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pytorch-image-models/models/helpers.py

90 lines
3.6 KiB

import torch
import torch.utils.model_zoo as model_zoo
import os
from collections import OrderedDict
def load_checkpoint(model, checkpoint_path):
if checkpoint_path and os.path.isfile(checkpoint_path):
print("=> Loading checkpoint '{}'".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
if k.startswith('module'):
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
else:
model.load_state_dict(checkpoint)
print("=> Loaded checkpoint '{}'".format(checkpoint_path))
else:
print("=> Error: No checkpoint found at '{}'".format(checkpoint_path))
raise FileNotFoundError()
def resume_checkpoint(model, checkpoint_path, start_epoch=None):
start_epoch = 0 if start_epoch is None else start_epoch
optimizer_state = None
if os.path.isfile(checkpoint_path):
print("=> loading checkpoint '{}'".format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
if k.startswith('module'):
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
if 'optimizer' in checkpoint:
optimizer_state = checkpoint['optimizer']
print("=> loaded checkpoint '{}' (epoch {})".format(checkpoint_path, checkpoint['epoch']))
start_epoch = checkpoint['epoch'] if start_epoch is None else start_epoch
else:
model.load_state_dict(checkpoint)
return optimizer_state, start_epoch
else:
print("=> No checkpoint found at '{}'".format(checkpoint_path))
raise FileNotFoundError()
def load_pretrained(model, default_cfg, num_classes=1000, in_chans=3, filter_fn=None):
state_dict = model_zoo.load_url(default_cfg['url'])
if in_chans == 1:
conv1_name = default_cfg['first_conv']
print('Converting first conv (%s) from 3 to 1 channel' % conv1_name)
conv1_weight = state_dict[conv1_name + '.weight']
state_dict[conv1_name + '.weight'] = conv1_weight.sum(dim=1, keepdim=True)
elif in_chans != 3:
assert False, "Invalid in_chans for pretrained weights"
strict = True
classifier_name = default_cfg['classifier']
if num_classes == 1000 and default_cfg['num_classes'] == 1001:
# special case for imagenet trained models with extra background class in pretrained weights
classifier_weight = state_dict[classifier_name + '.weight']
state_dict[classifier_name + '.weight'] = classifier_weight[1:]
classifier_bias = state_dict[classifier_name + '.bias']
state_dict[classifier_name + '.bias'] = classifier_bias[1:]
elif num_classes != default_cfg['num_classes']:
# completely discard fully connected for all other differences between pretrained and created model
del state_dict[classifier_name + '.weight']
del state_dict[classifier_name + '.bias']
strict = False
if filter_fn is not None:
state_dict = filter_fn(state_dict)
model.load_state_dict(state_dict, strict=strict)