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train.py
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#!/usr/bin/python
# _____________________________________________________________________________
import datetime
import json
# ----------------
# import libraries
# ----------------
# standard libraries
# -----
import os
import time
from argparse import Namespace
from tqdm import tqdm
import torch
import config
from utils.augmentations import get_transformations
from utils.constants import DATASETS
from utils.finetuning import finetune
from utils.finetuning_all import finetune_all
# configuration module
# -----
# custom libraries
# -----
from utils.general import init_target_net, prepare_device, save_model, is_target_needed, update_target_net, load_model, \
run_forward
from utils.getters import get_datasets, get_train_iterator, get_scheduler, get_network, get_modules, get_networks, \
get_arguments, get_optimizer, apply_transform
# custom function
# -----
def train():
# Initialization
run_name = f'{datetime.datetime.now().strftime("%d-%m-%y_%H-%M")}_{args.name}_{args.seed}'
fabric = prepare_device(args)
dataloader_train, dataloader_train_eval, dataloader_test, dataset_train, dataset_train_eval, dataset_test = get_datasets(args, run_name, fabric)
train_t, val_t = get_transformations(args, crop_size=DATASETS[args.dataset]['img_size'])
net, net_target, method_modules = get_networks(args, fabric, dataset_train)
optimizer = get_optimizer(args, net)
scheduler = get_scheduler(args, optimizer, len(dataloader_train)*args.n_epochs)
net, optimizer= fabric.setup(net, optimizer)
dataloader_train, dataloader_train_eval, dataloader_test = fabric.setup_dataloaders(dataloader_train, dataloader_train_eval, dataloader_test, move_to_device=True)
if args.path_load_model:
load_model(fabric, net,args, optimizer=optimizer, scheduler=scheduler)
epoch_loop = tqdm(range(args.n_epochs), ncols=80)
if args.name != "test":
dataset_test.eval(net, dataloader_train_eval, dataloader_test, modules=method_modules, tf= train_t, tv=val_t)
# Training
for epoch in epoch_loop:
epoch_loop.set_description(f"Method: {run_name.split('~')[0]}, Epoch: {epoch + 1}")
training_loop = get_train_iterator(args, dataloader_train)
net.train()
for i, data in enumerate(training_loop):
optimizer.zero_grad()
(x_pair1, x_pair, a), labels = data[0], data[1]
with torch.no_grad():
x = apply_transform(args, train_t, x_pair1, x_pair)
rep = run_forward(args, x, net)
rep_target=None
if is_target_needed(args):
with torch.no_grad():
rep_target = run_forward(args, x, net_target)
loss = 0
for m in method_modules:
loss = loss+ m.apply(net, net_target=net_target, action=a, rep=rep, rep_target=rep_target, data=data, epoch=epoch)
fabric.backward(loss)
optimizer.step()
scheduler.step()
if is_target_needed(args):
update_target_net(net, net_target, args.tau)
training_loop.set_description(f'Loss: {loss.item():>8.4f}')
if (epoch+1) % args.test_every == 0:
dataset_test.eval(net, dataloader_train_eval, dataloader_test, epoch=epoch +1, modules=method_modules, tf= train_t, tv=val_t)
if args.save_model and (epoch+1) % args.save_every == 0:
save_model(fabric, net, dataset_test.get_log_dir(), epoch, optimizer=optimizer, scheduler=scheduler)
fabric.barrier()
if args.finetune:
finetune_all(args, dataloader_train, dataset_train, dataloader_test, net, os.path.join(args.log_dir, run_name), fabric, train_t, val_t)
def end_finetune(all=False):
if args.path_load_model != "":
with open(os.path.join(args.path_load_model,'../config.json'), 'r') as f:
config = json.load(f)
config = config[next(iter(config))]
new_args = Namespace(**config)
args.seed = new_args.seed
fabric = prepare_device(args)
run_name = f'{datetime.datetime.now().strftime("%d-%m-%y_%H-%M")}_{args.name}_{args.seed}'
dataloader_train, dataloader_train_eval, dataloader_test, dataset_train, dataset_train_eval, dataset_test = get_datasets(args, run_name, fabric)
net, _, method_modules = get_networks(args, fabric, dataset_train)
net = fabric.setup(net)
# dataloader_train, dataset_train_eval, dataloader_test = fabric.setup_dataloaders(dataloader_train, dataloader_train_eval, dataloader_test, move_to_device=True)
dataloader_train, dataloader_test = fabric.setup_dataloaders(dataloader_train, dataloader_test, move_to_device=True)
if args.path_load_model != "": load_model(fabric, net, args, strict=False)
train_t, val_t = get_transformations(args, crop_size=DATASETS[args.dataset]['img_size'])
if all:
finetune_all(args, dataloader_train, dataset_train, dataloader_test, net, os.path.join(args.log_dir, run_name),fabric, train_t, val_t, name=args.name_eval)
else:
finetune(args, dataloader_train, dataset_train, dataloader_test, net, os.path.join(args.log_dir, run_name), fabric, train_t, val_t, name=args.name_eval)
# ----------------
# main program
# ----------------
if __name__ == '__main__':
args = get_arguments(config.parser).parse_args()
if args.mode == "train":
train()
if args.mode == "finetune_all":
end_finetune(all=True)
if args.mode == "finetune":
end_finetune()
# for i in range(args.n_repeat):
# config.RUN_NAME = config.RUN_NAME
# train()
# _____________________________________________________________________________
# Stick to 80 characters per line
# Use PEP8 Style
# Comment your code
# -----------------
# top-level comment
# -----------------
# medium level comment
# -----
# low level comment