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January 23, 2018 18:00
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import PIL.Image as Image\n", | |
| "import tensorflow as tf\n", | |
| "import numpy as np\n", | |
| "\n", | |
| "from matplotlib import pyplot as plt\n", | |
| "import matplotlib.patches as patches" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "with open('/root/workspace/infer/ssd_mobilenet_v1_android_export.pb', 'rb') as f:\n", | |
| " serialized = f.read()\n", | |
| " \n", | |
| "tf.reset_default_graph()\n", | |
| "original_gdef = tf.GraphDef()\n", | |
| "original_gdef.ParseFromString(serialized)\n", | |
| "graph = tf.import_graph_def(original_gdef)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "img = Image.open(\"./golden-retriever.jpg\").resize([300,300], Image.ANTIALIAS)\n", | |
| "# img = Image.open(\"./two-dogs.jpg\").resize([300,300], Image.ANTIALIAS)\n", | |
| "img_array = np.array(img).astype(np.float32) * 2.0 / 255 - 1\n", | |
| "img_array = img_array[None, :, :, :]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## List all Tensors in full graph" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "import/image_tensor\n", | |
| "import/ToFloat\n", | |
| "import/Preprocessor/map/Shape\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/Shape\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_1_depthwise/depthwise_weights/read/_174__cf__174\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_2_depthwise/depthwise_weights/read/_168__cf__168\n", | |
| "import/MultipleGridAnchorGenerator/div_17/_162__cf__162\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_11/ExpandedShape/concat/_161__cf__161\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_depthwise/depthwise_weights/read/_157__cf__157\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_3_depthwise/depthwise_weights/read/_146__cf__146\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_4_depthwise/depthwise_weights/read/_140__cf__140\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_5_depthwise/depthwise_weights/read/_134__cf__134\n", | |
| "import/MultipleGridAnchorGenerator/div_19/_112__cf__112\n", | |
| "import/MultipleGridAnchorGenerator/strided_slice/_111__cf__111\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_17/ExpandedShape/concat/_110__cf__110\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_8_depthwise/depthwise_weights/read/_108__cf__108\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_9_depthwise/depthwise_weights/read/_102__cf__102\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_6_depthwise/depthwise_weights/read/_92__cf__92\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_7_depthwise/depthwise_weights/read/_86__cf__86\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_10_depthwise/depthwise_weights/read/_81__cf__81\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_11_depthwise/depthwise_weights/read/_75__cf__75\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_12_depthwise/depthwise_weights/read/_69__cf__69\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_8/ExpandedShape/concat/_66__cf__66\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_8/ExpandedShape_1/concat/_65__cf__65\n", | |
| "import/MultipleGridAnchorGenerator/mul_29/_64__cf__64\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_7/ExpandedShape/concat/_63__cf__63\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_7/ExpandedShape_1/concat/_62__cf__62\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_6/_61__cf__61\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_4/_60__cf__60\n", | |
| "import/MultipleGridAnchorGenerator/div_16/_59__cf__59\n", | |
| "import/MultipleGridAnchorGenerator/div_18/_58__cf__58\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_14/ExpandedShape/concat/_57__cf__57\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_14/ExpandedShape_1/concat/_56__cf__56\n", | |
| "import/MultipleGridAnchorGenerator/mul_43/_55__cf__55\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_13/ExpandedShape/concat/_54__cf__54\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_13/ExpandedShape_1/concat/_53__cf__53\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_10/_52__cf__52\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_17/ExpandedShape_1/concat/_51__cf__51\n", | |
| "import/MultipleGridAnchorGenerator/mul_50/_50__cf__50\n", | |
| "import/MultipleGridAnchorGenerator/strided_slice_1/_49__cf__49\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_16/ExpandedShape/concat/_48__cf__48\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_16/ExpandedShape_1/concat/_47__cf__47\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/All/_46__cf__46\n", | |
| "import/MultipleGridAnchorGenerator/strided_slice_2/_45__cf__45\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_11/ExpandedShape_1/concat/_43__cf__43\n", | |
| "import/MultipleGridAnchorGenerator/mul_36/_42__cf__42\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_10/ExpandedShape/concat/_41__cf__41\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_10/ExpandedShape_1/concat/_40__cf__40\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_8/_39__cf__39\n", | |
| "import/MultipleGridAnchorGenerator/Reshape/_38__cf__38\n", | |
| "import/MultipleGridAnchorGenerator/div_14/_37__cf__37\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_2/ExpandedShape/concat/_36__cf__36\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_2/ExpandedShape_1/concat/_35__cf__35\n", | |
| "import/MultipleGridAnchorGenerator/mul_15/_34__cf__34\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_1/ExpandedShape/concat/_33__cf__33\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_1/ExpandedShape_1/concat/_32__cf__32\n", | |
| "import/BoxPredictor_0/ClassPredictor/weights/read/_31__cf__31\n", | |
| "import/BoxPredictor_0/ClassPredictor/biases/read/_30__cf__30\n", | |
| "import/BoxPredictor_1/ClassPredictor/weights/read/_29__cf__29\n", | |
| "import/BoxPredictor_1/ClassPredictor/biases/read/_28__cf__28\n", | |
| "import/BoxPredictor_2/ClassPredictor/weights/read/_27__cf__27\n", | |
| "import/BoxPredictor_2/ClassPredictor/biases/read/_26__cf__26\n", | |
| "import/BoxPredictor_3/ClassPredictor/weights/read/_25__cf__25\n", | |
| "import/BoxPredictor_3/ClassPredictor/biases/read/_24__cf__24\n", | |
| "import/BoxPredictor_4/ClassPredictor/weights/read/_23__cf__23\n", | |
| "import/BoxPredictor_4/ClassPredictor/biases/read/_22__cf__22\n", | |
| "import/BoxPredictor_5/ClassPredictor/weights/read/_21__cf__21\n", | |
| "import/BoxPredictor_5/ClassPredictor/biases/read/_20__cf__20\n", | |
| "import/MultipleGridAnchorGenerator/div_15/_19__cf__19\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_5/ExpandedShape/concat/_18__cf__18\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_5/ExpandedShape_1/concat/_17__cf__17\n", | |
