366 lines
16 KiB
Python
366 lines
16 KiB
Python
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from __future__ import print_function
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import sys
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import argparse
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import cv2 as cv
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import tensorflow as tf
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import numpy as np
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import struct
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if sys.version_info > (3,):
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long = int
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from tensorflow.python.tools import optimize_for_inference_lib
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from tensorflow.tools.graph_transforms import TransformGraph
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from tensorflow.core.framework.node_def_pb2 import NodeDef
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from google.protobuf import text_format
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parser = argparse.ArgumentParser(description="Use this script to create TensorFlow graph "
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"with weights from OpenCV's face detection network. "
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"Only backbone part of SSD model is converted this way. "
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"Look for .pbtxt configuration file at "
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"https://github.com/opencv/opencv_extra/tree/master/testdata/dnn/opencv_face_detector.pbtxt")
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parser.add_argument('--model', help='Path to .caffemodel weights', required=True)
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parser.add_argument('--proto', help='Path to .prototxt Caffe model definition', required=True)
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parser.add_argument('--pb', help='Path to output .pb TensorFlow model', required=True)
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parser.add_argument('--pbtxt', help='Path to output .pbxt TensorFlow graph', required=True)
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parser.add_argument('--quantize', help='Quantize weights to uint8', action='store_true')
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parser.add_argument('--fp16', help='Convert weights to half precision floats', action='store_true')
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args = parser.parse_args()
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assert(not args.quantize or not args.fp16)
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dtype = tf.float16 if args.fp16 else tf.float32
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################################################################################
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cvNet = cv.dnn.readNetFromCaffe(args.proto, args.model)
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def dnnLayer(name):
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return cvNet.getLayer(long(cvNet.getLayerId(name)))
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def scale(x, name):
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with tf.variable_scope(name):
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layer = dnnLayer(name)
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w = tf.Variable(layer.blobs[0].flatten(), dtype=dtype, name='mul')
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if len(layer.blobs) > 1:
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b = tf.Variable(layer.blobs[1].flatten(), dtype=dtype, name='add')
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return tf.nn.bias_add(tf.multiply(x, w), b)
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else:
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return tf.multiply(x, w, name)
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def conv(x, name, stride=1, pad='SAME', dilation=1, activ=None):
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with tf.variable_scope(name):
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layer = dnnLayer(name)
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w = tf.Variable(layer.blobs[0].transpose(2, 3, 1, 0), dtype=dtype, name='weights')
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if dilation == 1:
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conv = tf.nn.conv2d(x, filter=w, strides=(1, stride, stride, 1), padding=pad)
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else:
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assert(stride == 1)
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conv = tf.nn.atrous_conv2d(x, w, rate=dilation, padding=pad)
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if len(layer.blobs) > 1:
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b = tf.Variable(layer.blobs[1].flatten(), dtype=dtype, name='bias')
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conv = tf.nn.bias_add(conv, b)
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return activ(conv) if activ else conv
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def batch_norm(x, name):
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with tf.variable_scope(name):
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# Unfortunately, TensorFlow's batch normalization layer doesn't work with fp16 input.
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# Here we do a cast to fp32 but remove it in the frozen graph.
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if x.dtype != tf.float32:
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x = tf.cast(x, tf.float32)
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layer = dnnLayer(name)
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assert(len(layer.blobs) >= 3)
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mean = layer.blobs[0].flatten()
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std = layer.blobs[1].flatten()
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scale = layer.blobs[2].flatten()
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eps = 1e-5
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hasBias = len(layer.blobs) > 3
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hasWeights = scale.shape != (1,)
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if not hasWeights and not hasBias:
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mean /= scale[0]
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std /= scale[0]
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mean = tf.Variable(mean, dtype=tf.float32, name='mean')
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std = tf.Variable(std, dtype=tf.float32, name='std')
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gamma = tf.Variable(scale if hasWeights else np.ones(mean.shape), dtype=tf.float32, name='gamma')
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beta = tf.Variable(layer.blobs[3].flatten() if hasBias else np.zeros(mean.shape), dtype=tf.float32, name='beta')
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bn = tf.nn.fused_batch_norm(x, gamma, beta, mean, std, eps,
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is_training=False)[0]
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if bn.dtype != dtype:
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bn = tf.cast(bn, dtype)
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return bn
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def l2norm(x, name):
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with tf.variable_scope(name):
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layer = dnnLayer(name)
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w = tf.Variable(layer.blobs[0].flatten(), dtype=dtype, name='mul')
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return tf.nn.l2_normalize(x, 3, epsilon=1e-10) * w
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### Graph definition ###########################################################
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inp = tf.placeholder(dtype, [1, 300, 300, 3], 'data')
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data_bn = batch_norm(inp, 'data_bn')
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data_scale = scale(data_bn, 'data_scale')
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# Instead of tf.pad we use tf.space_to_batch_nd layers which override convolution's padding strategy to explicit numbers
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# data_scale = tf.pad(data_scale, [[0, 0], [3, 3], [3, 3], [0, 0]])
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data_scale = tf.space_to_batch_nd(data_scale, [1, 1], [[3, 3], [3, 3]], name='Pad')
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conv1_h = conv(data_scale, stride=2, pad='VALID', name='conv1_h')
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conv1_bn_h = batch_norm(conv1_h, 'conv1_bn_h')
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conv1_scale_h = scale(conv1_bn_h, 'conv1_scale_h')
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conv1_relu = tf.nn.relu(conv1_scale_h)
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conv1_pool = tf.layers.max_pooling2d(conv1_relu, pool_size=(3, 3), strides=(2, 2),
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padding='SAME', name='conv1_pool')
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layer_64_1_conv1_h = conv(conv1_pool, 'layer_64_1_conv1_h')
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layer_64_1_bn2_h = batch_norm(layer_64_1_conv1_h, 'layer_64_1_bn2_h')
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layer_64_1_scale2_h = scale(layer_64_1_bn2_h, 'layer_64_1_scale2_h')
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layer_64_1_relu2 = tf.nn.relu(layer_64_1_scale2_h)
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layer_64_1_conv2_h = conv(layer_64_1_relu2, 'layer_64_1_conv2_h')
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layer_64_1_sum = layer_64_1_conv2_h + conv1_pool
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layer_128_1_bn1_h = batch_norm(layer_64_1_sum, 'layer_128_1_bn1_h')
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layer_128_1_scale1_h = scale(layer_128_1_bn1_h, 'layer_128_1_scale1_h')
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layer_128_1_relu1 = tf.nn.relu(layer_128_1_scale1_h)
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layer_128_1_conv1_h = conv(layer_128_1_relu1, stride=2, name='layer_128_1_conv1_h')
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layer_128_1_bn2 = batch_norm(layer_128_1_conv1_h, 'layer_128_1_bn2')
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layer_128_1_scale2 = scale(layer_128_1_bn2, 'layer_128_1_scale2')
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layer_128_1_relu2 = tf.nn.relu(layer_128_1_scale2)
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layer_128_1_conv2 = conv(layer_128_1_relu2, 'layer_128_1_conv2')
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layer_128_1_conv_expand_h = conv(layer_128_1_relu1, stride=2, name='layer_128_1_conv_expand_h')
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layer_128_1_sum = layer_128_1_conv2 + layer_128_1_conv_expand_h
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layer_256_1_bn1 = batch_norm(layer_128_1_sum, 'layer_256_1_bn1')
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layer_256_1_scale1 = scale(layer_256_1_bn1, 'layer_256_1_scale1')
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layer_256_1_relu1 = tf.nn.relu(layer_256_1_scale1)
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# layer_256_1_conv1 = tf.pad(layer_256_1_relu1, [[0, 0], [1, 1], [1, 1], [0, 0]])
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layer_256_1_conv1 = tf.space_to_batch_nd(layer_256_1_relu1, [1, 1], [[1, 1], [1, 1]], name='Pad_1')
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layer_256_1_conv1 = conv(layer_256_1_conv1, stride=2, pad='VALID', name='layer_256_1_conv1')
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layer_256_1_bn2 = batch_norm(layer_256_1_conv1, 'layer_256_1_bn2')
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layer_256_1_scale2 = scale(layer_256_1_bn2, 'layer_256_1_scale2')
