def test_checkpoint(self):
savedir_base = '.results'
# create exp folder
exp_dict = {'model':{'name':'mlp', 'n_layers':30}, 'dataset':'mnist', 'batch_size':1}
savedir = os.path.join(savedir_base, hu.hash_dict(exp_dict))
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
hu.torch_save(os.path.join(savedir, "model.pth"), torch.zeros(10))
hu.torch_load(os.path.join(savedir, "model.pth"))
assert(os.path.exists(savedir))
# delete exp folder
hc.delete_experiment(savedir)
assert(not os.path.exists(savedir))
# check backup folder
os.rmdir(savedir_base)
def test_checkpoint():
savedir_base = ".results"
# create exp folder
exp_dict = {
"model": {
"name": "mlp",
"n_layers": 30
},
"dataset": "mnist",
"batch_size": 1
}
savedir = os.path.join(savedir_base, hu.hash_dict(exp_dict))
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
hu.torch_save(os.path.join(savedir, "model.pth"), torch.zeros(10))
hu.torch_load(os.path.join(savedir, "model.pth"))
hc.load_checkpoint(exp_dict, savedir_base, fname="model.pth")
assert os.path.exists(savedir)
# delete exp folder
hc.delete_experiment(savedir)
assert not os.path.exists(savedir)
# check backup folder
os.rmdir(savedir_base)
def trainval(exp_dict, savedir_base, reset=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
# Dataset
# -----------
# train loader
train_loader = datasets.get_loader(dataset_name=exp_dict['dataset'],
datadir=savedir_base,
split='train')
# val loader
val_loader = datasets.get_loader(dataset_name=exp_dict['dataset'],
datadir=savedir_base,
split='val')
# Model
# -----------
model = models.get_model(model_name=exp_dict['model'])
# Checkpoint
# -----------
model_path = os.path.join(savedir, 'model.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ------------
print('Starting experiment at epoch %d' % (s_epoch))
for e in range(s_epoch, 10):
score_dict = {}
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate the model
val_dict = model.val_on_loader(val_loader)
# Get metrics
score_dict['train_loss'] = train_dict['train_loss']
score_dict['val_acc'] = val_dict['val_acc']
score_dict['epoch'] = e
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print(score_df.tail())
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print('Checkpoint Saved: %s' % savedir)
print('experiment completed')
def trainval(exp_dict, savedir_base, datadir_base, reset=False,
num_workers=0, pin_memory=False, ngpu=1, cuda_deterministic=False):
# bookkeeping
# ==================
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
if DEVICE.type == "cuda":
if cuda_deterministic:
cudnn.benchmark = False
cudnn.deterministic = True
else:
cudnn.benchmark = True
# Dataset
# ==================
trainset = get_dataset(exp_dict['dataset'], 'train',
exp_dict=exp_dict, datadir_base=datadir_base,
n_samples=exp_dict['dataset_size']['train'],
transform_lvl=exp_dict['dataset']['transform_lvl'],
colorjitter=exp_dict['dataset'].get('colorjitter')
)
valset = get_dataset(exp_dict['dataset'], 'validation',
exp_dict=exp_dict, datadir_base=datadir_base,
n_samples=exp_dict['dataset_size']['train'],
transform_lvl=0,
val_transform=exp_dict['dataset']['val_transform'])
testset = get_dataset(exp_dict['dataset'], 'test',
exp_dict=exp_dict, datadir_base=datadir_base,
n_samples=exp_dict['dataset_size']['test'],
transform_lvl=0,
val_transform=exp_dict['dataset']['val_transform'])
print("Dataset defined.")
# define dataloaders
if exp_dict['dataset']['name'] == 'bach':
testloader = torch.utils.data.DataLoader(testset, batch_size=1,
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory)
else:
testloader = torch.utils.data.DataLoader(testset, batch_size=exp_dict['batch']['size'],
shuffle=False,
num_workers=num_workers,
pin_memory=pin_memory)
print("Testloader defined.")
# Model
# ==================
model = get_model(exp_dict, trainset, device=DEVICE)
print("Model loaded")
model_path = os.path.join(savedir, 'model.pth')
model_best_path = os.path.join(savedir, 'model_best.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
# checkpoint management
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = len(score_list)
else:
# restart experiment
score_list = []
s_epoch = 0
# define and log random seed for reproducibility
assert('fixedSeed' in exp_dict)
seed = exp_dict['fixedSeed']
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
print("Seed defined.")
