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main_finetune.py
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main_finetune.py
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# DAMA fine-tuning code
# References: MAE https://github.com/facebookresearch/mae
import argparse
import os
import sys
import datetime
import time
import math
import json
from pathlib import Path
from functools import partial
import numpy as np
from PIL import Image
from typing import Iterable, Optional
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
from torchvision import datasets, transforms
from torchvision import models as torchvision_models
from torch.utils.tensorboard import SummaryWriter
import timm
from timm.models.layers import trunc_normal_
from timm.data.mixup import Mixup
from timm.data import Mixup as Mixup_fn_lib
from timm.utils import accuracy
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
import utils
from utils import NativeScalerWithGradNormCount as NativeScaler
import DAMA.vision_transformer_finetune as vits
import DAMA.pos_embed as pos_embed
from data_transform import *
from cell_data import CellCustomImageDataset_CLS
import tifffile
torchvision_archs = sorted(name for name in torchvision_models.__dict__
if name.islower() and not name.startswith("__")
and callable(torchvision_models.__dict__[name]))
model_names = ['main_vit_tiny', 'main_vit_base'] + torchvision_archs
def get_args_parser():
parser = argparse.ArgumentParser('DAMA', add_help=False)
# Model params
parser.add_argument('--arch', default='main_vit_vit', type=str, metavar='ARCH',
choices=model_names, help='Name of model to train')
parser.add_argument('--img_size', default=128, type=int,
help='images input size')
parser.add_argument('--patch_size', default=16, type=int,
help='vits patch size')
parser.add_argument('--in_chans', default=7, type=int,
help='images channels')
parser.add_argument('--is_momentum', action='store_true')
parser.add_argument('--batch_size', default=64, type=int,
help='Batch size per GPU (effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--epochs', default=50, type=int)
parser.add_argument('--accum_iter', default=1, type=int,
help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')
parser.add_argument('--drop_path', type=float, default=0.1, metavar='PCT',
help='Drop path rate (default: 0.1)')
# Optimizer parameters
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--lr', type=float, default=None, metavar='LR',
help='learning rate (absolute lr)')
parser.add_argument('--blr', type=float, default=1e-3, metavar='LR',
help='base learning rate: absolute_lr = base_lr * total_batch_size / 256')
parser.add_argument('--layer_decay', type=float, default=0.75,
help='layer-wise lr decay from ELECTRA/BEiT')
parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0')
parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N',
help='epochs to warmup LR')
# Augmentation parameters
parser.add_argument('--color_jitter', type=float, default=None, metavar='PCT',
help='Color jitter factor (enabled only when not using Auto/RandAug)')
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
parser.add_argument('--smoothing', type=float, default=0.1,
help='Label smoothing (default: 0.1)')
# * Random Erase params
parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
help='Random erase prob (default: 0.25)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,
help='Random erase count (default: 1)')
parser.add_argument('--resplit', action='store_true', default=False,
help='Do not random erase first (clean) augmentation split')
# * Mixup params
parser.add_argument('--mixup', type=float, default=0,
help='mixup alpha, mixup enabled if > 0.')
parser.add_argument('--cutmix', type=float, default=0,
help='cutmix alpha, cutmix enabled if > 0.')
