165 lines
4.6 KiB
Python
165 lines
4.6 KiB
Python
import os
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import torch
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import math
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from pytorch_lightning.root_module.memory import ModelSummary
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from pytorch_lightning.root_module.grads import GradInformation
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from pytorch_lightning.root_module.model_saving import ModelIO, load_hparams_from_tags_csv
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from pytorch_lightning.root_module.optimization import OptimizerConfig
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from pytorch_lightning.root_module.hooks import ModelHooks
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class LightningModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
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def __init__(self, hparams):
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super(LightningModule, self).__init__()
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self.hparams = hparams
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self.dtype = torch.FloatTensor
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self.exp_save_path = None
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self.current_epoch = 0
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self.global_step = 0
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self.loaded_optimizer_states_dict = {}
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self.trainer = None
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self.experiment = None
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# track if gpu was requested for checkpointing
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self.on_gpu = False
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# computed vars for the dataloaders
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self._tng_dataloader = None
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self._val_dataloader = None
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self._test_dataloader = None
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def forward(self, *args, **kwargs):
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"""
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Expand model in into whatever you need.
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Also need to return the target
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:param x:
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:return:
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"""
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raise NotImplementedError
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def validation_step(self, data_batch, batch_nb):
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"""
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return whatever outputs will need to be aggregated in validation_end
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:param data_batch:
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:return:
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"""
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raise NotImplementedError
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def validation_end(self, outputs):
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"""
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Outputs has the appended output after each validation step
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:param outputs:
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:return: dic_with_metrics for tqdm
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"""
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raise NotImplementedError
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def training_step(self, data_batch, batch_nb):
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"""
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return loss, dict with metrics for tqdm
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:param data_batch:
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:return:
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"""
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raise NotImplementedError
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def configure_optimizers(self):
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"""
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Return array of optimizers
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:return:
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"""
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raise NotImplementedError
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def update_tng_log_metrics(self, logs):
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"""
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Chance to update metrics to be logged for training step.
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For example, add music, images, etc... to log
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:param logs:
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:return:
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"""
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raise NotImplementedError
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def loss(self, *args, **kwargs):
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"""
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Expand model_out into your components
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:param model_out:
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:return:
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"""
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raise NotImplementedError
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def summarize(self):
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model_summary = ModelSummary(self)
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print(model_summary)
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def freeze(self):
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for param in self.parameters():
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param.requires_grad = False
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def unfreeze(self):
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for param in self.parameters():
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param.requires_grad = True
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@property
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def tng_dataloader(self):
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"""
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Implement a function to load an h5py of this data
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:return:
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"""
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raise NotImplementedError
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@property
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def test_dataloader(self):
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"""
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Implement a function to load an h5py of this data
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:return:
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"""
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raise NotImplementedError
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@property
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def val_dataloader(self):
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"""
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Implement a function to load an h5py of this data
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:return:
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"""
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raise NotImplementedError
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@staticmethod
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def get_process_position(gpus):
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try:
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current_gpu = os.environ["CUDA_VISIBLE_DEVICES"]
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gpu_ids = gpus.split(',')
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process_position = gpu_ids.index(current_gpu)
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return process_position, current_gpu
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except Exception as e:
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return 0, 0
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@classmethod
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def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):
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"""
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Primary way of loading model from csv weights path
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:param weights_path:
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:param tags_csv:
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:param on_gpu:
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:param map_location: dic for mapping storage {'cuda:1':'cuda:0'}
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:return:
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"""
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hparams = load_hparams_from_tags_csv(tags_csv)
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hparams.__setattr__('on_gpu', on_gpu)
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if on_gpu:
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if map_location is not None:
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checkpoint = torch.load(weights_path, map_location=map_location)
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else:
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checkpoint = torch.load(weights_path)
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else:
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checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)
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model = cls(hparams)
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# allow model to load
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model.load_model_specific(checkpoint)
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model.load_state_dict(checkpoint['state_dict'], strict=False)
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return model
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