def main():
'''
Main Function
'''
#Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
writer = prep_experiment(args, parser)
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = loss.get_loss(args)
net = network.get_net(args, criterion)
optim, scheduler = optimizer.get_optimizer(args, net)
torch.cuda.empty_cache()
if args.evaluate:
# Early evaluation for benchmarking
validate(val_loader, net, criterion_val, optim, epoch, writer)
evaluate(val_loader, net)
return
#Main Loop
for epoch in range(args.start_epoch, args.max_epoch):
# Update EPOCH CTR
cfg.immutable(False)
cfg.EPOCH = epoch
cfg.immutable(True)
scheduler.step()
train(train_loader, net, criterion, optim, epoch, writer)
validate(val_loader, net, criterion_val, optim, epoch, writer)
def main():
"""
Main Function
"""
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args, parser)
writer = None
_, _, _, extra_val_loaders, _ = datasets.setup_loaders(args)
criterion, criterion_val = loss.get_loss(args)
criterion_aux = loss.get_loss_aux(args)
net = network.get_net(args, criterion, criterion_aux)
optim, scheduler = optimizer.get_optimizer(args, net)
net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net)
net = network.warp_network_in_dataparallel(net, args.local_rank)
epoch = 0
i = 0
if args.snapshot:
epoch, mean_iu = optimizer.load_weights(net, optim, scheduler,
args.snapshot, args.restore_optimizer)
print("#### iteration", i)
torch.cuda.empty_cache()
# Main Loop
# for epoch in range(args.start_epoch, args.max_epoch):
for dataset, val_loader in extra_val_loaders.items():
print("Extra validating... This won't save pth file")
validate(val_loader, dataset, net, criterion_val, optim, scheduler, epoch, writer, i, save_pth=False)
def setup_net(snapshot):
"""Quickly create a network for the given snapshot.
Arguments:
snapshot {string} -- Input snapshot, IE. kitti_best.pth
Returns:
[net, transform] -- PyTorch model & the image transform function.
"""
cudnn.benchmark = False
torch.cuda.empty_cache()
args = Args('./save', 'network.deepv3.DeepWV3Plus', snapshot)
assert_and_infer_cfg(args, train_mode=False)
# get net
net = network.get_net(args, criterion=None)
net = torch.nn.DataParallel(net).cuda()
print('Net built.')
net, _ = restore_snapshot(net,
optimizer=None,
snapshot=snapshot,
restore_optimizer_bool=False)
net.eval()
print('Net restored.')
# get data
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
img_transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(*mean_std)])
return net, img_transform, args
def main(_):
parser = argparse.ArgumentParser(
description='Classification model training')
parser.add_argument('--config_file', type=str, default=None,
help='Optional config file for params')
parser.add_argument('opts', help='see config.py for all options',
default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.config_file is not None:
cfg_from_file(args.config_file)
if args.opts is not None:
cfg_from_list(args.opts)
assert_and_infer_cfg()
print_cfg()
os.environ["CUDA_VISIBLE_DEVICES"] = str(cfg.GPU_ID)
logger = utils.setup_custom_logger('root')
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
tf_config = tf.ConfigProto(device_count=dict(
GPU=1), gpu_options=tf.GPUOptions(allow_growth=True))
tf.enable_resource_variables()
train(tf_config, logger)
test(tf_config, logger)
def main():
"""
Main Function
"""
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
writer = prep_experiment(args, parser)
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = loss.get_loss(args)
net = network.get_net(args, criterion)
optim, scheduler = optimizer.get_optimizer(args, net)
if args.fix_bn:
net.apply(set_bn_eval)
print("Fix bn for finetuning")
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level="O1")
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.snapshot:
optimizer.load_weights(net, optim,
args.snapshot, args.restore_optimizer)
if args.evaluateF:
assert args.snapshot is not None, "must load weights for evaluation"
evaluate(val_loader, net, args)
return
torch.cuda.empty_cache()
# Main Loop
for epoch in range(args.start_epoch, args.max_epoch):
# Update EPOCH CTR
cfg.immutable(False)
cfg.EPOCH = epoch
cfg.immutable(True)
scheduler.step()
train(train_loader, net, optim, epoch, writer)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
validate(val_loader, net, criterion_val,
optim, epoch, writer)
if args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.build_epoch(cut=True)
if args.apex:
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
def get_segmentation(self):
# Get Segmentation Net
assert_and_infer_cfg(self.opt, train_mode=False)
self.opt.dataset_cls = cityscapes
net = network.get_net(self.opt, criterion=None)
net = torch.nn.DataParallel(net).cuda()
print('Segmentation Net Built.')
snapshot = os.path.join(os.getcwd(), os.path.dirname(__file__),
self.opt.snapshot)
self.seg_net, _ = restore_snapshot(net,
optimizer=None,
snapshot=snapshot,
restore_optimizer_bool=False)
self.seg_net.eval()
print('Segmentation Net Restored.')
def main():
"""
Main Function
"""
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
writer = prep_experiment(args, parser)
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = loss.get_loss(args)
net = network.get_net(args, criterion)
optim, scheduler = optimizer.get_optimizer(args, net)
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level="O1")
net = network.warp_network_in_dataparallel(net, args.apex)
if args.snapshot:
optimizer.load_weights(net, optim, args.snapshot,
args.restore_optimizer)
torch.cuda.empty_cache()
# Main Loop
for epoch in range(args.start_epoch, args.max_epoch):
# Update EPOCH CTR
cfg.immutable(False)
cfg.EPOCH = epoch
cfg.immutable(True)
#snapshot="/srv/beegfs02/scratch/language_vision/data/Sound_Event_Prediction/audio/semanticPred/logs/ckpt/default/Omni-network.deepv3_audioBG_Spec_diffmask_Comp_noBG_Paralleltask_depth_noSeman.DeepWV3Plus/models_depth/SOP_epoch_"+str(14)+".pth"
#optimizer.load_weights(net, optim,
# snapshot, args.restore_optimizer)
scheduler.step()
train(train_loader, net, optim, epoch, writer)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
validate(val_loader, net, criterion_val, optim, epoch, writer)
if args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.build_epoch(cut=True)
if args.apex:
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
def main():
"""
Main Function
"""
if AutoResume:
AutoResume.init()
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True,
hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
train_loader, val_loader, train_obj = \
datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
auto_resume_details = None
if AutoResume:
auto_resume_details = AutoResume.get_resume_details()
if auto_resume_details:
checkpoint_fn = auto_resume_details.get("RESUME_FILE", None)
checkpoint = torch.load(checkpoint_fn,
map_location=torch.device('cpu'))
args.result_dir = auto_resume_details.get("TENSORBOARD_DIR", None)
args.start_epoch = int(auto_resume_details.get("EPOCH", None)) + 1
args.restore_net = True
args.restore_optimizer = True
msg = ("Found details of a requested auto-resume: checkpoint={}"
" tensorboard={} at epoch {}")
logx.msg(msg.format(checkpoint_fn, args.result_dir, args.start_epoch))
elif args.resume:
checkpoint = torch.load(args.resume, map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
msg = "Resuming from: checkpoint={}, epoch {}, arch {}"
logx.msg(msg.format(args.resume, args.start_epoch, args.arch))
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH',
cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
msg = "Loading weights from: checkpoint={}".format(args.snapshot)
logx.msg(msg)
#define the NASA optimizer parameter
iter_tot = len(train_loader) * args.max_epoch
# tau = args.tau_factor/sqrt(iter_tot)
tau = 1
net = network.get_net(args, criterion)
k = 1
# optim, scheduler = get_optimizer(args, net)
optim, scheduler = get_optimizer(args, net, tau, k)
# Visualize feature maps
#activation = {}
#def get_activation(name):
#def hook(model, input, output):
#activation[name] = output.detach()
#return hook
#net.layer[0].register_forward_hook(get_activation('conv1'))
#data, _ = dataset[0]
#data.unsqueeze_(0)
#output = model(data)
#act = activation['conv1'].squeeze()
#fig, axarr = plt.subplots(act.size(0))
#for idx in range(act.size(0)):
#axarr[idx].imshow(act[idx])
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level=args.amp_opt_level)
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.summary:
from thop import profile
img = torch.randn(1, 3, 640, 640).cuda()
mask = torch.randn(1, 1, 640, 640).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
if args.restore_net:
restore_net(net, checkpoint)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
scheduler.step(args.start_epoch)
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
if args.eval == 'test':
validate(val_loader,
net,
criterion=None,
optim=None,
epoch=0,
calc_metrics=False,
dump_assets=args.dump_assets,
dump_all_images=True,
testing=True,
grid=city)
return 0
if args.eval == 'val':
if args.dump_topn:
validate_topn(val_loader, net, criterion_val, optim, 0, args)
else:
validate(val_loader,
net,
criterion=criterion_val,
optim=optim,
epoch=0,
dump_assets=args.dump_assets,
dump_all_images=args.dump_all_images,
calc_metrics=not args.no_metrics)
return 0
elif args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
validate(val_loader,
net,
criterion=criterion_val,
optim=optim,
epoch=0,
calc_metrics=False,
dump_assets=args.dump_assets,
dump_all_images=True)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
for epoch in range(args.start_epoch, args.max_epoch):
update_epoch(epoch)
if args.only_coarse:
train_obj.only_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
else:
pass
train(train_loader, net, optim, epoch)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
if epoch % args.val_freq == 0:
validate(val_loader, net, criterion_val, optim, epoch)
scheduler.step()
if check_termination(epoch):
return 0
def main():
"""
Main Function
"""
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
# args2 = copy.deepcopy(args)
assert_and_infer_cfg(args)
# assert_and_infer_cfg(args2)
# args2.dataset = 'kitti_trav'
# print(args.dataset)
# print(args2.dataset)
writer = prep_experiment(args, parser)
# writer = prep_experiment(args2, parser)
# Dataset
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
# train_loader2, val_loader2, train_obj2 = datasets.setup_loaders(args2)
criterion, criterion_val = loss.get_loss(args, data_type='semantic')
criterion2, criterion_val2 = loss.get_loss(args, data_type='trav')
net = network.get_net(args, criterion, criterion2)
#parameters list
# param1_lists = list(net.mod1.parameters()) + list(net.mod2.parameters()) + list(net.mod3.parameters()) + list(net.mod4.parameters()) + list(net.mod5.parameters()) + list(net.mod6.parameters()) + list(net.mod7.parameters()) + list(net.pool2.parameters()) + list(net.pool3.parameters()) + list(net.aspp.parameters()) + list(net.bot_fine.parameters()) + list(net.bot_aspp.parameters()) + list(net.final.parameters()) + [log_sigma_A]
# param2_lists = list(net.mod1.parameters()) + list(net.mod2.parameters()) + list(net.mod3.parameters()) + list(net.mod4.parameters()) + list(net.mod5.parameters()) + list(net.mod6.parameters()) + list(net.mod7.parameters()) + list(net.pool2.parameters()) + list(net.pool3.parameters()) + list(net.aspp.parameters()) + list(net.bot_fine.parameters()) + list(net.bot_aspp.parameters()) + list(net.final2.parameters()) + [log_sigma_B]
#optimizers
optim, scheduler = optimizer.get_optimizer(args, net)
# optim2, scheduler2 = optimizer.get_optimizer(args, param2_lists)
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level="O1")
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.snapshot:
optimizer.load_weights(net, optim, args.snapshot, args.snapshot2,
args.restore_optimizer)
# optimizer.load_weights(net, optim2,
# args.snapshot, args.snapshot2, args.restore_optimizer)
torch.cuda.empty_cache()
# Main Loop
for epoch in range(args.start_epoch, args.max_epoch):
# Update EPOCH CTR
cfg.immutable(False)
cfg.EPOCH = epoch
cfg.immutable(True)
scheduler.step()
train(train_loader, net, optim, epoch, writer)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
# train_loader2.sampler.set_epoch(epoch + 1)
validate(val_loader, net, criterion_val, criterion_val2, optim, epoch,
writer)
if args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.build_epoch(cut=True)
# train_obj2.build_epoch(cut=True)
if args.apex:
train_loader.sampler.set_num_samples()
# train_loader2.sampler.set_num_samples()
else:
train_obj.build_epoch()
def main():
'''
Main Function
'''
#Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
writer = prep_experiment(args,parser)
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = loss.get_loss(args)
net = network.get_net(args, criterion)
optim, scheduler = optimizer.get_optimizer(args, net)
torch.cuda.empty_cache()
if args.mode=="test":
test_sv_path = args.test_sv_path
print(f"Saving prediction {test_sv_path}")
net.eval()
for vi, data in enumerate(tqdm(val_loader)):
input, mask, img_name, img_path = data
assert len(input.size()) == 4 and len(mask.size()) == 3
assert input.size()[2:] == mask.size()[1:]
b, h, w = mask.size()
batch_pixel_size = input.size(0) * input.size(2) * input.size(3)
input, mask_cuda = input.cuda(), mask.cuda()
with torch.no_grad():
seg_out, edge_out = net(input) # output = (1, 19, 713, 713)
seg_predictions = seg_out.data.cpu().numpy()
edge_predictions = edge_out.cpu().numpy()
for i in range(b):
_,file_name = os.path.split(img_path[i])
file_name = file_name.replace("jpg","png")
seq = img_path[i][:5]
seg_path = os.path.join(test_sv_path,"gscnn","seg",seq)
if not os.path.exists(seg_path):
os.makedirs(seg_path)
edge_path = os.path.join(test_sv_path,"gscnn","edge",seq)
edgenp_path = os.path.join(test_sv_path,"gscnn","edgenp",seq)
if not os.path.exists(edge_path):
os.makedirs(edge_path)
os.makedirs(edgenp_path)
seg_arg = np.argmax(seg_predictions[i],axis=0).astype(np.uint8)
edge_arg = np.argmax(edge_predictions[i],axis=0).astype(np.uint8)
seg_img = np.stack((seg_arg,seg_arg,seg_arg),axis=2)
edge_img = np.stack((edge_arg,edge_arg,edge_arg),axis=2)
seg_img = Image.fromarray(seg_img)
seg_img.save(os.path.join(seg_path,file_name))
edge_img = Image.fromarray(edge_img)
edge_img.save(os.path.join(edge_path,file_name))
np.save(os.path.join(edge_path,file_name.replace("png","npy")),edge_predictions[i])
return
if args.evaluate:
# Early evaluation for benchmarking
default_eval_epoch = 1
validate(val_loader, net, criterion_val,
optim, default_eval_epoch, writer)
evaluate(val_loader, net)
return
#Main Loop
for epoch in range(args.start_epoch, args.max_epoch):
# Update EPOCH CTR
cfg.immutable(False)
cfg.EPOCH = epoch
cfg.immutable(True)
scheduler.step()
train(train_loader, net, criterion, optim, epoch, writer)
validate(val_loader, net, criterion_val,
optim, epoch, writer)
def main():
"""
Main Function
"""
if AutoResume:
AutoResume.init()
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True, hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
train_loader, val_loader, train_obj = \
datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
auto_resume_details = None
if AutoResume:
auto_resume_details = AutoResume.get_resume_details()
if auto_resume_details:
checkpoint_fn = auto_resume_details.get("RESUME_FILE", None)
checkpoint = torch.load(checkpoint_fn,
map_location=torch.device('cpu'))
args.result_dir = auto_resume_details.get("TENSORBOARD_DIR", None)
args.start_epoch = int(auto_resume_details.get("EPOCH", None)) + 1
args.restore_net = True
args.restore_optimizer = True
msg = ("Found details of a requested auto-resume: checkpoint={}"
" tensorboard={} at epoch {}")
logx.msg(msg.format(checkpoint_fn, args.result_dir,
args.start_epoch))
elif args.resume:
checkpoint = torch.load(args.resume,
map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
msg = "Resuming from: checkpoint={}, epoch {}, arch {}"
logx.msg(msg.format(args.resume, args.start_epoch, args.arch))
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH', cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
msg = "Loading weights from: checkpoint={}".format(args.snapshot)
logx.msg(msg)
net = network.get_net(args, criterion)
optim, scheduler = get_optimizer(args, net)
if args.fp16:
net, optim = amp.initialize(net, optim, opt_level=args.amp_opt_level)
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.summary:
print(str(net))
from pytorchOpCounter.thop import profile
img = torch.randn(1, 3, 1024, 2048).cuda()
mask = torch.randn(1, 1, 1024, 2048).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
if args.restore_net:
restore_net(net, checkpoint)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
scheduler.step(args.start_epoch)
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
if args.eval == 'val':
if args.dump_topn:
validate_topn(val_loader, net, criterion_val, optim, 0, args)
else:
validate(val_loader, net, criterion=criterion_val, optim=optim, epoch=0,
dump_assets=args.dump_assets,
dump_all_images=args.dump_all_images,
calc_metrics=not args.no_metrics)
return 0
elif args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
validate(val_loader, net, criterion=None, optim=None, epoch=0,
calc_metrics=False, dump_assets=args.dump_assets,
dump_all_images=True)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
for epoch in range(args.start_epoch, args.max_epoch):
update_epoch(epoch)
if args.only_coarse:
train_obj.only_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
if args.apex:
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
else:
pass
train(train_loader, net, optim, epoch)
if args.apex:
train_loader.sampler.set_epoch(epoch + 1)
if epoch % args.val_freq == 0:
validate(val_loader, net, criterion_val, optim, epoch)
scheduler.step()
if check_termination(epoch):
return 0
parser.add_argument('--cv_split', type=int, default=None)
parser.add_argument('--mode', type=str, default='fine')
parser.add_argument('--split_index', type=int, default=0)
parser.add_argument('--split_count', type=int, default=1)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--resume',
action='store_true',
default=False,
help='Resume Inference')
parser.add_argument('--batch_size',
type=int,
default=1,
help='Only in pooling mode')
args = parser.parse_args()
assert_and_infer_cfg(
args, train_mode=False) #把args传递进去之后就会进行参数的推断，这里设置了数据集相关信息以及BN类型等
#assert执行后会引入一个全局变量cfg他在config文件中的名称是 __C
args.apex = False # No support for apex eval
cudnn.benchmark = False
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
date_str = str(datetime.now().strftime('%Y_%m_%d_%H_%M_%S'))
def sliding_window_cropping(data, scale=1.0):
"""
Sliding Window Cropping
Take the image and create a mapping and multiple crops
"""
sliding_window_cropping = None
mapping = {}
crop_ctr = 0
def main():
"""
Main Function
"""
if AutoResume:
AutoResume.init()
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=False,
hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
auto_resume_details = None
if AutoResume:
auto_resume_details = AutoResume.get_resume_details()
if auto_resume_details:
checkpoint_fn = auto_resume_details.get("RESUME_FILE", None)
checkpoint = torch.load(checkpoint_fn,
map_location=torch.device('cpu'))
args.result_dir = auto_resume_details.get("TENSORBOARD_DIR", None)
args.start_epoch = int(auto_resume_details.get("EPOCH", None)) + 1
args.restore_net = True
args.restore_optimizer = True
msg = ("Found details of a requested auto-resume: checkpoint={}"
" tensorboard={} at epoch {}")
logx.msg(msg.format(checkpoint_fn, args.result_dir, args.start_epoch))
elif args.resume:
checkpoint = torch.load(args.resume, map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
msg = "Resuming from: checkpoint={}, epoch {}, arch {}"
logx.msg(msg.format(args.resume, args.start_epoch, args.arch))
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH',
cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
msg = "Loading weights from: checkpoint={}".format(args.snapshot)
logx.msg(msg)
net = network.get_net(args, criterion)
optim, scheduler = get_optimizer(args, net)
net = network.wrap_network_in_dataparallel(net, args.apex)
if args.restore_optimizer:
restore_opt(optim, checkpoint)
if args.restore_net:
restore_net(net, checkpoint)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
scheduler.step(args.start_epoch)
if args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
# validate(val_loader, net, criterion=None, optim=None, epoch=0,
# calc_metrics=False, dump_assets=args.dump_assets,
# dump_all_images=True)
if not os.path.exists(args.result_dir + 'image_2/'):
os.mkdir(args.result_dir + 'image_2/')
if not os.path.exists(args.result_dir + 'image_3/'):
os.mkdir(args.result_dir + 'image_3/')
num_image = 7481
for idx in tqdm(range(num_image)):
sample_idx = "%06d" % idx
eval_minibatch(sample_idx, "image_2/", net, args)
eval_minibatch(sample_idx, "image_3/", net, args)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
'fwavacc': 0
}
#Enable CUDNN Benchmarking optimization
torch.backends.cudnn.benchmark = True
args.world_size = 1
#Test Mode run two epochs with a few iterations of training and val
if args.test_mode:
args.max_epoch = 2
if 'WORLD_SIZE' in os.environ:
args.world_size = int(os.environ['WORLD_SIZE'])
print("Total world size: ", int(os.environ['WORLD_SIZE']))
#Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
writer = prep_experiment(args, parser)
default_eval_epoch = 1
def main(eval_args=None):
'''
Main Function
'''
# Parse arguments from rest_communication.py
#args = parser.parse_args(eval_args)
if args.snapshot == None:
args.snapshot = "checkpoints/best_cityscapes_checkpoint.pth"
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
from options.test_options import TestOptions
import sys
sys.path.insert(0, './image_segmentation')
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
TestOptions = TestOptions()
opt = TestOptions.parse()
assert_and_infer_cfg(opt, train_mode=False)
cudnn.benchmark = False
torch.cuda.empty_cache()
# Get segmentation Net
opt.dataset_cls = cityscapes
net = network.get_net(opt, criterion=None)
net = torch.nn.DataParallel(net).cuda()
print('Segmentation Net built.')
net, _ = restore_snapshot(net,
optimizer=None,
snapshot=opt.snapshot,
restore_optimizer_bool=False)
net.eval()
print('Segmentation Net Restored.')
def main():
"""
Main Function
"""
rank = args.rank
cfg.GLOBAL_RANK = rank
args.gpus = torch.cuda.device_count()
device = torch.device("cpu")
loc_dist = True if args.gpus > 1 else False
loc_rank = rank % args.gpus
args.gpu = loc_rank
args.local_rank = loc_rank
if loc_dist:
device = "cuda:" + str(loc_rank)
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "19500"
os.environ["NCCL_SOCKET_IFNAME"] = "ib"
torch.cuda.set_device(device)
torch.distributed.init_process_group(backend="nccl",
rank=loc_rank,
world_size=args.gpus)
# torch.cuda.set_device(device)
elif args.gpus == 1:
args.gpus = torch.cuda.device_count()
device = "cuda:0"
args.local_rank = 0
torch.cuda.set_device(device)
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True,
hparams=vars(args),
global_rank=args.global_rank)
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
# args.ngpu = torch.cuda.device_count()
# args.best_record = {'mean_iu': -1, 'epoch': 0}
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
cwd = os.getcwd()
sz = ht.MPI_WORLD.size
filename = cwd + "/citys-heat-checkpoint-" + str(sz) + ".pth.tar"
if args.resume and os.path.isfile(filename):
checkpoint = torch.load(filename, map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
logx.msg(f"Resuming from: checkpoint={args.resume}, "
f"epoch {args.start_epoch}, arch {args.arch}")
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH',
cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
logx.msg(f"Loading weights from: checkpoint={args.snapshot}")
net = network.get_net(args, criterion)
net = net.to(device)
# args.lr = (1. / args.world_size * (5 * (args.world_size - 1) / 6.)) * 0.0125 * args.world_size
optim, scheduler = get_optimizer(args, net)
# the scheduler in this code is only run at the end of each epoch
# todo: make heat an option not this whole file
# if args.heat:
dp_optim = ht.optim.DASO(
local_optimizer=optim,
total_epochs=args.max_epoch,
max_global_skips=4,
)
#if args.no_cycling:
dp_optim.disable_cycling(global_skips=args.batch_skip,
batches_to_wait=args.gs)
# this is where the network is wrapped with DDDP (w/apex) or DP
htnet = ht.nn.DataParallelMultiGPU(net,
comm=ht.MPI_WORLD,
optimizer=dp_optim)
if args.summary:
print(str(net))
from thop import profile
img = torch.randn(1, 3, 1024, 2048).cuda()
mask = torch.randn(1, 1, 1024, 2048).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print0(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
dp_optim.stability.load_dict(checkpoint["skip_stable"])
if args.restore_net:
#restore_net(net, checkpoint)
htnet.load_state_dict(checkpoint["state_dict"])
#dp_optim.module.load_state_dist(checkpoint["state_dict"])
# htnet = ht.nn.DataParallelMultiGPU(net, ht.MPI_WORLD, dp_optim)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
if args.start_epoch != 0:
# TODO: need a loss value for the restart at a certain epoch...
scheduler.step(args.start_epoch)
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
# todo: HeAT fixes -- not urgent --
if args.eval == 'val':
if args.dump_topn:
validate_topn(val_loader, net, criterion_val, optim, 0, args)
else:
validate(val_loader,
net,
criterion=criterion_val,
optim=optim,
epoch=0,
dump_assets=args.dump_assets,
dump_all_images=args.dump_all_images,
calc_metrics=not args.no_metrics)
return 0
elif args.eval == 'folder':
# Using a folder for evaluation means to not calculate metrics
validate(val_loader,
net,
criterion=None,
optim=None,
epoch=0,
calc_metrics=False,
dump_assets=args.dump_assets,
dump_all_images=True)
return 0
elif args.eval is not None:
raise 'unknown eval option {}'.format(args.eval)
scaler = amp.GradScaler()
if dp_optim.comm.rank == 0:
print("scheduler", args.lr_schedule)
dp_optim.add_scaler(scaler)
nodes = str(int(dp_optim.comm.size / torch.cuda.device_count()))
cwd = os.getcwd()
fname = cwd + "/" + nodes + "-heat-citys-benchmark"
if args.resume and rank == 0 and os.path.isfile(fname + ".pkl"):
with open(fname + ".pkl", "rb") as f:
out_dict = pickle.load(f)
else:
out_dict = {
"epochs": [],
nodes + "-avg-batch-time": [],
nodes + "-total-train-time": [],
nodes + "-train-loss": [],
nodes + "-val-loss": [],
nodes + "-val-iou": [],
nodes + "-val-time": [],
}
print0("Output dict:", fname)
for epoch in range(args.start_epoch, args.max_epoch):
# todo: HeAT fixes -- possible conflict between processes
update_epoch(epoch)
if args.only_coarse: # default: false
train_obj.only_coarse()
train_obj.build_epoch()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
else:
train_obj.build_epoch()
else:
pass
ls, bt, btt = train(train_loader, htnet, dp_optim, epoch, scaler)
dp_optim.epoch_loss_logic(ls, loss_globally_averaged=True)
# if epoch % args.val_freq == 0:
vls, iu, vtt = validate(val_loader, htnet, criterion_val, dp_optim,
epoch)
if args.lr_schedule == "plateau":
if dp_optim.comm.rank == 0:
print("loss", ls, 'best:',
scheduler.best * (1. - scheduler.threshold),
scheduler.num_bad_epochs)
scheduler.step(ls) # val_loss)
else:
scheduler.step()
if args.rank == 0:
save_checkpoint({
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": htnet.state_dict(),
"optimizer": optim.state_dict(),
"skip_stable": dp_optim.stability.get_dict()
})
out_dict["epochs"].append(epoch)
out_dict[nodes + "-train-loss"].append(ls)
out_dict[nodes + "-avg-batch-time"].append(bt)
out_dict[nodes + "-total-train-time"].append(btt)
out_dict[nodes + "-val-loss"].append(vls)
out_dict[nodes + "-val-iou"].append(iu)
out_dict[nodes + "-val-time"].append(vtt)
if args.rank == 0:
save_obj(out_dict, fname)
if args.rank == 0:
print("\nRESULTS\n")
import pandas as pd
df = pd.DataFrame.from_dict(out_dict).set_index("epochs")
with pd.option_context("display.max_rows", None, "display.max_columns",
None):
# more options can be specified also
print(df)
if args.benchmarking:
try:
fulldf = pd.read_csv(cwd + "/heat-bench-results.csv")
fulldf = pd.concat([df, fulldf], axis=1)
except FileNotFoundError:
fulldf = df
fulldf.to_csv(cwd + "/heat-bench-results.csv")
def main():
"""
Main Function
"""
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
writer = prep_experiment(args, parser)
train_loader, val_loaders, train_obj, extra_val_loaders, covstat_val_loaders = datasets.setup_loaders(
args)
criterion, criterion_val = loss.get_loss(args)
criterion_aux = loss.get_loss_aux(args)
net = network.get_net(args, criterion, criterion_aux)
optim, scheduler = optimizer.get_optimizer(args, net)
net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net)
net = network.warp_network_in_dataparallel(net, args.local_rank)
epoch = 0
i = 0
if args.snapshot:
epoch, mean_iu = optimizer.load_weights(net, optim, scheduler,
args.snapshot,
args.restore_optimizer)
if args.restore_optimizer is True:
iter_per_epoch = len(train_loader)
i = iter_per_epoch * epoch
else:
epoch = 0
print("#### iteration", i)
torch.cuda.empty_cache()
# Main Loop
# for epoch in range(args.start_epoch, args.max_epoch):
while i < args.max_iter:
# Update EPOCH CTR
cfg.immutable(False)
cfg.ITER = i
cfg.immutable(True)
i = train(train_loader, net, optim, epoch, writer, scheduler,
args.max_iter)
train_loader.sampler.set_epoch(epoch + 1)
if (args.dynamic and args.use_isw and epoch % (args.cov_stat_epoch + 1) == args.cov_stat_epoch) \
or (args.dynamic is False and args.use_isw and epoch == args.cov_stat_epoch):
net.module.reset_mask_matrix()
for trial in range(args.trials):
for dataset, val_loader in covstat_val_loaders.items(
): # For get the statistics of covariance
validate_for_cov_stat(val_loader,
dataset,
net,
criterion_val,
optim,
scheduler,
epoch,
writer,
i,
save_pth=False)
net.module.set_mask_matrix()
if args.local_rank == 0:
print("Saving pth file...")
evaluate_eval(args,
net,
optim,
scheduler,
None,
None, [],
writer,
epoch,
"None",
None,
i,
save_pth=True)
if args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.build_epoch(cut=True)
train_loader.sampler.set_num_samples()
else:
train_obj.build_epoch()
epoch += 1
# Validation after epochs
if len(val_loaders) == 1:
# Run validation only one time - To save models
for dataset, val_loader in val_loaders.items():
validate(val_loader, dataset, net, criterion_val, optim, scheduler,
epoch, writer, i)
else:
if args.local_rank == 0:
print("Saving pth file...")
evaluate_eval(args,
net,
optim,
scheduler,
None,
None, [],
writer,
epoch,
"None",
None,
i,
save_pth=True)
for dataset, val_loader in extra_val_loaders.items():
print("Extra validating... This won't save pth file")
validate(val_loader,
dataset,
net,
criterion_val,
optim,
scheduler,
epoch,
writer,
i,
save_pth=False)
def main():
"""
Main Function
"""
rank = args.rank
cfg.GLOBAL_RANK = rank
args.gpus = torch.cuda.device_count()
device = torch.device("cpu")
hvd.init()
torch.manual_seed(999999)
#if args.cuda:
args.cuda = True
# Horovod: pin GPU to local rank.
torch.cuda.set_device(hvd.local_rank())
#torch.cuda.manual_seed(args.seed)
assert args.result_dir is not None, 'need to define result_dir arg'
logx.initialize(logdir=args.result_dir,
tensorboard=True,
hparams=vars(args),
global_rank=args.global_rank)
#print("vefore assert and infer")
# Set up the Arguments, Tensorboard Writer, Dataloader, Loss Fn, Optimizer
assert_and_infer_cfg(args)
prep_experiment(args)
# args.ngpu = torch.cuda.device_count()
# args.best_record = {'mean_iu': -1, 'epoch': 0}
#print("before datasets / loss")
train_loader, val_loader, train_obj = datasets.setup_loaders(args)
criterion, criterion_val = get_loss(args)
cwd = os.getcwd()
sz = ht.MPI_WORLD.size
filename = cwd + "/citys-hvd-checkpoint-" + str(sz) + ".pth.tar"
if args.resume and os.path.isfile(filename):
checkpoint = torch.load(filename, map_location=torch.device('cpu'))
args.arch = checkpoint['arch']
args.start_epoch = int(checkpoint['epoch']) + 1
args.restore_net = True
args.restore_optimizer = True
logx.msg(f"Resuming from: checkpoint={args.resume}, " \
f"epoch {args.start_epoch}, arch {args.arch}")
elif args.snapshot:
if 'ASSETS_PATH' in args.snapshot:
args.snapshot = args.snapshot.replace('ASSETS_PATH',
cfg.ASSETS_PATH)
checkpoint = torch.load(args.snapshot,
map_location=torch.device('cpu'))
args.restore_net = True
logx.msg(f"Loading weights from: checkpoint={args.snapshot}")
# todo: HeAT fixes -- urgent -- DDDP / optim / scheduler
net = network.get_net(args, criterion)
# net = net.to(device)
# todo: optim -> direct wrap after this, scheduler stays the same?
optim, scheduler = get_optimizer(args, net)
# if args.fp16:
# net, optim = amp.initialize(net, optim, opt_level=args.amp_opt_level)
compression = hvd.Compression.fp16 # if args.fp16_allreduce else hvd.Compression.none
optim = hvd.DistributedOptimizer(
optim,
named_parameters=net.named_parameters(),
compression=compression,
backward_passes_per_step=1, # args.batches_per_allreduce,
op=hvd.Average,
gradient_predivide_factor=1.0, # args.gradient_predivide_factor)
)
#print("after hvd optimizer setup")
if args.summary:
print(str(net))
from thop import profile
img = torch.randn(1, 3, 1024, 2048).cuda()
mask = torch.randn(1, 1, 1024, 2048).cuda()
macs, params = profile(net, inputs={'images': img, 'gts': mask})
print0(f'macs {macs} params {params}')
sys.exit()
if args.restore_optimizer:
restore_opt(optim, checkpoint)
if args.restore_net:
#net.loat_state_dict(checkpoint["state_dict"])
restore_net(net, checkpoint)
if args.init_decoder:
net.module.init_mods()
torch.cuda.empty_cache()
#print("before parameter broadcasts")
#hvd.broadcast_parameters(net.state_dict(), root_rank=0)
#hvd.broadcast_optimizer_state(optim, root_rank=0)
if args.start_epoch != 0:
# TODO: need a loss value for the restart at a certain epoch...
scheduler.step(args.start_epoch)
#net = net.cuda()
# There are 4 options for evaluation:
# --eval val just run validation
# --eval val --dump_assets dump all images and assets
# --eval folder just dump all basic images
# --eval folder --dump_assets dump all images and assets
# todo: HeAT fixes -- not urgent --
# if args.eval == 'val':
# if args.dump_topn:
# validate_topn(val_loader, net, criterion_val, optim, 0, args)
# else:
# validate(val_loader, net, criterion=criterion_val, optim=optim, epoch=0,
# dump_assets=args.dump_assets,
# dump_all_images=args.dump_all_images,
# calc_metrics=not args.no_metrics)
# return 0
# elif args.eval == 'folder':
# # Using a folder for evaluation means to not calculate metrics
# validate(val_loader, net, criterion=None, optim=None, epoch=0,
# calc_metrics=False, dump_assets=args.dump_assets,
# dump_all_images=True)
# return 0
# elif args.eval is not None:
# raise 'unknown eval option {}'.format(args.eval)
scaler = None #amp.GradScaler()
args.amp = False #True
nodes = str(int(hvd.size() / torch.cuda.device_count()))
cwd = os.getcwd()
fname = cwd + "/" + nodes + "-hvd-citys-benchmark"
if args.resume and rank == 0 and os.path.isfile(fname + ".pkl"):
with open(fname + ".pkl", "rb") as f:
out_dict = pickle.load(f)
else:
out_dict = {
"epochs": [],
nodes + "-avg-batch-time": [],
nodes + "-total-train-time": [],
nodes + "-train-loss": [],
nodes + "-val-loss": [],
nodes + "-val-iou": [],
nodes + "-val-time": [],
}
print0("Output dict:", fname)
# train_losses, train_btimes, train_ttime = [], [], []
# val_losses, val_iu, val_ttime = [], [], []
for epoch in range(args.start_epoch, args.max_epoch):
# todo: HeAT fixes -- possible conflict between processes
update_epoch(epoch)
if args.only_coarse: # default: false
train_obj.only_coarse()
train_obj.build_epoch()
elif args.class_uniform_pct:
if epoch >= args.max_cu_epoch:
train_obj.disable_coarse()
train_obj.build_epoch()
else:
train_obj.build_epoch()
else:
pass
ls, bt, btt = train(train_loader, net, optim, epoch, scaler)
# dp_optim.epoch_loss_logic(ls, loss_globally_averaged=True)
# if epoch % args.val_freq == 0:
vls, iu, vtt = validate(val_loader, net, criterion_val, optim, epoch)
if args.lr_schedule == "plateau":
scheduler.step(ls) # val_loss)
else:
scheduler.step()
if args.rank == 0:
save_checkpoint({
"epoch": epoch + 1,
"arch": args.arch,
"state_dict": net.state_dict(),
"optimizer": optim.state_dict(),
# "skip_stable": optim.stability.get_dict()
})
out_dict["epochs"].append(epoch)
out_dict[nodes + "-train-loss"].append(ls)
out_dict[nodes + "-avg-batch-time"].append(bt)
out_dict[nodes + "-total-train-time"].append(btt)
out_dict[nodes + "-val-loss"].append(vls)
out_dict[nodes + "-val-iou"].append(iu)
out_dict[nodes + "-val-time"].append(vtt)
if args.rank == 0:
save_obj(out_dict, fname)
if args.rank == 0:
print("\nRESULTS\n")
import pandas as pd
df = pd.DataFrame.from_dict(out_dict).set_index("epochs")
with pd.option_context("display.max_rows", None, "display.max_columns",
None):
# more options can be specified also
print(df)
if args.benchmarking:
try:
fulldf = pd.read_csv(cwd + "/hvd-bench-results.csv")
fulldf = pd.concat([df, fulldf], axis=1)
except FileNotFoundError:
fulldf = df
fulldf.to_csv(cwd + "/hvd-bench-results.csv")
parser.add_argument('--cv_split', type=int, default=None)
parser.add_argument('--mode', type=str, default='fine')
parser.add_argument('--split_index', type=int, default=0)
parser.add_argument('--split_count', type=int, default=1)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--resume',
action='store_true',
default=False,
help='Resume Inference')
parser.add_argument('--batch_size',
type=int,
default=1,
help='Only in pooling mode')
args = parser.parse_args()
assert_and_infer_cfg(args, train_mode=False)
args.apex = False # No support for apex eval
cudnn.benchmark = False
mean_std = ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
date_str = str(datetime.now().strftime('%Y_%m_%d_%H_%M_%S'))
def sliding_window_cropping(data, scale=1.0):
"""
Sliding Window Cropping滑动窗口裁剪
Take the image and create a mapping and multiple crops获取图像并创建一个映射和多个裁剪
"""
sliding_window_cropping = None
mapping = {}
crop_ctr = 0
if scale < 1.0:
def convert_segmentation_model(model_name='segmentation.onnx'):
assert_and_infer_cfg(opt, train_mode=False)
cudnn.benchmark = False
torch.cuda.empty_cache()
# Get segmentation Net
opt.dataset_cls = cityscapes
net = network.get_net(opt, criterion=None)
net = torch.nn.DataParallel(net).cuda()
print('Segmentation Net built.')
net, _ = restore_snapshot(net,
optimizer=None,
snapshot=opt.snapshot,
restore_optimizer_bool=False)
net.eval()
print('Segmentation Net Restored.')
# Input to the model
batch_size = 1
x = torch.randn(batch_size, 3, 1024, 2048, requires_grad=True).cuda()
torch_out = net(x)
# Export the model
torch.onnx.export(
net.module, # model being run
x, # model input (or a tuple for multiple inputs)
model_name, # where to save the model (can be a file or file-like object)
export_params=
True, # store the trained parameter weights inside the model file
opset_version=11, # the ONNX version to export the model to
do_constant_folding=
True, # whether to execute constant folding for optimization
input_names=['input'], # the model's input names
output_names=['output'], # the model's output names
dynamic_axes={
'input': {
0: 'batch_size'
}, # variable lenght axes
'output': {
0: 'batch_size'
}
})
ort_session = onnxruntime.InferenceSession(model_name)
def to_numpy(tensor):
return tensor.detach().cpu().numpy(
) if tensor.requires_grad else tensor.cpu().numpy()
ort_inputs = {ort_session.get_inputs()[0].name: to_numpy(x)}
ort_outs = ort_session.run(None, ort_inputs)
# compare ONNX Runtime and PyTorch results
np.testing.assert_allclose(to_numpy(torch_out),
ort_outs[0],
rtol=1e-03,
atol=1e-03)
print(
"Exported model has been tested with ONNXRuntime, and the result looks good!"
)
