diff --git a/src/symbols/fmobilenet.py b/src/symbols/fmobilenet.py new file mode 100644 index 0000000..2da1378 --- /dev/null +++ b/src/symbols/fmobilenet.py @@ -0,0 +1,109 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +import mxnet as mx + +def Act(data, act_type, name): + #ignore param act_type, set it in this function + body = mx.sym.LeakyReLU(data = data, act_type='prelu', name = name) + #act = mx.sym.Activation(data=bn, act_type='relu', name='%s%s_relu' %(name, suffix)) + return body + +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 = Act(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 + data = data-127.5 + data = data*0.0078125 + version_input = kwargs.get('version_input', 0) + assert version_input>=0 + version_output = kwargs.get('version_output', 'A') + fc_type = version_output + version_unit = kwargs.get('version_unit', 1) + print(version_input, version_output, version_unit) + if version_input==0: + conv_1 = Conv(data, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(2, 2), name="conv_1") # 224/112 + else: + conv_1 = Conv(data, num_filter=32, kernel=(3, 3), pad=(1, 1), stride=(1, 1), 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 + body = conv_14 + + if fc_type=='E': + body = mx.symbol.Dropout(data=body, p=0.4) + fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1') + fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1') + elif fc_type=='F': + body = mx.symbol.Dropout(data=body, p=0.4) + fc1 = mx.sym.FullyConnected(data=body, num_hidden=num_classes, name='pre_fc1') + fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1') + fc1 = Act(data=fc1, act_type='relu', name='fc1_relu') + else: + pool = mx.sym.Pooling(data=conv_14, global_pool=True, kernel=(7, 7), stride=(1, 1), pool_type="avg", name="global_pool") + flat = mx.sym.Flatten(data=pool, name="flatten") + if fc_type=='A': + fc1 = flat + else: + if fc_type=='G' or fc_type=='H': + fc1 = mx.symbol.Dropout(data=flat, p=0.2) + fc1 = mx.sym.FullyConnected(data=fc1, num_hidden=num_classes, name='pre_fc1') + if fc_type=='G': + return fc1 + else: + fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1') + return fc1 + else: + #B-D + #B + fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='pre_fc1') + if fc_type=='C': + fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1') + elif fc_type=='D': + fc1 = mx.sym.BatchNorm(data=fc1, fix_gamma=True, name='fc1') + fc1 = Act(data=fc1, act_type='relu', name='fc1_relu') + return fc1 + diff --git a/src/train_softmax.py b/src/train_softmax.py index 9d92d72..7371c81 100644 --- a/src/train_softmax.py +++ b/src/train_softmax.py @@ -24,7 +24,7 @@ sys.path.append(os.path.join(os.path.dirname(__file__), 'symbols')) import fresnet import finception_resnet_v2 import spherenet -import marginalnet +import fmobilenet #import inceptions #import xception #import lfw @@ -143,8 +143,10 @@ def get_symbol(args, arg_params, aux_params): if args.network[0]=='s': embedding = spherenet.get_symbol(512, args.num_layers) elif args.network[0]=='m': - print('init marginal', args.num_layers) - embedding = marginalnet.get_symbol(512, args.num_layers) + print('init mobilenet', args.num_layers) + embedding = fmobilenet.get_symbol(512, + use_se=args.use_se, version_input=args.version_input, + version_output=args.version_output, version_unit=args.version_unit) elif args.network[0]=='i': print('init inception-resnet-v2', args.num_layers) embedding = finception_resnet_v2.get_symbol(512) @@ -355,8 +357,8 @@ def train_net(args): data_shape_dict = {'data': (args.batch_size,)+data_shape, 'softmax_label': (args.batch_size,)} if args.network[0]=='s': arg_params, aux_params = spherenet.init_weights(sym, data_shape_dict, args.num_layers) - elif args.network[0]=='m': - arg_params, aux_params = marginalnet.init_weights(sym, data_shape_dict, args.num_layers) + #elif args.network[0]=='m': + # arg_params, aux_params = marginalnet.init_weights(sym, data_shape_dict, args.num_layers) #resnet_dcn.init_weights(sym, data_shape_dict, arg_params, aux_params) else: #sym, arg_params, aux_params = mx.model.load_checkpoint(pretrained, load_epoch)