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February 11, 2016 22:42
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Inception-v3
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| # Inception-v3: http://arxiv.org/abs/1512.00567 | |
| from lasagne.layers import InputLayer | |
| from lasagne.layers import Conv2DLayer | |
| from lasagne.layers import Pool2DLayer | |
| from lasagne.layers import DenseLayer | |
| from lasagne.layers import GlobalPoolLayer | |
| from lasagne.layers import ConcatLayer | |
| from lasagne.layers.normalization import batch_norm | |
| from lasagne.nonlinearities import softmax | |
| def preprocess(im): | |
| # Expected input: RGB uint8 image | |
| # Input to network should be bc01, 299x299 pixels, scaled to [-1, 1]. | |
| import skimage.transform | |
| import numpy as np | |
| im = skimage.transform.resize(im, (299, 299), preserve_range=True) | |
| im = (im - 128) / 128. | |
| im = np.rollaxis(im, 2)[np.newaxis].astype('float32') | |
| return im | |
| def bn_conv(input_layer, **kwargs): | |
| l = Conv2DLayer(input_layer, **kwargs) | |
| l = batch_norm(l, epsilon=0.001) | |
| return l | |
| def inceptionA(input_layer, nfilt): | |
| # Corresponds to a modified version of figure 5 in the paper | |
| l1 = bn_conv(input_layer, num_filters=nfilt[0][0], filter_size=1) | |
| l2 = bn_conv(input_layer, num_filters=nfilt[1][0], filter_size=1) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][1], filter_size=5, pad=2) | |
| l3 = bn_conv(input_layer, num_filters=nfilt[2][0], filter_size=1) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][1], filter_size=3, pad=1) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][2], filter_size=3, pad=1) | |
| l4 = Pool2DLayer( | |
| input_layer, pool_size=3, stride=1, pad=1, mode='average_exc_pad') | |
| l4 = bn_conv(l4, num_filters=nfilt[3][0], filter_size=1) | |
| return ConcatLayer([l1, l2, l3, l4]) | |
| def inceptionB(input_layer, nfilt): | |
| # Corresponds to a modified version of figure 10 in the paper | |
| l1 = bn_conv(input_layer, num_filters=nfilt[0][0], filter_size=3, stride=2) | |
| l2 = bn_conv(input_layer, num_filters=nfilt[1][0], filter_size=1) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][1], filter_size=3, pad=1) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][2], filter_size=3, stride=2) | |
| l3 = Pool2DLayer(input_layer, pool_size=3, stride=2) | |
| return ConcatLayer([l1, l2, l3]) | |
| def inceptionC(input_layer, nfilt): | |
| # Corresponds to figure 6 in the paper | |
| l1 = bn_conv(input_layer, num_filters=nfilt[0][0], filter_size=1) | |
| l2 = bn_conv(input_layer, num_filters=nfilt[1][0], filter_size=1) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][1], filter_size=(1, 7), pad=(0, 3)) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][2], filter_size=(7, 1), pad=(3, 0)) | |
| l3 = bn_conv(input_layer, num_filters=nfilt[2][0], filter_size=1) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][1], filter_size=(7, 1), pad=(3, 0)) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][2], filter_size=(1, 7), pad=(0, 3)) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][3], filter_size=(7, 1), pad=(3, 0)) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][4], filter_size=(1, 7), pad=(0, 3)) | |
| l4 = Pool2DLayer( | |
| input_layer, pool_size=3, stride=1, pad=1, mode='average_exc_pad') | |
| l4 = bn_conv(l4, num_filters=nfilt[3][0], filter_size=1) | |
| return ConcatLayer([l1, l2, l3, l4]) | |
| def inceptionD(input_layer, nfilt): | |
| # Corresponds to a modified version of figure 10 in the paper | |
| l1 = bn_conv(input_layer, num_filters=nfilt[0][0], filter_size=1) | |
