131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
from typing import *
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import copy
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import torch
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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from easydict import EasyDict as edict
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from ..basic import BasicTrainer
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class SparseStructureVaeTrainer(BasicTrainer):
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"""
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Trainer for Sparse Structure VAE.
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Args:
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models (dict[str, nn.Module]): Models to train.
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dataset (torch.utils.data.Dataset): Dataset.
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output_dir (str): Output directory.
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load_dir (str): Load directory.
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step (int): Step to load.
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batch_size (int): Batch size.
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batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
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batch_split (int): Split batch with gradient accumulation.
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max_steps (int): Max steps.
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optimizer (dict): Optimizer config.
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lr_scheduler (dict): Learning rate scheduler config.
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elastic (dict): Elastic memory management config.
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grad_clip (float or dict): Gradient clip config.
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ema_rate (float or list): Exponential moving average rates.
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fp16_mode (str): FP16 mode.
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- None: No FP16.
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- 'inflat_all': Hold a inflated fp32 master param for all params.
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- 'amp': Automatic mixed precision.
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fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
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finetune_ckpt (dict): Finetune checkpoint.
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log_param_stats (bool): Log parameter stats.
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i_print (int): Print interval.
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i_log (int): Log interval.
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i_sample (int): Sample interval.
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i_save (int): Save interval.
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i_ddpcheck (int): DDP check interval.
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loss_type (str): Loss type. 'bce' for binary cross entropy, 'l1' for L1 loss, 'dice' for Dice loss.
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lambda_kl (float): KL divergence loss weight.
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"""
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def __init__(
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self,
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*args,
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loss_type='bce',
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lambda_kl=1e-6,
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**kwargs
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):
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super().__init__(*args, **kwargs)
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self.loss_type = loss_type
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self.lambda_kl = lambda_kl
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def training_losses(
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self,
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ss: torch.Tensor,
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**kwargs
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) -> Tuple[Dict, Dict]:
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"""
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Compute training losses.
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Args:
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ss: The [N x 1 x H x W x D] tensor of binary sparse structure.
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Returns:
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a dict with the key "loss" containing a scalar tensor.
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may also contain other keys for different terms.
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"""
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z, mean, logvar = self.training_models['encoder'](ss.float(), sample_posterior=True, return_raw=True)
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logits = self.training_models['decoder'](z)
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terms = edict(loss = 0.0)
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if self.loss_type == 'bce':
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terms["bce"] = F.binary_cross_entropy_with_logits(logits, ss.float(), reduction='mean')
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terms["loss"] = terms["loss"] + terms["bce"]
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elif self.loss_type == 'l1':
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terms["l1"] = F.l1_loss(F.sigmoid(logits), ss.float(), reduction='mean')
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terms["loss"] = terms["loss"] + terms["l1"]
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elif self.loss_type == 'dice':
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logits = F.sigmoid(logits)
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terms["dice"] = 1 - (2 * (logits * ss.float()).sum() + 1) / (logits.sum() + ss.float().sum() + 1)
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terms["loss"] = terms["loss"] + terms["dice"]
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else:
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raise ValueError(f'Invalid loss type {self.loss_type}')
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terms["kl"] = 0.5 * torch.mean(mean.pow(2) + logvar.exp() - logvar - 1)
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terms["loss"] = terms["loss"] + self.lambda_kl * terms["kl"]
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return terms, {}
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@torch.no_grad()
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def snapshot(self, suffix=None, num_samples=64, batch_size=1, verbose=False):
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super().snapshot(suffix=suffix, num_samples=num_samples, batch_size=batch_size, verbose=verbose)
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@torch.no_grad()
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def run_snapshot(
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self,
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num_samples: int,
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batch_size: int,
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verbose: bool = False,
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) -> Dict:
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dataloader = DataLoader(
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copy.deepcopy(self.dataset),
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batch_size=batch_size,
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shuffle=True,
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num_workers=0,
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collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
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)
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# inference
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gts = []
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recons = []
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for i in range(0, num_samples, batch_size):
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batch = min(batch_size, num_samples - i)
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data = next(iter(dataloader))
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args = {k: v[:batch].cuda() if isinstance(v, torch.Tensor) else v[:batch] for k, v in data.items()}
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z = self.models['encoder'](args['ss'].float(), sample_posterior=False)
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logits = self.models['decoder'](z)
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recon = (logits > 0).long()
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gts.append(args['ss'])
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recons.append(recon)
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sample_dict = {
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'gt': {'value': torch.cat(gts, dim=0), 'type': 'sample'},
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'recon': {'value': torch.cat(recons, dim=0), 'type': 'sample'},
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}
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return sample_dict
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