def main():
args = vars(parser.parse_args())
check_args(args)
set_seeds(2020)
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"])
run_cfg = config.RunConfig(args["run_config"], eval=True)
output, save_prefix = set_output(args, "evaluate_model_log")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
torch.zeros((1)).to(device)
## Loading datasets
start = Print(" ".join(['start loading datasets']), output)
dataset_idxs, datasets, iterators = data_cfg.path.keys(), [], []
for idx in dataset_idxs:
dataset = get_dataset_from_configs(data_cfg, idx)
iterator = torch.utils.data.DataLoader(dataset, run_cfg.batch_size, shuffle=False, pin_memory=True, num_workers=4)
datasets.append(dataset)
iterators.append(iterator)
end = Print(" ".join(['loaded', str(len(dataset)), idx, 'samples']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
model, params = get_model(model_cfg, data_cfg.with_esa)
end = Print('end initializing a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
trainer = Trainer(model)
trainer.load_model(args["checkpoint"], output)
trainer.set_device(device)
end = Print('end setting trainer configurations', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start evaluating a model', output)
### validation
for idx, dataset, iterator in zip(dataset_idxs, datasets, iterators):
Print(" ".join(['processing', idx]), output)
### validation
for B, batch in enumerate(iterator):
trainer.evaluate(batch, device)
if B % 5 == 0: print('# {} {:.1%}'.format(idx, B / len(iterator)), end='\r', file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### save outputs
trainer.aggregate(dataset.set_labels)
trainer.save_outputs(idx, save_prefix)
end = Print('end evaluating a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
if not output == sys.stdout: output.close()
def main():
args = vars(parser.parse_args())
check_args(args)
set_seeds(2021)
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"])
run_cfg = config.RunConfig(args["run_config"],
eval=True,
sanity_check=args["sanity_check"])
output, save_prefix = set_output(args, "evaluate_model_log")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
torch.zeros((1)).to(device)
## Loading a dataset
start = Print(" ".join(['start loading a dataset']), output)
dataset_test = get_dataset_from_configs(data_cfg,
"test",
model_cfg.embedder,
sanity_check=args["sanity_check"])
iterator_test = torch.utils.data.DataLoader(dataset_test,
run_cfg.batch_size,
shuffle=False,
pin_memory=True,
num_workers=4)
end = Print(
" ".join(['loaded',
str(len(dataset_test)), 'dataset_test samples']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
model, params = get_model(model_cfg, run_cfg)
get_profile(model, dataset_test, output)
end = Print('end initializing a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
trainer = Trainer(model)
trainer.load_model(args["checkpoint"], output)
trainer.set_device(device)
end = Print('end setting trainer configurations', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start evaluating a model', output)
trainer.headline(output)
### validation
for B, batch in enumerate(iterator_test):
trainer.evaluate(batch, device)
if B % 5 == 0:
print('# {:.1%}'.format(B / len(iterator_test)),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log
trainer.save_outputs(save_prefix)
trainer.log(data_cfg.data_idx, output)
end = Print('end evaluating a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
if not output == sys.stdout: output.close()
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
cfgs = []
data_cfg = config.DataConfig(args["data_config"])
cfgs.append(data_cfg)
if args["lm_model_config"] is None:
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
num_classes=1)
cfgs += [model_cfg]
else:
lm_model_cfg = config.ModelConfig(args["lm_model_config"],
idx="lm_model_config",
input_dim=len(alphabet))
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
lm_dim=lm_model_cfg.num_layers *
lm_model_cfg.hidden_dim * 2,
num_classes=1)
cfgs += [model_cfg, lm_model_cfg]
if model_cfg.model_type == "RNN":
pr_model_cfg = config.ModelConfig(args["pr_model_config"],
idx="pr_model_config",
model_type="MLP",
num_classes=1)
if pr_model_cfg.projection:
pr_model_cfg.set_input_dim(model_cfg.embedding_dim)
else:
pr_model_cfg.set_input_dim(model_cfg.hidden_dim * 2)
cfgs.append(pr_model_cfg)
run_cfg = config.RunConfig(args["run_config"],
sanity_check=args["sanity_check"])
cfgs.append(run_cfg)
output, save_prefix = set_output(args, "eval_stability_log", test=True)
os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args[
"device"] is not None else ""
device, data_parallel = torch.device("cuda" if torch.cuda.is_available(
) else "cpu"), torch.cuda.device_count() > 1
config.print_configs(args, cfgs, device, output)
flag_rnn = (model_cfg.model_type == "RNN")
flag_lm_model = (args["lm_model_config"] is not None)
## load test datasets
start = Print(
" ".join(['start loading a test dataset', data_cfg.path["test"]]),
output)
dataset_test = ss.load_stability(data_cfg, "test", alphabet,
args["sanity_check"])
dataset_test = dataset.Seq_dataset(*dataset_test, alphabet, run_cfg,
flag_rnn, model_cfg.max_len)
collate_fn = dataset.collate_sequences if flag_rnn else None
iterator_test = torch.utils.data.DataLoader(dataset_test,
run_cfg.batch_size_eval,
collate_fn=collate_fn)
end = Print(" ".join(['loaded',
str(len(dataset_test)), 'sequences']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
models_list = [
] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
### model
if not flag_rnn: model = plus_tfm.PLUS_TFM(model_cfg)
elif not flag_lm_model: model = plus_rnn.PLUS_RNN(model_cfg)
else: model = p_elmo.P_ELMo(model_cfg)
models_list.append([model, "", flag_lm_model, flag_rnn, False])
### lm_model
if flag_lm_model:
lm_model = p_elmo.P_ELMo_lm(lm_model_cfg)
models_list.append([lm_model, "lm", True, False, False])
### pr_model
if flag_rnn:
pr_model = mlp.MLP(pr_model_cfg, per_seq=True)
