Transfer learning on TensorFlow-Slim image classification model library
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.## Licensed 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.# =============================================================================="""Mobilenet V3 conv defs and helper functions.# pylint: disable=line-too-longModel definitions and layer breakdowns:====================== V3 LARGE ====================== Conv2D MobilenetV3/Conv/Conv2D 351.2 k 1x224x224x3 432.0 5.42 M 1x112x112x16 Relu6 MobilenetV3/Conv/hard_swish/Relu6 ? - ? ? 1x112x112x16 DepthConv MobilenetV3/expanded_conv/depthwise/depthwise 401.4 k - 144.0 1.81 M 1x112x112x16 Relu MobilenetV3/expanded_conv/depthwise/Relu ? - ? ? 1x112x112x16 Conv2D MobilenetV3/expanded_conv/project/Conv2D 401.4 k 1x112x112x16 256.0 3.21 M 1x112x112x16 Conv2D MobilenetV3/expanded_conv_1/expand/Conv2D 1.00 M 1x112x112x16 1.02 k 12.8 M 1x112x112x64 Relu MobilenetV3/expanded_conv_1/expand/Relu ? - ? ? 1x112x112x64 DepthConv MobilenetV3/expanded_conv_1/depthwise/depthwise 1.00 M - 576.0 1.81 M 1x56x56x64 Relu MobilenetV3/expanded_conv_1/depthwise/Relu ? - ? ? 1x56x56x64 Conv2D MobilenetV3/expanded_conv_1/project/Conv2D 276.0 k 1x56x56x64 1.54 k 4.82 M 1x56x56x24 Conv2D MobilenetV3/expanded_conv_2/expand/Conv2D 301.1 k 1x56x56x24 1.73 k 5.42 M 1x56x56x72 Relu MobilenetV3/expanded_conv_2/expand/Relu ? - ? ? 1x56x56x72 DepthConv MobilenetV3/expanded_conv_2/depthwise/depthwise 451.6 k - 648.0 2.03 M 1x56x56x72 Relu MobilenetV3/expanded_conv_2/depthwise/Relu ? - ? ? 1x56x56x72 Conv2D MobilenetV3/expanded_conv_2/project/Conv2D 301.1 k 1x56x56x72 1.73 k 5.42 M 1x56x56x24 Conv2D MobilenetV3/expanded_conv_3/expand/Conv2D 301.1 k 1x56x56x24 1.73 k 5.42 M 1x56x56x72 Relu MobilenetV3/expanded_conv_3/expand/Relu ? - ? ? 1x56x56x72 DepthConv MobilenetV3/expanded_conv_3/depthwise/depthwise 282.2 k - 1.80 k 1.41 M 1x28x28x72 Relu MobilenetV3/expanded_conv_3/depthwise/Relu ? - ? ? 1x28x28x72 Conv2D MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Conv2D 96.0 1x1x1x72 1.73 k 1.73 k 1x1x1x24 Relu MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x24 Conv2D MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Conv2D 96.0 1x1x1x24 1.73 k 1.73 k 1x1x1x72 Relu6 MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x72 Conv2D MobilenetV3/expanded_conv_3/project/Conv2D 87.8 k 1x28x28x72 2.88 k 2.26 M 1x28x28x40 Conv2D MobilenetV3/expanded_conv_4/expand/Conv2D 125.4 k 1x28x28x40 4.80 k 3.76 M 1x28x28x120 Relu MobilenetV3/expanded_conv_4/expand/Relu ? - ? ? 1x28x28x120 DepthConv MobilenetV3/expanded_conv_4/depthwise/depthwise 188.2 k - 3.00 k 2.35 M 1x28x28x120 Relu MobilenetV3/expanded_conv_4/depthwise/Relu ? - ? ? 1x28x28x120 Conv2D MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Conv2D 152.0 1x1x1x120 3.84 k 3.84 k 1x1x1x32 Relu MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x32 Conv2D MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Conv2D 152.0 1x1x1x32 3.84 k 3.84 k 1x1x1x120 Relu6 MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x120 Conv2D MobilenetV3/expanded_conv_4/project/Conv2D 125.4 k 1x28x28x120 4.80 k 3.76 M 1x28x28x40 Conv2D MobilenetV3/expanded_conv_5/expand/Conv2D 125.4 k 1x28x28x40 4.80 k 3.76 M 1x28x28x120 Relu MobilenetV3/expanded_conv_5/expand/Relu ? - ? ? 1x28x28x120 DepthConv MobilenetV3/expanded_conv_5/depthwise/depthwise 188.2 k - 3.00 k 2.35 M 1x28x28x120 Relu MobilenetV3/expanded_conv_5/depthwise/Relu ? - ? ? 1x28x28x120 Conv2D MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Conv2D 152.0 1x1x1x120 3.84 k 3.84 k 1x1x1x32 Relu MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x32 Conv2D MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Conv2D 152.0 1x1x1x32 3.84 k 3.84 k 1x1x1x120 Relu6 MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x120 Conv2D MobilenetV3/expanded_conv_5/project/Conv2D 125.4 k 1x28x28x120 4.80 k 3.76 M 1x28x28x40 Conv2D MobilenetV3/expanded_conv_6/expand/Conv2D 219.5 k 1x28x28x40 9.60 k 7.53 M 1x28x28x240 Relu6 MobilenetV3/expanded_conv_6/expand/hard_swish/Relu6 ? - ? ? 1x28x28x240 DepthConv MobilenetV3/expanded_conv_6/depthwise/depthwise 235.2 k - 2.16 k 423.4 k 1x14x14x240 Relu6 MobilenetV3/expanded_conv_6/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x240 Conv2D MobilenetV3/expanded_conv_6/project/Conv2D 62.7 k 1x14x14x240 19.2 k 3.76 M 1x14x14x80 Conv2D MobilenetV3/expanded_conv_7/expand/Conv2D 54.9 k 1x14x14x80 16.0 k 3.14 M 1x14x14x200 Relu6 MobilenetV3/expanded_conv_7/expand/hard_swish/Relu6 ? - ? ? 1x14x14x200 DepthConv MobilenetV3/expanded_conv_7/depthwise/depthwise 78.4 k - 1.80 k 352.8 k 1x14x14x200 Relu6 MobilenetV3/expanded_conv_7/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x200 Conv2D MobilenetV3/expanded_conv_7/project/Conv2D 54.9 k 1x14x14x200 16.0 k 3.14 M 1x14x14x80 Conv2D MobilenetV3/expanded_conv_8/expand/Conv2D 51.7 k 1x14x14x80 14.7 k 2.89 M 1x14x14x184 Relu6 MobilenetV3/expanded_conv_8/expand/hard_swish/Relu6 ? - ? ? 