def __init__(self, device: Union[None, str, torch.device] = None, **kwargs):
super().__init__(device, **kwargs)
# This seed is copied from PLUS
set_seeds(2020)
# We inlined the config json files since they aren't shipped with the package
self._alphabet = Protein()
self._model_cfg = ModelConfig(input_dim=len(self._alphabet))
self._model_cfg.model_type = "RNN"
self._model_cfg.rnn_type = "B"
self._model_cfg.num_layers = 3
self._model_cfg.hidden_dim = 512
self._model_cfg.embedding_dim = 100
self._run_cfg = RunConfig(sanity_check=True)
self._run_cfg.batch_size_eval = 512
self._model = PLUS_RNN(self._model_cfg)
self._model.load_weights(self._options["model_file"])
self._model = self._model.to(self._device)
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=8)
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=8)
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=8)
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_secstr_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
idxs_test, datasets_test, iterators_test = [
key for key in data_cfg.path.keys() if "test" in key
], [], []
start = Print(" ".join(['start loading test datasets'] + idxs_test),
output)
collate_fn = dataset.collate_sequences if flag_rnn else None
for idx_test in idxs_test:
dataset_test = secstr.load_secstr(data_cfg, idx_test, alphabet,
args["sanity_check"])
dataset_test = dataset.Seq_dataset(*dataset_test,
alphabet,
run_cfg,
flag_rnn,
model_cfg.max_len,
truncate=False)
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)), '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)
tasks_list = [] # list of lists [idx, metrics_train, metrics_eval]
tasks_list.append(["cls", [], ["acc8", "acc3"]])
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
if flag_rnn: trainer_args["evaluate_cls"] = plus_rnn.evaluate_cls_amino
else: trainer_args["evaluate_cls"] = plus_tfm.evaluate_cls_amino
trainer_args["evaluate"] = ["cls", secstr.evaluate_secstr]
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
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=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():
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_plus = (model_cfg.rnn_type == "B") if flag_rnn else False
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, augment=flag_plus, sanity_check=args["sanity_check"])
if flag_rnn and flag_paired: collate_fn = dataset.collate_paired_sequences
elif flag_rnn and flag_plus: collate_fn = dataset.collate_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, augment=flag_plus, 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 flag_plus: 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=10)
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=10)
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=10)
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_localization_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 a train dataset:', data_cfg.path["train"]]),
output)
dataset_train = localization.load_localization(data_cfg, "train", alphabet,
args["sanity_check"])
dataset_train = dataset.Seq_dataset(*dataset_train, alphabet, run_cfg,
flag_rnn, model_cfg.max_len)
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 a dev dataset:', data_cfg.path["dev"]]),
output)
dataset_dev = localization.load_localization(data_cfg, "dev", alphabet,
args["sanity_check"])
dataset_dev = dataset.Seq_dataset(*dataset_dev, alphabet, run_cfg,
flag_rnn, model_cfg.max_len)
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, 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)
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"]])
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_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)
## 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()
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))
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)
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, "embedding_log", embedding=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 dataset:', data_cfg.path["test"]]), output)
test_dataset = load_fasta(data_cfg, "test", alphabet, sanity_check=args["sanity_check"])
test_dataset = dataset.Embedding_dataset(test_dataset, alphabet, run_cfg, flag_rnn)
collate_fn = dataset.collate_sequences_for_embedding if flag_rnn else None
iterator_test = torch.utils.data.DataLoader(test_dataset, run_cfg.batch_size_eval, collate_fn=collate_fn)
end = Print(" ".join(['loaded', str(len(test_dataset)), '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, "", 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])
load_models(args, models_list, device, data_parallel, output, tfm_cls=flag_rnn)
get_loss = plus_rnn.get_embedding if flag_rnn else plus_tfm.get_embedding
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]
trainer = Trainer(models_list, get_loss, run_cfg, tasks_list)
trainer_args = {"data_parallel": data_parallel}
end = Print('end setting trainer configurations', output)
Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
## evaluate a model
start = Print('start embedding protein sequences', output)
### evaluate cls
for b, batch in enumerate(iterator_test):
batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
trainer.embed(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)
trainer.save_embeddings(save_prefix)
trainer.reset()
end = Print('end embedding protein sequences', 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_plus = (model_cfg.rnn_type == "B") if flag_rnn else False
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,
augment=flag_plus,
sanity_check=args["sanity_check"])
if flag_rnn and flag_paired: collate_fn = dataset.collate_paired_sequences
elif flag_rnn and flag_plus: collate_fn = dataset.collate_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 flag_plus: 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()