models_list.append([pr_model, "pr", False, True, False])
params, pr_params = [], []
for model, idx, frz, _, _ in models_list:
if frz: continue
elif idx != "pr":
params += [p for p in model.parameters() if p.requires_grad]
else:
pr_params += [p for p in model.parameters() if p.requires_grad]
load_models(args,
models_list,
device,
data_parallel,
output,
tfm_cls=flag_rnn)
get_loss = plus_rnn.get_loss if flag_rnn else plus_tfm.get_loss
end = Print('end initializing a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
tasks_list.append(["cls", [], ["rho", "r"]])
if not flag_lm_model: tasks_list.append(["lm", [], ["acc"]])
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list)
trainer_args = {}
trainer_args["data_parallel"] = data_parallel
trainer_args["paired"] = False
if flag_rnn: trainer_args["projection"] = pr_model_cfg.projection
trainer_args["regression"] = True
if flag_rnn: trainer_args["evaluate_cls"] = plus_rnn.evaluate_cls_protein
else: trainer_args["evaluate_cls"] = plus_tfm.evaluate_cls_protein
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start evaluating a model', output)
Print(trainer.get_headline(test=True), output)
### evaluate cls
dataset_test.set_augment(False)
trainer.set_exec_flags(["cls", 'lm'], [True, False])
for b, batch in enumerate(iterator_test):
batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# cls {:.1%} loss={:.4f}'.format(b / len(iterator_test),
trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate lm
if not flag_lm_model:
dataset_test.set_augment(True)
trainer.set_exec_flags(["cls", 'lm'], [False, True])
for b, batch in enumerate(iterator_test):
batch = [
t.to(device) if type(t) is torch.Tensor else t for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# lm {:.1%} loss={:.4f}'.format(b / len(iterator_test),
trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
Print(trainer.get_log(test_idx="Stability", args=trainer_args), output)
trainer.reset()
end = Print('end evaluating a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
output.close()
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
cfgs = []
data_cfg = config.DataConfig(args["data_config"])
cfgs.append(data_cfg)
if args["lm_model_config"] is None:
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
num_classes=3)
cfgs += [model_cfg]
else:
lm_model_cfg = config.ModelConfig(args["lm_model_config"],
idx="lm_model_config",
input_dim=len(alphabet))
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
lm_dim=lm_model_cfg.num_layers *
lm_model_cfg.hidden_dim * 2,
num_classes=3)
cfgs += [model_cfg, lm_model_cfg]
if model_cfg.model_type == "RNN":
pr_model_cfg = config.ModelConfig(args["pr_model_config"],
idx="pr_model_config",
model_type="MLP",
num_classes=3)
if pr_model_cfg.projection:
pr_model_cfg.set_input_dim(model_cfg.embedding_dim)
else:
pr_model_cfg.set_input_dim(model_cfg.hidden_dim * 2)
cfgs.append(pr_model_cfg)
run_cfg = config.RunConfig(args["run_config"],
sanity_check=args["sanity_check"])
cfgs.append(run_cfg)
output, save_prefix = set_output(args, "train_transmembrane_log")
os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args[
"device"] is not None else ""
device, data_parallel = torch.device("cuda" if torch.cuda.is_available(
) else "cpu"), torch.cuda.device_count() > 1
config.print_configs(args, cfgs, device, output)
flag_rnn = (model_cfg.model_type == "RNN")
flag_lm_model = (args["lm_model_config"] is not None)
flag_lm_loss = (run_cfg.lm_loss_lambda != -1)
## load a train dataset
start = Print(
" ".join(['start loading train datasets', data_cfg.path["train"]]),
output)
dataset_train = transmembrane.load_transmembrane(data_cfg, "train",
alphabet,
args["sanity_check"])
dataset_train = dataset.Seq_dataset(*dataset_train,
alphabet,
run_cfg,
flag_rnn,
model_cfg.max_len,
truncate=False)
collate_fn = dataset.collate_sequences if flag_rnn else None
iterator_train = torch.utils.data.DataLoader(dataset_train,
run_cfg.batch_size_train,
collate_fn=collate_fn,
shuffle=True)
end = Print(" ".join(['loaded',
str(len(dataset_train)), 'sequences']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## load a dev dataset
start = Print(
" ".join(['start loading dev datasets', data_cfg.path["dev"]]), output)
dataset_dev = transmembrane.load_transmembrane(data_cfg, "dev", alphabet,
args["sanity_check"])
dataset_dev = dataset.Seq_dataset(*dataset_dev,
alphabet,
run_cfg,
flag_rnn,
model_cfg.max_len,
truncate=False)
iterator_dev = torch.utils.data.DataLoader(dataset_dev,
run_cfg.batch_size_eval,
collate_fn=collate_fn)
end = Print(" ".join(['loaded',
str(len(dataset_dev)), 'sequences']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
models_list = [
] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
### model
if not flag_rnn: model = plus_tfm.PLUS_TFM(model_cfg)
elif not flag_lm_model: model = plus_rnn.PLUS_RNN(model_cfg)
else: model = p_elmo.P_ELMo(model_cfg)
models_list.append([model, "", flag_lm_model, flag_rnn, False])
### lm_model
if flag_lm_model:
lm_model = p_elmo.P_ELMo_lm(lm_model_cfg)
models_list.append([lm_model, "lm", True, False, False])
### pr_model
if flag_rnn:
pr_model = mlp.MLP(pr_model_cfg)
models_list.append([pr_model, "pr", False, False, False])
params, pr_params = [], []
for model, idx, frz, _, _ in models_list:
if frz: continue
elif idx != "pr":
params += [p for p in model.parameters() if p.requires_grad]
else:
pr_params += [p for p in model.parameters() if p.requires_grad]
load_models(args,
models_list,
device,
data_parallel,
output,
tfm_cls=flag_rnn)
get_loss = plus_rnn.get_loss if flag_rnn else plus_tfm.get_loss
end = Print('end initializing a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
optim = torch.optim.Adam([{
'params': params,
'lr': run_cfg.learning_rate
}, {
'params': pr_params,
'lr': run_cfg.pr_learning_rate
}])
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
tasks_list.append(["cls", [], ["acc", "acc_p"]])
