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 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
"""
# -- Datasets
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=args.datadir)
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=args.datadir)
# -- Model
model = models.Model(exp_dict, device=torch.device('cuda'))
# -- Train & Val Loop
score_list = []
for e in range(0, 50):
# Compute metrics
score_dict = {"epoch": e}
score_dict["train_loss"] = model.val_on_dataset(
val_set, metric_name='softmax_loss')
score_dict["val_acc"] = model.val_on_dataset(val_set,
metric_name='softmax_acc')
score_list += [score_dict]
# Train model for one epoch
model.train_on_dataset(train_set)
# Visualize
images = model.vis_on_dataset(val_set,
fname=os.path.join(
savedir, 'images', 'results.png'))
# Report & Save
score_df = pd.DataFrame(score_list)
print("\n", score_df.tail(), "\n")
hu.save_pkl(os.path.join(savedir, 'score_list.pkl'), score_list)
hu.torch_save(os.path.join(savedir, 'model.pth'), model.state_dict())
print("Checkpoint Saved: %s" % savedir)
print('Experiment completed et epoch %d' % e)
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, 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,
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
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
"""
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
if args.use_cuda:
device = 'cuda'
torch.cuda.manual_seed_all(seed)
assert torch.cuda.is_available(
), 'cuda is not, available please run with "-c 0"'
else:
device = 'cpu'
print('Running on device: %s' % device)
# Dataset
# Load val set and train set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="val",
transform=exp_dict.get("transform"),
datadir=args.datadir)
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
transform=exp_dict.get("transform"),
datadir=args.datadir)
# Load train loader, val loader, and vis loader
train_loader = DataLoader(train_set,
sampler=RandomSampler(
train_set,
replacement=True,
num_samples=max(min(500, len(train_set)),
len(val_set))),
batch_size=exp_dict["batch_size"])
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=exp_dict["batch_size"])
vis_loader = DataLoader(val_set,
sampler=ut.SubsetSampler(train_set,
indices=[0, 1, 2]),
batch_size=1)
# Create model, opt, wrapper
model_original = models.get_model(exp_dict["model"],
exp_dict=exp_dict).cuda()
opt = torch.optim.Adam(model_original.parameters(),
lr=1e-5,
weight_decay=0.0005)
model = wrappers.get_wrapper(exp_dict["wrapper"],
model=model_original,
opt=opt).cuda()
score_list = []
# Checkpointing
# =============
score_list_path = os.path.join(savedir, "score_list.pkl")
model_path = os.path.join(savedir, "model_state_dict.pth")
opt_path = os.path.join(savedir, "opt_state_dict.pth")
if os.path.exists(score_list_path):
# resume experiment
score_list = hu.load_pkl(score_list_path)
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_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}
# visualize
model.vis_on_loader(vis_loader,
savedir=os.path.join(savedir, "images"))
# validate
score_dict.update(model.val_on_loader(val_loader))
# train
score_dict.update(model.train_on_loader(train_loader))
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved in %s" % savedir)
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,
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 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
"""
datadir = args.datadir
# 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=exp_dict["batch_size"],
collate_fn=ut.collate_fn,
num_workers=args.num_workers,
drop_last=False)
test_loader = DataLoader(test_set,
# sampler=val_sampler,
batch_size=1,
collate_fn=ut.collate_fn,
num_workers=args.num_workers)
# Model
# ==================
model = models.get_model(model_dict=exp_dict['model'],
exp_dict=exp_dict,
train_set=train_set).cuda()
chk_dict = hw.get_checkpoint(savedir)
score_list = chk_dict['score_list']
# Train & Val
# ==================
model.waiting = 0
model.val_score_best = -np.inf
sampler = exp_dict['dataset'].get('sampler', 'random')
if sampler == 'random':
train_sampler = torch.utils.data.RandomSampler(
train_set, replacement=True,
num_samples=len(val_set))
elif sampler == 'balanced':
train_sampler = samplers.BalancedSampler(
train_set, n_samples=len(val_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=args.num_workers)
for e in range(chk_dict['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 the model
val_dict = model.val_on_loader(val_loader,
savedir_images=os.path.join(savedir, "images"), n_images=5)
score_dict.update(val_dict)
# 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
hw.save_checkpoint(savedir, score_list=score_list)
if model.waiting > 100:
break
print('Experiment completed et epoch %d' % e)
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, 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
"""
# set seed
# ==================
seed = 42
np.random.seed(seed)
torch.manual_seed(seed)
#helen commented out the following lines to hard code in that the device was 'cpu' to resolve errors
#if args.use_cuda:
#device = 'cuda'
#torch.cuda.manual_seed_all(seed)
#assert torch.cuda.is_available(), 'cuda is not, available please run with "-c 0"'
#else:
device = 'cpu'
print('Running on device: %s' % device)
# Dataset
# Load val set and train set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="val",
transform=exp_dict.get("transform"),
datadir=args.datadir)
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
split="train",
transform=exp_dict.get("transform"),
datadir=args.datadir)
# Load train loader, val loader, and vis loader
train_loader = DataLoader(train_set,
