Alexander Alekhin
5 years ago
commit
c3e8a82c9c
25 changed files with 837 additions and 73 deletions
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// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#ifndef __OPENCV_DNN_COMMON_HPP__ |
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#define __OPENCV_DNN_COMMON_HPP__ |
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#include <opencv2/dnn.hpp> |
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namespace cv { namespace dnn { |
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CV__DNN_INLINE_NS_BEGIN |
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#define IS_DNN_OPENCL_TARGET(id) (id == DNN_TARGET_OPENCL || id == DNN_TARGET_OPENCL_FP16) |
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Mutex& getInitializationMutex(); |
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void initializeLayerFactory(); |
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namespace detail { |
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struct NetImplBase |
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{ |
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const int networkId; // network global identifier
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int networkDumpCounter; // dump counter
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int dumpLevel; // level of information dumps (initialized through OPENCV_DNN_NETWORK_DUMP parameter)
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NetImplBase(); |
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std::string getDumpFileNameBase(); |
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}; |
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} // namespace detail
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CV__DNN_INLINE_NS_END |
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}} // namespace
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#endif // __OPENCV_DNN_COMMON_HPP__
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@ -0,0 +1,236 @@ |
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# This file is a part of OpenCV project. |
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# It is a subject to the license terms in the LICENSE file found in the top-level directory |
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# of this distribution and at http://opencv.org/license.html. |
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# |
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# Copyright (C) 2020, Intel Corporation, all rights reserved. |
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# Third party copyrights are property of their respective owners. |
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# |
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# Use this script to get the text graph representation (.pbtxt) of EfficientDet |
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# deep learning network trained in https://github.com/google/automl. |
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# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function. |
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# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API |
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import argparse |
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import re |
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from math import sqrt |
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from tf_text_graph_common import * |
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class AnchorGenerator: |
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def __init__(self, min_level, aspect_ratios, num_scales, anchor_scale): |
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self.min_level = min_level |
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self.aspect_ratios = aspect_ratios |
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self.anchor_scale = anchor_scale |
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self.scales = [2**(float(s) / num_scales) for s in range(num_scales)] |
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def get(self, layer_id): |
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widths = [] |
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heights = [] |
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for s in self.scales: |
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for a in self.aspect_ratios: |
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base_anchor_size = 2**(self.min_level + layer_id) * self.anchor_scale |
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heights.append(base_anchor_size * s * a[1]) |
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widths.append(base_anchor_size * s * a[0]) |
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return widths, heights |
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def createGraph(modelPath, outputPath, min_level, aspect_ratios, num_scales, |
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anchor_scale, num_classes, image_width, image_height): |
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print('Min level: %d' % min_level) |
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print('Anchor scale: %f' % anchor_scale) |
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print('Num scales: %d' % num_scales) |
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print('Aspect ratios: %s' % str(aspect_ratios)) |
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print('Number of classes: %d' % num_classes) |
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print('Input image size: %dx%d' % (image_width, image_height)) |
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# Read the graph. |
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_inpNames = ['image_arrays'] |
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outNames = ['detections'] |
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writeTextGraph(modelPath, outputPath, outNames) |
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graph_def = parseTextGraph(outputPath) |
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def getUnconnectedNodes(): |
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unconnected = [] |
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for node in graph_def.node: |
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if node.op == 'Const': |
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continue |
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unconnected.append(node.name) |
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for inp in node.input: |
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if inp in unconnected: |
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unconnected.remove(inp) |
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return unconnected |
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nodesToKeep = ['truediv'] # Keep preprocessing nodes |
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removeIdentity(graph_def) |
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scopesToKeep = ('image_arrays', 'efficientnet', 'resample_p6', 'resample_p7', |
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'fpn_cells', 'class_net', 'box_net', 'Reshape', 'concat') |
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addConstNode('scale_w', [2.0], graph_def) |
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addConstNode('scale_h', [2.0], graph_def) |
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nodesToKeep += ['scale_w', 'scale_h'] |
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for node in graph_def.node: |
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if re.match('efficientnet-(.*)/blocks_\d+/se/mul_1', node.name): |
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node.input[0], node.input[1] = node.input[1], node.input[0] |
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if re.match('fpn_cells/cell_\d+/fnode\d+/resample(.*)/nearest_upsampling/Reshape_1$', node.name): |
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node.op = 'ResizeNearestNeighbor' |
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node.input[1] = 'scale_w' |
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node.input.append('scale_h') |
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for inpNode in graph_def.node: |
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if inpNode.name == node.name[:node.name.rfind('_')]: |
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node.input[0] = inpNode.input[0] |
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if re.match('box_net/box-predict(_\d)*/separable_conv2d$', node.name): |
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node.addAttr('loc_pred_transposed', True) |
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# Replace RealDiv to Mul with inversed scale for compatibility |
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if node.op == 'RealDiv': |
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for inpNode in graph_def.node: |
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if inpNode.name != node.input[1] or not 'value' in inpNode.attr: |
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continue |
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tensor = inpNode.attr['value']['tensor'][0] |
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if not 'float_val' in tensor: |
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continue |
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scale = float(inpNode.attr['value']['tensor'][0]['float_val'][0]) |
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addConstNode(inpNode.name + '/inv', [1.0 / scale], graph_def) |
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nodesToKeep.append(inpNode.name + '/inv') |