if flag_lm_loss: tasks_list.append(["lm", [], ["acc"]])
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list, optim)
trainer_args = {}
trainer_args["data_parallel"] = data_parallel
trainer_args["paired"] = False
if flag_rnn: trainer_args["projection"] = pr_model_cfg.projection
if flag_rnn:
trainer_args["evaluate_cls"] = plus_rnn.evaluate_transmembrane
else:
trainer_args["evaluate_cls"] = plus_tfm.evaluate_cls_amino
trainer_args["evaluate"] = ["cls", plus_tfm.evaluate_transmembrane]
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## train a model
start = Print('start training a model', output)
Print(trainer.get_headline(), output)
for epoch in range(run_cfg.num_epochs):
### train
dataset_train.set_augment(flag_lm_loss)
for B, batch in enumerate(iterator_train):
batch = [
t.to(device) if type(t) is torch.Tensor else t for t in batch
]
trainer.train(batch, trainer_args)
if B % 10 == 0:
print('# epoch [{}/{}] train {:.1%} loss={:.4f}'.format(
epoch + 1, run_cfg.num_epochs, B / len(iterator_train),
trainer.loss_train),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate cls
dataset_dev.set_augment(False)
trainer.set_exec_flags(["cls", 'lm'], [True, False])
for b, batch in enumerate(iterator_dev):
batch = [
t.to(device) if type(t) is torch.Tensor else t for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# cls {:.1%} loss={:.4f}'.format(
b / len(iterator_dev), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate lm
if flag_lm_loss:
dataset_dev.set_augment(True)
trainer.set_exec_flags(["cls", 'lm'], [False, True])
for b, batch in enumerate(iterator_dev):
batch = [
t.to(device) if type(t) is torch.Tensor else t
for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# lm {:.1%} loss={:.4f}'.format(
b / len(iterator_dev), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log and save models
trainer.save(save_prefix)
Print(trainer.get_log(epoch + 1, args=trainer_args), output)
trainer.set_exec_flags(["cls", "lm"], [True, True])
trainer.reset()
if trainer.patience == 0: break
end = Print('end training a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
num_classes=2)
run_cfg = config.RunConfig(args["run_config"],
sanity_check=args["sanity_check"])
output, save_prefix = set_output(args, "train_pfam_log")
os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args[
"device"] is not None else ""
device, data_parallel = torch.device("cuda" if torch.cuda.is_available(
) else "cpu"), torch.cuda.device_count() > 1
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
flag_rnn = (model_cfg.model_type == "RNN")
flag_paired = ("testpairs" in data_cfg.path)
## load a train dataset
start = Print(
" ".join(['start loading a train dataset:', data_cfg.path["train"]]),
output)
dataset_train = pfam.load_pfam(data_cfg, "train", alphabet,
args["sanity_check"])
dataset_train = dataset.Pfam_dataset(*dataset_train,
alphabet,
run_cfg,
flag_rnn,
model_cfg.max_len,
random_pairing=flag_paired,
sanity_check=args["sanity_check"])
if flag_rnn and flag_paired: collate_fn = dataset.collate_paired_sequences
elif flag_rnn: collate_fn = dataset.collate_sequences_pelmo
else: collate_fn = None
iterator_train = torch.utils.data.DataLoader(dataset_train,
run_cfg.batch_size_train,
collate_fn=collate_fn,
shuffle=True)
end = Print(" ".join(['loaded',
str(len(dataset_train)), 'sequences']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## load a test dataset
start = Print(
" ".join([
'start loading a test dataset:',
data_cfg.path["testpairs" if flag_paired else "test"]
]), output)
if flag_paired:
dataset_test = pfam.load_pfam_pairs(data_cfg, "testpairs", alphabet,
args["sanity_check"])
dataset_test = dataset.PairedPfam_dataset(*dataset_test, alphabet,
run_cfg, flag_rnn,
model_cfg.max_len)
else:
dataset_test = pfam.load_pfam(data_cfg, "test", alphabet,
args["sanity_check"])
dataset_test = dataset.Pfam_dataset(*dataset_test,
alphabet,
run_cfg,
flag_rnn,
model_cfg.max_len,
random_pairing=flag_paired,
sanity_check=args["sanity_check"])
iterator_test = torch.utils.data.DataLoader(dataset_test,
run_cfg.batch_size_eval,
collate_fn=collate_fn)
end = Print(
" ".join(['loaded',
str(len(dataset_test)), 'sequence(pair)s']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
models_list = [
] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
if not flag_rnn:
model = plus_tfm.PLUS_TFM(model_cfg)
run_cfg.set_total_steps(len(dataset_train))
elif model_cfg.rnn_type == "B":
model = plus_rnn.PLUS_RNN(model_cfg)
else:
model = p_elmo.P_ELMo_lm(model_cfg)
models_list.append([model, "", False, flag_rnn, flag_rnn and flag_paired])
params = []
for model, _, frz, _, _ in models_list:
if not frz:
params += [p for p in model.parameters() if p.requires_grad]
load_models(args, models_list, device, data_parallel, output)
get_loss = plus_rnn.get_loss if flag_rnn else plus_tfm.get_loss
end = Print('end initializing a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
if flag_rnn: optim = torch.optim.Adam(params, lr=run_cfg.learning_rate)
else: optim = get_BertAdam_optimizer(run_cfg, models_list[0][0])
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
if run_cfg.lm_loss_lambda != -1: tasks_list.append(["lm", [], ["acc"]])
if run_cfg.cls_loss_lambda != -1: tasks_list.append(["cls", [], ["acc"]])
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list, optim)
trainer_args = {}
trainer_args["data_parallel"] = data_parallel
trainer_args["paired"] = flag_paired
if flag_paired and flag_rnn:
trainer_args["evaluate_cls"] = plus_rnn.evaluate_sfp
elif flag_paired:
trainer_args["evaluate_cls"] = plus_tfm.evaluate_sfp
else:
trainer_args["num_alphabets"] = len(alphabet)
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## train a model
start = Print('start training a model', output)
Print(trainer.get_headline(), output)
for epoch in range(run_cfg.num_epochs):
### train
for B, batch in enumerate(iterator_train):
batch = [
t.to(device) if type(t) is torch.Tensor else t for t in batch
