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* @license\n * Copyright 2017 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\nimport {Rank} from '../types';\n\ndescribeWithFlags('conv2dTranspose', ALL_ENVS, () => {\n it('input=2x2x1,d2=1,f=2,s=1,p=0', async () => {\n const origInputDepth = 1;\n const origOutputDepth = 1;\n const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n const fSize = 2;\n const origPad = 0;\n const origStride = 1;\n\n const x = tf.tensor3d([2], inputShape);\n const w = tf.tensor4d(\n [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);\n const expected = [6, 2, 10, 0];\n\n expect(result.shape).toEqual([2, 2, 1]);\n expectArraysClose(await result.data(), expected);\n });\n\n it('input=3x3x1,d2=1,f=3,s=2,p=0', async () => {\n const origInputDepth = 1;\n const origOutputDepth = 4;\n const inputShape: [number, number, number, number] =\n [1, 2, 2, origOutputDepth];\n const fSize = 2;\n const origPad = 'same';\n const origStride = 2;\n\n const x = tf.tensor4d(\n [\n 1.24, 1.66, 0.9, 1.39, 0.16, 0.27, 0.42, 0.61, 0.04, 0.17, 0.34, 0.28,\n 0., 0.06, 0.14, 0.24\n ],\n inputShape);\n const w = tf.tensor4d(\n [0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15.],\n [fSize, fSize, origInputDepth, origOutputDepth]);\n\n const result = tf.conv2dTranspose(x, w, [1, 3, 3, 1], origStride, origPad);\n const expected = [7.63, 28.39, 2.94, 49.15, 69.91, 14.62, 1.69, 5.01, 1.06];\n\n expect(result.shape).toEqual([1, 3, 3, 1]);\n expectArraysClose(await result.data(), expected);\n });\n\n it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', async () => {\n const origInputDepth = 1;\n const origOutputDepth = 1;\n const inputShape: [number, number, number, number] =\n [2, 1, 1, origOutputDepth];\n const fSize = 2;\n const origPad = 0;\n const origStride = 1;\n\n const x = tf.tensor4d([2, 3], inputShape);\n const w = tf.tensor4d(\n [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n const result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);\n const expected = [6, 2, 10, 0, 9, 3, 15, 0];\n\n expect(result.shape).toEqual([2, 2, 2, 1]);\n expectArraysClose(await result.data(), expected);\n });\n\n // Reference (Python) TensorFlow code:\n //\n // ```py\n // import numpy as np\n // import tensorflow as tf\n //\n // tf.enable_eager_execution()\n //\n // x = tf.constant(np.array([[\n // [[-0.14656299], [0.32942239], [-1.90302866]],\n // [[-0.06487813], [-2.02637842], [-1.83669377]],\n // [[0.82650784], [-0.89249092], [0.01207666]]\n // ]]).astype(np.float32))\n // filt = tf.constant(np.array([\n // [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],\n // [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]\n // ]).astype(np.float32))\n //\n // with tf.GradientTape() as g:\n // g.watch(x)\n // g.watch(filt)\n // y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])\n // print(y)\n // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n //\n // print(\"x_grad = %s\" % x_grad)\n // print(\"filt_grad = %s\" % filt_grad)\n // ```\n it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid',\n async () => {\n const inputDepth = 1;\n const outputDepth = 2;\n const inputShape: [number, number, number, number] =\n [1, 3, 3, inputDepth];\n const filterSize = 2;\n const stride = 1;\n const pad = 'valid';\n\n const filterShape: [number, number, number, number] =\n [filterSize, filterSize, outputDepth, inputDepth];\n\n const x = tf.tensor4d(\n [[\n [[-0.14656299], [0.32942239], [-1.90302866]],\n [[-0.06487813], [-2.02637842], [-1.83669377]],\n [[0.82650784], [-0.89249092], [0.01207666]]\n ]],\n inputShape);\n const filt = tf.tensor4d(\n [\n [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],\n [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]\n ],\n filterShape);\n\n const grads = tf.grads(\n (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n tf.conv2dTranspose(\n x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride,\n pad)\n .clone());\n const dy = tf.ones([1, 4, 4, outputDepth]);\n const [xGrad, filtGrad] = grads([x, filt], dy);\n\n const expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));\n expectArraysClose(await xGrad.data(), await expectedXGrad.data());\n const expectedFiltGrad =\n tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));\n expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());\n });\n\n // Reference (Python) TensorFlow code:\n //\n // ```py\n // import numpy as np\n // import tensorflow as tf\n //\n // tf.enable_eager_execution()\n //\n // x = tf.constant(np.array([\n // [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]\n // ]).astype(np.float32))\n // filt = tf.constant(np.array([\n // [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],\n // [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]\n // ]).astype(np.float32))\n //\n // with tf.GradientTape() as g:\n // g.watch(x)\n // g.watch(filt)\n // y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,\n // 2)) print(y)\n // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n //\n // print(\"x_grad = %s\" % -x_grad)\n // print(\"filt_grad = %s\" % -filt_grad)\n // ```\n it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', async () => {\n const inputDepth = 1;\n const outputDepth = 2;\n const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];\n const filterSize = 2;\n const stride: [number, number] = [2, 2];\n const pad = 'valid';\n\n const filterShape: [number, number, number, number] =\n [filterSize, filterSize, outputDepth, inputDepth];\n\n const x = tf.tensor4d(\n [[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]],\n inputShape);\n const filt = tf.tensor4d(\n [\n [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],\n [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]\n ],\n filterShape);\n\n const grads = tf.grads(\n (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad));\n const dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));\n const [xGrad, filtGrad] = grads([x, filt], dy);\n\n const expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));\n expectArraysClose(await xGrad.data(), await expectedXGrad.data());\n expect(xGrad.shape).toEqual([1, 2, 2, 1]);\n\n const expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));\n expectArraysClose(await filtGrad.data(), await expectedFiltGrad.data());\n expect(filtGrad.shape).toEqual([2, 2, 2, 1]);\n });\n\n // Reference (Python) TensorFlow code:\n //\n // ```py\n // import numpy as np\n // import tensorflow as tf\n //\n // tf.enable_eager_execution()\n //\n // x = tf.constant(np.array([[\n // [[1.52433065], [-0.77053435], [-0.64562341]],\n // [[0.77962889], [1.58413887], [-0.25581856]],\n // [[-0.58966221], [0.05411662], [0.70749138]]\n // ]]).astype(np.float32))\n // filt = tf.constant(np.array([\n // [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n // [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n // ]).astype(np.float32))\n //\n // with tf.GradientTape() as g:\n // g.watch(x)\n // g.watch(filt)\n // y = tf.keras.backend.conv2d_transpose(\n // x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')\n // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n //\n // print(\"x_grad = %s\" % x_grad)\n // print(\"filt_grad = %s\" % filt_grad)\n // ```\n it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', async () => {\n const inputDepth = 1;\n const outputDepth = 2;\n const inputShape: [number, number, number, number] = [1, 3, 3, inputDepth];\n const filterSize = 2;\n const stride: [number, number] = [1, 1];\n const pad = 'same';\n\n const filterShape: [number, number, number, number] =\n [filterSize, filterSize, outputDepth, inputDepth];\n\n const x = tf.tensor4d(\n [[\n [[1.52433065], [-0.77053435], [-0.64562341]],\n [[0.77962889], [1.58413887], [-0.25581856]],\n [[-0.58966221], [0.05411662], [0.70749138]]\n ]],\n inputShape);\n const filt = tf.tensor4d(\n [\n [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n ],\n filterShape);\n\n const grads = tf.grads(\n (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n const dy = tf.ones([1, 3, 3, outputDepth]);\n const [xGrad, filtGrad] = grads([x, filt], dy);\n\n expectArraysClose(await xGrad.array(), [[\n [[1.30709858], [1.30709858], [-0.92814366]],\n [[1.30709858], [1.30709858], [-0.92814366]],\n [[1.19666437], [1.19666437], [-0.85476589]]\n ]]);\n expectArraysClose(await filtGrad.array(), [\n [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],\n [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]\n ]);\n });\n\n // Reference (Python) TensorFlow code:\n //\n // ```py\n // import numpy as np\n // import tensorflow as tf\n //\n // tf.enable_eager_execution()\n //\n // x = tf.constant(np.array([[\n // [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],\n // ]]).astype(np.float32))\n // filt = tf.constant(np.array([\n // [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],\n // [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]\n // ]).astype(np.float32))\n //\n // with