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114 lines
4.7 KiB
114 lines
4.7 KiB
""" Model creation / weight loading / state_dict helpers
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Hacked together by / Copyright 2020 Ross Wightman
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"""
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import logging
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import os
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from collections import OrderedDict
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import torch
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import timm.models._builder
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_logger = logging.getLogger(__name__)
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def clean_state_dict(state_dict):
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# 'clean' checkpoint by removing .module prefix from state dict if it exists from parallel training
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cleaned_state_dict = OrderedDict()
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for k, v in state_dict.items():
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name = k[7:] if k.startswith('module.') else k
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cleaned_state_dict[name] = v
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return cleaned_state_dict
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def load_state_dict(checkpoint_path, use_ema=True):
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if checkpoint_path and os.path.isfile(checkpoint_path):
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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state_dict_key = ''
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if isinstance(checkpoint, dict):
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if use_ema and checkpoint.get('state_dict_ema', None) is not None:
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state_dict_key = 'state_dict_ema'
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elif use_ema and checkpoint.get('model_ema', None) is not None:
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state_dict_key = 'model_ema'
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elif 'state_dict' in checkpoint:
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state_dict_key = 'state_dict'
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elif 'model' in checkpoint:
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state_dict_key = 'model'
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state_dict = clean_state_dict(checkpoint[state_dict_key] if state_dict_key else checkpoint)
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_logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
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return state_dict
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else:
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_logger.error("No checkpoint found at '{}'".format(checkpoint_path))
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raise FileNotFoundError()
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def load_checkpoint(model, checkpoint_path, use_ema=True, strict=True, remap=False):
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if os.path.splitext(checkpoint_path)[-1].lower() in ('.npz', '.npy'):
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# numpy checkpoint, try to load via model specific load_pretrained fn
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if hasattr(model, 'load_pretrained'):
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timm.models._model_builder.load_pretrained(checkpoint_path)
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else:
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raise NotImplementedError('Model cannot load numpy checkpoint')
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return
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state_dict = load_state_dict(checkpoint_path, use_ema)
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if remap:
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state_dict = remap_checkpoint(model, state_dict)
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incompatible_keys = model.load_state_dict(state_dict, strict=strict)
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return incompatible_keys
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def remap_checkpoint(model, state_dict, allow_reshape=True):
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""" remap checkpoint by iterating over state dicts in order (ignoring original keys).
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This assumes models (and originating state dict) were created with params registered in same order.
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"""
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out_dict = {}
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for (ka, va), (kb, vb) in zip(model.state_dict().items(), state_dict.items()):
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assert va.numel == vb.numel, f'Tensor size mismatch {ka}: {va.shape} vs {kb}: {vb.shape}. Remap failed.'
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if va.shape != vb.shape:
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if allow_reshape:
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vb = vb.reshape(va.shape)
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else:
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assert False, f'Tensor shape mismatch {ka}: {va.shape} vs {kb}: {vb.shape}. Remap failed.'
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out_dict[ka] = vb
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return out_dict
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def resume_checkpoint(model, checkpoint_path, optimizer=None, loss_scaler=None, log_info=True):
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resume_epoch = None
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if os.path.isfile(checkpoint_path):
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checkpoint = torch.load(checkpoint_path, map_location='cpu')
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if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
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if log_info:
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_logger.info('Restoring model state from checkpoint...')
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state_dict = clean_state_dict(checkpoint['state_dict'])
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model.load_state_dict(state_dict)
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if optimizer is not None and 'optimizer' in checkpoint:
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if log_info:
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_logger.info('Restoring optimizer state from checkpoint...')
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optimizer.load_state_dict(checkpoint['optimizer'])
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if loss_scaler is not None and loss_scaler.state_dict_key in checkpoint:
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if log_info:
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_logger.info('Restoring AMP loss scaler state from checkpoint...')
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loss_scaler.load_state_dict(checkpoint[loss_scaler.state_dict_key])
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if 'epoch' in checkpoint:
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resume_epoch = checkpoint['epoch']
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if 'version' in checkpoint and checkpoint['version'] > 1:
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resume_epoch += 1 # start at the next epoch, old checkpoints incremented before save
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if log_info:
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_logger.info("Loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
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else:
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model.load_state_dict(checkpoint)
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if log_info:
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_logger.info("Loaded checkpoint '{}'".format(checkpoint_path))
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return resume_epoch
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else:
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_logger.error("No checkpoint found at '{}'".format(checkpoint_path))
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raise FileNotFoundError()
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