parser.add_argument('--cutmix_minmax', type=float, nargs='+', default=None,
help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
parser.add_argument('--mixup_prob', type=float, default=1.0,
help='Probability of performing mixup or cutmix when either/both is enabled')
parser.add_argument('--mixup_switch_prob', type=float, default=0.5,
help='Probability of switching to cutmix when both mixup and cutmix enabled')
parser.add_argument('--mixup_mode', type=str, default='batch',
help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
# * Finetuning params
parser.add_argument('--finetune', default='',
help='finetune from checkpoint')
parser.add_argument('--global_pool', action='store_true')
parser.set_defaults(global_pool=True)
parser.add_argument('--cls_token', action='store_false', dest='global_pool',
help='Use class token instead of global pool for classification')
# Dataset parameters
parser.add_argument('--data_path', default='/datasets01/imagenet_full_size/061417/', type=str,
help='dataset path')
parser.add_argument('--num_classes', default=1000, type=int,
help='number of the classification types')
parser.add_argument('--output_dir', default='./output_dir',
help='path where to save, empty for no saving')
parser.add_argument('--log_dir', default='./output_dir',
help='path where to tensorboard log')
parser.add_argument('--device', default='cuda',
help='device to use for training / testing')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--resume', default='',
help='resume from checkpoint')
parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
help='start epoch')
parser.add_argument('--eval', action='store_true',
help='Perform evaluation only')
parser.add_argument('--dist_eval', action='store_true', default=False,
help='Enabling distributed evaluation (recommended during training for faster monitor')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem')
parser.set_defaults(pin_mem=True)
# distributed training parameters
parser.add_argument('--world_size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--local_rank', default=-1, type=int)
parser.add_argument('--dist_on_itp', action='store_true')
parser.add_argument('--dist_url', default='env://',
help='url used to set up distributed training')
return parser
def main(args):
utils.init_distributed_mode(args)
utils.fix_random_seeds(args.seed) # fix the seed for reproducibility
print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
print("{}".format(args).replace(', ', ',\n'))
device = torch.device(args.device)
cudnn.benchmark = True
# ============ preparing data ... ============
# simple augmentation
if args.in_chans != 7 :
transform_train = transforms.Compose([
transforms.RandomResizedCrop(args.img_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()])
dataset_train = datasets.ImageFolder(os.path.join(args.data_path, 'train'), transform=transform_train)
transform_val = transforms.Compose([
transforms.CenterCrop(50),
transforms.Resize(args.img_size),
transforms.ToTensor()])
dataset_val = datasets.ImageFolder(os.path.join(args.data_path, 'val'), transform=transform_val)
elif args.in_chans == 7:
def my_tiff_loader(filename):
return tifffile.imread(filename)
transform_train = Compose([
RandomResizedCrop(args.img_size//2, args.img_size), # 3 is bicubic
RandomRotation(90),
RandomShift(0.3),
RandomHorizontalFlip(),
RandomVerticalFlip(),
ToTensor()])
dataset_train = datasets.ImageFolder(os.path.join(args.data_path, 'train'), loader=my_tiff_loader,
transform=transform_train)
transform_val = Compose([
CenterCrop(48),
Resize(args.img_size),
ToTensor()])
dataset_val = datasets.ImageFolder(os.path.join(args.data_path, 'val'), loader=my_tiff_loader,
transform=transform_val)
print(dataset_train)
print(f"Train data loaded: there are {len(dataset_train)} images.")
print(dataset_val)
print(f"Val data loaded: there are {len(dataset_val)} images.")
if True: # args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
sampler_train = torch.utils.data.DistributedSampler(
dataset_train, num_replicas=num_tasks, rank=global_rank, shuffle=True
)
print("Sampler_train = %s" % str(sampler_train))
if args.dist_eval:
if len(dataset_val) % num_tasks != 0:
print('Warning: Enabling distributed evaluation with an eval dataset not divisible by process number. '
'This will slightly alter validation results as extra duplicate entries are added to achieve '
'equal num of samples per-process.')
sampler_val = torch.utils.data.DistributedSampler(
dataset_val, num_replicas=num_tasks, rank=global_rank, shuffle=True) # shuffle=True to reduce monitor bias
else:
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
sampler_val = torch.utils.data.SequentialSampler(dataset_val)
if global_rank == 0 and args.log_dir is not None and not args.eval:
os.makedirs(args.log_dir, exist_ok=True)
log_writer = SummaryWriter(log_dir=args.log_dir)
else:
log_writer = None
data_loader_train = torch.utils.data.DataLoader(
dataset_train, sampler=sampler_train,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=True,
)
data_loader_val = torch.utils.data.DataLoader(
dataset_val, sampler=sampler_val,
batch_size=args.batch_size,
num_workers=args.num_workers,
pin_memory=args.pin_mem,
drop_last=False
)
mixup_fn = None
mixup_active = args.mixup > 0 or args.cutmix > 0. or args.cutmix_minmax is not None
if mixup_active:
print("Mixup is activated!")
