(config,total_iterations)
| 7 | from competitors_models.DDRNet_Reimplementation import get_ddrnet_23,get_ddrnet_23slim |
| 8 | |
| 9 | def get_lr_function(config,total_iterations): |
| 10 | # get the learning rate multiplier function for LambdaLR |
| 11 | name=config["lr_scheduler"] |
| 12 | warmup_iters=config["warmup_iters"] |
| 13 | warmup_factor=config["warmup_factor"] |
| 14 | if "poly"==name: |
| 15 | p=config["poly_power"] |
| 16 | return lambda x : poly_lr_scheduler(x,total_iterations,warmup_iters,warmup_factor,p) |
| 17 | elif "cosine"==name: |
| 18 | return lambda x : cosine_lr_scheduler(x,total_iterations,warmup_iters,warmup_factor) |
| 19 | elif "step"==name: |
| 20 | return lambda x : step_lr_scheduler(x,total_iterations,warmup_iters,warmup_factor) |
| 21 | elif "exp"==name: |
| 22 | beta=config["exp_beta"] |
| 23 | return lambda x : exp_lr_scheduler(x,total_iterations,warmup_iters,warmup_factor,beta) |
| 24 | else: |
| 25 | raise NotImplementedError() |
| 26 | |
| 27 | def get_loss_fun(config): |
| 28 | train_crop_size=config["train_crop_size"] |
no test coverage detected