#!/usr/bin/env python from paddle.trainer_config_helpers import * height=224 width=224 num_class = 1000 batch_size = get_config_arg('batch_size', int, 128) args={'height':height, 'width':width, 'color':True, 'num_class':num_class} define_py_data_sources2("train.list", None, module="provider", obj="process", args=args) settings( batch_size = batch_size, learning_rate = 0.01 / batch_size, learning_method = MomentumOptimizer(0.9), regularization = L2Regularization(0.0005 * batch_size) ) def inception2(name, input, channels, \ filter1, filter3R, filter3, filter5R, filter5, proj): conv1 = name + '_1' conv3r = name + '_3r' conv3 = name + '_3' conv5r = name + '_5r' conv5 = name + '_5' maxpool = name + '_max' convproj = name + '_proj' cov1 = img_conv_layer(name=conv1, input=input, filter_size=1, num_channels=channels, num_filters=filter1, stride=1, padding=0) cov3r = img_conv_layer(name=conv3r, input=input, filter_size=1, num_channels=channels, num_filters=filter3R, stride=1, padding=0) cov3 = img_conv_layer(name=conv3, input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1) cov5r = img_conv_layer(name=conv5r, input=input, filter_size=1, num_channels=channels, num_filters=filter5R, stride=1, padding=0) cov5 = img_conv_layer(name=conv5, input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2) pool1 = img_pool_layer(name=maxpool, input=input, pool_size=3, num_channels=channels, stride=1, padding=1) covprj = img_conv_layer(name=convproj, input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0) cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj]) return cat def inception(name, input, channels, \ filter1, filter3R, filter3, filter5R, filter5, proj): cov1 = conv_projection(input=input, filter_size=1, num_channels=channels, num_filters=filter1, stride=1, padding=0) cov3r = img_conv_layer(name=name + '_3r', input=input, filter_size=1, num_channels=channels, num_filters=filter3R, stride=1, padding=0) cov3 = conv_projection(input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1) cov5r = img_conv_layer(name=name + '_5r', input=input, filter_size=1, num_channels=channels, num_filters=filter5R, stride=1, padding=0) cov5 = conv_projection(input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2) pool1 = img_pool_layer(name=name + '_max', input=input, pool_size=3, num_channels=channels, stride=1, padding=1) covprj = conv_projection(input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0) cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj], bias_attr=True, act=ReluActivation()) return cat lab = data_layer(name="label", size=1000) data = data_layer(name="input", size=3 * height * width) # stage 1 conv1 = img_conv_layer(name="conv1", input=data, filter_size=7, num_channels=3, num_filters=64, stride=2, padding=3) pool1 = img_pool_layer(name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2) # stage 2 conv2_1 = img_conv_layer(name="conv2_1", input=pool1, filter_size=1, num_filters=64, stride=1, padding=0) conv2_2 = img_conv_layer(name="conv2_2", input=conv2_1, filter_size=3, num_filters=192, stride=1, padding=1) pool2 = img_pool_layer(name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2) # stage 3 ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32) ince3b = inception("ince3b", ince3a, 256, 128, 128,192, 32, 96, 64) pool3 = img_pool_layer(name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2) # stage 4 ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64) ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64) ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64) ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64) ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128) pool4 = img_pool_layer(name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2) # stage 5 ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128) ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128) pool5 = img_pool_layer(name="pool5", input=ince5b, num_channels=1024, pool_size=7, stride=7, pool_type=AvgPooling()) # We remove loss1 and loss2 for all system when testing benchmark # output 1 # pool_o1 = img_pool_layer(name="pool_o1", input=ince4a, num_channels=512, pool_size=5, stride=3, pool_type=AvgPooling()) # conv_o1 = img_conv_layer(name="conv_o1", input=pool_o1, filter_size=1, num_filters=128, stride=1, padding=0) # fc_o1 = fc_layer(name="fc_o1", input=conv_o1, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation()) # out1 = fc_layer(name="output1", input=fc_o1, size=1000, act=SoftmaxActivation()) # loss1 = cross_entropy(name='loss1', input=out1, label=lab, coeff=0.3) # output 2 #pool_o2 = img_pool_layer(name="pool_o2", input=ince4d, num_channels=528, pool_size=5, stride=3, pool_type=AvgPooling()) #conv_o2 = img_conv_layer(name="conv_o2", input=pool_o2, filter_size=1, num_filters=128, stride=1, padding=0) #fc_o2 = fc_layer(name="fc_o2", input=conv_o2, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation()) #out2 = fc_layer(name="output2", input=fc_o2, size=1000, act=SoftmaxActivation()) #loss2 = cross_entropy(name='loss2', input=out2, label=lab, coeff=0.3) # output 3 dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4) out3 = fc_layer(name="output3", input=dropout, size=1000, act=SoftmaxActivation()) loss3 = cross_entropy(name='loss3', input=out3, label=lab) outputs(loss3)