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Add Conv3D gradient test and vjp fixes #460

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Sep 24, 2019
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Add Conv3D gradient test and vjp fixes #460

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08d1f4d
vjp fixes in conv3d
Shashi456 Aug 21, 2019
a37b4cc
Removing test
Shashi456 Aug 21, 2019
23fcd8a
Adding test
Shashi456 Aug 21, 2019
4d575e9
formatting errors
Shashi456 Aug 21, 2019
242d2aa
formatting errors
Shashi456 Aug 21, 2019
ef4e0fb
add python tensorflow example
Shashi456 Aug 21, 2019
b632ad1
review changes
Shashi456 Aug 23, 2019
679b8e2
Merge branch 'master' of https://github.com/tensorflow/swift-apis int…
Shashi456 Aug 23, 2019
1242686
Standardization wrt #499
Shashi456 Aug 29, 2019
2a696a5
print gradients in example
Shashi456 Aug 29, 2019
8602440
change message
Shashi456 Aug 29, 2019
4106fc6
Fix indentation
Shashi456 Aug 30, 2019
86c28f8
minor indentation errors
Shashi456 Aug 31, 2019
b0e9bb3
Merge branch 'master' into conv3d
rxwei Sep 24, 2019
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28 changes: 11 additions & 17 deletions Sources/TensorFlow/Operators/NN.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -229,12 +229,10 @@ func _vjpConv3D<Scalar: TensorFlowFloatingPoint>(
let value = conv3D(x, filter: filter, strides: strides,
padding: padding)
return (value, { v in
return (
conv3DBackpropInput(v, shape: x.shapeTensor, filter: filter,
strides: strides, padding: padding),
conv3DBackpropFilter(v, input: x, filterSizes: filter.shapeTensor,
strides: strides, padding: padding)
)
(conv3DBackpropInput(v, shape: x.shapeTensor, filter: filter,
strides: strides, padding: padding),
conv3DBackpropFilter(v, input: x, filterSizes: filter.shapeTensor,
strides: strides, padding: padding))
})
}

Expand Down Expand Up @@ -268,11 +266,9 @@ func _vjpConv3DBackpropInput<Scalar: TensorFlowFloatingPoint>(
let value = conv3DBackpropInput(x, shape: shape, filter: filter, strides: strides,
padding: padding)
return (value, { v in
return (
conv3DBackpropFilter(x, input: v, filterSizes: shape, strides: strides,
padding: padding),
conv3D(v, filter: filter, strides: strides, padding: padding)
)
(conv3D(v, filter: filter, strides: strides, padding: padding),
conv3DBackpropFilter(x, input: v, filterSizes: filter.shapeTensor, strides: strides,
padding: padding))
})
}

Expand All @@ -287,7 +283,7 @@ func conv3DBackpropFilter<Scalar: TensorFlowFloatingPoint>(
padding: Padding = .valid
) -> Tensor<Scalar> {
return Raw.conv3DBackpropFilterV2(
x,
input,
filterSizes: filterSizes,
outBackprop: x,
strides: [Int32(strides.0), Int32(strides.1), Int32(strides.2),
Expand All @@ -306,11 +302,9 @@ func _vjpConv3DBackpropFilter<Scalar: TensorFlowFloatingPoint>(
let value = conv3DBackpropFilter(x, input: input, filterSizes: filterSizes,
strides: strides, padding: padding)
return (value, { v in
return (
conv3DBackpropInput(x, shape: filterSizes, filter: v, strides: strides,
padding: padding),
conv3D(input, filter: v, strides: strides, padding: padding)
)
(conv3D(input, filter: v, strides: strides, padding: padding),
conv3DBackpropInput(x, shape: x.shapeTensor, filter: v, strides: strides,
padding: padding))
})
}

Expand Down
50 changes: 50 additions & 0 deletions Tests/TensorFlowTests/LayerTests.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -198,6 +198,55 @@ final class LayerTests: XCTestCase {
XCTAssertEqual(output, expected)
}

func testConv3DGradient() {
let filter = Tensor(shape: [1, 4, 4, 1, 1], scalars: (0..<16).map(Float.init))
let bias = Tensor<Float>(ones: [2])
let layer = Conv3D(filter: filter,
bias: bias,
activation: identity,
strides: (2, 2, 2),
padding: .same)
let input = Tensor(shape: [1, 4, 4, 4, 1], scalars: (0..<64).map(Float.init))
let grads = gradient(at: input, layer) { $1($0).sum() }
// The expected value of the gradient was computed using the following Python code:
// ```
// import tensorflow as tf
// x = tf.reshape(tf.range(64, dtype=tf.float32), [1, 4, 4, 4, 1])
// filter = tf.reshape(tf.range(72, dtype=tf.float32), [1, 4, 4, 1, 1])
// bias = tf.ones([2])
// with tf.GradientTape() as tape:
// tape.watch([x, filter, bias])
// y = tf.math.reduce_sum(tf.nn.conv3d(input=x,
// filters=filter,
// strides=[1, 2, 2, 2, 1],
// padding="SAME") + bias)
// print(tape.gradient(y, [x, filter, bias]))
// ```
XCTAssertEqual(grads.0,
[[[[[10.0], [20.0], [24.0], [12.0]],
[[20.0], [40.0], [48.0], [24.0]],
[[36.0], [72.0], [80.0], [40.0]],
[[18.0], [36.0], [40.0], [20.0]]],
[[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
[[ 0.0], [ 0.0], [ 0.0], [ 0.0]]],
[[[10.0], [20.0], [24.0], [12.0]],
[[20.0], [40.0], [48.0], [24.0]],
[[36.0], [72.0], [80.0], [40.0]],
[[18.0], [36.0], [40.0], [20.0]]],
[[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
[[ 0.0], [ 0.0], [ 0.0], [ 0.0]]]]])
XCTAssertEqual(grads.1.filter,
[[[[[ 84.0]], [[168.0]], [[176.0]], [[ 88.0]]],
[[[168.0]], [[336.0]], [[352.0]], [[176.0]]],
[[[200.0]], [[400.0]], [[416.0]], [[208.0]]],
[[[100.0]], [[200.0]], [[208.0]], [[104.0]]]]])
XCTAssertEqual(grads.1.bias, [8.0, 8.0])
}

func testDepthwiseConv2D() {
let filter = Tensor(shape: [2, 2, 2, 2], scalars: (0..<16).map(Float.init))
let bias = Tensor<Float>([1, 2, 3, 4])
Expand Down Expand Up @@ -1237,6 +1286,7 @@ final class LayerTests: XCTestCase {
("testConv2DGradient", testConv2DGradient),
("testConv2DDilation", testConv2DDilation),
("testConv3D", testConv3D),
("testConv3DGradient", testConv3DGradient),
("testDepthwiseConv2D", testDepthwiseConv2D),
("testDepthwiseConv2DGradient", testDepthwiseConv2DGradient),
("testSeparableConv1D", testSeparableConv1D),
Expand Down