def test_model_with_objectness(self):
image_dim = 64
backbone = resnet.ResNetBackbone(
input_shape=(image_dim, image_dim, 3),
units_per_block=(1, 1),
num_last_blocks=2,
)
tasks = [standard_tasks.get_objectness_task()]
builder = FPNBuilder(backbone=backbone, tasks=tasks)
self.train_export_model(builder)
def test_simple_cnn_model_with_many_tasks_many_fms(self):
image_dim = 64
backbone = SimpleCNNBackbone(input_shape=(image_dim, image_dim, 3),
init_filters=16,
num_last_blocks=1)
tasks = [
standard_tasks.get_objectness_task(),
standard_tasks.get_box_shape_task(),
standard_tasks.get_multiclass_task(num_classes=10,
fl_gamma=0,
activation="softmax"),
]
builder = FPNBuilder(backbone=backbone, tasks=tasks)
self.train_export_model(builder)
def __init__(
self,
num_classes: int,
image_dim: int = 128,
mnet_alpha: float = 1.0,
min_fm_size: int = 10,
weights: str = "imagenet",
label_smoothing: float = 0.01,
box_shape_task: str = "box_shape",
last_conv_filters: int = 64,
class_activation: str = "sigmoid",
box_obj_task: str = "center_ignore_margin",
):
self._image_dim = image_dim
self._num_classes = num_classes
backbone = mobilenetv2.MobileNetV2Backbone(
input_shape=(self._image_dim, self._image_dim, 3),
alpha=mnet_alpha,
min_fm_size=min_fm_size,
weights=weights,
)
tasks = [
standard_tasks.get_objectness_task(
obj_class=box_obj_task,
label_smoothing=label_smoothing,
num_filters=last_conv_filters,
),
standard_tasks.get_box_shape_task(box_shape_task,
num_filters=last_conv_filters),
standard_tasks.get_multiclass_task(
self._num_classes,
fl_gamma=0.0,
label_smoothing=label_smoothing,
num_filters=last_conv_filters,
activation=class_activation,
),
]
builder = FPNBuilder(backbone=backbone, tasks=tasks)
super().__init__(builder)
def test_debug_quantized_model(self):
image_dim = 64
backbone = resnet.ResNetBackbone(
input_shape=(image_dim, image_dim, 3),
units_per_block=(1, 1),
num_last_blocks=2,
)
tasks = [standard_tasks.get_objectness_task()]
builder = FPNBuilder(backbone=backbone, tasks=tasks)
builder.build()
image_dim = builder.input_shape[1]
raw_dataset = datasets_ops.from_numpy_generator(
utils.create_fake_detection_dataset_generator(num_steps=100))
train_dataset = datasets_ops.prepare_dataset(
raw_dataset,
model_image_size=(image_dim, image_dim),
augmentation_fn=aug_fn,
num_epochs=-1,
batch_size=2,
shuffle_buffer_size=1,
prefetch_buffer_size=1,
)
prepared_train_dataset = train_dataset.map(
builder.get_build_training_targets_fn())
def representative_dataset():
for features, labels in prepared_train_dataset:
for image in features["image"]:
yield np.expand_dims(image, 0)
quantized_model = builder.build_quantized()
outputs_diffs = tflite_debugger.debug_model_quantization(
representative_dataset(), quantized_model, max_samples=1)
print(outputs_diffs)