official/projects/yolo/modeling/yolo_model.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""Yolo models."""
from typing import Mapping, Union, Any, Dict
import tensorflow as tf, tf_keras
from official.projects.yolo.modeling.layers import nn_blocks
class Yolo(tf_keras.Model):
"""The YOLO model class."""
def __init__(self,
backbone,
decoder,
head,
detection_generator,
**kwargs):
"""Detection initialization function.
Args:
backbone: `tf_keras.Model` a backbone network.
decoder: `tf_keras.Model` a decoder network.
head: `RetinaNetHead`, the RetinaNet head.
detection_generator: the detection generator.
**kwargs: keyword arguments to be passed.
"""
super(Yolo, self).__init__(**kwargs)
self._config_dict = {
'backbone': backbone,
'decoder': decoder,
'head': head,
'detection_generator': detection_generator
}
# model components
self._backbone = backbone
self._decoder = decoder
self._head = head
self._detection_generator = detection_generator
self._fused = False
def call(self,
inputs: tf.Tensor,
training: bool = None,
mask: Any = None) -> Dict[str, tf.Tensor]:
maps = self.backbone(inputs)
decoded_maps = self.decoder(maps)
raw_predictions = self.head(decoded_maps)
if training:
return {'raw_output': raw_predictions}
else:
# Post-processing.
predictions = self.detection_generator(raw_predictions)
predictions.update({'raw_output': raw_predictions})
return predictions
@property
def backbone(self):
return self._backbone
@property
def decoder(self):
return self._decoder
@property
def head(self):
return self._head
@property
def detection_generator(self):
return self._detection_generator
def get_config(self):
return self._config_dict
@classmethod
def from_config(cls, config):
return cls(**config)
@property
def checkpoint_items(
self) -> Mapping[str, Union[tf_keras.Model, tf_keras.layers.Layer]]:
"""Returns a dictionary of items to be additionally checkpointed."""
items = dict(backbone=self.backbone, head=self.head)
if self.decoder is not None:
items.update(decoder=self.decoder)
return items
def fuse(self):
"""Fuses all Convolution and Batchnorm layers to get better latency."""
print('Fusing Conv Batch Norm Layers.')
if not self._fused:
self._fused = True
for layer in self.submodules:
if isinstance(layer, nn_blocks.ConvBN):
layer.fuse()
self.summary()
return