def __init__(self,
model,
mask_prob: float = 0.15,
clip: int = 1,
optimizer=None):
self.model = model
self.clip = clip
self.optimizer = optimizer
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model = self.model.to(self.device)
self.mask_prob = mask_prob
self.criterion = nn.NLLLoss(
ignore_index=model.text_processor.pad_token_id())
num_gpu = torch.cuda.device_count()
if num_gpu > 1:
print("Let's use", num_gpu, "GPUs!")
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
self.best_dev_loss = float("inf")
self.best_train_loss = float("inf")
self.last_train_loss = float("inf")
def createModels(args, userNum, itemNum):
if args.model == 'SPUIGACF':
model = SPUIGACF(userNum,
itemNum,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'SPUIMultiGACF':
model = SPUIMultiGACF(userNum,
itemNum,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'SPUIGAGPCF':
model = SPUIGAGPCF(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
if args.train_mode == 'PairSampling':
lossfn = BPRLoss()
if args.parallel == True:
model = DataParallelModel(model)
lossfn = DataParallelCriterion2(lossfn)
elif args.train_mode == 'NegSampling':
lossfn = BCEWithLogitsLoss()
if args.parallel == True:
model = DataParallelModel(model) # 并行化model
lossfn = DataParallelCriterion(lossfn) # 并行化损失函数
optim = Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
return model, lossfn, optim
def __init__(self, cfg: Namespace, data: Dataset):
"""
Args:
cfg: configuration
data: train dataset
"""
self.cfg = cfg
self.train, self.valid = data.split(0.8)
RATING_FIELD.build_vocab(self.train)
self.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu') # pylint: disable=no-member
self.batch_size = cfg.batch_size
if torch.cuda.is_available():
self.batch_size *= torch.cuda.device_count()
self.trn_itr = BucketIterator(
self.train,
device=self.device,
batch_size=self.batch_size,
shuffle=True,
train=True,
sort_within_batch=True,
sort_key=lambda exam: -len(exam.comment_text))
self.vld_itr = BucketIterator(
self.valid,
device=self.device,
batch_size=self.batch_size,
shuffle=False,
train=False,
sort_within_batch=True,
sort_key=lambda exam: -len(exam.comment_text))
self.log_step = 1000
if len(self.vld_itr) < 100:
self.log_step = 10
elif len(self.vld_itr) < 1000:
self.log_step = 100
bert_path = cfg.bert_path if cfg.bert_path else 'bert-base-cased'
self.model = BertForSequenceClassification.from_pretrained(
bert_path, num_labels=2)
pos_weight = (
len([exam for exam in self.train.examples if exam.target < 0.5]) /
len([exam for exam in self.train.examples if exam.target >= 0.5]))
pos_wgt_tensor = torch.tensor([1.0, pos_weight], device=self.device) # pylint: disable=not-callable
self.criterion = nn.CrossEntropyLoss(weight=pos_wgt_tensor)
if torch.cuda.is_available():
self.model = DataParallelModel(self.model.cuda())
self.criterion = DataParallelCriterion(self.criterion)
self.optimizer = optim.Adam(self.model.parameters(), cfg.learning_rate)
def __init__(self,
model,
caption_model,
mask_prob: float = 0.3,
clip: int = 1,
optimizer=None,
beam_width: int = 5,
max_len_a: float = 1.1,
max_len_b: int = 5,
len_penalty_ratio: float = 0.8,
nll_loss: bool = False,
fp16: bool = False,
mm_mode="mixed"):
super().__init__(model, mask_prob, clip, optimizer, beam_width,
max_len_a, max_len_b, len_penalty_ratio, nll_loss,
fp16, mm_mode)
self.caption_model = caption_model
self.caption_model.eval()
self.caption_model = self.caption_model.to(self.device)
if self.num_gpu == 1 and fp16:
self.caption_model = amp.initialize(self.caption_model,
opt_level="O2")
if self.num_gpu > 1:
print("Let's use", self.num_gpu, "GPUs!")
self.caption_model = DataParallelModel(self.caption_model)
def train():
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
loaders = create_datasets(num_workers=32, batch_size=600)
# info = pd.read_csv("./flower_data/train.csv")[["image","label"]]
# class_weights = torch.tensor(1.0/info.groupby(["label"]).count().values.astype(np.float32))
# del info
models_ensamble = [
# {"name":"vgg", "model":models.vgg16_bn(pretrained=True)},
{"name":"resnet", "model":models.resnet50(pretrained=True)},
# {"name":"densenet", "model":models.densenet121(pretrained=True) },
{"name":"resnet", "model":models.resnet101(pretrained=True) },
]
# model = Ensemble(models_ensamble, name="star_ensemble")
model = load_checkpoint("ensemble_iso_star_5118.pt")
ft, cl =model.get_parameters()
# model = nn.DataParallel(model)
model = DataParallelModel(model)
model = model.to(device)
weight = torch.from_numpy(weight_train[0]).to(device)
criterion = nn.NLLLoss(weight)
criterion = DataParallelCriterion(criterion)
optimizers = [ optim.Adam(ft, lr=5e-4), optim.Adam(cl, lr=5e-3)]
# # print("")
# # print('-' * 40)
# # print("lr = {} bs= {}".format(lr,bs) )
# # print('-' * 40)
# # Decay LR by a factor of 0.1 every 7 epochs
exp_lr_schedulers = [lr_scheduler.StepLR(optimizers[0], step_size = 1, gamma = 0.995),
lr_scheduler.StepLR(optimizers[1], step_size = 1, gamma = 0.992) ]
model = [model, criterion, optimizers, exp_lr_schedulers, device]
model = train_model(*model, loaders, num_epochs = 100)
def build_model(options):
model = Seq2Seq.load(ImageCaptioning,
options.model_path,
tok_dir=options.tokenizer_path,
use_obj=options.obj)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
num_gpu = torch.cuda.device_count()
generator = BeamDecoder(model,
beam_width=options.beam_width,
max_len_a=options.max_len_a,
max_len_b=options.max_len_b,
len_penalty_ratio=options.len_penalty_ratio)
if options.fp16:
generator = amp.initialize(generator, opt_level="O2")
if num_gpu > 1:
generator = DataParallelModel(generator)
return generator, model.text_processor
def main():
parser = setup_parser()
args = parser.parse_args()
processors = {
'stsb': StsbProcessor,
'mednli': MednliProcessor,
'medsts': MedstsProcessor
}
output_modes = {
'mnli': 'classification',
'stsb': 'regression',
'mednli': 'classification',
'medsts': 'regression'
}
bert_types = {
'discharge':
'/home/dc925/project/data/clinicalbert/biobert_pretrain_output_disch_100000',
'all':
'/home/dc925/project/data/clinicalbert/biobert_pretrain_output_all_notes_150000',
'base_uncased': 'bert-base-uncased',
'base_cased': 'bert-base-cased'
}
##################################################################################################
################################### SETUP DATA, DEVICE, MODEL ####################################
##################################################################################################
if args.local_rank == -1 or args.no_cuda:
device = torch.device('cuda' if torch.cuda.is_available()
and not args.no_cuda else 'cpu')
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device('cuda', args.local_rank)
n_gpu = 1
#Initialize the distributed backend which will take care of synchronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: {}".format(task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels(output_mode)
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(
args.local_rank))
model = BertForSequenceClassification.from_pretrained(
args.bert_model, cache_dir=cache_dir, num_labels=num_labels)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelModel(model)
##################################################################################################
########################################### OPTIMIZER ############################################
##################################################################################################
if args.do_train:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
if args.discriminative_finetuning:
group1 = ['layer.0', 'layer.1.']
group2 = ['layer.2', 'layer.3']
group3 = ['layer.4', 'layer.5']
group4 = ['layer.6', 'layer.7']
group5 = ['layer.8', 'layer.9']
group6 = ['layer.10', 'layer.11']
group_all = ['layer.0', 'layer.1.', 'layer.2', 'layer.3', 'layer.4', 'layer.5', \
'layer.6', 'layer.7', 'layer.8', 'layer.9', 'layer.10', 'layer.11']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and not any(nd in n for nd in group_all)], \
'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group1)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**5},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group2)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**4},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group3)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**3},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group4)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**2},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group5)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group6)], \
'weight_decay': 0.01, 'lr': args.learning_rate},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and not any(nd in n for nd in group_all)], \
'weight_decay': 0.0},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group1)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**5},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group2)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**4},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group3)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**3},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group4)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**2},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group5)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group6)], \
'weight_decay': 0.0, 'lr': args.learning_rate},
]
else:
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params': [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
##################################################################################################
############################################# TRAIN ##############################################
##################################################################################################
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer, output_mode)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer, output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.float)
all_pids = np.array([f.pid for f in eval_features])
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size,
drop_last=True)
model.train()
epoch_metric = {}
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model(input_ids, segment_ids, input_mask, labels=None)
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
loss_fct = DataParallelCriterion(loss_fct)
logits = [
logits[i].view(-1, num_labels)
for i in range(len(logits))
]
loss = loss_fct(logits, label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
loss_fct = DataParallelCriterion(loss_fct)
logits = [logits[i].view(-1) for i in range(len(logits))]
loss = loss_fct(logits, label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() #average on multi-gpu
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
#modify lr with special warm up BERT uses
#if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear.get_lr(
global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
with torch.no_grad():
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = []
i = 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids,
segment_ids,
input_mask,
labels=None)
if output_mode == 'classification':
# loss_fct = CrossEntropyLoss()
# tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
loss_fct = CrossEntropyLoss()
loss_fct = DataParallelCriterion(loss_fct)
logits = [
logits[i].view(-1, num_labels)
for i in range(len(logits))
]
tmp_eval_loss = loss_fct(logits, label_ids.view(-1))
elif output_mode == 'regression':
# loss_fct = MSELoss()
# tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
loss_fct = MSELoss()
loss_fct = DataParallelCriterion(loss_fct)
logits = [
logits[i].view(-1) for i in range(len(logits))
]
tmp_eval_loss = loss_fct(logits, label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
logits = parallel.gather(logits, target_device='cuda:0')
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
if output_mode == 'classification':
preds = np.argmax(preds, axis=1)
elif output_mode == 'regression':
preds = np.squeeze(preds)
all_label_ids = all_label_ids[:preds.shape[0]]
all_pids = all_pids[:preds.shape[0]]
errors = generate_errors(preds, all_label_ids.numpy(),
all_pids)
result = compute_metrics(task_name, preds,
all_label_ids.numpy())
loss = tr_loss / global_step if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step
result['loss'] = loss
logger.info('***** Eval Results *****')
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
epoch_metric[_] = result[
'pearson'] if output_mode == 'regression' else result['acc']
output_eval_file = os.path.join(args.output_dir, 'eval_results.txt')
with open(output_eval_file, 'w') as writer:
logger.info('***** Eval Results *****')
# for key in sorted(result.keys()):
# logger.info(" %s = %s", key, str(result[key]))
# writer.write("%s = %s\n" % (key, str(result[key])))
# writer.write("{} {}\n".format("epoch","pearson"))
for key in sorted(epoch_metric.keys()):
writer.write("{}\t{}\t{}\t{}\n".format(key,
str(epoch_metric[key]),
args.learning_rate,
args.train_batch_size))
errors.to_csv('errors.txt', sep='\t', index=False)
##################################################################################################
########################################## SAVE & RELOAD #########################################
##################################################################################################
if args.do_train:
#Save a trained model, config, and tokenizer
model_to_save = model.module if hasattr(
model, 'module') else model #only save the model itself
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
model = BertForSequenceClassification.from_pretrained(
args.output_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
model.to(device)
def main():
epoches = 32
gpu_id = 7
ctx_list = [mx.gpu(x) for x in [7, 8]]
log_interval = 100
batch_size = 32
start_epoch = 0
# trainer_resume = resume + ".states" if resume is not None else None
trainer_resume = None
resume = None
from mxnet.gluon.data.vision import transforms
transform_fn = transforms.Compose([
LeftTopPad(dest_shape=(256, 256)),
transforms.ToTensor(),
transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
dataset = CaptionDataSet(
image_root="/data3/zyx/yks/coco2017/train2017",
annotation_path=
"/data3/zyx/yks/coco2017/annotations/captions_train2017.json",
transforms=transform_fn,
feature_hdf5="output/train2017.h5")
val_dataset = CaptionDataSet(
image_root="/data3/zyx/yks/coco2017/val2017",
annotation_path=
"/data3/zyx/yks/coco2017/annotations/captions_val2017.json",
words2index=dataset.words2index,
index2words=dataset.index2words,
transforms=transform_fn,
feature_hdf5="output/val2017.h5")
dataloader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1,
pin_memory=True,
last_batch="discard")
val_loader = DataLoader(dataset=val_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1,
pin_memory=True)
num_words = dataset.words_count
# set up logger
save_prefix = "output/res50_"
logging.basicConfig()
logger = logging.getLogger()
logger.setLevel(logging.INFO)
log_file_path = save_prefix + '_train.log'
log_dir = os.path.dirname(log_file_path)
if log_dir and not os.path.exists(log_dir):
os.makedirs(log_dir)
fh = logging.FileHandler(log_file_path)
logger.addHandler(fh)
net = EncoderDecoder(num_words=num_words, test_max_len=val_dataset.max_len)
if resume is not None:
net.collect_params().load(resume,
allow_missing=True,
ignore_extra=True)
logger.info("Resumed form checkpoint {}.".format(resume))
params = net.collect_params()
for key in params.keys():
if params[key]._data is not None:
continue
else:
if "bias" in key or "mean" in key or "beta" in key:
params[key].initialize(init=mx.init.Zero())
logging.info("initialized {} using Zero.".format(key))
elif "weight" in key:
params[key].initialize(init=mx.init.Normal())
logging.info("initialized {} using Normal.".format(key))
elif "var" in key or "gamma" in key:
params[key].initialize(init=mx.init.One())
logging.info("initialized {} using One.".format(key))
else:
params[key].initialize(init=mx.init.Normal())
logging.info("initialized {} using Normal.".format(key))
net.collect_params().reset_ctx(ctx=ctx_list)
trainer = mx.gluon.Trainer(
net.collect_params(),
'adam',
{
'learning_rate': 4e-4,
'clip_gradient': 5,
'multi_precision': True
},
)
if trainer_resume is not None:
trainer.load_states(trainer_resume)
logger.info(
"Loaded trainer states form checkpoint {}.".format(trainer_resume))
criterion = Criterion()
accu_top3_metric = TopKAccuracy(top_k=3)
accu_top1_metric = Accuracy(name="batch_accu")
ctc_loss_metric = Loss(name="ctc_loss")
alpha_metric = Loss(name="alpha_loss")
batch_bleu = BleuMetric(name="batch_bleu",
pred_index2words=dataset.index2words,
label_index2words=dataset.index2words)
epoch_bleu = BleuMetric(name="epoch_bleu",
pred_index2words=dataset.index2words,
label_index2words=dataset.index2words)
btic = time.time()
logger.info(batch_size)
logger.info(num_words)
logger.info(len(dataset.words2index))
logger.info(len(dataset.index2words))
logger.info(dataset.words2index["<PAD>"])
logger.info(val_dataset.words2index["<PAD>"])
logger.info(len(val_dataset.words2index))
# net.hybridize(static_alloc=True, static_shape=True)
net_parallel = DataParallelModel(net, ctx_list=ctx_list, sync=True)
for nepoch in range(start_epoch, epoches):
if nepoch > 15:
trainer.set_learning_rate(4e-5)
logger.info("Current lr: {}".format(trainer.learning_rate))
accu_top1_metric.reset()
accu_top3_metric.reset()
ctc_loss_metric.reset()
alpha_metric.reset()
epoch_bleu.reset()
batch_bleu.reset()
for nbatch, batch in enumerate(tqdm.tqdm(dataloader)):
batch = [mx.gluon.utils.split_and_load(x, ctx_list) for x in batch]
inputs = [[x[n] for x in batch] for n, _ in enumerate(ctx_list)]
losses = []
with ag.record():
net_parallel.sync = nbatch > 1
outputs = net_parallel(*inputs)
for s_batch, s_outputs in zip(inputs, outputs):
image, label, label_len = s_batch
predictions, alphas = s_outputs
ctc_loss = criterion(predictions, label, label_len)
loss2 = 1.0 * ((1. - alphas.sum(axis=1))**2).mean()
losses.extend([ctc_loss, loss2])
ag.backward(losses)
trainer.step(batch_size=batch_size, ignore_stale_grad=True)
for n, l in enumerate(label_len):
l = int(l.asscalar())
la = label[n, 1:l]
pred = predictions[n, :(l - 1)]
accu_top3_metric.update(la, pred)
accu_top1_metric.update(la, pred)
epoch_bleu.update(la, predictions[n, :])
batch_bleu.update(la, predictions[n, :])
ctc_loss_metric.update(None,
preds=nd.sum(ctc_loss) / image.shape[0])
alpha_metric.update(None, preds=loss2)
if nbatch % log_interval == 0 and nbatch > 0:
msg = ','.join([
'{}={:.3f}'.format(*metric.get()) for metric in [
epoch_bleu, batch_bleu, accu_top1_metric,
accu_top3_metric, ctc_loss_metric, alpha_metric
]
])
logger.info(
'[Epoch {}][Batch {}], Speed: {:.3f} samples/sec, {}'.
