model package

Subpackages

• model.classify package
  • Submodules
  • model.classify.dataloader module
    • EMRIDatasetTorch
    • NpzDatasetTorch
    • TinyEMRIDataset
      • TinyEMRIDataset.save()
    • load_waveform()
    • save_waveform()
  • model.classify.tcn module
    • Chomp1d
      • Chomp1d.forward()
    • TCN
      • TCN.clear_activation_maps()
      • TCN.forward()
      • TCN.get_activation_maps()
    • TemporalBlock
      • TemporalBlock.forward()
      • TemporalBlock.init_weights()
    • TemporalConvNet
      • TemporalConvNet.clear_activation_maps()
      • TemporalConvNet.forward()
      • TemporalConvNet.get_activation_maps()
      • TemporalConvNet.hook_fn()
  • model.classify.trainer module
    • Trainer
      • Trainer.accuracy()
      • Trainer.calculate_accuracy()
      • Trainer.count_parameters()
      • Trainer.evaluate()
      • Trainer.format_number_with_unit()
      • Trainer.get_gpu_tensors_sorted_by_size()
      • Trainer.get_loss_fn()
      • Trainer.get_lr_scheduler()
      • Trainer.get_optimizer()
      • Trainer.load_checkpoint()
      • Trainer.print_gpu_tensors()
      • Trainer.train()
      • Trainer.train_epoch()
      • Trainer.update_average()
  • Module contents
    • get_version()
• model.denoising package
  • Subpackages
    • model.denoising.data package
      • Submodules
      • model.denoising.data.data_samplers module
      • model.denoising.data.dataset_utils module
      • model.denoising.data.gw_dataset module
      • Module contents
    • model.denoising.fused_kernels package
      • Module contents
    • model.denoising.model package
      • Submodules
      • model.denoising.model.distributed module
      • model.denoising.model.enums module
      • model.denoising.model.fused_bias_gelu module
      • model.denoising.model.fused_layer_norm module
      • model.denoising.model.fused_softmax module
      • model.denoising.model.module module
      • model.denoising.model.transformer module
      • model.denoising.model.utils module
      • model.denoising.model.waveform_model module
      • model.denoising.model.waveformer_model module
      • Module contents
    • model.denoising.mpu package
      • Submodules
      • model.denoising.mpu.data module
      • model.denoising.mpu.initialize module
      • model.denoising.mpu.layers module
      • model.denoising.mpu.mappings module
      • model.denoising.mpu.random module
      • model.denoising.mpu.utils module
      • Module contents
    • model.denoising.optimizer package
      • Submodules
      • model.denoising.optimizer.clip_grads module
      • model.denoising.optimizer.grad_scaler module
      • model.denoising.optimizer.optimizer module
      • Module contents
  • Submodules
  • model.denoising.arguments module
    • parse_args()
  • model.denoising.checkpointing module
    • check_checkpoint_args()
    • ensure_directory_exists()
    • fix_query_key_value_ordering()
    • get_checkpoint_name()
    • get_checkpoint_tracker_filename()
    • get_checkpoint_version()
    • load_biencoder_checkpoint()
    • load_checkpoint()
    • save_checkpoint()
    • set_checkpoint_version()
  • model.denoising.global_vars module
    • Timers
      • Timers.log()
      • Timers.write()
    • get_adlr_autoresume()
    • get_args()
    • get_current_global_batch_size()
    • get_num_microbatches()
    • get_tensorboard_writer()
    • get_timers()
    • get_tokenizer()
    • set_global_variables()
    • update_num_microbatches()
  • model.denoising.initialize module
    • initialize_megatron()
    • write_args_to_tensorboard()
  • model.denoising.learning_rates module
    • AnnealingLR
      • AnnealingLR.get_lr()
      • AnnealingLR.load_state_dict()
      • AnnealingLR.state_dict()
      • AnnealingLR.step()
  • model.denoising.memory module
    • MemoryBuffer
      • MemoryBuffer.add()
      • MemoryBuffer.get_data()
      • MemoryBuffer.is_in_use()
      • MemoryBuffer.numel_in_use()
      • MemoryBuffer.print_average_usage()
      • MemoryBuffer.reset()
    • RingMemBuffer
      • RingMemBuffer.get_next_buffer()
    • allocate_mem_buff()
    • get_mem_buff()
  • model.denoising.microbatches module
    • ConstantNumMicroBatches
      • ConstantNumMicroBatches.update()
    • NumMicroBatchesCalculator
      • NumMicroBatchesCalculator.get()
      • NumMicroBatchesCalculator.get_current_global_batch_size()
      • NumMicroBatchesCalculator.update()
    • RampupBatchsizeNumMicroBatches
      • RampupBatchsizeNumMicroBatches.update()
    • build_num_microbatches_calculator()
  • model.denoising.p2p_communication module
    • recv_backward()
    • recv_forward()
    • send_backward()
    • send_backward_recv_backward()
    • send_backward_recv_forward()
    • send_forward()
    • send_forward_backward_recv_forward_backward()
    • send_forward_recv_backward()
    • send_forward_recv_forward()
  • model.denoising.package_info module
  • model.denoising.schedules module
    • backward_step()
    • dummy_handler()
    • forward_backward_no_pipelining()
    • forward_backward_pipelining_with_interleaving()
    • forward_backward_pipelining_without_interleaving()
    • forward_step()
    • forward_step_wrapper()
    • get_forward_backward_func()
  • model.denoising.training module
    • build_train_valid_test_data_iterators()
    • cyclic_iter()
    • evaluate()
    • evaluate_and_print_results()
    • get_learning_rate_scheduler()
    • get_model()
    • pretrain()
    • print_datetime()
    • save_checkpoint_and_time()
    • setup_model_and_optimizer()
    • train()
    • train_step()
    • training_log()
    • update_train_iters()
  • model.denoising.utils module
    • average_losses_across_data_parallel_group()
    • calc_params_l2_norm()
    • check_adlr_autoresume_termination()
    • get_ltor_masks_and_position_ids()
    • print_params_min_max_norm()
    • report_memory()
    • unwrap_model()
  • Module contents
    • is_last_rank()
    • print_rank_0()
    • print_rank_last()
• model.detection package
  • Submodules
  • model.detection.dataloader module
    • GWSEDataset
      • GWSEDataset.load_waveform()
      • GWSEDataset.save_waveform()
    • WaveformDatasetTorch
      • WaveformDatasetTorch.merge_dataset()
  • Module contents
    • get_version()

Module contents

Detect EMRI signal using DNN

model.get_version() → str