mirror of
https://github.com/wassname/keras-js.git
synced 2026-09-09 11:25:25 +08:00
update SeparableConvolution2D layer
This commit is contained in:
Leon Chen
@@ -51,7 +51,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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@@ -114,7 +114,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 67,
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"metadata": {
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"collapsed": false
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},
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@@ -168,6 +168,130 @@
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"print('out:', format_decimal(result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"out shape: (3, 3, 2)\n",
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"out: [0.0918, -0.250826, 0.752085, -0.396841, 1.239056, -0.569188, 0.67132, -0.204467, -0.241087, 0.062854, 0.829858, -0.047888, 2.479452, -1.706853, 0.834468, -1.525005, 0.699256, -0.386894]\n"
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]
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}
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],
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"source": [
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"from keras.layers.convolutional import Convolution2D\n",
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"test_layer_0 = Input(shape=(5, 5, 1))\n",
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"test_layer_1 = Convolution2D(2, 3, 3, activation='linear', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=False)(test_layer_0)\n",
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"test_model = Model(input=test_layer_0, output=test_layer_1)\n",
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"test_model.set_weights([weights[0][:,:,0:1,:]])\n",
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"test_result = test_model.predict(np.array([data_in[:,:,0:1]]))\n",
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"print('out shape:', test_result[0].shape)\n",
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"print('out:', format_decimal(test_result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"out shape: (3, 3, 2)\n",
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"out: [1.301919, 1.174606, -0.754712, -0.528116, -0.431178, -0.245584, 0.482141, -0.370073, -0.96779, -0.070169, -0.373107, -0.637402, 0.148506, 0.112963, -0.600912, -0.627807, 0.361648, -0.466574]\n"
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]
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}
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],
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"source": [
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"from keras.layers.convolutional import Convolution2D\n",
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"test_layer_0 = Input(shape=(5, 5, 1))\n",
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"test_layer_1 = Convolution2D(2, 3, 3, activation='linear', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=False)(test_layer_0)\n",
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"test_model = Model(input=test_layer_0, output=test_layer_1)\n",
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"test_model.set_weights([weights[0][:,:,1:2,:]])\n",
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"test_result = test_model.predict(np.array([data_in[:,:,1:2]]))\n",
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"print('out shape:', test_result[0].shape)\n",
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"print('out:', format_decimal(test_result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"din=np.concatenate([np.array([0.0918, -0.250826, 0.752085, -0.396841, 1.239056, -0.569188, 0.67132, -0.204467, -0.241087, 0.062854, 0.829858, -0.047888, 2.479452, -1.706853, 0.834468, -1.525005, 0.699256, -0.386894]).reshape((3,3,2)),\n",
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" np.array([1.301919, 1.174606, -0.754713, -0.528116, -0.431178, -0.245584, 0.482141, -0.370073, -0.96779, -0.070169, -0.373107, -0.637402, 0.148506, 0.112963, -0.600912, -0.627807, 0.361648, -0.466574]).reshape((3,3,2))], axis=2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 77,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0.0918 , -0.250826, 1.301919, 1.174606, 0.752085, -0.396841,\n",
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" -0.754713, -0.528116, 1.239056, -0.569188, -0.431178, -0.245584,\n",
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" 0.67132 , -0.204467, 0.482141, -0.370073, -0.241087, 0.062854,\n",
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" -0.96779 , -0.070169, 0.829858, -0.047888, -0.373107, -0.637402,\n",
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" 2.479452, -1.706853, 0.148506, 0.112963, 0.834468, -1.525005,\n",
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" -0.600912, -0.627807, 0.699256, -0.386894, 0.361648, -0.466574])"
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]
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},
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"execution_count": 77,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"din.ravel()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"out shape: (3, 3, 4)\n",
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"out: [2.19505, -0.849839, -0.064323, -1.029974, 1.195129, -0.305476, -0.933523, -1.710334, 1.986473, -0.608713, -1.095067, -2.246176, 1.623991, -0.91593, -0.62239, -2.083865, 0.185464, 0.333382, -0.209328, -0.04148, 1.187908, -0.495996, -0.657218, -1.762427, 4.113845, -1.490486, -2.210908, -4.352599, 1.904537, -0.706008, -1.924138, -2.949446, 1.652338, -0.920442, -0.81737, -2.268492]\n"
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]
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}
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],
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"source": [
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"from keras.layers.convolutional import Convolution2D\n",
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"test_layer_0 = Input(shape=(3, 3, 4))\n",
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"test_layer_1 = Convolution2D(4, 1, 1, activation='linear', border_mode='valid', subsample=(1, 1), dim_ordering='tf', bias=False)(test_layer_0)\n",
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"test_model = Model(input=test_layer_0, output=test_layer_1)\n",
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"test_model.set_weights([\n",
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" weights[1]\n",
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"])\n",
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"test_result = test_model.predict(np.array([din]))\n",
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"for i in range(4):\n",
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" test_result[0][:,:,i] += weights[2][i]\n",
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"print('out shape:', test_result[0].shape)\n",
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"print('out:', format_decimal(test_result[0].ravel().tolist()))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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@@ -492,8 +616,9 @@
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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@@ -3,6 +3,143 @@ import Tensor from '../../Tensor'
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import Layer from '../../Layer'
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import Convolution2D from './Convolution2D'
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import ops from 'ndarray-ops'
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import gemm from 'ndarray-gemm'
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/**
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* _DepthwiseConvolution2D layer class
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*/
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class _DepthwiseConvolution2D extends Convolution2D {
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constructor (attrs = {}) {
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super(attrs)
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}
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/**
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* Convert input image to column matrix
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* @param {Tensor} x
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* @returns {Tensor} x
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*/
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_im2col (x) {
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const [inputRows, inputCols, inputChannels] = x.tensor.shape
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const nbRow = this.kernelShape[1]
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const nbCol = this.kernelShape[2]
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const outputRows = this.outputShape[0]