]
trainer.train(batch, trainer_args)
if B % 10 == 0:
print('# epoch [{}/{}] train {:.1%} loss={:.4f}'.format(
epoch + 1, run_cfg.num_epochs, B / len(iterator_train),
trainer.loss_train),
end='\r',
file=sys.stderr)
if trainer.global_step % 20000 == 0 or args["sanity_check"]:
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate lm
if run_cfg.lm_loss_lambda != -1:
if flag_paired: dataset_test.set_augment(True)
trainer.set_exec_flags(["lm", "cls"], [True, False])
for b, batch in enumerate(iterator_test):
batch = [
t.to(device) if type(t) is torch.Tensor else t
for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# lm {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate cls
if run_cfg.cls_loss_lambda != -1:
dataset_test.set_augment(False)
trainer.set_exec_flags(["lm", "cls"], [False, True])
for b, batch in enumerate(iterator_test):
batch = [
t.to(device) if type(t) is torch.Tensor else t
for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# cls {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log and save models
trainer.save(save_prefix)
Print(trainer.get_log(epoch + 1, args=trainer_args), output)
trainer.set_exec_flags(["lm", "cls"], [True, True])
trainer.reset()
end = Print('end trainin a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
output.close()
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
cfgs = []
data_cfg = config.DataConfig(args["data_config"])
cfgs.append(data_cfg)
if args["lm_model_config"] is None:
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
num_classes=5)
cfgs += [model_cfg]
else:
lm_model_cfg = config.ModelConfig(args["lm_model_config"],
idx="lm_model_config",
input_dim=len(alphabet))
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
lm_dim=lm_model_cfg.num_layers *
lm_model_cfg.hidden_dim * 2,
num_classes=5)
cfgs += [model_cfg, lm_model_cfg]
run_cfg = config.RunConfig(args["run_config"],
eval=True,
sanity_check=args["sanity_check"])
cfgs.append(run_cfg)
output, save_prefix = set_output(args, "eval_homology_log", test=True)
os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args[
"device"] is not None else ""
device, data_parallel = torch.device("cuda" if torch.cuda.is_available(
) else "cpu"), torch.cuda.device_count() > 1
config.print_configs(args, cfgs, device, output)
flag_rnn = (model_cfg.model_type == "RNN")
flag_lm_model = (args["pretrained_lm_model"] is not None)
flag_cm_model = (args["pretrained_cm_model"] is not None)
## load test datasets
idxs_test, datasets_test, iterators_test = [
key for key in data_cfg.path.keys() if "pairs" in key
], [], []
start = Print(" ".join(['start loading test datasets'] + idxs_test),
output)
collate_fn = dataset.collate_paired_sequences if flag_rnn else None
for idx_test in idxs_test:
dataset_test = homology.load_homology_pairs(data_cfg, idx_test,
alphabet, flag_cm_model,
args["sanity_check"])
dataset_test = dataset.PairedHomology_dataset(*dataset_test, alphabet,
run_cfg, flag_rnn,
model_cfg.max_len)
iterator_test = torch.utils.data.DataLoader(dataset_test,
run_cfg.batch_size_eval,
collate_fn=collate_fn)
datasets_test.append(dataset_test)
iterators_test.append(iterator_test)
end = Print(
" ".join(['loaded',
str(len(dataset_test)), 'sequence pairs']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
models_list = [
] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
### model
if not flag_rnn: model = plus_tfm.PLUS_TFM(model_cfg)
elif not flag_lm_model: model = plus_rnn.PLUS_RNN(model_cfg)
else: model = p_elmo.P_ELMo(model_cfg)
models_list.append([model, "", True, False, False])
### lm_model
if flag_lm_model:
lm_model = p_elmo.P_ELMo_lm(lm_model_cfg)
models_list.append([lm_model, "lm", True, False, False])
### cm_model
if flag_cm_model:
cm_model = cnn.ConvNet2D(model_cfg.embedding_dim)
models_list.append([cm_model, "cm", True, False, False])
load_models(args, models_list, device, data_parallel, output)
get_loss = plus_rnn.get_loss if flag_rnn else plus_tfm.get_loss
end = Print('end initializing a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
tasks_list.append([
"cls", [],
["acc", "r", "rho", "aupr_cl", "aupr_fo", "aupr_sf", "aupr_fa"]
])
if not flag_lm_model: tasks_list.append(["lm", [], ["acc"]])
if flag_cm_model: tasks_list.append(["cm", [], ["pr", "re", "f1"]])
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list)
trainer_args = {}
trainer_args["data_parallel"] = data_parallel
trainer_args["paired"] = True
if flag_rnn: trainer_args["evaluate_cls"] = plus_rnn.evaluate_homology
else: trainer_args["evaluate_cls"] = plus_tfm.evaluate_homology
trainer_args["evaluate"] = ["cls", homology.evaluate_homology]
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start evaluating a model', output)
Print(trainer.get_headline(test=True), output)
for idx_test, dataset_test, iterator_test in zip(idxs_test, datasets_test,
iterators_test):
### evaluate cls and cm
dataset_test.set_augment(False)
trainer.set_exec_flags(["cls", 'lm', "cm"], [True, False, True])
for b, batch in enumerate(iterator_test):
batch = [
t.to(device) if type(t) is torch.Tensor else t for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# cls {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate lm
if not flag_lm_model:
dataset_test.set_augment(True)
trainer.set_exec_flags(["cls", 'lm', "cm"], [False, True, False])
for b, batch in enumerate(iterator_test):
batch = [
t.to(device) if type(t) is torch.Tensor else t
for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# lm {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
Print(trainer.get_log(test_idx=idx_test, args=trainer_args), output)
trainer.reset()
end = Print('end evaluating a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
output.close()
def main():
hp = arg_parser.parse_args()
validate_args(hp)
logger = Logger(hp, model_name='Baseline', write_mode=hp.write_mode)
if not hp.test:
print(f"Running experiment {logger.model_hash}.")