mixup_fn = Mixup(
mixup_alpha=args.mixup, cutmix_alpha=args.cutmix, cutmix_minmax=args.cutmix_minmax,
prob=args.mixup_prob, switch_prob=args.mixup_switch_prob, mode=args.mixup_mode,
label_smoothing=args.smoothing, num_classes=args.num_classes)
model = vits.__dict__[args.arch](
img_size=args.img_size,
patch_size=args.patch_size,
in_chans = args.in_chans,
num_classes=args.num_classes,
drop_path_rate=args.drop_path,
global_pool=args.global_pool
)
# load from pre-trained, before DistributedDataParallel constructor
if args.finetune and not args.eval:
print("=> loading checkpoint '{}'".format(args.finetune))
checkpoint = torch.load(args.finetune, map_location="cpu")
state_dict = checkpoint['model']
if not args.is_momentum:
for k in list(state_dict.keys()):
# retain only base_encoder (student) up to 1st layer of decoder layer
if k.startswith('base_encoder') and 'decoder' not in k and 'mask' not in k and 'momentum_encoder' not in k:
# remove prefix
state_dict[k[len("base_encoder."):]] = state_dict[k]
del state_dict[k]
else:
for k in list(state_dict.keys()):
# retain only base_encoder (student) up to 1st layer of decoder layer
if k.startswith('momentum_encoder') and 'decoder' not in k and 'mask' not in k and 'base_encoder' not in k:
# remove prefix
state_dict[k[len("momentum_encoder."):]] = state_dict[k]
del state_dict[k]
print('Loading momentum model.')
# interpolate position embedding
pos_embed.interpolate_pos_embed(model, checkpoint)
msg = model.load_state_dict(state_dict, strict=False)
print(msg)
if args.global_pool:
assert set(msg.missing_keys) == {'head.weight', 'head.bias', 'fc_norm.weight', 'fc_norm.bias'}
else:
assert set(msg.missing_keys) == {'head.weight', 'head.bias'}
# manually initialize fc layer
trunc_normal_(model.head.weight, std=2e-5)
model.to(device)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print("Model = %s" % str(model_without_ddp))
print('number of params (M): %.2f' % (n_parameters / 1.e6))
eff_batch_size = args.batch_size * args.accum_iter * utils.get_world_size()
if args.lr is None: # only base_lr is specified
args.lr = args.blr * eff_batch_size / 256
print("base lr: %.2e" % (args.lr * 256 / eff_batch_size))
print("actual lr: %.2e" % args.lr)
print("accumulate grad iterations: %d" % args.accum_iter)
print("effective batch size: %d" % eff_batch_size)
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
# build optimizer with layer-wise lr decay (lrd)
param_groups = param_groups_lrd(model_without_ddp, args.weight_decay,
no_weight_decay_list=model_without_ddp.no_weight_decay(),
layer_decay=args.layer_decay
)
optimizer = torch.optim.AdamW(param_groups, lr=args.lr)
loss_scaler = NativeScaler()
if mixup_fn is not None:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif args.smoothing > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
else:
criterion = torch.nn.CrossEntropyLoss()
print("criterion = %s" % str(criterion))
utils.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
if args.eval:
test_stats = evaluate(data_loader_val, model, device)
print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
exit(0)
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
max_accuracy = 0.0
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
data_loader_train.sampler.set_epoch(epoch)
train_stats = train_one_epoch(
model, criterion, data_loader_train,
optimizer, device, epoch, loss_scaler,
args.clip_grad, mixup_fn,
log_writer=log_writer,
args=args
)
test_stats = evaluate(data_loader_val, model, device)
print(f"Accuracy of the network on the {len(dataset_val)} test images: {test_stats['acc1']:.1f}%")
save_best = test_stats["acc1"] >= max_accuracy
max_accuracy = max(max_accuracy, test_stats["acc1"])
print(f'Max accuracy: {max_accuracy:.2f}%')
if args.output_dir and epoch%15==0:
utils.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch, is_best=save_best)
elif args.output_dir and epoch == (args.epochs - 1):
utils.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch, is_best=save_best)
if log_writer is not None:
log_writer.add_scalar('perf/test_acc1', test_stats['acc1'], epoch)
log_writer.add_scalar('perf/test_acc5', test_stats['acc5'], epoch)
log_writer.add_scalar('perf/test_loss', test_stats['loss'], epoch)
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch,
