def build_classification_model(
input_specs: tf.keras.layers.InputSpec,
model_config: classification_cfg.ImageClassificationModel,
l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
skip_logits_layer: bool = False,
backbone: Optional[tf.keras.Model] = None) -> tf.keras.Model:
"""Builds the classification model."""
norm_activation_config = model_config.norm_activation
if not backbone:
backbone = backbones.factory.build_backbone(
input_specs=input_specs,
backbone_config=model_config.backbone,
norm_activation_config=norm_activation_config,
l2_regularizer=l2_regularizer)
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=model_config.num_classes,
input_specs=input_specs,
dropout_rate=model_config.dropout_rate,
kernel_initializer=model_config.kernel_initializer,
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,
skip_logits_layer=skip_logits_layer)
return model
def test_sync_bn_multiple_devices(self, strategy, use_sync_bn):
"""Test for sync bn on TPU and GPU devices."""
inputs = np.random.rand(64, 128, 128, 3)
tf.keras.backend.set_image_data_format('channels_last')
with strategy.scope():
backbone = backbones.ResNet(model_id=50, use_sync_bn=use_sync_bn)
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=1000,
dropout_rate=0.2,
)
_ = model(inputs)
def test_serialize_deserialize(self):
"""Validate the classification net can be serialized and deserialized."""
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.ResNet(model_id=50)
model = classification_model.ClassificationModel(
backbone=backbone, num_classes=1000)
config = model.get_config()
new_model = classification_model.ClassificationModel.from_config(config)
# Validate that the config can be forced to JSON.
_ = new_model.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(model.get_config(), new_model.get_config())
def test_mobilenet_network_creation(self, mobilenet_model_id,
filter_size_scale):
"""Test for creation of a MobileNet classifier."""
inputs = np.random.rand(2, 224, 224, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.MobileNet(
model_id=mobilenet_model_id, filter_size_scale=filter_size_scale)
num_classes = 1001
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
def test_data_format_gpu(self, strategy, data_format, input_dim):
"""Test for different data formats on GPU devices."""
if data_format == 'channels_last':
inputs = np.random.rand(2, 128, 128, input_dim)
else:
inputs = np.random.rand(2, input_dim, 128, 128)
input_specs = tf.keras.layers.InputSpec(shape=inputs.shape)
tf.keras.backend.set_image_data_format(data_format)
with strategy.scope():
backbone = backbones.ResNet(model_id=50, input_specs=input_specs)
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=1000,
input_specs=input_specs,
)
_ = model(inputs)
def test_revnet_network_creation(self):
"""Test for creation of a RevNet-56 classifier."""
revnet_model_id = 56
inputs = np.random.rand(2, 224, 224, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.RevNet(model_id=revnet_model_id)
self.assertEqual(backbone.count_params(), 19473792)
num_classes = 1000
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
add_head_batch_norm=True,
)
self.assertEqual(model.count_params(), 22816104)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
def test_resnet_network_creation(
self, input_size, resnet_model_id, activation):
"""Test for creation of a ResNet-50 classifier."""
inputs = np.random.rand(2, input_size, input_size, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.ResNet(
model_id=resnet_model_id, activation=activation)
self.assertEqual(backbone.count_params(), 23561152)
num_classes = 1000
model = classification_model.ClassificationModel(
backbone=backbone,
num_classes=num_classes,
dropout_rate=0.2,
)
self.assertEqual(model.count_params(), 25610152)
logits = model(inputs)
self.assertAllEqual([2, num_classes], logits.numpy().shape)
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