mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
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365 lines
16 KiB
365 lines
16 KiB
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 |
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min_sizes = [30, 60, 111, 162, 213, 264] |
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max_sizes = [60, 111, 162, 213, 264, 315] |
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steps = [8, 16, 32, 64, 100, 300] |
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aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]] |
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layers = [conv4_3_norm, fc7, conv6_2_h, conv7_2_h, conv8_2_h, conv9_2_h] |
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for i in range(6): |
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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(layers[i].name[:layers[i].name.find(':')]) |
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priorBox.input.append(inp_nodes[0]) # data |
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text_format.Merge('i: %d' % min_sizes[i], priorBox.attr["min_size"]) |
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text_format.Merge('i: %d' % max_sizes[i], priorBox.attr["max_size"]) |
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text_format.Merge('b: true', priorBox.attr["flip"]) |
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text_format.Merge('b: false', priorBox.attr["clip"]) |
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text_format.Merge(tensorMsg(aspect_ratios[i]), priorBox.attr["aspect_ratio"]) |
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text_format.Merge(tensorMsg([0.1, 0.1, 0.2, 0.2]), priorBox.attr["variance"]) |
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text_format.Merge('f: %f' % steps[i], priorBox.attr["step"]) |
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text_format.Merge('f: 0.5', priorBox.attr["offset"]) |
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graph_def.node.extend([priorBox]) |
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# Concatenate prior boxes |
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concat = NodeDef() |
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concat.name = 'mbox_priorbox' |
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concat.op = 'ConcatV2' |
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for i in range(6): |
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concat.input.append('PriorBox_%d' % i) |
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concat.input.append('mbox_loc/axis') |
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graph_def.node.extend([concat]) |
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# DetectionOutput layer |
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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('mbox_loc') |
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detectionOut.input.append('mbox_conf_flatten') |
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detectionOut.input.append('mbox_priorbox') |
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text_format.Merge('i: 2', detectionOut.attr['num_classes']) |
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text_format.Merge('b: true', detectionOut.attr['share_location']) |
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text_format.Merge('i: 0', detectionOut.attr['background_label_id']) |
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text_format.Merge('f: 0.45', detectionOut.attr['nms_threshold']) |
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text_format.Merge('i: 400', detectionOut.attr['top_k']) |
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text_format.Merge('s: "CENTER_SIZE"', detectionOut.attr['code_type']) |
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text_format.Merge('i: 200', detectionOut.attr['keep_top_k']) |
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text_format.Merge('f: 0.01', detectionOut.attr['confidence_threshold']) |
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graph_def.node.extend([detectionOut]) |
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# Replace L2Normalization subgraph onto a single node. |
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for i in reversed(range(len(graph_def.node))): |
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if graph_def.node[i].name in ['conv4_3_norm/l2_normalize/Square', |
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'conv4_3_norm/l2_normalize/Sum', |
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'conv4_3_norm/l2_normalize/Maximum', |
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'conv4_3_norm/l2_normalize/Rsqrt']: |
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del graph_def.node[i] |
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for node in graph_def.node: |
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if node.name == 'conv4_3_norm/l2_normalize': |
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node.op = 'L2Normalize' |
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node.input.pop() |
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node.input.pop() |
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node.input.append(layer_256_1_relu1.name) |
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node.input.append('conv4_3_norm/l2_normalize/Sum/reduction_indices') |
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break |
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softmaxShape = NodeDef() |
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softmaxShape.name = 'reshape_before_softmax' |
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softmaxShape.op = 'Const' |
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text_format.Merge( |
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'tensor {' |
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' dtype: DT_INT32' |
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' tensor_shape { dim { size: 3 } }' |
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' int_val: 0' |
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' int_val: -1' |
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' int_val: 2' |
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'}', softmaxShape.attr["value"]) |
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graph_def.node.extend([softmaxShape]) |
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for node in graph_def.node: |
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if node.name == 'mbox_conf_reshape': |
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node.input[1] = softmaxShape.name |
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elif node.name == 'mbox_conf_softmax': |
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text_format.Merge('i: 2', node.attr['axis']) |
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elif node.name in flattenLayersNames: |
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node.op = 'Flatten' |
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inpName = node.input[0] |
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node.input.pop() |
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node.input.pop() |
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node.input.append(inpName) |
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tf.train.write_graph(graph_def, "", args.pbtxt, as_text=True)
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