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Full_Supervise.py
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import os
from datetime import datetime
from argparse import ArgumentParser
from tqdm import tqdm
from yacs.config import CfgNode
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import SGD, Adam
from torch.optim.lr_scheduler import MultiStepLR
from torch.utils.tensorboard import SummaryWriter
import random
import numpy as np
# from src.models.model_by_2dTo3d import model_by_2dTo3d
# from src.models.model_by_3dTo2d import model_by_3dTo2d
from src.models.model_by_mlp import model_by_mlp
from src.models.loss import Dice_Loss
from src.data.data_factory import *
from src.utils.confusion import BinaryConfusionMatrix
from src.data.nuscenes.utils import NUSCENES_CLASS_NAMES
from src.data.argoverse.utils import ARGOVERSE_CLASS_NAMES
from src.utils.visualise import colorise
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark=True # speed up
def train(labeled_train_loader, train_sampler, bev_seg_model, criterion, optimizer, config, epoch):
bev_seg_model.train()
# Initialise confusion matrix
confusion = BinaryConfusionMatrix(config.num_class)
train_sampler.set_epoch(epoch)
if (torch.distributed.get_rank() == 0):
dataloader = enumerate(tqdm(labeled_train_loader))
else:
dataloader = enumerate(labeled_train_loader)
# Iterate over dataloader
iteration = (epoch - 1) * len(labeled_train_loader)
for i, batch in dataloader:
labeled_data = [t.cuda() for t in batch]
image, label, mask, calib = labeled_data
bev_feature, bev_seg = bev_seg_model(image, calib) # b x 256 x 200 x 200, b x n x 196 x 200
segmentation_loss = criterion(bev_seg, label)
optimizer.zero_grad()
segmentation_loss.backward()
optimizer.step()
# Update confusion matrix
scores = bev_seg.cpu().sigmoid() # 0~1
confusion.update(scores > config.score_thresh, label > 0, mask.cpu() > 0)
# Update tensorboard
if i % config.log_interval == 0 and torch.distributed.get_rank() == 0:
print('\n segmentation_loss =', float(segmentation_loss),
'\n lr =', optimizer.param_groups[0]['lr'])
# summary.add_scalar('train/segmentation_loss', float(segmentation_loss), iteration)
# Visualise
# if i % config.vis_interval == 0:
# visualise(summary, image, scores, label, mask, iteration,
# config.train_dataset, split='train')
iteration += 1
# Print and record results
if torch.distributed.get_rank() == 0:
print('Results on nuscenes training set:')
display_results(confusion, config.train_dataset)
# log_results(confusion, config.train_dataset, summary, 'train', epoch)
def evaluate(dataloader, model, criterion, config, epoch):
model.eval()
# Initialise confusion matrix
confusion = BinaryConfusionMatrix(config.num_class)
data = enumerate(tqdm(dataloader)) if (torch.distributed.get_rank() == 0) else enumerate(dataloader)
# Iterate over dataset
for i, batch in data:
# Move tensors to GPU
batch = [t.cuda() for t in batch]
# Predict class occupancy scores and compute loss
image, label, mask, calib = batch
with torch.no_grad():
_, logits = model(image, calib)
loss = criterion(logits, label)
# Update confusion matrix
scores = logits.cpu().sigmoid()
confusion.update(scores > config.score_thresh, label > 0, mask.cpu() > 0)
'''
# Update tensorboard
if i % config.log_interval == 0:
summary.add_scalar('val/loss', float(loss), epoch)
# Visualise
if i % config.vis_interval == 0:
visualise(summary, image, scores, label, mask, epoch,
config.train_dataset, split='val')
'''
# Print and record results
mean_iou = display_results(confusion, config.train_dataset)
# log_results(confusion, config.train_dataset, summary, 'val', epoch)
return mean_iou
'''
def visualise(summary, image, scores, label, mask, step, dataset, split):
class_names = NUSCENES_CLASS_NAMES if dataset == 'nuscenes' \
else ARGOVERSE_CLASS_NAMES
summary.add_image(split + '/image', image[0], step, dataformats='CHW')
summary.add_image(split + '/pred', colorise(scores[0], 'coolwarm', 0, 1),
step, dataformats='NHWC')
summary.add_image(split + '/gt', colorise(label[0], 'coolwarm', 0, 1),
step, dataformats='NHWC')
'''
def display_results(confusion, dataset):
torch.distributed.barrier()
# Display confusion matrix summary
class_names = NUSCENES_CLASS_NAMES if dataset == 'nuscenes' else ARGOVERSE_CLASS_NAMES
iou = confusion.iou.cuda().clone()
torch.distributed.all_reduce(iou, op=torch.distributed.ReduceOp.SUM)
iou /= torch.distributed.get_world_size()
if torch.distributed.get_rank() == 0:
for name, iou_score in zip(class_names, iou):
print('{:20s} {:.3f}'.format(name, iou_score))
mean_iou = torch.tensor(confusion.mean_iou).cuda().clone()
torch.distributed.all_reduce(mean_iou, op=torch.distributed.ReduceOp.SUM)
mean_iou /= torch.distributed.get_world_size()
if torch.distributed.get_rank() == 0:
print('{:20s} {:.3f}'.format('MEAN', mean_iou))
return mean_iou
'''
def log_results(confusion, dataset, summary, split, epoch):
# Display and record epoch IoU scores
class_names = NUSCENES_CLASS_NAMES if dataset == 'nuscenes' \
else ARGOVERSE_CLASS_NAMES
for name, iou_score in zip(class_names, confusion.iou):
summary.add_scalar(f'{split}/iou/{name}', iou_score, epoch)