# Train & Val
# ==================
print("Starting experiment at epoch %d/%d" % (s_epoch, exp_dict['niter']))
for epoch in range(s_epoch, exp_dict['niter']):
s_time = time.time()
# Sample new train val
trainloader, valloader = get_train_val_dataloader(exp_dict,
trainset, valset,
mixtrainval=exp_dict['mixTrainVal'],
num_workers=num_workers,
pin_memory=pin_memory)
# Train & validate
train_dict = model.train_on_loader(trainloader, valloader, epoch=epoch,
exp_dict=exp_dict)
# Test phase
train_dict_2 = model.test_on_loader(trainloader)
val_dict = model.test_on_loader(valloader)
test_dict = model.test_on_loader(testloader)
# Vis phase
model.vis_on_loader('train', trainset, savedir_images=os.path.join(
savedir, 'images'), epoch=epoch)
score_dict = {}
score_dict["epoch"] = epoch
score_dict["test_acc"] = test_dict['acc']
score_dict["val_acc"] = val_dict['acc']
score_dict["train_acc"] = train_dict_2['acc']
score_dict["train_loss"] = train_dict['loss']
score_dict["time_taken"] = time.time() - s_time
score_dict["netC_lr"] = train_dict['netC_lr']
if exp_dict['model']['netA'] is not None:
if 'transformations_mean' in train_dict:
for i in range(len(train_dict['transformations_mean'])):
score_dict[str(
i) + "_mean"] = train_dict['transformations_mean'][i].item()
if 'transformations_std' in train_dict:
for i in range(len(train_dict['transformations_std'])):
score_dict[str(
i) + "_std"] = train_dict['transformations_std'][i].item()
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# Update best score
if epoch == 0 or (score_dict["test_acc"] >= score_df["test_acc"][:-1].max()):
hu.save_pkl(os.path.join(
savedir, "score_list_best.pkl"), score_list)
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
print('experiment completed')
def test(exp_dict,
savedir_base,
datadir,
num_workers=0,
model_path=None,
scan_id=None):
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# val set
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
if str(scan_id) != 'None':
test_set.active_data = test_set.get_scan(scan_id)
test_sampler = torch.utils.data.SequentialSampler(test_set)
test_loader = DataLoader(test_set,
sampler=test_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=num_workers)
# Model
# ==================
# chk = torch.load('best_model.ckpt')
model = models.get_model_for_onnx_export(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=test_set).cuda()
epoch = -1
if str(model_path) != 'None':
model_path = model_path
model.load_state_dict(hu.torch_load(model_path))
else:
try:
exp_dict_train = copy.deepcopy(exp_dict)
del exp_dict_train['test_mode']
savedir_train = os.path.join(savedir_base,
hu.hash_dict(exp_dict_train))
model_path = os.path.join(savedir_train, "model_best.pth")
score_list = hu.load_pkl(
os.path.join(savedir_train, 'score_list_best.pkl'))
epoch = score_list[-1]['epoch']
print('Loaded model at epoch %d with score %.3f' % epoch)
model.load_state_dict(hu.torch_load(model_path))
except:
pass
s_time = time.time()
savedir_images = os.path.join(savedir, 'images')
# delete image folder if exists
if os.path.exists(savedir_images):
shutil.rmtree(savedir_images)
os.makedirs(savedir_images, exist_ok=True)
# for i in range(20):
# score_dict = model.train_on_loader(test_loader)
score_dict = model.val_on_loader(test_loader,
savedir_images=savedir_images,
n_images=30000,
save_preds=True)
score_dict['epoch'] = epoch
score_dict["time"] = time.time() - s_time
score_dict["saved_at"] = hu.time_to_montreal()
# save test_score_list
test_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(test_path):
test_score_list = [
sd for sd in hu.load_pkl(test_path) if sd['epoch'] != epoch
]
else:
test_score_list = []
# append score_dict to last result
test_score_list += [score_dict]
hu.save_pkl(test_path, test_score_list)
print('Final Score is ', str(score_dict["val_score"]) + "\n")
def trainval(exp_dict, savedir_base, datadir, reset=False, num_workers=0):
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# test set
test_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="test",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(
val_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=num_workers)
test_loader = DataLoader(
test_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
# model.opt = optimizers.get_optim(exp_dict['opt'], model)
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
model.waiting = 0
model.val_score_best = -np.inf
train_sampler = torch.utils.data.RandomSampler(train_set,
replacement=True,
num_samples=2 *
len(test_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
collate_fn=ut.collate_fn,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate the model
val_dict = model.val_on_loader(val_loader)
score_dict["val_score"] = val_dict["val_score"]
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = e
score_dict["waiting"] = model.waiting
model.waiting += 1
# Add to score_list and save checkpoint
score_list += [score_dict]
# Save Best Checkpoint
score_df = pd.DataFrame(score_list)
if score_dict["val_score"] >= model.val_score_best:
test_dict = model.val_on_loader(test_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
score_dict.update(test_dict)
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