tf.GradientTape() as g:\n // g.watch(x)\n // g.watch(filt)\n // y = tf.keras.backend.conv2d_transpose(\n // x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')\n // print(y.shape)\n // (x_grad, filt_grad) = g.gradient(y, [x, filt])\n //\n // print(\"x_grad = %s\" % x_grad)\n // print(\"filt_grad = %s\" % filt_grad)\n // ```\n it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', async () => {\n const inputDepth = 2;\n const outputDepth = 2;\n const inputShape: [number, number, number, number] = [1, 2, 2, inputDepth];\n const filterSize = 2;\n const stride: [number, number] = [2, 2];\n const pad = 'same';\n\n const filterShape: [number, number, number, number] =\n [filterSize, filterSize, outputDepth, inputDepth];\n\n const x = tf.tensor4d(\n [[\n [[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],\n [[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]\n ]],\n inputShape);\n const filt = tf.tensor4d(\n [\n [\n [[0.19529686, -0.79594708], [0.70314057, -0.06081263]],\n [[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]\n ],\n [\n [[0.51872197, -1.17569193], [1.28316791, -0.81225092]],\n [[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]\n ]\n ],\n filterShape);\n\n const grads = tf.grads(\n (x: tf.Tensor4D, filter: tf.Tensor4D) =>\n tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad));\n const dy = tf.ones([1, 3, 3, outputDepth]);\n const [xGrad, filtGrad] = grads([x, filt], dy);\n\n expectArraysClose(await xGrad.data(), [\n 1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,\n 0.89843743, -0.85675972\n ]);\n expect(xGrad.shape).toEqual([1, 2, 2, 2]);\n expectArraysClose(await filtGrad.data(), [\n -1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,\n -2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,\n -1.81506593, 1.00900095, -1.81506593, 1.00900095\n ]);\n expect(filtGrad.shape).toEqual([2, 2, 2, 2]);\n });\n\n it('throws when x is not rank 3', () => {\n const origInputDepth = 1;\n const origOutputDepth = 1;\n const fSize = 2;\n const origPad = 0;\n const origStride = 1;\n\n // tslint:disable-next-line:no-any\n const x: any = tf.tensor2d([2, 2], [2, 1]);\n const w = tf.tensor4d(\n [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))\n .toThrowError();\n });\n\n it('throws when weights is not rank 4', () => {\n const origInputDepth = 1;\n const origOutputDepth = 1;\n const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n const fSize = 2;\n const origPad = 0;\n const origStride = 1;\n\n const x = tf.tensor3d([2], inputShape);\n // tslint:disable-next-line:no-any\n const w: any = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);\n\n expect(() => tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad))\n .toThrowError();\n });\n\n it('throws when x depth does not match weights original output depth', () => {\n const origInputDepth = 1;\n const origOutputDepth = 2;\n const wrongOrigOutputDepth = 3;\n const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n const fSize = 2;\n const origPad = 0;\n const origStride = 1;\n\n const x = tf.tensor3d([2, 2], inputShape);\n const w = tf.randomNormal<Rank.R4>(\n [fSize, fSize, origInputDepth, wrongOrigOutputDepth]);\n\n expect(() => tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad))\n .toThrowError();\n });\n\n it('throws when passed x as a non-tensor', () => {\n const origInputDepth = 1;\n const origOutputDepth = 1;\n const fSize = 2;\n const origPad = 0;\n const origStride = 1;\n\n const w = tf.tensor4d(\n [3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);\n\n expect(\n () => tf.conv2dTranspose(\n {} as tf.Tensor3D, w, [2, 2, 1], origStride, origPad))\n .toThrowError(\n /Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);\n });\n\n it('throws when passed filter as a non-tensor', () => {\n const origOutputDepth = 1;\n const inputShape: [number, number, number] = [1, 1, origOutputDepth];\n const origPad = 0;\n const origStride = 1;\n\n const x = tf.tensor3d([2], inputShape);\n\n expect(\n () => tf.conv2dTranspose(\n x, {} as tf.Tensor4D, [2, 2, 1], origStride, origPad))\n .toThrowError(\n /Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const origPad = 0;\n const origStride = 1;\n\n const x = [[[2]]]; // 1x1x1\n const w = [[[[3]], [[1]]], [[[5]], [[0]]]]; // 2x2x1x1\n\n const result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);\n const expected = [6, 2, 10, 0];\n\n expect(result.shape).toEqual([2, 2, 1]);\n expectArraysClose(await result.data(), expected);\n });\n});\n"]} |