def initialize_weights():
weight_reader = WeightReader(wt_path)
weight_reader.reset()
nb_conv = 23
for i in range(1, nb_conv + 1):
conv_layer = model.get_layer('conv_' + str(i))
if i < nb_conv:
norm_layer = model.get_layer('norm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = weight_reader.read_bytes(size)
gamma = weight_reader.read_bytes(size)
mean = weight_reader.read_bytes(size)
var = weight_reader.read_bytes(size)
weights = norm_layer.set_weights([gamma, beta, mean, var])
if len(conv_layer.get_weights()) > 1:
bias = weight_reader.read_bytes(
np.prod(conv_layer.get_weights()[1].shape))
kernel = weight_reader.read_bytes(
np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(
list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2, 3, 1, 0])
conv_layer.set_weights([kernel, bias])
else:
kernel = weight_reader.read_bytes(
np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(
list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2, 3, 1, 0])
conv_layer.set_weights([kernel])
layer = model.layers[-4] # the last convolutional layer
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (GRID_H * GRID_W)
new_bias = np.random.normal(size=weights[1].shape) / (GRID_H * GRID_W)
layer.set_weights([new_kernel, new_bias])
def load_weights():
"""
This method loads the pretrained weights from YOLO
"""
weight_reader = WeightReader(WEIGHT_PATH)
weight_reader.reset()
nb_conv = 23
for i in range(1, nb_conv + 1):
conv_layer = model.get_layer('conv_' + str(i))
if i < nb_conv:
norm_layer = model.get_layer('norm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = weight_reader.read_bytes(size)
gamma = weight_reader.read_bytes(size)
mean = weight_reader.read_bytes(size)
var = weight_reader.read_bytes(size)
weights = norm_layer.set_weights([gamma, beta, mean, var])
if len(conv_layer.get_weights()) > 1:
bias = weight_reader.read_bytes(
np.prod(conv_layer.get_weights()[1].shape))
kernel = weight_reader.read_bytes(
np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(
list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2, 3, 1, 0])
conv_layer.set_weights([kernel, bias])
else:
kernel = weight_reader.read_bytes(
np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(
list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2, 3, 1, 0])
conv_layer.set_weights([kernel])
# Randomize the weights of the last layer
# We would only be training the last layer again
layer = model.layers[-4]
weights = layer.get_weights()
new_kernel = np.random.normal(size=weights[0].shape) / (GRID_H * GRID_W)
new_bias = np.random.normal(size=weights[1].shape) / (GRID_H * GRID_W)
layer.set_weights([new_kernel, new_bias])
strides=(1, 1),
padding='same',
name='conv_23')(x)
output = Reshape((GRID_H, GRID_W, BOX, 4 + 1 + CLASS))(x)
# small hack to allow true_boxes to be registered when Keras build the model
# for more information: https://github.com/fchollet/keras/issues/2790
output = Lambda(lambda args: args[0])([output, true_boxes])
model = Model([input_image, true_boxes], output)
model.summary()
weight_reader = WeightReader(wt_path)
weight_reader.reset()
nb_conv = 23
for i in range(1, nb_conv + 1):
conv_layer = model.get_layer('conv_' + str(i))
if i < nb_conv:
norm_layer = model.get_layer('norm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = weight_reader.read_bytes(size)
gamma = weight_reader.read_bytes(size)
mean = weight_reader.read_bytes(size)
var = weight_reader.read_bytes(size)
# for more information: https://github.com/fchollet/keras/issues/2790
# output = Lambda(lambda args: args[0])([output, true_boxes])
model = Model([input_image, true_boxes], output)
model.summary()
yolo = Model([input_image, true_boxes], output)
'''Load pre-trained weights'''
# Load pretrained weights
# **Load the weights originally provided by YOLO**
print("**Load the weights originally provided by YOLO**")
weight_reader = WeightReader(wt_path)
weight_reader.reset() # don't worry! it doesn't delete the weights.
nb_conv = 23
for i in range(1, nb_conv + 1):
conv_layer = model.get_layer('conv_' + str(i))
if i < nb_conv:
norm_layer = model.get_layer('norm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = weight_reader.read_bytes(size)
gamma = weight_reader.read_bytes(size)
mean = weight_reader.read_bytes(size)
var = weight_reader.read_bytes(size)