if hp.write_mode != 'NONE':
logger.write_hyperparams()
print(f"Hyperparameters and checkpoints will be saved in "
f"{logger.run_savepath}")
torch.manual_seed(hp.seed)
torch.cuda.manual_seed_all(
hp.seed) # silently ignored if there are no GPUs
CUDA = False
if torch.cuda.is_available() and not hp.no_cuda:
CUDA = True
USE_POS = False
if hp.pos_emb_dim is not None:
USE_POS = True
SAVE_IN_SPREADSHEET = False
if hp.spreadsheet:
SAVE_IN_SPREADSHEET = True
corpus = IESTCorpus(config.corpora_dict,
hp.corpus,
force_reload=hp.force_reload,
train_data_proportion=hp.train_data_proportion,
dev_data_proportion=hp.dev_data_proportion,
batch_size=hp.batch_size,
lowercase=hp.lowercase,
use_pos=USE_POS)
if hp.embeddings != "random":
# Load pre-trained embeddings
embeddings = Embeddings(
config.embedding_dict[hp.embeddings],
k_most_frequent=None,
force_reload=hp.force_reload,
)
# Get subset of embeddings corresponding to our vocabulary
embedding_matrix = embeddings.generate_embedding_matrix(
corpus.lang.token2id)
print(
f"{len(embeddings.unknown_tokens)} words from vocabulary not found "
f"in {hp.embeddings} embeddings. ")
else:
word_vocab_size = len(corpus.lang.token2id)
embedding_matrix = np.random.uniform(-0.05,
0.05,
size=(word_vocab_size, 300))
# Repeat process for character embeddings with the difference that they are
# not pretrained
# Initialize character embedding matrix randomly
char_vocab_size = len(corpus.lang.char2id)
char_embedding_matrix = np.random.uniform(-0.05,
0.05,
size=(char_vocab_size,
hp.char_emb_dim))
pos_embedding_matrix = None
if USE_POS:
# Initialize pos embedding matrix randomly
pos_vocab_size = len(corpus.pos_lang.token2id)
pos_embedding_matrix = np.random.uniform(-0.05,
0.05,
size=(pos_vocab_size,
hp.pos_emb_dim))
if hp.model_hash:
# WARNING: This feature should be used only when testing. We
# haven't implemented a proper way to resume training yet.
experiment_path = os.path.join(config.RESULTS_PATH,
hp.model_hash + '*')
ext_experiment_path = glob(experiment_path)
assert len(
ext_experiment_path) == 1, 'Try provinding a longer model hash'
ext_experiment_path = ext_experiment_path[0]
model_path = os.path.join(ext_experiment_path, 'best_model.pth')
model = torch.load(model_path)
else:
num_classes = len(corpus.label2id)
batch_size = corpus.train_batches.batch_size
hidden_sizes = hp.lstm_hidden_size
model = IESTClassifier(
num_classes,
batch_size,
embedding_matrix=embedding_matrix,
char_embedding_matrix=char_embedding_matrix,
pos_embedding_matrix=pos_embedding_matrix,
word_encoding_method=hp.word_encoding_method,
word_char_aggregation_method=hp.word_char_aggregation_method,
sent_encoding_method=hp.model,
hidden_sizes=hidden_sizes,
use_cuda=CUDA,
pooling_method=hp.pooling_method,
batch_first=True,
dropout=hp.dropout,
lstm_layer_dropout=hp.lstm_layer_dropout,
sent_enc_dropout=hp.sent_enc_dropout,
sent_enc_layers=hp.sent_enc_layers)
if CUDA:
model.cuda()
if hp.write_mode != 'NONE':
logger.write_architecture(str(model))
logger.write_current_run_details(str(model))
if hp.model == 'transformer':
core_optimizer = torch.optim.Adam(model.parameters(),
lr=0,
betas=(0.9, 0.98),
eps=1e-9)
transformer_scheduler = TransformerScheduler(
1024, factor=1, warmup_steps=hp.warmup_iters)
optimizer = ScheduledOptim(core_optimizer, transformer_scheduler)
else:
# optimizer = OptimWithDecay(model.parameters(),
# method=hp.optim,
# initial_lr=hp.learning_rate,
# max_grad_norm=hp.grad_clipping,
# lr_decay=hp.learning_rate_decay,
# start_decay_at=hp.start_decay_at,
# decay_every=hp.decay_every)
core_optimizer = torch.optim.Adam(
[param for param in model.parameters() if param.requires_grad],
lr=0,
)
max_iter = corpus.train_batches.num_batches * hp.epochs
slanted_triangular_scheduler = SlantedTriangularScheduler(
max_iter, max_lr=hp.max_lr, cut_fraction=0.1, ratio=32)
optimizer = ScheduledOptim(core_optimizer,
slanted_triangular_scheduler)
loss_function = torch.nn.CrossEntropyLoss()
trainer = Trainer(model,
optimizer,
loss_function,
num_epochs=hp.epochs,
use_cuda=CUDA,
log_interval=hp.log_interval)
# FIXME: This test block of code looks ugly here <2018-06-29 11:41:51, Jorge Balazs>
if hp.test:
if hp.model_hash is None:
raise RuntimeError(
'You should have provided a pre-trained model hash with the '
'--model_hash flag')
print(f'Testing model {model_path}')
eval_dict = trainer.evaluate(corpus.test_batches)
probs = np_softmax(eval_dict['output'])
probs_filepath = os.path.join(ext_experiment_path,
'test_probabilities.csv')
np.savetxt(probs_filepath, probs, delimiter=',', fmt='%.8f')
print(f'Saved prediction probs in {probs_filepath}')
labels_filepath = os.path.join(ext_experiment_path,
'test_predictions.txt')
labels = [label + '\n' for label in eval_dict['labels']]
with open(labels_filepath, 'w', encoding='utf-8') as f:
f.writelines(labels)
print(f'Saved prediction file in {labels_filepath}')
representations_filepath = os.path.join(
ext_experiment_path, 'sentence_representations.txt')
if hp.save_sent_reprs:
with open(representations_filepath, 'w', encoding='utf-8') as f:
np.savetxt(representations_filepath,
eval_dict['sent_reprs'],
delimiter=' ',
fmt='%.8f')
exit()
# Main Training Loop
writer = None
if hp.write_mode != 'NONE':
writer = SummaryWriter(logger.run_savepath)
try:
best_accuracy = None
for epoch in tqdm(range(hp.epochs), desc='Epoch'):
total_loss = 0
trainer.train_epoch(corpus.train_batches, epoch, writer)
corpus.train_batches.shuffle_examples()
eval_dict = trainer.evaluate(corpus.dev_batches, epoch, writer)
if hp.update_learning_rate and hp.model != 'transformer':
# hp.update_learning_rate is not supposed to be used with
# scheduled learning rates
optim_updated, new_lr = trainer.optimizer.updt_lr_accuracy(
epoch, eval_dict['accuracy'])
# TODO: lr_threshold shouldn't be hardcoded
lr_threshold = 1e-5
if new_lr < lr_threshold:
tqdm.write(f'Learning rate smaller than {lr_threshold}, '
f'stopping.')