format(nepoch, nbatch,
log_interval * batch_size / (time.time() - btic),
msg))
btic = time.time()
batch_bleu.reset()
accu_top1_metric.reset()
accu_top3_metric.reset()
ctc_loss_metric.reset()
alpha_metric.reset()
bleu, acc_top1 = validate(net,
gpu_id=gpu_id,
val_loader=val_loader,
train_index2words=dataset.index2words,
val_index2words=val_dataset.index2words)
save_path = save_prefix + "_weights-%d-bleu-%.4f-%.4f.params" % (
nepoch, bleu, acc_top1)
net.collect_params().save(save_path)
trainer.save_states(fname=save_path + ".states")
logger.info("Saved checkpoint to {}.".format(save_path))
def main(args):
init(args)
#Args setup:
save_dir = os.path.join(args.output_dir, args.experiment_name,
"checkpoints")
save_dir_local = "checkpoints_local"
desc = args.desc
data_dir = args.data_dir
log_dir = os.path.join(args.output_dir, args.experiment_name, "logs")
os.makedirs(log_dir, exist_ok=True)
os.makedirs(save_dir, exist_ok=True)
os.makedirs(save_dir_local, exist_ok=True)
train_log_interval = args.train_log_interval
val_log_interval = args.val_log_interval
beam = args.beam
p = args.p
n_ctx = args.n_ctx
gen_len = args.gen_len
k = args.k
decoding_strategy = args.decoding_strategy
accum_iter = args.accum_iter
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print("device", device, "n_gpu", n_gpu)
logger = Logger(log_dir)
#Text Encoder
if args.use_offline_gpt2:
text_encoder = GPT2Tokenizer.from_pretrained('./gpt2model')
elif args.debug_mode:
text_encoder = GPT2Tokenizer.from_pretrained('gpt2')
else:
text_encoder = GPT2Tokenizer.from_pretrained('gpt2-medium')
text_encoder.add_special_tokens({
'bos_token':
'_start_',
'cls_token':
'_classify_',
'eos_token':
'_end_',
'additional_special_tokens':
['_kw_', '_endkw_', '_t_', '_i_', '_b_', '_c_']
})
vocab = len(text_encoder)
print("Loading dataset...")
if args.use_model == "base":
train_loader = get_paragraph_input_loader(
os.path.join(data_dir, "train_encoded.jsonl"),
args.n_batch,
text_encoder,
num_workers=3,
shuffle=True,
gen_len=gen_len,
n_ctx=n_ctx,
include_discourse_type=args.use_discourse,
include_neigh=args.use_neighbor_feat,
max_size=args.max_ex,
include_kw=not args.exclude_kw,
dim=args.n_embd,
debug_mode=args.debug_mode)
val_loader = get_paragraph_input_loader(
os.path.join(data_dir, "val_encoded.jsonl"),
n_gpu,
text_encoder,
num_workers=0,
shuffle=False,
gen_len=gen_len,
n_ctx=n_ctx,
include_discourse_type=args.use_discourse,
include_neigh=args.use_neighbor_feat,
max_size=args.num_val_examples,
include_kw=not args.exclude_kw,
dim=args.n_embd,
debug_mode=args.debug_mode)
print("Train length: {}, Validation length: {}".format(
len(train_loader), len(val_loader)))
doc_model = GPT2BaseModel(args,
vocab=vocab,
n_ctx=n_ctx,
gen_len=gen_len,
lastidx=text_encoder.eos_token_id,
includeprev=args.use_neighbor_feat,
use_offline_gpt2=args.use_offline_gpt2)
elif args.use_model == "plotmachines":
#asli
train_loader = get_paragraph_memory_input_loader(
os.path.join(data_dir, "train_encoded.jsonl"),
args.n_batch,
text_encoder,
num_workers=3,
shuffle=True,
gen_len=gen_len,
n_ctx=n_ctx,
include_discourse_type=args.use_discourse,
include_neigh=args.use_neighbor_feat,
max_size=args.max_ex,
include_kw=not args.exclude_kw,
memsize=args.memstatesize,
dim=args.n_embd,
use_kwmem=True,
debug_mode=args.debug_mode)
val_loader = get_paragraph_memory_input_loader(
os.path.join(data_dir, "val_encoded.jsonl"),
n_gpu,
text_encoder,
num_workers=0,
shuffle=False,
gen_len=gen_len,
n_ctx=n_ctx,
include_discourse_type=args.use_discourse,
include_neigh=args.use_neighbor_feat,
max_size=args.num_val_examples,
include_kw=not args.exclude_kw,
memsize=args.memstatesize,
dim=args.n_embd,
use_kwmem=True,
debug_mode=args.debug_mode)
print("Train length: {}, Validation length: {}".format(
len(train_loader), len(val_loader)))
doc_model = PlotMachinesModel(args,
vocab=vocab,
n_ctx=n_ctx,
gen_len=gen_len,
lastidx=text_encoder.eos_token_id,
includeprev=args.use_neighbor_feat,
use_offline_gpt2=args.use_offline_gpt2)
n_updates_total = (len(train_loader) //
args.accum_iter) * (args.num_epochs)
if args.debug_mode:
print_model_params(log_dir, doc_model)
criterion = nn.CrossEntropyLoss(reduction="none")
model_opt = AdamW(filter(lambda p: p.requires_grad,
doc_model.parameters()),
lr=args.lr,
betas=(args.b1, args.b2),
eps=args.e)
lm_loss = ParagraphLoss(criterion, n_ctx=n_ctx, gen_len=gen_len)
print("Loading Model")
doc_model.to(device)
if n_gpu > 1:
doc_model = DataParallelModel(doc_model)
lm_loss = DataParallelCriterion(lm_loss)
print("Parallelized")
bestloss = -1
start_iter, running_loss = 1, 0
prevloss = 1000
start_iter, running_loss = load_checkpoint(args.checkpoint, doc_model,
model_opt)
for i in range(args.num_epochs):
start_iter, running_loss, bestloss, updates, val_loss1 = run_epoch(
bestloss,
start_iter,
running_loss,
doc_model,
lm_loss,
model_opt,
train_loader,
val_loader,
train_log_interval,
val_log_interval,
device,
beam,
gen_len,
k,
p,
decoding_strategy,
accum_iter,
"FT Training Epoch [{}/{}]".format(i + 1, args.num_epochs),
save_dir,
logger,
text_encoder,
show_progress=args.show_progress,
my_local_dir=save_dir_local)
print("VAL LOSS: ", str(val_loss1))
if val_loss1 > prevloss or math.isnan(val_loss1):
break
prevloss = val_loss1
print('Done training...')
print('Evaluating on validation with best checkpoint...')
bestcheck = os.path.join(save_dir, "checkpoint_best.pt")
checkpoint = torch.load(bestcheck, map_location='cpu')
state_dict = checkpoint["state_dict"]
if state_dict.get('module.pos_emb_mask') is None and doc_model.state_dict(
).get('module.pos_emb_mask') is not None:
state_dict['module.pos_emb_mask'] = doc_model.state_dict().get(
'module.pos_emb_mask')
doc_model.load_state_dict(state_dict)
evaluate_doc_model(doc_model, val_loader, text_encoder, device, beam,
gen_len, k, p, args.decoding_strategy,
os.path.join(save_dir, 'valeval.log'), 'gen', 'tgt',
gen_len, [], args)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_corpus",
default=None,
type=str,
required=True,
help="The input train corpus.")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese."
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument(
"--on_memory",
action='store_true',
help="Whether to load train samples into memory or use disk")
parser.add_argument(
"--do_lower_case",
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumualte before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--discriminative_finetuning',
action='store_true',
help='Whether to use discriminative fine-tuning')
args = parser.parse_args()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train:
raise ValueError(
"Training is currently the only implemented execution option. Please set `do_train`."
)
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
#train_examples = None
num_train_optimization_steps = None
if args.do_train:
print("Loading Train Dataset", args.train_corpus)
train_dataset = BERTDataset(args.train_corpus,
tokenizer,
seq_len=args.max_seq_length,
corpus_lines=None,
on_memory=args.on_memory)
num_train_optimization_steps = int(
len(train_dataset) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
#############################################################################
# model = BertForPreTraining.from_pretrained(args.bert_model)
model = BertForMaskedLM.from_pretrained(args.bert_model)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelModel(model)
# Prepare optimizer
if args.do_train:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
if args.discriminative_finetuning:
group1 = ['layer.0', 'layer.1.']
group2 = ['layer.2', 'layer.3']
group3 = ['layer.4', 'layer.5']
group4 = ['layer.6', 'layer.7']
group5 = ['layer.8', 'layer.9']
group6 = ['layer.10', 'layer.11']
group_all = ['layer.0', 'layer.1', 'layer.2', 'layer.3', 'layer.4', 'layer.5', \
'layer.6', 'layer.7', 'layer.8', 'layer.9', 'layer.10', 'layer.11']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and not any(nd in n for nd in group_all)], \
'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group1)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**5},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group2)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**4},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group3)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**3},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group4)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6**2},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group5)], \
'weight_decay': 0.01, 'lr': args.learning_rate/2.6},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and any(nd in n for nd in group6)], \
'weight_decay': 0.01, 'lr': args.learning_rate},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and not any(nd in n for nd in group_all)], \
'weight_decay': 0.0},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group1)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**5},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group2)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**4},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group3)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**3},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group4)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6**2},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group5)], \
'weight_decay': 0.0, 'lr': args.learning_rate/2.6},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and any(nd in n for nd in group6)], \
'weight_decay': 0.0, 'lr': args.learning_rate},
]
else:
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params': [
p for n, p in param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
#TODO: check if this works with current data generator from disk that relies on next(file)
# (it doesn't return item back by index)
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size,
drop_last=True)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
logits = model(input_ids, segment_ids, input_mask)
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss_fct = DataParallelCriterion(loss_fct)
logits = [
logits[i].view(-1, model.module.config.vocab_size)
for i in range(len(logits))
]
loss = loss_fct(logits, lm_label_ids.view(-1))
# loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
# loss = model(input_ids, segment_ids, input_mask, lm_label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear.get_lr(
global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
logger.info("** ** * Saving fine - tuned model ** ** * ")
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
def main(args):
init(args)
#Args setup:
beam = args.beam
p = args.p
n_ctx = args.n_ctx
gen_len = args.gen_len
k = args.k
decoding_strategy = args.decoding_strategy
accum_iter = args.accum_iter
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print("device", device, "n_gpu", n_gpu)
data_dir = args.data_dir
#Text Encoder
if args.debug_mode:
text_encoder = GPT2Tokenizer.from_pretrained('gpt2')
else:
text_encoder = GPT2Tokenizer.from_pretrained('gpt2-medium')
text_encoder.add_special_tokens({
'bos_token':
'_start_',
'cls_token':
'_classify_',
'eos_token':
'_end_',
'additional_special_tokens':
['_kw_', '_endkw_', '_t_', '_i_', '_b_', '_c_']
})
vocab = len(text_encoder)
datafile = os.path.join(
data_dir, "test_encoded.jsonl") if args.testset else os.path.join(
data_dir, "val_encoded.jsonl")
print("Loading dataset...")
val_loader = get_fullstory_loader(datafile,
args.n_batch,
text_encoder,
num_workers=0,
shuffle=False,
gen_len=gen_len,
n_ctx=n_ctx,
include_kw=not args.exclude_kw,
max_size=args.max_ex)
print(len(val_loader))
if args.use_model == "plotmachines":
doc_model = PlotMachinesModel(args,
vocab=vocab,
n_ctx=n_ctx,
gen_len=gen_len,
lastidx=text_encoder.eos_token_id,
includeprev=args.use_neighbor_feat)
else:
doc_model = GPT2BaseModel(args,
vocab=vocab,
n_ctx=n_ctx,
gen_len=gen_len,
lastidx=text_encoder.eos_token_id,
includeprev=args.use_neighbor_feat)
doc_model.to(device)
if n_gpu > 1:
doc_model = DataParallelModel(doc_model)
if args.debug_mode:
gptclf = GPT2Model.from_pretrained('gpt2')
gptclf.eval()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
gptclf.to(device)
#gpttok = gptTokenizer.from_pretrained('openai-gpt')
gpttok = GPT2Tokenizer.from_pretrained('gpt2')
else:
gptclf = GPT2Model.from_pretrained('gpt2-medium')
gptclf.eval()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
gptclf.to(device)
#gpttok = gptTokenizer.from_pretrained('openai-gpt')
gpttok = GPT2Tokenizer.from_pretrained('gpt2-medium')
prevloss = []
upd = []
start_iter, running_loss = 1, 0
load_dir = args.load_dir
bestcheck = os.path.join(load_dir, "checkpoint_best.pt")
checkpoint = torch.load(bestcheck, map_location='cpu')
state_dict = checkpoint["state_dict"]
if n_gpu == 1:
if state_dict.get(
'module.pos_emb_mask') is None and doc_model.state_dict().get(
'pos_emb_mask') is not None:
state_dict['module.pos_emb_mask'] = doc_model.state_dict().get(
'pos_emb_mask')
for k in list(state_dict.keys()):
state_dict[k[7:]] = state_dict[k]
del state_dict[k]
else:
if state_dict.get(
'module.pos_emb_mask') is None and doc_model.state_dict().get(
'module.pos_emb_mask') is not None:
state_dict['module.pos_emb_mask'] = doc_model.state_dict().get(
'module.pos_emb_mask')
doc_model.load_state_dict(state_dict)
print("Parallelized")
tagset = ['_i_'] + args.bodynum * ['_b_'] + ['_c_']
vort = 'test' if args.testset else 'val'
generatedocs(doc_model,
gptclf,
gpttok,
val_loader,
text_encoder,
device,
beam,
gen_len,
k,
p,
args.decoding_strategy,
os.path.join(args.save_dir, vort + '.gens.tsv'),
'gen',
'tgt',
gen_len, [],
args,
tags=tagset,
dim=args.n_embd,
save_dir=args.save_dir,
localfile=os.path.join('/tmp', vort + '.gens.tsv'))
print('done decoding....')