# score_df.to_csv(os.path.join(savedir, "score_best_df.csv"))
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
model.waiting = 0
model.val_score_best = score_dict["val_score"]
print("Saved Best: %s" % savedir)
# Report & Save
score_df = pd.DataFrame(score_list)
# score_df.to_csv(os.path.join(savedir, "score_df.csv"))
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
if model.waiting > 100:
break
print('Experiment completed et epoch %d' % e)
def trainval(exp_dict, savedir_base, datadir_base, reset=False):
# bookkeeping stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# load train and acrtive set
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
datadir_base=datadir_base,
exp_dict=exp_dict)
active_set = ActiveLearningDataset(train_set, random_state=42)
# val set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="val",
datadir_base=datadir_base,
exp_dict=exp_dict)
val_loader = DataLoader(val_set, batch_size=exp_dict["batch_size"])
# Model
# ==================
model = models.get_model(model_name=exp_dict['model']['name'],
exp_dict=exp_dict).cuda()
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
active_set.load_state_dict(
hu.load_pkl(os.path.join(savedir, "active_set.pkl")))
score_list = hu.load_pkl(score_list_path)
inner_s_epoch = score_list[-1]['inner_epoch'] + 1
s_cycle = score_list[-1]['cycle']
else:
# restart experiment
score_list = []
inner_s_epoch = 0
s_cycle = 0
# Train & Val
# ==================
print("Starting experiment at cycle %d epoch %d" %
(s_cycle, inner_s_epoch))
for c in range(s_cycle, exp_dict['max_cycle']):
# Set seed
np.random.seed(c)
torch.manual_seed(c)
torch.cuda.manual_seed_all(c)
if inner_s_epoch == 0:
active_set.label_next_batch(model)
hu.save_pkl(os.path.join(savedir, "active_set.pkl"),
active_set.state_dict())
train_loader = DataLoader(active_set,
sampler=samplers.get_sampler(
exp_dict['sampler']['train'],
active_set),
batch_size=exp_dict["batch_size"])
# Visualize the model
model.vis_on_loader(vis_loader,
savedir=os.path.join(savedir, "images"))
for e in range(inner_s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
if e == 0:
score_dict.update(model.val_on_loader(val_loader))
# Train the model
score_dict.update(model.train_on_loader(train_loader))
# Validate the model
score_dict["epoch"] = len(score_list)
score_dict["inner_epoch"] = e
score_dict["cycle"] = c
score_dict['n_ratio'] = active_set.n_labelled_ratio
score_dict["n_train"] = len(train_loader.dataset)
score_dict["n_pool"] = len(train_loader.dataset.pool)
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
inner_s_epoch = 0
def trainval(exp_dict, savedir_base, data_root, reset=False, tensorboard=True):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
np.random.seed(exp_dict["seed"])
torch.manual_seed(exp_dict["seed"])
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
writer = tensorboardX.SummaryWriter(savedir) \
if tensorboard == 1 else None
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
pprint.pprint(exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# -----------
train_dataset, val_dataset = get_dataset(['train', 'val'], data_root,
exp_dict)
# val_dataset = get_dataset('val', exp_dict)
# train and val loader
if exp_dict["episodic"] == False:
train_loader = DataLoader(train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
val_loader = DataLoader(val_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
else: # to support episodes TODO: move inside each model
from datasets.episodic_dataset import EpisodicDataLoader
train_loader = EpisodicDataLoader(train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
val_loader = EpisodicDataLoader(val_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
# Model
# -----------
model = get_model(exp_dict,
labelset=train_dataset.raw_labelset,
writer=writer)
print("Model with:",
sum(p.numel() for p in model.parameters() if p.requires_grad),
"parameters")
# Checkpoint
# -----------
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ------------
print("Starting experiment at epoch %d" % (s_epoch))
for e in range(s_epoch, exp_dict['max_epoch']):
score_dict = {}
# Train the model
score_dict.update(model.train_on_loader(e, train_loader))
# Validate the model
score_dict.update(model.val_on_loader(e, val_loader))
score_dict["epoch"] = e
if tensorboard:
for key, value in score_dict.items():
writer.add_scalar(key, value, e)
writer.flush()
# Visualize the model
# model.vis_on_loader(vis_loader, savedir=savedir+"/images/")
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail())
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# if model.is_end():
# print("Early stopping")
# break
print('experiment completed')
# Cleanup
if tensorboard == 1:
writer.close()
def trainval(exp_dict, savedir_base, reset=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print(exp_dict)
print("Experiment saved in %s" % savedir)
# Set Seed
# -------
seed = exp_dict.get('seed')
np.random.seed(seed)
torch.manual_seed(seed)
# Dataset
# -----------
train_dataset = get_dataset('train', exp_dict['dataset'])
val_dataset = get_dataset('test', exp_dict['dataset'])
# train and val loader
train_loader = DataLoader(
train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x