| l1 = bn_conv(l1, num_filters=nfilt[0][1], filter_size=3, stride=2) | |
| l2 = bn_conv(input_layer, num_filters=nfilt[1][0], filter_size=1) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][1], filter_size=(1, 7), pad=(0, 3)) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][2], filter_size=(7, 1), pad=(3, 0)) | |
| l2 = bn_conv(l2, num_filters=nfilt[1][3], filter_size=3, stride=2) | |
| l3 = Pool2DLayer(input_layer, pool_size=3, stride=2) | |
| return ConcatLayer([l1, l2, l3]) | |
| def inceptionE(input_layer, nfilt, pool_mode): | |
| # Corresponds to figure 7 in the paper | |
| l1 = bn_conv(input_layer, num_filters=nfilt[0][0], filter_size=1) | |
| l2 = bn_conv(input_layer, num_filters=nfilt[1][0], filter_size=1) | |
| l2a = bn_conv(l2, num_filters=nfilt[1][1], filter_size=(1, 3), pad=(0, 1)) | |
| l2b = bn_conv(l2, num_filters=nfilt[1][2], filter_size=(3, 1), pad=(1, 0)) | |
| l3 = bn_conv(input_layer, num_filters=nfilt[2][0], filter_size=1) | |
| l3 = bn_conv(l3, num_filters=nfilt[2][1], filter_size=3, pad=1) | |
| l3a = bn_conv(l3, num_filters=nfilt[2][2], filter_size=(1, 3), pad=(0, 1)) | |
| l3b = bn_conv(l3, num_filters=nfilt[2][3], filter_size=(3, 1), pad=(1, 0)) | |
| l4 = Pool2DLayer( | |
| input_layer, pool_size=3, stride=1, pad=1, mode=pool_mode) | |
| l4 = bn_conv(l4, num_filters=nfilt[3][0], filter_size=1) | |
| return ConcatLayer([l1, l2a, l2b, l3a, l3b, l4]) | |
| def build_network(): | |
| net = {} | |
| net['input'] = InputLayer((None, 3, 299, 299)) | |
| net['conv'] = bn_conv(net['input'], | |
| num_filters=32, filter_size=3, stride=2) | |
| net['conv_1'] = bn_conv(net['conv'], num_filters=32, filter_size=3) | |
| net['conv_2'] = bn_conv(net['conv_1'], | |
| num_filters=64, filter_size=3, pad=1) | |
| net['pool'] = Pool2DLayer(net['conv_2'], pool_size=3, stride=2, mode='max') | |
| net['conv_3'] = bn_conv(net['pool'], num_filters=80, filter_size=1) | |
| net['conv_4'] = bn_conv(net['conv_3'], num_filters=192, filter_size=3) | |
| net['pool_1'] = Pool2DLayer(net['conv_4'], | |
| pool_size=3, stride=2, mode='max') | |
| net['mixed/join'] = inceptionA( | |
| net['pool_1'], nfilt=((64,), (48, 64), (64, 96, 96), (32,))) | |
| net['mixed_1/join'] = inceptionA( | |
| net['mixed/join'], nfilt=((64,), (48, 64), (64, 96, 96), (64,))) | |
| net['mixed_2/join'] = inceptionA( | |
| net['mixed_1/join'], nfilt=((64,), (48, 64), (64, 96, 96), (64,))) | |
| net['mixed_3/join'] = inceptionB( | |
| net['mixed_2/join'], nfilt=((384,), (64, 96, 96))) | |
| net['mixed_4/join'] = inceptionC( | |
| net['mixed_3/join'], | |
| nfilt=((192,), (128, 128, 192), (128, 128, 128, 128, 192), (192,))) | |
| net['mixed_5/join'] = inceptionC( | |
| net['mixed_4/join'], | |
| nfilt=((192,), (160, 160, 192), (160, 160, 160, 160, 192), (192,))) | |
| net['mixed_6/join'] = inceptionC( | |
| net['mixed_5/join'], | |
| nfilt=((192,), (160, 160, 192), (160, 160, 160, 160, 192), (192,))) | |
| net['mixed_7/join'] = inceptionC( | |
| net['mixed_6/join'], | |
| nfilt=((192,), (192, 192, 192), (192, 192, 192, 192, 192), (192,))) | |
| net['mixed_8/join'] = inceptionD( | |
| net['mixed_7/join'], | |
| nfilt=((192, 320), (192, 192, 192, 192))) | |
| net['mixed_9/join'] = inceptionE( | |
| net['mixed_8/join'], | |
| nfilt=((320,), (384, 384, 384), (448, 384, 384, 384), (192,)), | |
| pool_mode='average_exc_pad') | |
| net['mixed_10/join'] = inceptionE( | |
| net['mixed_9/join'], | |
| nfilt=((320,), (384, 384, 384), (448, 384, 384, 384), (192,)), | |
| pool_mode='max') | |
| net['pool3'] = GlobalPoolLayer(net['mixed_10/join']) | |
| net['softmax'] = DenseLayer( | |
| net['pool3'], num_units=1008, nonlinearity=softmax) | |
| return net |
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