def train(args, train_dataset, model, tokenizer):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (
len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
args.warmup_steps = t_total // 100
# Prepare optimizer and schedule (linear warmup and decay)
optimizer_grouped_parameters = get_param_groups(args, model)
optimizer = RAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=args.warmup_steps,
t_total=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelModel(model)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
args.logging_steps = len(train_dataloader) // 1
args.save_steps = args.logging_steps
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch")
set_seed(args)
for _ in train_iterator:
args.current_epoch = _
epoch_iterator = tqdm(train_dataloader, desc="Iteration")
for step, batch in enumerate(epoch_iterator):
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
'input_ids':
batch[0],
'attention_mask':
batch[1],
'token_type_ids':
batch[2] if args.model_type in ['bert', 'xlnet'] else None,
} # XLM and RoBERTa don't use segment_ids
# 'labels': batch[3]}
outputs = model(**inputs)
outputs = [outputs[i][0] for i in range(len(outputs))]
loss_fct = CrossEntropyLoss()
loss_fct = DataParallelCriterion(loss_fct)
loss = loss_fct(outputs, batch[3])
if args.n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer),
args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
if args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value,
global_step)
tb_writer.add_scalar('lr',
scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss) /
args.logging_steps, global_step)
logging_loss = tr_loss
if args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(
args.output_dir, 'checkpoint-{}'.format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(
model, 'module'
) else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
torch.save(args,
os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
lr_rate = 0.03
milestones = [5, 7, 8, 10, 12, 14, 16, 17, 18]
img_size = 384
gamma = 0.5
#use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#use_cuda = False
segm_model = ResNetLinkModel(input_channels=1, pretrained=True, num_classes=3)
if torch.cuda.device_count() > 1:
#dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
#segm_model = nn.DataParallel(segm_model)
#segm_model = encoding.parallel.DataParallelModel(segm_model, device_ids=[0,1,2,3,4,5,6,7])
segm_model = DataParallelModel(segm_model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
segm_model.to(device)
'''if use_cuda:
segm_model.cuda()
seg_model = nn.DataParallel(seg_model)'''
mul_transf = [
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor()
]
#optimizer = optim.SGD(segm_model.parameters(), lr=lr_rate, momentum=momentum)
optimizer = optim.Adam(segm_model.parameters(), lr=0.0001)
#criterion = nn.BCEWithLogitsLoss().cuda() if use_cuda else nn.BCEWithLogitsLoss()
criterion = nn.BCEWithLogitsLoss()
criterion = DataParallelCriterion(criterion)
def __init__(self,
model,
vocab_size,
train_dataloader,
test_dataloader=None,
lr: float = 1e-4,
betas=(0.9, 0.999),
weight_decay: float = 0.01,
warmup_steps=10000,
with_cuda: bool = True,
cuda_devices=None,
log_freq: int = 10,
include_next=False,
include_vision=True,
total_epochs=1):
"""
:param bert: BERT model which you want to train
:param vocab_size: total word vocab size
:param train_dataloader: train dataset data loader
:param test_dataloader: test dataset data loader [can be None]
:param lr: learning rate of optimizer
:param betas: Adam optimizer betas
:param weight_decay: Adam optimizer weight decay param
:param with_cuda: traning with cuda
:param log_freq: logging frequency of the batch iteration
"""
# Setup cuda device for BERT training, argument -c, --cuda should be true
cuda_condition = torch.cuda.is_available() and with_cuda
self.device = torch.device("cuda:0" if cuda_condition else "cpu")
n_gpu = torch.cuda.device_count()
print("device", device, "n_gpu", n_gpu)
# Initialize the BERT Language Model, with BERT model
self.model = model.to(self.device)
self.bert = self.model.bert
self.padding_idx = 0
self.include_next = include_next
self.include_vision = include_vision
# Distributed GPU training if CUDA can detect more than 1 GPU
if with_cuda and torch.cuda.device_count() > 1:
print("Using %d GPUS for BERT" % torch.cuda.device_count())
#self.model = nn.DataParallel(self.model, device_ids=range(torch.cuda.device_count()))
self.model = DataParallelModel(self.model,
device_ids=range(
torch.cuda.device_count()))
# Setting the train and test data loader
self.train_data = train_dataloader
self.test_data = test_dataloader
# Setting the Adam optimizer with hyper-param
self.optim = optim.Adamax(self.model.parameters(),
lr=lr,
betas=betas,
weight_decay=weight_decay)
if self.model.__class__.__name__ in [
'DataParallel', 'DataParallelModel'
]:
self.optim_schedule = ScheduledOptim(
self.optim,
self.model.module.bert.transformer_hidden_size,
n_warmup_steps=warmup_steps)
else:
self.optim_schedule = ScheduledOptim(
self.optim,
self.model.bert.transformer_hidden_size,
n_warmup_steps=warmup_steps)
# Using Negative Log Likelihood Loss function for predicting the masked_token
self.criterion = nn.NLLLoss(ignore_index=0)
if with_cuda and torch.cuda.device_count() > 1:
print("Using %d GPUS for BERT" % torch.cuda.device_count())
#self.model = nn.DataParallel(self.model, device_ids=range(torch.cuda.device_count()))
self.criterion = DataParallelCriterion(
self.criterion, device_ids=range(torch.cuda.device_count()))
self.log_freq = log_freq
self.total_iters = total_epochs * len(train_dataloader)
print("Total Parameters:",
sum([p.nelement() for p in self.model.parameters()]))
class BERTTrainer:
"""
BERTTrainer make the pretrained BERT model with two LM training method.
1. Masked Language Model : 3.3.1 Task #1: Masked LM
2. Next Sentence prediction : 3.3.2 Task #2: Next Sentence Prediction
"""
def __init__(self,
model,
vocab_size,
train_dataloader,
test_dataloader=None,
lr: float = 1e-4,
betas=(0.9, 0.999),
weight_decay: float = 0.01,
warmup_steps=10000,
with_cuda: bool = True,
cuda_devices=None,
log_freq: int = 10,
include_next=False,
include_vision=True,
total_epochs=1):
"""
:param bert: BERT model which you want to train
:param vocab_size: total word vocab size
:param train_dataloader: train dataset data loader
:param test_dataloader: test dataset data loader [can be None]
:param lr: learning rate of optimizer
:param betas: Adam optimizer betas
:param weight_decay: Adam optimizer weight decay param
:param with_cuda: traning with cuda
:param log_freq: logging frequency of the batch iteration
"""
# Setup cuda device for BERT training, argument -c, --cuda should be true
cuda_condition = torch.cuda.is_available() and with_cuda
self.device = torch.device("cuda:0" if cuda_condition else "cpu")
n_gpu = torch.cuda.device_count()
print("device", device, "n_gpu", n_gpu)
# Initialize the BERT Language Model, with BERT model
self.model = model.to(self.device)
self.bert = self.model.bert
self.padding_idx = 0
self.include_next = include_next
self.include_vision = include_vision
# Distributed GPU training if CUDA can detect more than 1 GPU
if with_cuda and torch.cuda.device_count() > 1:
print("Using %d GPUS for BERT" % torch.cuda.device_count())
#self.model = nn.DataParallel(self.model, device_ids=range(torch.cuda.device_count()))
self.model = DataParallelModel(self.model,
device_ids=range(
torch.cuda.device_count()))
# Setting the train and test data loader
self.train_data = train_dataloader
self.test_data = test_dataloader
# Setting the Adam optimizer with hyper-param
self.optim = optim.Adamax(self.model.parameters(),
lr=lr,
betas=betas,
weight_decay=weight_decay)
if self.model.__class__.__name__ in [
'DataParallel', 'DataParallelModel'
]:
self.optim_schedule = ScheduledOptim(
self.optim,
self.model.module.bert.transformer_hidden_size,
n_warmup_steps=warmup_steps)
else:
self.optim_schedule = ScheduledOptim(
self.optim,
self.model.bert.transformer_hidden_size,
n_warmup_steps=warmup_steps)
# Using Negative Log Likelihood Loss function for predicting the masked_token
self.criterion = nn.NLLLoss(ignore_index=0)
if with_cuda and torch.cuda.device_count() > 1:
print("Using %d GPUS for BERT" % torch.cuda.device_count())
#self.model = nn.DataParallel(self.model, device_ids=range(torch.cuda.device_count()))
self.criterion = DataParallelCriterion(
self.criterion, device_ids=range(torch.cuda.device_count()))
self.log_freq = log_freq
self.total_iters = total_epochs * len(train_dataloader)
print("Total Parameters:",
sum([p.nelement() for p in self.model.parameters()]))
def train(self, epoch):
self.iteration(epoch, self.train_data)
def test(self, epoch):
self.iteration(epoch, self.test_data, train=False)
def iteration(self, epoch, data_loader, train=True):
"""
loop over the data_loader for training or testing
if on train status, backward operation is activated
and also auto save the model every peoch
:param epoch: current epoch index
:param data_loader: torch.utils.data.DataLoader for iteration
:param train: boolean value of is train or test
:return: None
"""
str_code = "train" if train else "test"
# Setting the tqdm progress bar
data_iter = tqdm.tqdm(enumerate(data_loader),
desc="EP_%s:%d" % (str_code, epoch),
total=len(data_loader),
bar_format="{l_bar}{r_bar}",
disable=True)
avg_loss = 0.0
total_correct = 0
total_element = 0
for i, data in data_iter:
# 0. prepare the text sequence tensor
#data = {key: value.to(self.device) for key, value in data.items()}
seq_tensor = data['masked_text_seq']
labels = data['masked_text_label']
seq_lengths = np.argmax(seq_tensor == self.padding_idx, axis=1)
seq_lengths[seq_lengths == 0] = seq_tensor.shape[1] # Full length
# Sort sequences by lengths
seq_lengths, perm_idx = seq_lengths.sort(0, True)
sorted_tensor = seq_tensor[perm_idx]
mask = (sorted_tensor == padding_idx)[:, :seq_lengths[0]]
f_t_all = data['feature_all']
isnext = data["isnext"]
f_t_all = f_t_all[perm_idx]
isnext = isnext[perm_idx]
labels = labels[perm_idx]
# 1. forward the next_sentence_prediction and masked_lm model
if self.include_vision:
#next_sent_output, mask_lm_output = self.model.forward(sorted_tensor.cuda(), mask.cuda(),seq_lengths.cuda(),f_t_all.cuda())
output = self.model.forward(sorted_tensor.cuda(), mask.cuda(),
seq_lengths.cuda(), f_t_all.cuda())
length_output = len(output)
print("You got %d outputs" % (length_output))
next_sent_output, mask_lm_output = zip(*output)
print("vision test shape is %d " % (next_sent_output[1].shape))
print("lm test shape is %d " % (mask_lm_output[1].shape))
else:
#next_sent_output, mask_lm_output = self.model.forward(sorted_tensor.cuda(), mask.cuda(),seq_lengths.cuda(),None)
output = self.model.forward(sorted_tensor.cuda(), mask.cuda(),
seq_lengths.cuda(), None)
length_output = len(output)
print("You got %d outputs" % (length_output))
next_sent_output, mask_lm_output = zip(*output)
# 2-1. NLL(negative log likelihood) loss of is_next classification result
next_loss = 0
if self.include_vision and self.include_next:
next_loss = self.criterion(next_sent_output, isnext.cuda())
# 2-2. NLLLoss of predicting masked token word
mask_loss = self.criterion(mask_lm_output.transpose(1, 2),
labels[:, :seq_lengths[0]].cuda())
# 2-3. Adding next_loss and mask_loss : 3.4 Pre-training Procedure
#loss = next_loss + mask_loss
# 3. backward and optimization only in train
loss = loss.mean()
if train:
self.optim_schedule.zero_grad()
loss.backward()
self.optim_schedule.step_and_update_lr()
# next vision prediction accuracy
if self.include_next:
correct = next_sent_output.argmax(dim=-1).eq(
isnext.cuda()).sum().item()
total_correct += correct
total_element += data["isnext"].nelement()
avg_loss += loss.item()
if self.include_next:
post_fix = {
"epoch": epoch,
"iter": i,
"avg_loss": avg_loss / (i + 1),
"avg_acc": total_correct / total_element * 100,
"loss": loss.item()
}
else:
post_fix = {
"epoch": epoch,
"iter": i,
"avg_loss": avg_loss / (i + 1),
"loss": loss.item()
}
#if i % self.log_freq == 0:
# data_iter.write(str(post_fix))
if i % 100 == 0:
#print("PROGRESS: {}%".format(round((myidx) * 100 / n_iters, 4)))
print("\n")
print("PROGRESS: {}%".format(
round((epoch * len(data_loader) + i) * 100 /
self.total_iters, 4)))
print("EVALERR: {}%".format(avg_loss / (i + 1)))
#print("EP%d_%s, avg_loss=" % (epoch, str_code), avg_loss / len(data_iter))
def save(self, epoch, file_path="pretrained_models/addbert_trained.model"):
"""
Saving the current BERT model on file_path
:param epoch: current epoch number
:param file_path: model output path which gonna be file_path+"ep%d" % epoch
:return: final_output_path
"""
output_path = file_path + ".ep%d" % epoch
torch.save(self.bert.cpu(), output_path)
self.bert.to(self.device)
print("EP:%d Model Saved on:" % epoch, output_path)
return output_path
for (key, value) in vars(args).items():
print("{0:16} | {1}".format(key, value))
# check if processed data file exists or not
data_mean = [0.485, 0.456, 0.406]
data_std = [0.229, 0.224, 0.225]
# load the model
model = BiSalNet()
model.eval()
if args.onGPU and torch.cuda.device_count() > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelModel(model)
if args.onGPU:
model = model.cuda()
# compose the data with transforms
val_transforms = transforms.Compose([
transforms.Resize((args.inHeight, args.inWidth)),
transforms.ToTensor(),
transforms.Normalize(data_mean, data_std)
])
if os.path.isfile(join(args.savedir, "checkpoint.pth")):
print("=> loading checkpoint '{}'".format(
join(args.savedir, "checkpoint.pth")))
checkpoint = torch.load(join(args.savedir, "checkpoint.pth"))["state_dict"]
if list(checkpoint.keys())[0][:7] == "module." and not isinstance(
class ImageMTTrainer:
def __init__(self,
model,
mask_prob: float = 0.3,
clip: int = 1,
optimizer=None,
beam_width: int = 5,
max_len_a: float = 1.1,
max_len_b: int = 5,
len_penalty_ratio: float = 0.8,
nll_loss: bool = False,
fp16: bool = False,
mm_mode="mixed",
rank: int = -1):
self.model = model
self.clip = clip
self.optimizer = optimizer
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.num_gpu = torch.cuda.device_count()
self.mask_prob = mask_prob
if nll_loss:
self.criterion = nn.NLLLoss(
ignore_index=model.text_processor.pad_token_id())
else:
self.criterion = SmoothedNLLLoss(
ignore_index=model.text_processor.pad_token_id())
self.num_gpu = torch.cuda.device_count()
self.fp16 = False
self.rank = rank
if rank >= 0:
self.device = torch.device('cuda', rank)
torch.cuda.set_device(self.device)
self.model = self.model.to(self.device)
if fp16:
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level="O2")
self.fp16 = True
self.generator = BeamDecoder(self.model,
beam_width=beam_width,
max_len_a=max_len_a,
max_len_b=max_len_b,
len_penalty_ratio=len_penalty_ratio)
if rank >= 0:
self.model = DistributedDataParallel(self.model,
device_ids=[self.rank],
output_device=self.rank,
find_unused_parameters=True)
self.generator = DistributedDataParallel(
self.generator,
device_ids=[self.rank],
output_device=self.rank,
find_unused_parameters=True)
elif self.num_gpu > 1:
print("Let's use", self.num_gpu, "GPUs!")