if exp_dict['batch_size'] == 1 else default_collate,
# to handle episodes
num_workers=args.num_workers)
val_loader = DataLoader(
val_dataset,
batch_size=exp_dict['batch_size'],
collate_fn=lambda x: x
if exp_dict['batch_size'] == 1 else default_collate,
shuffle=True,
num_workers=args.num_workers)
# Model
# -----------
model = get_model(exp_dict)
# Checkpoint
# -----------
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ------------
print("Starting experiment at epoch %d" % (s_epoch))
for e in range(s_epoch, exp_dict['max_epoch']):
score_dict = {}
# Train the model
score_dict.update(model.train_on_loader(train_loader))
# Validate the model
savepath = os.path.join(savedir_base, exp_dict['dataset']['name'])
score_dict.update(model.val_on_loader(val_loader, savedir=savepath))
model.on_train_end(savedir=savedir, epoch=e)
score_dict["epoch"] = e
# Visualize the model
# model.vis_on_loader(vis_loader, savedir=savedir+"/images/")
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail())
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
print('experiment completed')
def trainval(exp_dict, savedir_base, datadir, reset=False, num_workers=0):
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=1,
num_workers=num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
# model.opt = optimizers.get_optim(exp_dict['opt'], model)
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
train_sampler = torch.utils.data.RandomSampler(train_set,
replacement=True,
num_samples=2 *
len(val_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate and Visualize the model
val_dict = model.val_on_loader(val_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
score_dict.update(val_dict)
# model.vis_on_loader(
# vis_loader, savedir=os.path.join(savedir, "images"))
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = len(score_list)
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# Save Best Checkpoint
if e == 0 or (score_dict.get("val_score", 0) >
score_df["val_score"][:-1].fillna(0).max()):
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
print('Experiment completed et epoch %d' % e)
def trainval(exp_dict,
savedir_base,
data_root,
reset=False,
wandb='None',
wandb_key='None'):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
print(exp_dict)
print("Experiment saved in %s" % savedir)
model_name = exp_dict['model'] + \
"_lr_" + str(exp_dict['lr']) +\
"_hs_" + str(exp_dict['backbone']['hidden_size']) +\
"_pa_" + str(exp_dict['patience'])
if exp_dict['model'] == 'MAML':
model_name += "_ilr_" + str(exp_dict['inner_lr']) +\
"_nii_" + str(exp_dict['n_inner_iter'])
#TODO add seed
if wandb is not 'None':
# https://docs.wandb.com/quickstart
import wandb as logger
if wandb_key is not 'None':
logger.login(key=wandb_key)
logger.init(project=wandb, group=model_name)
logger.config.update(exp_dict)
# Dataset
# -----------
train_dataset = get_dataset('train', data_root, exp_dict)
val_dataset = get_dataset('val', data_root, exp_dict)
test_dataset = get_dataset('test', data_root, exp_dict)
if 'ood' in exp_dict['dataset']['task']:
ood_dataset = get_dataset('ood', data_root, exp_dict)
ood = True
else:
ood = False
# train and val loader
if exp_dict["episodic"] == False:
train_loader = DataLoader(train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
val_loader = DataLoader(val_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
test_loader = DataLoader(test_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
else: # to support episodes TODO: move inside each model
from datasets.episodic_dataset import EpisodicDataLoader
train_loader = EpisodicDataLoader(train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
val_loader = EpisodicDataLoader(val_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
test_loader = EpisodicDataLoader(test_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
if ood:
ood_loader = EpisodicDataLoader(ood_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
# Model
# -----------
model = get_model(exp_dict)
# Checkpoint
# -----------
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
patience_counter = 0
# Train & Val
# ------------
print("Starting experiment at epoch %d" % (s_epoch))
for e in range(s_epoch, exp_dict['max_epoch']):
score_dict = {}
# Train the model
score_dict.update(model.train_on_loader(train_loader))
# Validate and Test the model
score_dict.update(
model.val_on_loader(val_loader,
mode='val',
savedir=os.path.join(
savedir_base,
exp_dict['dataset']['name'])))
score_dict.update(model.val_on_loader(test_loader, mode='test'))
if ood:
score_dict.update(model.val_on_loader(ood_loader, mode='ood'))
score_dict["epoch"] = e
# Visualize the model
# model.vis_on_loader(vis_loader, savedir=savedir+"/images/")
# Test error at best validation:
if score_dict["val_accuracy"] > model.best_val:
score_dict["test_accuracy_at_best_val"] = score_dict[
"test_accuracy"]
score_dict["ood_accuracy_at_best_val"] = score_dict["ood_accuracy"]
model.best_val = score_dict["val_accuracy"]
patience_counter = 0
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail())
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
if wandb is not 'None':
for key, values in score_dict.items():
logger.log({key: values})
patience_counter += 1
