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train_graph.py
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train_graph.py
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# -*- coding: utf-8 -*-
# @Author : Xiangyi Zhang
# @File : train_graph.py
# @Email : [email protected]
import os
import shutil
import torch
import torch.nn as nn
import torch.optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
import torch.backends.cudnn as cudnn
from torchvision.transforms import Compose
from models.SemiFewShotPartGraph import SemiFewShotSegPartGraph
from dataloaders.customized import voc_fewshot
from dataloaders.transforms import RandomMirror, Resize, ToTensorNormalize
from util.utils import set_seed, CLASS_LABELS, get_params
from config import ex
from util.metric import Metric
import tqdm
import torch.nn.functional as F
from tensorboardX import SummaryWriter
import matplotlib.pyplot as plt
from util.utils import check_dir
import numpy as np
import time
import os.path as osp
import pprint
@ex.automain
def main(_run, _config, _log):
logdir = f'{_run.observers[0].dir}/'
print(logdir)
category = ['aeroplane','bicycle','bird','boat','bottle','bus','car','cat','chair','cow','diningtable','dog','horse','motorbike','person','pottedplant','sheep','sofa','train','tvmonitor']
training_tags = {'loss': "ATraining/total_loss", "query_loss": "ATraining/query_loss",
'aligned_loss': "ATraining/aligned_loss", 'base_loss': "ATraining/base_loss",}
if _run.observers:
os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
for source_file, _ in _run.experiment_info['sources']:
os.makedirs(os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),exist_ok=True)
_run.observers[0].save_file(source_file, f'source/{source_file}')
shutil.rmtree(f'{_run.observers[0].basedir}/_sources')
cfg = _config
data_name = _config['dataset']
max_label=20 if data_name == 'VOC' else 80
set_seed(_config['seed'])
cudnn.enabled = True
cudnn.benchmark = True
torch.cuda.set_device(device=_config['gpu_id'])
torch.set_num_threads(1)
_log.info(f'###### Setting: {_run.observers[0].dir} ######')
print(_config['ckpt_dir'])
tbwriter = SummaryWriter(osp.join(_config['ckpt_dir']))
infer_tags = {
'mean_iou': "MeanIoU/mean_iou",
"mean_iou_binary": "MeanIoU/mean_iou_binary",
}
_log.info('###### Create model ######')
model = SemiFewShotSegPartGraph(cfg=_config)
_log.info('Model: SemiFewShotSegPartGraph')
model = nn.DataParallel(model.cuda(), device_ids=[_config['gpu_id'],])
model.train()
_log.info('###### Load data ######')
data_name = _config['dataset']
make_data = voc_fewshot
labels = CLASS_LABELS[data_name][_config['label_sets']]
transforms = Compose([Resize(size=_config['input_size']),RandomMirror()])
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['n_steps'] * _config['batch_size'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'],
n_unlabel=_config['task']['n_unlabels'],
cfg=_config
)
trainloader = DataLoader(
dataset,
batch_size=_config['batch_size'],
shuffle=True,
num_workers=_config['num_workers'],
pin_memory=True,
drop_last=True
)
_log.info('###### Set optimizer ######')
# Optimizer
if _config['fix']:
print('Optimizer: fix')
optimizer = torch.optim.SGD(
params=[
{
"params": model.module.encoder.layer3.parameters(),
"lr": _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
},
{
"params": model.module.encoder.layer4.parameters(),
"lr": _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
},
{
"params": get_params(model, key='20x'),
"lr": 20 * _config['optim']['lr'],
"weight_decay": 0
},
{
"params": get_params(model, key='10x'),
"lr": 10 * _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