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
self.generator = DataParallelModel(self.generator)
self.reference = None
self.best_bleu = -1.0
self.mm_mode = mm_mode
def train_epoch(self,
img_data_iter: List[data_utils.DataLoader],
step: int,
saving_path: str = None,
mass_data_iter: List[data_utils.DataLoader] = None,
mt_dev_iter: List[data_utils.DataLoader] = None,
mt_train_iter: List[data_utils.DataLoader] = None,
max_step: int = 300000,
accum=1,
beam_width=1,
fine_tune: bool = False,
lang_directions: dict = False,
lex_dict=None,
save_opt: bool = False,
**kwargs):
"Standard Training and Logging Function"
start = time.time()
total_tokens, total_loss, tokens, cur_loss = 0, 0, 0, 0
cur_loss = 0
batch_zip, shortest = self.get_batch_zip(img_data_iter, mass_data_iter,
mt_train_iter)
model = (self.model.module
if hasattr(self.model, "module") else self.model)
self.optimizer.zero_grad()
for i, batches in enumerate(batch_zip):
for batch in batches:
is_img_batch = isinstance(batch,
list) and "captions" in batch[0]
is_mass_batch = not is_img_batch and "dst_texts" not in batch
is_contrastive = False
try:
if fine_tune and (is_img_batch or is_mass_batch):
id2lid = lambda r: model.text_processor.languages[
model.text_processor.id2token(lang_directions[int(
r)])]
if is_mass_batch:
src_inputs = batch["src_texts"].squeeze(0)
src_pad_mask = src_inputs != model.text_processor.pad_token_id(
)
pad_indices = batch["pad_idx"].squeeze(0)
proposal = batch["proposal"].squeeze(
0) if lex_dict is not None else None
target_langs = torch.LongTensor([
lang_directions[int(l)]
for l in src_inputs[:, 0]
])
dst_langs = torch.LongTensor(
[id2lid(l) for l in src_inputs[:, 0]])
else:
src_inputs = [b["captions"] for b in batch]
src_pad_mask = [b["caption_mask"] for b in batch]
pad_indices = [b["pad_idx"] for b in batch]
proposal = [
b["proposal"] if lex_dict is not None else None
for b in batch
]
target_langs = [
torch.LongTensor([
lang_directions[int(l)] for l in src[:, 0]
]) for src in src_inputs
]
dst_langs = [
torch.LongTensor(
[id2lid(l) for l in src[:, 0]])
for src in src_inputs
]
if len(src_inputs) < self.num_gpu:
continue
if is_mass_batch:
langs = batch["langs"].squeeze(0)
else:
langs = [b["langs"] for b in batch]
model.eval()
with torch.no_grad():
# We do not backpropagate the data generator following the MASS paper.
images = None
if is_img_batch:
images = [b["images"] for b in batch]
outputs = self.generator(
src_inputs=src_inputs,
src_sizes=pad_indices,
first_tokens=target_langs,
src_langs=langs,
tgt_langs=dst_langs,
pad_idx=model.text_processor.pad_token_id(),
src_mask=src_pad_mask,
unpad_output=False,
beam_width=beam_width,
images=images,
proposals=proposal)
if self.num_gpu > 1 and self.rank < 0:
if is_mass_batch:
new_outputs = []
for output in outputs:
new_outputs += output
outputs = new_outputs
if is_mass_batch or self.num_gpu <= 1:
translations = pad_sequence(
outputs,
batch_first=True,
padding_value=model.text_processor.
pad_token_id())
translation_proposals = None
if lex_dict is not None:
translation_proposals = list(
map(
lambda o: dataset.
get_lex_suggestions(
lex_dict, o,
model.text_processor.
pad_token_id()), outputs))
translation_proposals = pad_sequence(
translation_proposals,
batch_first=True,
padding_value=model.text_processor.
pad_token_id())
translation_pad_mask = (
translations !=
model.text_processor.pad_token_id())
else:
translation_proposals = None
if lex_dict is not None:
translation_proposals = [
pad_sequence(
list(
map(
lambda o: dataset.
get_lex_suggestions(
lex_dict, o,
model.text_processor.
pad_token_id()),
output)),
batch_first=True,
padding_value=model.text_processor.
pad_token_id())
for output in outputs
]
translations = [
pad_sequence(output,
batch_first=True,
padding_value=model.
text_processor.pad_token_id())
for output in outputs
]
translation_pad_mask = [
t != model.text_processor.pad_token_id()
for t in translations
]
model.train()
if is_mass_batch:
langs = batch["langs"].squeeze(0)
else:
langs = torch.cat([b["langs"] for b in batch])
# Now use it for back-translation loss.
predictions = model(
src_inputs=translations,
tgt_inputs=src_inputs,
src_pads=translation_pad_mask,
pad_idx=model.text_processor.pad_token_id(),
src_langs=dst_langs,
tgt_langs=langs,
proposals=translation_proposals,
log_softmax=True)
if is_mass_batch:
src_targets = src_inputs[:,
1:].contiguous().view(-1)
src_mask_flat = src_pad_mask[:,
1:].contiguous().view(
-1)
else:
src_targets = torch.cat(
list(map(lambda s: s[:, 1:], src_inputs)))
src_mask_flat = torch.cat(
list(map(lambda s: s[:, 1:], src_pad_mask)))
targets = src_targets[src_mask_flat]
ntokens = targets.size(0)
elif is_img_batch:
src_inputs = [b["captions"] for b in batch]
src_pad_mask = [b["caption_mask"] for b in batch]
proposals = [b["proposal"] for b in batch
] if lex_dict is not None else None
langs = [b["langs"] for b in batch]
if (self.mm_mode == "mixed" and random.random() <= .5
) or self.mm_mode == "masked":
pad_indices = [b["pad_idx"] for b in batch]
if len(batch) < self.num_gpu:
continue
# For image masking, we are allowed to mask more than mask_prob
mask_prob = random.uniform(self.mask_prob, 1.0)
masked_info = list(
map(
lambda pi, si: mass_mask(
mask_prob, pi, si, model.text_processor
), pad_indices, src_inputs))
predictions = self.model(
src_inputs=list(
map(lambda m: m["src_text"], masked_info)),
tgt_inputs=list(
map(lambda m: m["to_recover"],
masked_info)),
tgt_positions=list(
map(lambda m: m["positions"],
masked_info)),
src_pads=src_pad_mask,
pad_idx=model.text_processor.pad_token_id(),
src_langs=langs,
batch=batch,
proposals=proposals,
log_softmax=True)
targets = torch.cat(
list(map(lambda m: m["targets"], masked_info)))
ntokens = targets.size(0)
else:
neg_samples = [b["neg"] for b in batch]
neg_mask = [b["neg_mask"] for b in batch]
loss = self.model(
src_inputs=src_inputs,
src_pads=src_pad_mask,
neg_samples=neg_samples,
neg_mask=neg_mask,
pad_idx=model.text_processor.pad_token_id(),
src_langs=langs,
batch=batch,
proposals=proposals,
log_softmax=True)
is_contrastive = True
elif not is_mass_batch: # MT data
src_inputs = batch["src_texts"].squeeze(0)
src_mask = batch["src_pad_mask"].squeeze(0)
tgt_inputs = batch["dst_texts"].squeeze(0)
tgt_mask = batch["dst_pad_mask"].squeeze(0)
src_langs = batch["src_langs"].squeeze(0)
dst_langs = batch["dst_langs"].squeeze(0)
proposals = batch["proposal"].squeeze(
0) if lex_dict is not None else None
if src_inputs.size(0) < self.num_gpu:
continue
predictions = self.model(
src_inputs=src_inputs,
tgt_inputs=tgt_inputs,
src_pads=src_mask,
tgt_mask=tgt_mask,
src_langs=src_langs,
tgt_langs=dst_langs,
proposals=proposals,
pad_idx=model.text_processor.pad_token_id(),
log_softmax=True)
targets = tgt_inputs[:, 1:].contiguous().view(-1)
tgt_mask_flat = tgt_mask[:, 1:].contiguous().view(-1)
targets = targets[tgt_mask_flat]
ntokens = targets.size(0)
else: # MASS data
src_inputs = batch["src_texts"].squeeze(0)
pad_indices = batch["pad_idx"].squeeze(0)
proposals = batch["proposal"].squeeze(
0) if lex_dict is not None else None
if src_inputs.size(0) < self.num_gpu:
continue
masked_info = mass_mask(self.mask_prob, pad_indices,
src_inputs,
model.text_processor)
predictions = self.model(
src_inputs=masked_info["src_text"],
tgt_inputs=masked_info["to_recover"],
tgt_positions=masked_info["positions"],
pad_idx=model.text_processor.pad_token_id(),
src_langs=batch["langs"].squeeze(0),
proposals=proposals,
log_softmax=True)
targets = masked_info["targets"]
ntokens = targets.size(0)
if is_contrastive: # Nothing to predict!
backward(loss, self.optimizer, self.fp16)
loss = loss.data
elif ntokens > 0:
if self.num_gpu == 1:
targets = targets.to(predictions.device)
if self.rank >= 0: targets = targets.to(self.device)
loss = self.criterion(predictions, targets).mean()
backward(loss, self.optimizer, self.fp16)
loss = float(loss.data) * ntokens
tokens += ntokens
total_tokens += ntokens
total_loss += loss
cur_loss += loss
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.clip)
step += 1
if step % accum == 0:
self.optimizer.step()
self.optimizer.zero_grad()
if is_mass_batch and not fine_tune:
mass_unmask(masked_info["src_text"],
masked_info["src_mask"],
masked_info["mask_idx"])
if not is_contrastive and is_img_batch and not fine_tune:
map(
lambda m: mass_unmask(m["src_text"], m["src_mask"],
m["mask_idx"]), masked_info)
if step % 50 == 0 and tokens > 0:
elapsed = time.time() - start
print(
self.rank, "->", datetime.datetime.now(),
"Epoch Step: %d Loss: %f Tokens per Sec: %f " %
(step, cur_loss / tokens, tokens / elapsed))
if mt_dev_iter is not None and step % 5000 == 0 and self.rank <= 0:
bleu = self.eval_bleu(mt_dev_iter, saving_path)
print("BLEU:", bleu)
if step % 10000 == 0:
if self.rank <= 0:
if self.rank < 0:
model.cpu().save(saving_path + ".latest")
elif self.rank == 0:
model.save(saving_path + ".latest")
if save_opt:
with open(
os.path.join(
saving_path + ".latest",
"optim"), "wb") as fp:
pickle.dump(self.optimizer, fp)
if self.rank < 0:
model = model.to(self.device)
start, tokens, cur_loss = time.time(), 0, 0
except RuntimeError as err:
print(repr(err))
print("Error processing", is_img_batch)
if (isinstance(model, ImageMassSeq2Seq)) and is_img_batch:
for b in batch:
print("->", len(b["images"]), b["captions"].size())
torch.cuda.empty_cache()
if i == shortest - 1:
break
if step >= max_step:
break
try:
if self.rank <= 0:
print("Total loss in this epoch: %f" %
(total_loss / total_tokens))
if self.rank < 0:
model.cpu().save(saving_path + ".latest")
model = model.to(self.device)
elif self.rank == 0:
model.save(saving_path + ".latest")
if mt_dev_iter is not None:
bleu = self.eval_bleu(mt_dev_iter, saving_path)
print("BLEU:", bleu)
except RuntimeError as err:
print(repr(err))
return step
def get_batch_zip(self, img_data_iter, mass_data_iter, mt_train_iter):
# if img_data_iter is not None and mt_train_iter is not None:
# img_data_iter *= 5
# if mass_data_iter is not None and mt_train_iter is not None:
# mass_data_iter *= 5
iters = list(
chain(*filter(lambda x: x != None,
[img_data_iter, mass_data_iter, mt_train_iter])))
shortest = min(len(l) for l in iters)
return zip(*iters), shortest
def eval_bleu(self, dev_data_iter, saving_path, save_opt: bool = False):
mt_output = []
src_text = []
model = (self.model.module
if hasattr(self.model, "module") else self.model)
model.eval()
with torch.no_grad():
for iter in dev_data_iter:
for batch in iter:
src_inputs = batch["src_texts"].squeeze(0)
src_mask = batch["src_pad_mask"].squeeze(0)
tgt_inputs = batch["dst_texts"].squeeze(0)
src_langs = batch["src_langs"].squeeze(0)
dst_langs = batch["dst_langs"].squeeze(0)
src_pad_idx = batch["pad_idx"].squeeze(0)
proposal = batch["proposal"].squeeze(
0) if batch["proposal"] is not None else None
src_ids = get_outputs_until_eos(
model.text_processor.sep_token_id(),
src_inputs,
remove_first_token=True)
src_text += list(
map(
lambda src: model.text_processor.tokenizer.decode(
src.numpy()), src_ids))
outputs = self.generator(
src_inputs=src_inputs,
src_sizes=src_pad_idx,
first_tokens=tgt_inputs[:, 0],
src_mask=src_mask,
src_langs=src_langs,
tgt_langs=dst_langs,
pad_idx=model.text_processor.pad_token_id(),
proposals=proposal)
if self.num_gpu > 1 and self.rank < 0:
new_outputs = []
for output in outputs:
new_outputs += output
outputs = new_outputs
mt_output += list(
map(
lambda x: model.text_processor.tokenizer.decode(x[
1:].numpy()), outputs))
model.train()
bleu = sacrebleu.corpus_bleu(mt_output,
[self.reference[:len(mt_output)]],
lowercase=True,
tokenize="intl")
with open(os.path.join(saving_path, "bleu.output"), "w") as writer:
writer.write("\n".join([
src + "\n" + ref + "\n" + o + "\n\n***************\n"
for src, ref, o in zip(src_text, mt_output,
self.reference[:len(mt_output)])
]))
if bleu.score > self.best_bleu:
self.best_bleu = bleu.score
print("Saving best BLEU", self.best_bleu)
with open(os.path.join(saving_path, "bleu.best.output"),
"w") as writer:
writer.write("\n".join([
src + "\n" + ref + "\n" + o + "\n\n***************\n"
for src, ref, o in zip(src_text, mt_output,
self.reference[:len(mt_output)])
]))
if self.rank < 0:
model.cpu().save(saving_path)
model = model.to(self.device)
elif self.rank == 0:
model.save(saving_path)
if save_opt:
with open(os.path.join(saving_path, "optim"), "wb") as fp:
pickle.dump(self.optimizer, fp)
return bleu.score
@staticmethod
def train(options):
lex_dict = None
if options.dict_path is not None:
lex_dict = get_lex_dict(options.dict_path)
if options.local_rank <= 0 and not os.path.exists(options.model_path):
os.makedirs(options.model_path)
text_processor = TextProcessor(options.tokenizer_path)
assert text_processor.pad_token_id() == 0
num_processors = max(torch.cuda.device_count(),
1) if options.local_rank < 0 else 1
if options.pretrained_path is not None:
mt_model = Seq2Seq.load(ImageMassSeq2Seq,
options.pretrained_path,
tok_dir=options.tokenizer_path)
else:
mt_model = ImageMassSeq2Seq(
use_proposals=lex_dict is not None,
tie_embed=options.tie_embed,
text_processor=text_processor,
resnet_depth=options.resnet_depth,
lang_dec=options.lang_decoder,
enc_layer=options.encoder_layer,
dec_layer=options.decoder_layer,
embed_dim=options.embed_dim,
intermediate_dim=options.intermediate_layer_dim)
if options.lm_path is not None:
lm = LM(text_processor=text_processor,
enc_layer=options.encoder_layer,
embed_dim=options.embed_dim,
intermediate_dim=options.intermediate_layer_dim)
mt_model.init_from_lm(lm)
print("Model initialization done!")