# Patience:
if patience_counter > exp_dict['patience'] * 3:
print('training done, out of patience')
break
print('experiment completed')
def train(exp_dict, savedir_base, reset, compute_fid=False):
# Book keeping
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
ut.rmtree(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
print('Experiment saved in %s' % savedir)
device = \
torch.device('cuda:' + exp_dict['gpu'] if torch.cuda.is_available() else 'cpu')
# 1. Load dataset and loader
train_set, test_set, num_channels, num_train_classes, num_test_classes = \
datasets.get_dataset(exp_dict['dataset'],
dataset_path=savedir_base,
image_size=exp_dict['image_size'])
train_loader, test_loader = \
dataloaders.get_dataloader(exp_dict['dataloader'],
train_set, test_set, exp_dict)
# 2. Fetch model to train
model = models.get_model(exp_dict['model'], num_train_classes,
num_test_classes, num_channels, device, exp_dict)
# 3. Resume experiment or start from scratch
score_list_path = os.path.join(savedir, 'score_list.pkl')
if os.path.exists(score_list_path):
# Resume experiment if it exists
model_path = os.path.join(savedir, 'model_state_dict.pth')
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
meta_dict_path = os.path.join(savedir, 'meta_dict.pkl')
meta_dict = hu.load_pkl(meta_dict_path)
print('Resuming experiment at episode %d epoch %d' %
(meta_dict['episode'], meta_dict['epoch']))
else:
# Start experiment from scratch
meta_dict = {'episode': 1, 'epoch': 1}
score_list = []
# Remove TensorBoard logs from previous runs
ut.rmtree(os.path.join(savedir, 'tensorboard_logs'))
print('Starting experiment at episode %d epoch %d' %
(meta_dict['episode'], meta_dict['epoch']))
# 4. Train and eval loop
s_epoch = meta_dict['epoch']
for e in range(s_epoch, exp_dict['num_epochs'] + 1):
# 0. Initialize dicts
score_dict = {'epoch': e}
meta_dict['epoch'] = e
# 1. Train on loader
train_dict = model.train_on_loader(train_loader)
# 1b. Compute FID
if compute_fid == 1:
if e % 20 == 0 or e == 1 or e == exp_dict['num_epochs']:
print('Starting FID computation...')
train_dict['fid'] = fid(model, train_loader.dataset,
train_loader.sampler, save_dir)
score_dict.update(train_dict)
# 2. Eval on loader
eval_dict = model.val_on_loader(test_loader, savedir, e)
score_dict.update(eval_dict)
# 3. Report and save model state, optimizer state, and scores
score_list += [score_dict]
score_df = pd.DataFrame(score_list)
print('\n', score_df.tail(), '\n')
if e % 10 == 0:
hu.torch_save(os.path.join(savedir, 'model_state_dict.pth'),
model.get_state_dict())
hu.save_pkl(os.path.join(savedir, 'score_list.pkl'), score_list)
hu.save_pkl(os.path.join(savedir, 'meta_dict.pkl'), meta_dict)
def trainval(exp_dict, savedir_base, data_root, reset=False, test_only=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
np.random.seed(exp_dict["seed"])
torch.manual_seed(exp_dict["seed"])
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, "exp_dict.json"), exp_dict)
pprint.pprint(exp_dict)
print("Experiment saved in %s" % savedir)
# Dataset
# -----------
# train and val loader
if exp_dict["episodic"] == False:
if (int(test_only) == 0):
train_dataset, val_dataset, test_dataset = get_dataset(
['train', 'val', 'test'], data_root, exp_dict)
train_loader = DataLoader(train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
val_loader = DataLoader(val_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
test_loader = DataLoader(test_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
if hasattr(train_dataset, "mask"):
# assert((train_dataset.mask == val_dataset.mask).all())
# assert((train_dataset.mask == test_dataset.mask).all())
np.save(os.path.join(savedir, "mask.npy"), train_dataset.mask)
else:
test_dataset, = get_dataset(['test'], exp_dict)
test_loader = DataLoader(test_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
num_workers=args.num_workers)
else: # to support episodes TODO: move inside each model
from datasets.episodic_dataset import EpisodicDataLoader
train_loader = EpisodicDataLoader(train_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
val_loader = EpisodicDataLoader(val_dataset,
batch_size=exp_dict['batch_size'],
shuffle=True,
collate_fn=lambda x: x,
num_workers=args.num_workers)
# Model
# -----------
model = get_model(exp_dict)
print("Parameters: ", sum([torch.numel(v) for v in model.parameters()]))
# Checkpoint
# -----------
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
print("Resuming from", model_path)
model.set_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
if int(test_only) == 0:
# Train & Val
# ------------
print("Starting experiment at epoch %d" % (s_epoch))
for e in range(s_epoch, exp_dict['max_epoch']):
score_dict = {}
# Train the model
score_dict.update(model.train_on_loader(train_loader))
# Validate the model
score_dict.update(
model.val_on_loader(val_loader,
savedir=os.path.join(
savedir_base,
exp_dict['dataset']['name'])))
score_dict["epoch"] = e
# Visualize the model
# model.vis_on_loader(vis_loader, savedir=savedir+"/images/")
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail())
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
if model.is_end():
print("Early stopping")
break
print('experiment completed')
print("Testing...")
