Added a wrapper module for convolution functions.
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jcmartin
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-- Copyright 2020 TensorFlow authors.
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--
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-- Licensed under the Apache License, Version 2.0 (the "License");
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-- you may not use this file except in compliance with the License.
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-- You may obtain a copy of the License at
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--
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-- http://www.apache.org/licenses/LICENSE-2.0
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--
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-- Unless required by applicable law or agreed to in writing, software
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-- distributed under the License is distributed on an "AS IS" BASIS,
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-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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-- See the License for the specific language governing permissions and
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-- limitations under the License.
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{-# LANGUAGE ConstraintKinds #-}
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{-# LANGUAGE DataKinds #-}
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{-# LANGUAGE OverloadedStrings #-}
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module TensorFlow.Convolution
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( Padding(..)
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, DataFormat(..)
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, conv2D
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, conv2D'
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, conv2DBackpropFilter
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, conv2DBackpropFilter'
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, conv2DBackpropInput
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, conv2DBackpropInput'
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, conv3D
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, conv3D'
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, conv3DBackpropFilter
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, conv3DBackpropFilter'
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, conv3DBackpropFilterV2
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, conv3DBackpropFilterV2'
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, conv3DBackpropInput
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, conv3DBackpropInput'
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, conv3DBackpropInputV2
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, conv3DBackpropInputV2'
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, depthwiseConv2dNative
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, depthwiseConv2dNative'
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, depthwiseConv2dNativeBackpropFilter
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, depthwiseConv2dNativeBackpropFilter'
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, depthwiseConv2dNativeBackpropInput
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, depthwiseConv2dNativeBackpropInput'
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) where
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import Data.Word(Word16)
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import Data.Int(Int32,Int64)
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import Data.ByteString(ByteString)
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import Lens.Family2 ((.~), (&))
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import qualified TensorFlow.BuildOp as TF
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.GenOps.Core as TF
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-- TODO: Support other convolution parameters such as stride.
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-- | Convolution padding.
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data Padding = PaddingValid -- ^ output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])
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| PaddingSame -- ^ output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i])
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paddingToByteString :: Padding -> ByteString
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paddingToByteString x = case x of
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PaddingValid -> "VALID"
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PaddingSame -> "SAME"
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-- | Matrix format.
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data DataFormat = ChannelLast -- ^ Channel is the last dimension (e.g. NWC, NHWC, NDHWC)
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| ChannelFirst -- ^ Channel is the first dimension after N (e.g. NCW, NCHW, NCDHW)
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-- TODO: Address 1D convolution.
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--dataFormat1D :: DataFormat -> ByteString
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--dataFormat1D x = case x of
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-- ChannelLast -> "NWC"
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-- ChannelFirst -> "NCW"
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dataFormat2D :: DataFormat -> ByteString
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dataFormat2D x = case x of
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ChannelLast -> "NHWC"
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ChannelFirst -> "NCHW"
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dataFormat3D :: DataFormat -> ByteString
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dataFormat3D x = case x of
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ChannelLast -> "NDHWC"
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ChannelFirst -> "NCDHW"
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-- | 2D Convolution with default parameters.
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conv2D :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor TF.Build t -- ^ output
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conv2D = conv2D' id PaddingValid ChannelLast
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conv2D' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor TF.Build t -- ^ output
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conv2D' params padding dataformat = TF.conv2D'
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(params . (TF.opAttr "data_format" .~ dataFormat2D dataformat))
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(paddingToByteString padding)
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-- | 2D convolution backpropagation filter with default parameters.
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conv2DBackpropFilter :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 Int32 -- ^ filter_sizes
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv2DBackpropFilter = conv2DBackpropFilter' id PaddingValid ChannelLast
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conv2DBackpropFilter' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 Int32 -- ^ filter_sizes
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv2DBackpropFilter' params padding dataformat = TF.conv2DBackpropFilter'
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(params . (TF.opAttr "data_format" .~ dataFormat2D dataformat))
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(paddingToByteString padding)
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-- | 2D convolution backpropagation input with default parameters.
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conv2DBackpropInput :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 Int32 -- ^ input_sizes
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv2DBackpropInput = conv2DBackpropInput' id PaddingValid ChannelLast
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conv2DBackpropInput' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 Int32 -- ^ input_sizes
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv2DBackpropInput' params padding dataformat = TF.conv2DBackpropInput'
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(params . (TF.opAttr "data_format" .~ dataFormat2D dataformat))
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(paddingToByteString padding)
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-- | 3D Convolution with default parameters.