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node.input[1] = inpNode.name + '/inv' |
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node.op = 'Mul' |
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break |
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def to_remove(name, op): |
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if name in nodesToKeep: |
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return False |
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return op == 'Const' or not name.startswith(scopesToKeep) |
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removeUnusedNodesAndAttrs(to_remove, graph_def) |
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# Attach unconnected preprocessing |
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assert(graph_def.node[1].name == 'truediv' and graph_def.node[1].op == 'RealDiv') |
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graph_def.node[1].input.insert(0, 'image_arrays') |
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graph_def.node[2].input.insert(0, 'truediv') |
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priors_generator = AnchorGenerator(min_level, aspect_ratios, num_scales, anchor_scale) |
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priorBoxes = [] |
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for i in range(5): |
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inpName = '' |
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for node in graph_def.node: |
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if node.name == 'Reshape_%d' % (i * 2 + 1): |
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inpName = node.input[0] |
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break |
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priorBox = NodeDef() |
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priorBox.name = 'PriorBox_%d' % i |
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priorBox.op = 'PriorBox' |
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priorBox.input.append(inpName) |
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priorBox.input.append(graph_def.node[0].name) # image_tensor |
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priorBox.addAttr('flip', False) |
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priorBox.addAttr('clip', False) |
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widths, heights = priors_generator.get(i) |
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priorBox.addAttr('width', widths) |
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priorBox.addAttr('height', heights) |
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priorBox.addAttr('variance', [1.0, 1.0, 1.0, 1.0]) |
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graph_def.node.extend([priorBox]) |
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priorBoxes.append(priorBox.name) |
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addConstNode('concat/axis_flatten', [-1], graph_def) |
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def addConcatNode(name, inputs, axisNodeName): |
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concat = NodeDef() |
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concat.name = name |
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concat.op = 'ConcatV2' |
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for inp in inputs: |
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concat.input.append(inp) |
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concat.input.append(axisNodeName) |
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graph_def.node.extend([concat]) |
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addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten') |
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sigmoid = NodeDef() |
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sigmoid.name = 'concat/sigmoid' |
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sigmoid.op = 'Sigmoid' |
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sigmoid.input.append('concat') |
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graph_def.node.extend([sigmoid]) |
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addFlatten(sigmoid.name, sigmoid.name + '/Flatten', graph_def) |
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addFlatten('concat_1', 'concat_1/Flatten', graph_def) |
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detectionOut = NodeDef() |
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detectionOut.name = 'detection_out' |
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detectionOut.op = 'DetectionOutput' |
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detectionOut.input.append('concat_1/Flatten') |
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detectionOut.input.append(sigmoid.name + '/Flatten') |
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detectionOut.input.append('PriorBox/concat') |
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detectionOut.addAttr('num_classes', num_classes) |
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detectionOut.addAttr('share_location', True) |
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detectionOut.addAttr('background_label_id', num_classes + 1) |
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detectionOut.addAttr('nms_threshold', 0.6) |
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detectionOut.addAttr('confidence_threshold', 0.2) |
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detectionOut.addAttr('top_k', 100) |
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detectionOut.addAttr('keep_top_k', 100) |
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detectionOut.addAttr('code_type', "CENTER_SIZE") |
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graph_def.node.extend([detectionOut]) |
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graph_def.node[0].attr['shape'] = { |
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'shape': { |
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'dim': [ |
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{'size': -1}, |
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{'size': image_height}, |
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{'size': image_width}, |
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{'size': 3} |
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] |
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} |
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} |
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while True: |
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unconnectedNodes = getUnconnectedNodes() |
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unconnectedNodes.remove(detectionOut.name) |
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if not unconnectedNodes: |
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break |
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for name in unconnectedNodes: |
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for i in range(len(graph_def.node)): |
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if graph_def.node[i].name == name: |
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del graph_def.node[i] |
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break |
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# Save as text |
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graph_def.save(outputPath) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description='Run this script to get a text graph of ' |
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'SSD model from TensorFlow Object Detection API. ' |
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'Then pass it with .pb file to cv::dnn::readNetFromTensorflow function.') |
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parser.add_argument('--input', required=True, help='Path to frozen TensorFlow graph.') |
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parser.add_argument('--output', required=True, help='Path to output text graph.') |
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parser.add_argument('--min_level', default=3, type=int, help='Parameter from training config') |
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parser.add_argument('--num_scales', default=3, type=int, help='Parameter from training config') |
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parser.add_argument('--anchor_scale', default=4.0, type=float, help='Parameter from training config') |
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parser.add_argument('--aspect_ratios', default=[1.0, 1.0, 1.4, 0.7, 0.7, 1.4], |
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nargs='+', type=float, help='Parameter from training config') |
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parser.add_argument('--num_classes', default=90, type=int, help='Number of classes to detect') |
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parser.add_argument('--width', default=512, type=int, help='Network input width') |
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parser.add_argument('--height', default=512, type=int, help='Network input height') |
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args = parser.parse_args() |
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ar = args.aspect_ratios |
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assert(len(ar) % 2 == 0) |
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ar = list(zip(ar[::2], ar[1::2])) |
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createGraph(args.input, args.output, args.min_level, ar, args.num_scales, |
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args.anchor_scale, args.num_classes, args.width, args.height) |
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