1x14x14x184 DepthConv MobilenetV3/expanded_conv_8/depthwise/depthwise 72.1 k - 1.66 k 324.6 k 1x14x14x184 Relu6 MobilenetV3/expanded_conv_8/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x184 Conv2D MobilenetV3/expanded_conv_8/project/Conv2D 51.7 k 1x14x14x184 14.7 k 2.89 M 1x14x14x80 Conv2D MobilenetV3/expanded_conv_9/expand/Conv2D 51.7 k 1x14x14x80 14.7 k 2.89 M 1x14x14x184 Relu6 MobilenetV3/expanded_conv_9/expand/hard_swish/Relu6 ? - ? ? 1x14x14x184 DepthConv MobilenetV3/expanded_conv_9/depthwise/depthwise 72.1 k - 1.66 k 324.6 k 1x14x14x184 Relu6 MobilenetV3/expanded_conv_9/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x184 Conv2D MobilenetV3/expanded_conv_9/project/Conv2D 51.7 k 1x14x14x184 14.7 k 2.89 M 1x14x14x80 Conv2D MobilenetV3/expanded_conv_10/expand/Conv2D 109.8 k 1x14x14x80 38.4 k 7.53 M 1x14x14x480 Relu6 MobilenetV3/expanded_conv_10/expand/hard_swish/Relu6 ? - ? ? 1x14x14x480 DepthConv MobilenetV3/expanded_conv_10/depthwise/depthwise 188.2 k - 4.32 k 846.7 k 1x14x14x480 Relu6 MobilenetV3/expanded_conv_10/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x480 Conv2D MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Conv2D 600.0 1x1x1x480 57.6 k 57.6 k 1x1x1x120 Relu MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x120 Conv2D MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Conv2D 600.0 1x1x1x120 57.6 k 57.6 k 1x1x1x480 Relu6 MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x480 Conv2D MobilenetV3/expanded_conv_10/project/Conv2D 116.0 k 1x14x14x480 53.8 k 10.5 M 1x14x14x112 Conv2D MobilenetV3/expanded_conv_11/expand/Conv2D 153.7 k 1x14x14x112 75.3 k 14.8 M 1x14x14x672 Relu6 MobilenetV3/expanded_conv_11/expand/hard_swish/Relu6 ? - ? ? 1x14x14x672 DepthConv MobilenetV3/expanded_conv_11/depthwise/depthwise 263.4 k - 6.05 k 1.19 M 1x14x14x672 Relu6 MobilenetV3/expanded_conv_11/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x672 Conv2D MobilenetV3/expanded_conv_11/squeeze_excite/Conv/Conv2D 840.0 1x1x1x672 112.9 k 112.9 k 1x1x1x168 Relu MobilenetV3/expanded_conv_11/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x168 Conv2D MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/Conv2D 840.0 1x1x1x168 112.9 k 112.9 k 1x1x1x672 Relu6 MobilenetV3/expanded_conv_11/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x672 Conv2D MobilenetV3/expanded_conv_11/project/Conv2D 153.7 k 1x14x14x672 75.3 k 14.8 M 1x14x14x112 Conv2D MobilenetV3/expanded_conv_12/expand/Conv2D 153.7 k 1x14x14x112 75.3 k 14.8 M 1x14x14x672 Relu6 MobilenetV3/expanded_conv_12/expand/hard_swish/Relu6 ? - ? ? 1x14x14x672 DepthConv MobilenetV3/expanded_conv_12/depthwise/depthwise 164.6 k - 16.8 k 823.2 k 1x7x7x672 Relu6 MobilenetV3/expanded_conv_12/depthwise/hard_swish/Relu6 ? - ? ? 1x7x7x672 Conv2D MobilenetV3/expanded_conv_12/squeeze_excite/Conv/Conv2D 840.0 1x1x1x672 112.9 k 112.9 k 1x1x1x168 Relu MobilenetV3/expanded_conv_12/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x168 Conv2D MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/Conv2D 840.0 1x1x1x168 112.9 k 112.9 k 1x1x1x672 Relu6 MobilenetV3/expanded_conv_12/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x672 Conv2D MobilenetV3/expanded_conv_12/project/Conv2D 40.8 k 1x7x7x672 107.5 k 5.27 M 1x7x7x160 Conv2D MobilenetV3/expanded_conv_13/expand/Conv2D 54.9 k 1x7x7x160 153.6 k 7.53 M 1x7x7x960 Relu6 MobilenetV3/expanded_conv_13/expand/hard_swish/Relu6 ? - ? ? 1x7x7x960 DepthConv MobilenetV3/expanded_conv_13/depthwise/depthwise 94.1 k - 24.0 k 1.18 M 1x7x7x960 Relu6 MobilenetV3/expanded_conv_13/depthwise/hard_swish/Relu6 ? - ? ? 1x7x7x960 Conv2D MobilenetV3/expanded_conv_13/squeeze_excite/Conv/Conv2D 1.20 k 1x1x1x960 230.4 k 230.4 k 1x1x1x240 Relu MobilenetV3/expanded_conv_13/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x240 Conv2D MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/Conv2D 1.20 k 1x1x1x240 230.4 k 230.4 k 1x1x1x960 Relu6 MobilenetV3/expanded_conv_13/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x960 Conv2D MobilenetV3/expanded_conv_13/project/Conv2D 54.9 k 1x7x7x960 153.6 k 7.53 M 1x7x7x160 Conv2D MobilenetV3/expanded_conv_14/expand/Conv2D 54.9 k 1x7x7x160 153.6 k 7.53 M 1x7x7x960 Relu6 MobilenetV3/expanded_conv_14/expand/hard_swish/Relu6 ? - ? ? 1x7x7x960 DepthConv MobilenetV3/expanded_conv_14/depthwise/depthwise 94.1 k - 24.0 k 1.18 M 1x7x7x960 Relu6 MobilenetV3/expanded_conv_14/depthwise/hard_swish/Relu6 ? - ? ? 1x7x7x960 Conv2D MobilenetV3/expanded_conv_14/squeeze_excite/Conv/Conv2D 1.20 k 1x1x1x960 230.4 k 230.4 k 1x1x1x240 Relu MobilenetV3/expanded_conv_14/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x240 Conv2D MobilenetV3/expanded_conv_14/squeeze_excite/Conv_1/Conv2D 1.20 k 1x1x1x240 230.4 k 230.4 k 1x1x1x960 Relu6 MobilenetV3/expanded_conv_14/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x960 Conv2D MobilenetV3/expanded_conv_14/project/Conv2D 54.9 k 1x7x7x960 153.6 k 7.53 M 1x7x7x160 Conv2D MobilenetV3/Conv_1/Conv2D 54.9 k 1x7x7x160 153.6 k 7.53 M 1x7x7x960 Relu6 MobilenetV3/Conv_1/hard_swish/Relu6 ? - ? ? 