def build_qat_segmentation_model(
model: tf.keras.Model, quantization: common.Quantization,
input_specs: tf.keras.layers.InputSpec) -> tf.keras.Model:
"""Applies quantization aware training for segmentation model.
Args:
model: The model applying quantization aware training.
quantization: The Quantization config.
input_specs: The shape specifications of input tensor.
Returns:
The model that applied optimization techniques.
"""
original_checkpoint = quantization.pretrained_original_checkpoint
if original_checkpoint is not None:
ckpt = tf.train.Checkpoint(model=model, **model.checkpoint_items)
status = ckpt.read(original_checkpoint)
status.expect_partial().assert_existing_objects_matched()
# Build quantization compatible model.
model = qat_segmentation_model.SegmentationModelQuantized(
model.backbone, model.decoder, model.head, input_specs)
scope_dict = {
'L2': tf.keras.regularizers.l2,
}
# Apply QAT to backbone (a tf.keras.Model) first.
with tfmot.quantization.keras.quantize_scope(scope_dict):
annotated_backbone = tfmot.quantization.keras.quantize_annotate_model(
model.backbone)
optimized_backbone = tfmot.quantization.keras.quantize_apply(
annotated_backbone, scheme=schemes.Default8BitQuantizeScheme())
backbone_optimized_model = qat_segmentation_model.SegmentationModelQuantized(
optimized_backbone, model.decoder, model.head, input_specs)
# Copy over all remaining layers.
for from_layer, to_layer in zip(model.layers,
backbone_optimized_model.layers):
if from_layer != model.backbone:
to_layer.set_weights(from_layer.get_weights())
with tfmot.quantization.keras.quantize_scope(scope_dict):
def apply_quantization_to_layers(layer):
if isinstance(layer, (segmentation_heads.SegmentationHead,
nn_layers.SpatialPyramidPooling, aspp.ASPP)):
return tfmot.quantization.keras.quantize_annotate_layer(layer)
return layer
annotated_model = tf.keras.models.clone_model(
backbone_optimized_model,
clone_function=apply_quantization_to_layers,
)
optimized_model = tfmot.quantization.keras.quantize_apply(
annotated_model, scheme=schemes.Default8BitQuantizeScheme())
return optimized_model
def build_qat_retinanet(
model: tf.keras.Model, quantization: common.Quantization,
model_config: configs.retinanet.RetinaNet) -> tf.keras.Model:
"""Applies quantization aware training for RetinaNet model.
Args:
model: The model applying quantization aware training.
quantization: The Quantization config.
model_config: The model config.
Returns:
The model that applied optimization techniques.
"""
original_checkpoint = quantization.pretrained_original_checkpoint
if original_checkpoint is not None:
ckpt = tf.train.Checkpoint(
model=model,
**model.checkpoint_items)
status = ckpt.read(original_checkpoint)
status.expect_partial().assert_existing_objects_matched()
scope_dict = {
'L2': tf.keras.regularizers.l2,
}
with tfmot.quantization.keras.quantize_scope(scope_dict):
annotated_backbone = tfmot.quantization.keras.quantize_annotate_model(
model.backbone)
optimized_backbone = tfmot.quantization.keras.quantize_apply(
annotated_backbone,
scheme=schemes.Default8BitQuantizeScheme())
head = model.head
if quantization.quantize_detection_head:
if not isinstance(head, dense_prediction_heads.RetinaNetHead):
raise ValueError('Currently only supports RetinaNetHead.')
head = (
dense_prediction_heads_qat.RetinaNetHeadQuantized.from_config(
head.get_config()))
optimized_model = retinanet_model.RetinaNetModel(
optimized_backbone,
model.decoder,
head,
model.detection_generator,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_scales=model_config.anchor.num_scales,
aspect_ratios=model_config.anchor.aspect_ratios,
anchor_size=model_config.anchor.anchor_size)
return optimized_model
def build_qat_classification_model(
model: tf.keras.Model,
quantization: common.Quantization,
input_specs: tf.keras.layers.InputSpec,
model_config: configs.image_classification.ImageClassificationModel,
l2_regularizer: tf.keras.regularizers.Regularizer = None
) -> tf.keras.Model: # pytype: disable=annotation-type-mismatch # typed-keras
"""Apply model optimization techniques.
Args:
model: The model applying model optimization techniques.
quantization: The Quantization config.
input_specs: `tf.keras.layers.InputSpec` specs of the input tensor.
model_config: The model config.
l2_regularizer: tf.keras.regularizers.Regularizer object. Default to None.
Returns:
model: The model that applied optimization techniques.