score_dict = model.test_on_loader(train_loader, tag="train")
score_dict.update(model.test_on_loader(val_loader, tag="val"))
score_dict.update(model.test_on_loader(test_loader, tag="test"))
# Report & Save
score_list_path = os.path.join(savedir, "score_list_test.pkl")
hu.save_pkl(score_list_path, score_dict)
else:
print("Testing...")
score_dict = model.test_on_loader(test_loader, "test")
# Report & Save
score_list_path = os.path.join(savedir, "score_list_test.pkl")
hu.save_pkl(score_list_path, score_dict)
def trainval(exp_dict, savedir, args):
"""
exp_dict: dictionary defining the hyperparameters of the experiment
savedir: the directory where the experiment will be saved
args: arguments passed through the command line
"""
# Dataset
# ==================
# train set
train_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="train",
datadir=args.datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
# val set
val_set = datasets.get_dataset(dataset_dict=exp_dict["dataset"],
split="val",
datadir=args.datadir,
exp_dict=exp_dict,
dataset_size=exp_dict['dataset_size'])
val_sampler = torch.utils.data.SequentialSampler(val_set)
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=1,
num_workers=args.num_workers)
# Model
# ==================
model = models.CountingModel(exp_dict=exp_dict, train_set=train_set).cuda()
# model.opt = optimizers.get_optim(exp_dict['opt'], model)
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
train_sampler = torch.utils.data.RandomSampler(train_set,
replacement=True,
num_samples=2 *
len(val_set))
train_loader = DataLoader(train_set,
sampler=train_sampler,
batch_size=exp_dict["batch_size"],
drop_last=True,
num_workers=args.num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate and Visualize the model
val_dict = model.val_on_loader(val_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=3)
score_dict.update(val_dict)
# model.vis_on_loader(
# vis_loader, savedir=os.path.join(savedir, "images"))
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = len(score_list)
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# Save Best Checkpoint
if e == 0 or (score_dict.get("val_score", 0) >
score_df["val_score"][:-1].fillna(0).max()):
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
print('Experiment completed et epoch %d' % e)
def trainval(exp_dict, savedir_base, datadir, reset=False, num_workers=0):
# bookkeepting stuff
# ==================
savedir = os.path.join(savedir_base, hu.hash_dict(exp_dict))
os.makedirs(savedir, exist_ok=True)
if reset:
hc.delete_and_backup_experiment(savedir)
print("Experiment saved in %s" % savedir)
# Dataset
# ==================
# train set
data_transform = A.Compose(
[
A.Flip(p=0.3),
A.IAAAffine(p=0.3),
A.Rotate(p=0.3),
A.HueSaturationValue(hue_shift_limit=10,
sat_shift_limit=15,
val_shift_limit=10,
p=0.3),
A.GaussianBlur(3, p=0.3),
A.GaussNoise(30, p=0.3)
],
keypoint_params=A.KeypointParams(format='xy'),
additional_targets={
'mask0': 'mask',
'mask1': 'mask',
'mask2': 'mask',
'keypoints0': 'keypoints',
'keypoints1': 'keypoints',
'keypoints2': 'keypoints',
'keypoints3': 'keypoints',
'keypoints4': 'keypoints',
'keypoints5': 'keypoints'
})
# random.seed(20201009)
random_seed = random.randint(0, 20201009)
train_set = HEDataset_Fast(data_dir=datadir,
n_classes=exp_dict["n_classes"],
transform=data_transform,
option="Train",
random_seed=random_seed,
obj_option=exp_dict["obj"],
patch_size=exp_dict["patch_size"],
bkg_option=exp_dict["bkg"])
test_transform = A.Compose([A.Resize(1024, 1024)],
keypoint_params=A.KeypointParams(format='xy'),
additional_targets={
'mask0': 'mask',
'mask1': 'mask'
})
# val set
val_set = HEDataset(data_dir=datadir,
transform=test_transform,
option="Validation")
val_loader = DataLoader(val_set, batch_size=1, num_workers=num_workers)
# test set
test_set = HEDataset(data_dir=datadir,
transform=test_transform,
option="Test")
test_loader = DataLoader(test_set, batch_size=1, num_workers=num_workers)
# Model
# ==================
# torch.manual_seed(20201009)
model = models.get_model(exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
model_path = os.path.join(savedir, "model.pth")
