37ada14728975dcb9fc971bf37ea1fb397e97716,src/graph_transpiler/webdnn/frontend/keras/layers/convolutional.py,,_convert_conv2d_transpose,#Any#Any#,63

Before Change

    ksize = tuple(k_op.kernel_size)
    stride = tuple(k_op.strides)

    if k_op.padding == "valid":
        padding = (0, 0)

    elif k_op.padding == "same":
        // @see https://github.com/tensorflow/tensorflow/blob/e5cf6f0c13b6053e4c58af6a951b204fde263172/tensorflow/python/ops/nn_ops.py//L507-L519
        pad_extra_shape = [k - 1 for k in ksize]

        if any(p % 2 != 0 for p in pad_extra_shape):
            raise NotImplementedError(f
[KerasConverter] Currently WebDNN doesn"t supports different size padding: 
    (pad_extra_shape)=f{pad_extra_shape})

        padding = tuple(p // 2 for p in pad_extra_shape)

    else:
        raise ValueError(f"[KerasConverter] Unknown padding: {k_op.padding}")

    y, = Deconvolution2D(None, ksize=ksize, stride=stride, padding=padding)(x, w)
    if k_op.use_bias:
        b = converter.convert_to_constant_variable(k_op.bias, OrderC)
        y = y + b

After Change

    if dilation_rate != (1, 1):
        raise NotImplementedError("[KerasConverter] keras.layers.Convolution2DTranspose with large dilation_rate is not supported")

    padding = (parse_padding(k_op.padding, ksize[0], dilation_rate[0]), parse_padding(k_op.padding, ksize[1], dilation_rate[1]))

    y, = Deconvolution2D(None, ksize=ksize, stride=stride, padding=padding)(x, w)
    if k_op.use_bias:

Instances