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SSD with FPN proposals from TensorFlow

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This commit is contained in:
Dmitry Kurtaev
2019-01-11 10:50:52 +03:00
parent 71aee662bd
commit 4ae5df5509
4 changed files with 132 additions and 42 deletions
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samples/dnn/tf_text_graph_common.py
+1 -1
View File
@@ -289,7 +289,7 @@ def removeUnusedNodesAndAttrs(to_remove, graph_def):
op = graph_def.node[i].op
name = graph_def.node[i].name
if op == 'Const' or to_remove(name, op):
if to_remove(name, op):
if op != 'Const':
removedNodes.append(name)
samples/dnn/tf_text_graph_faster_rcnn.py
+1 -1
View File
@@ -49,7 +49,7 @@ def createFasterRCNNGraph(modelPath, configPath, outputPath):
removeIdentity(graph_def)
def to_remove(name, op):
return name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
return op == 'Const' or name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
(name.startswith('CropAndResize') and op != 'CropAndResize')
removeUnusedNodesAndAttrs(to_remove, graph_def)
samples/dnn/tf_text_graph_mask_rcnn.py
+1 -1
View File
@@ -55,7 +55,7 @@ graph_def = parseTextGraph(args.output)
removeIdentity(graph_def)
def to_remove(name, op):
return name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
return op == 'Const' or name.startswith(scopesToIgnore) or not name.startswith(scopesToKeep) or \
(name.startswith('CropAndResize') and op != 'CropAndResize')
removeUnusedNodesAndAttrs(to_remove, graph_def)
samples/dnn/tf_text_graph_ssd.py
+129 -39
View File
@@ -10,14 +10,60 @@
# Then you can import it with a binary frozen graph (.pb) using readNetFromTensorflow() function.
# See details and examples on the following wiki page: https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API
import argparse
import re
from math import sqrt
from tf_text_graph_common import *
class SSDAnchorGenerator:
def __init__(self, min_scale, max_scale, num_layers, aspect_ratios,
reduce_boxes_in_lowest_layer, image_width, image_height):
self.min_scale = min_scale
self.aspect_ratios = aspect_ratios
self.reduce_boxes_in_lowest_layer = reduce_boxes_in_lowest_layer
self.image_width = image_width
self.image_height = image_height
self.scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
for i in range(num_layers)] + [1.0]
def get(self, layer_id):
if layer_id == 0 and self.reduce_boxes_in_lowest_layer:
widths = [0.1, self.min_scale * sqrt(2.0), self.min_scale * sqrt(0.5)]
heights = [0.1, self.min_scale / sqrt(2.0), self.min_scale / sqrt(0.5)]
else:
widths = [self.scales[layer_id] * sqrt(ar) for ar in self.aspect_ratios]
heights = [self.scales[layer_id] / sqrt(ar) for ar in self.aspect_ratios]
widths += [sqrt(self.scales[layer_id] * self.scales[layer_id + 1])]
heights += [sqrt(self.scales[layer_id] * self.scales[layer_id + 1])]
widths = [w * self.image_width for w in widths]
heights = [h * self.image_height for h in heights]
return widths, heights
class MultiscaleAnchorGenerator:
def __init__(self, min_level, aspect_ratios, scales_per_octave, anchor_scale):
self.min_level = min_level
self.aspect_ratios = aspect_ratios
self.anchor_scale = anchor_scale
self.scales = [2**(float(s) / scales_per_octave) for s in range(scales_per_octave)]
def get(self, layer_id):
widths = []
heights = []
for a in self.aspect_ratios:
for s in self.scales:
base_anchor_size = 2**(self.min_level + layer_id) * self.anchor_scale
ar = sqrt(a)
heights.append(base_anchor_size * s / ar)
widths.append(base_anchor_size * s * ar)
return widths, heights
def createSSDGraph(modelPath, configPath, outputPath):