"""
original_checkpoint = quantization.pretrained_original_checkpoint
if original_checkpoint:
ckpt = tf.train.Checkpoint(
model=model,
**model.checkpoint_items)
status = ckpt.read(original_checkpoint)
status.expect_partial().assert_existing_objects_matched()
scope_dict = {
'L2': tf.keras.regularizers.l2,
}
with tfmot.quantization.keras.quantize_scope(scope_dict):
annotated_backbone = tfmot.quantization.keras.quantize_annotate_model(
model.backbone)
if quantization.change_num_bits:
backbone = tfmot.quantization.keras.quantize_apply(
annotated_backbone,
scheme=n_bit_schemes.DefaultNBitQuantizeScheme(
num_bits_weight=quantization.num_bits_weight,
num_bits_activation=quantization.num_bits_activation))
else:
backbone = tfmot.quantization.keras.quantize_apply(
annotated_backbone,
scheme=schemes.Default8BitQuantizeScheme())
norm_activation_config = model_config.norm_activation
backbone_optimized_model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=model_config.num_classes,
input_specs=input_specs,
dropout_rate=model_config.dropout_rate,
kernel_regularizer=l2_regularizer,
add_head_batch_norm=model_config.add_head_batch_norm,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon)
for from_layer, to_layer in zip(
model.layers, backbone_optimized_model.layers):
if from_layer != model.backbone:
to_layer.set_weights(from_layer.get_weights())
with tfmot.quantization.keras.quantize_scope(scope_dict):
def apply_quantization_to_dense(layer):
if isinstance(layer, (tf.keras.layers.Dense,
tf.keras.layers.Dropout,
tf.keras.layers.GlobalAveragePooling2D)):
return tfmot.quantization.keras.quantize_annotate_layer(layer)
return layer
annotated_model = tf.keras.models.clone_model(
backbone_optimized_model,
clone_function=apply_quantization_to_dense,
)
if quantization.change_num_bits:
optimized_model = tfmot.quantization.keras.quantize_apply(
annotated_model,
scheme=n_bit_schemes.DefaultNBitQuantizeScheme(
num_bits_weight=quantization.num_bits_weight,
num_bits_activation=quantization.num_bits_activation))
else:
optimized_model = tfmot.quantization.keras.quantize_apply(
annotated_model)
return optimized_model
def build_qat_retinanet(
model: tf.keras.Model, quantization: common.Quantization,
model_config: configs.retinanet.RetinaNet) -> tf.keras.Model:
"""Applies quantization aware training for RetinaNet model.
Args:
model: The model applying quantization aware training.
quantization: The Quantization config.
model_config: The model config.
Returns:
The model that applied optimization techniques.
"""
original_checkpoint = quantization.pretrained_original_checkpoint
if original_checkpoint is not None:
ckpt = tf.train.Checkpoint(model=model, **model.checkpoint_items)
status = ckpt.read(original_checkpoint)
status.expect_partial().assert_existing_objects_matched()
scope_dict = {
'L2': tf.keras.regularizers.l2,
'BatchNormalizationWrapper': qat_nn_layers.BatchNormalizationWrapper,
}
with tfmot.quantization.keras.quantize_scope(scope_dict):
annotated_backbone = tfmot.quantization.keras.quantize_annotate_model(
model.backbone)
optimized_backbone = tfmot.quantization.keras.quantize_apply(
annotated_backbone, scheme=schemes.Default8BitQuantizeScheme())
decoder = model.decoder
if quantization.quantize_detection_decoder:
if not isinstance(decoder, fpn.FPN):
raise ValueError('Currently only supports FPN.')
decoder = tf.keras.models.clone_model(
decoder,
clone_function=_clone_function_for_fpn,
)
decoder = tfmot.quantization.keras.quantize_apply(decoder)
decoder = tfmot.quantization.keras.remove_input_range(decoder)
head = model.head
if quantization.quantize_detection_head:
if not isinstance(head, dense_prediction_heads.RetinaNetHead):
raise ValueError('Currently only supports RetinaNetHead.')
head = (
dense_prediction_heads_qat.RetinaNetHeadQuantized.from_config(
head.get_config()))
optimized_model = retinanet_model.RetinaNetModel(
optimized_backbone,
decoder,
head,
model.detection_generator,
min_level=model_config.min_level,
max_level=model_config.max_level,
num_scales=model_config.anchor.num_scales,
aspect_ratios=model_config.anchor.aspect_ratios,
anchor_size=model_config.anchor.anchor_size)
if quantization.quantize_detection_head:
# Call the model with dummy input to build the head part.
dummpy_input = tf.zeros([1] + model_config.input_size)
optimized_model(dummpy_input, training=True)
helper.copy_original_weights(model.head, optimized_model.head)
return optimized_model