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const outputCols = this.outputShape[1]
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const nbPatches = outputRows * outputCols
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const patchLen = nbRow * nbCol
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if (!this._imColsMat) {
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this._imColsMat = new Tensor([], [nbPatches * inputChannels, patchLen])
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}
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let patch = new Tensor([], [nbRow, nbCol, 1])
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let patchRaveled = new Tensor([], [patchLen])
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let n = 0
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for (let c = 0; c < inputChannels; c++) {
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for (let i = 0, limit = inputRows - nbRow; i <= limit; i += this.subsample[0]) {
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for (let j = 0, limit = inputCols - nbCol; j <= limit; j += this.subsample[1]) {
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ops.assign(patch.tensor, x.tensor.hi(i + nbRow, j + nbCol, c + 1).lo(i, j, c))
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patchRaveled.replaceTensorData(patch.tensor.data)
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ops.assign(this._imColsMat.tensor.pick(n, null), patchRaveled.tensor)
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n += 1
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}
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}
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}
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if (this._useWeblas) {
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this._imColsMat.createWeblasTensor()
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}
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return this._imColsMat
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}
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/**
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* Convert filter weights to row matrix
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* @returns {Tensor|weblas.pipeline.Tensor} wRowsMat
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*/
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_w2row () {
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const inputChannels = this.weights.W.tensor.shape[2]
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const [nbFilter, nbRow, nbCol] = this.kernelShape
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const patchLen = nbRow * nbCol
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this._wRowsMat = new Tensor([], [patchLen, nbFilter * inputChannels])
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let patch = new Tensor([], [nbRow, nbCol])
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let patchRaveled = new Tensor([], [patchLen])
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let p = 0
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for (let c = 0; c < inputChannels; c++) {
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for (let n = 0; n < nbFilter; n++) {
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ops.assign(patch.tensor, this.weights.W.tensor.pick(null, null, c, n))
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patchRaveled.replaceTensorData(patch.tensor.data)
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ops.assign(this._wRowsMat.tensor.pick(null, p), patchRaveled.tensor)
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p += 1
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}
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}
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return this._wRowsMat
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}
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/**
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* Method for layer computational logic
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* @param {Tensor} x
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* @returns {Tensor} x
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*/
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call (x) {
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// convert to tf ordering
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if (this.dimOrdering === 'th') {
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x.tensor = x.tensor.transpose(1, 2, 0)
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}
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this._calcOutputShape(x)
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this._padInput(x)
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this._im2col(x)
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const nbFilter = this.kernelShape[0]
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const outputRows = this.outputShape[0]
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const outputCols = this.outputShape[1]
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const nbPatches = outputRows * outputCols
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const matMul = new Tensor([], [nbPatches * x.tensor.shape[2], nbFilter * x.tensor.shape[2]])
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if (this._useWeblas) {
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// GPU
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if (this._imColsMat.weblasTensorsSplit) {
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// split matrix multiply if this._imColsMat dimension > webgl.MAX_TEXTURE_SIZE
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let offset = 0
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this._imColsMat.weblasTensorsSplit.forEach(imColsMatSplit => {
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const matMulSplitData = weblas.pipeline.sgemm(
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1, imColsMatSplit, this._wRowsMat.weblasTensor,
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1, this._zerosVec.weblasTensor
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).transfer()
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matMul.tensor.data.set(matMulSplitData, offset)
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offset += matMulSplitData.length
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})
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} else {
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// normal matrix multiply
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matMul.tensor.data = weblas.pipeline.sgemm(
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1, this._imColsMat.weblasTensor, this._wRowsMat.weblasTensor,
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1, this._zerosVec.weblasTensor
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).transfer()
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}
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} else {
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// CPU
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gemm(matMul.tensor, this._imColsMat.tensor, this._wRowsMat.tensor, 1, 1)
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}
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let output = new Tensor([], [outputRows, outputCols, x.tensor.shape[2] * nbFilter])
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const outputDataLength = outputRows * outputCols * x.tensor.shape[2] * nbFilter
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let dataFiltered = new Float32Array(outputDataLength)
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for (let c = 0; c < x.tensor.shape[2]; c++) {
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for (let i = 0, n = c * outputDataLength + c * nbFilter, len = (c + 1) * outputDataLength; n < len; i++, n += nbFilter * x.tensor.shape[2]) {
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for (let m = 0; m < nbFilter; m++) {
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dataFiltered[n + m - c * outputDataLength] = matMul.tensor.data[n + m]
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}
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}
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}
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output.replaceTensorData(dataFiltered)
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x.tensor = output.tensor
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return x
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}
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}
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/**
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* SeparableConvolution2D layer class
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@@ -58,10 +195,20 @@ export default class SeparableConvolution2D extends Layer {
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// SeparableConvolution2D has two components: depthwise, and pointwise.