# Nodes that should be kept.
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu6', 'Placeholder', 'FusedBatchNorm',
keepOps = ['Conv2D', 'BiasAdd', 'Add', 'Relu', 'Relu6', 'Placeholder', 'FusedBatchNorm',
'DepthwiseConv2dNative', 'ConcatV2', 'Mul', 'MaxPool', 'AvgPool', 'Identity',
'Sub']
'Sub', 'ResizeNearestNeighbor', 'Pad']
# Node with which prefixes should be removed
prefixesToRemove = ('MultipleGridAnchorGenerator/', 'Postprocessor/', 'Preprocessor/map')
@@ -27,26 +73,50 @@ def createSSDGraph(modelPath, configPath, outputPath):
config = config['model'][0]['ssd'][0]
num_classes = int(config['num_classes'][0])
ssd_anchor_generator = config['anchor_generator'][0]['ssd_anchor_generator'][0]
min_scale = float(ssd_anchor_generator['min_scale'][0])
max_scale = float(ssd_anchor_generator['max_scale'][0])
num_layers = int(ssd_anchor_generator['num_layers'][0])
aspect_ratios = [float(ar) for ar in ssd_anchor_generator['aspect_ratios']]
reduce_boxes_in_lowest_layer = True
if 'reduce_boxes_in_lowest_layer' in ssd_anchor_generator:
reduce_boxes_in_lowest_layer = ssd_anchor_generator['reduce_boxes_in_lowest_layer'][0] == 'true'
fixed_shape_resizer = config['image_resizer'][0]['fixed_shape_resizer'][0]
image_width = int(fixed_shape_resizer['width'][0])
image_height = int(fixed_shape_resizer['height'][0])
box_predictor = 'convolutional' if 'convolutional_box_predictor' in config['box_predictor'][0] else 'weight_shared_convolutional'
anchor_generator = config['anchor_generator'][0]
if 'ssd_anchor_generator' in anchor_generator:
ssd_anchor_generator = anchor_generator['ssd_anchor_generator'][0]
min_scale = float(ssd_anchor_generator['min_scale'][0])
max_scale = float(ssd_anchor_generator['max_scale'][0])
num_layers = int(ssd_anchor_generator['num_layers'][0])
aspect_ratios = [float(ar) for ar in ssd_anchor_generator['aspect_ratios']]
reduce_boxes_in_lowest_layer = True
if 'reduce_boxes_in_lowest_layer' in ssd_anchor_generator:
reduce_boxes_in_lowest_layer = ssd_anchor_generator['reduce_boxes_in_lowest_layer'][0] == 'true'
priors_generator = SSDAnchorGenerator(min_scale, max_scale, num_layers,
aspect_ratios, reduce_boxes_in_lowest_layer,
image_width, image_height)
print('Scale: [%f-%f]' % (min_scale, max_scale))
print('Aspect ratios: %s' % str(aspect_ratios))
print('Reduce boxes in the lowest layer: %s' % str(reduce_boxes_in_lowest_layer))
elif 'multiscale_anchor_generator' in anchor_generator:
multiscale_anchor_generator = anchor_generator['multiscale_anchor_generator'][0]
min_level = int(multiscale_anchor_generator['min_level'][0])
max_level = int(multiscale_anchor_generator['max_level'][0])
anchor_scale = float(multiscale_anchor_generator['anchor_scale'][0])
aspect_ratios = [float(ar) for ar in multiscale_anchor_generator['aspect_ratios']]
scales_per_octave = int(multiscale_anchor_generator['scales_per_octave'][0])
num_layers = max_level - min_level + 1
priors_generator = MultiscaleAnchorGenerator(min_level, aspect_ratios,
scales_per_octave, anchor_scale)
print('Levels: [%d-%d]' % (min_level, max_level))
print('Anchor scale: %f' % anchor_scale)
print('Scales per octave: %d' % scales_per_octave)
print('Aspect ratios: %s' % str(aspect_ratios))
else:
print('Unknown anchor_generator')
exit(0)
print('Number of classes: %d' % num_classes)
print('Number of layers: %d' % num_layers)
print('Scale: [%f-%f]' % (min_scale, max_scale))
print('Aspect ratios: %s' % str(aspect_ratios))
print('Reduce boxes in the lowest layer: %s' % str(reduce_boxes_in_lowest_layer))
print('box predictor: %s' % box_predictor)
print('Input image size: %dx%d' % (image_width, image_height))
@@ -67,8 +137,8 @@ def createSSDGraph(modelPath, configPath, outputPath):
return unconnected
# Detect unfused batch normalization nodes and fuse them.
def fuse_batch_normalization():
def fuse_nodes(nodesToKeep):
# Detect unfused batch normalization nodes and fuse them.
# Add_0 <-- moving_variance, add_y
# Rsqrt <-- Add_0
# Mul_0 <-- Rsqrt, gamma
@@ -77,9 +147,15 @@ def createSSDGraph(modelPath, configPath, outputPath):
# Sub_0 <-- beta, Mul_2
# Add_1 <-- Mul_1, Sub_0
nodesMap = {node.name: node for node in graph_def.node}
subgraph = ['Add',
subgraphBatchNorm = ['Add',
['Mul', 'input', ['Mul', ['Rsqrt', ['Add', 'moving_variance', 'add_y']], 'gamma']],
['Sub', 'beta', ['Mul', 'moving_mean', 'Mul_0']]]
