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301 lines
12 KiB
301 lines
12 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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import math
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from collections import OrderedDict
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from copy import deepcopy
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from typing import Callable
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import torch
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import torch.nn as nn
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import torch.utils.model_zoo as model_zoo
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from .features import FeatureListNet, FeatureDictNet, FeatureHookNet
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from .layers import Conv2dSame
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_logger = logging.getLogger(__name__)
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def load_state_dict(checkpoint_path, use_ema=False):
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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 = 'state_dict'
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if isinstance(checkpoint, dict):
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if use_ema and 'state_dict_ema' in checkpoint:
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state_dict_key = 'state_dict_ema'
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if state_dict_key and state_dict_key in checkpoint:
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new_state_dict = OrderedDict()
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for k, v in checkpoint[state_dict_key].items():
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# strip `module.` prefix
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name = k[7:] if k.startswith('module') else k
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new_state_dict[name] = v
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state_dict = new_state_dict
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else:
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state_dict = 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=False, strict=True):
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state_dict = load_state_dict(checkpoint_path, use_ema)
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model.load_state_dict(state_dict, strict=strict)
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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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new_state_dict = OrderedDict()
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for k, v in checkpoint['state_dict'].items():
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name = k[7:] if k.startswith('module') else k
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new_state_dict[name] = v
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model.load_state_dict(new_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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def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None, strict=True):
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if cfg is None:
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cfg = getattr(model, 'default_cfg')
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if cfg is None or 'url' not in cfg or not cfg['url']:
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_logger.warning("Pretrained model URL is invalid, using random initialization.")
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return
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state_dict = model_zoo.load_url(cfg['url'], progress=False, map_location='cpu')
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if filter_fn is not None:
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state_dict = filter_fn(state_dict)
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if in_chans == 1:
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conv1_name = cfg['first_conv']
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_logger.info('Converting first conv (%s) pretrained weights from 3 to 1 channel' % conv1_name)
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conv1_weight = state_dict[conv1_name + '.weight']
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# Some weights are in torch.half, ensure it's float for sum on CPU
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conv1_type = conv1_weight.dtype
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conv1_weight = conv1_weight.float()
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O, I, J, K = conv1_weight.shape
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if I > 3:
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assert conv1_weight.shape[1] % 3 == 0
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# For models with space2depth stems
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conv1_weight = conv1_weight.reshape(O, I // 3, 3, J, K)
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conv1_weight = conv1_weight.sum(dim=2, keepdim=False)
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else:
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conv1_weight = conv1_weight.sum(dim=1, keepdim=True)
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conv1_weight = conv1_weight.to(conv1_type)
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state_dict[conv1_name + '.weight'] = conv1_weight
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elif in_chans != 3:
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conv1_name = cfg['first_conv']
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conv1_weight = state_dict[conv1_name + '.weight']
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conv1_type = conv1_weight.dtype
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conv1_weight = conv1_weight.float()
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O, I, J, K = conv1_weight.shape
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if I != 3:
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_logger.warning('Deleting first conv (%s) from pretrained weights.' % conv1_name)
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del state_dict[conv1_name + '.weight']
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strict = False
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else:
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# NOTE this strategy should be better than random init, but there could be other combinations of
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# the original RGB input layer weights that'd work better for specific cases.
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_logger.info('Repeating first conv (%s) weights in channel dim.' % conv1_name)
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repeat = int(math.ceil(in_chans / 3))
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conv1_weight = conv1_weight.repeat(1, repeat, 1, 1)[:, :in_chans, :, :]
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conv1_weight *= (3 / float(in_chans))
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conv1_weight = conv1_weight.to(conv1_type)
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state_dict[conv1_name + '.weight'] = conv1_weight
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classifier_name = cfg['classifier']
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if num_classes == 1000 and cfg['num_classes'] == 1001:
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# special case for imagenet trained models with extra background class in pretrained weights
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classifier_weight = state_dict[classifier_name + '.weight']
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state_dict[classifier_name + '.weight'] = classifier_weight[1:]
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classifier_bias = state_dict[classifier_name + '.bias']
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state_dict[classifier_name + '.bias'] = classifier_bias[1:]
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elif num_classes != cfg['num_classes']:
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# completely discard fully connected for all other differences between pretrained and created model
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del state_dict[classifier_name + '.weight']
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del state_dict[classifier_name + '.bias']
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strict = False
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model.load_state_dict(state_dict, strict=strict)
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def extract_layer(model, layer):
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layer = layer.split('.')