1x7x7x960 AvgPool MobilenetV3/AvgPool2D/AvgPool ? 1x7x7x960 ? 47.0 k 1x1x1x960 Conv2D MobilenetV3/Conv_2/Conv2D 2.24 k 1x1x1x960 1.23 M 1.23 M 1x1x1x1280 Relu6 MobilenetV3/Conv_2/hard_swish/Relu6 ? - ? ? 1x1x1x1280 Conv2D MobilenetV3/Logits/Conv2d_1c_1x1/Conv2D 2.28 k 1x1x1x1280 1.28 M 1.28 M 1x1x1x1001-----====================== V3 SMALL ====================== op name ActMem ConvInput ConvParameters Madds OutputTensor Conv2D MobilenetV3/Conv/Conv2D 351.2 k 1x224x224x3 432.0 5.42 M 1x112x112x16 Relu6 MobilenetV3/Conv/hard_swish/Relu6 ? - ? ? 1x112x112x16 DepthConv MobilenetV3/expanded_conv/depthwise/depthwise 250.9 k - 144.0 451.6 k 1x56x56x16 Relu MobilenetV3/expanded_conv/depthwise/Relu ? - ? ? 1x56x56x16 Conv2D MobilenetV3/expanded_conv/squeeze_excite/Conv/Conv2D 24.0 1x1x1x16 128.0 128.0 1x1x1x8 Relu MobilenetV3/expanded_conv/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x8 Conv2D MobilenetV3/expanded_conv/squeeze_excite/Conv_1/Conv2D 24.0 1x1x1x8 128.0 128.0 1x1x1x16 Relu6 MobilenetV3/expanded_conv/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x16 Conv2D MobilenetV3/expanded_conv/project/Conv2D 100.4 k 1x56x56x16 256.0 802.8 k 1x56x56x16 Conv2D MobilenetV3/expanded_conv_1/expand/Conv2D 276.0 k 1x56x56x16 1.15 k 3.61 M 1x56x56x72 Relu MobilenetV3/expanded_conv_1/expand/Relu ? - ? ? 1x56x56x72 DepthConv MobilenetV3/expanded_conv_1/depthwise/depthwise 282.2 k - 648.0 508.0 k 1x28x28x72 Relu MobilenetV3/expanded_conv_1/depthwise/Relu ? - ? ? 1x28x28x72 Conv2D MobilenetV3/expanded_conv_1/project/Conv2D 75.3 k 1x28x28x72 1.73 k 1.35 M 1x28x28x24 Conv2D MobilenetV3/expanded_conv_2/expand/Conv2D 87.8 k 1x28x28x24 2.11 k 1.66 M 1x28x28x88 Relu MobilenetV3/expanded_conv_2/expand/Relu ? - ? ? 1x28x28x88 DepthConv MobilenetV3/expanded_conv_2/depthwise/depthwise 138.0 k - 792.0 620.9 k 1x28x28x88 Relu MobilenetV3/expanded_conv_2/depthwise/Relu ? - ? ? 1x28x28x88 Conv2D MobilenetV3/expanded_conv_2/project/Conv2D 87.8 k 1x28x28x88 2.11 k 1.66 M 1x28x28x24 Conv2D MobilenetV3/expanded_conv_3/expand/Conv2D 94.1 k 1x28x28x24 2.30 k 1.81 M 1x28x28x96 Relu6 MobilenetV3/expanded_conv_3/expand/hard_swish/Relu6 ? - ? ? 1x28x28x96 DepthConv MobilenetV3/expanded_conv_3/depthwise/depthwise 94.1 k - 2.40 k 470.4 k 1x14x14x96 Relu6 MobilenetV3/expanded_conv_3/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x96 Conv2D MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Conv2D 120.0 1x1x1x96 2.30 k 2.30 k 1x1x1x24 Relu MobilenetV3/expanded_conv_3/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x24 Conv2D MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Conv2D 120.0 1x1x1x24 2.30 k 2.30 k 1x1x1x96 Relu6 MobilenetV3/expanded_conv_3/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x96 Conv2D MobilenetV3/expanded_conv_3/project/Conv2D 26.7 k 1x14x14x96 3.84 k 752.6 k 1x14x14x40 Conv2D MobilenetV3/expanded_conv_4/expand/Conv2D 54.9 k 1x14x14x40 9.60 k 1.88 M 1x14x14x240 Relu6 MobilenetV3/expanded_conv_4/expand/hard_swish/Relu6 ? - ? ? 1x14x14x240 DepthConv MobilenetV3/expanded_conv_4/depthwise/depthwise 94.1 k - 6.00 k 1.18 M 1x14x14x240 Relu6 MobilenetV3/expanded_conv_4/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x240 Conv2D MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Conv2D 304.0 1x1x1x240 15.4 k 15.4 k 1x1x1x64 Relu MobilenetV3/expanded_conv_4/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x64 Conv2D MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Conv2D 304.0 1x1x1x64 15.4 k 15.4 k 1x1x1x240 Relu6 MobilenetV3/expanded_conv_4/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x240 Conv2D MobilenetV3/expanded_conv_4/project/Conv2D 54.9 k 1x14x14x240 9.60 k 1.88 M 1x14x14x40 Conv2D MobilenetV3/expanded_conv_5/expand/Conv2D 54.9 k 1x14x14x40 9.60 k 1.88 M 1x14x14x240 Relu6 MobilenetV3/expanded_conv_5/expand/hard_swish/Relu6 ? - ? ? 1x14x14x240 DepthConv MobilenetV3/expanded_conv_5/depthwise/depthwise 94.1 k - 6.00 k 1.18 M 1x14x14x240 Relu6 MobilenetV3/expanded_conv_5/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x240 Conv2D MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Conv2D 304.0 1x1x1x240 15.4 k 15.4 k 1x1x1x64 Relu MobilenetV3/expanded_conv_5/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x64 Conv2D MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Conv2D 304.0 1x1x1x64 15.4 k 15.4 k 1x1x1x240 Relu6 MobilenetV3/expanded_conv_5/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x240 Conv2D MobilenetV3/expanded_conv_5/project/Conv2D 54.9 k 1x14x14x240 9.60 k 1.88 M 1x14x14x40 Conv2D MobilenetV3/expanded_conv_6/expand/Conv2D 31.4 k 1x14x14x40 4.80 k 940.8 k 1x14x14x120 Relu6 MobilenetV3/expanded_conv_6/expand/hard_swish/Relu6 ? - ? ? 