# We assume that the collator function returns a list with the size of number of gpus (in case of cpus,
collator = dataset.ImageTextCollator()
num_batches = max(1, torch.cuda.device_count())
if options.continue_train:
with open(os.path.join(options.pretrained_path, "optim"),
"rb") as fp:
optimizer = pickle.load(fp)
else:
optimizer = build_optimizer(mt_model,
options.learning_rate,
warump_steps=options.warmup)
trainer = ImageMTTrainer(model=mt_model,
mask_prob=options.mask_prob,
optimizer=optimizer,
clip=options.clip,
beam_width=options.beam_width,
max_len_a=options.max_len_a,
max_len_b=options.max_len_b,
len_penalty_ratio=options.len_penalty_ratio,
fp16=options.fp16,
mm_mode=options.mm_mode,
rank=options.local_rank)
pin_memory = torch.cuda.is_available()
img_train_loader = ImageMTTrainer.get_img_loader(
collator,
dataset.ImageCaptionDataset,
options.train_path,
mt_model,
num_batches,
options,
pin_memory,
lex_dict=lex_dict)
mass_train_data, mass_train_loader, finetune_loader, mt_dev_loader = None, None, None, None
if options.mass_train_path is not None:
mass_train_paths = options.mass_train_path.strip().split(",")
if options.step > 0:
mass_train_data, mass_train_loader = ImageMTTrainer.get_mass_loader(
mass_train_paths,
mt_model,
num_processors,
options,
pin_memory,
keep_examples=options.finetune_step > 0,
lex_dict=lex_dict)
if options.finetune_step > 0:
finetune_loader, finetune_data = ImageMTTrainer.get_mass_finetune_data(
mass_train_data,
mass_train_paths,
mt_model,
num_processors,
options,
pin_memory,
lex_dict=lex_dict)
mt_train_loader = None
if options.mt_train_path is not None:
mt_train_loader = ImageMTTrainer.get_mt_train_data(
mt_model,
num_processors,
options,
pin_memory,
lex_dict=lex_dict)
mt_dev_loader = None
if options.mt_dev_path is not None:
mt_dev_loader = ImageMTTrainer.get_mt_dev_data(mt_model,
options,
pin_memory,
text_processor,
trainer,
lex_dict=lex_dict)
step, train_epoch = 0, 1
while options.step > 0 and step < options.step:
print("train epoch", train_epoch)
step = trainer.train_epoch(img_data_iter=img_train_loader,
mass_data_iter=mass_train_loader,
mt_train_iter=mt_train_loader,
max_step=options.step,
lex_dict=lex_dict,
mt_dev_iter=mt_dev_loader,
saving_path=options.model_path,
step=step,
save_opt=options.save_opt,
accum=options.accum)
train_epoch += 1
finetune_epoch = 0
# Resetting the optimizer for the purpose of finetuning.
trainer.optimizer.reset()
lang_directions = ImageMTTrainer.get_lang_dirs(options.bt_langs,
text_processor)
print(options.local_rank, "lang dirs", lang_directions)
print(options.local_rank,
"Reloading image train data with new batch size...")
if options.finetune_step > 0 and img_train_loader is not None:
img_train_loader = ImageMTTrainer.get_img_loader(
collator,
dataset.ImageCaptionDataset,
options.train_path,
mt_model,
num_batches,
options,
pin_memory,
denom=2,
lex_dict=lex_dict)
if options.ignore_mt_mass:
mt_train_loader = None
print(options.local_rank,
"Reloading image train data with new batch size done!")
while options.finetune_step > 0 and step <= options.finetune_step + options.step:
print(options.local_rank, "finetune epoch", finetune_epoch)
step = trainer.train_epoch(img_data_iter=img_train_loader,
mass_data_iter=finetune_loader,
mt_train_iter=mt_train_loader,
max_step=options.finetune_step +
options.step,
mt_dev_iter=mt_dev_loader,
saving_path=options.model_path,
step=step,
fine_tune=True,
lang_directions=lang_directions,
lex_dict=lex_dict,
save_opt=options.save_opt,
accum=options.accum,
beam_width=options.bt_beam_width)
finetune_epoch += 1
@staticmethod
def get_lang_dirs(bt_langs, text_processor: TextProcessor):
langs = ["<" + l + ">" for l in bt_langs.strip().split(",")]
langs = set([text_processor.token_id(l) for l in langs])
if len(langs) < 2:
return None
assert len(langs) <= 2
lang_directions = {}
for lang1 in langs:
for lang2 in langs:
if lang1 != lang2:
# Assuming that we only have two languages!
lang_directions[lang1] = lang2
return lang_directions
@staticmethod
def get_mt_dev_data(mt_model,
options,
pin_memory,
text_processor,
trainer,
lex_dict=None):
mt_dev_loader = []
dev_paths = options.mt_dev_path.split(",")
trainer.reference = []
for dev_path in dev_paths:
mt_dev_data = dataset.MTDataset(
batch_pickle_dir=dev_path,
max_batch_capacity=options.total_capacity,
keep_pad_idx=True,
max_batch=int(options.batch / (options.beam_width * 2)),
pad_idx=mt_model.text_processor.pad_token_id(),
lex_dict=lex_dict)
dl = data_utils.DataLoader(mt_dev_data,
batch_size=1,
shuffle=False,
pin_memory=pin_memory)
mt_dev_loader.append(dl)
print(options.local_rank, "creating reference")
generator = (trainer.generator.module if hasattr(
trainer.generator, "module") else trainer.generator)
for batch in dl:
tgt_inputs = batch["dst_texts"].squeeze()
refs = get_outputs_until_eos(text_processor.sep_token_id(),
tgt_inputs,
remove_first_token=True)
ref = [
generator.seq2seq_model.text_processor.tokenizer.decode(
ref.numpy()) for ref in refs
]
trainer.reference += ref
return mt_dev_loader
@staticmethod
def get_mt_train_data(mt_model,
num_processors,
options,
pin_memory,
lex_dict=None):
mt_train_loader = []
train_paths = options.mt_train_path.split(",")
for train_path in train_paths:
mt_train_data = dataset.MTDataset(
batch_pickle_dir=train_path,
max_batch_capacity=int(num_processors *
options.total_capacity / 2),
max_batch=int(num_processors * options.batch / 2),
pad_idx=mt_model.text_processor.pad_token_id(),
lex_dict=lex_dict,
keep_pad_idx=False)
mtl = data_utils.DataLoader(
mt_train_data,
sampler=None if options.local_rank < 0 else DistributedSampler(
mt_train_data, rank=options.local_rank),
batch_size=1,
shuffle=(options.local_rank < 0),
pin_memory=pin_memory)
mt_train_loader.append(mtl)
return mt_train_loader
@staticmethod
def get_mass_finetune_data(mass_train_data,
mass_train_paths,
mt_model,
num_processors,
options,
pin_memory,
lex_dict=None):
finetune_data, finetune_loader = [], []
for i, mass_train_path in enumerate(mass_train_paths):
fd = dataset.MassDataset(
batch_pickle_dir=mass_train_path,
max_batch_capacity=int(num_processors *
options.total_capacity /
max(2, options.bt_beam_width)),
max_batch=int(num_processors * options.batch /
max(2, options.bt_beam_width)),
pad_idx=mt_model.text_processor.pad_token_id(),
max_seq_len=options.max_seq_len,
keep_examples=False,
example_list=None if mass_train_data is None else
mass_train_data[i].examples_list,
lex_dict=lex_dict)
finetune_data.append(fd)
fl = data_utils.DataLoader(
fd,
sampler=None if options.local_rank < 0 else DistributedSampler(
fd, rank=options.local_rank),
batch_size=1,
shuffle=(options.local_rank < 0),
pin_memory=pin_memory)
finetune_loader.append(fl)
if mass_train_data is not None:
mass_train_data[i].examples_list = []
return finetune_loader, finetune_data
@staticmethod
def get_mass_loader(mass_train_paths,
mt_model,
num_processors,
options,
pin_memory,
keep_examples,
lex_dict=None):
mass_train_data, mass_train_loader = [], []
for i, mass_train_path in enumerate(mass_train_paths):
td = dataset.MassDataset(
batch_pickle_dir=mass_train_path,
max_batch_capacity=num_processors * options.total_capacity,
max_batch=num_processors * options.batch,
pad_idx=mt_model.text_processor.pad_token_id(),
max_seq_len=options.max_seq_len,
keep_examples=keep_examples,
lex_dict=lex_dict)
mass_train_data.append(td)
dl = data_utils.DataLoader(
td,
sampler=None if options.local_rank < 0 else DistributedSampler(
td, rank=options.local_rank),
batch_size=1,
shuffle=(options.local_rank < 0),
pin_memory=pin_memory)
mass_train_loader.append(dl)
return mass_train_data, mass_train_loader
@staticmethod
def get_img_loader(collator,
dataset_class,
paths,
mt_model,
num_batches,
options,
pin_memory,
denom=1,
lex_dict=None,
shuffle=True):
if paths is not None:
img_loader = []
for pth in paths.strip().split(","):
data = dataset_class(
root_img_dir=options.image_dir,
data_bin_file=pth,
max_capacity=int(options.img_capacity / denom),
text_processor=mt_model.text_processor,
max_img_per_batch=options.max_image / denom,
lex_dict=lex_dict)
print(options.local_rank, pth, "Length of training data",
len(data))
tl = data_utils.DataLoader(
data,
sampler=None if options.local_rank < 0 else
DistributedSampler(data, rank=options.local_rank),
batch_size=num_batches,
shuffle=shuffle,
pin_memory=pin_memory,
collate_fn=collator)
img_loader.append(tl)
return img_loader
return None
class Trainer:
"""
trainer class
"""
def __init__(self, cfg: Namespace, data: Dataset):
"""
Args:
cfg: configuration
data: train dataset
"""
self.cfg = cfg
self.train, self.valid = data.split(0.8)
RATING_FIELD.build_vocab(self.train)
self.device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu') # pylint: disable=no-member
self.batch_size = cfg.batch_size
if torch.cuda.is_available():
self.batch_size *= torch.cuda.device_count()
self.trn_itr = BucketIterator(
self.train,
device=self.device,
batch_size=self.batch_size,
shuffle=True,
train=True,
sort_within_batch=True,
sort_key=lambda exam: -len(exam.comment_text))
self.vld_itr = BucketIterator(
self.valid,
device=self.device,
batch_size=self.batch_size,
shuffle=False,
train=False,
sort_within_batch=True,
sort_key=lambda exam: -len(exam.comment_text))
self.log_step = 1000
if len(self.vld_itr) < 100:
self.log_step = 10
elif len(self.vld_itr) < 1000:
self.log_step = 100
bert_path = cfg.bert_path if cfg.bert_path else 'bert-base-cased'
self.model = BertForSequenceClassification.from_pretrained(
bert_path, num_labels=2)
pos_weight = (
len([exam for exam in self.train.examples if exam.target < 0.5]) /
len([exam for exam in self.train.examples if exam.target >= 0.5]))
pos_wgt_tensor = torch.tensor([1.0, pos_weight], device=self.device) # pylint: disable=not-callable
self.criterion = nn.CrossEntropyLoss(weight=pos_wgt_tensor)
if torch.cuda.is_available():
self.model = DataParallelModel(self.model.cuda())
self.criterion = DataParallelCriterion(self.criterion)
self.optimizer = optim.Adam(self.model.parameters(), cfg.learning_rate)
def run(self):
"""
do train
"""
max_f_score = -9e10
max_epoch = -1
for epoch in range(self.cfg.epoch):
train_loss = self._train_epoch(epoch)
metrics = self._evaluate(epoch)
max_f_score_str = f' < {max_f_score:.2f}'
if metrics['f_score'] > max_f_score:
max_f_score_str = ' is max'
max_f_score = metrics['f_score']
max_epoch = epoch
torch.save(self.model.state_dict(), self.cfg.model_path)
logging.info('EPOCH[%d]: train loss: %.6f, valid loss: %.6f, acc: %.2f,' \
' F: %.2f%s', epoch, train_loss, metrics['loss'],
metrics['accuracy'], metrics['f_score'], max_f_score_str)
if (epoch - max_epoch) >= self.cfg.patience:
logging.info('early stopping...')
