#num_samples = 1000000
all_data_dict = dict()
max_length = 100
head_hidd_list = list()
tail_hidd_list = list()
#device = "cpu"
device = "cuda"
pretrained_weights = model
tokenizer = RobertaTokenizer.from_pretrained(pretrained_weights)
fine_tuned_weight = model
#model = RobertaForMaskedLM.from_pretrained(pretrained_weights, output_hidden_states=True,return_dict=True)
model = RobertaForMaskedLMDomainTask.from_pretrained(pretrained_weights,
output_hidden_states=True,
return_dict=True,
num_labels=3)
#model.load_state_dict(torch.load(fine_tuned_weight), strict=False)
#model.to(device).half()
model.to(device)
model.eval()
#num_samples = 1000000
old = torch.FloatTensor(768)
with open(file_in) as f:
#data = json.load(f)
for index, d in tqdm(enumerate(f)):
#print(type(index),index)
#if index == 1000000:
def main():
parser = argparse.ArgumentParser()
## Required parameters
###############
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--pretrain_model",
default='bert-case-uncased',
type=str,
required=True,
help="Pre-trained model")
parser.add_argument("--num_labels_task",
default=None,
type=int,
required=True,
help="num_labels_task")
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",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
default=False,
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--learning_rate",
default=5e-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",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
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 accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
default=False,
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("--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--task",
default=None,
type=int,
required=True,
help="Choose Task")
###############
args = parser.parse_args()
processors = Processor_1
num_labels = args.num_labels_task
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 = int(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(
"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))
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = RobertaTokenizer.from_pretrained(args.pretrain_model)
train_examples = None
num_train_steps = None
aspect_list = None
sentiment_list = None
processor = processors()
num_labels = num_labels
train_examples, aspect_list, sentiment_list = processor.get_train_examples(
args.data_dir)
if args.task == 1:
num_labels = len(aspect_list)
elif args.task == 2:
num_labels = len(sentiment_list)
else:
print("What's task?")
exit()
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
#model = RobertaForSequenceClassification.from_pretrained(args.pretrain_model, num_labels=args.num_labels_task, output_hidden_states=False, output_attentions=False, return_dict=True)
model = RobertaForMaskedLMDomainTask.from_pretrained(
args.pretrain_model,
num_labels=args.num_labels_task,
output_hidden_states=False,
output_attentions=False,
return_dict=True)
# Prepare optimizer
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
model.to(device)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
#no_decay = ['bias', 'LayerNorm.weight']
no_grad = [
'bert.encoder.layer.11.output.dense_ent',
'bert.encoder.layer.11.output.LayerNorm_ent'
]
param_optimizer = [(n, p) for n, p in param_optimizer
if not any(nd in n for nd in no_grad)]
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
args.weight_decay
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=int(t_total *
0.1),
num_training_steps=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."
)
exit()
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
aspect_list,
sentiment_list,
args.max_seq_length,
tokenizer, args.task)
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_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_attention_mask = torch.tensor(
[f.attention_mask for f in train_features], dtype=torch.long)
if args.task == 1:
print("Excuting the task 1")
elif args.task == 2:
all_segment_ids = torch.tensor(
[f.segment_ids for f in train_features], dtype=torch.long)
else:
print("Wrong here2")
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
if args.task == 1:
train_data = TensorDataset(all_input_ids, all_attention_mask,
all_label_ids)
elif args.task == 2:
train_data = TensorDataset(all_input_ids, all_attention_mask,
all_segment_ids, all_label_ids)
else:
print("Wrong here1")
'''
print("========")
print(train_data)
print(type(train_data))
exit()
'''
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)
output_loss_file = os.path.join(args.output_dir, "loss")
loss_fout = open(output_loss_file, 'w')
model.train()
##########Pre-Pprocess#########
###############################
for epoch 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) if i != 3 else t for i, t in enumerate(batch))
batch = tuple(t.to(device) for i, t in enumerate(batch))
if args.task == 1:
input_ids, attention_mask, label_ids = batch
elif args.task == 2:
input_ids, attention_mask, segment_ids, label_ids = batch
else:
print("Wrong here3")
if args.task == 1:
#loss, logits, hidden_states, attentions
#output = model(input_ids=input_ids, token_type_ids=None, attention_mask=attention_mask, labels=label_ids)
#loss = output.loss
loss, logit = model(input_ids_org=input_ids,
token_type_ids=None,
attention_mask=attention_mask,
sentence_label=label_ids,
func="task_class")
elif args.task == 2:
#loss, logits, hidden_states, attentions
#output = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=attention_mask, labels=label_ids)
#output = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=attention_mask, labels=label_ids)
#output = model(input_ids=input_ids, token_type_ids=None, attention_mask=attention_mask, labels=label_ids)
#loss = output.loss
loss, logit = model(input_ids_org=input_ids,
token_type_ids=None,
attention_mask=attention_mask,
sentence_label=label_ids,
func="task_class")
else:
print("Wrong!!")