}], lr=_config['optim']['lr'], weight_decay=_config['optim']['weight_decay'],
momentum=_config['optim']['momentum'])
else:
print('Optimizer: Not fix')
optimizer = torch.optim.SGD(
params=[
{
"params": model.module.encoder.parameters(),
"lr": _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
},
{
"params": get_params(model, key='20x'),
"lr": 20 * _config['optim']['lr'],
"weight_decay": 0
},
{
"params": get_params(model, key='10x'),
"lr": 10 * _config['optim']['lr'],
"weight_decay": _config['optim']['weight_decay']
}], lr=_config['optim']['lr'], weight_decay=_config['optim']['weight_decay'], momentum=_config['optim']['momentum'])
print('scheduler: MultiStepLR')
scheduler = MultiStepLR(optimizer, milestones=_config['lr_milestones'], gamma=0.1)
criterion = nn.CrossEntropyLoss(ignore_index=_config['ignore_label'])
log_loss = {'loss': 0, 'align_loss': 0, 'base_loss': 0, 'align_loss_cs': 0}
_log.info('###### Training ######')
highest_iou = 0
metrics = {}
device = torch.device('cuda')
for i_iter, sample_batched in enumerate(trainloader):
if _config['fix']:
model.module.encoder.conv1.eval()
model.module.encoder.bn1.eval()
model.module.encoder.layer1.eval()
model.module.encoder.layer2.eval()
if _config['eval']:
if i_iter == 0:
break
# Prepare input
support_images = [[shot.cuda() for shot in way] for way in sample_batched['support_images']]
support_fg_mask = [[shot[f'fg_mask'].float().cuda() for shot in way] for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_mask'].float().cuda() for shot in way] for way in sample_batched['support_mask']]
query_images = [query_image.cuda() for query_image in sample_batched['query_images']]
query_labels = torch.cat([query_label.long().cuda() for query_label in sample_batched['query_labels']], dim=0)#1*417*417
unlabel_images = [[unlabel_images.cuda() for unlabel_images in way] for way in sample_batched['unlabel_images']]
unlabel_segment = [[un.float().to(device) for un in way] for way in sample_batched['unlabel_segment']]
# Forward and Backward
optimizer.zero_grad()
query_pred, output_semantic, align_loss = model(support_images, support_fg_mask, support_bg_mask, query_images, unlabel_images, unlabel_segment)
support_label_base = torch.cat([torch.cat([shot.long().cuda() for shot in way]) for way in sample_batched['support_labels_base']]) # 2*1*417*417
query_labels_base = torch.cat([query_label.long().cuda() for query_label in sample_batched['query_labels_base']], dim=0) # 1*417*417
label_base = torch.cat((support_label_base, query_labels_base)) # 3*53*53
query_loss = criterion(query_pred, query_labels) #1*3*417*417, 1*417*417
if cfg['model']['sem']:
base_loss = criterion(output_semantic, label_base) # 3*16*53*53, 3*53*53
else:
base_loss = torch.zeros(1).to(device)
loss = query_loss + align_loss * _config['align_loss_scaler'] + base_loss * _config['base_loss_scaler']
loss.backward()
optimizer.step()
scheduler.step(epoch=i_iter)
# Log loss
query_loss = query_loss.detach().data.cpu().numpy()
align_loss = align_loss.detach().data.cpu().numpy()
base_loss = base_loss.detach().data.cpu().numpy()
# _run.log_scalar('loss', query_loss)
# _run.log_scalar('align_loss', align_loss)
log_loss['loss'] += query_loss
log_loss['align_loss'] += align_loss
log_loss['base_loss'] += base_loss
# print loss and take snapshots
if (i_iter + 1) % _config['print_interval'] == 0:
loss = log_loss['loss'] / (i_iter + 1)
align_loss = log_loss['align_loss'] / (i_iter + 1)
base_loss = log_loss['base_loss'] / (i_iter + 1)
print(f'step {i_iter+1}: loss: {loss}, align_loss: {align_loss}, base_loss: {base_loss}')
_log.info(f'step {i_iter+1}: loss: {loss}, align_loss: {align_loss}, base_loss: {base_loss}')