| "import/MultipleGridAnchorGenerator/mul_22/_16__cf__16\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_4/ExpandedShape/concat/_15__cf__15\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_4/ExpandedShape_1/concat/_14__cf__14\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_2/_13__cf__13\n", | |
| "import/strided_slice_14/_12__cf__12\n", | |
| "import/BoxPredictor_0/BoxEncodingPredictor/weights/read/_11__cf__11\n", | |
| "import/BoxPredictor_0/BoxEncodingPredictor/biases/read/_10__cf__10\n", | |
| "import/BoxPredictor_1/BoxEncodingPredictor/weights/read/_9__cf__9\n", | |
| "import/BoxPredictor_1/BoxEncodingPredictor/biases/read/_8__cf__8\n", | |
| "import/BoxPredictor_2/BoxEncodingPredictor/weights/read/_7__cf__7\n", | |
| "import/BoxPredictor_2/BoxEncodingPredictor/biases/read/_6__cf__6\n", | |
| "import/BoxPredictor_3/BoxEncodingPredictor/weights/read/_5__cf__5\n", | |
| "import/BoxPredictor_3/BoxEncodingPredictor/biases/read/_4__cf__4\n", | |
| "import/BoxPredictor_4/BoxEncodingPredictor/weights/read/_3__cf__3\n", | |
| "import/BoxPredictor_4/BoxEncodingPredictor/biases/read/_2__cf__2\n", | |
| "import/BoxPredictor_5/BoxEncodingPredictor/weights/read/_1__cf__1\n", | |
| "import/BoxPredictor_5/BoxEncodingPredictor/biases/read/_0__cf__0\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/weights/read/_180__cf__180\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/weights/read/_183__cf__183\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/weights/read/_122__cf__122\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/weights/read/_125__cf__125\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/weights/read/_128__cf__128\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/weights/read/_131__cf__131\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/weights/read/_148__cf__148\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/weights/read/_151__cf__151\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise/weights/read/_154__cf__154\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_12_pointwise/weights/read/_160__cf__160\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_11_pointwise/weights/read/_72__cf__72\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_10_pointwise/weights/read/_78__cf__78\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_9_pointwise/weights/read/_99__cf__99\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_8_pointwise/weights/read/_105__cf__105\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_7_pointwise/weights/read/_83__cf__83\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_6_pointwise/weights/read/_89__cf__89\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_5_pointwise/weights/read/_95__cf__95\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_4_pointwise/weights/read/_137__cf__137\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_3_pointwise/weights/read/_143__cf__143\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_2_pointwise/weights/read/_165__cf__165\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_1_pointwise/weights/read/_171__cf__171\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_177__cf__177\n", | |
| "import/Preprocessor/map/strided_slice/stack\n", | |
| "import/Preprocessor/map/strided_slice/stack_1\n", | |
| "import/Preprocessor/map/strided_slice/stack_2\n", | |
| "import/Preprocessor/map/strided_slice\n", | |
| "import/Preprocessor/map/TensorArray\n", | |
| "import/Preprocessor/map/while/TensorArrayReadV3/Enter\n", | |
| "import/Preprocessor/map/TensorArray_1\n", | |
| "import/Preprocessor/map/while/Enter_1\n", | |
| "import/Preprocessor/map/while/Merge_1\n", | |
| "import/Preprocessor/map/while/TensorArrayWrite/TensorArrayWriteV3/Enter\n", | |
| "import/Preprocessor/map/while/Less/Enter\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/strided_slice/stack\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/strided_slice/stack_1\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/strided_slice/stack_2\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/strided_slice\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/range/start\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/range/delta\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/range\n", | |
| "import/Preprocessor/map/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3\n", | |
| "import/Preprocessor/map/while/TensorArrayReadV3/Enter_1\n", | |
| "import/Preprocessor/map/Const\n", | |
| "import/Preprocessor/map/while/Enter\n", | |
| "import/Preprocessor/map/while/Merge\n", | |
| "import/Preprocessor/map/while/Less\n", | |
| "import/Preprocessor/map/while/LoopCond\n", | |
| "import/Preprocessor/map/while/Switch_1\n", | |
| "import/Preprocessor/map/while/Identity_1\n", | |
| "import/Preprocessor/map/while/Exit_1\n", | |
| "import/Preprocessor/map/TensorArrayStack/TensorArraySizeV3\n", | |
| "import/Preprocessor/map/while/Switch\n", | |
| "import/Preprocessor/map/while/Identity\n", | |
| "import/Preprocessor/map/while/TensorArrayReadV3\n", | |
| "import/Preprocessor/map/while/ResizeImage/ExpandDims/dim\n", | |
| "import/Preprocessor/map/while/ResizeImage/ExpandDims\n", | |
| "import/Preprocessor/map/while/ResizeImage/size\n", | |
| "import/Preprocessor/map/while/ResizeImage/ResizeBilinear\n", | |
| "import/Preprocessor/map/while/ResizeImage/Squeeze\n", | |
| "import/Preprocessor/map/while/TensorArrayWrite/TensorArrayWriteV3\n", | |
| "import/Preprocessor/map/while/NextIteration_1\n", | |
| "import/Preprocessor/map/while/add/y\n", | |
| "import/Preprocessor/map/while/add\n", | |
| "import/Preprocessor/map/while/NextIteration\n", | |
| "import/Preprocessor/map/TensorArrayStack/range/start\n", | |
| "import/Preprocessor/map/TensorArrayStack/range/delta\n", | |
| "import/Preprocessor/map/TensorArrayStack/range\n", | |
| "import/Preprocessor/map/TensorArrayStack/TensorArrayGatherV3\n", | |
| "import/Preprocessor/mul/x\n", | |
| "import/Preprocessor/mul\n", | |
| "import/Preprocessor/sub/y\n", | |
| "import/Preprocessor/sub\n", | |
| "import/FeatureExtractor/Shape\n", | |
| "import/FeatureExtractor/Shape_1\n", | |
| "import/Shape_6\n", | |
| "import/FeatureExtractor/strided_slice/stack\n", | |
| "import/FeatureExtractor/strided_slice/stack_1\n", | |
| "import/FeatureExtractor/strided_slice/stack_2\n", | |
| "import/FeatureExtractor/strided_slice\n", | |
| "import/FeatureExtractor/GreaterEqual/y\n", | |
| "import/FeatureExtractor/GreaterEqual\n", | |
| "import/FeatureExtractor/strided_slice_1/stack\n", | |
| "import/FeatureExtractor/strided_slice_1/stack_1\n", | |
| "import/FeatureExtractor/strided_slice_1/stack_2\n", | |
