print(">> LOAD VALID DATASET FORM:", valid_annotation_json_filepath)
dataloader_valid = DataLoader(
images_dir_path=valid_images_dir_path,
annotation_json_path=valid_annotation_json_filepath,
config_training=config_training,
config_model=config_model,
config_preproc=config_preproc)
number_of_keypoints = dataloader_train.number_of_keypoints # 17
# train dataset
dataset_train = dataloader_train.input_fn()
dataset_valid = dataloader_valid.input_fn()
# validation images
val_images, val_heatmaps = dataloader_valid.get_images(
0, batch_size=25) # from 22 index 6 images and 6 labels
# ================================================
# =============== build model ====================
# ================================================
from model_provider import get_model
model = get_model(model_name=model_name,
model_subname=model_subname,
number_of_keypoints=number_of_keypoints,
config_extra=config_extra)
loss_object = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizers.Adam(
config_training["learning_rate"], epsilon=config_training["epsilon"])
train_loss = tf.keras.metrics.Mean(name="train_loss")
valid_loss = tf.keras.metrics.Mean(name="valid_loss")
config_model=config_model,
config_preproc=config_preproc)
number_of_keypoints = dataloader_train.number_of_keypoints # 17
# train dataset
dataset_train = dataloader_train.input_fn()
dataset_valid = dataloader_valid.input_fn(
) if dataloader_valid is not None else None
#dataset_train = strategy.experimental_distribute_dataset(dataset_train)
#dataset_valid = strategy.experimental_distribute_dataset(dataset_valid)
# validation images
val_images, val_heatmaps = dataloader_valid.get_images(
0, batch_size=25
) if dataloader_valid is not None else None, None # from 22 index 6 images and 6 labels
# ================================================
# =============== build model ====================
# ================================================
from model_provider import get_model
if strategy is not None:
with strategy.scope():
model = get_model(model_name=model_name,
model_subname=model_subname,
number_of_keypoints=number_of_keypoints,
config_extra=config_extra,
backbone_name=model_backbone_name)
else:
model = get_model(model_name=model_name,