1x14x14x120 DepthConv MobilenetV3/expanded_conv_6/depthwise/depthwise 47.0 k - 3.00 k 588.0 k 1x14x14x120 Relu6 MobilenetV3/expanded_conv_6/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x120 Conv2D MobilenetV3/expanded_conv_6/squeeze_excite/Conv/Conv2D 152.0 1x1x1x120 3.84 k 3.84 k 1x1x1x32 Relu MobilenetV3/expanded_conv_6/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x32 Conv2D MobilenetV3/expanded_conv_6/squeeze_excite/Conv_1/Conv2D 152.0 1x1x1x32 3.84 k 3.84 k 1x1x1x120 Relu6 MobilenetV3/expanded_conv_6/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x120 Conv2D MobilenetV3/expanded_conv_6/project/Conv2D 32.9 k 1x14x14x120 5.76 k 1.13 M 1x14x14x48 Conv2D MobilenetV3/expanded_conv_7/expand/Conv2D 37.6 k 1x14x14x48 6.91 k 1.35 M 1x14x14x144 Relu6 MobilenetV3/expanded_conv_7/expand/hard_swish/Relu6 ? - ? ? 1x14x14x144 DepthConv MobilenetV3/expanded_conv_7/depthwise/depthwise 56.4 k - 3.60 k 705.6 k 1x14x14x144 Relu6 MobilenetV3/expanded_conv_7/depthwise/hard_swish/Relu6 ? - ? ? 1x14x14x144 Conv2D MobilenetV3/expanded_conv_7/squeeze_excite/Conv/Conv2D 184.0 1x1x1x144 5.76 k 5.76 k 1x1x1x40 Relu MobilenetV3/expanded_conv_7/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x40 Conv2D MobilenetV3/expanded_conv_7/squeeze_excite/Conv_1/Conv2D 184.0 1x1x1x40 5.76 k 5.76 k 1x1x1x144 Relu6 MobilenetV3/expanded_conv_7/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x144 Conv2D MobilenetV3/expanded_conv_7/project/Conv2D 37.6 k 1x14x14x144 6.91 k 1.35 M 1x14x14x48 Conv2D MobilenetV3/expanded_conv_8/expand/Conv2D 65.9 k 1x14x14x48 13.8 k 2.71 M 1x14x14x288 Relu6 MobilenetV3/expanded_conv_8/expand/hard_swish/Relu6 ? - ? ? 1x14x14x288 DepthConv MobilenetV3/expanded_conv_8/depthwise/depthwise 70.6 k - 7.20 k 352.8 k 1x7x7x288 Relu6 MobilenetV3/expanded_conv_8/depthwise/hard_swish/Relu6 ? - ? ? 1x7x7x288 Conv2D MobilenetV3/expanded_conv_8/squeeze_excite/Conv/Conv2D 360.0 1x1x1x288 20.7 k 20.7 k 1x1x1x72 Relu MobilenetV3/expanded_conv_8/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x72 Conv2D MobilenetV3/expanded_conv_8/squeeze_excite/Conv_1/Conv2D 360.0 1x1x1x72 20.7 k 20.7 k 1x1x1x288 Relu6 MobilenetV3/expanded_conv_8/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x288 Conv2D MobilenetV3/expanded_conv_8/project/Conv2D 18.8 k 1x7x7x288 27.6 k 1.35 M 1x7x7x96 Conv2D MobilenetV3/expanded_conv_9/expand/Conv2D 32.9 k 1x7x7x96 55.3 k 2.71 M 1x7x7x576 Relu6 MobilenetV3/expanded_conv_9/expand/hard_swish/Relu6 ? - ? ? 1x7x7x576 DepthConv MobilenetV3/expanded_conv_9/depthwise/depthwise 56.4 k - 14.4 k 705.6 k 1x7x7x576 Relu6 MobilenetV3/expanded_conv_9/depthwise/hard_swish/Relu6 ? - ? ? 1x7x7x576 Conv2D MobilenetV3/expanded_conv_9/squeeze_excite/Conv/Conv2D 720.0 1x1x1x576 82.9 k 82.9 k 1x1x1x144 Relu MobilenetV3/expanded_conv_9/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x144 Conv2D MobilenetV3/expanded_conv_9/squeeze_excite/Conv_1/Conv2D 720.0 1x1x1x144 82.9 k 82.9 k 1x1x1x576 Relu6 MobilenetV3/expanded_conv_9/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x576 Conv2D MobilenetV3/expanded_conv_9/project/Conv2D 32.9 k 1x7x7x576 55.3 k 2.71 M 1x7x7x96 Conv2D MobilenetV3/expanded_conv_10/expand/Conv2D 32.9 k 1x7x7x96 55.3 k 2.71 M 1x7x7x576 Relu6 MobilenetV3/expanded_conv_10/expand/hard_swish/Relu6 ? - ? ? 1x7x7x576 DepthConv MobilenetV3/expanded_conv_10/depthwise/depthwise 56.4 k - 14.4 k 705.6 k 1x7x7x576 Relu6 MobilenetV3/expanded_conv_10/depthwise/hard_swish/Relu6 ? - ? ? 1x7x7x576 Conv2D MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Conv2D 720.0 1x1x1x576 82.9 k 82.9 k 1x1x1x144 Relu MobilenetV3/expanded_conv_10/squeeze_excite/Conv/Relu ? - ? ? 1x1x1x144 Conv2D MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Conv2D 720.0 1x1x1x144 82.9 k 82.9 k 1x1x1x576 Relu6 MobilenetV3/expanded_conv_10/squeeze_excite/Conv_1/Relu6 ? - ? ? 1x1x1x576 Conv2D MobilenetV3/expanded_conv_10/project/Conv2D 32.9 k 1x7x7x576 55.3 k 2.71 M 1x7x7x96 Conv2D MobilenetV3/Conv_1/Conv2D 32.9 k 1x7x7x96 55.3 k 2.71 M 1x7x7x576 Relu6 MobilenetV3/Conv_1/hard_swish/Relu6 ? - ? ? 1x7x7x576 AvgPool MobilenetV3/AvgPool2D/AvgPool ? 1x7x7x576 ? 28.2 k 1x1x1x576 Conv2D MobilenetV3/Conv_2/Conv2D 1.60 k 1x1x1x576 589.8 k 589.8 k 1x1x1x1024 Relu6 MobilenetV3/Conv_2/hard_swish/Relu6 ? - ? ? 1x1x1x1024 Conv2D MobilenetV3/Logits/Conv2d_1c_1x1/Conv2D 2.02 k 1x1x1x1024 1.03 M 1.03 M 1x1x1x1001----- Total Total 2.96 M - 2.53 M 56.5 M -======================== V3 EDGETPU ======================== op name ActMem ConvInput ConvParameters Madds OutputTensor Conv2D MobilenetEdgeTPU/Conv/Conv2D 551.9 k 1x224x224x3 864.0 10.8 M 1x112x112x32 Relu MobilenetEdgeTPU/Conv/Relu ? - ? ? 1x112x112x32 Conv2D MobilenetEdgeTPU/expanded_conv/project/Conv2D 602.1 k 1x112x112x32 512.0 6.42 M 1x112x112x16 Conv2D MobilenetEdgeTPU/expanded_conv_1/expand/Conv2D 602.1 k 1x112x112x16 18.4 k 57.8 M 1x56x56x128 Relu MobilenetEdgeTPU/expanded_conv_1/expand/Relu ? - ? ? 