`. Example: >>> input_shape = (4, 4) >>> kernel_shape = (2, 1) >>> output_position = (1, 1) >>> strides = (1, 1) >>> padding = "valid" >>> conv_connected_inputs(input_shape, kernel_shape, output_position, ... strides, padding) [range(1, 3), range(1, 2)] Args: input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the input. kernel_shape: tuple of size N, spatial shape of the convolutional kernel / receptive field. output_position: tuple of size N: `(p_out1, ..., p_outN)`, a single position in the output of the convolution. strides: tuple of size N, strides along each spatial dimension. padding: type of padding, string `"same"` or `"valid"`. `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. Returns: N ranges `[[p_in_left1, ..., p_in_right1], ..., [p_in_leftN, ..., p_in_rightN]]` specifying the region in the input connected to output_position. r*rr)rr4rr-minappend) r>r?rGr@r%rangesZndimsdZ left_shiftZ right_shiftcenterstartendrrrr=s' r=cs@tt}fdd|Dtfdd|DS)aReturn the output shape of an N-D convolution. Forces dimensions where input is empty (size 0) to remain empty. Args: input_shape: tuple of size N: `(d_in1, ..., d_inN)`, spatial shape of the input. kernel_shape: tuple of size N, spatial shape of the convolutional kernel / receptive field. strides: tuple of size N, strides along each spatial dimension. padding: type of padding, string `"same"` or `"valid"`. `"valid"` means no padding. `"same"` results in padding evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input. Returns: tuple of size N: `(d_out1, ..., d_outN)`, spatial shape of the output. cs&g|]}t|||qSr)r))r5rT)r>r?r%r@rrr7sz%conv_output_shape..cs$g|]}|dkrdn|qS)rr)r5rT)r>rDrrr7s)r4rr)r>r?r@r%rOr)r>r?rDr%r@rr9s  r9c Cs*td|j}|| d}|s>t|| d}|d| }|sht|d| }t|tjrt|dg| }nt|tj dg|fdd}||}|j| d}|st|| d}t|tj ||fdd} | |jd| | j| d| SQRXdS)aReturns `unsqueeze_batch(op(squeeze_batch(inp)))`. Where `squeeze_batch` reshapes `inp` to shape `[prod(inp.shape[:-inner_rank])] + inp.shape[-inner_rank:]` and `unsqueeze_batch` does the reverse reshape but on the output. Args: inp: A tensor with dims `batch_shape + inner_shape` where `inner_shape` is length `inner_rank`. op: A callable that takes a single input tensor and returns a single. output tensor. inner_rank: A python integer. Returns: `unsqueeze_batch_op(squeeze_batch(inp))`. squeeze_batch_dimsN)Zaxis) rZ name_scope_v2shapeZis_fully_definedrrrZ TensorShapeZreshapeas_listconcat set_shape) ZinpopZ inner_rankrZZ inner_shapeZ batch_shapeZ inp_reshapedZ out_reshapedZout_inner_shapeoutrrrrXs( &rX)r)Nrr)__doc__r;numpyr:Ztensorflow.python.frameworkrrZtensorflow.python.kerasrZtensorflow.python.opsrrrr)r,r/r1r3rJrPr=r9rXrrrrs(    '  ,  O^;