break
if optim_updated:
tqdm.write(f'Learning rate decayed to {new_lr}')
accuracy = eval_dict['accuracy']
if not best_accuracy or accuracy > best_accuracy:
best_accuracy = accuracy
logger.update_results({
'best_valid_acc': best_accuracy,
'best_epoch': epoch
})
if hp.write_mode != 'NONE':
probs = np_softmax(eval_dict['output'])
probs_filepath = os.path.join(
logger.run_savepath, 'best_dev_probabilities.csv')
np.savetxt(probs_filepath,
probs,
delimiter=',',
fmt='%.8f')
labels_filepath = os.path.join(logger.run_savepath,
'best_dev_predictions.txt')
labels = [label + '\n' for label in eval_dict['labels']]
with open(labels_filepath, 'w', encoding='utf-8') as f:
f.writelines(labels)
if hp.save_model:
logger.torch_save_file('best_model_state_dict.pth',
model.state_dict(),
progress_bar=tqdm)
logger.torch_save_file('best_model.pth',
model,
progress_bar=tqdm)
except KeyboardInterrupt:
pass
finally:
if SAVE_IN_SPREADSHEET:
logger.insert_in_googlesheets()
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
cfgs = []
data_cfg = config.DataConfig(args["data_config"]); cfgs.append(data_cfg)
if args["lm_model_config"] is None:
model_cfg = config.ModelConfig(args["model_config"], input_dim=len(alphabet), num_classes=5)
cfgs += [model_cfg]
else:
lm_model_cfg = config.ModelConfig(args["lm_model_config"], idx="lm_model_config", input_dim=len(alphabet))
model_cfg = config.ModelConfig(args["model_config"], input_dim=len(alphabet),
lm_dim=lm_model_cfg.num_layers * lm_model_cfg.hidden_dim * 2, num_classes=5)
cfgs += [model_cfg, lm_model_cfg]
run_cfg = config.RunConfig(args["run_config"], sanity_check=args["sanity_check"]); cfgs.append(run_cfg)
output, save_prefix = set_output(args, "train_homology_log")
os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args["device"] is not None else ""
device, data_parallel = torch.device("cuda" if torch.cuda.is_available() else "cpu"), torch.cuda.device_count() > 1
config.print_configs(args, cfgs, device, output)
flag_rnn = (model_cfg.model_type == "RNN")
flag_lm_model = (args["lm_model_config"] is not None)
flag_lm_loss = (run_cfg.lm_loss_lambda != -1)
flag_cm_loss = (run_cfg.cm_loss_lambda != -1)
## load a train dataset
start = Print(" ".join(['start loading a train dataset:', data_cfg.path["train"]]), output)
dataset_train = homology.load_homology(data_cfg, "train", alphabet, flag_cm_loss, args["sanity_check"])
dataset_train = dataset.Homology_dataset(*dataset_train, alphabet, run_cfg, flag_rnn, model_cfg.max_len)
sampler = dataset.HomolgySampler(dataset_train.labels, run_cfg)
collate_fn = dataset.collate_paired_sequences if flag_rnn else None
iterator_train = torch.utils.data.DataLoader(dataset_train, run_cfg.batch_size_train, collate_fn=collate_fn, sampler=sampler)
end = Print(" ".join(['loaded', str(int(np.sqrt(len(dataset_train)))), 'sequences']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## load a dev dataset
start = Print(" ".join(['start loading a dev dataset:', data_cfg.path["devpairs"]]), output)
dataset_test = homology.load_homology_pairs(data_cfg, "devpairs", alphabet, flag_cm_loss, args["sanity_check"])
dataset_test = dataset.PairedHomology_dataset(*dataset_test, alphabet, run_cfg, flag_rnn, model_cfg.max_len)
iterator_test = torch.utils.data.DataLoader(dataset_test, run_cfg.batch_size_eval, collate_fn=collate_fn)
end = Print(" ".join(['loaded', str(len(dataset_test)), 'sequence pairs']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
models_list = [] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
### model
if not flag_rnn: model = plus_tfm.PLUS_TFM(model_cfg)
elif not flag_lm_model: model = plus_rnn.PLUS_RNN(model_cfg)
else: model = p_elmo.P_ELMo(model_cfg)
models_list.append([model, "", False, flag_rnn, flag_rnn])
### lm_model
if flag_lm_model:
lm_model = p_elmo.P_ELMo_lm(lm_model_cfg)
models_list.append([lm_model, "lm", True, False, False])
### cm_model
if flag_cm_loss:
cm_model = cnn.ConvNet2D(model_cfg.embedding_dim)
models_list.append([cm_model, "cm", False, False, True])
params = []
for model, _, frz, _, _ in models_list:
if not frz: params += [p for p in model.parameters() if p.requires_grad]
load_models(args, models_list, device, data_parallel, output, tfm_cls=flag_rnn)
get_loss = plus_rnn.get_loss if flag_rnn else plus_tfm.get_loss
end = Print('end initializing a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
optim = torch.optim.Adam(params, lr=run_cfg.learning_rate)
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
tasks_list.append(["cls", [], ["acc", "r", "rho"]])
if flag_lm_loss: tasks_list.append(["lm", [], ["acc"]])
if flag_cm_loss: tasks_list.append(["cm", [], ["pr", "re", "f1"]])
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list, optim)
trainer_args = {}
trainer_args["data_parallel"] = data_parallel
trainer_args["paired"] = True
if flag_rnn: trainer_args["evaluate_cls"] = plus_rnn.evaluate_homology
else: trainer_args["evaluate_cls"] = plus_tfm.evaluate_homology
trainer_args["evaluate"] = ["cls", homology.evaluate_homology]
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## train a model
start = Print('start training a model', output)
Print(trainer.get_headline(), output)
for epoch in range(run_cfg.num_epochs):
### train
for B, batch in enumerate(iterator_train):
batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
trainer.train(batch, trainer_args)
if B % 10 == 0: print('# epoch [{}/{}] train {:.1%} loss={:.4f}'.format(
epoch + 1, run_cfg.num_epochs, B / len(iterator_train), trainer.loss_train), end='\r', file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate cls and cm
dataset_test.set_augment(False)
trainer.set_exec_flags(["cls", 'lm', "cm"], [True, False, flag_cm_loss])
for b, batch in enumerate(iterator_test):
batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0: print('# cls {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval), end='\r', file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate lm
