关于TF2 Model Zoo中SSD模型pipeline.config文件train_config内total_steps与num_steps的关系及配置疑问

Great question! Let's break down the relationship between these two parameters, why you're seeing the behavior you described, and whether they need to be set to the same value.

Core Differences Between the Two Parameters #

• num_steps: This is the hard stop condition for your training loop, controlled directly by the TensorFlow Object Detection API's trainer. Once your training reaches this number of steps, the entire training process will terminate—regardless of what you set total_steps to. That's exactly why modifying total_steps didn't change how long your training ran; the trainer only looks at num_steps to know when to stop.
• total_steps: This parameter is exclusively used by learning rate schedulers (like the cosine_decay_learning_rate in your config). It defines the total number of steps the scheduler uses to calculate learning rate decay. For cosine decay specifically, it determines how many steps it takes for the learning rate to drop from its base value to the minimum value, and it has no control over when training stops.

Why They're Usually Set to the Same Value #

In most standard training workflows, setting num_steps and total_steps to the same value is intentional and recommended:

• It ensures your learning rate decay cycle perfectly aligns with your total training duration. For example, if you train for 50,000 steps (num_steps=50000), setting total_steps=50000 means the cosine decay will finish its full cycle right as training ends, which is the intended behavior for this learning rate strategy.
• If you set them to different values:
  • If num_steps < total_steps: Training will stop before the learning rate completes its full decay cycle, leaving you with a higher final learning rate than intended.
  • If num_steps > total_steps: Once training passes total_steps, the cosine decay will just hold the learning rate at its minimum value for the remaining steps. No error is thrown because the scheduler only calculates the rate based on its formula, it doesn't enforce a stop.

Context for Your Specific Config #

Looking at your provided train_config snippet:

train_config {
 batch_size: 128
 data_augmentation_options {
 random_horizontal_flip { }
 }
 data_augmentation_options {
 random_crop_image {
 min_object_covered: 0.0
 min_aspect_ratio: 0.75
 max_aspect_ratio: 3.0
 min_area: 0.75
 max_area: 1.0
 overlap_thresh: 0.0
 }
 }
 sync_replicas: true
 optimizer {
 momentum_optimizer {
 learning_rate {
 cosine_decay_learning_rate {
 learning_rate_base: 0.07999999821186066
 total_steps: 50000
 warmup_learning_rate: 0.026666000485420227
 warmup_steps: 1000
 }
 }
 momentum_optimizer_value: 0.8999999761581421
 }
 use_moving_average: false
}
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED"
num_steps: 50000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "classification"
fine_tune_checkpoint_version: V2

You're using cosine decay, which relies on total_steps to manage its decay rhythm. Setting num_steps and total_steps to 50000 here is the right choice—it makes sure your learning rate follows the full cosine curve over the entire course of your training.

内容的提问来源于stack exchange，提问作者Hoang97