import tensorflow as tf

from core.yolov3 import YOLOV3

darknet_weights = '<your darknet weights' path>'

ckpt_file = './checkpoint/yolov3.ckpt'

def load_weights(var_list, weights_file):

"""

Loads and converts pre-trained weights.

:param var_list: list of network variables.

:param weights_file: name of the binary file.

:return: list of assign ops

"""

with open(weights_file, "rb") as fp:

_ = np.fromfile(fp, dtype=np.int32, count=5)

weights = np.fromfile(fp, dtype=np.float32) # np.ndarray

print('weights_num:', weights.shape[0])

ptr = 0

i = 0

assign_ops = []

while i < len(var_list) - 1:

var1 = var_list[i]

var2 = var_list[i + 1]

# do something only if we process conv layer

if 'conv' in var1.name.split('/')[-2]:

# check type of next layer

if 'batch_normalization' in var2.name.split('/')[-2]:

# load batch norm params

gamma, beta, mean, var = var_list[i + 1:i + 5]

batch_norm_vars = [beta, gamma, mean, var]

for vari in batch_norm_vars:

shape = vari.shape.as_list()

num_params = np.prod(shape)

vari_weights = weights[ptr:ptr + num_params].reshape(shape)

ptr += num_params

assign_ops.append(

tf.assign(vari, vari_weights, validate_shape=True))

i += 4

elif 'conv' in var2.name.split('/')[-2]:

# load biases

bias = var2

bias_shape = bias.shape.as_list()

bias_params = np.prod(bias_shape)

bias_weights = weights[ptr:ptr +

bias_params].reshape(bias_shape)

ptr += bias_params

assign_ops.append(

tf.assign(bias, bias_weights, validate_shape=True))

i += 1

shape = var1.shape.as_list()

num_params = np.prod(shape)

var_weights = weights[ptr:ptr + num_params].reshape(

(shape[3], shape[2], shape[0], shape[1]))

# remember to transpose to column-major

var_weights = np.transpose(var_weights, (2, 3, 1, 0))

ptr += num_params

assign_ops.append(

tf.assign(var1, var_weights, validate_shape=True))

i += 1

print('ptr:', ptr)

return assign_ops

with tf.name_scope('input'):

input_data = tf.placeholder(dtype=tf.float32,shape=(None, 608, 608, 3), name='input_data')

model = YOLOV3(input_data, trainable=False)

load_ops = load_weights(tf.global_variables(), darknet_weights)

saver = tf.train.Saver(tf.global_variables())

with tf.Session() as sess:

sess.run(load_ops)

save_path = saver.save(sess, save_path=ckpt_file)

print('Model saved in path: {}'.format(save_path))