summary.add_scalar(f'{split}/iou/MEAN', confusion.mean_iou, epoch)
'''
def save_checkpoint(path, model, optimizer, scheduler, epoch):
if isinstance(model, nn.parallel.distributed.DistributedDataParallel):
model = model.module
ckpt = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'epoch': epoch
}
torch.save(ckpt, path)
def load_checkpoint(path, model, optimizer, scheduler):
ckpt = torch.load(path)
# Load model weights
if isinstance(model, nn.parallel.distributed.DistributedDataParallel):
model = model.module
model.load_state_dict(ckpt['model'])
# Load optimiser state
optimizer.load_state_dict(ckpt['optimizer'])
# Load scheduler state
scheduler.load_state_dict(ckpt['scheduler'])
return ckpt['epoch']
def create_experiment(config, tag, resume=None):
# Restore an existing experiment if a directory is specified
if resume is not None:
print("\n==> Restoring experiment from directory:\n" + resume)
logdir = resume
else:
# Otherwise, generate a run directory based on the current time
name = datetime.now().strftime('{}_%y-%m-%d--%H-%M-%S').format(tag)
logdir = os.path.join(os.path.expandvars(config.logdir), name)
print("\n==> Creating new experiment in directory:\n" + logdir)
os.makedirs(logdir)
# Display the config options on-screen
print(config.dump())
# Save the current config
with open(os.path.join(logdir, 'config.yml'), 'w') as f:
f.write(config.dump())
return logdir
def main():
parser = ArgumentParser()
parser.add_argument('--tag', type=str, default='run',
help='optional tag to identify the run')
parser.add_argument('--resume', default=None,
help='path to an experiment to resume')
parser.add_argument('--options', nargs='*', default=[],
help='list of addition config options as key-val pairs')
parser.add_argument('--model', choices=['2dTo3d_based', '3dTo2d_based', 'mlp_based'], default='mlp_based', help='type of bev seg model')
parser.add_argument('--img_size', help='resolution of input img', nargs="+", type=int, default=[800, 600]) # 224 x 448, 300x400, 600 x 800
parser.add_argument('--dataset', choices=['nuscenes', 'argoverse'], default='nuscenes', help='dataset to train on')
args = parser.parse_args()
# Load configuration
with open('./configs/config.yml') as f:
config = CfgNode.load_cfg(f)
config['model'] = args.model
config['img_size'] = args.img_size
config['train_dataset'] = args.dataset
# if args.dataset == 'nuscenes':
config['num_class'] = 14
# 1) 初始化
torch.distributed.init_process_group(backend="nccl")
# 2) 配置每个进程的gpu
local_rank = torch.distributed.get_rank()
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)
setup_seed(42 + torch.distributed.get_rank())
# Setup experiment
bev_seg_model = model_by_mlp(config).to(device) # B x 3 x 600 x 800, B x 3 x 3 ==> B x n x 196 x 200
bev_seg_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(bev_seg_model)
bev_seg_model = DDP(bev_seg_model, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True)
criterion = Dice_Loss().cuda()
if torch.distributed.get_rank() == 0:
# print('test: random.random() =', random.random())
# print('test: np.random.random() =', np.random.random())
# print('test: torch.rand(1) =', torch.randn(1))
print('num of trainable parameters =', sum(p.numel() for p in bev_seg_model.parameters() if p.requires_grad))
train_data, test_data = build_fullNu_datasets(config)
train_sampler = DistributedSampler(train_data)
train_loader = DataLoader(train_data, batch_size=config.batch_size // torch.cuda.device_count(),
num_workers=config.num_workers // torch.cuda.device_count(), sampler=train_sampler)
test_sampler = DistributedSampler(test_data)
test_loader = DataLoader(test_data, batch_size=4, num_workers=8, sampler=test_sampler)
# Build optimiser and learning rate scheduler
optimizer = Adam(bev_seg_model.parameters(), lr=config.learning_rate)
lr_scheduler = MultiStepLR(optimizer, config.lr_milestones, 0.5)
if torch.distributed.get_rank() == 0:
# Create a directory for the experiment
logdir = create_experiment(config, args.tag, args.resume)
# Create tensorboard summary
# summary = SummaryWriter(logdir)
epoch, best_iou = 1, 0
'''
if torch.distributed.get_rank() == 0:
print('Results on nuscenes testing set:')
test_iou = evaluate(nuscenes_test_loader, bev_seg_model, criterion, config, 0)
'''
# Main training loop
while epoch <= config.num_epochs:
if torch.distributed.get_rank() == 0:
print('\n\n=== Beginning epoch {} of {} ==='.format(epoch, config.num_epochs))
# Train model for one epoch
train(train_loader, train_sampler, bev_seg_model, criterion, optimizer, config, epoch)
# Update learning rate
lr_scheduler.step()
'''
if epoch <= config.lr_milestones[0]:
epoch += 1
continue
'''
# Evaluate on the test set
if torch.distributed.get_rank() == 0:
print('Results on nuscenes testing set:')
test_iou = evaluate(test_loader, bev_seg_model, criterion, config, epoch)
if torch.distributed.get_rank() == 0:
if test_iou > best_iou:
best_iou = test_iou
save_checkpoint(os.path.join(logdir, 'iou_{}.pth'.format(best_iou)), bev_seg_model, optimizer, lr_scheduler, epoch)
print('Best IOU =', best_iou)
epoch += 1
print("\n Process {} complete!".format(torch.distributed.get_rank()))
if __name__ == '__main__':
main()