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conv3D :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor TF.Build t -- ^ output
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conv3D = conv3D' id PaddingValid ChannelLast
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conv3D' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor TF.Build t -- ^ output
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conv3D' params padding dataformat = TF.conv3D'
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(params . (TF.opAttr "data_format" .~ dataFormat3D dataformat))
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(paddingToByteString padding)
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-- | 3D convolution backpropagation filter with default parameters.
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conv3DBackpropFilter :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropFilter = conv3DBackpropFilter' id PaddingValid ChannelLast
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conv3DBackpropFilter' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropFilter' params padding dataformat = TF.conv3DBackpropFilter'
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(params . (TF.opAttr "data_format" .~ dataFormat3D dataformat))
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(paddingToByteString padding)
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-- | 3D convolution backpropagation filter with default parameters.
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conv3DBackpropFilterV2 :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 Int32 -- ^ filter_sizes
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropFilterV2 = conv3DBackpropFilterV2' id PaddingValid ChannelLast
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conv3DBackpropFilterV2' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 Int32 -- ^ filter_sizes
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropFilterV2' params padding dataformat = TF.conv3DBackpropFilterV2'
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(params . (TF.opAttr "data_format" .~ dataFormat3D dataformat))
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(paddingToByteString padding)
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-- | 3D convolution backpropagation input with default parameters.
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conv3DBackpropInput :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropInput = conv3DBackpropInput' id PaddingValid ChannelLast
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conv3DBackpropInput' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropInput' params padding dataformat = TF.conv3DBackpropInput'
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(params . (TF.opAttr "data_format" .~ dataFormat3D dataformat))
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(paddingToByteString padding)
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-- | 3D convolution backpropagation input with default parameters.
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conv3DBackpropInputV2 :: (TF.OneOf '[Word16, Double, Float] t, TF.OneOf '[Int32, Int64] tshape)
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=> TF.Tensor v1 tshape -- ^ input_sizes
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropInputV2 = conv3DBackpropInputV2' id PaddingValid ChannelLast
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conv3DBackpropInputV2' :: (TF.OneOf '[Word16, Double, Float] t, TF.OneOf '[Int32, Int64] tshape)
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 tshape -- ^ input_sizes
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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conv3DBackpropInputV2' params padding dataformat = TF.conv3DBackpropInputV2'
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(params . (TF.opAttr "data_format" .~ dataFormat3D dataformat))
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(paddingToByteString padding)
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-- | Depth-wise 2D convolution native with default parameters.
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depthwiseConv2dNative :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor TF.Build t -- ^ output
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depthwiseConv2dNative = depthwiseConv2dNative' id PaddingValid ChannelLast
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depthwiseConv2dNative' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 t -- ^ filter
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-> TF.Tensor TF.Build t -- ^ output
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depthwiseConv2dNative' params padding dataformat = TF.depthwiseConv2dNative'
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(params . (TF.opAttr "data_format" .~ dataFormat2D dataformat))
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(paddingToByteString padding)
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-- | Depth-wise 2D convolution native backpropagation filter with default parameters.
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depthwiseConv2dNativeBackpropFilter :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 Int32 -- ^ filter_sizes
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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depthwiseConv2dNativeBackpropFilter = depthwiseConv2dNativeBackpropFilter' id PaddingValid ChannelLast
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depthwiseConv2dNativeBackpropFilter' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 t -- ^ input
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-> TF.Tensor v2 Int32 -- ^ filter_sizes
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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depthwiseConv2dNativeBackpropFilter' params padding dataformat = TF.depthwiseConv2dNativeBackpropFilter'
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(params . (TF.opAttr "data_format" .~ dataFormat2D dataformat))
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(paddingToByteString padding)
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-- | Depth-wise 2D convolution native backpropagation input with default parameters.
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depthwiseConv2dNativeBackpropInput :: TF.OneOf '[Word16, Double, Float] t
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=> TF.Tensor v1 Int32 -- ^ input_sizes
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-> TF.Tensor v2 t -- ^ input
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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depthwiseConv2dNativeBackpropInput = depthwiseConv2dNativeBackpropInput' id PaddingValid ChannelLast
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depthwiseConv2dNativeBackpropInput' :: TF.OneOf '[Word16, Double, Float] t
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=> TF.OpParams
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-> Padding
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-> DataFormat
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-> TF.Tensor v1 Int32 -- ^ input_sizes
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-> TF.Tensor v2 t -- ^ input
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-> TF.Tensor v3 t -- ^ out_backprop
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-> TF.Tensor TF.Build t -- ^ output
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depthwiseConv2dNativeBackpropInput' params padding dataformat = TF.depthwiseConv2dNativeBackpropInput'
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(params . (TF.opAttr "data_format" .~ dataFormat2D dataformat))
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(paddingToByteString padding)
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@ -16,6 +16,7 @@ library
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hs-source-dirs: src
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exposed-modules: TensorFlow.Gradient
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, TensorFlow.Ops
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, TensorFlow.Convolution
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, TensorFlow.EmbeddingOps
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, TensorFlow.Minimize
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, TensorFlow.NN
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@ -32,7 +32,8 @@ import Control.Monad(forM_, replicateM, zipWithM)
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import Control.Monad.IO.Class (liftIO)
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import qualified TensorFlow.Core as TF
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import qualified TensorFlow.GenOps.Core as TF (conv2DBackpropInput', max, maximum, resizeBilinear', tile, pad, batchToSpaceND, spaceToBatchND, squeeze, sqrt, slice, shape, diag, depthwiseConv2dNative', depthwiseConv2dNativeBackpropInput', batchMatMul, batchMatMul', conjugateTranspose)
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import qualified TensorFlow.GenOps.Core as TF (max, maximum, resizeBilinear', tile, pad, batchToSpaceND, spaceToBatchND, squeeze, sqrt, slice, shape, diag, batchMatMul, batchMatMul', conjugateTranspose)
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import qualified TensorFlow.Convolution as TF
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import qualified TensorFlow.Gradient as TF
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import qualified TensorFlow.Ops as TF hiding (zeroInitializedVariable, shape)
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import qualified TensorFlow.Output as TF
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@ -715,10 +716,8 @@ testConv2DBackpropInputGrad = testCase "testConv2DBackpropInputGrad" $ do
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let filterShape = TF.vector [2, 2, 1, 1 :: Int32] -- [fh, fw, inc, out]
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filter' <- TF.render $ TF.fill filterShape (TF.scalar (1::Float))
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let y = TF.conv2DBackpropInput'
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( (TF.opAttr "strides" .~ [1::Int64, 1, 1, 1])
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. (TF.opAttr "data_format" .~ (BS.pack "NHWC"))
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)
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"VALID" conv_input_shape filter' x
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(TF.opAttr "strides" .~ [1::Int64, 1, 1, 1])
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TF.PaddingValid TF.ChannelLast conv_input_shape filter' x
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[dx] <- TF.gradients y [x]
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TF.run (dx, TF.shape dx, TF.shape x)
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@ -734,10 +733,8 @@ testDepthwiseConv2dGrad = testCase "testDepthwiseConv2dGrad" $ do
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let filterShape = TF.vector [2, 2, 1, 1 :: Int32]
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filter' <- TF.render $ TF.fill filterShape (TF.scalar (1 :: Float))
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let y = TF.depthwiseConv2dNative'
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( (TF.opAttr "strides" .~ [1 :: Int64, 1, 1, 1])
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. (TF.opAttr "data_format" .~ (BS.pack "NHWC"))
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)
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"VALID" x filter'
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(TF.opAttr "strides" .~ [1 :: Int64, 1, 1, 1])
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TF.PaddingValid TF.ChannelLast x filter'
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[dx] <- TF.gradients y [x]
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TF.run (dx, TF.shape dx, TF.shape x)
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@ -755,10 +752,8 @@ testDepthwiseConv2dBackpropInputGrad = testCase "testDepthwiseConv2dBackpropInpu
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let filterShape = TF.vector [2, 2, 1, 1 :: Int32]
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filter' <- TF.render $ TF.fill filterShape (TF.scalar (1 :: Float))
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let y = TF.depthwiseConv2dNativeBackpropInput'
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( (TF.opAttr "strides" .~ [1 :: Int64, 1, 1, 1])
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. (TF.opAttr "data_format" .~ (BS.pack "NHWC"))
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)
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"VALID" conv_input_shape filter' x
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(TF.opAttr "strides" .~ [1 :: Int64, 1, 1, 1])
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TF.PaddingValid TF.ChannelLast conv_input_shape filter' x
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[dx] <- TF.gradients y [x]
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TF.run (dx, TF.shape dx, TF.shape x)
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