1x56x56x128 Conv2D MobilenetEdgeTPU/expanded_conv_1/project/Conv2D 501.8 k 1x56x56x128 4.10 k 12.8 M 1x56x56x32 Conv2D MobilenetEdgeTPU/expanded_conv_2/expand/Conv2D 501.8 k 1x56x56x32 36.9 k 115.6 M 1x56x56x128 Relu MobilenetEdgeTPU/expanded_conv_2/expand/Relu ? - ? ? 1x56x56x128 Conv2D MobilenetEdgeTPU/expanded_conv_2/project/Conv2D 501.8 k 1x56x56x128 4.10 k 12.8 M 1x56x56x32 Conv2D MobilenetEdgeTPU/expanded_conv_3/expand/Conv2D 501.8 k 1x56x56x32 36.9 k 115.6 M 1x56x56x128 Relu MobilenetEdgeTPU/expanded_conv_3/expand/Relu ? - ? ? 1x56x56x128 Conv2D MobilenetEdgeTPU/expanded_conv_3/project/Conv2D 501.8 k 1x56x56x128 4.10 k 12.8 M 1x56x56x32 Conv2D MobilenetEdgeTPU/expanded_conv_4/expand/Conv2D 501.8 k 1x56x56x32 36.9 k 115.6 M 1x56x56x128 Relu MobilenetEdgeTPU/expanded_conv_4/expand/Relu ? - ? ? 1x56x56x128 Conv2D MobilenetEdgeTPU/expanded_conv_4/project/Conv2D 501.8 k 1x56x56x128 4.10 k 12.8 M 1x56x56x32 Conv2D MobilenetEdgeTPU/expanded_conv_5/expand/Conv2D 301.1 k 1x56x56x32 73.7 k 57.8 M 1x28x28x256 Relu MobilenetEdgeTPU/expanded_conv_5/expand/Relu ? - ? ? 1x28x28x256 Conv2D MobilenetEdgeTPU/expanded_conv_5/project/Conv2D 238.3 k 1x28x28x256 12.3 k 9.63 M 1x28x28x48 Conv2D MobilenetEdgeTPU/expanded_conv_6/expand/Conv2D 188.2 k 1x28x28x48 82.9 k 65.0 M 1x28x28x192 Relu MobilenetEdgeTPU/expanded_conv_6/expand/Relu ? - ? ? 1x28x28x192 Conv2D MobilenetEdgeTPU/expanded_conv_6/project/Conv2D 188.2 k 1x28x28x192 9.22 k 7.23 M 1x28x28x48 Conv2D MobilenetEdgeTPU/expanded_conv_7/expand/Conv2D 188.2 k 1x28x28x48 82.9 k 65.0 M 1x28x28x192 Relu MobilenetEdgeTPU/expanded_conv_7/expand/Relu ? - ? ? 1x28x28x192 Conv2D MobilenetEdgeTPU/expanded_conv_7/project/Conv2D 188.2 k 1x28x28x192 9.22 k 7.23 M 1x28x28x48 Conv2D MobilenetEdgeTPU/expanded_conv_8/expand/Conv2D 188.2 k 1x28x28x48 82.9 k 65.0 M 1x28x28x192 Relu MobilenetEdgeTPU/expanded_conv_8/expand/Relu ? - ? ? 1x28x28x192 Conv2D MobilenetEdgeTPU/expanded_conv_8/project/Conv2D 188.2 k 1x28x28x192 9.22 k 7.23 M 1x28x28x48 Conv2D MobilenetEdgeTPU/expanded_conv_9/expand/Conv2D 338.7 k 1x28x28x48 18.4 k 14.5 M 1x28x28x384 Relu MobilenetEdgeTPU/expanded_conv_9/expand/Relu ? - ? ? 1x28x28x384 DepthConv MobilenetEdgeTPU/expanded_conv_9/depthwise/depthwise 376.3 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_9/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_9/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_10/expand/Conv2D 94.1 k 1x14x14x96 36.9 k 7.23 M 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_10/expand/Relu ? - ? ? 1x14x14x384 DepthConv MobilenetEdgeTPU/expanded_conv_10/depthwise/depthwise 150.5 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_10/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_10/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_11/expand/Conv2D 94.1 k 1x14x14x96 36.9 k 7.23 M 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_11/expand/Relu ? - ? ? 1x14x14x384 DepthConv MobilenetEdgeTPU/expanded_conv_11/depthwise/depthwise 150.5 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_11/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_11/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_12/expand/Conv2D 94.1 k 1x14x14x96 36.9 k 7.23 M 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_12/expand/Relu ? - ? ? 1x14x14x384 DepthConv MobilenetEdgeTPU/expanded_conv_12/depthwise/depthwise 150.5 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_12/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_12/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_13/expand/Conv2D 169.3 k 1x14x14x96 73.7 k 14.5 M 1x14x14x768 Relu MobilenetEdgeTPU/expanded_conv_13/expand/Relu ? - ? ? 1x14x14x768 DepthConv MobilenetEdgeTPU/expanded_conv_13/depthwise/depthwise 301.1 k - 6.91 k 1.35 M 1x14x14x768 Relu MobilenetEdgeTPU/expanded_conv_13/depthwise/Relu ? - ? ? 1x14x14x768 Conv2D MobilenetEdgeTPU/expanded_conv_13/project/Conv2D 169.3 k 1x14x14x768 73.7 k 14.5 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_14/expand/Conv2D 94.1 k 1x14x14x96 36.9 k 7.23 M 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_14/expand/Relu ? - ? ? 1x14x14x384 DepthConv MobilenetEdgeTPU/expanded_conv_14/depthwise/depthwise 150.5 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_14/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_14/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_15/expand/Conv2D 94.1 k 1x14x14x96 36.9 k 7.23 M 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_15/expand/Relu ? - ? ? 1x14x14x384 DepthConv MobilenetEdgeTPU/expanded_conv_15/depthwise/depthwise 150.5 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_15/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_15/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_16/expand/Conv2D 94.1 k 1x14x14x96 36.9 k 7.23 M 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_16/expand/Relu ? - ? ? 1x14x14x384 DepthConv MobilenetEdgeTPU/expanded_conv_16/depthwise/depthwise 150.5 k - 3.46 k 677.4 k 1x14x14x384 Relu MobilenetEdgeTPU/expanded_conv_16/depthwise/Relu ? - ? ? 1x14x14x384 Conv2D MobilenetEdgeTPU/expanded_conv_16/project/Conv2D 94.1 k 1x14x14x384 36.9 k 7.23 M 1x14x14x96 Conv2D MobilenetEdgeTPU/expanded_conv_17/expand/Conv2D 169.3 k 1x14x14x96 73.7 k 14.5 M 1x14x14x768 Relu MobilenetEdgeTPU/expanded_conv_17/expand/Relu ? - ? ? 1x14x14x768 DepthConv MobilenetEdgeTPU/expanded_conv_17/depthwise/depthwise 188.2 k - 19.2 k 940.8 k 1x7x7x768 Relu MobilenetEdgeTPU/expanded_conv_17/depthwise/Relu ? - ? ? 1x7x7x768 Conv2D MobilenetEdgeTPU/expanded_conv_17/project/Conv2D 45.5 k 1x7x7x768 122.9 k 6.02 M 1x7x7x160 Conv2D MobilenetEdgeTPU/expanded_conv_18/expand/Conv2D 39.2 k 1x7x7x160 102.4 k 5.02 M 1x7x7x640 Relu MobilenetEdgeTPU/expanded_conv_18/expand/Relu ? - ? ? 1x7x7x640 DepthConv MobilenetEdgeTPU/expanded_conv_18/depthwise/depthwise 62.7 k - 16.0 k 784.0 k 1x7x7x640 Relu MobilenetEdgeTPU/expanded_conv_18/depthwise/Relu ? - ? ? 1x7x7x640 Conv2D MobilenetEdgeTPU/expanded_conv_18/project/Conv2D 39.2 k 1x7x7x640 102.4 k 5.02 M 1x7x7x160 Conv2D MobilenetEdgeTPU/expanded_conv_19/expand/Conv2D 39.2 k 1x7x7x160 102.4 k 5.02 M 1x7x7x640 Relu MobilenetEdgeTPU/expanded_conv_19/expand/Relu ? - ? ? 1x7x7x640 DepthConv MobilenetEdgeTPU/expanded_conv_19/depthwise/depthwise 62.7 k - 16.0 k 784.0 k 1x7x7x640 Relu MobilenetEdgeTPU/expanded_conv_19/depthwise/Relu ? - ? ? 1x7x7x640 Conv2D MobilenetEdgeTPU/expanded_conv_19/project/Conv2D 39.2 k 1x7x7x640 102.4 k 5.02 M 1x7x7x160 Conv2D MobilenetEdgeTPU/expanded_conv_20/expand/Conv2D 39.2 k 1x7x7x160 102.4 k 5.02 M 1x7x7x640 Relu MobilenetEdgeTPU/expanded_conv_20/expand/Relu ? - ? ? 1x7x7x640 DepthConv MobilenetEdgeTPU/expanded_conv_20/depthwise/depthwise 62.7 k - 16.0 k 784.0 k 1x7x7x640 Relu MobilenetEdgeTPU/expanded_conv_20/depthwise/Relu ? - ? ? 1x7x7x640 Conv2D MobilenetEdgeTPU/expanded_conv_20/project/Conv2D 39.2 k 1x7x7x640 102.4 k 5.02 M 1x7x7x160 Conv2D MobilenetEdgeTPU/expanded_conv_21/expand/Conv2D 70.6 k 1x7x7x160 204.8 k 10.0 M 1x7x7x1280 Relu MobilenetEdgeTPU/expanded_conv_21/expand/Relu ? - ? ? 1x7x7x1280 DepthConv MobilenetEdgeTPU/expanded_conv_21/depthwise/depthwise 125.4 k - 11.5 k 564.5 k 1x7x7x1280 Relu MobilenetEdgeTPU/expanded_conv_21/depthwise/Relu ? - ? ? 1x7x7x1280 Conv2D MobilenetEdgeTPU/expanded_conv_21/project/Conv2D 72.1 k 1x7x7x1280 245.8 k 12.0 M 1x7x7x192 Conv2D MobilenetEdgeTPU/Conv_1/Conv2D 72.1 k 1x7x7x192 245.8 k 12.0 M 1x7x7x1280 Relu MobilenetEdgeTPU/Conv_1/Relu ? - ? ? 1x7x7x1280 AvgPool MobilenetEdgeTPU/Logits/AvgPool2D ? 1x7x7x1280 ? 62.7 k 1x1x1x1280 Conv2D MobilenetEdgeTPU/Logits/Conv2d_1c_1x1/Conv2D 2.28 k 1x1x1x1280 1.28 M 1.28 M 1x1x1x1001----- Total Total 11.6 M - 4.05 M 990.7 M -# pylint: enable=line-too-long"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionimport copyimport functoolsimport numpy as npimport tensorflow.compat.v1 as tfimport tf_slim as slimfrom nets.mobilenet import conv_blocks as opsfrom nets.mobilenet import mobilenet as libop = lib.opexpand_input = ops.expand_input_by_factor# Squeeze Excite with all parameters filled-in, we use hard-sigmoid# for gating function and relu for inner activation function.squeeze_excite = functools.partial( ops.squeeze_excite, squeeze_factor=4, inner_activation_fn=tf.nn.relu, gating_fn=lambda x: tf.nn.relu6(x+3)*0.16667)# Wrap squeeze excite op as expansion_transform that takes# both expansion and input tensor._se4 = lambda expansion_tensor, input_tensor: squeeze_excite(expansion_tensor)def hard_swish(x): with tf.name_scope('hard_swish'): return x * tf.nn.relu6(x + np.float32(3)) * np.float32(1. / 6.)def reduce_to_1x1(input_tensor, default_size=7, **kwargs): h, w = input_tensor.shape.as_list()[1:3] if h is not None and w == h: k = [h, h] else: k = [default_size, default_size] return slim.avg_pool2d(input_tensor, kernel_size=k, **kwargs)def mbv3_op(ef, n, k, s=1, act=tf.nn.relu, se=None, **kwargs): """Defines a single Mobilenet V3 convolution block. Args: ef: expansion factor n: number of output channels k: stride of depthwise s: stride act: activation function in inner layers se: squeeze excite function. **kwargs: passed to expanded_conv Returns: An object (lib._Op) for inserting in conv_def, representing this operation. """ return op( ops.expanded_conv, expansion_size=expand_input(ef), kernel_size=(k, k), stride=s, num_outputs=n, inner_activation_fn=act, expansion_transform=se, **kwargs)def mbv3_fused(ef, n, k, s=1, **kwargs): """Defines a single Mobilenet V3 convolution block. Args: ef: expansion factor n: number of output channels k: stride of depthwise s: stride **kwargs: will be passed to mbv3_op Returns: An object (lib._Op) for inserting in conv_def, representing this operation. """ expansion_fn = functools.partial(slim.conv2d, kernel_size=k, stride=s) return