| "import/FeatureExtractor/strided_slice_1\n", | |
| "import/FeatureExtractor/GreaterEqual_1/y\n", | |
| "import/FeatureExtractor/GreaterEqual_1\n", | |
| "import/FeatureExtractor/LogicalAnd\n", | |
| "import/FeatureExtractor/Assert/Assert/data_0\n", | |
| "import/FeatureExtractor/Assert/Assert\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/sub/_182__cf__182\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/sub/_179__cf__179\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/sub/_176__cf__176\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/sub/_173__cf__173\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/mul/_172__cf__172\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/sub/_170__cf__170\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/sub/_167__cf__167\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/mul/_166__cf__166\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/sub/_164__cf__164\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/sub/_159__cf__159\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/sub/_156__cf__156\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul/_155__cf__155\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/sub/_153__cf__153\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/sub/_150__cf__150\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/sub/_147__cf__147\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/sub/_145__cf__145\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/mul/_144__cf__144\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/sub/_142__cf__142\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/sub/_139__cf__139\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul/_138__cf__138\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/sub/_136__cf__136\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/sub/_133__cf__133\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/sub/_130__cf__130\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/sub/_127__cf__127\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/sub/_124__cf__124\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/sub/_121__cf__121\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/sub/_107__cf__107\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul/_106__cf__106\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/sub/_104__cf__104\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/sub/_101__cf__101\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul/_100__cf__100\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/sub/_98__cf__98\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul/_96__cf__96\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/sub/_94__cf__94\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/sub/_91__cf__91\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul/_90__cf__90\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/sub/_88__cf__88\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/sub/_85__cf__85\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul/_84__cf__84\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/sub/_82__cf__82\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/sub/_80__cf__80\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul/_79__cf__79\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/sub/_77__cf__77\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/sub/_74__cf__74\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/mul/_73__cf__73\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/sub/_71__cf__71\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/sub/_68__cf__68\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul/_67__cf__67\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_128/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Shape_2\n", | |
| "import/BoxPredictor_2/BoxEncodingPredictor/convolution\n", | |
| "import/BoxPredictor_2/BoxEncodingPredictor/BiasAdd\n", | |
| "import/BoxPredictor_2/ClassPredictor/convolution\n", | |
| "import/BoxPredictor_2/ClassPredictor/BiasAdd\n", | |
| "import/BoxPredictor_2/Shape\n", | |
| "import/BoxPredictor_1/BoxEncodingPredictor/convolution\n", | |
| "import/BoxPredictor_1/BoxEncodingPredictor/BiasAdd\n", | |
| "import/BoxPredictor_1/ClassPredictor/convolution\n", | |
| "import/BoxPredictor_1/ClassPredictor/BiasAdd\n", | |
| "import/BoxPredictor_1/Shape\n", | |
| "import/BoxPredictor_0/BoxEncodingPredictor/convolution\n", | |
| "import/BoxPredictor_0/BoxEncodingPredictor/BiasAdd\n", | |
| "import/BoxPredictor_0/ClassPredictor/convolution\n", | |
| "import/BoxPredictor_0/ClassPredictor/BiasAdd\n", | |
| "import/BoxPredictor_0/Shape\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_2/stack\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_2/stack_1\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_2/stack_2\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_2\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros/shape/1\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros/shape/2\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros/shape/3\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros/shape\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros/Const\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros\n", | |
| "import/FeatureExtractor/MobilenetV1/concat/axis\n", | |
| "import/FeatureExtractor/MobilenetV1/concat\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_1/shape/1\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_1/shape/2\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_1/shape/3\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_1/shape\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_1/Const\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_1\n", | |
| "import/FeatureExtractor/MobilenetV1/concat_1/axis\n", | |
| "import/FeatureExtractor/MobilenetV1/concat_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_128/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Shape_3\n", | |
| "import/BoxPredictor_3/BoxEncodingPredictor/convolution\n", | |
| "import/BoxPredictor_3/BoxEncodingPredictor/BiasAdd\n", | |
| "import/BoxPredictor_3/ClassPredictor/convolution\n", | |
| "import/BoxPredictor_3/ClassPredictor/BiasAdd\n", | |
| "import/BoxPredictor_3/Shape\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_3/stack\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_3/stack_1\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_3/stack_2\n", | |
| "import/FeatureExtractor/MobilenetV1/strided_slice_3\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_2/shape/1\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_2/shape/2\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_2/shape/3\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_2/shape\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_2/Const\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_2\n" | |
| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "import/FeatureExtractor/MobilenetV1/concat_2/axis\n", | |
| "import/FeatureExtractor/MobilenetV1/concat_2\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_3/shape/1\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_3/shape/2\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_3/shape/3\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_3/shape\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_3/Const\n", | |
| "import/FeatureExtractor/MobilenetV1/zeros_3\n", | |
| "import/FeatureExtractor/MobilenetV1/concat_3/axis\n", | |
| "import/FeatureExtractor/MobilenetV1/concat_3\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_64/Relu6\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/mul_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/BatchNorm/batchnorm/add_1\n", | |
| "import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6\n", | |
| "import/BoxPredictor_5/BoxEncodingPredictor/convolution\n", | |
| "import/BoxPredictor_5/BoxEncodingPredictor/BiasAdd\n", | |
| "import/BoxPredictor_5/ClassPredictor/convolution\n", | |
| "import/BoxPredictor_5/ClassPredictor/BiasAdd\n", | |
| "import/BoxPredictor_5/Shape\n", | |
| "import/BoxPredictor_4/BoxEncodingPredictor/convolution\n", | |
| "import/BoxPredictor_4/BoxEncodingPredictor/BiasAdd\n", | |
| "import/BoxPredictor_4/ClassPredictor/convolution\n", | |
| "import/BoxPredictor_4/ClassPredictor/BiasAdd\n", | |
| "import/BoxPredictor_4/Shape\n", | |
| "import/strided_slice_6/stack\n", | |
| "import/strided_slice_6/stack_1\n", | |
| "import/strided_slice_6/stack_2\n", | |
| "import/strided_slice_6\n", | |
| "import/MultipleGridAnchorGenerator/ToFloat\n", | |
| "import/strided_slice_7/stack\n", | |
| "import/strided_slice_7/stack_1\n", | |
| "import/strided_slice_7/stack_2\n", | |
| "import/strided_slice_7\n", | |
| "import/MultipleGridAnchorGenerator/ToFloat_1\n", | |
| "import/MultipleGridAnchorGenerator/Minimum\n", | |
| "import/MultipleGridAnchorGenerator/div_12\n", | |
| "import/MultipleGridAnchorGenerator/mul_12\n", | |
| "import/MultipleGridAnchorGenerator/mul_14\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_2/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_2/Tile\n", | |
| "import/MultipleGridAnchorGenerator/mul_21\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_5/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_5/Tile\n", | |
| "import/MultipleGridAnchorGenerator/mul_28\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_8/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_8/Tile\n", | |
| "import/MultipleGridAnchorGenerator/mul_35\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_11/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_11/Tile\n", | |
| "import/MultipleGridAnchorGenerator/mul_42\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_14/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_14/Tile\n", | |
| "import/MultipleGridAnchorGenerator/mul_49\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_17/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_17/Tile\n", | |
| "import/MultipleGridAnchorGenerator/div_13\n", | |
| "import/MultipleGridAnchorGenerator/mul_13\n", | |
| "import/MultipleGridAnchorGenerator/mul_16\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_1/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_1/Tile\n", | |
| "import/MultipleGridAnchorGenerator/stack_1\n", | |
| "import/MultipleGridAnchorGenerator/mul_23\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_4/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_4/Tile\n", | |
| "import/MultipleGridAnchorGenerator/stack_3\n", | |
| "import/MultipleGridAnchorGenerator/mul_30\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_7/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_7/Tile\n", | |
| "import/MultipleGridAnchorGenerator/stack_5\n", | |
| "import/MultipleGridAnchorGenerator/mul_37\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_10/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_10/Tile\n", | |
| "import/MultipleGridAnchorGenerator/stack_7\n", | |
| "import/MultipleGridAnchorGenerator/mul_44\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_13/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_13/Tile\n", | |
| "import/MultipleGridAnchorGenerator/stack_9\n", | |
| "import/MultipleGridAnchorGenerator/mul_51\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_16/Reshape\n", | |
| "import/MultipleGridAnchorGenerator/Meshgrid_16/Tile\n", | |
| "import/MultipleGridAnchorGenerator/stack_11\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_1/shape\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_1\n", | |
| "import/MultipleGridAnchorGenerator/mul_19/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_19\n", | |
| "import/MultipleGridAnchorGenerator/sub\n", | |
| "import/MultipleGridAnchorGenerator/mul_20/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_20\n", | |
| "import/MultipleGridAnchorGenerator/add_2\n", | |
| "import/MultipleGridAnchorGenerator/concat/axis\n", | |
| "import/MultipleGridAnchorGenerator/concat\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_3/shape\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_3\n", | |
| "import/MultipleGridAnchorGenerator/mul_26/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_26\n", | |
| "import/MultipleGridAnchorGenerator/sub_1\n", | |
| "import/MultipleGridAnchorGenerator/mul_27/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_27\n", | |
| "import/MultipleGridAnchorGenerator/add_5\n", | |
| "import/MultipleGridAnchorGenerator/concat_1/axis\n", | |
| "import/MultipleGridAnchorGenerator/concat_1\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_5/shape\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_5\n", | |
| "import/MultipleGridAnchorGenerator/mul_33/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_33\n", | |
| "import/MultipleGridAnchorGenerator/sub_2\n", | |
| "import/MultipleGridAnchorGenerator/mul_34/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_34\n", | |
| "import/MultipleGridAnchorGenerator/add_8\n", | |
| "import/MultipleGridAnchorGenerator/concat_2/axis\n", | |
| "import/MultipleGridAnchorGenerator/concat_2\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_7/shape\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_7\n", | |
| "import/MultipleGridAnchorGenerator/mul_40/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_40\n", | |
| "import/MultipleGridAnchorGenerator/sub_3\n", | |
| "import/MultipleGridAnchorGenerator/mul_41/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_41\n", | |
