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utils.py
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utils.py
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# Copyright (c) [2012]-[2021] YUAN Li@NUS.
#
# This source code is licensed under the Clear BSD License
# LICENSE file in the root directory of this file
# All rights reserved.
'''
- load_for_transfer_learning: load pretrained paramters to model in transfer learning
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
import torch
import torch.nn as nn
import torch.nn.init as init
import logging
import os
from collections import OrderedDict
_logger = logging.getLogger(__name__)
def resize_pos_embed(posemb, posemb_new): # example: 224:(14x14+1)-> 384: (24x24+1)
# Rescale the grid of position embeddings when loading from state_dict. Adapted from
# https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
ntok_new = posemb_new.shape[1]
if True:
posemb_tok, posemb_grid = posemb[:, :1], posemb[0, 1:] # posemb_tok is for cls token, posemb_grid for the following tokens
ntok_new -= 1
else:
posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
gs_old = int(math.sqrt(len(posemb_grid))) # 14
gs_new = int(math.sqrt(ntok_new)) # 24
_logger.info('Position embedding grid-size from %s to %s', gs_old, gs_new)
posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) # [1, 196, dim]->[1, 14, 14, dim]->[1, dim, 14, 14]
posemb_grid = F.interpolate(posemb_grid, size=(gs_new, gs_new), mode='bicubic') # [1, dim, 14, 14] -> [1, dim, 24, 24]
posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new * gs_new, -1) # [1, dim, 24, 24] -> [1, 24*24, dim]
posemb = torch.cat([posemb_tok, posemb_grid], dim=1) # [1, 24*24+1, dim]
return posemb
def load_state_dict(checkpoint_path, model, num_classes, use_ema=False, del_posemb=False):
if checkpoint_path and os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path, map_location='cpu')
state_dict_key = 'state_dict'
if isinstance(checkpoint, dict):
if use_ema and 'state_dict_ema' in checkpoint:
state_dict_key = 'state_dict_ema'
if state_dict_key and state_dict_key in checkpoint:
new_state_dict = OrderedDict()
for k, v in checkpoint[state_dict_key].items():
# strip `module.` prefix
name = k[7:] if k.startswith('module') else k
new_state_dict[name] = v
state_dict = new_state_dict
else:
state_dict = checkpoint
_logger.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
print(f'num classes: {num_classes}')
if num_classes != 1000:
# completely discard fully connected for all other differences between pretrained and created model
print('delete the original class')
del state_dict['head' + '.weight']
del state_dict['head' + '.bias']
if del_posemb==True:
del state_dict['pos_embed']
#old_posemb = state_dict['pos_embed']
#if model.pos_embed.shape != old_posemb.shape: # need resize the position embedding by interpolate
# new_posemb = resize_pos_embed(old_posemb, model.pos_embed)
# state_dict['pos_embed'] = new_posemb
return state_dict
else:
_logger.error("No checkpoint found at '{}'".format(checkpoint_path))
raise FileNotFoundError()
def load_for_transfer_learning(model, checkpoint_path, num_classes, use_ema=False, strict=True):
print(f'num classes: {num_classes}')
state_dict = load_state_dict(checkpoint_path, use_ema, num_classes)
model.load_state_dict(state_dict, strict=strict)
def get_mean_and_std(dataset):
'''Compute the mean and std value of dataset.'''
dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2)
mean = torch.zeros(3)
std = torch.zeros(3)
print('==> Computing mean and std..')
for inputs, targets in dataloader:
for i in range(3):
mean[i] += inputs[:,i,:,:].mean()
std[i] += inputs[:,i,:,:].std()
mean.div_(len(dataset))
std.div_(len(dataset))
return mean, std
def init_params(net):
'''Init layer parameters.'''
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal(m.weight, mode='fan_out')
if m.bias:
init.constant(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant(m.weight, 1)
init.constant(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal(m.weight, std=1e-3)
if m.bias:
init.constant(m.bias, 0)
_, term_width = os.popen('stty size', 'r').read().split()
term_width = int(term_width)
TOTAL_BAR_LENGTH = 65.
last_time = time.time()
begin_time = last_time
def progress_bar(current, total, msg=None):
global last_time, begin_time
if current == 0:
begin_time = time.time() # Reset for new bar.
cur_len = int(TOTAL_BAR_LENGTH*current/total)
rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1
sys.stdout.write(' [')
for i in range(cur_len):
sys.stdout.write('=')
sys.stdout.write('>')
for i in range(rest_len):
sys.stdout.write('.')
sys.stdout.write(']')
cur_time = time.time()
step_time = cur_time - last_time
last_time = cur_time
tot_time = cur_time - begin_time
L = []
L.append(' Step: %s' % format_time(step_time))
L.append(' | Tot: %s' % format_time(tot_time))
if msg:
L.append(' | ' + msg)
msg = ''.join(L)
sys.stdout.write(msg)
for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3):
sys.stdout.write(' ')
# Go back to the center of the bar.
for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2):
sys.stdout.write('\b')
sys.stdout.write(' %d/%d ' % (current+1, total))
if current < total-1:
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
sys.stdout.flush()
def format_time(seconds):
days = int(seconds / 3600/24)
seconds = seconds - days*3600*24
hours = int(seconds / 3600)
seconds = seconds - hours*3600
minutes = int(seconds / 60)
seconds = seconds - minutes*60
secondsf = int(seconds)
seconds = seconds - secondsf
millis = int(seconds*1000)
f = ''
i = 1
if days > 0:
f += str(days) + 'D'
i += 1
if hours > 0 and i <= 2:
f += str(hours) + 'h'
i += 1
if minutes > 0 and i <= 2:
f += str(minutes) + 'm'
i += 1
if secondsf > 0 and i <= 2:
f += str(secondsf) + 's'
i += 1
if millis > 0 and i <= 2:
f += str(millis) + 'ms'
i += 1
if f == '':
f = '0ms'
return f