if flag_lm_loss:
dataset_test.set_augment(True)
trainer.set_exec_flags(["cls", 'lm', "cm"], [False, True, False])
for b, batch in enumerate(iterator_test):
batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0: print('# lm {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval), end='\r', file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log and save models
trainer.save(save_prefix)
Print(trainer.get_log(epoch + 1, args=trainer_args), output)
trainer.set_exec_flags(["cls", "lm", "cm"], [True, True, True])
trainer.reset()
if trainer.patience == 0: break
end = Print('end training a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
output.close()
def main():
set_seeds(2020)
args = vars(parser.parse_args())
alphabet = Protein()
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"],
input_dim=len(alphabet),
num_classes=2)
run_cfg = config.RunConfig(args["run_config"],
eval=True,
sanity_check=args["sanity_check"])
output, save_prefix = set_output(args, "eval_pfam_log", test=True)
os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args[
"device"] is not None else ""
device, data_parallel = torch.device("cuda" if torch.cuda.is_available(
) else "cpu"), torch.cuda.device_count() > 1
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
flag_rnn = (model_cfg.model_type == "RNN")
flag_paired = ("testpairs" in data_cfg.path)
## load a test dataset
start = Print(
" ".join([
'start loading a test dataset:',
data_cfg.path["testpairs" if flag_paired else "test"]
]), output)
if flag_paired:
dataset_test = pfam.load_pfam_pairs(data_cfg, "testpairs", alphabet,
args["sanity_check"])
dataset_test = dataset.PairedPfam_dataset(*dataset_test, alphabet,
run_cfg, flag_rnn,
model_cfg.max_len)
else:
dataset_test = pfam.load_pfam(data_cfg, "test", alphabet,
args["sanity_check"])
dataset_test = dataset.Pfam_dataset(*dataset_test,
alphabet,
run_cfg,
flag_rnn,
model_cfg.max_len,
random_pairing=flag_paired,
sanity_check=args["sanity_check"])
if flag_rnn and flag_paired: collate_fn = dataset.collate_paired_sequences
elif flag_rnn: collate_fn = dataset.collate_sequences_pelmo
else: collate_fn = None
iterator_test = torch.utils.data.DataLoader(dataset_test,
run_cfg.batch_size_eval,
collate_fn=collate_fn)
end = Print(
" ".join(['loaded',
str(len(dataset_test)), 'sequence(pair)s']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
models_list = [
] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
if not flag_rnn: model = plus_tfm.PLUS_TFM(model_cfg)
elif model_cfg.rnn_type == "B": model = plus_rnn.PLUS_RNN(model_cfg)
else: model = p_elmo.P_ELMo_lm(model_cfg)
models_list.append([model, "", True, False, False])
load_models(args, models_list, device, data_parallel, output)
get_loss = plus_rnn.get_loss if flag_rnn else plus_tfm.get_loss
end = Print('end initializing a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
tasks_list.append(["lm", [], ["acc"]])
if flag_paired: tasks_list.append(["cls", [], ["acc"]])
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list)
trainer_args = {}
trainer_args["data_parallel"] = data_parallel
trainer_args["paired"] = flag_paired
if flag_paired and flag_rnn:
trainer_args["evaluate_cls"] = plus_rnn.evaluate_sfp
elif flag_paired:
trainer_args["evaluate_cls"] = plus_tfm.evaluate_sfp
else:
trainer_args["num_alphabets"] = len(alphabet)
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start evaluating a model', output)
Print(trainer.get_headline(test=True), output)
### evaluate lm
if flag_paired: dataset_test.set_augment(True)
trainer.set_exec_flags(["lm", "cls"], [True, False])
for b, batch in enumerate(iterator_test):
batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# lm {:.1%} loss={:.4f}'.format(b / len(iterator_test),
trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### evaluate cls
if flag_paired:
dataset_test.set_augment(False)
trainer.set_exec_flags(["lm", "cls"], [False, True])
for b, batch in enumerate(iterator_test):
batch = [
t.to(device) if type(t) is torch.Tensor else t for t in batch
]
trainer.evaluate(batch, trainer_args)
if b % 10 == 0:
print('# cls {:.1%} loss={:.4f}'.format(
b / len(iterator_test), trainer.loss_eval),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
Print(trainer.get_log(test_idx="Pfam", args=trainer_args), output)
end = Print('end evaluating a model', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
output.close()
def main():
args = vars(parser.parse_args())
check_args(args)
set_seeds(2020)
data_cfg = config.DataConfig(args["data_config"])
model_cfg = config.ModelConfig(args["model_config"])
run_cfg = config.RunConfig(args["run_config"], eval=False)
output, save_prefix = set_output(args, "train_model_log")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config.print_configs(args, [data_cfg, model_cfg, run_cfg], device, output)
torch.zeros((1)).to(device)
## Loading datasets
start = Print(" ".join(['start loading datasets']), output)
dataset_train = get_dataset_from_configs(data_cfg, split_idx="train")
dataset_val = get_dataset_from_configs(data_cfg, split_idx="val")
iterator_train = torch.utils.data.DataLoader(dataset_train,
run_cfg.batch_size,
shuffle=True,
pin_memory=True,
num_workers=4)
iterator_val = torch.utils.data.DataLoader(dataset_val,
run_cfg.batch_size,
shuffle=False,
pin_memory=True,
num_workers=4)
end = Print(
" ".join(['loaded',
str(len(dataset_train)), 'dataset_train samples']), output)
end = Print(
" ".join(['loaded',
str(len(dataset_val)), 'dataset_val samples']), output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## initialize a model
start = Print('start initializing a model', output)
model, params = get_model(model_cfg, data_cfg.with_esa,
run_cfg.dropout_rate)
end = Print('end initializing a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## setup trainer configurations
start = Print('start setting trainer configurations', output)