break
logging.info('epoch: %d, f-score: %.2f', max_epoch, max_f_score)
def _train_epoch(self, epoch: int) -> float:
"""
train single epoch
Args:
epoch: epoch number
Returns:
average loss
"""
self.model.train()
progress = tqdm(self.trn_itr,
f'EPOCH[{epoch}]',
mininterval=1,
ncols=100)
losses = []
for step, batch in enumerate(progress, start=1):
outputs = self.model(batch.comment_text)
# output of model wrapped with DataParallelModel is a list of outputs from each GPU
# make input of DataParallelCriterion as a list of tuples
if isinstance(self.model, DataParallelModel):
loss = self.criterion([(output, ) for output in outputs],
batch.target)
else:
loss = self.criterion(outputs, batch.target)
losses.append(loss.item())
if step % self.log_step == 0:
avg_loss = sum(losses) / len(losses)
progress.set_description(f'EPOCH[{epoch}] ({avg_loss:.6f})')
loss.backward()
self.optimizer.step()
self.optimizer.zero_grad()
return sum(losses) / len(losses)
def _evaluate(self, epoch: int) -> Dict[str, float]:
"""
evaluate on validation data
Args:
epoch: epoch number
Returns:
metrics
"""
self.model.eval()
progress = tqdm(self.vld_itr,
f' EVAL[{epoch}]',
mininterval=1,
ncols=100)
losses = []
preds = []
golds = []
for step, batch in enumerate(progress, start=1):
with torch.no_grad():
outputs = self.model(batch.comment_text)
if isinstance(self.model, DataParallelModel):
loss = self.criterion([(output, ) for output in outputs],
batch.target)
for output in outputs:
preds.extend([(0 if o[0] < o[1] else 1)
for o in output])
else:
loss = self.criterion(outputs, batch.target)
preds.extend([(0 if output[0] < output[1] else 1)
for output in outputs])
losses.append(loss.item())
golds.extend([gold.item() for gold in batch.target])
if step % self.log_step == 0:
avg_loss = sum(losses) / len(losses)
progress.set_description(
f' EVAL[{epoch}] ({avg_loss:.6f})')
metrics = self._get_metrics(preds, golds)
metrics['loss'] = sum(losses) / len(losses)
return metrics
@classmethod
def _get_metrics(cls, preds: List[float],
golds: List[float]) -> Dict[str, float]:
"""
get metric values
Args:
preds: predictions
golds: gold standards
Returns:
metric
"""
assert len(preds) == len(golds)
true_pos = 0
false_pos = 0
false_neg = 0
true_neg = 0
for pred, gold in zip(preds, golds):
if pred >= 0.5:
if gold >= 0.5:
true_pos += 1
else:
false_pos += 1
else:
if gold >= 0.5:
false_neg += 1
else:
true_neg += 1
accuracy = (true_pos + true_neg) / (true_pos + false_pos + false_neg +
true_neg)
precision = 0.0
if (true_pos + false_pos) > 0:
precision = true_pos / (true_pos + false_pos)
recall = 0.0
if (true_pos + false_neg) > 0:
recall = true_pos / (true_pos + false_neg)
f_score = 0.0
if (precision + recall) > 0.0:
f_score = 2.0 * precision * recall / (precision + recall)
return {
'accuracy': 100.0 * accuracy,
'precision': 100.0 * precision,
'recall': 100.0 * recall,
'f_score': 100.0 * f_score,
}
def main(args):
init(args)
# Constants
n_ctx = args.n_ctx
data_dir = args.data_dir
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
print("device", device, "n_gpu", n_gpu)
text_encoder = TextEncoder(args.encoder_path, args.bpe_path)
encoder = text_encoder.encoder
n_vocab = len(text_encoder.encoder)
text_encoder.decoder[len(encoder)] = '_start_'
encoder['_start_'] = len(encoder)
text_encoder.decoder[len(encoder)] = '_delimiter_'
encoder['_delimiter_'] = len(encoder)
text_encoder.decoder[len(encoder)] = '_classify_'
encoder['_classify_'] = len(encoder)
n_special = 3 # XD: useless for language modeling task
vocab = n_vocab + n_special + n_ctx
lm_model = LMModel(args,
vocab,
n_ctx,
return_probs=True,
doc_embed=args.doc_model)
load_openai_pretrained_model(lm_model.transformer,
n_ctx=n_ctx,
n_special=n_special)
if args.checkpoint != "none":
checkpoint = torch.load(args.checkpoint, map_location='cpu')
state_dict = checkpoint["state_dict"]
for key in list(state_dict.keys()):
state_dict[key[7:]] = state_dict[key]
del state_dict[key]
pos_emb_mask = torch.zeros(1, 1, vocab)
pos_emb_mask[:, :, -n_ctx] = -1e12
state_dict['pos_emb_mask'] = pos_emb_mask
lm_model.load_state_dict(state_dict)
lm_model.to(device)
lm_model = DataParallelModel(lm_model)
train_bar = get_loader(os.path.join(data_dir, "val_encoded.jsonl"),
n_gpu,
encoder,
num_workers=1,
shuffle=True,
max_size=args.n_iter)
srcs, hyps, refs = [], [], []
with torch.no_grad():
lm_model.eval()
for i, (pad_output, mask_output) in enumerate(tqdm(train_bar), 1):
src_strs, tgt_strs, gen_strs = generate_outputs(
lm_model, pad_output, mask_output, text_encoder, device,
args.beam, args.gen_len, args.k, args.decoding_strategy)
srcs.extend(src_strs)
hyps.extend(gen_strs)
refs.extend(tgt_strs)
for i in range(len(hyps)):
print("*" * 50)
print("Source: {}".format(srcs[i]))
print('Hypothesis: {}'.format(hyps[i]))
print("Reference: {}".format(refs[i]))
def train(config):
net = BertForMaskedLM.from_pretrained(config.model)
lossFunc = KLDivLoss(config)
if torch.cuda.is_available():
net = net.cuda()
lossFunc = lossFunc.cuda()
if config.dataParallel:
net = DataParallelModel(net)
lossFunc = DataParallelCriterion(lossFunc)
options = optionsLoader(LOG, config.optionFrames, disp=False)
Tokenizer = BertTokenizer.from_pretrained(config.model)
prepareFunc = prepare_data
trainSet = Dataset('train', config.batch_size,
lambda x: len(x[0]) + len(x[1]), prepareFunc, Tokenizer,
options['dataset'], LOG, 'train')
validSet = Dataset('valid', config.batch_size,
lambda x: len(x[0]) + len(x[1]), prepareFunc, Tokenizer,
options['dataset'], LOG, 'valid')
print(trainSet.__len__())
Q = []
best_vloss = 1e99
counter = 0
lRate = config.lRate
prob_src = config.prob_src
prob_tgt = config.prob_tgt
num_train_optimization_steps = trainSet.__len__(
) * options['training']['stopConditions']['max_epoch']
param_optimizer = list(net.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=lRate,
e=1e-9,
t_total=num_train_optimization_steps,
warmup=0.0)
for epoch_idx in range(options['training']['stopConditions']['max_epoch']):
total_seen = 0
total_similar = 0
total_unseen = 0
total_source = 0
trainSet.setConfig(config, prob_src, prob_tgt)
trainLoader = data.DataLoader(dataset=trainSet,
batch_size=1,
shuffle=True,
num_workers=config.dataLoader_workers,
pin_memory=True)
validSet.setConfig(config, 0.0, prob_tgt)
validLoader = data.DataLoader(dataset=validSet,
batch_size=1,
shuffle=False,
num_workers=config.dataLoader_workers,
pin_memory=True)
for batch_idx, batch_data in enumerate(trainLoader):
if (batch_idx + 1) % 10000 == 0:
gc.collect()
start_time = time.time()
net.train()
inputs, positions, token_types, labels, masks, batch_seen, batch_similar, batch_unseen, batch_source = batch_data
inputs = inputs[0].cuda()
positions = positions[0].cuda()
token_types = token_types[0].cuda()
labels = labels[0].cuda()
masks = masks[0].cuda()
total_seen += batch_seen
total_similar += batch_similar
total_unseen += batch_unseen
total_source += batch_source
n_token = int((labels.data != 0).data.sum())
predicts = net(inputs, positions, token_types, masks)
loss = lossFunc(predicts, labels, n_token).sum()
Q.append(float(loss))
if len(Q) > 200:
Q.pop(0)
loss_avg = sum(Q) / len(Q)
optimizer.zero_grad()
loss.backward()
optimizer.step()
LOG.log(
'Epoch %2d, Batch %6d, Loss %9.6f, Average Loss %9.6f, Time %9.6f'
% (epoch_idx + 1, batch_idx + 1, loss, loss_avg,
time.time() - start_time))
# Checkpoints
idx = epoch_idx * trainSet.__len__() + batch_idx + 1
if (idx >= options['training']['checkingPoints']['checkMin']) and (
idx % options['training']['checkingPoints']['checkFreq']
== 0):
if config.do_eval:
vloss = 0
total_tokens = 0
for bid, batch_data in enumerate(validLoader):
inputs, positions, token_types, labels, masks, batch_seen, batch_similar, batch_unseen, batch_source = batch_data
inputs = inputs[0].cuda()
positions = positions[0].cuda()
token_types = token_types[0].cuda()
labels = labels[0].cuda()
masks = masks[0].cuda()
n_token = int((labels.data != config.PAD).data.sum())
with torch.no_grad():
net.eval()
predicts = net(inputs, positions, token_types,
masks)
vloss += float(lossFunc(predicts, labels).sum())
total_tokens += n_token
vloss /= total_tokens
is_best = vloss < best_vloss
best_vloss = min(vloss, best_vloss)
LOG.log(
'CheckPoint: Validation Loss %11.8f, Best Loss %11.8f'
% (vloss, best_vloss))
if is_best:
LOG.log('Best Model Updated')
save_check_point(
{
'epoch': epoch_idx + 1,
'batch': batch_idx + 1,
'options': options,
'config': config,
'state_dict': net.state_dict(),
'best_vloss': best_vloss
},
is_best,
path=config.save_path,
fileName='latest.pth.tar')
counter = 0
else:
counter += options['training']['checkingPoints'][
'checkFreq']
if counter >= options['training']['stopConditions'][
'rateReduce_bound']:
counter = 0
for param_group in optimizer.param_groups:
lr_ = param_group['lr']
param_group['lr'] *= 0.55
_lr = param_group['lr']
LOG.log(
'Reduce Learning Rate from %11.8f to %11.8f' %
(lr_, _lr))
LOG.log('Current Counter = %d' % (counter))
else:
save_check_point(
{
'epoch': epoch_idx + 1,
'batch': batch_idx + 1,
'options': options,
'config': config,
'state_dict': net.state_dict(),
'best_vloss': 1e99
},
False,
path=config.save_path,
fileName='checkpoint_Epoch' + str(epoch_idx + 1) +
'_Batch' + str(batch_idx + 1) + '.pth.tar')
LOG.log('CheckPoint Saved!')
if options['training']['checkingPoints']['everyEpoch']:
save_check_point(
{
'epoch': epoch_idx + 1,
'batch': batch_idx + 1,
'options': options,
'config': config,
'state_dict': net.state_dict(),
'best_vloss': 1e99
},
False,
path=config.save_path,
fileName='checkpoint_Epoch' + str(epoch_idx + 1) + '.pth.tar')
LOG.log('Epoch Finished.')
LOG.log(
'Total Seen: %d, Total Unseen: %d, Total Similar: %d, Total Source: %d.'
% (total_seen, total_unseen, total_similar, total_source))
gc.collect()
def test(config):
Best_Model = torch.load(config.test_model)
Tokenizer = BertTokenizer.from_pretrained(config.model)
f_in = open(config.inputFile, 'r')
net = BertForMaskedLM.from_pretrained(config.model)
# When loading from a model not trained from DataParallel
#net.load_state_dict(Best_Model['state_dict'])
#net.eval()
if torch.cuda.is_available():
net = net.cuda(0)
if config.dataParallel:
net = DataParallelModel(net)
# When loading from a model trained from DataParallel
net.load_state_dict(Best_Model['state_dict'])
net.eval()
mySearcher = Searcher(net, config)
f_top1 = open('summary' + config.suffix + '.txt', 'w', encoding='utf-8')
f_topK = open('summary' + config.suffix + '.txt.' +
str(config.answer_size),
'w',
encoding='utf-8')
ed = '\n------------------------\n'
for idx, line in enumerate(f_in):
source_ = line.strip().split()
source = Tokenizer.tokenize(line.strip())
mapping = mapping_tokenize(source_, source)
source = Tokenizer.convert_tokens_to_ids(source)
print(idx)
print(detokenize(translate(source, Tokenizer), mapping), end=ed)
l_pred = mySearcher.length_Predict(source)
Answers = mySearcher.search(source)
baseline = sum(Answers[0][0])
if config.reranking_method == 'none':
Answers = sorted(Answers, key=lambda x: sum(x[0]))
elif config.reranking_method == 'length_norm':
Answers = sorted(Answers, key=lambda x: length_norm(x[0]))
elif config.reranking_method == 'bounded_word_reward':
Answers = sorted(
Answers,
key=lambda x: bounded_word_reward(x[0], config.reward, l_pred))
elif config.reranking_method == 'bounded_adaptive_reward':
Answers = sorted(
Answers,
key=lambda x: bounded_adaptive_reward(x[0], x[2], l_pred))
texts = [
detokenize(translate(Answers[k][1], Tokenizer), mapping)
for k in range(len(Answers))
]
if baseline != sum(Answers[0][0]):
print('Reranked!')
print(texts[0], end=ed)
print(texts[0], file=f_top1)
print(len(texts), file=f_topK)
for i in range(len(texts)):
print(Answers[i][0], file=f_topK)
print(texts[i], file=f_topK)
f_top1.close()
f_topK.close()
# target dimension[0] / 2
# tar = target.contiguous().view(-1)
# out = output.contiguous().view(target.size(0),-1)
target = tar.contiguous().view(-1)
output = out[:tar.size(0)]
normalize = output.size(0) * output.size(1)
output = output.contiguous().view(target.size(0), -1)
loss = self.NLL(output, target) / normalize
return loss
if not eval_model:
criterion = NLLLoss(ignore_index=PAD)
parallel_model = DataParallelModel(model) # Encapsulate the model
parallel_loss = DataParallelCriterion(criterion)
# In[5]:
# ---------------------------
# def merge_res(res):
# ((inds1, log_probs1, enc_out1),(inds2, log_probs2, enc_out2)) = res
# inds = T.cat([inds1, inds2], dim = 0).cpu()
# enc_out = T.cat([enc_out1, enc_out2], dim = 0).cpu()
# if type(log_probs1) != list:
# log_probs = T.cat([log_probs1, log_probs2], dim = 0)
# return inds, log_probs, enc_out
# else:
# return inds, _, enc_out
if config.model_type=='LSTM':
model = LSTMLM(input_size=len(vocab),
embedding_size=config.embedding_size,
hidden_size=config.hidden_size,
output_size=len(vocab),
n_layers=config.n_layers,
dropout_p=config.dropout_p)
elif config.model_type=='BiLSTM':
model = BiLSTMLM(input_size=len(vocab),
embedding_size=config.embedding_size,
hidden_size=config.hidden_size,
output_size=len(vocab),
n_layers=config.n_layers,
dropout_p=config.dropout_p)
loss_fn = nn.NLLLoss(ignore_index=vocab.stoi[vocab.pad_token])
optimizer = optim.Adam(model.parameters())
if config.cuda:
if config.multi_gpu:
from parallel import DataParallelModel, DataParallelCriterion
model = DataParallelModel(model).cuda()
loss_fn = DataParallelCriterion(loss_fn).cuda()
else:
model = model.cuda()
loss_fn = loss_fn.cuda()
print('=========MODEL=========\n',model)
# Train
for epoch in range(1, config.epochs+1):
train()
def parallelize(self):
self.parallel = True
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
def main():
args = setup_parser()
args.final_eval = False
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup CUDA, GPU & distributed training
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
args.device = device
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1),
args.fp16)
# Set seed
set_seed(args)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels(args.data_dir)
num_labels = len(label_list)
args.num_labels = num_labels
# Load pretrained model and tokenizer
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name
if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case)
model = model_class.from_pretrained(args.model_name_or_path, config=config)
model.to(args.device)
# logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train:
# Create output directory if needed
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
model.to(args.device)
if args.n_gpu > 1:
model = DataParallelModel(model)
# Evaluation
results = {}
if args.do_eval:
tokenizer = tokenizer_class.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
if args.n_gpu > 1:
model = DataParallelModel(model)
args.final_eval = True
result = evaluate(args, model, tokenizer, prefix=global_step)
result = dict(
(k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
if args.save_embeddings:
save_embeddings(args, model, tokenizer)
return results
def __init__(self,
model,
mask_prob: float = 0.3,
clip: int = 1,
optimizer=None,
beam_width: int = 5,
max_len_a: float = 1.1,
max_len_b: int = 5,
len_penalty_ratio: float = 0.8,
nll_loss: bool = False,
fp16: bool = False,
mm_mode="mixed",
rank: int = -1):
self.model = model
self.clip = clip
self.optimizer = optimizer
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.num_gpu = torch.cuda.device_count()
self.mask_prob = mask_prob
if nll_loss:
self.criterion = nn.NLLLoss(
ignore_index=model.text_processor.pad_token_id())
else:
self.criterion = SmoothedNLLLoss(
ignore_index=model.text_processor.pad_token_id())
self.num_gpu = torch.cuda.device_count()
self.fp16 = False
self.rank = rank
if rank >= 0:
self.device = torch.device('cuda', rank)
torch.cuda.set_device(self.device)
self.model = self.model.to(self.device)
if fp16:
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level="O2")
self.fp16 = True
self.generator = BeamDecoder(self.model,
beam_width=beam_width,
max_len_a=max_len_a,
max_len_b=max_len_b,
len_penalty_ratio=len_penalty_ratio)
if rank >= 0:
self.model = DistributedDataParallel(self.model,
device_ids=[self.rank],
output_device=self.rank,
find_unused_parameters=True)
self.generator = DistributedDataParallel(
self.generator,
device_ids=[self.rank],
output_device=self.rank,
find_unused_parameters=True)
elif self.num_gpu > 1:
print("Let's use", self.num_gpu, "GPUs!")