mbv3_op( ef, n, k=1, s=s, depthwise_location=None, expansion_fn=expansion_fn, **kwargs)mbv3_op_se = functools.partial(mbv3_op, se=_se4)DEFAULTS = { (ops.expanded_conv,): dict( normalizer_fn=slim.batch_norm, residual=True), (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu, }, (slim.batch_norm,): { 'center': True, 'scale': True },}DEFAULTS_GROUP_NORM = { (ops.expanded_conv,): dict(normalizer_fn=slim.group_norm, residual=True), (slim.conv2d, slim.fully_connected, slim.separable_conv2d): { 'normalizer_fn': slim.group_norm, 'activation_fn': tf.nn.relu, }, (slim.group_norm,): { 'groups': 8 },}# Compatible checkpoint: http://mldash/5511169891790690458#scalarsV3_LARGE = dict( defaults=dict(DEFAULTS), spec=([ # stage 1 op(slim.conv2d, stride=2, num_outputs=16, kernel_size=(3, 3), activation_fn=hard_swish), mbv3_op(ef=1, n=16, k=3), mbv3_op(ef=4, n=24, k=3, s=2), mbv3_op(ef=3, n=24, k=3, s=1), mbv3_op_se(ef=3, n=40, k=5, s=2), mbv3_op_se(ef=3, n=40, k=5, s=1), mbv3_op_se(ef=3, n=40, k=5, s=1), mbv3_op(ef=6, n=80, k=3, s=2, act=hard_swish), mbv3_op(ef=2.5, n=80, k=3, s=1, act=hard_swish), mbv3_op(ef=184/80., n=80, k=3, s=1, act=hard_swish), mbv3_op(ef=184/80., n=80, k=3, s=1, act=hard_swish), mbv3_op_se(ef=6, n=112, k=3, s=1, act=hard_swish), mbv3_op_se(ef=6, n=112, k=3, s=1, act=hard_swish), mbv3_op_se(ef=6, n=160, k=5, s=2, act=hard_swish), mbv3_op_se(ef=6, n=160, k=5, s=1, act=hard_swish), mbv3_op_se(ef=6, n=160, k=5, s=1, act=hard_swish), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=960, activation_fn=hard_swish), op(reduce_to_1x1, default_size=7, stride=1, padding='VALID'), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280, normalizer_fn=None, activation_fn=hard_swish) ]))# 72.2% accuracy.V3_LARGE_MINIMALISTIC = dict( defaults=dict(DEFAULTS), spec=([ # stage 1 op(slim.conv2d, stride=2, num_outputs=16, kernel_size=(3, 3)), mbv3_op(ef=1, n=16, k=3), mbv3_op(ef=4, n=24, k=3, s=2), mbv3_op(ef=3, n=24, k=3, s=1), mbv3_op(ef=3, n=40, k=3, s=2), mbv3_op(ef=3, n=40, k=3, s=1), mbv3_op(ef=3, n=40, k=3, s=1), mbv3_op(ef=6, n=80, k=3, s=2), mbv3_op(ef=2.5, n=80, k=3, s=1), mbv3_op(ef=184 / 80., n=80, k=3, s=1), mbv3_op(ef=184 / 80., n=80, k=3, s=1), mbv3_op(ef=6, n=112, k=3, s=1), mbv3_op(ef=6, n=112, k=3, s=1), mbv3_op(ef=6, n=160, k=3, s=2), mbv3_op(ef=6, n=160, k=3, s=1), mbv3_op(ef=6, n=160, k=3, s=1), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=960), op(reduce_to_1x1, default_size=7, stride=1, padding='VALID'), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280, normalizer_fn=None) ]))# Compatible run: http://mldash/2023283040014348118#scalarsV3_SMALL = dict( defaults=dict(DEFAULTS), spec=([ # stage 1 op(slim.conv2d, stride=2, num_outputs=16, kernel_size=(3, 3), activation_fn=hard_swish), mbv3_op_se(ef=1, n=16, k=3, s=2), mbv3_op(ef=72./16, n=24, k=3, s=2), mbv3_op(ef=(88./24), n=24, k=3, s=1), mbv3_op_se(ef=4, n=40, k=5, s=2, act=hard_swish), mbv3_op_se(ef=6, n=40, k=5, s=1, act=hard_swish), mbv3_op_se(ef=6, n=40, k=5, s=1, act=hard_swish), mbv3_op_se(ef=3, n=48, k=5, s=1, act=hard_swish), mbv3_op_se(ef=3, n=48, k=5, s=1, act=hard_swish), mbv3_op_se(ef=6, n=96, k=5, s=2, act=hard_swish), mbv3_op_se(ef=6, n=96, k=5, s=1, act=hard_swish), mbv3_op_se(ef=6, n=96, k=5, s=1, act=hard_swish), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=576, activation_fn=hard_swish), op(reduce_to_1x1, default_size=7, stride=1, padding='VALID'), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1024, normalizer_fn=None, activation_fn=hard_swish) ]))# 62% accuracy.V3_SMALL_MINIMALISTIC = dict( defaults=dict(DEFAULTS), spec=([ # stage 1 op(slim.conv2d, stride=2, num_outputs=16, kernel_size=(3, 3)), mbv3_op(ef=1, n=16, k=3, s=2), mbv3_op(ef=72. / 16, n=24, k=3, s=2), mbv3_op(ef=(88. / 24), n=24, k=3, s=1), mbv3_op(ef=4, n=40, k=3, s=2), mbv3_op(ef=6, n=40, k=3, s=1), mbv3_op(ef=6, n=40, k=3, s=1), mbv3_op(ef=3, n=48, k=3, s=1), mbv3_op(ef=3, n=48, k=3, s=1), mbv3_op(ef=6, n=96, k=3, s=2), mbv3_op(ef=6, n=96, k=3, s=1), mbv3_op(ef=6, n=96, k=3, s=1), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=576), op(reduce_to_1x1, default_size=7, stride=1, padding='VALID'), op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1024, normalizer_fn=None) ]))# EdgeTPU friendly variant of MobilenetV3 that uses fused convolutions# instead of depthwise in the early layers.V3_EDGETPU = dict( defaults=dict(DEFAULTS), spec=[ op(slim.conv2d, stride=2, num_outputs=32, kernel_size=(3, 3)), mbv3_fused(k=3, s=1, ef=1, n=16), mbv3_fused(k=3, s=2, ef=8, n=32), mbv3_fused(k=3, s=1, ef=4, n=32), mbv3_fused(k=3, s=1, ef=4, n=32), mbv3_fused(k=3, s=1, ef=4, n=32), mbv3_fused(k=3, s=2, ef=8, n=48), mbv3_fused(k=3, s=1, ef=4, n=48), mbv3_fused(k=3, s=1, ef=4, n=48), mbv3_fused(k=3, s=1, ef=4, n=48), mbv3_op(k=3, s=2, ef=8, n=96), mbv3_op(k=3, s=1, ef=4, n=96), mbv3_op(k=3, s=1, ef=4, n=96), mbv3_op(k=3, s=1, ef=4, n=96), mbv3_op(k=3, s=1, ef=8, n=96, residual=False), mbv3_op(k=3, s=1, ef=4, n=96), mbv3_op(k=3, s=1, ef=4, n=96), mbv3_op(k=3, s=1, ef=4, n=96), mbv3_op(k=5, s=2, ef=8, n=160), mbv3_op(k=5, s=1, ef=4, n=160), mbv3_op(k=5, s=1, ef=4, n=160), mbv3_op(k=5, s=1, ef=4, n=160), mbv3_op(k=3, s=1, ef=8, n=192), op(slim.conv2d, stride=1, num_outputs=1280, kernel_size=(1, 