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module = model
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if hasattr(model, 'module') and layer[0] != 'module':
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module = model.module
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if not hasattr(model, 'module') and layer[0] == 'module':
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layer = layer[1:]
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for l in layer:
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if hasattr(module, l):
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if not l.isdigit():
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module = getattr(module, l)
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else:
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module = module[int(l)]
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else:
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return module
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return module
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def set_layer(model, layer, val):
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layer = layer.split('.')
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module = model
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if hasattr(model, 'module') and layer[0] != 'module':
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module = model.module
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lst_index = 0
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module2 = module
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for l in layer:
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if hasattr(module2, l):
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if not l.isdigit():
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module2 = getattr(module2, l)
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else:
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module2 = module2[int(l)]
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lst_index += 1
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lst_index -= 1
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for l in layer[:lst_index]:
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if not l.isdigit():
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module = getattr(module, l)
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else:
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module = module[int(l)]
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l = layer[lst_index]
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setattr(module, l, val)
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def adapt_model_from_string(parent_module, model_string):
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separator = '***'
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state_dict = {}
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lst_shape = model_string.split(separator)
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for k in lst_shape:
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k = k.split(':')
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key = k[0]
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shape = k[1][1:-1].split(',')
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if shape[0] != '':
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state_dict[key] = [int(i) for i in shape]
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new_module = deepcopy(parent_module)
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for n, m in parent_module.named_modules():
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old_module = extract_layer(parent_module, n)
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if isinstance(old_module, nn.Conv2d) or isinstance(old_module, Conv2dSame):
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if isinstance(old_module, Conv2dSame):
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conv = Conv2dSame
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else:
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conv = nn.Conv2d
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s = state_dict[n + '.weight']
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in_channels = s[1]
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out_channels = s[0]
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g = 1
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if old_module.groups > 1:
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in_channels = out_channels
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g = in_channels
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new_conv = conv(
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in_channels=in_channels, out_channels=out_channels, kernel_size=old_module.kernel_size,
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bias=old_module.bias is not None, padding=old_module.padding, dilation=old_module.dilation,
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groups=g, stride=old_module.stride)
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set_layer(new_module, n, new_conv)
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if isinstance(old_module, nn.BatchNorm2d):
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new_bn = nn.BatchNorm2d(
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num_features=state_dict[n + '.weight'][0], eps=old_module.eps, momentum=old_module.momentum,
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affine=old_module.affine, track_running_stats=True)
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set_layer(new_module, n, new_bn)
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if isinstance(old_module, nn.Linear):
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# FIXME extra checks to ensure this is actually the FC classifier layer and not a diff Linear layer?
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num_features = state_dict[n + '.weight'][1]
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new_fc = nn.Linear(
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in_features=num_features, out_features=old_module.out_features, bias=old_module.bias is not None)
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set_layer(new_module, n, new_fc)
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if hasattr(new_module, 'num_features'):
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new_module.num_features = num_features
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new_module.eval()
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parent_module.eval()
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return new_module
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def adapt_model_from_file(parent_module, model_variant):
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adapt_file = os.path.join(os.path.dirname(__file__), 'pruned', model_variant + '.txt')
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with open(adapt_file, 'r') as f:
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return adapt_model_from_string(parent_module, f.read().strip())
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def build_model_with_cfg(
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model_cls: Callable,
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variant: str,
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pretrained: bool,
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default_cfg: dict,
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model_cfg: dict = None,
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feature_cfg: dict = None,
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pretrained_strict: bool = True,
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pretrained_filter_fn: Callable = None,
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**kwargs):
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pruned = kwargs.pop('pruned', False)
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features = False
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feature_cfg = feature_cfg or {}
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if kwargs.pop('features_only', False):
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features = True
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feature_cfg.setdefault('out_indices', (0, 1, 2, 3, 4))
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if 'out_indices' in kwargs:
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feature_cfg['out_indices'] = kwargs.pop('out_indices')
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model = model_cls(**kwargs) if model_cfg is None else model_cls(cfg=model_cfg, **kwargs)
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model.default_cfg = deepcopy(default_cfg)
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if pruned:
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model = adapt_model_from_file(model, variant)
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# for classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats
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num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000))
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if pretrained:
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load_pretrained(
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model,
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num_classes=num_classes_pretrained, in_chans=kwargs.get('in_chans', 3),
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filter_fn=pretrained_filter_fn, strict=pretrained_strict)
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if features:
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feature_cls = FeatureListNet
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if 'feature_cls' in feature_cfg:
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feature_cls = feature_cfg.pop('feature_cls')
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if isinstance(feature_cls, str):
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feature_cls = feature_cls.lower()
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if 'hook' in feature_cls:
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feature_cls = FeatureHookNet
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else:
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assert False, f'Unknown feature class {feature_cls}'
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model = feature_cls(model, **feature_cfg)
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return model
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