lrate = LearningRateScheduler(_step_decay)
# sgd optimizer with lr multipliers
multisgd = MultiSGD(lr=base_lr,
momentum=momentum,
decay=0.0,
nesterov=False,
lr_mult=lr_multipliers)
loss_funcs = get_loss_funcs(batch_size)
model.compile(loss=loss_funcs, optimizer=multisgd, metrics=["accuracy"])
load_any_weights(model, multigpu=args.gpus > 1)
df = get_dataflow(
annot_path='%s/annotations/person_keypoints_%s2017.json' %
(DATA_DIR, args.dataset),
img_dir='%s/%s2017/' % (DATA_DIR, args.dataset))
train_samples = df.size()
print('Collected %d val samples...' % train_samples)
train_df = batch_dataflow(df,
batch_size,
time_steps=args.time_steps,
format=__format)
train_gen = gen(train_df)
print(model.inputs[0].get_shape())
# print(model.outputs)
from snapshot import Snapshot
model.fit_generator(train_gen,
annot_path_train = os.path.join(curr_dir, '../dataset/annotations/person_keypoints_train2017.json')
img_dir_train = os.path.abspath(os.path.join(curr_dir, '../dataset/train2017/'))
annot_path_val = os.path.join(curr_dir, '../dataset/annotations/person_keypoints_val2017.json')
img_dir_val = os.path.abspath(os.path.join(curr_dir, '../dataset/val2017/'))
# get dataflow of samples from training set and validation set (we use validation set for training as well)
coco_data_train = COCODataPaths(
annot_path=annot_path_train,
img_dir=img_dir_train
)
coco_data_val = COCODataPaths(
annot_path=annot_path_val,
img_dir=img_dir_val
)
df = get_dataflow([coco_data_train, coco_data_val])
train_samples = df.size()
# get generator of batches
batch_df = batch_dataflow(df, batch_size)
train_gen = gen(batch_df)
# setup lr multipliers for conv layers
lr_multipliers = get_lr_multipliers(model)
# configure callbacks
#/////////////////////////////////////////////////////////////////
iterations_per_epoch = train_samples // batch_size
_step_decay = partial(step_decay,
last_epoch = restore_weights(weights_best_file, model)
# prepare generators
curr_dir = os.path.dirname(__file__)
train_annot_path = os.path.join(
curr_dir, '../dataset/my_keypoints_final_train.json')
train_img_dir = os.path.abspath(
os.path.join(curr_dir, '../dataset/train_tracking_data/'))
val_annot_path = os.path.join(curr_dir,
'../dataset/my_keypoints_final_test.json')
val_img_dir = os.path.abspath(
os.path.join(curr_dir, '../dataset/train_tracking_data/'))
# get dataflow of samples
df1 = get_dataflow(annot_path=train_annot_path, img_dir=train_img_dir)
train_samples = df1.size()
df2 = get_dataflow(annot_path=val_annot_path, img_dir=train_img_dir)
val_samples = df2.size()
# get generator of batches
batch_df1 = batch_dataflow(df1, batch_size)
train_gen = gen(batch_df1)
batch_df2 = batch_dataflow(df2, batch_size)
val_gen = gen(batch_df2)
# setup lr multipliers for conv layers
lr_multipliers = get_lr_multipliers(model)
# configure callbacks
# restore weights
last_epoch = restore_weights(weights_best_file, model)
#输出模型结果
# model.summary()
# prepare generators
# curr_dir = os.path.dirname(__file__)
# annot_path = os.path.join(curr_dir, '../dataset/annotations/person_keypoints_train2017.json')
# img_dir = os.path.abspath(os.path.join(curr_dir, '../dataset/train2017/'))
annot_path = "/media/han/E/mWork/datasets/COCO2017/annotations/person_keypoints_val2017.json"
img_dir = '/media/han/E/mWork/datasets/COCO2017/val2017/'
# get dataflow of samples
df = get_dataflow( #数据集读取和处理（mask,augment,heatmap,paf等）多线程
annot_path=annot_path,
img_dir=img_dir)
train_samples = df.size()
# get generator of batches
batch_df = batch_dataflow(df, batch_size)
train_gen = gen(batch_df)
# setup lr multipliers for conv layers
lr_multipliers = get_lr_multipliers(model)
# configure callbacks
iterations_per_epoch = train_samples // batch_size
_step_decay = partial(step_decay,
iterations_per_epoch=iterations_per_epoch)
lrate = LearningRateScheduler(_step_decay)
model = get_training_model(weight_decay)
# restore weights
last_epoch = restore_weights(weights_best_file, model)
# prepare generators
curr_dir = os.path.dirname(__file__)
annot_path = os.path.join(
curr_dir, '../dataset/annotations/person_keypoints_train2017.json')
img_dir = os.path.abspath(os.path.join(curr_dir, '../dataset/train2017/'))
# get dataflow of samples
df = get_dataflow(annot_path=annot_path, img_dir=img_dir)
train_samples = df.size()
# get generator of batches
batch_df = batch_dataflow(df, batch_size)
train_gen = gen(batch_df)
# setup lr multipliers for conv layers
lr_multipliers = get_lr_multipliers(model)
# configure callbacks
iterations_per_epoch = train_samples // batch_size
_step_decay = partial(step_decay,
# get the model
model = get_training_model(weight_decay)
# restore weights
last_epoch = restore_weights(weights_best_file, model)
# prepare generators
curr_dir = os.path.dirname(__file__)
annot_path = os.path.join(
curr_dir, '../dataset/annotations/person_keypoints_train2017.json')
img_dir = os.path.abspath(os.path.join(curr_dir, '../dataset/train2017/'))
df = get_dataflow(annot_path=annot_path,
img_dir=img_dir,
batch_size=batch_size)
train_gen = df.get_data()
train_samples = df.size()
# setup lr multipliers for conv layers
lr_multipliers = get_lr_multipliers(model)
# configure callbacks
iterations_per_epoch = train_samples // batch_size
_step_decay = partial(step_decay,
iterations_per_epoch=iterations_per_epoch)
lrate = LearningRateScheduler(_step_decay)
checkpoint = ModelCheckpoint(weights_best_file,