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spark/dl/src/test/scala/com/intel/analytics/bigdl/nn/SpatialSeperableConvolutionSpec.scala
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| /* | ||
| * Copyright 2016 The BigDL Authors. | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
| package com.intel.analytics.bigdl.nn | ||
|
|
||
| import com.intel.analytics.bigdl.nn.abstractnn.DataFormat | ||
| import com.intel.analytics.bigdl.tensor.Tensor | ||
| import org.scalatest.{FlatSpec, Matchers} | ||
|
|
||
| class SpatialSeperableConvolutionSpec extends FlatSpec with Matchers { | ||
| "SpatialSeperableConvolution NHWC and NCHW" should "have same output" in { | ||
| val depthWeightNHWC = Tensor[Float](2, 2, 3, 1).rand() | ||
| val depthWeightNCHW = depthWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) | ||
| .contiguous() | ||
| val pointWeightNHWC = Tensor[Float](1, 1, 3, 6).rand() | ||
| val pointWeightNCHW = pointWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) | ||
| .contiguous() | ||
| val convNHWC = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NHWC, | ||
| initDepthWeight = depthWeightNHWC, initPointWeight = pointWeightNHWC) | ||
| val convNCHW = SpatialSeperableConvolution[Float](3, 6, 1, 2, 2, dataFormat = DataFormat.NCHW, | ||
| initDepthWeight = depthWeightNCHW, initPointWeight = pointWeightNCHW) | ||
| val inputNHWC = Tensor[Float](2, 24, 24, 3).rand() | ||
| val inputNCHW = inputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| val outputNHWC = convNHWC.forward(inputNHWC) | ||
| val outputNCHW = convNCHW.forward(inputNCHW) | ||
| val convert = outputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| convert.almostEqual(outputNCHW, 1e-5) should be(true) | ||
| val gradOutputNHWC = Tensor[Float](2, 23, 23, 6).rand() | ||
| val gradOutputNCHW = gradOutputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| val gradInputNHWC = convNHWC.backward(inputNHWC, gradOutputNHWC) | ||
| val gradInputNCHW = convNCHW.backward(inputNCHW, gradOutputNCHW) | ||
| val convertGradInput = gradInputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| convertGradInput.almostEqual(gradInputNCHW, 1e-5) should be(true) | ||
|
|
||
| convNHWC.parameters()._2.zip(convNCHW.parameters()._2).map { case(p1, p2) => | ||
| if (p1.nDimension() == 4) { | ||
| val convert = p2.transpose(1, 4).transpose(1, 3).transpose(2, 3) | ||
| p1.almostEqual(convert, 1e-3) should be(true) | ||
| } else { | ||
| p1.almostEqual(p2, 1e-3) should be(true) | ||
| } | ||
| } | ||
| } | ||
|
|
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| "SpatialSeperableConvolution NHWC and NCHW" should "have same output when depth mul is 2" in { | ||
| val depthWeightNHWC = Tensor[Float](2, 2, 3, 2).rand() | ||
| val depthWeightNCHW = depthWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) | ||
| .contiguous() | ||
| val pointWeightNHWC = Tensor[Float](1, 1, 6, 6).rand() | ||
| val pointWeightNCHW = pointWeightNHWC.transpose(1, 4).transpose(2, 4).transpose(2, 3) | ||
| .contiguous() | ||
| val convNHWC = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NHWC, | ||
| initDepthWeight = depthWeightNHWC, initPointWeight = pointWeightNHWC) | ||
| val convNCHW = SpatialSeperableConvolution[Float](3, 6, 2, 2, 2, dataFormat = DataFormat.NCHW, | ||
| initDepthWeight = depthWeightNCHW, initPointWeight = pointWeightNCHW) | ||
| val inputNHWC = Tensor[Float](2, 24, 24, 3).rand() | ||
| val inputNCHW = inputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| val outputNHWC = convNHWC.forward(inputNHWC) | ||
| val outputNCHW = convNCHW.forward(inputNCHW) | ||
| val convert = outputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| convert.almostEqual(outputNCHW, 1e-5) should be(true) | ||
| val gradOutputNHWC = Tensor[Float](2, 23, 23, 6).rand() | ||
| val gradOutputNCHW = gradOutputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| val gradInputNHWC = convNHWC.backward(inputNHWC, gradOutputNHWC) | ||
| val gradInputNCHW = convNCHW.backward(inputNCHW, gradOutputNCHW) | ||
| val convertGradInput = gradInputNHWC.transpose(2, 4).transpose(3, 4).contiguous() | ||
| convertGradInput.almostEqual(gradInputNCHW, 1e-5) should be(true) | ||
|
|
||
| convNHWC.parameters()._2.zip(convNCHW.parameters()._2).map { case(p1, p2) => | ||
| if (p1.nDimension() == 4) { | ||
| val convert = p2.transpose(1, 4).transpose(1, 3).transpose(2, 3) | ||
| p1.almostEqual(convert, 1e-3) should be(true) | ||
| } else { | ||
| p1.almostEqual(p2, 1e-3) should be(true) | ||
| } | ||
| } | ||
| } | ||
| } |