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layer_256_1_relu2 = tf.nn.relu(layer_256_1_scale2)
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layer_256_1_conv2 = conv(layer_256_1_relu2, 'layer_256_1_conv2')
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layer_256_1_conv_expand = conv(layer_256_1_relu1, stride=2, name='layer_256_1_conv_expand')
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layer_256_1_sum = layer_256_1_conv2 + layer_256_1_conv_expand
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layer_512_1_bn1 = batch_norm(layer_256_1_sum, 'layer_512_1_bn1')
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layer_512_1_scale1 = scale(layer_512_1_bn1, 'layer_512_1_scale1')
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layer_512_1_relu1 = tf.nn.relu(layer_512_1_scale1)
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layer_512_1_conv1_h = conv(layer_512_1_relu1, 'layer_512_1_conv1_h')
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layer_512_1_bn2_h = batch_norm(layer_512_1_conv1_h, 'layer_512_1_bn2_h')
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layer_512_1_scale2_h = scale(layer_512_1_bn2_h, 'layer_512_1_scale2_h')
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layer_512_1_relu2 = tf.nn.relu(layer_512_1_scale2_h)
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layer_512_1_conv2_h = conv(layer_512_1_relu2, dilation=2, name='layer_512_1_conv2_h')
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layer_512_1_conv_expand_h = conv(layer_512_1_relu1, 'layer_512_1_conv_expand_h')
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layer_512_1_sum = layer_512_1_conv2_h + layer_512_1_conv_expand_h
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last_bn_h = batch_norm(layer_512_1_sum, 'last_bn_h')
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last_scale_h = scale(last_bn_h, 'last_scale_h')
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fc7 = tf.nn.relu(last_scale_h, name='last_relu')
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conv6_1_h = conv(fc7, 'conv6_1_h', activ=tf.nn.relu)
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conv6_2_h = conv(conv6_1_h, stride=2, name='conv6_2_h', activ=tf.nn.relu)
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conv7_1_h = conv(conv6_2_h, 'conv7_1_h', activ=tf.nn.relu)
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# conv7_2_h = tf.pad(conv7_1_h, [[0, 0], [1, 1], [1, 1], [0, 0]])
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conv7_2_h = tf.space_to_batch_nd(conv7_1_h, [1, 1], [[1, 1], [1, 1]], name='Pad_2')
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conv7_2_h = conv(conv7_2_h, stride=2, pad='VALID', name='conv7_2_h', activ=tf.nn.relu)
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conv8_1_h = conv(conv7_2_h, pad='SAME', name='conv8_1_h', activ=tf.nn.relu)
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conv8_2_h = conv(conv8_1_h, pad='VALID', name='conv8_2_h', activ=tf.nn.relu)
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conv9_1_h = conv(conv8_2_h, 'conv9_1_h', activ=tf.nn.relu)
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conv9_2_h = conv(conv9_1_h, pad='VALID', name='conv9_2_h', activ=tf.nn.relu)
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conv4_3_norm = l2norm(layer_256_1_relu1, 'conv4_3_norm')
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### Locations and confidences ##################################################
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locations = []
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confidences = []
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flattenLayersNames = [] # Collect all reshape layers names that should be replaced to flattens.
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for top, suffix in zip([locations, confidences], ['_mbox_loc', '_mbox_conf']):
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for bottom, name in zip([conv4_3_norm, fc7, conv6_2_h, conv7_2_h, conv8_2_h, conv9_2_h],
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['conv4_3_norm', 'fc7', 'conv6_2', 'conv7_2', 'conv8_2', 'conv9_2']):
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name += suffix
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flat = tf.layers.flatten(conv(bottom, name))
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flattenLayersNames.append(flat.name[:flat.name.find(':')])
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top.append(flat)
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mbox_loc = tf.concat(locations, axis=-1, name='mbox_loc')
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mbox_conf = tf.concat(confidences, axis=-1, name='mbox_conf')
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total = int(np.prod(mbox_conf.shape[1:]))
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mbox_conf_reshape = tf.reshape(mbox_conf, [-1, 2], name='mbox_conf_reshape')
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mbox_conf_softmax = tf.nn.softmax(mbox_conf_reshape, name='mbox_conf_softmax')
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mbox_conf_flatten = tf.reshape(mbox_conf_softmax, [-1, total], name='mbox_conf_flatten')
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flattenLayersNames.append('mbox_conf_flatten')
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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### Check correctness ######################################################
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out_nodes = ['mbox_loc', 'mbox_conf_flatten']
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inp_nodes = [inp.name[:inp.name.find(':')]]
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np.random.seed(2701)
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inputData = np.random.standard_normal([1, 3, 300, 300]).astype(np.float32)
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cvNet.setInput(inputData)
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cvNet.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
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outDNN = cvNet.forward(out_nodes)
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outTF = sess.run([mbox_loc, mbox_conf_flatten], feed_dict={inp: inputData.transpose(0, 2, 3, 1)})
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print('Max diff @ locations: %e' % np.max(np.abs(outDNN[0] - outTF[0])))
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print('Max diff @ confidence: %e' % np.max(np.abs(outDNN[1] - outTF[1])))
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# Save a graph
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graph_def = sess.graph.as_graph_def()
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# Freeze graph. Replaces variables to constants.
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graph_def = tf.graph_util.convert_variables_to_constants(sess, graph_def, out_nodes)
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# Optimize graph. Removes training-only ops, unused nodes.
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graph_def = optimize_for_inference_lib.optimize_for_inference(graph_def, inp_nodes, out_nodes, dtype.as_datatype_enum)
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# Fuse constant operations.
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transforms = ["fold_constants(ignore_errors=True)"]
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if args.quantize:
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transforms += ["quantize_weights(minimum_size=0)"]
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transforms += ["sort_by_execution_order"]
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graph_def = TransformGraph(graph_def, inp_nodes, out_nodes, transforms)
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# By default, float16 weights are stored in repeated tensor's field called
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# `half_val`. It has type int32 with leading zeros for unused bytes.
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# This type is encoded by Variant that means only 7 bits are used for value
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# representation but the last one is indicated the end of encoding. This way
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# float16 might takes 1 or 2 or 3 bytes depends on value. To improve compression,
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# we replace all `half_val` values to `tensor_content` using only 2 bytes for everyone.
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for node in graph_def.node:
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if 'value' in node.attr:
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halfs = node.attr["value"].tensor.half_val
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if not node.attr["value"].tensor.tensor_content and halfs:
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node.attr["value"].tensor.tensor_content = struct.pack('H' * len(halfs), *halfs)
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node.attr["value"].tensor.ClearField('half_val')
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# Serialize
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with tf.gfile.FastGFile(args.pb, 'wb') as f:
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f.write(graph_def.SerializeToString())
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################################################################################
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# Write a text graph representation
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################################################################################
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def tensorMsg(values):
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msg = 'tensor { dtype: DT_FLOAT tensor_shape { dim { size: %d } }' % len(values)
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for value in values:
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msg += 'float_val: %f ' % value
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return msg + '}'
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# Remove Const nodes and unused attributes.
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for i in reversed(range(len(graph_def.node))):
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if graph_def.node[i].op in ['Const', 'Dequantize']:
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del graph_def.node[i]
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for attr in ['T', 'data_format', 'Tshape', 'N', 'Tidx', 'Tdim',
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'use_cudnn_on_gpu', 'Index', 'Tperm', 'is_training',
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'Tpaddings', 'Tblock_shape', 'Tcrops']:
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if attr in graph_def.node[i].attr:
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del graph_def.node[i].attr[attr]
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|
# Append prior box generators
|
||
|
|
min_sizes = [30, 60, 111, 162, 213, 264]
|
||
|
|
max_sizes = [60, 111, 162, 213, 264, 315]
|
||
|
|
steps = [8, 16, 32, 64, 100, 300]
|
||
|
|
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
|
||
|
|
layers = [conv4_3_norm, fc7, conv6_2_h, conv7_2_h, conv8_2_h, conv9_2_h]
|
||
|
|
for i in range(6):
|
||
|
|
priorBox = NodeDef()
|
||
|
|
priorBox.name = 'PriorBox_%d' % i
|
||
|
|
priorBox.op = 'PriorBox'
|
||
|
|
priorBox.input.append(layers[i].name[:layers[i].name.find(':')])
|
||
|
|
priorBox.input.append(inp_nodes[0]) # data
|
||
|
|
|
||
|
|
text_format.Merge('i: %d' % min_sizes[i], priorBox.attr["min_size"])
|
||
|
|
text_format.Merge('i: %d' % max_sizes[i], priorBox.attr["max_size"])
|
||
|
|
text_format.Merge('b: true', priorBox.attr["flip"])
|
||
|
|
text_format.Merge('b: false', priorBox.attr["clip"])
|
||
|
|
text_format.Merge(tensorMsg(aspect_ratios[i]), priorBox.attr["aspect_ratio"])
|
||
|
|
text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"])
|
||
|
|
text_format.Merge('f: %f' % steps[i], priorBox.attr["step"])
|
||
|
|
text_format.Merge('f: 0.5', priorBox.attr["offset"])
|
||
|
|
graph_def.node.extend([priorBox])
|
||
|
|
|
||
|
|
# Concatenate prior boxes
|
||
|
|
concat = NodeDef()
|
||
|
|
concat.name = 'mbox_priorbox'
|
||
|
|
concat.op = 'ConcatV2'
|
||
|
|
for i in range(6):
|
||
|
|
concat.input.append('PriorBox_%d' % i)
|
||
|
|
concat.input.append('mbox_loc/axis')
|
||
|
|
graph_def.node.extend([concat])
|
||
|
|
|
||
|
|
# DetectionOutput layer
|
||
|
|
detectionOut = NodeDef()
|
||
|
|
detectionOut.name = 'detection_out'
|
||
|
|
detectionOut.op = 'DetectionOutput'
|
||
|
|
|
||
|
|
detectionOut.input.append('mbox_loc')
|
||
|
|
detectionOut.input.append('mbox_conf_flatten')
|
||
|
|
detectionOut.input.append('mbox_priorbox')
|
||
|
|
|
||
|
|
text_format.Merge('i: 2', detectionOut.attr['num_classes'])
|
||
|
|
text_format.Merge('b: true', detectionOut.attr['share_location'])
|
||
|
|
text_format.Merge('i: 0', detectionOut.attr['background_label_id'])
|
||
|
|
text_format.Merge('f: 0.45', detectionOut.attr['nms_threshold'])
|
||
|
|
text_format.Merge('i: 400', detectionOut.attr['top_k'])
|
||
|
|
text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type'])
|
||
|
|
text_format.Merge('i: 200', detectionOut.attr['keep_top_k'])
|
||
|
|
text_format.Merge('f: 0.01', detectionOut.attr['confidence_threshold'])
|
||
|
|
|
||
|
|
graph_def.node.extend([detectionOut])
|
||
|
|
|
||
|
|
# Replace L2Normalization subgraph onto a single node.
|
||
|
|
for i in reversed(range(len(graph_def.node))):
|
||
|
|
if graph_def.node[i].name in ['conv4_3_norm/l2_normalize/Square',
|
||
|
|
'conv4_3_norm/l2_normalize/Sum',
|
||
|
|
'conv4_3_norm/l2_normalize/Maximum',
|
||
|
|
'conv4_3_norm/l2_normalize/Rsqrt']:
|
||
|
|
del graph_def.node[i]
|
||
|
|
for node in graph_def.node:
|
||
|
|
if node.name == 'conv4_3_norm/l2_normalize':
|
||
|
|
node.op = 'L2Normalize'
|
||
|
|
node.input.pop()
|
||
|
|
node.input.pop()
|
||
|
|
node.input.append(layer_256_1_relu1.name)
|
||
|
|
node.input.append('conv4_3_norm/l2_normalize/Sum/reduction_indices')
|
||
|
|
break
|
||
|
|
|
||
|
|
softmaxShape = NodeDef()
|
||
|
|
softmaxShape.name = 'reshape_before_softmax'
|
||
|
|
softmaxShape.op = 'Const'
|
||
|
|
text_format.Merge(
|
||
|
|
'tensor {'
|
||
|
|
' dtype: DT_INT32'
|
||
|
|
' tensor_shape { dim { size: 3 } }'
|
||
|
|
' int_val: 0'
|
||
|
|
' int_val: -1'
|
||
|
|
' int_val: 2'
|
||
|
|
'}', softmaxShape.attr["value"])
|
||
|
|
graph_def.node.extend([softmaxShape])
|
||
|
|
|
||
|
|
for node in graph_def.node:
|
||
|
|
if node.name == 'mbox_conf_reshape':
|
||
|
|
node.input[1] = softmaxShape.name
|
||
|
|
elif node.name == 'mbox_conf_softmax':
|
||
|
|
text_format.Merge('i: 2', node.attr['axis'])
|
||
|
|
elif node.name in flattenLayersNames:
|
||
|
|
node.op = 'Flatten'
|
||
|
|
inpName = node.input[0]
|
||
|
|
node.input.pop()
|
||
|
|
node.input.pop()
|
||
|
|
node.input.append(inpName)
|
||
|
|
|
||
|
|
tf.train.write_graph(graph_def, "", args.pbtxt, as_text=True)
|