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weights_loader.py
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weights_loader.py
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import tensorflow as tf
import os
import numpy as np
import os.path
import test
import net
# IMPORTANT: Weights order in the binary file is [ 'biases','gamma','moving_mean','moving_variance','kernel']
# IMPORTANT: biases ARE NOT the usual biases to add after the conv2d! They refer to the betas (offsets) in the Batch Normalization!
# IMPORTANT: the biases added after the conv2d are set to zero!
# IMPORTANT: to use the weights they actually need to be de-normalized because of the Batch Normalization! ( see later )
def load_conv_layer_bn(name,loaded_weights,shape,offset):
# Conv layer with Batch norm
n_kernel_weights = shape[0]*shape[1]*shape[2]*shape[3]
n_output_channels = shape[-1]
n_bn_mean = n_output_channels
n_bn_var = n_output_channels
n_biases = n_output_channels
n_bn_gamma = n_output_channels
n_weights_conv_bn = (n_kernel_weights + n_output_channels * 4)
# IMPORTANT: This is where (n_kernel_weights + n_output_channels * 4) comes from:
# n_params = kernel_shape + n_biases + n_bn_means + n_bn_var + n_bn_gammas
# n_params = kernel_shape + n_biases + n_output_channels + n_output_channels + n_output_channels
# n_params = kernel_shape + n_output_channels + n_output_channels + n_output_channels + n_output_channels
# n_params = kernel_shape + n_output_channels*4
# IMPORTANT: YOLOv2 sets the biases in every convolution = 0 and keeps only the betas (offsets) of the Batch Normalization!
# So in the end there will be only mean,var,beta(offset),gamma(scale) for every single output channel!
print('Loading '+str(n_weights_conv_bn)+' weights of '+name+' ...')
biases = loaded_weights[offset:offset+n_biases]
offset = offset + n_biases
gammas = loaded_weights[offset:offset+n_bn_gamma]
offset = offset + n_bn_gamma
means = loaded_weights[offset:offset+n_bn_mean]
offset = offset + n_bn_mean
var = loaded_weights[offset:offset+n_bn_var]
offset = offset + n_bn_var
kernel_weights = loaded_weights[offset:offset+n_kernel_weights]
offset = offset + n_kernel_weights
# IMPORTANT: DarkNet conv_weights are serialized Caffe-style: (out_dim, in_dim, height, width)
kernel_weights = np.reshape(kernel_weights,(shape[3],shape[2],shape[0],shape[1]),order='C')
# IMPORTANT: Denormalize the weights with the Batch Normalization parameters
for i in range(n_output_channels):
scale = gammas[i] / np.sqrt(var[i] + net.bn_epsilon)
kernel_weights[i,:,:,:] = kernel_weights[i,:,:,:] * scale
biases[i] = biases[i] - means[i] * scale
# IMPORTANT: Set weights to Tensorflow order: (height, width, in_dim, out_dim)
kernel_weights = np.transpose(kernel_weights,[2,3,1,0])
return biases,kernel_weights,offset
def load_conv_layer(name,loaded_weights,shape,offset):
# Conv layer without Batch norm
n_kernel_weights = shape[0]*shape[1]*shape[2]*shape[3]
n_output_channels = shape[-1]
n_biases = n_output_channels
n_weights_conv = (n_kernel_weights + n_output_channels)
# The number of weights is a conv layer without batchnorm is: (kernel_height*kernel_width + n_biases)
print('Loading '+str(n_weights_conv)+' weights of '+name+' ...')
biases = loaded_weights[offset:offset+n_biases]
offset = offset + n_biases
kernel_weights = loaded_weights[offset:offset+n_kernel_weights]
offset = offset + n_kernel_weights
# IMPORTANT: DarkNet conv_weights are serialized Caffe-style: (out_dim, in_dim, height, width)
# IMPORTANT: We would like to set these to Tensorflow order: (height, width, in_dim, out_dim)
kernel_weights = np.reshape(kernel_weights,(shape[3],shape[2],shape[0],shape[1]),order='C')
kernel_weights = np.transpose(kernel_weights,[2,3,1,0])
return biases,kernel_weights,offset
def load(sess,weights_path,ckpt_folder_path,saver):
if(os.path.exists(ckpt_folder_path)):
print('Found a checkpoint!')
checkpoint_files_path = os.path.join(ckpt_folder_path,"model.ckpt")
saver.restore(sess,checkpoint_files_path)
print('Loaded weights from checkpoint!')
return True
print('No checkpoint found!')
print('Loading weights from file and creating new checkpoint...')