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)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
###
else:
loss.backward()
loss_fout.write("{}\n".format(loss.item()))
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses
###
if args.fp16:
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
###
if epoch < 2:
continue
else:
model_to_save = model.module if hasattr(model,
'module') else model
#output_model_file = os.path.join(args.output_dir, "pytorch_model.bin_{}".format(global_step))
output_model_file = os.path.join(
args.output_dir, "pytorch_model.bin_{}".format(epoch))
torch.save(model_to_save.state_dict(), output_model_file)
# Save a trained 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, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
def main():
parser = argparse.ArgumentParser()
## Required parameters
###############
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--pretrain_model",
default='bert-case-uncased',
type=str,
required=True,
help="Pre-trained model")
parser.add_argument("--num_labels_task",
default=None,
type=int,
required=True,
help="num_labels_task")
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",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
default=False,
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--eval_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--learning_rate",
default=5e-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",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
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 accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
default=False,
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("--weight_decay",
default=0.0,
type=float,
help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-8,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--task",
default=None,
type=int,
required=True,
help="Choose Task")
###############
args = parser.parse_args()
#print(args.do_train, args.do_eval)
#exit()
processors = Processor_1
num_labels = args.num_labels_task
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 = int(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_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))
'''
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = RobertaTokenizer.from_pretrained(args.pretrain_model)
train_examples = None
num_train_steps = None
aspect_list = None
sentiment_list = None
processor = processors()
num_labels = num_labels
#train_examples, aspect_list, sentiment_list = processor.get_train_examples(args.data_dir)
filenames = os.listdir(args.output_dir)
filenames = [x for x in filenames if "pytorch_model.bin_" in x]
print(filenames)
file_mark = []
model_performace = dict()
for x in filenames:
#file_mark.append([x, True])
file_mark.append([x, False])
####
####
test_examples, aspect_list, sentiment_list = processor.get_test_examples(
args.data_dir)
if args.task == 1:
num_labels = len(aspect_list)
elif args.task == 2:
num_labels = len(sentiment_list)
else:
print("What's task?")
exit()
test = convert_examples_to_features(test_examples, aspect_list,
sentiment_list, args.max_seq_length,
tokenizer, args.task)
eval_examples = test_examples
###
for x, mark in file_mark:
print(x, mark)
output_model_file = os.path.join(args.output_dir, x)
#model = RobertaForSequenceClassification.from_pretrained(args.pretrain_model, num_labels=num_labels, output_hidden_states=False, output_attentions=False, return_dict=True)
model = RobertaForMaskedLMDomainTask.from_pretrained(
args.pretrain_model,
output_hidden_states=False,
output_attentions=False,
return_dict=True,
num_labels=args.num_labels_task)
model.load_state_dict(torch.load(output_model_file), strict=False)
#strict False: ignore non-matching keys
#param_optimizer = [para[0] for para in model.named_parameters()]
#param_optimizer = [para for para in model.named_parameters()][-2]
#print(param_optimizer)
model.to(device)
if mark:
eval_features = dev
else:
eval_features = test
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_attention_mask = torch.tensor(