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
self.generator = DataParallelModel(self.generator)
self.reference = None
self.best_bleu = -1.0
self.mm_mode = mm_mode
class TransferNetworkImg(Network):
def __init__(self,
model_name='DenseNet',
model_type='cv_transfer',
lr=0.02,
criterion=nn.CrossEntropyLoss(),
optimizer_name='Adam',
dropout_p=0.45,
pretrained=True,
device=None,
best_accuracy=0.,
best_validation_loss=None,
best_model_file='best_model.pth',
head={
'num_outputs': 10,
'layers': [],
'model_type': 'classifier'
},
class_names=[],
num_classes=None,
add_extra=True,
set_params=True,
set_head=True):
super().__init__(device=device)
self.set_transfer_model(model_name,
pretrained=pretrained,
add_extra=add_extra,
dropout_p=dropout_p)
if set_head:
self.set_model_head(model_name=model_name,
head=head,
dropout_p=dropout_p,
criterion=criterion,
device=device)
if set_params:
self.set_model_params(criterion=criterion,
optimizer_name=optimizer_name,
lr=lr,
dropout_p=dropout_p,
model_name=model_name,
model_type=model_type,
best_accuracy=best_accuracy,
best_validation_loss=best_validation_loss,
best_model_file=best_model_file,
class_names=class_names,
num_classes=num_classes)
self.model = self.model.to(device)
def set_model_params(self,
criterion=nn.CrossEntropyLoss(),
optimizer_name='Adam',
lr=0.1,
dropout_p=0.45,
model_name='DenseNet',
model_type='cv_transfer',
best_accuracy=0.,
best_validation_loss=None,
best_model_file='best_model_file.pth',
class_names=[],
num_classes=None):
print('Transfer Learning: current best accuracy = {:.3f}'.format(
best_accuracy))
super(TransferNetworkImg,
self).set_model_params(criterion=criterion,
optimizer_name=optimizer_name,
lr=lr,
dropout_p=dropout_p,
model_name=model_name,
model_type=model_type,
best_accuracy=best_accuracy,
best_validation_loss=best_validation_loss,
best_model_file=best_model_file)
self.class_names = class_names
self.num_classes = num_classes
if len(class_names) == 0:
self.class_names = {
k: str(v)
for k, v in enumerate(list(range(self.head['num_outputs'])))
}
def forward(self, x):
return self.model(x)
def freeze(self, train_classifier=True):
super(TransferNetworkImg, self).freeze()
if train_classifier:
for param in self.model.fc.parameters():
param.requires_grad = True
def parallelize(self):
self.parallel = True
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
def set_transfer_model(self,
mname,
pretrained=True,
add_extra=True,
dropout_p=0.45):
self.model = None
models_dict = {
'densenet': {
'model': models.densenet121(pretrained=pretrained),
'conv_channels': 1024
},
'resnet34': {
'model': models.resnet34(pretrained=pretrained),
'conv_channels': 512
},
'resnet50': {
'model': models.resnet50(pretrained=pretrained),
'conv_channels': 2048
}
}
meta = models_dict[mname.lower()]
try:
model = meta['model']
for param in model.parameters():
param.requires_grad = False
self.model = model
print(
'Setting transfer learning model: self.model set to {}'.format(
mname))
except:
print(
'Setting transfer learning model: model name {} not supported'.
format(mname))
# creating and adding extra layers to the model
dream_model = None
if add_extra:
channels = meta['conv_channels']
dream_model = nn.Sequential(
nn.Conv2d(channels, channels, 3, 1, 1),
# Printer(),
nn.BatchNorm2d(channels),
nn.ReLU(True),
nn.Dropout2d(dropout_p),
nn.Conv2d(channels, channels, 3, 1, 1),
nn.BatchNorm2d(channels),
nn.ReLU(True),
nn.Dropout2d(dropout_p),
nn.Conv2d(channels, channels, 3, 1, 1),
nn.BatchNorm2d(channels),
nn.ReLU(True),
nn.Dropout2d(dropout_p))
self.dream_model = dream_model
def set_model_head(
self,
model_name='DenseNet',
head={
'num_outputs': 10,
'layers': [],
'class_names': None,
'model_type': 'classifier'
},
criterion=nn.NLLLoss(),
adaptive=True,
dropout_p=0.45,
device=None):
models_meta = {
'resnet34': {
'conv_channels': 512,
'head_id': -2,
'adaptive_head': [DAI_AvgPool],
'normal_head': [nn.AvgPool2d(7, 1)]
},
'resnet50': {
'conv_channels': 2048,
'head_id': -2,
'adaptive_head': [DAI_AvgPool],
'normal_head': [nn.AvgPool2d(7, 1)]
},
'densenet': {
'conv_channels': 1024,
'head_id': -1,
'adaptive_head': [nn.ReLU(inplace=True), DAI_AvgPool],
'normal_head': [nn.ReLU(inplace=True),
nn.AvgPool2d(7, 1)]
}
}
name = model_name.lower()
meta = models_meta[name]
modules = list(self.model.children())
l = modules[:meta['head_id']]
if self.dream_model:
l += self.dream_model
if type(head).__name__ != 'dict':
model = nn.Sequential(*l)
for layer in head.children():
if (type(layer).__name__) == 'StdConv':
conv_module = layer
break
conv_layer = conv_module.conv
temp_args = [
conv_layer.out_channels, conv_layer.kernel_size,
conv_layer.stride, conv_layer.padding
]
temp_args.insert(0, meta['conv_channels'])
conv_layer = nn.Conv2d(*temp_args)
conv_module.conv = conv_layer
model.add_module('custom_head', head)
else:
head['criterion'] = criterion
if head['model_type'].lower() == 'classifier':
head['output_non_linearity'] = None
self.num_outputs = head['num_outputs']
fc = modules[-1]
try:
in_features = fc.in_features
except:
in_features = fc.model.out.in_features
fc = FC(num_inputs=in_features,
num_outputs=head['num_outputs'],
layers=head['layers'],
model_type=head['model_type'],
output_non_linearity=head['output_non_linearity'],
dropout_p=dropout_p,
criterion=head['criterion'],
optimizer_name=None,
device=device)
if adaptive:
l += meta['adaptive_head']
else:
l += meta['normal_head']
model = nn.Sequential(*l)
model.add_module('fc', fc)
self.model = model
self.head = head
if type(head).__name__ == 'dict':
print('Model: {}, Setting head: inputs: {} hidden:{} outputs: {}'.
format(model_name, in_features, head['layers'],
head['num_outputs']))
else:
print('Model: {}, Setting head: {}'.format(model_name,
type(head).__name__))
def _get_dropout(self):
return self.dropout_p
def _set_dropout(self, p=0.45):
if self.model.classifier is not None:
print('{}: setting head (FC) dropout prob to {:.3f}'.format(
self.model_name, p))
self.model.fc._set_dropout(p=p)
def get_model_params(self):
params = super(TransferNetworkImg, self).get_model_params()
params['class_names'] = self.class_names
params['num_classes'] = self.num_classes
params['head'] = self.head
return params
def createModels(args, userNum, itemNum, adj):
if args.model == 'NCF':
model = NCF(userNum, itemNum, 64, layers=[128, 64, 32, 16, 8]).cuda()
if args.model == 'NMF':
model = NMF(args.model, userNum, itemNum, 3, args.embedSize,
args.droprate).cuda()
elif args.model == 'NGCFMF':
model = NGCFMF(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMLP':
model = NGCFMLP(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMFMLP':
model = NGCFMFMLP(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMF_concat_MF':
model = NGCFMF_concat_MF(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMF_concat_MLP':
model = NGCFMF_concat_MLP(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMLP_concat_MF':
model = NGCFMLP_concat_MF(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMLP_concat_MLP':
model = NGCFMLP_concat_MLP(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMF_concat_MF_MLP':
model = NGCFMF_concat_MF_MLP(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'NGCFMLP_concat_MF_MLP':
model = NGCFMLP_concat_MF_MLP(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers).cuda()
elif args.model == 'GACFV1':
model = GACFV1(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'GACFV2':
model = GACFV2(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'GACFMask':
model = GACFMask(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'SPGA':
model = SPGACF(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'GACFV3':
model = GACFV3(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'GACFV4':
model = GACFV4(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'GACFV5':
model = GACFV5(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
elif args.model == 'GACFV6':
model = GACFV6(userNum,
itemNum,
adj,
embedSize=args.embedSize,
layers=args.layers,
droprate=args.droprate).cuda()
if args.train_mode == 'PairSampling':
lossfn = BPRLoss()
if args.parallel == True:
model = DataParallelModel(model)
lossfn = DataParallelCriterion2(lossfn)
elif args.train_mode == 'NegSampling':
lossfn = BCEWithLogitsLoss()
if args.parallel == True:
model = DataParallelModel(model) # 并行化model
lossfn = DataParallelCriterion(lossfn) # 并行化损失函数
optim = Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
return model, lossfn, optim
class FoodIngredients(Network):
def __init__(self,
model_name='DenseNet',
model_type='food',
lr=0.02,
optimizer_name='Adam',
criterion1=nn.CrossEntropyLoss(),
criterion2=nn.BCEWithLogitsLoss(),
dropout_p=0.45,
pretrained=True,
device=None,
best_accuracy=0.,
best_validation_loss=None,
best_model_file='best_model.pth',
head1={
'num_outputs': 10,
'layers': [],
'model_type': 'classifier'
},
head2={
'num_outputs': 10,
'layers': [],
'model_type': 'multi_label_classifier'
},
class_names=[],
num_classes=None,
ingredient_names=[],
num_ingredients=None,
add_extra=True,
set_params=True,
set_head=True):
super().__init__(device=device)
self.set_transfer_model(model_name,
pretrained=pretrained,
add_extra=add_extra,
dropout_p=dropout_p)
if set_head:
self.set_model_head(model_name=model_name,
head1=head1,
head2=head2,
dropout_p=dropout_p,
criterion1=criterion1,
criterion2=criterion2,
device=device)
if set_params:
self.set_model_params(
optimizer_name=optimizer_name,
lr=lr,
dropout_p=dropout_p,
model_name=model_name,
model_type=model_type,
best_accuracy=best_accuracy,
best_validation_loss=best_validation_loss,
best_model_file=best_model_file,
class_names=class_names,
num_classes=num_classes,
ingredient_names=ingredient_names,
num_ingredients=num_ingredients,
)
self.model = self.model.to(device)
def set_model_params(self,
criterion1=nn.CrossEntropyLoss(),
criterion2=nn.BCEWithLogitsLoss(),
optimizer_name='Adam',
lr=0.1,
dropout_p=0.45,
model_name='DenseNet',
model_type='cv_transfer',
best_accuracy=0.,
best_validation_loss=None,
best_model_file='best_model_file.pth',
head1={
'num_outputs': 10,
'layers': [],
'model_type': 'classifier'
},
head2={
'num_outputs': 10,
'layers': [],
'model_type': 'muilti_label_classifier'
},
class_names=[],
num_classes=None,
ingredient_names=[],
num_ingredients=None):
print(
'Food Names: current best accuracy = {:.3f}'.format(best_accuracy))
if best_validation_loss is not None:
print('Food Ingredients: current best loss = {:.3f}'.format(
best_validation_loss))
super(FoodIngredients,
self).set_model_params(optimizer_name=optimizer_name,
lr=lr,
dropout_p=dropout_p,
model_name=model_name,
model_type=model_type,
best_accuracy=best_accuracy,
best_validation_loss=best_validation_loss,
best_model_file=best_model_file)
self.class_names = class_names
self.num_classes = num_classes
self.ingredeint_names = ingredient_names
self.num_ingredients = num_ingredients
self.criterion1 = criterion1
self.criterion2 = criterion2
def forward(self, x):
l = list(self.model.children())
for m in l[:-2]:
x = m(x)
food = l[-2](x)
ingredients = l[-1](x)
return (food, ingredients)
def compute_loss(self, outputs, labels, w1=1., w2=1.):
out1, out2 = outputs
label1, label2 = labels
loss1 = self.criterion1(out1, label1)
loss2 = self.criterion2(out2, label2)
return [(loss1 * w1) + (loss2 * w2)]
def freeze(self, train_classifier=True):
super(FoodIngredients, self).freeze()
if train_classifier:
for param in self.model.fc1.parameters():
param.requires_grad = True
for param in self.model.fc2.parameters():
param.requires_grad = True
def parallelize(self):
self.parallel = True
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
def set_transfer_model(self,
mname,
pretrained=True,
add_extra=True,
dropout_p=0.45):
self.model = None
models_dict = {
'densenet': {
'model': models.densenet121(pretrained=pretrained),
'conv_channels': 1024
},
'resnet34': {
'model': models.resnet34(pretrained=pretrained),
'conv_channels': 512
},
'resnet50': {
'model': models.resnet50(pretrained=pretrained),
'conv_channels': 2048
}
}
meta = models_dict[mname.lower()]
try:
model = meta['model']
for param in model.parameters():
param.requires_grad = False
self.model = model
print(
'Setting transfer learning model: self.model set to {}'.format(
mname))
except:
print(
'Setting transfer learning model: model name {} not supported'.
format(mname))
# creating and adding extra layers to the model
dream_model = None
if add_extra:
channels = meta['conv_channels']
dream_model = nn.Sequential(
nn.Conv2d(channels, channels, 3, 1, 1),
# Printer(),
nn.BatchNorm2d(channels),
nn.ReLU(True),
nn.Dropout2d(dropout_p),
nn.Conv2d(channels, channels, 3, 1, 1),
nn.BatchNorm2d(channels),
nn.ReLU(True),
nn.Dropout2d(dropout_p),
nn.Conv2d(channels, channels, 3, 1, 1),
nn.BatchNorm2d(channels),
nn.ReLU(True),
nn.Dropout2d(dropout_p))
self.dream_model = dream_model
def set_model_head(
self,
model_name='DenseNet',
head1={
'num_outputs': 10,
'layers': [],
'class_names': None,
'model_type': 'classifier'
},
head2={
'num_outputs': 10,
'layers': [],
'class_names': None,
'model_type': 'muilti_label_classifier'
},
criterion1=nn.CrossEntropyLoss(),
criterion2=nn.BCEWithLogitsLoss(),
adaptive=True,
dropout_p=0.45,
device=None):
models_meta = {
'resnet34': {
'conv_channels': 512,
'head_id': -2,
'adaptive_head': [DAI_AvgPool],
'normal_head': [nn.AvgPool2d(7, 1)]
},
'resnet50': {
'conv_channels': 2048,
'head_id': -2,
'adaptive_head': [DAI_AvgPool],
'normal_head': [nn.AvgPool2d(7, 1)]
},
'densenet': {
'conv_channels': 1024,
'head_id': -1,
'adaptive_head': [nn.ReLU(inplace=True), DAI_AvgPool],
'normal_head': [nn.ReLU(inplace=True),
nn.AvgPool2d(7, 1)]
}
}
name = model_name.lower()
meta = models_meta[name]
modules = list(self.model.children())
l = modules[:meta['head_id']]
if self.dream_model:
l += self.dream_model
heads = [head1, head2]
crits = [criterion1, criterion2]
fcs = []
for head, criterion in zip(heads, crits):
head['criterion'] = criterion
if head['model_type'].lower() == 'classifier':
head['output_non_linearity'] = None
fc = modules[-1]
try:
in_features = fc.in_features
except:
in_features = fc.model.out.in_features
fc = FC(num_inputs=in_features,
num_outputs=head['num_outputs'],
layers=head['layers'],
model_type=head['model_type'],
output_non_linearity=head['output_non_linearity'],
dropout_p=dropout_p,
criterion=head['criterion'],
optimizer_name=None,
device=device)
fcs.append(fc)
if adaptive:
l += meta['adaptive_head']
else:
l += meta['normal_head']
model = nn.Sequential(*l)
model.add_module('fc1', fcs[0])
model.add_module('fc2', fcs[1])
self.model = model
self.head1 = head1
self.head2 = head2
print('Multi-head set up complete.')