# Get the size in bytes of the binary
size = os.path.getsize(weights_path)
# Load the binary to an array of float32
loaded_weights = []
loaded_weights = np.fromfile(weights_path, dtype='f')
# Delete the first 4 that are not real params...
loaded_weights = loaded_weights[4:]
print('Total number of params to load = {}'.format(len(loaded_weights)))
# IMPORTANT: starting from offset=0, layer by layer, we will get the exact number of parameters required and assign them!
# Conv1 , 3x3, 3->16
offset = 0
biases,kernel_weights,offset = load_conv_layer_bn('conv1',loaded_weights,[3,3,3,16],offset)
sess.run(tf.assign(net.b1,biases))
sess.run(tf.assign(net.w1,kernel_weights))
# Conv2 , 3x3, 16->32
biases,kernel_weights,offset = load_conv_layer_bn('conv2',loaded_weights,[3,3,16,32],offset)
sess.run(tf.assign(net.b2,biases))
sess.run(tf.assign(net.w2,kernel_weights))
# Conv3 , 3x3, 32->64
biases,kernel_weights,offset = load_conv_layer_bn('conv3',loaded_weights,[3,3,32,64],offset)
sess.run(tf.assign(net.b3,biases))
sess.run(tf.assign(net.w3,kernel_weights))
# Conv4 , 3x3, 64->128
biases,kernel_weights,offset = load_conv_layer_bn('conv4',loaded_weights,[3,3,64,128],offset)
sess.run(tf.assign(net.b4,biases))
sess.run(tf.assign(net.w4,kernel_weights))
# Conv5 , 3x3, 128->256
biases,kernel_weights,offset = load_conv_layer_bn('conv5',loaded_weights,[3,3,128,256],offset)
sess.run(tf.assign(net.b5,biases))
sess.run(tf.assign(net.w5,kernel_weights))
# Conv6 , 3x3, 256->512
biases,kernel_weights,offset = load_conv_layer_bn('conv6',loaded_weights,[3,3,256,512],offset)
sess.run(tf.assign(net.b6,biases))
sess.run(tf.assign(net.w6,kernel_weights))
# Conv7 , 3x3, 512->1024
biases,kernel_weights,offset = load_conv_layer_bn('conv7',loaded_weights,[3,3,512,1024],offset)
sess.run(tf.assign(net.b7,biases))
sess.run(tf.assign(net.w7,kernel_weights))
# Conv8 , 3x3, 1024->1024
biases,kernel_weights,offset = load_conv_layer_bn('conv8',loaded_weights,[3,3,1024,1024],offset)
sess.run(tf.assign(net.b8,biases))
sess.run(tf.assign(net.w8,kernel_weights))
# Conv9 , 1x1, 1024->125
biases,kernel_weights,offset = load_conv_layer('conv9',loaded_weights,[1,1,1024,125],offset)
sess.run(tf.assign(net.b9,biases))
sess.run(tf.assign(net.w9,kernel_weights))
# These two numbers MUST be equal!
print('Final offset = {}'.format(offset))
print('Total number of params in the weight file = {}'.format(len(loaded_weights)))
# Saving checkpoint!
if not os.path.exists(ckpt_folder_path):
print('Saving new checkpoint to the new checkpoint directory ./ckpt/ !')
os.makedirs(ckpt_folder_path)
checkpoint_files_path = os.path.join(ckpt_folder_path, "model.ckpt")
saver.save(sess,checkpoint_files_path)
#######################################################################################################################################