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FS_train.py
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FS_train.py
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from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import torch
from torch.utils.tensorboard import SummaryWriter
import torch.optim as optim
import torch.cuda.amp as amp
import torchvision
from torchvision.transforms import InterpolationMode
from torch.utils import data
import torch.nn.functional as F
from utils.utils import reverse_one_hot, compute_global_accuracy, fast_hist, per_class_iu, colour_code_segmentation,poly_lr_scheduler
from utils.loss import CrossEntropy2d,DiceLoss
import numpy as np
import os
import os.path as osp
import random
import matplotlib.pyplot as plt
import collections
from PIL import Image
#from torchinfo import summary
#from fvcore.nn import FlopCountAnalysis
import json
import argparse
from tqdm import tqdm
# Dataset class:
from dataset.cityscapesDataSet import cityscapesDataSet
from dataset.GTA5DataSet import GTA5DataSet
# Discriminator
from model.discriminator import FCDiscriminator, LightWeightFCDiscriminator
# Network
from model.build_BiSeNet import BiSeNet
# Validation function
from eval import val
data_path = "/content/data"
## -- TRAINING --
def train(args, model, epoch_start_i, optimizer, dataloader_train, dataloader_val, miou_init=0):
model_description = f"checkpoints-m_{args.context_path}-{args.optimizer}-e_{args.num_epochs}-b_{args.batch_size}-c_{args.crop_width}_{args.crop_height}_horFlip_blur"
save_model_path = args.save_model_path + model_description
save_tb_path = args.save_tb_path + model_description
writer = SummaryWriter(log_dir = save_tb_path)
scaler = amp.GradScaler()
if args.loss == 'dice':
loss_func = DiceLoss()
elif args.loss == 'crossentropy':
loss_func = torch.nn.CrossEntropyLoss(ignore_index=255)
max_miou = miou_init
step = 0
for epoch in range(epoch_start_i, args.num_epochs):
lr = poly_lr_scheduler(optimizer, args.learning_rate, iter=epoch, max_iter=args.num_epochs)
model.train()
tq = tqdm(total=len(dataloader_train) * args.batch_size)
tq.set_description('epoch %d, lr %f' % (epoch, lr))
loss_record = []
for i, (data, label) in enumerate(dataloader_train):
if torch.cuda.is_available() and args.use_gpu:
data = data.cuda()
label = label.long().cuda()
optimizer.zero_grad()
with amp.autocast():
output, output_sup1, output_sup2 = model(data)
loss1 = loss_func(output, label)
loss2 = loss_func(output_sup1, label)
loss3 = loss_func(output_sup2, label)
loss = loss1 + loss2 + loss3
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
tq.update(args.batch_size)
#tq.set_postfix(loss='%.6f' % loss)
step += 1
writer.add_scalar('loss_step', loss, step)
loss_record.append(loss.item())
tq.close()
loss_train_mean = np.mean(loss_record)
writer.add_scalar('epoch/loss_epoch_train', float(loss_train_mean), epoch)
print('loss for train : %f' % (loss_train_mean))
if epoch % args.checkpoint_step == 0 and epoch != 0:
print("Saving checkpoint")
if not os.path.isdir(save_model_path):
os.mkdir(save_model_path)
torch.save({
'epoch': epoch,
'model_state_dict': model.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'max_miou': max_miou,
},
os.path.join(save_model_path, 'latest_CE_loss.pth'))
if epoch % args.validation_step == 0 and epoch != 0:
precision, miou = val(args, model, dataloader_val,batch_size=1)
if miou > max_miou:
max_miou = miou
os.makedirs(save_model_path, exist_ok=True)
torch.save({
'epoch': epoch,
'model_state_dict': model.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'max_miou': max_miou,
},
os.path.join(save_model_path, 'best_CE_loss.pth'))
writer.add_scalar('epoch/precision_val', precision, epoch)
writer.add_scalar('epoch/miou val', miou, epoch)
def get_arguments(params=[]):
"""Parse all the arguments provided from the CLI.
Returns:
A list of parsed arguments.
"""
# basic parameters
parser = argparse.ArgumentParser()
parser.add_argument('--num_epochs', type=int, default=300, help='Number of epochs to train for')
parser.add_argument('--epoch_start_i', type=int, default=0, help='Start counting epochs from this number')
parser.add_argument('--checkpoint_step', type=int, default=10, help='How often to save checkpoints (epochs)')
parser.add_argument('--validation_step', type=int, default=10, help='How often to perform validation (epochs)')
parser.add_argument('--dataset', type=str, default="Cityscapes", help='Dataset you are using.')
parser.add_argument('--crop_width', type=int, default=1024, help='Width of cropped/resized input image to network')
parser.add_argument('--crop_height', type=int, default=512, help='Height of cropped/resized input image to network')
parser.add_argument('--batch_size', type=int, default=32, help='Number of images in each batch')
parser.add_argument("--iter-size", type=int, default=125, help="Accumulate gradients for ITER_SIZE iterations.")
parser.add_argument('--context_path', type=str, default="resnet101",
help='The context path model you are using, resnet18, resnet101.')
parser.add_argument('--learning_rate', type=float, default=0.01, help='learning rate used for train')
parser.add_argument('--data', type=str, default='content/data', help='path of training data')
parser.add_argument('--num_workers', type=int, default=4, help='num of workers')
parser.add_argument('--num_classes', type=int, default=19, help='num of object classes')
parser.add_argument("--num-steps", type=int, default=50, help="Number of training steps.")