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// Activation function and bias is applied at the end.
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// Subsampling (striding) only performed on depthwise part, not the pointwise part.
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const depthwiseConvAttrs = { nbFilter: this.depthMultiplier, nbRow, nbCol, activation: 'linear', borderMode, subsample, dimOrdering, bias: false }
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const pointwiseConvAttrs = { nbFilter, nbRow: 1, nbCol: 1, activation: 'linear', borderMode, subsample: [1, 1], dimOrdering, bias: false }
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this._depthwiseConv = new Convolution2D(Object.assign(depthwiseConvAttrs, { gpu: attrs.gpu }))
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this._pointwiseConv = new Convolution2D(Object.assign(pointwiseConvAttrs, { gpu: attrs.gpu }))
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this.depthwiseConvAttrs = { nbFilter: this.depthMultiplier, nbRow, nbCol, activation: 'linear', borderMode, subsample, dimOrdering, bias: false, gpu: attrs.gpu }
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this.pointwiseConvAttrs = { nbFilter, nbRow: 1, nbCol: 1, activation: 'linear', borderMode, subsample: [1, 1], dimOrdering, bias: this.bias, gpu: attrs.gpu }
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}
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/**
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* Method for setting layer weights
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* Override `super` method since weights must be set in component Convolution2D layers
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* @param {Tensor[]} weightsArr - array of weights which are instances of Tensor
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*/
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setWeights (weightsArr) {
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this._depthwiseConv = new _DepthwiseConvolution2D(this.depthwiseConvAttrs)
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this._depthwiseConv.setWeights(weightsArr.slice(0, 1))
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this._pointwiseConv = new Convolution2D(this.pointwiseConvAttrs)
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this._pointwiseConv.setWeights(weightsArr.slice(1, 3))
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}
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/**
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@@ -71,53 +218,11 @@ export default class SeparableConvolution2D extends Layer {
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*/
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call (x) {
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// Perform depthwise ops
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this._depthwiseConv._calcOutputShape(x)
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const outputRows = this._depthwiseConv.outputShape[0]
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const outputCols = this._depthwiseConv.outputShape[1]
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let depthwiseOutput = new Tensor([], [outputRows, outputCols, this.depthMultiplier])
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// temporary tensor to hold input for a particular channel
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const [inputRows, inputCols, inputChannels] = x.tensor.shape
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let inputChannelSlice = new Tensor([], [inputRows, inputCols, 1])
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// temporary tensor to hold weights for a particular channel
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let depthwiseKernelSliceShape = this.weights.depthwise_kernel.tensor.shape.slice()
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depthwiseKernelSliceShape[2] = 1
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let depthwiseKernelSlice = new Tensor([], depthwiseKernelSliceShape)
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// tensor to hold combined output of depthwise ops
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const depthwiseOutputCombined = new Tensor([], [
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this._depthwiseConv.outputShape[0],
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this._depthwiseConv.outputShape[1],
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inputChannels * this.depthMultiplier
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])
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// perform convolution over each channel separately
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for (let c = 0; c < inputChannels; c++) {
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depthwiseKernelSlice.tensor = this.weights.depthwise_kernel.tensor
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.hi(depthwiseKernelSliceShape[0], depthwiseKernelSliceShape[1], c + 1, depthwiseKernelSliceShape[3])
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.lo(0, 0, c, 0)
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this._depthwiseConv.setWeights([depthwiseKernelSlice])
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inputChannelSlice.tensor = x.tensor.hi(inputRows, inputCols, c + 1).lo(0, 0, c)
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depthwiseOutput = this._depthwiseConv.call(inputChannelSlice)
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ops.assign(
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depthwiseOutputCombined.tensor
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.hi(outputRows, outputCols, (c + 1) * this.depthMultiplier)
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.lo(0, 0, c * this.depthMultiplier),
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depthwiseOutput.tensor
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)
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}
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const depthwiseOutput = this._depthwiseConv.call(x)
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// Perform depthwise ops
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this._pointwiseConv.setWeights([this.weights.pointwise_kernel])
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const pointwiseOutput = this._pointwiseConv.call(depthwiseOutputCombined)
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const pointwiseOutput = this._pointwiseConv.call(depthwiseOutput)
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// bias
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if (this.bias) {
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for (let n = 0; n < this.weights.b.tensor.shape[0]; n++) {
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ops.addseq(pointwiseOutput.tensor.pick(null, null, n), this.weights.b.tensor.get(n))
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}
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}
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x.tensor = pointwiseOutput.tensor
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// activation
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