metrics['loss'] = loss
metrics['query_loss'] = query_loss
metrics['align_loss'] = align_loss
metrics['base_loss'] = base_loss
# for k, v in metrics.items():
# tbwriter.add_scalar(training_tags[k], v, i_iter)
if (i_iter + 1) % _config['evaluate_interval'] == 0:
_log.info('###### Evaluation begins ######')
_log.info(f'###### Setting: {_run.observers[0].dir} ######')
print(_config['ckpt_dir'])
model.eval()
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
transforms = Compose(transforms)
metric = Metric(max_label=max_label, n_runs=_config['n_runs'])
with torch.no_grad():
for run in range(1):
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['infer_max_iters'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'],
n_unlabel=_config['task']['n_unlabels'],
cfg=_config
)
testloader = DataLoader(dataset, batch_size=_config['batch_size'], shuffle=False,
num_workers=_config['num_workers'], pin_memory=True, drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
label_ids = list(sample_batched['class_ids'])
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
suffix = 'mask'
support_fg_mask = [[shot[f'fg_{suffix}'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_{suffix}'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
unlabel_images = [[unlabel_images.cuda() for unlabel_images in way] for way in
sample_batched['unlabel_images']]
unlabel_segment = [[un.float().to(device) for un in way] for way in
sample_batched['unlabel_segment']]
query_images = [query_image.cuda()
for query_image in sample_batched['query_images']]
query_labels = torch.cat([query_label.cuda() for query_label in sample_batched['query_labels']], dim=0)
query_pred, _, _ = model(support_images, support_fg_mask, support_bg_mask, query_images, unlabel_images, unlabel_segment)
curr_iou = metric.record(query_pred.argmax(dim=1)[0], query_labels[0], labels=label_ids, n_run=run)
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels), n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
print(f'meanIoU: {meanIoU}, meanIoU_binary: {meanIoU_binary}')
metrics = {}
metrics['mean_iou'] = meanIoU
metrics['mean_iou_binary'] = meanIoU_binary
for k, v in metrics.items():
tbwriter.add_scalar(infer_tags[k], v, i_iter)
if meanIoU > highest_iou:
print(f'The highest iou is in iter: {i_iter} : {meanIoU}, save: {_config["ckpt_dir"]}/best.pth')
highest_iou = meanIoU
torch.save(model.state_dict(), os.path.join(f'{_config["ckpt_dir"]}/best.pth'))
else:
print(f'The highest iou is in iter: {i_iter} : {meanIoU}')
torch.save(model.state_dict(), os.path.join(f'{_config["ckpt_dir"]}/{i_iter + 1}.pth'))
model.train()
_log.info(f'###### Setting: {_run.observers[0].dir} ######')
print(_config['ckpt_dir'])
_log.info(' --------- Testing begins ---------')
labels = CLASS_LABELS[data_name]['all'] - CLASS_LABELS[data_name][_config['label_sets']]
transforms = [Resize(size=_config['input_size'])]
transforms = Compose(transforms)
ckpt = os.path.join(f'{_config["ckpt_dir"]}/best.pth')
model.load_state_dict(torch.load(ckpt, map_location='cpu'))
model.eval()
metric = Metric(max_label=max_label, n_runs=5)
with torch.no_grad():
for run in range(5):
n_iter = 0
_log.info(f'### Run {run + 1} ###')
set_seed(_config['seed'] + run)
_log.info(f'### Load data ###')
dataset = make_data(
base_dir=_config['path'][data_name]['data_dir'],
split=_config['path'][data_name]['data_split'],
transforms=transforms,
to_tensor=ToTensorNormalize(),
labels=labels,
max_iters=_config['infer_max_iters'],
n_ways=_config['task']['n_ways'],
n_shots=_config['task']['n_shots'],
n_queries=_config['task']['n_queries'],
n_unlabel=_config['task']['n_unlabels'],
