/
utils_simmim.py
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/
utils_simmim.py
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# --------------------------------------------------------
# SimMIM
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# Modified by Zhenda Xie
# --------------------------------------------------------
import os
import torch
import torch.distributed as dist
import numpy as np
from scipy import interpolate
def load_checkpoint(config, model, optimizer, lr_scheduler, scaler, logger):
logger.info(f">>>>>>>>>> Resuming from {config.MODEL.RESUME} ..........")
if config.MODEL.RESUME.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
config.MODEL.RESUME, map_location='cpu', check_hash=True)
else:
checkpoint = torch.load(config.MODEL.RESUME, map_location='cpu')
# re-map keys due to name change (only for loading provided models)
rpe_mlp_keys = [k for k in checkpoint['model'].keys() if "rpe_mlp" in k]
for k in rpe_mlp_keys:
checkpoint['model'][k.replace('rpe_mlp', 'cpb_mlp')] = checkpoint['model'].pop(k)
msg = model.load_state_dict(checkpoint['model'], strict=False)
logger.info(msg)
max_accuracy = 0.0
if not config.EVAL_MODE and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'scaler' in checkpoint and 'epoch' in checkpoint:
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
scaler.load_state_dict(checkpoint['scaler'])
config.defrost()
config.TRAIN.START_EPOCH = checkpoint['epoch'] + 1
config.freeze()
logger.info(f"=> loaded successfully '{config.MODEL.RESUME}' (epoch {checkpoint['epoch']})")
if 'max_accuracy' in checkpoint:
max_accuracy = checkpoint['max_accuracy']
else:
max_accuracy = 0.0
del checkpoint
torch.cuda.empty_cache()
return max_accuracy
def save_checkpoint(config, epoch, model, max_accuracy, optimizer, lr_scheduler, scaler, logger):
save_state = {'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'scaler': scaler.state_dict(),
'max_accuracy': max_accuracy,
'epoch': epoch,
'config': config}
save_path = os.path.join(config.OUTPUT, f'ckpt_epoch_{epoch}.pth')
logger.info(f"{save_path} saving......")
torch.save(save_state, save_path)
logger.info(f"{save_path} saved !!!")
def get_grad_norm(parameters, norm_type=2):
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
norm_type = float(norm_type)
total_norm = 0
for p in parameters:
param_norm = p.grad.data.norm(norm_type)
total_norm += param_norm.item() ** norm_type
total_norm = total_norm ** (1. / norm_type)
return total_norm
def auto_resume_helper(output_dir, logger):
checkpoints = os.listdir(output_dir)
checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')]
logger.info(f"All checkpoints founded in {output_dir}: {checkpoints}")
if len(checkpoints) > 0:
latest_checkpoint = max([os.path.join(output_dir, d) for d in checkpoints], key=os.path.getmtime)
logger.info(f"The latest checkpoint founded: {latest_checkpoint}")
resume_file = latest_checkpoint
else:
resume_file = None
return resume_file
def reduce_tensor(tensor):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.ReduceOp.SUM)
rt /= dist.get_world_size()
return rt
def load_pretrained(config, model, logger):
logger.info(f">>>>>>>>>> Fine-tuned from {config.MODEL.PRETRAINED} ..........")
checkpoint = torch.load(config.MODEL.PRETRAINED, map_location='cpu')
checkpoint_model = checkpoint['model']
if any([True if 'encoder.' in k else False for k in checkpoint_model.keys()]):
checkpoint_model = {k.replace('encoder.', ''): v for k, v in checkpoint_model.items() if k.startswith('encoder.')}
logger.info('Detect pre-trained model, remove [encoder.] prefix.')
else:
logger.info('Detect non-pre-trained model, pass without doing anything.')
if config.MODEL.TYPE in ['swin', 'swinv2']:
logger.info(f">>>>>>>>>> Remapping pre-trained keys for SWIN ..........")
checkpoint = remap_pretrained_keys_swin(model, checkpoint_model, logger)
else:
raise NotImplementedError
msg = model.load_state_dict(checkpoint_model, strict=False)
logger.info(msg)
del checkpoint
torch.cuda.empty_cache()
logger.info(f">>>>>>>>>> loaded successfully '{config.MODEL.PRETRAINED}'")
def remap_pretrained_keys_swin(model, checkpoint_model, logger):
state_dict = model.state_dict()
# Geometric interpolation when pre-trained patch size mismatch with fine-tuned patch size
all_keys = list(checkpoint_model.keys())
for key in all_keys:
if "relative_position_bias_table" in key:
relative_position_bias_table_pretrained = checkpoint_model[key]
relative_position_bias_table_current = state_dict[key]
L1, nH1 = relative_position_bias_table_pretrained.size()
L2, nH2 = relative_position_bias_table_current.size()
if nH1 != nH2:
logger.info(f"Error in loading {key}, passing......")
else:
if L1 != L2:
logger.info(f"{key}: Interpolate relative_position_bias_table using geo.")
src_size = int(L1 ** 0.5)
dst_size = int(L2 ** 0.5)
def geometric_progression(a, r, n):
return a * (1.0 - r ** n) / (1.0 - r)
left, right = 1.01, 1.5
while right - left > 1e-6:
q = (left + right) / 2.0
gp = geometric_progression(1, q, src_size // 2)
if gp > dst_size // 2:
right = q
else:
left = q
# if q > 1.090307:
# q = 1.090307
dis = []
cur = 1
for i in range(src_size // 2):
dis.append(cur)
cur += q ** (i + 1)
r_ids = [-_ for _ in reversed(dis)]
x = r_ids + [0] + dis
y = r_ids + [0] + dis
t = dst_size // 2.0
dx = np.arange(-t, t + 0.1, 1.0)
dy = np.arange(-t, t + 0.1, 1.0)
logger.info("Original positions = %s" % str(x))
logger.info("Target positions = %s" % str(dx))
all_rel_pos_bias = []
for i in range(nH1):
z = relative_position_bias_table_pretrained[:, i].view(src_size, src_size).float().numpy()
f_cubic = interpolate.interp2d(x, y, z, kind='cubic')
all_rel_pos_bias.append(torch.Tensor(f_cubic(dx, dy)).contiguous().view(-1, 1).to(
relative_position_bias_table_pretrained.device))
new_rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
checkpoint_model[key] = new_rel_pos_bias
# delete relative_position_index since we always re-init it
relative_position_index_keys = [k for k in checkpoint_model.keys() if "relative_position_index" in k]
for k in relative_position_index_keys:
del checkpoint_model[k]
# delete relative_coords_table since we always re-init it
relative_coords_table_keys = [k for k in checkpoint_model.keys() if "relative_coords_table" in k]
for k in relative_coords_table_keys:
del checkpoint_model[k]
# re-map keys due to name change
rpe_mlp_keys = [k for k in checkpoint_model.keys() if "rpe_mlp" in k]
for k in rpe_mlp_keys:
checkpoint_model[k.replace('rpe_mlp', 'cpb_mlp')] = checkpoint_model.pop(k)
# delete attn_mask since we always re-init it
attn_mask_keys = [k for k in checkpoint_model.keys() if "attn_mask" in k]
for k in attn_mask_keys:
del checkpoint_model[k]
return checkpoint_model