| "import/MultipleGridAnchorGenerator/add_11\n", | |
| "import/MultipleGridAnchorGenerator/concat_3/axis\n", | |
| "import/MultipleGridAnchorGenerator/concat_3\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_9/shape\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_9\n", | |
| "import/MultipleGridAnchorGenerator/mul_47/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_47\n", | |
| "import/MultipleGridAnchorGenerator/sub_4\n", | |
| "import/MultipleGridAnchorGenerator/mul_48/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_48\n", | |
| "import/MultipleGridAnchorGenerator/add_14\n", | |
| "import/MultipleGridAnchorGenerator/concat_4/axis\n", | |
| "import/MultipleGridAnchorGenerator/concat_4\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_11/shape\n", | |
| "import/MultipleGridAnchorGenerator/Reshape_11\n", | |
| "import/MultipleGridAnchorGenerator/mul_54/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_54\n", | |
| "import/MultipleGridAnchorGenerator/sub_5\n", | |
| "import/MultipleGridAnchorGenerator/mul_55/x\n", | |
| "import/MultipleGridAnchorGenerator/mul_55\n", | |
| "import/MultipleGridAnchorGenerator/add_17\n", | |
| "import/MultipleGridAnchorGenerator/concat_5/axis\n", | |
| "import/MultipleGridAnchorGenerator/concat_5\n", | |
| "import/MultipleGridAnchorGenerator/Concatenate/concat/axis\n", | |
| "import/MultipleGridAnchorGenerator/Concatenate/concat\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/x\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/Assert/Assert/data_0\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/Assert/Assert/data_1\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/Assert/Assert/data_2\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/Assert/Assert/data_4\n", | |
| "import/MultipleGridAnchorGenerator/assert_equal/Assert/Assert\n", | |
| "import/MultipleGridAnchorGenerator/Identity\n", | |
| "import/BoxPredictor_0/strided_slice/stack\n", | |
| "import/BoxPredictor_0/strided_slice/stack_1\n", | |
| "import/BoxPredictor_0/strided_slice/stack_2\n", | |
| "import/BoxPredictor_0/strided_slice\n", | |
| "import/BoxPredictor_0/stack/1\n", | |
| "import/BoxPredictor_0/stack/2\n", | |
| "import/BoxPredictor_0/stack/3\n", | |
| "import/BoxPredictor_0/stack\n", | |
| "import/BoxPredictor_0/Reshape\n", | |
| "import/Squeeze\n", | |
| "import/Shape_7\n", | |
| "import/BoxPredictor_0/stack_1/1\n", | |
| "import/BoxPredictor_0/stack_1/2\n", | |
| "import/BoxPredictor_0/stack_1\n", | |
| "import/BoxPredictor_0/Reshape_1\n", | |
| "import/BoxPredictor_1/strided_slice/stack\n", | |
| "import/BoxPredictor_1/strided_slice/stack_1\n", | |
| "import/BoxPredictor_1/strided_slice/stack_2\n", | |
| "import/BoxPredictor_1/strided_slice\n", | |
| "import/BoxPredictor_1/stack/1\n", | |
| "import/BoxPredictor_1/stack/2\n", | |
| "import/BoxPredictor_1/stack/3\n", | |
| "import/BoxPredictor_1/stack\n", | |
| "import/BoxPredictor_1/Reshape\n", | |
| "import/Squeeze_1\n", | |
| "import/Shape_8\n", | |
| "import/BoxPredictor_1/stack_1/1\n", | |
| "import/BoxPredictor_1/stack_1/2\n", | |
| "import/BoxPredictor_1/stack_1\n", | |
| "import/BoxPredictor_1/Reshape_1\n", | |
| "import/BoxPredictor_2/strided_slice/stack\n", | |
| "import/BoxPredictor_2/strided_slice/stack_1\n", | |
| "import/BoxPredictor_2/strided_slice/stack_2\n", | |
| "import/BoxPredictor_2/strided_slice\n", | |
| "import/BoxPredictor_2/stack/1\n", | |
| "import/BoxPredictor_2/stack/2\n", | |
| "import/BoxPredictor_2/stack/3\n", | |
| "import/BoxPredictor_2/stack\n", | |
| "import/BoxPredictor_2/Reshape\n", | |
| "import/Squeeze_2\n", | |
| "import/Shape_9\n", | |
| "import/BoxPredictor_2/stack_1/1\n", | |
| "import/BoxPredictor_2/stack_1/2\n", | |
| "import/BoxPredictor_2/stack_1\n", | |
| "import/BoxPredictor_2/Reshape_1\n", | |
| "import/BoxPredictor_3/strided_slice/stack\n", | |
| "import/BoxPredictor_3/strided_slice/stack_1\n", | |
| "import/BoxPredictor_3/strided_slice/stack_2\n", | |
| "import/BoxPredictor_3/strided_slice\n", | |
| "import/BoxPredictor_3/stack/1\n", | |
| "import/BoxPredictor_3/stack/2\n", | |
| "import/BoxPredictor_3/stack/3\n", | |
| "import/BoxPredictor_3/stack\n", | |
| "import/BoxPredictor_3/Reshape\n", | |
| "import/Squeeze_3\n", | |
| "import/Shape_10\n", | |
| "import/BoxPredictor_3/stack_1/1\n", | |
| "import/BoxPredictor_3/stack_1/2\n", | |
| "import/BoxPredictor_3/stack_1\n", | |
| "import/BoxPredictor_3/Reshape_1\n", | |
| "import/BoxPredictor_4/strided_slice/stack\n", | |
| "import/BoxPredictor_4/strided_slice/stack_1\n", | |
| "import/BoxPredictor_4/strided_slice/stack_2\n", | |
| "import/BoxPredictor_4/strided_slice\n", | |
| "import/BoxPredictor_4/stack/1\n", | |
| "import/BoxPredictor_4/stack/2\n", | |
| "import/BoxPredictor_4/stack/3\n", | |
| "import/BoxPredictor_4/stack\n", | |
| "import/BoxPredictor_4/Reshape\n", | |
| "import/Squeeze_4\n", | |
| "import/Shape_11\n", | |
| "import/BoxPredictor_4/stack_1/1\n", | |
| "import/BoxPredictor_4/stack_1/2\n", | |
| "import/BoxPredictor_4/stack_1\n", | |
| "import/BoxPredictor_4/Reshape_1\n", | |
| "import/BoxPredictor_5/strided_slice/stack\n", | |
| "import/BoxPredictor_5/strided_slice/stack_1\n", | |
| "import/BoxPredictor_5/strided_slice/stack_2\n", | |
| "import/BoxPredictor_5/strided_slice\n", | |
| "import/BoxPredictor_5/stack/1\n", | |
| "import/BoxPredictor_5/stack/2\n", | |
| "import/BoxPredictor_5/stack/3\n", | |
| "import/BoxPredictor_5/stack\n", | |
| "import/BoxPredictor_5/Reshape\n", | |
| "import/Squeeze_5\n", | |
| "import/Shape_12\n", | |
| "import/BoxPredictor_5/stack_1/1\n", | |
| "import/BoxPredictor_5/stack_1/2\n", | |
| "import/BoxPredictor_5/stack_1\n", | |
| "import/BoxPredictor_5/Reshape_1\n", | |
| "import/strided_slice_8/stack\n", | |
| "import/strided_slice_8/stack_1\n", | |
| "import/strided_slice_8/stack_2\n", | |
| "import/strided_slice_8\n", | |
| "import/strided_slice_9/stack\n", | |
| "import/strided_slice_9/stack_1\n", | |
| "import/strided_slice_9/stack_2\n", | |
| "import/strided_slice_9\n", | |
| "import/strided_slice_10/stack\n", | |
| "import/strided_slice_10/stack_1\n", | |
| "import/strided_slice_10/stack_2\n", | |
| "import/strided_slice_10\n", | |
| "import/strided_slice_11/stack\n", | |
| "import/strided_slice_11/stack_1\n", | |
| "import/strided_slice_11/stack_2\n", | |
| "import/strided_slice_11\n", | |
| "import/strided_slice_12/stack\n", | |
| "import/strided_slice_12/stack_1\n", | |
| "import/strided_slice_12/stack_2\n", | |
| "import/strided_slice_12\n", | |
| "import/strided_slice_13/stack\n", | |
| "import/strided_slice_13/stack_1\n", | |
| "import/strided_slice_13/stack_2\n", | |
| "import/strided_slice_13\n", | |
| "import/add/x\n", | |
| "import/add\n", | |
| "import/add_1\n", | |