def train_(self, e, trainloader, optimizer, print_every):
epoch, epochs = e
self.train()
t0 = time.time()
t1 = time.time()
batches = 0
running_loss = 0.
for data_batch in trainloader:
inputs, label1, label2 = data_batch[0], data_batch[1], data_batch[
2]
batches += 1
inputs = inputs.to(self.device)
label1 = label1.to(self.device)
label2 = label2.to(self.device)
labels = (label1, label2)
optimizer.zero_grad()
outputs = self.forward(inputs)
loss = self.compute_loss(outputs, labels)[0]
if self.parallel:
loss.sum().backward()
loss = loss.sum()
else:
loss.backward()
loss = loss.item()
optimizer.step()
running_loss += loss
if batches % print_every == 0:
elapsed = time.time() - t1
if elapsed > 60:
elapsed /= 60.
measure = 'min'
else:
measure = 'sec'
batch_time = time.time() - t0
if batch_time > 60:
batch_time /= 60.
measure2 = 'min'
else:
measure2 = 'sec'
print(
'+----------------------------------------------------------------------+\n'
f"{time.asctime().split()[-2]}\n"
f"Time elapsed: {elapsed:.3f} {measure}\n"
f"Epoch:{epoch+1}/{epochs}\n"
f"Batch: {batches+1}/{len(trainloader)}\n"
f"Batch training time: {batch_time:.3f} {measure2}\n"
f"Batch training loss: {loss:.3f}\n"
f"Average training loss: {running_loss/(batches):.3f}\n"
'+----------------------------------------------------------------------+\n'
)
t0 = time.time()
return running_loss / len(trainloader)
def evaluate(self, dataloader, metric='accuracy'):
running_loss = 0.
classifier = None
if self.model_type == 'classifier': # or self.num_classes is not None:
classifier = Classifier(self.class_names)
y_pred = []
y_true = []
self.eval()
rmse_ = 0.
with torch.no_grad():
for data_batch in dataloader:
inputs, label1, label2 = data_batch[0], data_batch[
1], data_batch[2]
inputs = inputs.to(self.device)
label1 = label1.to(self.device)
label2 = label2.to(self.device)
labels = (label1, label2)
outputs = self.forward(inputs)
loss = self.compute_loss(outputs, labels)[0]
if self.parallel:
running_loss += loss.sum()
outputs = parallel.gather(outputs, self.device)
else:
running_loss += loss.item()
if classifier is not None and metric == 'accuracy':
classifier.update_accuracies(outputs, labels)
y_true.extend(list(labels.squeeze(0).cpu().numpy()))
_, preds = torch.max(torch.exp(outputs), 1)
y_pred.extend(list(preds.cpu().numpy()))
elif metric == 'rmse':
rmse_ += rmse(outputs, labels).cpu().numpy()
self.train()
ret = {}
# print('Running_loss: {:.3f}'.format(running_loss))
if metric == 'rmse':
print('Total rmse: {:.3f}'.format(rmse_))
ret['final_rmse'] = rmse_ / len(dataloader)
ret['final_loss'] = running_loss / len(dataloader)
if classifier is not None:
ret['accuracy'], ret[
'class_accuracies'] = classifier.get_final_accuracies()
ret['report'] = classification_report(
y_true, y_pred, target_names=self.class_names)
ret['confusion_matrix'] = confusion_matrix(y_true, y_pred)
try:
ret['roc_auc_score'] = roc_auc_score(y_true, y_pred)
except:
pass
return ret
def evaluate_food(self, dataloader, metric='accuracy'):
running_loss = 0.
classifier = None
classifier = Classifier(self.class_names)
y_pred = []
y_true = []
self.eval()
rmse_ = 0.
with torch.no_grad():
for data_batch in dataloader:
inputs, labels = data_batch[0], data_batch[1]
inputs = inputs.to(self.device)
labels = labels.to(self.device)
outputs = self.forward(inputs)[0]
if classifier is not None and metric == 'accuracy':
try:
classifier.update_accuracies(outputs, labels)
y_true.extend(list(labels.squeeze(0).cpu().numpy()))
_, preds = torch.max(torch.exp(outputs), 1)
y_pred.extend(list(preds.cpu().numpy()))
except:
pass
elif metric == 'rmse':
rmse_ += rmse(outputs, labels).cpu().numpy()
self.train()
ret = {}
# print('Running_loss: {:.3f}'.format(running_loss))
if metric == 'rmse':
print('Total rmse: {:.3f}'.format(rmse_))
ret['final_rmse'] = rmse_ / len(dataloader)
ret['final_loss'] = running_loss / len(dataloader)
if classifier is not None:
ret['accuracy'], ret[
'class_accuracies'] = classifier.get_final_accuracies()
ret['report'] = classification_report(
y_true, y_pred, target_names=self.class_names)
ret['confusion_matrix'] = confusion_matrix(y_true, y_pred)
try:
ret['roc_auc_score'] = roc_auc_score(y_true, y_pred)
except:
pass
return ret
def find_lr(self,
trn_loader,
init_value=1e-8,
final_value=10.,
beta=0.98,
plot=False):
print('\nFinding the ideal learning rate.')
model_state = copy.deepcopy(self.model.state_dict())
optim_state = copy.deepcopy(self.optimizer.state_dict())
optimizer = self.optimizer
num = len(trn_loader) - 1
mult = (final_value / init_value)**(1 / num)
lr = init_value
optimizer.param_groups[0]['lr'] = lr
avg_loss = 0.
best_loss = 0.
batch_num = 0
losses = []
log_lrs = []
for data_batch in trn_loader:
batch_num += 1
inputs, label1, label2 = data_batch[0], data_batch[1], data_batch[
2]
inputs = inputs.to(self.device)
label1 = label1.to(self.device)
label2 = label2.to(self.device)
labels = (label1, label2)
optimizer.zero_grad()
outputs = self.forward(inputs)
loss = self.compute_loss(outputs, labels)[0]
#Compute the smoothed loss
if self.parallel:
avg_loss = beta * avg_loss + (1 - beta) * loss.sum()
else:
avg_loss = beta * avg_loss + (1 - beta) * loss.item()
smoothed_loss = avg_loss / (1 - beta**batch_num)
#Stop if the loss is exploding
if batch_num > 1 and smoothed_loss > 4 * best_loss:
self.log_lrs, self.find_lr_losses = log_lrs, losses
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(optim_state)
if plot:
self.plot_find_lr()
temp_lr = self.log_lrs[np.argmin(self.find_lr_losses) -
(len(self.log_lrs) // 8)]
self.lr = (10**temp_lr)
print('Found it: {}\n'.format(self.lr))
return self.lr
#Record the best loss
if smoothed_loss < best_loss or batch_num == 1:
best_loss = smoothed_loss
#Store the values
losses.append(smoothed_loss)
log_lrs.append(math.log10(lr))
#Do the SGD step
if self.parallel:
loss.sum().backward()
else:
loss.backward()
optimizer.step()
#Update the lr for the next step
lr *= mult
optimizer.param_groups[0]['lr'] = lr
self.log_lrs, self.find_lr_losses = log_lrs, losses
self.model.load_state_dict(model_state)
self.optimizer.load_state_dict(optim_state)
if plot:
self.plot_find_lr()
temp_lr = self.log_lrs[np.argmin(self.find_lr_losses) -
(len(self.log_lrs) // 10)]
self.lr = (10**temp_lr)
print('Found it: {}\n'.format(self.lr))
return self.lr
def plot_find_lr(self):
plt.ylabel("Loss")
plt.xlabel("Learning Rate (log scale)")
plt.plot(self.log_lrs, self.find_lr_losses)
plt.show()
def classify(self,
inputs,
thresh=0.4): #,show = False,mean = None,std = None):
outputs = self.predict(inputs)
food, ing = outputs
try:
_, preds = torch.max(torch.exp(food), 1)
except:
_, preds = torch.max(torch.exp(food.unsqueeze(0)), 1)
ing_outs = ing.sigmoid()
ings = (ing_outs >= thresh)
class_preds = [str(self.class_names[p]) for p in preds]
ing_preds = [
self.ingredeint_names[p.nonzero().squeeze(1).cpu()] for p in ings
]
return class_preds, ing_preds
def _get_dropout(self):
return self.dropout_p
def get_model_params(self):
params = super(FoodIngredients, self).get_model_params()
params['class_names'] = self.class_names
params['num_classes'] = self.num_classes
params['ingredient_names'] = self.ingredient_names
params['num_ingredients'] = self.num_ingredients
params['head1'] = self.head1
params['head2'] = self.head2
return params
class LMTrainer:
def __init__(self,
model,
mask_prob: float = 0.15,
clip: int = 1,
optimizer=None):
self.model = model
self.clip = clip
self.optimizer = optimizer
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.model = self.model.to(self.device)
self.mask_prob = mask_prob
self.criterion = nn.NLLLoss(
ignore_index=model.text_processor.pad_token_id())
num_gpu = torch.cuda.device_count()
if num_gpu > 1:
print("Let's use", num_gpu, "GPUs!")
self.model = DataParallelModel(self.model)
self.criterion = DataParallelCriterion(self.criterion)
self.best_dev_loss = float("inf")
self.best_train_loss = float("inf")
self.last_train_loss = float("inf")
def train_epoch(self, data_iter: data_utils.DataLoader,
dev_data_iter: data_utils.DataLoader, saving_path: str,
step: int):
"Standard Training and Logging Function"
start = time.time()
total_tokens, total_loss, tokens, cur_loss = 0, 0, 0, 0
cur_loss = 0
model = self.model.module if hasattr(self.model,
"module") else self.model
for i, batch in enumerate(data_iter):
if self.optimizer is not None:
self.optimizer.zero_grad()
mask, target, texts = mask_text(self.mask_prob, batch["pad_mask"],
batch["texts"],
model.text_processor)
try:
predictions = self.model(mask=mask,
texts=texts,
pads=batch["pad_mask"],
langs=batch["langs"])
ntokens = target.size(0)
if ntokens == 0: # Nothing to predict!
continue
loss = self.criterion(predictions, target).mean()
loss.backward()
unmask_text(mask, target, texts)
if self.optimizer is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(),
self.clip)
self.optimizer.step()
step += 1
loss = float(loss.data) * ntokens
total_loss += loss
cur_loss += loss
total_tokens += ntokens
tokens += ntokens
if step % 50 == 0:
elapsed = time.time() - start
print(
datetime.datetime.now(),
"Epoch Step: %d Loss: %f Tokens per Sec: %f" %
(step, cur_loss / tokens, tokens / elapsed))
if step % 500 == 0:
self.validate_and_save(saving_path, dev_data_iter)
start, tokens, cur_loss = time.time(), 0, 0
except RuntimeError as err:
print("Problem with batch item", texts.size())
torch.cuda.empty_cache()
pass
current_loss = total_loss / total_tokens
print("Total loss in this epoch: %f" % current_loss)
if current_loss < self.best_train_loss:
self.best_train_loss = current_loss
model_to_save = (self.model.module if hasattr(
self.model, "module") else self.model)
model_to_save.save(saving_path + ".latest")
with open(os.path.join(saving_path + ".latest", "optim"),
"wb") as fp:
pickle.dump(self.optimizer, fp)
self.last_train_loss = current_loss
self.validate_and_save(saving_path, dev_data_iter)
return step
def validate_and_save(self, saving_path, dev_data_iter):
with torch.no_grad():
model = self.model.module if hasattr(self.model,
"module") else self.model
model.eval()
total_dev_loss, total_dev_tokens = 0, 0
for batch in dev_data_iter:
mask, target, texts = mask_text(self.mask_prob,
batch["pad_mask"],
batch["texts"].clone(),
model.text_processor)
predictions = self.model(mask=mask,
texts=texts,
pads=batch["pad_mask"],
langs=batch["langs"])
ntokens = target.size(0)
if ntokens == 0: # Nothing to predict!
continue
loss = self.criterion(predictions,
target).mean().data * ntokens
total_dev_loss += float(loss)
total_dev_tokens += ntokens
dev_loss = total_dev_loss / total_dev_tokens
print("Current dev loss", dev_loss)
if self.best_dev_loss > float(dev_loss):
self.best_dev_loss = float(dev_loss)
print("saving best dev loss", self.best_dev_loss)
model_to_save = (self.model.module if hasattr(
self.model, "module") else self.model)
model_to_save.save(saving_path)
with open(os.path.join(saving_path, "optim"), "wb") as fp:
pickle.dump(self.optimizer, fp)
model.train()
@staticmethod
def config_dropout(model, dropout):
model.encoder.config.hidden_dropout_prob = dropout
model.encoder.config.attention_probs_dropout_prob = dropout
@staticmethod
def train(options):
if not os.path.exists(options.model_path):
os.makedirs(options.model_path)
text_processor = TextProcessor(options.tokenizer_path)
lm_class = ReformerLM if options.reformer else LM
if options.pretrained_path is None:
lm = lm_class(text_processor=text_processor,
size=options.model_size)
else:
lm = lm_class.load(options.pretrained_path)
if options.reformer:
lm.config.hidden_dropout_prob = options.dropout
lm.config.local_attention_probs_dropout_prob = options.dropout
lm.config.lsh_attention_probs_dropout_prob = options.dropout
else:
LMTrainer.config_dropout(lm, options.dropout)
train_data = dataset.TextDataset(save_cache_dir=options.train_path,
max_cache_size=options.cache_size)
dev_data = dataset.TextDataset(save_cache_dir=options.dev_path,
max_cache_size=options.cache_size,
load_all=True)
if options.continue_train:
with open(os.path.join(options.pretrained_path, "optim"),
"rb") as fp:
optimizer = pickle.load(fp)
else:
optimizer = build_optimizer(lm, options.learning_rate,
options.warmup)
trainer = LMTrainer(model=lm,
mask_prob=options.mask_prob,
optimizer=optimizer,
clip=options.clip)
collator = dataset.TextCollator(pad_idx=text_processor.pad_token_id())
train_sampler, dev_sampler = None, None
pin_memory = torch.cuda.is_available()
loader = data_utils.DataLoader(train_data,
batch_size=options.batch,
shuffle=False,
pin_memory=pin_memory,
collate_fn=collator,
sampler=train_sampler)
dev_loader = data_utils.DataLoader(dev_data,
batch_size=options.batch,
shuffle=False,
pin_memory=pin_memory,
collate_fn=collator,
sampler=dev_sampler)
step, train_epoch = 0, 1
while step <= options.step:
print("train epoch", train_epoch)
step = trainer.train_epoch(data_iter=loader,
dev_data_iter=dev_loader,
saving_path=options.model_path,
step=step)