'n_parameters': n_parameters}
if args.output_dir and utils.is_main_process():
if log_writer is not None:
log_writer.flush()
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
mixup_fn: Optional[Mixup_fn_lib] = None, log_writer=None,
args=None):
model.train(True)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 30
accum_iter = args.accum_iter
optimizer.zero_grad()
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
# we use a per iteration (instead of per epoch) lr scheduler
if data_iter_step % accum_iter == 0:
adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
with torch.cuda.amp.autocast():
outputs = model(samples)
loss = criterion(outputs, targets)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
loss /= accum_iter
loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=False,
update_grad=(data_iter_step + 1) % accum_iter == 0)
if (data_iter_step + 1) % accum_iter == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
min_lr = 10.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
loss_value_reduce = utils.all_reduce_mean(loss_value)
if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
""" We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('lr', max_lr, epoch_1000x)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader, model, device):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
for batch in metric_logger.log_every(data_loader, 10, header):
images = batch[0]
target = batch[-1]
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# compute output
with torch.cuda.amp.autocast():
output = model(images)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['acc1'].update(acc1.item(), n=batch_size)
metric_logger.meters['acc5'].update(acc5.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}'
.format(top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
def adjust_momentum(epoch, args):
"""Adjust moco momentum based on current epoch"""
m = 1. - 0.5 * (1. + math.cos(math.pi * epoch / args.epochs)) * (1. - args.main_m)
return m
def adjust_learning_rate(optimizer, epoch, args):
"""Decay the learning rate with half-cycle cosine after warmup"""
if epoch < args.warmup_epochs:
lr = args.lr * epoch / args.warmup_epochs
else:
lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \
(1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
for param_group in optimizer.param_groups:
if "lr_scale" in param_group:
param_group["lr"] = lr * param_group["lr_scale"]
else:
param_group["lr"] = lr
return lr
import json
def param_groups_lrd(model, weight_decay=0.05, no_weight_decay_list=[], layer_decay=.75):
"""
Parameter groups for layer-wise lr decay
Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L58
"""
param_group_names = {}
param_groups = {}
num_layers = len(model.blocks) + 1
layer_scales = list(layer_decay ** (num_layers - i) for i in range(num_layers + 1))
for n, p in model.named_parameters():
if not p.requires_grad:
continue
# no decay: all 1D parameters and model specific ones
if p.ndim == 1 or n in no_weight_decay_list:
g_decay = "no_decay"
this_decay = 0.
else:
g_decay = "decay"
this_decay = weight_decay
layer_id = get_layer_id_for_vit(n, num_layers)
group_name = "layer_%d_%s" % (layer_id, g_decay)
if group_name not in param_group_names:
this_scale = layer_scales[layer_id]
param_group_names[group_name] = {
"lr_scale": this_scale,
"weight_decay": this_decay,
"params": [],
}
param_groups[group_name] = {
"lr_scale": this_scale,
"weight_decay": this_decay,
"params": [],
}
param_group_names[group_name]["params"].append(n)
param_groups[group_name]["params"].append(p)
# print("parameter groups: \n%s" % json.dumps(param_group_names, indent=2))
return list(param_groups.values())
def get_layer_id_for_vit(name, num_layers):
"""
Assign a parameter with its layer id
Following BEiT: https://github.com/microsoft/unilm/blob/master/beit/optim_factory.py#L33
"""
if name in ['cls_token', 'pos_embed']:
return 0
elif name.startswith('patch_embed'):
return 0
elif name.startswith('blocks'):
return int(name.split('.')[1]) + 1
else:
return num_layers
if __name__ == '__main__':
args = get_args_parser()
args = args.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
print(args)
start_train = time.time()
main(args)
end_train = time.time()
print('Training time in: %s' %((end_train - start_train)/3600))