| "import/add_2\n", | |
| "import/add_3\n", | |
| "import/add_4\n", | |
| "import/add_5\n", | |
| "import/assert_equal/Equal\n", | |
| "import/assert_equal/Const\n", | |
| "import/assert_equal/All\n", | |
| "import/assert_equal/Assert/Assert/data_0\n", | |
| "import/assert_equal/Assert/Assert\n", | |
| "import/concat/axis\n", | |
| "import/concat\n", | |
| "import/Postprocessor/Shape\n", | |
| "import/concat_1/axis\n", | |
| "import/concat_1\n", | |
| "import/Postprocessor/strided_slice/stack\n", | |
| "import/Postprocessor/strided_slice/stack_1\n", | |
| "import/Postprocessor/strided_slice/stack_2\n", | |
| "import/Postprocessor/strided_slice\n", | |
| "import/Postprocessor/ExpandDims/dim\n", | |
| "import/Postprocessor/ExpandDims\n", | |
| "import/Postprocessor/Tile/multiples/1\n", | |
| "import/Postprocessor/Tile/multiples/2\n", | |
| "import/Postprocessor/Tile/multiples\n", | |
| "import/Postprocessor/Tile\n", | |
| "import/Postprocessor/Reshape/shape\n", | |
| "import/Postprocessor/Reshape\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/Rank\n", | |
| "import/Postprocessor/Reshape_1/shape\n", | |
| "import/Postprocessor/Reshape_1\n", | |
| "import/Postprocessor/Decode/transpose/Rank\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/sub/y\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/sub\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/Range/start\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/Range/delta\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/Range\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose/sub_1\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/transpose\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/unstack\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/sub\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/sub_1\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/div/y\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/div\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/add\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/div_1/y\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/div_1\n", | |
| "import/Postprocessor/Decode/get_center_coordinates_and_sizes/add_1\n", | |
| "import/Postprocessor/Decode/transpose/sub/y\n", | |
| "import/Postprocessor/Decode/transpose/sub\n", | |
| "import/Postprocessor/Decode/transpose/Range/start\n", | |
| "import/Postprocessor/Decode/transpose/Range/delta\n", | |
| "import/Postprocessor/Decode/transpose/Range\n", | |
| "import/Postprocessor/Decode/transpose/sub_1\n", | |
| "import/Postprocessor/Decode/transpose\n", | |
| "import/Postprocessor/Decode/unstack\n", | |
| "import/Postprocessor/Decode/div/y\n", | |
| "import/Postprocessor/Decode/div\n", | |
| "import/Postprocessor/Decode/mul_2\n", | |
| "import/Postprocessor/Decode/add\n", | |
| "import/Postprocessor/Decode/div_1/y\n", | |
| "import/Postprocessor/Decode/div_1\n", | |
| "import/Postprocessor/Decode/mul_3\n", | |
| "import/Postprocessor/Decode/add_1\n", | |
| "import/Postprocessor/Decode/div_2/y\n", | |
| "import/Postprocessor/Decode/div_2\n", | |
| "import/Postprocessor/Decode/Exp_1\n", | |
| "import/Postprocessor/Decode/mul_1\n", | |
| "import/Postprocessor/Decode/div_3/y\n", | |
| "import/Postprocessor/Decode/div_3\n", | |
| "import/Postprocessor/Decode/Exp\n", | |
| "import/Postprocessor/Decode/mul\n", | |
| "import/Postprocessor/Decode/div_4/y\n", | |
| "import/Postprocessor/Decode/div_4\n", | |
| "import/Postprocessor/Decode/sub\n", | |
| "import/Postprocessor/Decode/div_5/y\n", | |
| "import/Postprocessor/Decode/div_5\n", | |
| "import/Postprocessor/Decode/sub_1\n", | |
| "import/Postprocessor/Decode/div_6/y\n", | |
| "import/Postprocessor/Decode/div_6\n", | |
| "import/Postprocessor/Decode/add_2\n", | |
| "import/Postprocessor/Decode/div_7/y\n", | |
| "import/Postprocessor/Decode/div_7\n", | |
| "import/Postprocessor/Decode/add_3\n", | |
| "import/Postprocessor/Decode/stack\n", | |
| "import/Postprocessor/Decode/transpose_1/Rank\n", | |
| "import/Postprocessor/Decode/transpose_1/sub/y\n", | |
| "import/Postprocessor/Decode/transpose_1/sub\n", | |
| "import/Postprocessor/Decode/transpose_1/Range/start\n", | |
| "import/Postprocessor/Decode/transpose_1/Range/delta\n", | |
| "import/Postprocessor/Decode/transpose_1/Range\n", | |
| "import/Postprocessor/Decode/transpose_1/sub_1\n", | |
| "import/Postprocessor/Decode/transpose_1\n", | |
| "import/Postprocessor/stack/1\n", | |
| "import/Postprocessor/stack/2\n", | |
| "import/Postprocessor/stack\n", | |
| "import/Postprocessor/Reshape_2\n", | |
| "import/Postprocessor/ExpandDims_1/dim\n", | |
| "import/Postprocessor/ExpandDims_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/Shape\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/Shape\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayUnstack/Shape\n", | |
| "import/Postprocessor/Slice/begin\n", | |
| "import/Postprocessor/Slice/size\n", | |
| "import/Postprocessor/Slice\n", | |
| "import/Postprocessor/scale_logits/y\n", | |
| "import/Postprocessor/scale_logits\n", | |
| "import/Postprocessor/convert_scores\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayUnstack_1/Shape\n", | |
| "import/Postprocessor/Const\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ClipToWindow/unstack/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/strided_slice/stack\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/strided_slice/stack_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/strided_slice/stack_2\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/strided_slice\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/ones/shape\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/ones/Const\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/ones\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/mul/y\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/mul\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayUnstack_3/Shape\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/strided_slice/stack\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/strided_slice/stack_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/strided_slice/stack_2\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/strided_slice\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayReadV3/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayReadV3_1/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray_3\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayReadV3_3/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray_4\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Enter_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Merge_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayWrite/TensorArrayWriteV3/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray_5\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Enter_2\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Merge_2\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayWrite_1/TensorArrayWriteV3/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray_6\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Enter_3\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Merge_3\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayWrite_2/TensorArrayWriteV3/Enter\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArray_8\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Enter_5\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/Merge_5\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayWrite_4/TensorArrayWriteV3/Enter\n", | |
