else:
dictionary = load_dict()
dict_size = len(dictionary)
print("Dictionary size: ", dict_size)
print("Done reading babi!")
lstm = LSTM(args.hidden_dim_lstm, args.batch_size_stories, dict_size,
args.emb_dim, args.lstm_layers, device).to(device)
rn = RelationNetwork(args.hidden_dim_lstm, args.hidden_dims_g,
args.output_dim_g, args.hidden_dims_f, dict_size,
args.batch_size_stories, device).to(device)
if args.load:
load_models([(lstm, names_models[0]), (rn, names_models[1])],
saving_path_rn)
optimizer = torch.optim.Adam(chain(lstm.parameters(), rn.parameters()),
args.learning_rate,
weight_decay=args.weight_decay)
criterion = torch.nn.CrossEntropyLoss(reduction='mean')
if args.epochs > 0:
print("Start training")
avg_train_losses, avg_train_accuracies, val_losses, val_accuracies = train_single(
train_stories, validation_stories, args.epochs, lstm, rn, criterion,
optimizer, args.print_every, args.no_save)
print("End training!")
print("Testing...")
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()
model_dict = {}
model_dict['net_seg'] = net_seg
# Set test data
target_testset = loadTestDataWithoutLabelsWithFileName(test_settings.data_test,
normalize_type = "Max255")
data_loader_test = DataLoader(target_testset, batch_size=1, shuffle=False)
## set directories
img_dir = test_settings.save_dir
if not os.path.exists(img_dir):
os.makedirs(img_dir)
# Load net weight
checkpoint = torch.load(os.path.join(test_settings.weight_dir, test_settings.weight_name))
load_models(model_dict, checkpoint)
net_seg.eval()
for i_val, (images_val, image_name) in enumerate(data_loader_test):
viz_util.progress_bar(i_val,len(data_loader_test),"testing progress")
with torch.no_grad():
images_val = images_val.data.numpy()
images_val = np.transpose(images_val, (0, ) +tuple(range(2, 4)) + (1, ))
images_val = images_val[0]
prediction = predict_in_blocks_UNetDA2Out(net_seg, images_val, in_shape = [512,512], block_shape = [324, 324],
output_function=net_seg.forward_target, image_format="NHWC", device = device)
prediction = np.transpose(prediction, (2, ) +tuple(range(0, 2)))
prediction = np.argmax(prediction, axis = 0)
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()
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=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():
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"], 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()
args.o_dims,
device,
g_layers=1,
edge_attribute_dim=args.hidden_dim_lstm,
single_output=True).to(device)
if torch.cuda.device_count() > 1:
print("Using {} GPUs!".format(torch.cuda.device_count()))
rrn = nn.DataParallel(rrn)
elif torch.cuda.is_available():
print("Using 1 GPU!")
else:
print("No GPUs found, using CPU!")
if args.load:
load_models([(lstm, names_models[0]), (rrn, names_models[2]),
(mlp, names_models[3])], saving_path_rrn)
optimizer = torch.optim.Adam(chain(lstm.parameters(), rrn.parameters(),
mlp.parameters()),
args.learning_rate,
weight_decay=args.weight_decay)
criterion = torch.nn.CrossEntropyLoss(reduction='mean')
if args.epochs > 0:
print("Start training")
begin_time = time.time()
avg_train_losses, avg_train_accuracies, val_losses, val_accuracies = train_single(
train_stories, validation_stories, args.epochs, mlp, lstm, rrn,
criterion, optimizer, args.print_every, args.no_save, device)
end_time = time.time()
