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Update ops-related pbtxt files.
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Change: 149566601
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@tensorflower-gardener
tensorflower-gardener committed Mar 8, 2017
1 parent ce016c8 commit 34c3835
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164 changes: 164 additions & 0 deletions tensorflow/core/ops/compat/ops_history.v1.pbtxt
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Original file line number Diff line number Diff line change
Expand Up @@ -5031,6 +5031,101 @@ op {
}
}
}
op {
name: "DepthwiseConv2dNative"
input_arg {
name: "input"
type_attr: "T"
}
input_arg {
name: "filter"
type_attr: "T"
}
output_arg {
name: "output"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
attr {
name: "strides"
type: "list(int)"
}
attr {
name: "padding"
type: "string"
allowed_values {
list {
s: "SAME"
s: "VALID"
}
}
}
attr {
name: "data_format"
type: "string"
default_value {
s: "NHWC"
}
allowed_values {
list {
s: "NHWC"
s: "NCHW"
}
}
}
}
op {
name: "DepthwiseConv2dNativeBackpropFilter"
input_arg {
name: "input"
type_attr: "T"
}
input_arg {
name: "filter_sizes"
type: DT_INT32
}
input_arg {
name: "out_backprop"
type_attr: "T"
}
output_arg {
name: "output"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
attr {
name: "strides"
type: "list(int)"
}
attr {
name: "padding"
type: "string"
allowed_values {
list {
s: "SAME"
s: "VALID"
}
}
}
}
op {
name: "DepthwiseConv2dNativeBackpropFilter"
input_arg {
Expand Down Expand Up @@ -5073,6 +5168,62 @@ op {
}
}
}
attr {
name: "data_format"
type: "string"
default_value {
s: "NHWC"
}
allowed_values {
list {
s: "NHWC"
s: "NCHW"
}
}
}
}
op {
name: "DepthwiseConv2dNativeBackpropInput"
input_arg {
name: "input_sizes"
type: DT_INT32
}
input_arg {
name: "filter"
type_attr: "T"
}
input_arg {
name: "out_backprop"
type_attr: "T"
}
output_arg {
name: "output"
type_attr: "T"
}
attr {
name: "T"
type: "type"
allowed_values {
list {
type: DT_FLOAT
type: DT_DOUBLE
}
}
}
attr {
name: "strides"
type: "list(int)"
}
attr {
name: "padding"
type: "string"
allowed_values {
list {
s: "SAME"
s: "VALID"
}
}
}
}
op {
name: "DepthwiseConv2dNativeBackpropInput"
Expand Down Expand Up @@ -5116,6 +5267,19 @@ op {
}
}
}
attr {
name: "data_format"
type: "string"
default_value {
s: "NHWC"
}
allowed_values {
list {
s: "NHWC"
s: "NCHW"
}
}
}
}
op {
name: "Dequantize"
Expand Down
52 changes: 47 additions & 5 deletions tensorflow/core/ops/ops.pbtxt
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Original file line number Diff line number Diff line change
Expand Up @@ -5696,14 +5696,28 @@ op {
}
}
}
attr {
name: "data_format"
type: "string"
default_value {
s: "NHWC"
}
description: "Specify the data format of the input and output data. With the\ndefault format \"NHWC\", the data is stored in the order of:\n [batch, height, width, channels].\nAlternatively, the format could be \"NCHW\", the data storage order of:\n [batch, channels, height, width]."
allowed_values {
list {
s: "NHWC"
s: "NCHW"
}
}
}
summary: "Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors."
description: "Given an input tensor of shape `[batch, in_height, in_width, in_channels]`\nand a filter / kernel tensor of shape\n`[filter_height, filter_width, in_channels, channel_multiplier]`, containing\n`in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies\na different filter to each input channel (expanding from 1 channel to\n`channel_multiplier` channels for each), then concatenates the results\ntogether. Thus, the output has `in_channels * channel_multiplier` channels.\n\nfor k in 0..in_channels-1\n for q in 0..channel_multiplier-1\n output[b, i, j, k * channel_multiplier + q] =\n sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] *\n filter[di, dj, k, q]\n\nMust have `strides[0] = strides[3] = 1`. For the most common case of the same\nhorizontal and vertices strides, `strides = [1, stride, stride, 1]`."
}
op {
name: "DepthwiseConv2dNativeBackpropFilter"
input_arg {
name: "input"
description: "4-D with shape `[batch, in_height, in_width, in_channels]`."
description: "4-D with shape based on `data_format`. For example, if\n`data_format` is \'NHWC\' then `input` is a 4-D `[batch, in_height,\nin_width, in_channels]` tensor."
type_attr: "T"
}
input_arg {
Expand All @@ -5713,7 +5727,7 @@ op {
}
input_arg {
name: "out_backprop"
description: "4-D with shape `[batch, out_height, out_width, out_channels]`.\nGradients w.r.t. the output of the convolution."
description: "4-D with shape based on `data_format`.\nFor example, if `data_format` is \'NHWC\' then\nout_backprop shape is `[batch, out_height, out_width, out_channels]`.\nGradients w.r.t. the output of the convolution."
type_attr: "T"
}
output_arg {
Expand Down Expand Up @@ -5747,13 +5761,27 @@ op {
}
}
}
attr {
name: "data_format"
type: "string"
default_value {
s: "NHWC"
}
description: "Specify the data format of the input and output data. With the\ndefault format \"NHWC\", the data is stored in the order of:\n [batch, height, width, channels].\nAlternatively, the format could be \"NCHW\", the data storage order of:\n [batch, channels, height, width]."
allowed_values {
list {
s: "NHWC"
s: "NCHW"
}
}
}
summary: "Computes the gradients of depthwise convolution with respect to the filter."
}
op {
name: "DepthwiseConv2dNativeBackpropInput"
input_arg {
name: "input_sizes"
description: "An integer vector representing the shape of `input`,\nwhere `input` is a 4-D `[batch, height, width, channels]` tensor."
description: "An integer vector representing the shape of `input`, based\non `data_format`. For example, if `data_format` is \'NHWC\' then\n `input` is a 4-D `[batch, height, width, channels]` tensor."
type: DT_INT32
}
input_arg {
Expand All @@ -5763,12 +5791,12 @@ op {
}
input_arg {
name: "out_backprop"
description: "4-D with shape `[batch, out_height, out_width, out_channels]`.\nGradients w.r.t. the output of the convolution."
description: "4-D with shape based on `data_format`.\nFor example, if `data_format` is \'NHWC\' then\nout_backprop shape is `[batch, out_height, out_width, out_channels]`.\nGradients w.r.t. the output of the convolution."
type_attr: "T"
}
output_arg {
name: "output"
description: "4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient\nw.r.t. the input of the convolution."
description: "4-D with shape according to `data_format`. For example, if\n`data_format` is \'NHWC\', output shape is `[batch, in_height,\nin_width, in_channels]`. Gradient w.r.t. the input of the\nconvolution."
type_attr: "T"
}
attr {
Expand Down Expand Up @@ -5797,6 +5825,20 @@ op {
}
}
}
attr {
name: "data_format"
type: "string"
default_value {
s: "NHWC"
}
description: "Specify the data format of the input and output data. With the\ndefault format \"NHWC\", the data is stored in the order of:\n [batch, height, width, channels].\nAlternatively, the format could be \"NCHW\", the data storage order of:\n [batch, channels, height, width]."
allowed_values {
list {
s: "NHWC"
s: "NCHW"
}
}
}
summary: "Computes the gradients of depthwise convolution with respect to the input."
}
op {
Expand Down

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