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| ] | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ClipToWindow_22/Area/mul\n", | |
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| }, | |
| { | |
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| }, | |
| { | |
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| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/ClipToWindow_79/Where\n", | |
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| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_2/Merge\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayWrite_1/TensorArrayWriteV3\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/NextIteration_2\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_2/Shape/Switch\n", | |
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| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_2/cond/strided_slice/Switch\n", | |
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| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_2/cond/concat/Switch\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/PadOrClipBoxList/cond_2/cond/concat\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/strided_slice/stack\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/strided_slice/stack_1\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/strided_slice/stack_2\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/strided_slice\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayWrite_4/TensorArrayWriteV3\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/NextIteration_5\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/add/y\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/add\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/while/NextIteration\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack/range/start\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack/range/delta\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack/range\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack/TensorArrayGatherV3\n", | |
| "import/detection_boxes\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_1/range/start\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_1/range/delta\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_1/range\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_1/TensorArrayGatherV3\n", | |
| "import/detection_scores\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_2/range/start\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_2/range/delta\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_2/range\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_2/TensorArrayGatherV3\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_4/range/start\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_4/range/delta\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_4/range\n", | |
| "import/Postprocessor/BatchMultiClassNonMaxSuppression/map/TensorArrayStack_4/TensorArrayGatherV3\n", | |
| "import/Postprocessor/ToFloat\n", | |
| "import/num_detections\n", | |
| "import/add_6/y\n", | |
| "import/add_6\n", | |
| "import/detection_classes\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "with tf.Session(graph=graph) as sess:\n", | |
| " for op in tf.get_default_graph().get_operations():\n", | |
| " opname = str(op.name)\n", | |
| " print str(op.name) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Extracting Box Sizes From Feature Extractor\n", | |
| "\n", | |
| "This pulls out the 6 layers that are used to create box predictions. See Fig 2 of Liu _et al._" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "('box0 = ', (1, 19, 19, 512))\n", | |
| "('box1 = ', (1, 10, 10, 1024))\n", | |
| "('box2 = ', (1, 5, 5, 512))\n", | |
| "('box3 = ', (1, 3, 3, 256))\n", | |
| "('box4 = ', (1, 2, 2, 256))\n", | |
| "('box5 = ', (1, 1, 1, 128))\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "with tf.Session(graph=graph) as sess:\n", | |
| " image_input_tensor = sess.graph.get_tensor_by_name(\"import/Preprocessor/sub:0\")\n", | |
| " tensor_box0 = sess.graph.get_tensor_by_name(\"import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6:0\")\n", | |
| " tensor_box1 = sess.graph.get_tensor_by_name(\"import/FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6:0\")\n", | |
| " tensor_box2 = sess.graph.get_tensor_by_name(\"import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6:0\")\n", | |
| " tensor_box3 = sess.graph.get_tensor_by_name(\"import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_256/Relu6:0\")\n", | |
| " tensor_box4 = sess.graph.get_tensor_by_name(\"import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_256/Relu6:0\")\n", | |
| " tensor_box5 = sess.graph.get_tensor_by_name(\"import/FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_128/Relu6:0\")\n", | |
| " \n", | |
| " box0, box1, box2, box3, box4, box5 = sess.run([tensor_box0, tensor_box1, tensor_box2, tensor_box3, tensor_box4, tensor_box5], feed_dict={ image_input_tensor: img_array })\n", | |
| " \n", | |
| "print(\"box0 = \", np.shape(box0))\n", | |
| "print(\"box1 = \", np.shape(box1))\n", | |
| "print(\"box2 = \", np.shape(box2))\n", | |
| "print(\"box3 = \", np.shape(box3))\n", | |
| "print(\"box4 = \", np.shape(box4))\n", | |
| "print(\"box5 = \", np.shape(box5))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Extract out Tensors By Name\n", | |
| "\n", | |
| "Pull out tensors which are of interest to us:\n", | |
| "\n", | |
| " - `image_input_tensor`: This is the input tensor (300 x 300)\n", | |
| " - `detection_classes`, `detection_boxes`, `detection_scores`: These are the outputs of the TF model and what we want our CoreML model to match. \n", | |
| " - `coreml_boxes`, `coreml_classes`: These are the tensors in the converted CoreML model\n", | |
| " - `multiple_grid_generator2`, `multiple_grid_generator3`: These are the generated anchor boxes for the SSD" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "with tf.Session(graph=graph) as sess:\n", | |
