def evaluate(**kwargs):
model, params = efficientdet.checkpoint.load(kwargs['checkpoint'])
if kwargs['format'] == 'labelme':
classes = params['classes_names'].split(',')
class2idx = {c: i for i, c in enumerate(classes)}
n_classes = len(classes)
elif kwargs['format'] == 'VOC':
class2idx = efficientdet.data.voc.LABEL_2_IDX
classes = efficientdet.data.voc.IDX_2_LABEL
n_classes = 20
ds, class2idx = efficientdet.data.build_ds(
format=kwargs['format'],
annots_path=kwargs['test_dataset'],
images_path=kwargs['images_path'],
im_size=(model.config.input_size,) * 2,
class_names=params['classes_names'].split(','),
data_augmentation=False,
batch_size=8)
engine.evaluate(
model=model,
dataset=ds,
steps=sum(1 for _ in ds),
class2idx=class2idx)
def train(**kwargs):
save_checkpoint_dir = Path(kwargs['save_dir'])
save_checkpoint_dir.mkdir(exist_ok=True, parents=True)
model = efficientdet.models.EfficientDet(
kwargs['n_classes'],
D=kwargs['efficientdet'],
bidirectional=kwargs['bidirectional'],
freeze_backbone=kwargs['freeze_backbone'],
weights='imagenet')
if kwargs['checkpoint'] is not None:
print('Loading checkpoint from {}...'.format(kwargs['checkpoint']))
model.load_weights(kwargs['checkpoint'])
ds, class2idx = efficientdet.data.build_ds(
format=kwargs['format'],
annots_path=kwargs['train_dataset'],
images_path=kwargs['images_path'],
im_size=(model.config.input_size, ) * 2,
class_names=kwargs['classes_names'].split(','),
batch_size=kwargs['batch_size'])
val_ds = None
if kwargs['val_dataset']:
val_ds, _ = efficientdet.data.build_ds(
format=kwargs['format'],
annots_path=kwargs['val_dataset'],
images_path=kwargs['images_path'],
class_names=kwargs['classes_names'].split(','),
im_size=(model.config.input_size, ) * 2,
shuffle=False,
batch_size=kwargs['batch_size'] // 2)
anchors = generate_anchors(model.anchors_config, model.config.input_size)
optimizer = tf.optimizers.Adam(learning_rate=kwargs['learning_rate'],
clipnorm=0.001)
for epoch in range(kwargs['epochs']):
engine.train_single_epoch(model=model,
anchors=anchors,
dataset=ds,
optimizer=optimizer,
loss_fn=loss_fn,
num_classes=kwargs['n_classes'],
epoch=epoch)
if val_ds is not None:
engine.evaluate(model=model, dataset=val_ds, class2idx=class2idx)
model_type = 'bifpn' if kwargs['bidirectional'] else 'fpn'
data_format = kwargs['format']
fname = f'{model_type}_{data_format}_efficientdet_weights_{epoch}.tf'
fname = save_checkpoint_dir / fname
model.save_weights(str(fname))
def evaluate(**kwargs):
model = efficientdet.models.EfficientDet(
kwargs['n_classes'],
D=kwargs['efficientdet'],
bidirectional=kwargs['bidirectional'],
freeze_backbone=True,
weights=None)
model.load_weights(kwargs['checkpoint'])
ds, class2idx = efficientdet.data.build_ds(
format=kwargs['format'],
annots_path=kwargs['test_dataset'],
images_path=kwargs['images_path'],
im_size=(model.config.input_size, ) * 2,
class_names=kwargs['classes_names'].split(','),
batch_size=1)
engine.evaluate(model=model, dataset=ds, class2idx=class2idx)
def train(**kwargs):
save_checkpoint_dir = Path(kwargs['save_dir'])
save_checkpoint_dir.mkdir(exist_ok=True, parents=True)
if kwargs['checkpoint'] is not None:
print('Loading checkpoint from {}...'.format(kwargs['checkpoint']))
model = efficientdet.checkpoint.load(kwargs['checkpoint'])
elif kwargs['from_pretrained'] is not None:
model = (efficientdet.EfficientDet.from_pretrained(
kwargs['from_pretrained'], num_classes=kwargs['n_classes']))
for l in model.layers:
l.trainable = True
model.trainable = True
print('Training from a pretrained model...')
print('This will override any configuration related to EfficientNet'
' using the defined in the pretrained model.')
else:
model = efficientdet.models.EfficientDet(
kwargs['n_classes'],
D=kwargs['efficientdet'],
bidirectional=kwargs['bidirectional'],
freeze_backbone=kwargs['freeze_backbone'],
weights='imagenet')
ds, class2idx = efficientdet.data.build_ds(
format=kwargs['format'],
annots_path=kwargs['train_dataset'],
images_path=kwargs['images_path'],
im_size=(model.config.input_size, ) * 2,
class_names=kwargs['classes_names'].split(','),
batch_size=kwargs['batch_size'],
data_augmentation=True)
val_ds = None
if kwargs['val_dataset']:
val_ds, _ = efficientdet.data.build_ds(
format=kwargs['format'],
annots_path=kwargs['val_dataset'],
images_path=kwargs['images_path'],
class_names=kwargs['classes_names'].split(','),
im_size=(model.config.input_size, ) * 2,
shuffle=False,
data_augmentation=False,
batch_size=kwargs['batch_size'] // 2)
anchors = generate_anchors(model.anchors_config, model.config.input_size)
if kwargs['w_scheduler']:
lr = efficientdet.optim.EfficientDetLRScheduler(
kwargs['learning_rate'],
kwargs['epochs'], (ds_len(ds) // kwargs['grad_accum_steps']) + 1,
alpha=kwargs['alpha'])
else:
lr = kwargs['learning_rate']
optimizer = tf.optimizers.SGD(learning_rate=lr, momentum=0.9)
for epoch in range(kwargs['epochs']):
engine.train_single_epoch(model=model,
anchors=anchors,
dataset=ds,
optimizer=optimizer,
grad_accum_steps=kwargs['grad_accum_steps'],
loss_fn=loss_fn,
num_classes=kwargs['n_classes'],
epoch=epoch,
print_every=kwargs['print_freq'])
if val_ds is not None and (epoch + 1) % kwargs['validate_freq'] == 0:
engine.evaluate(model=model, dataset=val_ds, class2idx=class2idx)
model_type = 'bifpn' if kwargs['bidirectional'] else 'fpn'
data_format = kwargs['format']
arch = kwargs['efficientdet']
save_dir = (save_checkpoint_dir /
f'{arch}_{model_type}_{data_format}_{epoch}')
efficientdet.checkpoint.save(model, kwargs, save_dir)