# Detect unfused nearest neighbor resize.
subgraphResizeNN = ['Reshape',
['Mul', ['Reshape', 'input', ['Pack', 'shape_1', 'shape_2', 'shape_3', 'shape_4', 'shape_5']],
'ones'],
['Pack', ['StridedSlice', ['Shape', 'input'], 'stack', 'stack_1', 'stack_2'],
'out_height', 'out_width', 'out_channels']]
def checkSubgraph(node, targetNode, inputs, fusedNodes):
op = targetNode[0]
if node.op == op and (len(node.input) >= len(targetNode) - 1):
@@ -100,7 +176,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
for node in graph_def.node:
inputs = {}
fusedNodes = []
if checkSubgraph(node, subgraph, inputs, fusedNodes):
if checkSubgraph(node, subgraphBatchNorm, inputs, fusedNodes):
name = node.name
node.Clear()
node.name = name
@@ -112,15 +188,41 @@ def createSSDGraph(modelPath, configPath, outputPath):
node.input.append(inputs['moving_variance'])
node.addAttr('epsilon', 0.001)
nodesToRemove += fusedNodes[1:]
inputs = {}
fusedNodes = []
if checkSubgraph(node, subgraphResizeNN, inputs, fusedNodes):
name = node.name
node.Clear()
node.name = name
node.op = 'ResizeNearestNeighbor'
node.input.append(inputs['input'])
node.input.append(name + '/output_shape')
out_height_node = nodesMap[inputs['out_height']]
out_width_node = nodesMap[inputs['out_width']]
out_height = int(out_height_node.attr['value']['tensor'][0]['int_val'][0])
out_width = int(out_width_node.attr['value']['tensor'][0]['int_val'][0])
shapeNode = NodeDef()
shapeNode.name = name + '/output_shape'
shapeNode.op = 'Const'
shapeNode.addAttr('value', [out_height, out_width])
graph_def.node.insert(graph_def.node.index(node), shapeNode)
nodesToKeep.append(shapeNode.name)
nodesToRemove += fusedNodes[1:]
for node in nodesToRemove:
graph_def.node.remove(node)
fuse_batch_normalization()
nodesToKeep = []
fuse_nodes(nodesToKeep)
removeIdentity(graph_def)
def to_remove(name, op):
return (not op in keepOps) or name.startswith(prefixesToRemove)
return (not name in nodesToKeep) and \
(op == 'Const' or (not op in keepOps) or name.startswith(prefixesToRemove))
removeUnusedNodesAndAttrs(to_remove, graph_def)
@@ -169,19 +271,15 @@ def createSSDGraph(modelPath, configPath, outputPath):
graph_def.node.extend([flatten])
addConcatNode('%s/concat' % label, concatInputs, 'concat/axis_flatten')
idx = 0
num_matched_layers = 0
for node in graph_def.node:
if node.name == ('BoxPredictor_%d/BoxEncodingPredictor/Conv2D' % idx) or \
node.name == ('WeightSharedConvolutionalBoxPredictor_%d/BoxPredictor/Conv2D' % idx) or \
node.name == 'WeightSharedConvolutionalBoxPredictor/BoxPredictor/Conv2D':
if re.match('BoxPredictor_\d/BoxEncodingPredictor/Conv2D', node.name) or \
re.match('WeightSharedConvolutionalBoxPredictor(_\d)*/BoxPredictor/Conv2D', node.name):
node.addAttr('loc_pred_transposed', True)
idx += 1
assert(idx == num_layers)
num_matched_layers += 1
assert(num_matched_layers == num_layers)
# Add layers that generate anchors (bounding boxes proposals).
scales = [min_scale + (max_scale - min_scale) * i / (num_layers - 1)
for i in range(num_layers)] + [1.0]
priorBoxes = []
for i in range(num_layers):
priorBox = NodeDef()
@@ -199,17 +297,8 @@ def createSSDGraph(modelPath, configPath, outputPath):
priorBox.addAttr('flip', False)
priorBox.addAttr('clip', False)
if i == 0 and reduce_boxes_in_lowest_layer:
widths = [0.1, min_scale * sqrt(2.0), min_scale * sqrt(0.5)]
heights = [0.1, min_scale / sqrt(2.0), min_scale / sqrt(0.5)]
else:
widths = [scales[i] * sqrt(ar) for ar in aspect_ratios]
heights = [scales[i] / sqrt(ar) for ar in aspect_ratios]
widths, heights = priors_generator.get(i)
widths += [sqrt(scales[i] * scales[i + 1])]
heights += [sqrt(scales[i] * scales[i + 1])]
widths = [w * image_width for w in widths]
heights = [h * image_height for h in heights]
priorBox.addAttr('width', widths)
priorBox.addAttr('height', heights)
priorBox.addAttr('variance', [0.1, 0.1, 0.2, 0.2])
@@ -217,6 +306,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
graph_def.node.extend([priorBox])
priorBoxes.append(priorBox.name)
# Compare this layer's output with Postprocessor/Reshape
addConcatNode('PriorBox/concat', priorBoxes, 'concat/axis_flatten')
# Sigmoid for classes predictions and DetectionOutput layer