dwSun commented on a change in pull request #8894: Mobilenet URL: https://github.com/apache/incubator-mxnet/pull/8894#discussion_r155159584
########## File path: example/image-classification/symbols/mobilenet.py ########## @@ -14,48 +14,129 @@ # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. - +# -*- coding:utf-8 -*- import mxnet as mx -def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name=None, suffix=''): - conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' %(name, suffix)) - bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' %(name, suffix), fix_gamma=True) - act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix)) +__author__ = 'qingzhouzhen' +modified_date = '17/8/5' +__modify__ = 'dwSun' +modified_date = '17/11/30' + +''' +mobilenet + Suittable for image with around resolution x resolution, resolution is multiple of 32. + +Reference: + MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications + https://arxiv.org/abs/1704.04861 +''' + +alpha_values = [0.25, 0.50, 0.75, 1.0] + + +def Conv(data, num_filter=1, kernel=(1, 1), stride=(1, 1), pad=(0, 0), num_group=1, name='', suffix=''): + conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel, num_group=num_group, stride=stride, pad=pad, no_bias=True, name='%s%s_conv2d' % (name, suffix)) + bn = mx.sym.BatchNorm(data=conv, name='%s%s_batchnorm' % (name, suffix), fix_gamma=True) + act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' % (name, suffix)) return act -def get_symbol(num_classes, **kwargs): - data = mx.symbol.Variable(name="data") # 224 - conv_1 = Conv(data, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112 - conv_2_dw = Conv(conv_1, num_group=32, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112 - conv_2 = Conv(conv_2_dw, num_filter=64, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 112/112 - conv_3_dw = Conv(conv_2, num_group=64, num_filter=64, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_3_dw") # 112/56 - conv_3 = Conv(conv_3_dw, num_filter=128, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_3") # 56/56 - conv_4_dw = Conv(conv_3, num_group=128, num_filter=128, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_4_dw") # 56/56 - conv_4 = Conv(conv_4_dw, num_filter=128, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_4") # 56/56 - conv_5_dw = Conv(conv_4, num_group=128, num_filter=128, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_5_dw") # 56/28 - conv_5 = Conv(conv_5_dw, num_filter=256, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_5") # 28/28 - conv_6_dw = Conv(conv_5, num_group=256, num_filter=256, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_6_dw") # 28/28 - conv_6 = Conv(conv_6_dw, num_filter=256, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_6") # 28/28 - conv_7_dw = Conv(conv_6, num_group=256, num_filter=256, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_7_dw") # 28/14 - conv_7 = Conv(conv_7_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_7") # 14/14 - - conv_8_dw = Conv(conv_7, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_8_dw") # 14/14 - conv_8 = Conv(conv_8_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_8") # 14/14 - conv_9_dw = Conv(conv_8, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_9_dw") # 14/14 - conv_9 = Conv(conv_9_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_9") # 14/14 - conv_10_dw = Conv(conv_9, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_10_dw") # 14/14 - conv_10 = Conv(conv_10_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_10") # 14/14 - conv_11_dw = Conv(conv_10, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_11_dw") # 14/14 - conv_11 = Conv(conv_11_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_11") # 14/14 - conv_12_dw = Conv(conv_11, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_12_dw") # 14/14 - conv_12 = Conv(conv_12_dw, num_filter=512, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_12") # 14/14 - - conv_13_dw = Conv(conv_12, num_group=512, num_filter=512, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_13_dw") # 14/7 - conv_13 = Conv(conv_13_dw, num_filter=1024, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_13") # 7/7 - conv_14_dw = Conv(conv_13, num_group=1024, num_filter=1024, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_14_dw") # 7/7 - conv_14 = Conv(conv_14_dw, num_filter=1024, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_14") # 7/7 - - pool = mx.sym.Pooling(data=conv_14, kernel=(7, 7), stride=(1, 1), pool_type="avg", name="global_pool") + +def Conv_DPW(data, depth=1, stride=(1, 1), name='', idx=0, suffix=''): + conv_dw = Conv(data, num_group=depth, num_filter=depth, kernel=(3, 3), pad=(1, 1), stride=stride, name="conv_%d_dw" % (idx), suffix=suffix) + conv = Conv(conv_dw, num_filter=depth * stride[0], kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_%d" % (idx), suffix=suffix) + return conv + + +def get_symbol_compact(num_classes, alpha=1, resolution=224, **kwargs): + assert alpha in alpha_values, 'Invalid alpha={0}, must be one of {1}'.format(alpha, alpha_values) + assert resolution % 32 == 0, 'resolution must be multiple of 32' + + base = int(32 * alpha) + + data = mx.symbol.Variable(name="data") # 224 + conv_1 = Conv(data, num_filter=base, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 32*alpha, 224/112 + + conv_2_dw = Conv(conv_1, num_group=base, num_filter=base, kernel=(3, 3), pad=(1, 1), stride=(1, 1), name="conv_2_dw") # 112/112 + conv_2 = Conv(conv_2_dw, num_filter=base * 2, kernel=(1, 1), pad=(0, 0), stride=(1, 1), name="conv_2") # 32*alpha, 112/112 + + conv_3_dpw = Conv_DPW(conv_2, depth=base * 2, stride=(2, 2), idx=3) # 64*alpha, 112/56 => 56/56 + conv_4_dpw = Conv_DPW(conv_3_dpw, depth=base * 4, stride=(1, 1), idx=4) # 128*alpha, 56/56 =>56/56 + conv_5_dpw = Conv_DPW(conv_4_dpw, depth=base * 4, stride=(2, 2), idx=5) # 128*alpha, 56/28 => 28/28 + conv_6_dpw = Conv_DPW(conv_5_dpw, depth=base * 8, stride=(1, 1), idx=6) # 256*alpha, 28/28 => 28/28 + conv_7_dpw = Conv_DPW(conv_6_dpw, depth=base * 8, stride=(2, 2), idx=7) # 256*alpha, 28/14 => 14/14 + conv_dpw = conv_7_dpw + + for idx in range(8, 13): + conv_dpw = Conv_DPW(conv_dpw, depth=base * 16, stride=(1, 1), idx=idx) # 512*alpha, 14/14 + + conv_12_dpw = conv_dpw + conv_13_dpw = Conv_DPW(conv_12_dpw, depth=base * 16, stride=(2, 2), idx=13) # 512*alpha, 14/7 => 7/7 + conv_14_dpw = Conv_DPW(conv_13_dpw, depth=base * 32, stride=(1, 1), idx=14) # 1024*alpha, 7/7 => 7/7 + + pool_size = int(resolution / 32) + pool = mx.sym.Pooling(data=conv_14_dpw, kernel=(pool_size, pool_size), stride=(1, 1), pool_type="avg", name="global_pool") + flatten = mx.sym.Flatten(data=pool, name="flatten") + fc = mx.symbol.FullyConnected(data=flatten, num_hidden=num_classes, name='fc') + softmax = mx.symbol.SoftmaxOutput(data=fc, name='softmax') + return softmax + + +def get_symbol(num_classes, alpha=1, resolution=224, **kwargs): Review comment: This is the function prepared for train_imagenet.py ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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