sampler=RandomSampler(
train_set,
replacement=True,
num_samples=max(min(500, len(train_set)),
len(val_set))),
batch_size=exp_dict["batch_size"])
val_loader = DataLoader(val_set,
shuffle=False,
batch_size=exp_dict["batch_size"])
vis_loader = DataLoader(val_set,
sampler=ut.SubsetSampler(train_set,
indices=[0, 1, 2]),
batch_size=1)
# Create model, opt, wrapper
model_original = models.get_model(exp_dict["model"],
exp_dict=exp_dict).cuda()
opt = torch.optim.Adam(model_original.parameters(),
lr=1e-5,
weight_decay=0.0005)
model = wrappers.get_wrapper(exp_dict["wrapper"],
model=model_original,
opt=opt).cuda()
score_list = []
# Checkpointing
# =============
#score_list_path = os.path.join(savedir, "score_list.pkl") #helen commented out these three lines and hard coded the model and opt paths to resolve errors
#model_path = os.path.join(savedir, "model_state_dict.pth")
#opt_path = os.path.join(savedir, "opt_state_dict.pth")
score_list_path = '/Users/helenpropson/Documents/git/marepesca/results/testresults/score_list.pkl' #helen added this
model_path = '/Users/helenpropson/Documents/git/marepesca/results/testresults/model_state_dict.pth' #helen added this
opt_path = '/Users/helenpropson/Documents/git/marepesca/results/testresults/opt_state_dict.pth' #helen added this
#helen hard coded that the experiment would resume instead of restarting from epoch 0
#if os.path.exists(score_list_path):
# resume experiment
score_list = hu.load_pkl(
score_list_path
) #helen changed this from ut.load_pkl to hu.load_pkl to resolve error
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_path))
s_epoch = score_list[-1]["epoch"] + 1
#else:
# restart experiment
#score_list = []
#s_epoch = 0
# *************** helen added this code
im = Image.open("/Users/helenpropson/Documents/git/marepesca/tank.jpg")
# im.show() #this line will display the image you are running the model on if uncommented
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
normalize_transform = transforms.Normalize(mean=mean, std=std)
data_transform = transforms.Compose(
[transforms.ToTensor(),
normalize_transform]) #transformations we will use on our image
im_new = data_transform(
im) #transforms the image into a tensor and normalizes it
im_final = im_new.unsqueeze(
0) #adds another dimension so image is the correct shape for the model
print("now trying helen's code") #print statement for debugging
#model.vis_on_batch_helen(im_final, f'im_new') #uncomment this line to run model on image
# *************** this is the end of helen's code
# Run training and validation
for epoch in range(s_epoch, exp_dict["max_epoch"]):
score_dict = {"epoch": epoch}
# visualize
model.vis_on_loader(vis_loader,
savedir=os.path.join(savedir, "images"))
print("after vis_on_loader"
) #helen add this print statement as an update while iterating
# validate
score_dict.update(model.val_on_loader(val_loader))
print("after validate")
# train
score_dict.update(model.train_on_loader(train_loader))
print("after train")
# Add score_dict to score_list
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved in %s" % savedir)
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
# ==================
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, 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, 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 newminimum(exp_id,
savedir_base,
datadir,
name,
exp_dict,
metrics_flag=True):
# bookkeeping
# ---------------
# get experiment directory
old_modeldir = os.path.join(savedir_base, exp_id)
savedir = os.path.join(savedir_base, exp_id, name)
old_exp_dict = hu.load_json(os.path.join(old_modeldir, 'exp_dict.json'))
# TODO: compare exp dict for possible errors:
# optimizer have to be the same
# same network, dataset
# 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)
# set seed
# ---------------
seed = 42 + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# -----------
# Load Train Dataset
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=datadir,
exp_dict=exp_dict)
train_loader = torch.utils.data.DataLoader(
train_set,
drop_last=True,
shuffle=True,
batch_size=exp_dict["batch_size"])
# Load Val Dataset
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=datadir,
exp_dict=exp_dict)
# Model
# -----------
model = models.get_model(exp_dict["model"], train_set=train_set)
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# Load Optimizer
n_batches_per_epoch = len(train_set) / float(exp_dict["batch_size"])
opt = optimizers.get_optimizer(opt=exp_dict["opt"],
params=model.parameters(),
n_batches_per_epoch=n_batches_per_epoch)
# Checkpoint
# -----------
model_path = os.path.join(savedir, 'model.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
opt_path = os.path.join(savedir, 'opt_state_dict.pth')
old_model_path = os.path.join(old_modeldir, 'model.pth')
old_score_list_path = os.path.join(old_modeldir, 'score_list.pkl')
old_opt_path = os.path.join(old_modeldir, 'opt_state_dict.pth')
score_list = hu.load_pkl(old_score_list_path)
model.load_state_dict(torch.load(old_model_path))
opt.load_state_dict(torch.load(old_opt_path))
s_epoch = score_list[-1]['epoch'] + 1
# save current model state for comparison
minimum = []
for param in model.parameters():
minimum.append(param.clone())
# Train & Val
# ------------
print('Starting experiment at epoch %d/%d' %
(s_epoch, exp_dict['max_epoch']))
for epoch in range(s_epoch, exp_dict['max_epoch']):
# Set seed
np.random.seed(exp_dict['runs'] + epoch)
torch.manual_seed(exp_dict['runs'] + epoch)
# torch.cuda.manual_seed_all(exp_dict['runs']+epoch) not needed since no cuda available
score_dict = {"epoch": epoch}
if metrics_flag:
