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train_eval.py
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import torch.cuda
import torch.optim as optim
import time
import xlwt
from datetime import datetime
from pathlib import Path
from tensorboardX import SummaryWriter
from src.dataset.data_loader import GMDataset, get_dataloader
from src.displacement_layer import Displacement
from src.loss_func import *
from src.evaluation_metric import matching_accuracy
from src.parallel import DataParallel
from src.utils.model_sl import load_model, save_model
from eval import eval_model
from src.lap_solvers.hungarian import hungarian
from src.utils.data_to_cuda import data_to_cuda
from src.utils.config import cfg
from pygmtools.benchmark import Benchmark
def train_eval_model(model,
criterion,
optimizer,
optimizer_k,
image_dataset,
dataloader,
tfboard_writer,
benchmark,
num_epochs=25,
start_epoch=0,
xls_wb=None):
print('Start training...')
since = time.time()
dataset_size = len(dataloader['train'].dataset)
displacement = Displacement()
device = next(model.parameters()).device
print('model on device: {}'.format(device))
checkpoint_path = Path(cfg.OUTPUT_PATH) / 'params'
if not checkpoint_path.exists():
checkpoint_path.mkdir(parents=True)
model_path, optim_path, optim_k_path = '', '', ''
if start_epoch != 0:
model_path = str(checkpoint_path / 'params_{:04}.pt'.format(start_epoch))
optim_path = str(checkpoint_path / 'optim_{:04}.pt'.format(start_epoch))
if optimizer_k is not None:
optim_k_path = str(checkpoint_path / 'optim_k_{:04}.pt'.format(start_epoch))
if len(cfg.PRETRAINED_PATH) > 0:
model_path = cfg.PRETRAINED_PATH
if len(model_path) > 0:
print('Loading model parameters from {}'.format(model_path))
load_model(model, model_path, strict=False)
if len(optim_path) > 0:
print('Loading optimizer state from {}'.format(optim_path))
optimizer.load_state_dict(torch.load(optim_path))
if len(optim_k_path) > 0:
try:
print('Loading optimizer_k state from {}'.format(optim_k_path))
optimizer_k.load_state_dict(torch.load(optim_k_path))
except FileNotFoundError:
print('Creating new optimizer for AFA modules')
if optimizer_k is not None:
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=cfg.TRAIN.LR_STEP,
gamma=cfg.TRAIN.LR_DECAY,
last_epoch=-1) # cfg.TRAIN.START_EPOCH - 1
scheduler_k = optim.lr_scheduler.MultiStepLR(optimizer_k,
milestones=cfg.TRAIN.LR_STEP,
gamma=cfg.TRAIN.LR_DECAY,
last_epoch=-1) # cfg.TRAIN.START_EPOCH - 1
else:
scheduler = optim.lr_scheduler.MultiStepLR(optimizer,
milestones=cfg.TRAIN.LR_STEP,
gamma=cfg.TRAIN.LR_DECAY,
last_epoch=cfg.TRAIN.START_EPOCH - 1)
for epoch in range(start_epoch, num_epochs):
# Reset seed after evaluation per epoch
torch.manual_seed(cfg.RANDOM_SEED + epoch + 1)
dataloader['train'] = get_dataloader(image_dataset['train'], shuffle=True, fix_seed=False)
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
model.train() # Set model to training mode
model.module.trainings = True
print('lr = ' + ', '.join(['{:.2e}'.format(x['lr']) for x in optimizer.param_groups]))
if optimizer_k is not None:
print('K_regression_lr = ' + ', '.join(['{:.2e}'.format(x['lr']) for x in optimizer_k.param_groups]))
epoch_loss = 0.0
running_loss = 0.0
running_ks_loss = 0.0
running_ks_error = 0
running_since = time.time()
iter_num = 0
# Iterate over data.
for inputs in dataloader['train']:
if iter_num >= cfg.TRAIN.EPOCH_ITERS:
break
if model.module.device != torch.device('cpu'):
inputs = data_to_cuda(inputs)
iter_num = iter_num + 1
# zero the parameter gradients
optimizer.zero_grad()
if optimizer_k is not None:
optimizer_k.zero_grad()
with torch.set_grad_enabled(True):
# torch.autograd.set_detect_anomaly(True)
# forward
if 'common' in cfg.MODEL_NAME: # COMMON use the iter number to control the warmup temperature
outputs = model(inputs, training=True, iter_num=iter_num, epoch=epoch)
else:
outputs = model(inputs)
if cfg.PROBLEM.TYPE == '2GM':
assert 'ds_mat' in outputs
assert 'perm_mat' in outputs
assert 'gt_perm_mat' in outputs
# compute loss
if cfg.TRAIN.LOSS_FUNC == 'offset':
d_gt, grad_mask = displacement(outputs['gt_perm_mat'], *outputs['Ps'], outputs['ns'][0])
d_pred, _ = displacement(outputs['ds_mat'], *outputs['Ps'], outputs['ns'][0])
loss = criterion(d_pred, d_gt, grad_mask)
elif cfg.TRAIN.LOSS_FUNC in ['perm', 'ce', 'hung', 'ilp']:
loss = criterion(outputs['ds_mat'], outputs['gt_perm_mat'], *outputs['ns'])
elif cfg.TRAIN.LOSS_FUNC == 'hamming':
loss = criterion(outputs['perm_mat'], outputs['gt_perm_mat'])
elif cfg.TRAIN.LOSS_FUNC == 'custom':
loss = torch.sum(outputs['loss'])
else:
raise ValueError(
'Unsupported loss function {} for problem type {}'.format(cfg.TRAIN.LOSS_FUNC,
cfg.PROBLEM.TYPE))
if 'ks_loss' in outputs:
ks_loss = outputs['ks_loss']
ks_error = outputs['ks_error']
# compute accuracy
acc = matching_accuracy(outputs['perm_mat'], outputs['gt_perm_mat'], outputs['ns'], idx=0)
elif cfg.PROBLEM.TYPE in ['MGM', 'MGM3']:
assert 'ds_mat_list' in outputs
assert 'graph_indices' in outputs
assert 'perm_mat_list' in outputs
if not 'gt_perm_mat_list' in outputs:
assert 'gt_perm_mat' in outputs
gt_perm_mat_list = [outputs['gt_perm_mat'][idx] for idx in outputs['graph_indices']]
else:
gt_perm_mat_list = outputs['gt_perm_mat_list']
# compute loss & accuracy
if cfg.TRAIN.LOSS_FUNC in ['perm', 'ce' 'hung']:
loss = torch.zeros(1, device=model.module.device)
ns = outputs['ns']
for s_pred, x_gt, (idx_src, idx_tgt) in \
zip(outputs['ds_mat_list'], gt_perm_mat_list, outputs['graph_indices']):
l = criterion(s_pred, x_gt, ns[idx_src], ns[idx_tgt])
loss += l
loss /= len(outputs['ds_mat_list'])
elif cfg.TRAIN.LOSS_FUNC == 'plain':
loss = torch.sum(outputs['loss'])
else:
raise ValueError(
'Unsupported loss function {} for problem type {}'.format(cfg.TRAIN.LOSS_FUNC,
cfg.PROBLEM.TYPE))
# compute accuracy
acc = torch.zeros(1, device=model.module.device)
for x_pred, x_gt, (idx_src, idx_tgt) in \
zip(outputs['perm_mat_list'], gt_perm_mat_list, outputs['graph_indices']):
a = matching_accuracy(x_pred, x_gt, ns, idx=idx_src)
acc += torch.sum(a)
acc /= len(outputs['perm_mat_list'])
else:
raise ValueError('Unknown problem type {}'.format(cfg.PROBLEM.TYPE))
# backward + optimize
if cfg.FP16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
# with torch.autograd.detect_anomaly():
loss.backward()
if optimizer_k is not None:
ks_loss.backward()
# for n, p in model.named_parameters():
# if p.grad is not None and torch.any(torch.isnan(p.grad)):
# print('NaN!!!')
# print('name:', n, '-->require_grad:', p.requires_grad)
optimizer.step()
if optimizer_k is not None:
optimizer_k.step()
batch_num = inputs['batch_size']
# tfboard writer
loss_dict = dict()
loss_dict['loss'] = loss.item()
tfboard_writer.add_scalars('loss', loss_dict, epoch * cfg.TRAIN.EPOCH_ITERS + iter_num)
accdict = dict()
accdict['matching accuracy'] = torch.mean(acc)
tfboard_writer.add_scalars(
'training accuracy',
accdict,
epoch * cfg.TRAIN.EPOCH_ITERS + iter_num
)
# statistics
running_loss += loss.item() * batch_num
epoch_loss += loss.item() * batch_num
if 'ks_loss' in outputs:
running_ks_loss += ks_loss * batch_num
running_ks_error += ks_error * batch_num
if iter_num % cfg.STATISTIC_STEP == 0:
running_speed = cfg.STATISTIC_STEP * batch_num / (time.time() - running_since)
print('Epoch {:<4} Iteration {:<4} {:>4.2f}sample/s Loss={:<8.4f} Ks_Loss={:<8.4f} Ks_Error={:<8.4f}'
.format(epoch, iter_num, running_speed, running_loss / cfg.STATISTIC_STEP / batch_num, running_ks_loss / cfg.STATISTIC_STEP / batch_num, running_ks_error / cfg.STATISTIC_STEP / batch_num))
tfboard_writer.add_scalars(
'speed',
{'speed': running_speed},
epoch * cfg.TRAIN.EPOCH_ITERS + iter_num
)
tfboard_writer.add_scalars(
'learning rate',
{'lr_{}'.format(i): x['lr'] for i, x in enumerate(optimizer.param_groups)},
epoch * cfg.TRAIN.EPOCH_ITERS + iter_num
)
running_loss = 0.0
running_ks_loss = 0.0
running_ks_error = 0.0
running_since = time.time()
epoch_loss = epoch_loss / cfg.TRAIN.EPOCH_ITERS / batch_num
save_model(model, str(checkpoint_path / 'params_{:04}.pt'.format(epoch + 1)))
torch.save(optimizer.state_dict(), str(checkpoint_path / 'optim_{:04}.pt'.format(epoch + 1)))
if optimizer_k is not None:
torch.save(optimizer_k.state_dict(), str(checkpoint_path / 'optim_k_{:04}.pt'.format(epoch + 1)))
print('Epoch {:<4} Loss: {:.4f}'.format(epoch, epoch_loss))
print()
# Eval in each epoch
if dataloader['test'].dataset.cls not in ['none', 'all', None]:
clss = [dataloader['test'].dataset.cls]
else:
clss = dataloader['test'].dataset.bm.classes
l_e = (epoch == (num_epochs - 1))
accs = eval_model(model, clss, benchmark['test'], l_e,
xls_sheet=xls_wb.add_sheet('epoch{}'.format(epoch + 1)))
acc_dict = {"{}".format(cls): single_acc for cls, single_acc in zip(dataloader['test'].dataset.classes, accs)}
acc_dict['average'] = torch.mean(accs)
tfboard_writer.add_scalars(
'Eval acc',
acc_dict,
(epoch + 1) * cfg.TRAIN.EPOCH_ITERS
)
wb.save(wb.__save_path)
scheduler.step()
if optimizer_k is not None:
scheduler_k.step()
time_elapsed = time.time() - since
print('Training complete in {:.0f}h {:.0f}m {:.0f}s'
.format(time_elapsed // 3600, (time_elapsed // 60) % 60, time_elapsed % 60))
return model
if __name__ == '__main__':
from src.utils.dup_stdout_manager import DupStdoutFileManager
from src.utils.parse_args import parse_args
from src.utils.print_easydict import print_easydict
args = parse_args('Deep learning of graph matching training & evaluation code.')
import importlib
mod = importlib.import_module(cfg.MODULE)
Net = mod.Net
torch.manual_seed(cfg.RANDOM_SEED)
dataset_len = {'train': cfg.TRAIN.EPOCH_ITERS * cfg.BATCH_SIZE, 'test': cfg.EVAL.SAMPLES}
ds_dict = cfg[cfg.DATASET_FULL_NAME] if ('DATASET_FULL_NAME' in cfg) and (cfg.DATASET_FULL_NAME in cfg) else {}
benchmark = {
x: Benchmark(name=cfg.DATASET_FULL_NAME,
sets=x,
problem=cfg.PROBLEM.TYPE,
obj_resize=cfg.PROBLEM.RESCALE,
filter=cfg.PROBLEM.FILTER,
**ds_dict)
for x in ('train', 'test')}
image_dataset = {
x: GMDataset(cfg.DATASET_FULL_NAME,
benchmark[x],
dataset_len[x],
cfg.PROBLEM.TRAIN_ALL_GRAPHS if x == 'train' else cfg.PROBLEM.TEST_ALL_GRAPHS,
cfg.TRAIN.CLASS if x == 'train' else cfg.EVAL.CLASS,
cfg.PROBLEM.TYPE)
for x in ('train', 'test')}
dataloader = {x: get_dataloader(image_dataset[x], shuffle=True, fix_seed=(x == 'test'))
for x in ('train', 'test')}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = Net()
model = model.to(device)
if cfg.TRAIN.LOSS_FUNC.lower() == 'offset':
criterion = OffsetLoss(norm=cfg.TRAIN.RLOSS_NORM)
elif cfg.TRAIN.LOSS_FUNC.lower() == 'perm':
criterion = PermutationLoss()
elif cfg.TRAIN.LOSS_FUNC.lower() == 'ce':
criterion = CrossEntropyLoss()
elif cfg.TRAIN.LOSS_FUNC.lower() == 'focal':
criterion = FocalLoss(alpha=.5, gamma=0.)
elif cfg.TRAIN.LOSS_FUNC.lower() == 'hung':
criterion = PermutationLossHung()
elif cfg.TRAIN.LOSS_FUNC.lower() == 'hamming':
criterion = HammingLoss()
elif cfg.TRAIN.LOSS_FUNC.lower() == 'ilp':
criterion = ILP_attention_loss()
elif cfg.TRAIN.LOSS_FUNC.lower() == 'custom':
criterion = None
print('NOTE: You are setting the loss function as \'custom\', please ensure that there is a tensor with key '
'\'loss\' in your model\'s returned dictionary.')
else:
raise ValueError('Unknown loss function {}'.format(cfg.TRAIN.LOSS_FUNC))
optimizer_k = None
if cfg.TRAIN.SEPARATE_BACKBONE_LR:
if not cfg.TRAIN.SEPARATE_K_LR:
backbone_ids = [id(item) for item in model.backbone_params]
other_params = [param for param in model.parameters() if id(param) not in backbone_ids]
model_params = [
{'params': other_params},
{'params': model.backbone_params, 'lr': cfg.TRAIN.BACKBONE_LR}
]
else:
backbone_ids = [id(item) for item in model.backbone_params]
k_params = model.k_params_id
other_params = [param for param in model.parameters() if id(param) not in k_params and id(param) not in backbone_ids]
model_params = [
{'params': other_params},
{'params': model.backbone_params, 'lr': cfg.TRAIN.BACKBONE_LR}
]
k_reg_params = model.k_params
optimizer_k = optim.Adam(k_reg_params, lr=cfg.TRAIN.K_LR)
else:
model_params = model.parameters()
if cfg.TRAIN.OPTIMIZER.lower() == 'sgd':
optimizer = optim.SGD(model_params, lr=cfg.TRAIN.LR, momentum=cfg.TRAIN.MOMENTUM, nesterov=True)
elif cfg.TRAIN.OPTIMIZER.lower() == 'adam':
optimizer = optim.Adam(model_params, lr=cfg.TRAIN.LR)
else:
raise ValueError('Unknown optimizer {}'.format(cfg.TRAIN.OPTIMIZER))
if cfg.FP16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to enable FP16.")
model, optimizer = amp.initialize(model, optimizer)
model = DataParallel(model, device_ids=cfg.GPUS)
if not Path(cfg.OUTPUT_PATH).exists():
Path(cfg.OUTPUT_PATH).mkdir(parents=True)
now_time = datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
tfboardwriter = SummaryWriter(logdir=str(Path(cfg.OUTPUT_PATH) / 'tensorboard' / 'training_{}'.format(now_time)))
wb = xlwt.Workbook()
wb.__save_path = str(Path(cfg.OUTPUT_PATH) / ('train_eval_result_' + now_time + '.xls'))
with DupStdoutFileManager(str(Path(cfg.OUTPUT_PATH) / ('train_log_' + now_time + '.log'))) as _:
print_easydict(cfg)
model = train_eval_model(model, criterion, optimizer, optimizer_k, image_dataset, dataloader, tfboardwriter, benchmark,
num_epochs=cfg.TRAIN.NUM_EPOCHS,
start_epoch=cfg.TRAIN.START_EPOCH,
xls_wb=wb)
wb.save(wb.__save_path)