cfg=_config
)
testloader = DataLoader(dataset, batch_size=_config['batch_size'], shuffle=False,
num_workers=_config['num_workers'], pin_memory=True, drop_last=False)
_log.info(f"Total # of Data: {len(dataset)}")
for sample_batched in tqdm.tqdm(testloader):
label_ids = list(sample_batched['class_ids'])
support_images = [[shot.cuda() for shot in way]
for way in sample_batched['support_images']]
suffix = 'mask'
support_fg_mask = [[shot[f'fg_{suffix}'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
support_bg_mask = [[shot[f'bg_{suffix}'].float().cuda() for shot in way]
for way in sample_batched['support_mask']]
unlabel_images = [[unlabel_images.cuda() for unlabel_images in way] for way in
sample_batched['unlabel_images']]
unlabel_segment = [[un.float().to(device) for un in way] for way in
sample_batched['unlabel_segment']]
query_images = [query_image.cuda()
for query_image in sample_batched['query_images']]
query_labels = torch.cat([query_label.cuda() for query_label in sample_batched['query_labels']], dim=0)
query_pred, _, _ = model(support_images, support_fg_mask, support_bg_mask, query_images, unlabel_images, unlabel_segment)
curr_iou = metric.record(query_pred.argmax(dim=1)[0], query_labels[0], labels=label_ids, n_run=run)
n_iter += 1
classIoU, meanIoU = metric.get_mIoU(labels=sorted(labels), n_run=run)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=run)
_run.log_scalar('classIoU', classIoU.tolist())
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_log.info(f'classIoU: {classIoU}')
_log.info(f'meanIoU: {meanIoU}')
_log.info(f'classIoU_binary: {classIoU_binary}')
_log.info(f'meanIoU_binary: {meanIoU_binary}')
classIoU, classIoU_std, meanIoU, meanIoU_std = metric.get_mIoU(labels=sorted(labels))
classIoU_binary, classIoU_std_binary, meanIoU_binary, meanIoU_std_binary = metric.get_mIoU_binary()
_run.log_scalar('meanIoU', meanIoU.tolist())
_run.log_scalar('meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_classIoU', classIoU.tolist())
_run.log_scalar('final_classIoU_std', classIoU_std.tolist())
_run.log_scalar('final_meanIoU', meanIoU.tolist())
_run.log_scalar('final_meanIoU_std', meanIoU_std.tolist())
_run.log_scalar('final_classIoU_binary', classIoU_binary.tolist())
_run.log_scalar('final_classIoU_std_binary', classIoU_std_binary.tolist())
_run.log_scalar('final_meanIoU_binary', meanIoU_binary.tolist())
_run.log_scalar('final_meanIoU_std_binary', meanIoU_std_binary.tolist())
_log.info('----- Final Result -----')
_log.info(f'classIoU mean: {classIoU}')
_log.info(f'classIoU std: {classIoU_std}')
_log.info(f'meanIoU mean: {meanIoU}')
_log.info(f'meanIoU std: {meanIoU_std}')
_log.info(f'classIoU_binary mean: {classIoU_binary}')
_log.info(f'classIoU_binary std: {classIoU_std_binary}')
_log.info(f'meanIoU_binary mean: {meanIoU_binary}')
_log.info(f'meanIoU_binary std: {meanIoU_std_binary}')
_log.info("## ------------------------------------------ ##")
_log.info(f'###### Setting: {_run.observers[0].dir} ######')
_log.info("Running {num_run} runs, meanIoU:{miou:.4f}, meanIoU_binary:{mbiou:.4f} "
"meanIoU_std:{miou_std:.4f}, meanIoU_binary_std:{mbiou_std:.4f}".format(
num_run=5, miou=meanIoU, mbiou=meanIoU_binary, miou_std=meanIoU_std,
mbiou_std=meanIoU_std_binary))
_log.info(f"Current setting is {_run.observers[0].dir}")
print("Running {num_run} runs, meanIoU:{miou:.4f}, meanIoU_binary:{mbiou:.4f} "
"meanIoU_std:{miou_std:.4f}, meanIoU_binary_std:{mbiou_std:.4f}".format(
num_run=5, miou=meanIoU, mbiou=meanIoU_binary, miou_std=meanIoU_std,
mbiou_std=meanIoU_std_binary))
print(f"Current setting is {_run.observers[0].dir}")
print(_config['ckpt_dir'])
print(logdir)