parser.add_argument('--cuda', type=str, default='0', help='GPU ids used for training')
parser.add_argument('--use_gpu', type=bool, default=True, help='whether to user gpu for training')
parser.add_argument('--optimizer', type=str, default='rmsprop', help='optimizer, support rmsprop, sgd, adam')
parser.add_argument('--loss', type=str, default='crossentropy', help='loss function, dice or crossentropy')
parser.add_argument("--random-scale", action="store_true", help="Whether to randomly scale the inputs during the training.")
parser.add_argument("--random-mirror", action="store_true", help="Whether to randomly mirror the inputs during the training.")
parser.add_argument("--set-type", type=str, default='train', help="choose adaptation set.")
parser.add_argument('--load_pretrained_model', type=bool, default=False, help='load or not the last model from the saved checkpoint ')
parser.add_argument('--pretrained_model_path', type=str, default=None, help='path to pretrained model')
parser.add_argument('--save_model_path', type=str, default=None, help='path to save model')
parser.add_argument('--save_tb_path', type=str, default=None, help='path to save tensorboard graphs')
parser.add_argument("--random-seed", type=int, default=42, help="Random seed to have reproducible results.")
args = parser.parse_args(params)
return args
def main(params):
args = get_arguments(params)
# Set random seed
torch.manual_seed(args.random_seed)
torch.cuda.manual_seed(args.random_seed)
np.random.seed(args.random_seed)
random.seed(args.random_seed)
# create dataset and dataloader
data_root_path = os.path.join(args.data, args.dataset) # /content/data/Cityscapes
train_path = os.path.join(data_root_path, "train.txt") # /content/data/Cityscapes/train.txt
val_path = os.path.join(data_root_path, "val.txt") # /content/data/Cityscapes/val.txt
info_path = os.path.join(args.data, args.dataset, "info.json") # /content/data/Cityscapes/info.json
# preprocessing informations:
input_size = (int(args.crop_width), int(args.crop_height))
f = open(info_path)
info = json.load(f)
img_mean = np.array((104.00698793, 116.66876762, 122.67891434), dtype=np.float32)
img_mean = np.array(img_mean, dtype=np.float32)
# augmentation values
#AUG_VALUES = None
AUG_VALUES = {
"hor_flip": True,
"blur": True,
"prob" : 0.5,
"kernel_size" : 9,
"sigma" : (1,2)
}
# create dataloaders
train_dataset = cityscapesDataSet(root=data_root_path,
list_path = train_path,
info_json = info,
crop_size=input_size,
mean=img_mean,
augmentation=AUG_VALUES)
val_dataset = cityscapesDataSet(root=data_root_path,
list_path = val_path,
info_json = info,
crop_size=input_size,
mean=img_mean)
print(f'train_dataset: {len(train_dataset)}')
print(f'val_dataset: {len(val_dataset)}')
image, label = train_dataset[0]
print(f'images shape: {image.shape}')
print(f'label shape: {label.shape}')
# Define dataloaders
dataloader_train = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, drop_last = True, num_workers=args.num_workers, pin_memory=True)
# (batch size for dataloader_val must be 1)
dataloader_val = DataLoader(val_dataset, batch_size=1, shuffle=True, num_workers=args.num_workers, pin_memory=True)
# build model
os.environ['CUDA_VISIBLE_DEVICES'] = args.cuda
model = BiSeNet(args.num_classes, args.context_path)
if torch.cuda.is_available() and args.use_gpu:
model = torch.nn.DataParallel(model).cuda()
# build optimizer
if args.optimizer == 'rmsprop':
optimizer = torch.optim.RMSprop(model.parameters(), args.learning_rate)
elif args.optimizer == 'sgd':
optimizer = torch.optim.SGD(model.parameters(), args.learning_rate, momentum=0.9, weight_decay=1e-4)
elif args.optimizer == 'adam':
optimizer = torch.optim.Adam(model.parameters(), args.learning_rate)
else: # rmsprop
print('not supported optimizer \n')
return None
epoch_start_i = 0
miou_init = 0
# load pretrained model if exists
if (args.load_pretrained_model) and (args.pretrained_model_path is not None) and (os.path.isfile(args.pretrained_model_path)):
print('load model from %s ...' % args.pretrained_model_path)
checkpoint= torch.load(args.pretrained_model_path)
model.module.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch_start_i = int(checkpoint['epoch'])+1
miou_init = float(checkpoint['max_miou'])
print('Done!')
print('- Epoch:', epoch_start_i)
print('- Best miou:', miou_init)
# train
train(args, model, epoch_start_i, optimizer, dataloader_train, dataloader_val, miou_init)
# final val
val(args, model, dataloader_val, save=True, batch_size=1, save_path=data_path)
if __name__ == '__main__':
params = [
'--num_epochs', '50',
'--learning_rate', '2.5e-2',
'--data', data_path,
'--num_workers', '8',
'--num_classes', '19',
'--cuda', '0',
'--batch_size', '4',
'--save_model_path', '/gdrive/MyDrive/Project_AML/Models/segmentation/',
'--pretrained_model_path', '',
'--save_tb_path', '/gdrive/MyDrive/Project_AML/Graphs/segmentation/',
'--context_path', 'resnet18', # set resnet18 or resnet101, only support resnet18 and resnet101
'--optimizer', 'sgd',
'--checkpoint_step', '2',
'--validation_step', '2',
'--crop_width', '1024',
'--crop_height', '512',
'--load_pretrained_model', False
]
main(params)