| " writer = tf.summary.FileWriter(\"/root/workspace/infer\", sess.graph)\n", | |
| " writer.flush()\n", | |
| " writer.close()\n", | |
| " \n", | |
| " image_input_tensor = sess.graph.get_tensor_by_name(\"import/Preprocessor/sub:0\")\n", | |
| " num_detections = sess.graph.get_tensor_by_name(\"import/num_detections:0\")\n", | |
| " detection_classes = sess.graph.get_tensor_by_name(\"import/detection_classes:0\")\n", | |
| " detection_boxes = sess.graph.get_tensor_by_name(\"import/detection_boxes:0\")\n", | |
| " detection_scores = sess.graph.get_tensor_by_name(\"import/detection_scores:0\")\n", | |
| " \n", | |
| " coreml_boxes = sess.graph.get_tensor_by_name(\"import/concat:0\")\n", | |
| " coreml_classes = sess.graph.get_tensor_by_name(\"import/concat_1:0\")\n", | |
| " \n", | |
| " multiple_grid_generator = sess.graph.get_tensor_by_name(\"import/Postprocessor/ExpandDims_1:0\")\n", | |
| " multiple_grid_generator2 = sess.graph.get_tensor_by_name(\"import/MultipleGridAnchorGenerator/Identity:0\")\n", | |
| " multiple_grid_generator3 = sess.graph.get_tensor_by_name(\"import/Postprocessor/Reshape:0\")\n", | |
| " \n", | |
| " classes, boxes, scores, boxes_coreml, classes_coreml, anchors, anchors2, anchors3 = sess.run([ detection_classes, detection_boxes, detection_scores, coreml_boxes, coreml_classes, multiple_grid_generator, multiple_grid_generator2, multiple_grid_generator3], feed_dict={ image_input_tensor: img_array })\n", | |
| " \n", | |
| "anchors = np.squeeze(anchors)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def plot_box(box):\n", | |
| " bbox = 300 * box\n", | |
| " fig, ax = plt.subplots(1)\n", | |
| " ax.imshow(img)\n", | |
| " rect = patches.Rectangle((bbox[1], bbox[0]), bbox[3] - bbox[1], bbox[2] - bbox[0], linewidth=2, edgecolor='g', facecolor='none')\n", | |
| " ax.add_patch(rect)\n", | |
| "\n", | |
| " plt.show()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## decode_boxes\n", | |
| "\n", | |
| "This is the `numpy` version of [decode in KeypointBoxCoder](https://github.com/tensorflow/models/blob/master/research/object_detection/box_coders/keypoint_box_coder.py#L128)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def decode_boxes(preds, _anchors):\n", | |
| " nbatch, _ = _anchors.shape\n", | |
| " \n", | |
| " ya = (_anchors[:, 0] + _anchors[:, 2]) / 2\n", | |
| " xa = (_anchors[:, 1] + _anchors[:, 3]) / 2\n", | |
| " wa = (_anchors[:, 3] - _anchors[:, 1])\n", | |
| " ha = (_anchors[:, 2] - _anchors[:, 0])\n", | |
| " \n", | |
| " ty, tx, th, tw = np.transpose(preds)\n", | |
| " \n", | |
| " th /= 5.0\n", | |
| " tw /= 5.0\n", | |
| " ty /= 10.0\n", | |
| " tx /= 10.0\n", | |
| " \n", | |
| " w = np.exp(tw) * wa \n", | |
| " h = np.exp(th) * ha\n", | |
| " \n", | |
| " ycenter = ty * ha + ya\n", | |
| " xcenter = tx * wa + xa\n", | |
| " \n", | |
| " ymin = (ycenter - h / 2.0).reshape(nbatch, 1)\n", | |
| " xmin = (xcenter - w / 2.0).reshape(nbatch, 1)\n", | |
| " ymax = (ycenter + h / 2.0).reshape(nbatch, 1)\n", | |
| " xmax = (xcenter + w / 2.0).reshape(nbatch, 1)\n", | |
| " \n", | |
| " return np.concatenate((ymin, xmin, ymax, xmax), axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Converting CoreML output to boxes\n", | |
| "\n", | |
| "I show kind of how you can extract the CoreML output to create box predictions. It's not a full-blown postprocessing with non-maximum suppression but it gives you a sense for what to do." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "dog_scores = classes_coreml[0, :, 18]\n", | |
| "high_scoring_inds = np.where(dog_scores > 0.01)\n", | |
| "\n", | |
| "high_scoring_scores = dog_scores[high_scoring_inds]\n", | |
| "sorted_inds = np.flip(np.argsort(high_scoring_scores), 0)\n", | |
| "\n", | |
| "high_scoring_boxes = np.squeeze(boxes_coreml[0, high_scoring_inds, :])[sorted_inds, :]\n", | |
| "high_scoring_anchors = np.squeeze(anchors[high_scoring_inds, :])[sorted_inds, :]\n", | |
| "\n", | |
| "sorted_high_scores = np.concatenate((high_scoring_scores[sorted_inds].reshape(7, 1), high_scoring_boxes, high_scoring_anchors), axis=1)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "('Best Index = ', 1860)\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "best_box_index = high_scoring_inds[0][sorted_inds[0]]\n", | |
| "print(\"Best Index = \", best_box_index)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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Y8MGBrkgyJ8sMQkpSSgyDZdd0dH1Pkec3JbxGy/EcOx/x1lEVBX27/cRr+jPi4BQk7xn6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bqJqK06ohKwrVpXm8Tpt2u40sqxQq3TSqS+Qq3Tgdl4XZWaLQxbPbzM5Msrq6SrbS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\nnT6DOh1lXUspSqOV0alRvvv4QU6cOkKFSSQSjZITwixOnsZo1FKsL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5kmE3eh1MgpK+0k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\nFVZY4Q2x4ixWWGGFN8SKs1hhhRXeEP8XU9eOmxQ3LsYAAAAASUVORK5CYII=\n", | |
| "text/plain": [ | |
| "<matplotlib.figure.Figure at 0x7f6782395690>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "best_anchor_box = anchors2[best_box_index, :].reshape(1, 4)\n", | |
| "best_coreml_box = boxes_coreml[0, best_box_index, :].reshape(1, 4)\n", | |
| "best_decoded_box = np.squeeze(decode_boxes(best_coreml_box, best_anchor_box))\n", | |
| "\n", | |
| "plot_box(best_decoded_box)" | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 2", | |
| "language": "python", | |
| "name": "python2" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 2 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython2", | |
| "version": "2.7.12" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
first of all thanks for this git, it is very helpful ...
I have a question about what you did in ln[6]
I get the following error:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'image_tensor' with dtype uint8 and shape [?,?,?,3]
-- from my experience tensorflow cannot run the graph without any input given to the placeholders, so even-though there's the "Preprocessor/sub:0", it is not enough because the post processing block needs the image_tensor (for dimensions) - I fixed it by feeding a dummy image to the placeholder...
What tensorflow version are you using ? maybe the error I got is because of version differences?
Just trying to figure out what I'm missing here...
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I think this line is hard coded:
"sorted_high_scores = np.concatenate((high_scoring_scores[sorted_inds].reshape(7, 1), high_scoring_boxes, high_scoring_anchors), axis=1)"
Changing it to this would be better:
"sorted_high_scores = np.concatenate((high_scoring_scores[sorted_inds].reshape(high_scoring_scores.shape[0], 1), high_scoring_boxes, high_scoring_anchors), axis=1)"