# 1. Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model, train_set, metric_name='softmax_loss')
# metric_name=exp_dict["loss_func"])
# TODO: which loss should be used? (normal or with reguralizer?)
# 2. Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model, val_set, metric_name=exp_dict["acc_func"])
# 3. Train over train loader
model.train()
print("%d - Training model with %s..." %
(epoch, exp_dict["loss_func"]))
s_time = time.time()
for images, labels in tqdm.tqdm(train_loader):
# images, labels = images.cuda(), labels.cuda() no cuda available
opt.zero_grad()
loss = loss_function(model, images, labels, minimum,
0.1) # just works for custom loss function
loss.backward()
opt.step()
e_time = time.time()
# Record metrics
score_dict["step_size"] = opt.state["step_size"]
score_dict["n_forwards"] = opt.state["n_forwards"]
score_dict["n_backwards"] = opt.state["n_backwards"]
score_dict["batch_size"] = train_loader.batch_size
score_dict["train_epoch_time"] = e_time - s_time
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved: %s" % savedir)
with torch.nograd():
print('Current distance: %f',
metrics.computedistance(minimum, model))
print('Experiment completed')
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):
# 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, 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)
# set seed
# ---------------
seed = 42 + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Dataset
# -----------
# train loader
train_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=True,
datadir=savedir_base,
exp_dict=exp_dict)
train_loader = torch.utils.data.DataLoader(
train_set,
drop_last=True,
shuffle=True,
batch_size=exp_dict["batch_size"])
# val set
val_set = datasets.get_dataset(dataset_name=exp_dict["dataset"],
train_flag=False,
datadir=savedir_base,
exp_dict=exp_dict)
# Model
# -----------
model = models.get_model(exp_dict["model"], train_set=train_set).cuda()
# Choose loss and metric function
loss_function = metrics.get_metric_function(exp_dict["loss_func"])
# Compute fstar
# -------------
if exp_dict['opt'].get('fstar_flag'):
ut.compute_fstar(train_set, loss_function, savedir_base, exp_dict)
# Load Optimizer
n_batches_per_epoch = len(train_set) / float(exp_dict["batch_size"])
opt = optimizers.get_optimizer(opt_dict=exp_dict["opt"],
params=model.parameters(),
n_batches_per_epoch=n_batches_per_epoch)
# Checkpoint
# -----------
model_path = os.path.join(savedir, 'model.pth')
score_list_path = os.path.join(savedir, 'score_list.pkl')
opt_path = os.path.join(savedir, 'opt_state_dict.pth')
if os.path.exists(score_list_path):
# resume experiment
score_list = hu.load_pkl(score_list_path)
model.load_state_dict(torch.load(model_path))
opt.load_state_dict(torch.load(opt_path))
s_epoch = score_list[-1]['epoch'] + 1
else:
# restart experiment
score_list = []
s_epoch = 0
# Train & Val
# ------------
print('Starting experiment at epoch %d/%d' %
(s_epoch, exp_dict['max_epoch']))
for e in range(s_epoch, exp_dict['max_epoch']):
# Set seed
seed = e + exp_dict['runs']
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
score_dict = {}
# Compute train loss over train set
score_dict["train_loss"] = metrics.compute_metric_on_dataset(
model, train_set, metric_name=exp_dict["loss_func"])
# Compute val acc over val set
score_dict["val_acc"] = metrics.compute_metric_on_dataset(
model, val_set, metric_name=exp_dict["acc_func"])
# Train over train loader
model.train()
print("%d - Training model with %s..." % (e, exp_dict["loss_func"]))
# train and validate
s_time = time.time()
for batch in tqdm.tqdm(train_loader):
images, labels = batch["images"].cuda(), batch["labels"].cuda()
opt.zero_grad()
# closure
def closure():
return loss_function(model, images, labels, backwards=True)
opt.step(closure)
e_time = time.time()
# Record metrics
score_dict["epoch"] = e
score_dict["step_size"] = opt.state["step_size"]
score_dict["step_size_avg"] = opt.state["step_size_avg"]
score_dict["n_forwards"] = opt.state["n_forwards"]
score_dict["n_backwards"] = opt.state["n_backwards"]
score_dict["grad_norm"] = opt.state["grad_norm"]
score_dict["batch_size"] = train_loader.batch_size
score_dict["train_epoch_time"] = e_time - s_time
score_list += [score_dict]
# Report and save
print(pd.DataFrame(score_list).tail())
hu.save_pkl(score_list_path, score_list)
hu.torch_save(model_path, model.state_dict())
hu.torch_save(opt_path, opt.state_dict())
print("Saved: %s" % savedir)
print('Experiment completed')
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)