1)), ])@slim.add_arg_scopedef mobilenet(input_tensor, num_classes=6, depth_multiplier=1.0, scope='MobilenetV3', conv_defs=None, finegrain_classification_mode=False, use_groupnorm=False, **kwargs): """Creates mobilenet V3 network. Inference mode is created by default. To create training use training_scope below. with slim.arg_scope(mobilenet_v3.training_scope()): logits, endpoints = mobilenet_v3.mobilenet(input_tensor) Args: input_tensor: The input tensor num_classes: number of classes depth_multiplier: The multiplier applied to scale number of channels in each layer. scope: Scope of the operator conv_defs: Which version to create. Could be large/small or any conv_def (see mobilenet_v3.py for examples). finegrain_classification_mode: When set to True, the model will keep the last layer large even for small multipliers. Following https://arxiv.org/abs/1801.04381 it improves performance for ImageNet-type of problems. *Note* ignored if final_endpoint makes the builder exit earlier. use_groupnorm: When set to True, use group_norm as normalizer_fn. **kwargs: passed directly to mobilenet.mobilenet: prediction_fn- what prediction function to use. reuse-: whether to reuse variables (if reuse set to true, scope must be given). Returns: logits/endpoints pair Raises: ValueError: On invalid arguments """ if conv_defs is None: conv_defs = V3_LARGE if 'multiplier' in kwargs: raise ValueError('mobilenetv2 doesn\'t support generic ' 'multiplier parameter use "depth_multiplier" instead.') if use_groupnorm: conv_defs = copy.deepcopy(conv_defs) conv_defs['defaults'] = dict(DEFAULTS_GROUP_NORM) conv_defs['defaults'].update({ (slim.group_norm,): { 'groups': kwargs.pop('groups', 8) } }) if finegrain_classification_mode: conv_defs = copy.deepcopy(conv_defs) conv_defs['spec'][-1] = conv_defs['spec'][-1]._replace( multiplier_func=lambda params, multiplier: params) depth_args = {} with slim.arg_scope((lib.depth_multiplier,), **depth_args): return lib.mobilenet( input_tensor, num_classes=num_classes, conv_defs=conv_defs, scope=scope, multiplier=depth_multiplier, **kwargs)mobilenet.default_image_size = 224training_scope = lib.training_scope@slim.add_arg_scopedef mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs): """Creates base of the mobilenet (no pooling and no logits) .""" return mobilenet( input_tensor, depth_multiplier=depth_multiplier, base_only=True, **kwargs)def wrapped_partial(func, new_defaults=None, **kwargs): """Partial function with new default parameters and updated docstring.""" if not new_defaults: new_defaults = {} def func_wrapper(*f_args, **f_kwargs): new_kwargs = dict(new_defaults) new_kwargs.update(f_kwargs) return func(*f_args, **new_kwargs) functools.update_wrapper(func_wrapper, func) partial_func = functools.partial(func_wrapper, **kwargs) functools.update_wrapper(partial_func, func) return partial_funclarge = wrapped_partial(mobilenet, conv_defs=V3_LARGE)small = wrapped_partial(mobilenet, conv_defs=V3_SMALL)edge_tpu = wrapped_partial(mobilenet, new_defaults={'scope': 'MobilenetEdgeTPU'}, conv_defs=V3_EDGETPU)edge_tpu_075 = wrapped_partial( mobilenet, new_defaults={'scope': 'MobilenetEdgeTPU'}, conv_defs=V3_EDGETPU, depth_multiplier=0.75, finegrain_classification_mode=True)# Minimalistic model that does not have Squeeze Excite blocks,# Hardswish, or 5x5 depthwise convolution.# This makes the model very friendly for a wide range of hardwarelarge_minimalistic = wrapped_partial(mobilenet, conv_defs=V3_LARGE_MINIMALISTIC)small_minimalistic = wrapped_partial(mobilenet, conv_defs=V3_SMALL_MINIMALISTIC)def _reduce_consecutive_layers(conv_defs, start_id, end_id, multiplier=0.5): """Reduce the outputs of consecutive layers with multiplier. Args: conv_defs: Mobilenet conv_defs. start_id: 0-based index of the starting conv_def to be reduced. end_id: 0-based index of the last conv_def to be reduced. multiplier: The multiplier by which to reduce the conv_defs. Returns: Mobilenet conv_defs where the output sizes from layers [start_id, end_id], inclusive, are reduced by multiplier. Raises: ValueError if any layer to be reduced does not have the 'num_outputs' attribute. """ defs = copy.deepcopy(conv_defs) for d in defs['spec'][start_id:end_id+1]: d.params.update({ 'num_outputs': np.int(np.round(d.params['num_outputs'] * multiplier)) }) return defsV3_LARGE_DETECTION = _reduce_consecutive_layers(V3_LARGE, 13, 16)V3_SMALL_DETECTION = _reduce_consecutive_layers(V3_SMALL, 9, 12)__all__ = ['training_scope', 'mobilenet', 'V3_LARGE', 'V3_SMALL', 'large', 'small', 'V3_LARGE_DETECTION', 'V3_SMALL_DETECTION']