trainer = Trainer(model)
trainer.set_device(device)
trainer.set_optim_scheduler(run_cfg, params)
end = Print('end setting trainer configurations', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
## train a model
start = Print('start training a model', output)
Print(trainer.get_headline(), output)
for epoch in range(int(trainer.epoch), run_cfg.num_epochs):
### train
for B, batch in enumerate(iterator_train):
trainer.train(batch, device)
if B % 5 == 0:
print('# epoch [{}/{}] train {:.1%}'.format(
epoch + 1, run_cfg.num_epochs, B / len(iterator_train)),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### validation
for B, batch in enumerate(iterator_val):
trainer.evaluate(batch, device)
if B % 5 == 0:
print('# epoch [{}/{}] val {:.1%}'.format(
epoch + 1, run_cfg.num_epochs, B / len(iterator_val)),
end='\r',
file=sys.stderr)
print(' ' * 150, end='\r', file=sys.stderr)
### print log and save models
trainer.epoch += 1
trainer.scheduler_step()
trainer.save_model(save_prefix)
trainer.log("val", output)
end = Print('end training a model', output)
Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)
if not output == sys.stdout: output.close()
def main():
hp = arg_parser.parse_args()
logger = Logger(hp, model_name='elmo4irony', write_mode=hp.write_mode)
if hp.write_mode != 'NONE':
logger.write_hyperparams()
torch.manual_seed(hp.seed)
torch.cuda.manual_seed_all(hp.seed) # silently ignored if there are no GPUs
CUDA = False
if torch.cuda.is_available() and not hp.no_cuda:
CUDA = True
USE_POS = False
corpus = ClassificationCorpus(config.corpora_dict, hp.corpus,
force_reload=hp.force_reload,
train_data_proportion=hp.train_data_proportion,
dev_data_proportion=hp.dev_data_proportion,
batch_size=hp.batch_size,
lowercase=hp.lowercase,
use_pos=USE_POS)
if hp.model_hash:
experiment_path = os.path.join(config.RESULTS_PATH, hp.model_hash + '*')
ext_experiment_path = glob(experiment_path)
assert len(ext_experiment_path) == 1, 'Try providing a longer model hash'
ext_experiment_path = ext_experiment_path[0]
model_path = os.path.join(ext_experiment_path, 'best_model.pth')
model = torch.load(model_path)
else:
# Define some specific parameters for the model
num_classes = len(corpus.label2id)
batch_size = corpus.train_batches.batch_size
hidden_sizes = hp.lstm_hidden_size
model = Classifier(num_classes,
batch_size,
hidden_sizes=hidden_sizes,
use_cuda=CUDA,
pooling_method=hp.pooling_method,
batch_first=True,
dropout=hp.dropout,
sent_enc_dropout=hp.sent_enc_dropout,
sent_enc_layers=hp.sent_enc_layers)
if CUDA:
model.cuda()
if hp.write_mode != 'NONE':
logger.write_architecture(str(model))
logger.write_current_run_details(str(model))
optimizer = OptimWithDecay(model.parameters(),
method=hp.optim,
initial_lr=hp.learning_rate,
max_grad_norm=hp.grad_clipping,
lr_decay=hp.learning_rate_decay,
start_decay_at=hp.start_decay_at,
decay_every=hp.decay_every)
loss_function = torch.nn.CrossEntropyLoss()
trainer = Trainer(model, optimizer, loss_function, num_epochs=hp.epochs,
use_cuda=CUDA, log_interval=hp.log_interval)
if hp.test:
if hp.model_hash is None:
raise RuntimeError(
'You should have provided a pre-trained model hash with the '
'--model_hash flag'
)
print(f'Testing model {model_path}')
eval_dict = trainer.evaluate(corpus.test_batches)
probs = np_softmax(eval_dict['output'])
probs_filepath = os.path.join(ext_experiment_path,
'test_probs.csv')
np.savetxt(probs_filepath, probs,
delimiter=',', fmt='%.8f')
print(f'Saved prediction probs in {probs_filepath}')
labels_filepath = os.path.join(ext_experiment_path,
'predictions.txt')
labels = [label + '\n' for label in eval_dict['labels']]
with open(labels_filepath, 'w', encoding='utf-8') as f:
f.writelines(labels)
print(f'Saved prediction file in {labels_filepath}')
# We know the last segment of ext_experiment_path corresponds to the
# model hash
full_model_hash = os.path.basename(ext_experiment_path)
# WARNING: We are accessing an internal method of the Logger class;
# This could corrupt the results database if not used properly
logger._update_in_db({'test_acc': eval_dict['accuracy'],
'test_f1': eval_dict['f1'],
'test_p': eval_dict['precision'],
'test_r': eval_dict['recall']},
experiment_hash=full_model_hash)
exit()
writer = None
if hp.write_mode != 'NONE':
writer = SummaryWriter(logger.run_savepath)
try:
best_accuracy = None
for epoch in tqdm(range(hp.epochs), desc='Epoch'):
total_loss = 0
trainer.train_epoch(corpus.train_batches, epoch, writer)
corpus.train_batches.shuffle_examples()
eval_dict = trainer.evaluate(corpus.dev_batches, epoch, writer)
if hp.training_schedule == 'decay':
optim_updated, new_lr = trainer.optimizer.updt_lr_accuracy(epoch, eval_dict['accuracy'])
lr_threshold = 1e-5
if new_lr < lr_threshold:
tqdm.write(f'Learning rate smaller than {lr_threshold}, '
f'stopping.')
break
if optim_updated:
tqdm.write(f'Learning rate decayed to {new_lr}')
if hp.training_schedule == 'decay-nie':
optim_updated, new_lr = trainer.optimizer.update_learning_rate_nie(epoch)
if optim_updated:
tqdm.write(f'Learning rate decayed to {new_lr}')
accuracy = eval_dict['accuracy']
if not best_accuracy or accuracy > best_accuracy:
best_accuracy = accuracy
logger.update_results({'best_valid_acc': best_accuracy,
'best_epoch': epoch})
if hp.write_mode != 'NONE':
probs = np_softmax(eval_dict['output'])
probs_filepath = os.path.join(logger.run_savepath,
'best_eval_probs.csv')
np.savetxt(probs_filepath, probs,
delimiter=',', fmt='%.8f')
labels_filepath = os.path.join(logger.run_savepath,
'predictions_dev.txt')
labels = [label + '\n' for label in eval_dict['labels']]
with open(labels_filepath, 'w', encoding='utf-8') as f:
f.writelines(labels)
if hp.save_model:
logger.torch_save_file('best_model_state_dict.pth',
model.state_dict(),
progress_bar=tqdm)
logger.torch_save_file('best_model.pth',
model,
progress_bar=tqdm)
except KeyboardInterrupt:
pass
