def convert_convtranspose(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert transposed convolution layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting transposed convolution ...')

    if names == 'short':
        tf_name = 'C' + random_string(7)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    bias_name = '{0}.bias'.format(w_name)
    weights_name = '{0}.weight'.format(w_name)

    if len(weights[weights_name].numpy().shape) == 4:
        W = weights[weights_name].numpy().transpose(2, 3, 1, 0)
        height, width, n_filters, channels = W.shape

        n_groups = params['group']
        if n_groups > 1:
            raise AssertionError('Cannot convert conv1d with groups != 1')

        if params['dilations'][0] > 1:
            raise AssertionError('Cannot convert conv1d with dilation_rate != 1')

        if bias_name in weights:
            biases = weights[bias_name].numpy()
            has_bias = True
        else:
            biases = None
            has_bias = False

        input_name = inputs[0]

        if has_bias:
            weights = [W, biases]
        else:
            weights = [W]

        conv = keras.layers.Conv2DTranspose(
            filters=n_filters,
            kernel_size=(height, width),
            strides=(params['strides'][0], params['strides'][1]),
            padding='valid',
            output_padding=0,
            weights=weights,
            use_bias=has_bias,
            activation=None,
            dilation_rate=params['dilations'][0],
            bias_initializer='zeros', kernel_initializer='zeros',
            name=tf_name
        )

        layers[scope_name] = conv(layers[input_name])

        # Magic ad-hoc.
        # See the Keras issue: https://github.com/keras-team/keras/issues/6777
        layers[scope_name].set_shape(layers[scope_name]._keras_shape)

        pads = params['pads']
        if pads[0] > 0:
            assert(len(pads) == 2 or (pads[2] == pads[0] and pads[3] == pads[1]))

            crop = keras.layers.Cropping2D(
                pads[:2],
                name=tf_name + '_crop'
            )
            layers[scope_name] = crop(layers[scope_name])
    else:
        raise AssertionError('Layer is not supported for now')
def convert_convtranspose(params, w_name, scope_name, inputs, layers, weights, names):
    """
    Convert transposed convolution layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
    """
    print('Converting transposed convolution ...')

    if names == 'short':
        tf_name = 'C' + random_string(7)
    elif names == 'keep':
        tf_name = w_name
    else:
        tf_name = w_name + str(random.random())

    bias_name = '{0}.bias'.format(w_name)
    weights_name = '{0}.weight'.format(w_name)

    if len(weights[weights_name].numpy().shape) == 4:
        W = weights[weights_name].numpy().transpose(2, 3, 1, 0)
        height, width, n_filters, channels = W.shape

        n_groups = params['group']
        if n_groups > 1:
            raise AssertionError('Cannot convert conv1d with groups != 1')

        if params['dilations'][0] > 1:
            raise AssertionError('Cannot convert conv1d with dilation_rate != 1')

        if bias_name in weights:
            biases = weights[bias_name].numpy()
            has_bias = True
        else:
            biases = None
            has_bias = False

        input_name = inputs[0]

        if has_bias:
            weights = [W, biases]
        else:
            weights = [W]

        conv = keras.layers.Conv2DTranspose(
            filters=n_filters,
            kernel_size=(height, width),
            strides=(params['strides'][0], params['strides'][1]),
            padding='valid',
            output_padding=0,
            weights=weights,
            use_bias=has_bias,
            activation=None,
            dilation_rate=params['dilations'][0],
            bias_initializer='zeros', kernel_initializer='zeros',
            name=tf_name
        )

        layers[scope_name] = conv(layers[input_name])

        # Magic ad-hoc.
        # See the Keras issue: https://github.com/keras-team/keras/issues/6777
        layers[scope_name].set_shape(layers[scope_name]._keras_shape)

        pads = params['pads']
        if pads[0] > 0:
            assert(len(pads) == 2 or (pads[2] == pads[0] and pads[3] == pads[1]))

            crop = keras.layers.Cropping2D(
                pads[:2],
                name=tf_name + '_crop'
            )
            layers[scope_name] = crop(layers[scope_name])
    else:
        raise AssertionError('Layer is not supported for now')
def convert_convtranspose ( params , w_name , scope_name , inputs , layers , weights , names ) : print ( 'Converting transposed convolution ...' ) if names == 'short' : tf_name = 'C' + random_string ( 7 ) elif names == 'keep' : tf_name = w_name else : tf_name = w_name + str ( random . random ( ) ) bias_name = '{0}.bias' . format ( w_name ) weights_name = '{0}.weight' . format ( w_name ) if len ( weights [ weights_name ] . numpy ( ) . shape ) == 4 : W = weights [ weights_name ] . numpy ( ) . transpose ( 2 , 3 , 1 , 0 ) height , width , n_filters , channels = W . shape n_groups = params [ 'group' ] if n_groups > 1 : raise AssertionError ( 'Cannot convert conv1d with groups != 1' ) if params [ 'dilations' ] [ 0 ] > 1 : raise AssertionError ( 'Cannot convert conv1d with dilation_rate != 1' ) if bias_name in weights : biases = weights [ bias_name ] . numpy ( ) has_bias = True else : biases = None has_bias = False input_name = inputs [ 0 ] if has_bias : weights = [ W , biases ] else : weights = [ W ] conv = keras . layers . Conv2DTranspose ( filters = n_filters , kernel_size = ( height , width ) , strides = ( params [ 'strides' ] [ 0 ] , params [ 'strides' ] [ 1 ] ) , padding = 'valid' , output_padding = 0 , weights = weights , use_bias = has_bias , activation = None , dilation_rate = params [ 'dilations' ] [ 0 ] , bias_initializer = 'zeros' , kernel_initializer = 'zeros' , name = tf_name ) layers [ scope_name ] = conv ( layers [ input_name ] ) # Magic ad-hoc. # See the Keras issue: https://github.com/keras-team/keras/issues/6777 layers [ scope_name ] . set_shape ( layers [ scope_name ] . _keras_shape ) pads = params [ 'pads' ] if pads [ 0 ] > 0 : assert ( len ( pads ) == 2 or ( pads [ 2 ] == pads [ 0 ] and pads [ 3 ] == pads [ 1 ] ) ) crop = keras . layers . Cropping2D ( pads [ : 2 ] , name = tf_name + '_crop' ) layers [ scope_name ] = crop ( layers [ scope_name ] ) else : raise AssertionError ( 'Layer is not supported for now' )
Convert transposed convolution layer.

    Args:
        params: dictionary with layer parameters
        w_name: name prefix in state_dict
        scope_name: pytorch scope name
        inputs: pytorch node inputs
        layers: dictionary with keras tensors
        weights: pytorch state_dict
        names: use short names for keras layers
Convert transposed convolution layer .