score_list_path = os.path.join(savedir, "score_list.pkl")
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ==================
print("Starting experiment at epoch %d" % (s_epoch))
# train_sampler = torch.utils.data.RandomSampler(
# train_set, replacement=True, num_samples=2*len(val_set))
train_loader = DataLoader(train_set,
batch_size=exp_dict["batch_size"],
shuffle=True,
num_workers=num_workers)
for e in range(s_epoch, exp_dict['max_epoch']):
# Validate only at the start of each cycle
score_dict = {}
# Train the model
train_dict = model.train_on_loader(train_loader)
# Validate and Visualize the model
val_dict = model.val_on_loader(val_loader,
savedir_images=os.path.join(
savedir, "images"),
n_images=7)
score_dict.update(val_dict)
# Get new score_dict
score_dict.update(train_dict)
score_dict["epoch"] = len(score_list)
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir)
# Save Best Checkpoint
if e == 0 or (score_dict.get("val_score", 0) >
score_df["val_score"][:-1].fillna(0).max()):
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
hu.torch_save(os.path.join(savedir, "model_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
# if s_epoch==exp_dict['max_epoch']:
# e = s_epoch
model.load_state_dict(
hu.torch_load(os.path.join(savedir, "model_best.pth")))
test_dict = model.test_on_loader(test_loader)
hu.save_pkl(os.path.join(savedir, 'test_iou.pkl'), test_dict)
print('Test IoU:{}'.format(test_dict["test_iou"]))
print('Experiment completed et epoch %d' % e)
def trainval(exp_dict,
savedir_base,
reset=False,
num_workers=0,
run_ssl=False):
# bookkeeping
# ---------------
# get experiment directory
exp_id = hu.hash_dict(exp_dict)
savedir = os.path.join(savedir_base, exp_id)
if reset:
# delete and backup experiment
hc.delete_experiment(savedir, backup_flag=True)
# create folder and save the experiment dictionary
os.makedirs(savedir, exist_ok=True)
hu.save_json(os.path.join(savedir, 'exp_dict.json'), exp_dict)
pprint.pprint(exp_dict)
print('Experiment saved in %s' % savedir)
# load datasets
# ==========================
train_set = datasets.get_dataset(
dataset_name=exp_dict["dataset_train"],
data_root=exp_dict["dataset_train_root"],
split="train",
transform=exp_dict["transform_train"],
classes=exp_dict["classes_train"],
support_size=exp_dict["support_size_train"],
query_size=exp_dict["query_size_train"],
n_iters=exp_dict["train_iters"],
unlabeled_size=exp_dict["unlabeled_size_train"])
val_set = datasets.get_dataset(
dataset_name=exp_dict["dataset_val"],
data_root=exp_dict["dataset_val_root"],
split="val",
transform=exp_dict["transform_val"],
classes=exp_dict["classes_val"],
support_size=exp_dict["support_size_val"],
query_size=exp_dict["query_size_val"],
n_iters=exp_dict["val_iters"],
unlabeled_size=exp_dict["unlabeled_size_val"])
test_set = datasets.get_dataset(
dataset_name=exp_dict["dataset_test"],
data_root=exp_dict["dataset_test_root"],
split="test",
transform=exp_dict["transform_val"],
classes=exp_dict["classes_test"],
support_size=exp_dict["support_size_test"],
query_size=exp_dict["query_size_test"],
n_iters=exp_dict["test_iters"],
unlabeled_size=exp_dict["unlabeled_size_test"])
# get dataloaders
# ==========================
train_loader = torch.utils.data.DataLoader(
train_set,
batch_size=exp_dict["batch_size"],
shuffle=True,
num_workers=num_workers,
collate_fn=ut.get_collate(exp_dict["collate_fn"]),
drop_last=True)
val_loader = torch.utils.data.DataLoader(val_set,
batch_size=1,
shuffle=False,
num_workers=num_workers,
collate_fn=lambda x: x,
drop_last=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=num_workers,
collate_fn=lambda x: x,
drop_last=True)
# create model and trainer
# ==========================
# Create model, opt, wrapper
backbone = backbones.get_backbone(
backbone_name=exp_dict['model']["backbone"], exp_dict=exp_dict)
model = models.get_model(model_name=exp_dict["model"]['name'],
backbone=backbone,
n_classes=exp_dict["n_classes"],
exp_dict=exp_dict)
if run_ssl:
# runs the SSL experiments
score_list_path = os.path.join(savedir, 'score_list.pkl')
if not os.path.exists(score_list_path):
test_dict = model.test_on_loader(test_loader, max_iter=None)