[f.attention_mask for f in eval_features], dtype=torch.long)
if args.task == 1:
print("Excuting the task 1")
elif args.task == 2:
all_segment_ids = torch.tensor(
[f.segment_ids for f in eval_features], dtype=torch.long)
else:
print("Wrong here2")
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
if args.task == 1:
eval_data = TensorDataset(all_input_ids, all_attention_mask,
all_label_ids)
elif args.task == 2:
eval_data = TensorDataset(all_input_ids, all_attention_mask,
all_segment_ids, all_label_ids)
else:
print("Wrong here1")
if args.local_rank == -1:
eval_sampler = RandomSampler(eval_data)
else:
eval_sampler = DistributedSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
if mark:
output_eval_file = os.path.join(
args.output_dir,
"eval_results_{}.txt".format(x.split("_")[-1]))
output_file_pred = os.path.join(
args.output_dir, "eval_pred_{}.txt".format(x.split("_")[-1]))
output_file_glod = os.path.join(
args.output_dir, "eval_gold_{}.txt".format(x.split("_")[-1]))
else:
output_eval_file = os.path.join(
args.output_dir,
"test_results_{}.txt".format(x.split("_")[-1]))
output_file_pred = os.path.join(
args.output_dir, "test_pred_{}.txt".format(x.split("_")[-1]))
output_file_glod = os.path.join(
args.output_dir, "test_gold_{}.txt".format(x.split("_")[-1]))
fpred = open(output_file_pred, "w")
fgold = open(output_file_glod, "w")
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for step, batch in enumerate(tqdm(eval_dataloader, desc="Iteration")):
#batch = tuple(t.to(device) if i != 3 else t for i, t in enumerate(batch))
batch = tuple(t.to(device) for i, t in enumerate(batch))
if args.task == 1:
input_ids, attention_mask, label_ids = batch
elif args.task == 2:
input_ids, attention_mask, segment_ids, label_ids = batch
else:
print("Wrong here3")
if args.task == 1:
#loss, logits, hidden_states, attentions
'''
output = model(input_ids=input_ids, token_type_ids=None, attention_mask=attention_mask, labels=label_ids)
logits = output.logits
tmp_eval_loss = output.loss
'''
#
tmp_eval_loss, logits = model(input_ids_org=input_ids,
sentence_label=label_ids,
attention_mask=attention_mask,
func="task_class")
#logits = output.logits
#tmp_eval_loss = output.loss
elif args.task == 2:
#loss, logits, hidden_states, attentions
'''
output = model(input_ids=input_ids, token_type_ids=None, attention_mask=attention_mask, labels=label_ids)
logits = output.logits
tmp_eval_loss = output.loss
'''
#
tmp_eval_loss, logits = model(input_ids_org=input_ids,
sentence_label=label_ids,
attention_mask=attention_mask,
func="task_class")
#exit()
#logits = output.logits
#tmp_eval_loss = output.loss
else:
print("Wrong!!")
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy, pred = accuracy(logits, label_ids)
for a, b in zip(pred, label_ids):
fgold.write("{}\n".format(b))
fpred.write("{}\n".format(a))
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy}
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])))
model_performace[x] = eval_accuracy
#################
#################
'''
head_hidd_list = list()
tail_hidd_list = list()
#device = "cpu"
device = "cuda"
#pretrained_weights = model
tokenizer = RobertaTokenizer.from_pretrained(model)
#fine_tuned_weight = model
#model = RobertaForMaskedLM.from_pretrained(pretrained_weights, output_hidden_states=True,num_labels=3,return_dict=True)
#model = RobertaForSequenceClassification.from_pretrained(pretrained_weights, output_hidden_states=True,num_labels=3,return_dict=True)
#model = RobertaForMaskedLMDomainTask.from_pretrained('roberta-base', output_hidden_states=True,return_dict=True,num_labels=num_samples)
#model.load_state_dict(torch.load(fine_tuned_weight), strict=False)
model = RobertaForMaskedLMDomainTask.from_pretrained(
model,
output_hidden_states=False,
return_dict=True,
num_labels=num_labels,
output_attentions=False)
#model.to(device).half()
model.to(device)
model.eval()
#num_samples = 1000000
old = torch.FloatTensor(768)
with open(file_in) as f:
#data = json.load(f)
for index, d in tqdm(enumerate(f)):
#print(type(index),index)
#if index == 1000000: