def main(argv):
flags = parser(
description="freeze yolov3 graph from checkpoint file").parse_args()
classes = utils.read_coco_names("./data/coco.names")
num_classes = len(classes)
SIZE = flags.image_size
print("=> the input image size is [%d, %d]" % (SIZE, SIZE))
model = yolov3.yolov3(num_classes)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(
tf.float32, [1, SIZE, SIZE, 3]) # placeholder for detector inputs
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs, is_training=False)
boxes, confs, probs = model.predict(feature_map)
scores = confs * probs
print("=>", boxes, scores)
boxes, scores, labels = utils.gpu_nms(
boxes,
scores,
num_classes,
score_thresh=flags.score_threshold,
iou_thresh=flags.iou_threshold)
print("=>", boxes, scores, labels)
feature_map_1, feature_map_2, feature_map_3 = feature_map
print("=>", feature_map_1, feature_map_2, feature_map_3)
saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))
if flags.convert:
if not os.path.exists(flags.weights_path):
url = 'https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3.weights'
for i in range(3):
time.sleep(1)
print("=> %s does not exists ! " % flags.weights_path)
print("=> It will take a while to download it from %s" % url)
print('=> Downloading yolov3 weights ... ')
wget.download(url, flags.weights_path)
load_ops = utils.load_weights(tf.global_variables(scope='yolov3'),
flags.weights_path)
sess.run(load_ops)
save_path = saver.save(sess, save_path=flags.ckpt_file)
print('=> model saved in path: {}'.format(save_path))
if flags.freeze:
saver.restore(sess, flags.ckpt_file)
print('=> checkpoint file restored from ', flags.ckpt_file)
utils.freeze_graph(sess, './checkpoint/yolov3_cpu_nms.pb',
["concat_9", "mul_6"])
utils.freeze_graph(sess, './checkpoint/yolov3_gpu_nms.pb',
["concat_10", "concat_11", "concat_12"])
utils.freeze_graph(sess, './checkpoint/yolov3_feature.pb', [
"yolov3/yolo-v3/feature_map_1",
"yolov3/yolo-v3/feature_map_2",
"yolov3/yolo-v3/feature_map_3",
])
def main(argv):
flags = parser(
description="freeze yolov3 graph from checkpoint file").parse_args()
print("=> the input image size is [%d, %d]" %
(flags.image_h, flags.image_w))
anchors = utils.get_anchors(flags.anchors_path, flags.image_h,
flags.image_w)
model = yolov3.yolov3(flags.num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32,
[1, flags.image_h, flags.image_w, 3
]) # placeholder for detector inputs
print("=>", inputs)
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs, is_training=False)
boxes, confs, probs = model.predict(feature_map)
scores = confs * probs
print("=>", boxes.name[:-2], scores.name[:-2])
cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
boxes, scores, labels = utils.gpu_nms(
boxes,
scores,
flags.num_classes,
score_thresh=flags.score_threshold,
iou_thresh=flags.iou_threshold)
print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
gpu_out_node_names = [
boxes.name[:-2], scores.name[:-2], labels.name[:-2]
]
feature_map_1, feature_map_2, feature_map_3 = feature_map
saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))
if flags.convert:
if not os.path.exists(flags.weights_path):
url = 'https://github.com/YunYang1994/tensorflow-yolov3/releases/download/v1.0/yolov3.weights'
for i in range(3):
time.sleep(1)
print("=> %s does not exists ! " % flags.weights_path)
print("=> It will take a while to download it from %s" % url)
print('=> Downloading yolov3 weights ... ')
wget.download(url, flags.weights_path)
load_ops = utils.load_weights(tf.global_variables(scope='yolov3'),
flags.weights_path)
sess.run(load_ops)
save_path = saver.save(sess, save_path=flags.ckpt_file)
print('=> model saved in path: {}'.format(save_path))
if flags.freeze:
saver.restore(sess, flags.ckpt_file)
print('=> checkpoint file restored from ', flags.ckpt_file)
utils.freeze_graph(sess, './checkpoint/yolov3_cpu_nms.pb',
cpu_out_node_names)
utils.freeze_graph(sess, './checkpoint/yolov3_gpu_nms.pb',
gpu_out_node_names)
def convert_weights(self):
print(f"=> the input image size is [{self.img_h}, {self.img_w}]")
anchors = utils.get_anchors(self.anchors_path, self.img_h, self.img_w)
model = yolov3.yolov3(self.num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32,
[1, self.img_h, self.img_w, 1
]) # placeholder for detector inputs
print("=>", inputs)
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs,
n_filters_dn=self.n_filters_dn,
n_strides_dn=self.n_strides_dn,
n_ksizes_dn=self.n_ksizes_dn,
is_training=False)
boxes, confs, probs = model.predict(feature_map)
scores = confs * probs
print("=>", boxes.name[:-2], scores.name[:-2])
cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
boxes, scores, labels = utils.gpu_nms(boxes, scores,
self.num_classes)
print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
gpu_out_node_names = [
boxes.name[:-2], scores.name[:-2], labels.name[:-2]
]
saver = tf.train.Saver(var_list=tf.global_variables(
scope='yolov3'))
if self.convert:
load_ops = utils.load_weights(
tf.global_variables(scope='yolov3'), self.weights_dir)
sess.run(load_ops)
save_path = saver.save(sess, save_path=self.checkpoint_dir)
print(f'=> model saved in path: {save_path}')
if self.freeze:
ckpt_idx = self.checkpoint_dir + '-' + str(
self.checkpoint_step)
try:
saver.restore(sess, ckpt_idx)
except:
print(
f"Error: you tried to restore a checkpoint ({self.checkpoint_dir}) that doesn't exist."
)
print(
"Please clear the network and retrain, or load a different checkpoint by changing the steps parameter."
)
print('=> checkpoint file restored from ', ckpt_idx)
utils.freeze_graph(sess,
'../../data/checkpoint/yolov3_cpu_nms.pb',
cpu_out_node_names)
utils.freeze_graph(sess,
'../../data/checkpoint/yolov3_gpu_nms.pb',
gpu_out_node_names)
ANCHORS = utils.get_anchors('data/voc_anchors.txt', IMAGE_H, IMAGE_W)
NUM_CLASSES = len(CLASSES)
EVAL_INTERNAL = 100
SAVE_INTERNAL = 500
parser = Parser(IMAGE_H, IMAGE_W, ANCHORS, NUM_CLASSES)
trainset = dataset(parser, train_tfrecord, BATCH_SIZE, shuffle=SHUFFLE_SIZE)
testset = dataset(parser, test_tfrecord , BATCH_SIZE, shuffle=None)
is_training = tf.placeholder(tf.bool)
example = tf.cond(is_training, lambda: trainset.get_next(), lambda: testset.get_next())
images, *y_true = example
model = yolov3.yolov3(NUM_CLASSES, ANCHORS)
with tf.variable_scope('yolov3'):
pred_feature_map = model.forward(images, is_training=is_training)
loss = model.compute_loss(pred_feature_map, y_true)
y_pred = model.predict(pred_feature_map)
tf.summary.scalar("loss/coord_loss", loss[1])
tf.summary.scalar("loss/sizes_loss", loss[2])
tf.summary.scalar("loss/confs_loss", loss[3])
tf.summary.scalar("loss/class_loss", loss[4])
global_step = tf.Variable(0, trainable=False, collections=[tf.GraphKeys.LOCAL_VARIABLES])
write_op = tf.summary.merge_all()
writer_train = tf.summary.FileWriter("./data/bdd100k_train")
writer_test = tf.summary.FileWriter("./data/bdd100k_test")
sess = tf.Session()
classes = utils.read_coco_names('./data/coco.names')
num_classes = len(classes)
file_pattern = "./data/train_data/quick_train_data/tfrecords/quick_train_data*.tfrecords"
anchors = utils.get_anchors('./data/yolo_anchors.txt')
dataset = tf.data.TFRecordDataset(filenames=tf.gfile.Glob(file_pattern))
dataset = dataset.map(utils.parser(anchors, num_classes).parser_example,
num_parallel_calls=10)
dataset = dataset.repeat().shuffle(SHUFFLE_SIZE).batch(BATCH_SIZE).prefetch(
BATCH_SIZE)
iterator = dataset.make_one_shot_iterator()
example = iterator.get_next()
images, *y_true = example
model = yolov3.yolov3(num_classes)
with tf.variable_scope('yolov3'):
y_pred = model.forward(images, is_training=False)
loss = model.compute_loss(y_pred, y_true)
y_pred = model.predict(y_pred)
load_ops = utils.load_weights(tf.global_variables(scope='yolov3'),
weights_path)
sess.run(load_ops)
for epoch in range(EPOCHS):
run_items = sess.run([y_pred, y_true] + loss)
rec, prec, mAP = utils.evaluate(run_items[0],
run_items[1],
num_classes,
score_thresh=0.3,
import tensorflow as tf
from core import utils, yolov3
import cv2
from PIL import Image
import numpy as np
input_image = "../data/raccoon_data/images/raccoon-4.jpg"
image = Image.open(input_image)
# image = cv2.imread(input_image)
# image = Image.fromarray(image)
image_resize = cv2.resize(np.array(image) / 255., (416, 416))
image_place = tf.placeholder(dtype=tf.float32, shape=(None, 416, 416, 3))
CLASSES = utils.read_coco_names('../data/objects.names')
ANCHORE = utils.get_anchors("../data/objects.txt", 416, 416)
model = yolov3.yolov3(len(CLASSES), ANCHORE)
with tf.variable_scope('yolov3'):
pred_feature_map = model.forward(image_place, is_training=False)
pred = model.predict(pred_feature_map)
sess = tf.Session()
saver = tf.train.Saver()
model_dir = tf.train.latest_checkpoint("../data/raccoon_data/model/")
saver.restore(sess, model_dir)
boxes, confs, prods = sess.run(pred, feed_dict={image_place: np.expand_dims(image_resize, 0)})
boxes, confs, prods = utils.cpu_nms(boxes, confs * prods, len(CLASSES))
utils.draw_boxes(image, boxes, confs, prods, CLASSES, (416, 416), "../data/font/HuaWenXinWei-1.ttf")
print(boxes, confs, prods)
from core import utils, yolov3
sess = tf.Session()
classes = utils.read_coco_names('./data/coco.names')
num_classes = len(classes)
input_shape = [416, 416]
dataset = utils.read_image_box_from_text(
'./data/train_data/quick_train_data.txt')
anchors = utils.get_anchors('./data/yolo_anchors.txt')
inputs = tf.placeholder(tf.float32, shape=[1, 416, 416, 3])
y_true_13 = tf.placeholder(tf.float32, shape=[1, 13, 13, 3, 85])
y_true_26 = tf.placeholder(tf.float32, shape=[1, 26, 26, 3, 85])
y_true_52 = tf.placeholder(tf.float32, shape=[1, 52, 52, 3, 85])
model = yolov3.yolov3(80)
with tf.variable_scope('yolov3'):
feature_maps = model.forward(inputs, is_training=True)
load_ops = utils.load_weights(tf.global_variables(scope='yolov3'),
"./checkpoint/yolov3.weights")
sess.run(load_ops)
loss = model.compute_loss(feature_maps, [y_true_13, y_true_26, y_true_52])
optimizer = tf.train.GradientDescentOptimizer(0.001)
train_op = optimizer.minimize(loss)
sess.run(tf.global_variables_initializer())
for image_path in dataset.keys():
image = Image.open(image_path)
true_boxes, true_labels = dataset[image_path]
image, true_boxes = utils.resize_image_correct_bbox(
def main(argv):
flags = parser(
description="freeze yolov3 graph from checkpoint file").parse_args()
print("=> the input image size is [%d, %d]" %
(flags.image_h, flags.image_w))
anchors = utils.get_anchors(flags.anchors_path, flags.image_h,
flags.image_w)
# print(anchors)
# exit()
model = yolov3.yolov3(flags.num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32,
[1, flags.image_h, flags.image_w, 3
]) # placeholder for detector inputs
print("=>", inputs)
with tf.variable_scope('yolov3'):
feature_map = model.forward(
inputs, is_training=False) # 返回3个尺度的feature_map
# 获取网络给出绝对boxes(左上角,右下角)信息, 未经过最大抑制去除多余boxes
boxes, confs, probs = model.predict(feature_map)
scores = confs * probs
print("=>", boxes.name[:-2], scores.name[:-2])
# cpu 运行是恢复模型所需要的网络节点的名字
cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
boxes, scores, labels = utils.gpu_nms(
boxes,
scores,
flags.num_classes,
score_thresh=flags.score_threshold,
iou_thresh=flags.iou_threshold)
print("=>", boxes.name[:-2], scores.name[:-2], labels.name[:-2])
# gpu 运行是恢复模型所需要的网络节点的名字 , 直接运算得出最终结果
gpu_out_node_names = [
boxes.name[:-2], scores.name[:-2], labels.name[:-2]
]
feature_map_1, feature_map_2, feature_map_3 = feature_map
saver = tf.train.Saver(var_list=tf.global_variables(scope='yolov3'))
our_out_node_names = [
"yolov3/yolo-v3/feature_map_1", "yolov3/yolo-v3/feature_map_2",
"yolov3/yolo-v3/feature_map_3"
]
# 只有第一次需要读取预训练参数yolov3.weights
# if flags.convert:
# load_ops = utils.load_weights(tf.global_variables(scope='yolov3'), flags.weights_path)
# sess.run(load_ops)
# save_path = saver.save(sess, save_path=flags.ckpt_file)
# print('=> model saved in path: {}'.format(save_path))
# print(flags.freeze)
if flags.freeze:
saver.restore(sess, flags.ckpt_file)
print('=> checkpoint file restored from ', flags.ckpt_file)
utils.freeze_graph(sess, '../checkpoint/yolov3_cpu_nms.pb',
cpu_out_node_names)
utils.freeze_graph(sess, '../checkpoint/yolov3_gpu_nms.pb',
gpu_out_node_names)
utils.freeze_graph(sess, '../checkpoint/ouryolov3.pb',
our_out_node_names)
def train(self):
ANCHORS = utils.get_anchors(self.anchors_path, self.img_h, self.img_w)
parser = Parser(image_h=self.img_h,
image_w=self.img_w,
anchors=ANCHORS,
num_classes=self.num_classes)
trainset = dataset(parser,
self.train_records,
self.batch_size,
shuffle=self.shuffle_size)
testset = dataset(parser,
self.test_records,
self.batch_size,
shuffle=None)
is_training = tf.placeholder(tf.bool)
example = tf.cond(is_training, lambda: trainset.get_next(),
lambda: testset.get_next())
images, y_true = example
model = yolov3.yolov3(self.num_classes, ANCHORS)
with tf.variable_scope('yolov3'):
# Give the images to the network, and receive a prediction
# feature map
pred_feature_map = model.forward(images,
is_training=is_training,
n_filters_dn=self.n_filters_dn,
n_strides_dn=self.n_strides_dn,
n_ksizes_dn=self.n_ksizes_dn)
loss = model.compute_loss(pred_feature_map, y_true,
self.iou_threshold)
y_pred = model.predict(pred_feature_map)
tf.summary.scalar("loss/coord_loss", loss[1])
tf.summary.scalar("loss/sizes_loss", loss[2])
tf.summary.scalar("loss/confs_loss", loss[3])
tf.summary.scalar("loss/class_loss", loss[4])
global_step = tf.Variable(0,
trainable=True,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
write_op = tf.summary.merge_all()
writer_train = tf.summary.FileWriter("../../data/train_summary",
sess.graph)
writer_test = tf.summary.FileWriter("../../data/test_summary")
update_vars = tf.contrib.framework.get_variables_to_restore(
include=["yolov3/yolo-v3"])
lr = tf.train.exponential_decay(self.learning_rate,
global_step,
decay_steps=self.decay_steps,
decay_rate=self.decay_rate,
staircase=True)
optimizer = tf.train.AdamOptimizer(lr)
# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss[0],
var_list=update_vars,
global_step=global_step)
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
saver = tf.train.Saver(max_to_keep=2)
for step in range(self.steps):
run_items = sess.run([train_op, write_op, y_pred, y_true] + loss,
feed_dict={is_training: True})
if (step + 1) % self.eval_internal == 0:
train_rec_value, train_prec_value = utils.evaluate(
run_items[2], run_items[3])
writer_train.add_summary(run_items[1], global_step=step)
writer_train.flush() # Flushes the event file to disk
if (step + 1) % self.save_internal == 0:
saver.save(sess,
save_path=self.checkpoint_path,
global_step=step + 1)
if (step + 1) % self.print_every_n == 0:
print(f"=> STEP {step+1} [TRAIN]:\tloss_xy: " +
f"{run_items[5]:.4f} \tloss_wh:{run_items[6]:.4f} \t" +
f"loss_conf:{run_items[7]:.4f} \tloss_class:" +
f"{run_items[8]:.4f}")
run_items = sess.run([write_op, y_pred, y_true] + loss,
feed_dict={is_training: False})
if (step + 1) % self.eval_internal == 0:
test_rec_value, test_prec_value = utils.evaluate(
run_items[1], run_items[2])
print(f"\n{20*'='}> evaluation result <{20*'='}\n")
print(f"=> STEP {step+1} [TRAIN]:\trecall:" +
f"{train_rec_value:.2f} \tprecision:" +
f"{train_prec_value:.4f}")
print(f"=> STEP {step+1} [VALID]:\trecall:" +
f"{test_rec_value:.2f} \tprecision:" +
f"{test_prec_value:.4f}")
print(f"\n{20*'='}> evaluation result <{20*'='}\n")
writer_test.add_summary(run_items[0], global_step=step)
writer_test.flush() # Flushes the event file to disk
image_h , image_w = 416, 416
num_classes = 80
iou_threshold = 0.5
score_threshold = 0.5
ckpt_file = './checkpoint/yolov3.ckpt'
anchors_path = './data/raccoon_anchors.txt'
weights_path = './checkpoint/yolov3.weights'
if __name__ == '__main__':
print("the input image size is {}*{}".format(image_h, image_w))
anchors = utils.get_anchors(anchors_path, image_h, image_w)
model = yolov3.yolov3(num_classes, anchors)
with tf.Graph().as_default() as graph:
sess = tf.Session(graph=graph)
inputs = tf.placeholder(tf.float32, [1, image_h, image_w, 3])
print(inputs)
with tf.variable_scope('yolov3'):
feature_map = model.forward(inputs, is_training=False)
boxes ,confs, probs = model.predict(feature_map)
scores = confs * probs
print("=>", boxes.name[:-2], scores.name[:-2])
cpu_out_node_names = [boxes.name[:-2], scores.name[:-2]]
boxes, scores, labels = utils.gpu_nms(boxes, scores, num_classes,
import tensorflow as tf
from core import utils, yolov3
from core.dataset import dataset, Parser
from basicNet.mobilenetV2 import MobilenetV2
from config.config import *
parser = Parser(ANCHORS, NUM_CLASSES)
trainset = dataset(parser,
TEST_TFRECORD,
BATCH_SIZE,
shuffle=SHUFFLE_SIZE,
multi_image_size=False)
testset = dataset(parser, TEST_TFRECORD, BATCH_SIZE, shuffle=None)
example = trainset.get_next()
images, *y_true = example
model = yolov3.yolov3(NUM_CLASSES, ANCHORS, basic_net=MobilenetV2)
with tf.variable_scope('yolov3'):
model.set_anchor(images)
pred_feature_map = model.forward(images, is_training=False)
y_pred = model.predict(pred_feature_map)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, "./checkpoint/yolov3.ckpt-25000")
run_items = sess.run([images, y_pred])
for i in range(8):
image = run_items[0][i]
pred_boxes = run_items[1][0][i:i + 1]
pred_confs = run_items[1][1][i:i + 1]
pred_probs = run_items[1][2][i:i + 1]
pred_boxes, pred_scores, pred_labels = utils.cpu_nms(