hu.save_pkl(score_list_path, [test_dict])
return
# Checkpoint
# -----------
checkpoint_path = os.path.join(savedir, 'checkpoint.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
if os.path.exists(score_list_path):
# resume experiment
model.load_state_dict(hu.torch_load(checkpoint_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Run training and validation
for epoch in range(s_epoch, exp_dict["max_epoch"]):
score_dict = {"epoch": epoch}
score_dict.update(model.get_lr())
# train
score_dict.update(model.train_on_loader(train_loader))
# validate
score_dict.update(model.val_on_loader(val_loader))
score_dict.update(model.test_on_loader(test_loader))
# Add score_dict to score_list
score_list += [score_dict]
# Report
score_df = pd.DataFrame(score_list)
print(score_df.tail())
# Save checkpoint
hu.save_pkl(score_list_path, score_list)
hu.torch_save(checkpoint_path, model.get_state_dict())
print("Saved: %s" % savedir)
if "accuracy" in exp_dict["target_loss"]:
is_best = score_dict[exp_dict["target_loss"]] >= score_df[
exp_dict["target_loss"]][:-1].max()
else:
is_best = score_dict[exp_dict["target_loss"]] <= score_df[
exp_dict["target_loss"]][:-1].min()
# Save best checkpoint
if is_best:
hu.save_pkl(os.path.join(savedir, "score_list_best.pkl"),
score_list)
hu.torch_save(os.path.join(savedir, "checkpoint_best.pth"),
model.get_state_dict())
print("Saved Best: %s" % savedir)
# Check for end of training conditions
if model.is_end_of_training():
break
test_loader = DataLoader(test_set,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=0)
pprint.pprint(exp_dict)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
model_path = os.path.join(savedir_base, hash_id, 'model_best.pth')
# load best model
model.load_state_dict(hu.torch_load(model_path))
# loop over the val_loader and saves image
# get counts
habitats = []
for i, batch in enumerate(test_loader):
habitat = batch['meta'][0]['habitat']
habitats += [habitat]
habitats = np.array(habitats)
val_dict = {}
val_dict_lst = []
for h in np.unique(habitats):
val_meter = metrics.SegMeter(split=test_loader.dataset.split)
for i, batch in enumerate(tqdm.tqdm(test_loader)):
habitat = batch['meta'][0]['habitat']
def trainval(exp_dict, savedir_base, reset):
# ==================
# bookkeepting stuff
# ==================
pprint.pprint(exp_dict)
exp_id = hu.hash_dict(exp_dict)
savedir = savedir_base + "/%s/" % exp_id
if reset:
hc.delete_and_backup_experiment(savedir)
os.makedirs(savedir, exist_ok=True)
hu.save_json(savedir + "exp_dict.json", exp_dict)
print("Experiment saved in %s" % savedir)
# ==================
# Dataset
# ==================
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize((0.5, ), (0.5, ))
])
# train set
train_set = torchvision.datasets.MNIST(savedir_base,
train=True,
download=True,
transform=transform)
train_loader = DataLoader(train_set,
shuffle=True,
batch_size=exp_dict["batch_size"])
# val set
val_set = torchvision.datasets.MNIST(savedir_base,
train=False,
download=True,
transform=transform)
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=exp_dict["batch_size"])
# ==================
# Model
# ==================
model = MLP(n_classes=10).cuda()
model.opt = torch.optim.Adam(model.parameters(), lr=exp_dict["lr"])
model_path = savedir + "/model.pth"
score_list_path = savedir + "/score_list.pkl"
if os.path.exists(score_list_path):
# resume experiment
model.set_state_dict(hu.torch_load(model_path))
score_list = hu.load_pkl(score_list_path)
s_epoch = len(score_list)
else:
# restart experiment
score_list = []
s_epoch = 0
# ==================
# Train & Val
# ==================
print("Starting experiment at epoch %d" % s_epoch)
for e in range(s_epoch, 100):
score_dict = {}
# Train the model
score_dict.update(model.train_on_loader(train_loader))
# Validate the model
score_dict.update(model.val_on_loader(val_loader))
score_dict["epoch"] = e
# Add to score_list and save checkpoint
score_list += [score_dict]
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail()[["epoch", "train_loss", "val_acc"]], "\n")
hu.torch_save(model_path, model.get_state_dict())
hu.save_pkl(score_list_path, score_list)
print("Checkpoint Saved: %s" % savedir_base)
