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dae5c90 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import logging
import os
from timm.utils import get_state_dict
from pathlib import Path
import torch
from .distributed import is_main_process, save_on_master
def load_state_dict(model, state_dict, prefix='', ignore_missing="relative_position_index"):
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, '_metadata', None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=''):
local_metadata = {} if metadata is None else metadata.get(
prefix[:-1], {})
module._load_from_state_dict(
state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + '.')
load(model, prefix=prefix)
warn_missing_keys = []
ignore_missing_keys = []
for key in missing_keys:
keep_flag = True
for ignore_key in ignore_missing.split('|'):
if ignore_key in key:
keep_flag = False
break
if keep_flag:
warn_missing_keys.append(key)
else:
ignore_missing_keys.append(key)
missing_keys = warn_missing_keys
if len(missing_keys) > 0:
logging.warning("Weights of {} not initialized from pretrained model: {}".format(
model.__class__.__name__, missing_keys))
if len(unexpected_keys) > 0:
logging.warning("Weights from pretrained model not used in {}: {}".format(
model.__class__.__name__, unexpected_keys))
if len(ignore_missing_keys) > 0:
logging.warning("Ignored weights of {} not initialized from pretrained model: {}".format(
model.__class__.__name__, ignore_missing_keys))
if len(error_msgs) > 0:
logging.error('\n'.join(error_msgs))
def save_model(args, epoch, model, model_without_ddp, optimizer, save_path):
to_save = {
'epoch' : epoch,
'model_state_dict': model_without_ddp.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'args': args,
}
save_on_master(to_save, save_path)
if is_main_process():
_input = torch.randn(1, 3, args.input_size, args.input_size, device=args.device)
export_dir = os.path.join(args.output_dir, "exported_models")
onnx_path = os.path.join(export_dir, "model_onnx.onnx")
torchscript_path = os.path.join(export_dir, "model_torchscript.pt")
if not os.path.exists(export_dir):
os.makedirs(export_dir)
convert_to_torchscript(model, _input, torchscript_path)
convert_to_onnx(model, _input, onnx_path)
def convert_to_torchscript(model, input_tensor, output_path, set_to_eval=True):
if set_to_eval:
model.eval()
scripted_model = torch.jit.trace(model, input_tensor)
scripted_model.save(output_path)
logging.info(f"Model exported to Torchscript format at {output_path}")
def convert_to_onnx(model, input_tensor, output_path):
torch.onnx.export(model, input_tensor, output_path, export_params=True, opset_version=11,
do_constant_folding=True, input_names=['input'], output_names=['output'],
dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}})
logging.info(f"Model exported to ONNX format at {output_path}")
def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None):
output_dir = Path(args.output_dir)
if len(args.checkpoint) == 0:
import glob
all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth'))
latest_ckpt = -1
for ckpt in all_checkpoints:
t = ckpt.split('-')[-1].split('.')[0]
if t.isdigit():
latest_ckpt = max(int(t), latest_ckpt)
if latest_ckpt >= 0:
args.checkpoint = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt)
logging.info("Auto resume checkpoint: %s" % args.checkpoint)
if args.checkpoint:
if args.checkpoint.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
args.checkpoint, map_location='cpu', check_hash=True)
else:
checkpoint = torch.load(args.checkpoint, map_location='cpu')
if 'model_state_dict' in checkpoint:
state_dict = checkpoint['model_state_dict']
else:
state_dict = checkpoint['model']
model_without_ddp.load_state_dict(state_dict)
logging.info("Resume checkpoint %s" % args.checkpoint)
if 'optimizer' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
if not isinstance(checkpoint['epoch'], str): # does not support resuming with 'best', 'best-ema'
args.start_epoch = checkpoint['epoch'] + 1
else:
assert args.eval, 'Does not support resuming with checkpoint-best'
if hasattr(args, 'model_ema') and args.model_ema:
if 'model_ema' in checkpoint.keys():
model_ema.ema.load_state_dict(checkpoint['model_ema'])
else:
model_ema.ema.load_state_dict(checkpoint['model'])
if 'scaler' in checkpoint:
loss_scaler.load_state_dict(checkpoint['scaler'])
logging.info("With optim & sched!")
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