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train_tiny_imagenet.py
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train_tiny_imagenet.py
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""" Search cell """
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tensorboardX import SummaryWriter
from util_func.config import TrainCifarConfig
import util_func.utils as utils
# from models.search_cnn import SearchCNNController
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch.optim
import torch.utils.data
import pytorch_warmup as warmup
from models_util import *
from models_cifar import *
from train_util import *
from util_func.dataset import get_dataset, DATASETS
import math
import copy
config = TrainCifarConfig()
device = torch.device("cuda")
# tensorboard
writer = SummaryWriter(log_dir=os.path.join(config.path, "tb"))
writer.add_text('config', config.as_markdown(), 0)
logger = utils.get_logger(os.path.join(config.path, "{}.log".format(config.dataset)))
config.print_params(logger.info)
def main():
logger.info("Logger is set - training start")
# set default gpu device id
torch.cuda.set_device(config.gpus[0])
device = torch.device("cuda")
# set seed
np.random.seed(config.seed)
torch.manual_seed(config.seed)
torch.cuda.manual_seed_all(config.seed)
torch.backends.cudnn.benchmark = True
model = model_ReLU_RP(config)
criterion = nn.CrossEntropyLoss().to(device)
if config.distil:
config_teacher = copy.deepcopy(config)
config_teacher.act_type = 'nn.ReLU'
config_teacher.arch = config_teacher.teacher_arch
teacher_model = model_ReLU_RP(config_teacher)
model.criterion = criterion
# if 'vgg' in config.arch:
# model = vgg.__dict__[config.arch](config, criterion, config.act_type, config.pool_type)
# elif 'resnet' in config.arch:
# model = resnet.__dict__[config.arch](config, criterion, config.act_type, config.pool_type, num_classes = config.num_classes)
# elif config.arch == "mobilenet_v2":
# model = mbnet.__dict__[config.arch](config, criterion, config.act_type, num_classes = config.num_classes)
# config.pretrained_path = '/data3/hop20001/mpc_proj/PASNET_cifar10_search/pretrained/cifar10_baseline/vgg_checkpoint_best.tar'
# config.pretrained_path = '/data3/hop20001/mpc_proj/PASNET_cifar10_search/pretrained/cifar10_baseline/resnet18.pt'
### finetune from checkpoint
if config.pretrained_path:
print("==> Load pretrained")
model.load_pretrained(pretrained_path = config.pretrained_path)
if config.distil and (config.dataset != 'imagenet'):
teacher_model.load_pretrained(pretrained_path = config.teacher_path)
if(config.checkpoint_path):
config.start_epoch, best_top1 = model.load_check_point(check_point_path = config.checkpoint_path)
model = model.to(device)
if config.distil:
teacher_model = teacher_model.to(device)
teacher_model.eval()
criterion_kd = SoftTarget(4.0).to(device)
train_dataset = get_dataset(config, 'train')
val_dataset = get_dataset(config, 'test')
pin_memory = (config.dataset == "imagenet")
train_loader = torch.utils.data.DataLoader(train_dataset, shuffle=True, batch_size=config.batch_size,
num_workers=config.workers, pin_memory=pin_memory)
val_loader = torch.utils.data.DataLoader(val_dataset, shuffle=False, batch_size=config.batch_size,
num_workers=config.workers, pin_memory=pin_memory)
if config.evaluate:
# # ----------------------------
if config.evaluate != 'None':
checkpoint = torch.load(config.evaluate, map_location="cpu")
model.load_state_dict(checkpoint['state_dict'])
# print(model)
from torchinfo import summary
device = "cuda"
summary(model, input_size=(1, 3, 32, 32), device=device, depth=3, verbose=2,
col_names=["input_size",
"output_size",
"kernel_size"],
)
if config.precision == 'full':
validate(val_loader, model, 0, len(val_loader), device, config, logger, writer)
else:
validate_fp16(val_loader, model, 0, len(val_loader), device, config, logger, writer)
model.print_alphas(logger)
return
# # ----------------------------
# weights optimizer
# w_optim = torch.optim.SGD(model.weights(), config.w_mask_lr, momentum=config.w_momentum,
# weight_decay=config.w_weight_decay)
# #w_optim = torch.optim.Adam(model.weights(), config.w_mask_lr)
# # alphas optimizer
# if config.act_type != 'nn.ReLU':
# alpha_optim = torch.optim.Adam(model.alpha_aux(), config.alpha_lr, betas=(0.5, 0.999),
# weight_decay=config.alpha_weight_decay)
para_setting = [
{'params': model.weights(), 'lr': config.w_lr},
{'params': model.alpha_aux(), 'lr': config.w_mask_lr}
]
# w_optim = torch.optim.Adam(model.weights_and_alpha(), config.w_mask_lr)
w_optim = torch.optim.Adam(para_setting)
if config.enable_lookahead:
w_optim = Lookahead(w_optim)
# w_optim = torch.optim.Adam(model.weights_and_alpha(), config.w_mask_lr, weight_decay=config.w_weight_decay)
# w_optim = torch.optim.SGD(model.weights_and_alpha(), config.w_mask_lr)
# w_optim = torch.optim.SGD(model.weights_and_alpha(), config.w_mask_lr, momentum=config.w_momentum,
# weight_decay=config.w_weight_decay)
# param_groups = [
# {'optimizer':w_optim,'T_max':config.mask_epochs, 'eta_min':config.w_lr_min},
# {'optimizer':alpha_optim,'T_max':config.mask_epochs}
# ]
lr_scheduler_w = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer = w_optim, T_max = config.mask_epochs,
eta_min = config.w_lr_min)
# if config.act_type != 'nn.ReLU':
# lr_scheduler_alpha = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer = alpha_optim, T_max = config.mask_epochs)
# warmup_scheduler = warmup.UntunedLinearWarmup(alpha_optim)
#### Freeze batch normalization ####
# model.train_fz_bn(freeze_bn=True)
model.train_fz_bn(freeze_bn=False)
# model.change_mask_dropout_ratio(config.mask_dropout)
# lambda_l1 = 1e-6
# lambda_l2 = 5e-4
lambda0 = config.lamda
# training loop
best_top1 = 0.
for epoch in range(config.start_epoch, config.mask_epochs):
if config.act_type != 'nn.ReLU':
model.update_sparse_list()
model.print_sparse_list(logger)
if config.precision == 'full':
if(config.distil):
top1_train, global_density, total_mask = train_mask_distil(train_loader, model, w_optim, lambda0, teacher_model, criterion_kd, epoch,
device, config, logger, writer)
else:
top1_train, global_density, total_mask = train_mask(train_loader, model, w_optim, lambda0, epoch,
device, config, logger, writer)
else:
if(config.distil):
top1_train, global_density, total_mask = train_mask_distil_fp16(train_loader, model, w_optim, lambda0, teacher_model, criterion_kd, epoch,
device, config, logger, writer)
else:
top1_train, global_density, total_mask = train_mask_fp16(train_loader, model, w_optim, lambda0, epoch,
device, config, logger, writer)
# training without mask update
# adjust_learning_rate(w_optim, epoch, config)
lr_scheduler_w.step()
# lr_scheduler_alpha.step()
# validation
cur_step = (epoch+1) * len(train_loader)
if config.precision == 'full':
top1 = validate(val_loader, model, epoch, cur_step, device, config, logger, writer)
else:
top1 = validate_fp16(val_loader, model, epoch, cur_step, device, config, logger, writer)
# save
# save
relu_count = global_density * total_mask/1000
# if (global_density - config.ReLU_count*1000/total_mask) < 0.01:
if (relu_count - config.ReLU_count) < 2:
if best_top1 < top1:
best_top1 = top1
# best_genotype = genotype
is_best = True
else:
is_best = False
# utils.save_checkpoint(model, config.path, is_best)
# if is_best:
# save_path = os.path.join(config.path, 'best_mask_train.pth.tar')
# else:
# save_path = os.path.join(config.path, 'checkpoint_mask_train.pth.tar')
# model.save_checkpoint(epoch, best_top1, is_best, filename=save_path)
if is_best:
save_path = os.path.join(config.path, 'best_mask_train.pth.tar')
model.save_checkpoint(epoch, best_top1, is_best, filename=save_path)
logger.info("Current mask training best Prec@1 = {:.4%}".format(best_top1))
save_path = os.path.join(config.path, 'checkpoint_mask_train.pth.tar')
model.save_checkpoint(epoch, best_top1, False, filename=save_path)
#### Start to finetune ####
para_group = [{'params': model.weights(), 'initial_lr': config.w_lr}]
w_optim = torch.optim.SGD(para_group, config.w_lr, momentum=config.w_momentum,
weight_decay=config.w_weight_decay)
# param_groups = [
# {'optimizer':w_optim,'T_max':config.mask_epochs, 'eta_min':config.w_lr_min},
# ]
if config.optim == 'cosine_rst':
# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(w_optim, 1, T_mult=2, eta_min=config.w_lr_min) #, last_epoch = config.epochs
T_mult = 2
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(w_optim, 1, T_mult=T_mult, eta_min=config.w_lr_min) #, last_epoch = config.epochs
elif config.optim == 'cosine':
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
w_optim, config.epochs, eta_min=config.w_lr_min)
elif config.optim == 'cosine_finetune':
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
w_optim, T_max = 400, eta_min=config.w_lr_min, last_epoch= 400 - config.epochs)
# lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer = w_optim, T_max = config.epochs,
# eta_min = config.w_lr_min)
model.train_fz_bn(freeze_bn=False)
model.change_dropout_ratio(config.dropout)
model.change_mask_dropout_ratio(config.mask_dropout)
best_top1 = 0.
for epoch in range(config.start_epoch, config.epochs):
if config.act_type != 'nn.ReLU':
model.update_sparse_list()
model.print_sparse_list(logger)
if config.precision == 'full':
if(config.distil):
top1_train = train_distil(train_loader, model, w_optim, teacher_model, criterion_kd, epoch,
device, config, logger, writer)
else:
top1_train = train(train_loader, model, w_optim, epoch,
device, config, logger, writer)
else:
if(config.distil):
top1_train = train_distil_fp16(train_loader, model, w_optim, teacher_model, criterion_kd, epoch,
device, config, logger, writer)
else:
top1_train = train_fp16(train_loader, model, w_optim, epoch,
device, config, logger, writer)
# adjust_learning_rate(w_optim, epoch, config)
if config.optim == 'cos_modified':
cos_modified_learning_rate(w_optim, epoch, config)
else:
lr_scheduler.step()
# validation
cur_step = (epoch+1) * len(train_loader)
if config.precision == 'full':
top1 = validate(val_loader, model, epoch, cur_step, device, config, logger, writer)
else:
top1 = validate_fp16(val_loader, model, epoch, cur_step, device, config, logger, writer)
# save
if best_top1 < top1:
best_top1 = top1
# best_genotype = genotype
is_best = True
else:
is_best = False
# utils.save_checkpoint(model, config.path, is_best)
if is_best:
save_path = os.path.join(config.path, 'best.pth.tar')
else:
save_path = os.path.join(config.path, 'checkpoint.pth.tar')
model.save_checkpoint(epoch, best_top1, is_best, filename=save_path)
logger.info("Current best Prec@1 = {:.4%}".format(best_top1))
# if (epoch % 20) == 0:
# logger.info("Perform validation on training dataset. ")
# if config.precision == 'full':
# top1_train_wo_dropout = validate_train(train_loader, model, epoch, cur_step, device, config, logger, writer)
# else:
# top1_train_wo_dropout = validate_train_fp16(train_loader, model, epoch, cur_step, device, config, logger, writer)
# logger.info("Final train Prec@1 = {:.4%}".format(top1_train_wo_dropout))
logger.info("Final best validation Prec@1 = {:.4%}".format(best_top1))
# logger.info("Best Genotype = {}".format(best_genotype))
if __name__ == "__main__":
main()