# 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,
iterations_per_epoch=iterations_per_epoch
)
lrate = LearningRateScheduler(_step_decay)
checkpoint = ModelCheckpoint(weights_best_file, monitor='loss',
verbose=0, save_best_only=False,
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
iterations_per_epoch = train_samples // batch_size
lrate = ReduceLROnPlateau(monitor='val_loss',
factor=0.25,
patience=30,
mode='auto')
checkpoint = ModelCheckpoint(weights_best_file,