def load_model_weights(self):
weight_reader = WeightReader(self.wt_path)
#weight_reader.reset()
nb_conv = 23
for i in range(1, nb_conv + 1):
conv_layer = self.model.get_layer('conv_' + str(i))
if i < nb_conv:
norm_layer = self.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])
return
# Layer 23
x = Conv2D(BOX * (4 + 1 + CLASS), (1, 1),
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)
import torch
import YoloV3_model
from dataset import YoloV3DataModule
from collections import namedtuple
from utils import WeightReader, load_conv_block
import pytorch_lightning as pl
conv_config = namedtuple("ConvConfig", ["kernel_size", "filters", "stride", "pad"])
maxpool_config = namedtuple("MaxPoolConfig", ["kernel_size", "stride"])
DummyBlock = namedtuple("DummyBlock", ["conv"])
model = YoloV3_model.YoloV3Model()
data = YoloV3DataModule()
# +
weight_reader = WeightReader(
"yolov3.weights",
initial_offset=5,
)
def apply_weights_on_seq(seq_block):
for block in seq_block.children():
if "CNNBlock" in str(block.__class__):
load_conv_block(block, weight_reader)
elif "DoubleConvWithResidual" in str(block.__class__):
load_conv_block(block.conv1, weight_reader)
load_conv_block(block.conv2, weight_reader)
elif "Conv2d" in str(block.__class__):
load_conv_block(block, weight_reader, with_bn=False)
apply_weights_on_seq(model.base_model.part1)
# 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()
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)
repeat_block([conv_config(1, 512, 1, 0),
conv_config(3, 1024, 1, 1)], 2),
conv_config(3, 1024, 1, 1),
conv_config(3, 1024, 2, 1),
conv_config(3, 1024, 1, 1),
conv_config(3, 1024, 1, 1),
]
DummyBlock = namedtuple("DummyBlock", ["conv"])
model = YoloV1_model.YoloV1Model(
architecture=architecture_config,
split_size=7,
num_boxes=2,
num_classes=20,
)
weight_reader = WeightReader("./yolov1.weights", initial_offset=5)
def apply_weights_on_seq(seq_block):
for block in seq_block.children():
if "CNNBlock" in str(block.__class__):
load_conv_block(block, weight_reader)
apply_weights_on_seq(model.darknet)
print("After Loading:", weight_reader.offset)
for p in model.darknet.parameters():
p.requires_grad = False
data = YoloV1DataModule()
conv_config(3, 1024, 1, 1),
conv_config(1, 512, 1, 0),
conv_config(3, 1024, 1, 1),
conv_config(3, 1024, 1, 1),
conv_config(3, 1024, 1, 1),
]
DummyBlock = namedtuple("DummyBlock", ["conv"])
model = YoloV2_model.YoloV2Model(
architecture=[architecture_config1, architecture_config2],
split_size=13,
num_boxes=5,
num_classes=20,
)
weight_reader = WeightReader("yolov2-voc.weights", initial_offset=5)
def apply_weights_on_seq(seq_block):
for block in seq_block.children():
if "CNNBlock" in str(block.__class__):
load_conv_block(block, weight_reader)
apply_weights_on_seq(model.darknet_before_skip)
apply_weights_on_seq(model.darknet_after_skip)
load_conv_block(model.middle, weight_reader)
load_conv_block(model.conv_end1, weight_reader)
load_conv_block(model.final_conv, weight_reader, with_bn=False)
print("After Loading:", weight_reader.offset)
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])