class CometLogger(BaseLogger):
def __init__(self, experiment_id=None):
self.experiment = Experiment(auto_metric_logging=False)
if experiment_id is not None:
self.experiment.log_parameter('experiment_id', experiment_id)
def add_scalar(self, name, value, step):
self.experiment.log_metric(name, value, epoch=step)
def log_parameters(self, params_dict):
self.experiment.log_parameters(params_dict)
def log_metrics(self, metrics_dict, epoch):
self.experiment.log_metrics(metrics_dict, epoch=epoch)
def add_text(self, name, text):
self.experiment.log_text(f'{name}: {text}')
def set_context_prefix(self, prefix):
self.experiment.context = prefix
def reset_context_prefix(self):
self.experiment.context = None
def very_simple_param_count(model):
result = sum([p.numel() for p in model.parameters()])
return result
if __name__ == "__main__":
torch.set_num_threads(4) # 4 thread
experiment = Experiment(project_name=PROJECT_NAME, api_key=COMET_ML_API)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)
args = meow_parse()
print(args)
experiment.set_name(args.task_id)
experiment.set_cmd_args()
experiment.log_text(args.note)
DATA_PATH = args.input
TRAIN_PATH = os.path.join(DATA_PATH, "train_data")
TEST_PATH = os.path.join(DATA_PATH, "test_data")
dataset_name = args.datasetname
if dataset_name=="shanghaitech":
print("will use shanghaitech dataset with crop ")
elif dataset_name == "shanghaitech_keepfull":
print("will use shanghaitech_keepfull")
else:
print("cannot detect dataset_name")
print("current dataset_name is ", dataset_name)
# create list
train_list = create_image_list(TRAIN_PATH)
class CorefSolver():
def __init__(self, args):
self.args = args
self.data_utils = data_utils(args)
self.disable_comet = args.disable_comet
self.model = self.make_model(
src_vocab=self.data_utils.vocab_size,
tgt_vocab=self.data_utils.vocab_size,
N=args.num_layer,
dropout=args.dropout,
entity_encoder_type=args.entity_encoder_type)
print(self.model)
if self.args.train:
self.outfile = open(self.args.logfile, 'w')
self.model_dir = make_save_dir(args.model_dir)
# self.logfile = os.path.join(args.logdir, args.exp_name)
# self.log = SummaryWriter(self.logfile)
self.w_valid_file = args.w_valid_file
def make_model(self,
src_vocab,
tgt_vocab,
N=6,
dropout=0.1,
d_model=512,
entity_encoder_type='linear',
d_ff=2048,
h=8):
"Helper: Construct a model from hyperparameters."
c = copy.deepcopy
attn = MultiHeadedAttention(h, d_model)
attn_ner = MultiHeadedAttention(1, d_model, dropout)
ff = PositionwiseFeedForward(d_model, d_ff, dropout)
position = PositionalEncoding(d_model, dropout)
embed = Embeddings(d_model, src_vocab)
word_embed = nn.Sequential(embed, c(position))
print('pgen', self.args.pointer_gen)
if entity_encoder_type == 'transformer':
# entity_encoder = nn.Sequential(embed, Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), 1))
print('transformer')
entity_encoder = Seq_Entity_Encoder(
embed,
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), 2))
elif entity_encoder_type == 'albert':
albert_tokenizer = AlbertTokenizer.from_pretrained(
'albert-base-v2')
albert = AlbertModel.from_pretrained('albert-base-v2')
entity_encoder = Albert_Encoder(albert, albert_tokenizer, d_model)
elif entity_encoder_type == 'gru':
entity_encoder = RNNEncoder(embed,
'GRU',
d_model,
d_model,
num_layers=1,
dropout=0.1,
bidirectional=True)
print('gru')
elif entity_encoder_type == 'lstm':
entity_encoder = RNNEncoder(embed,
'LSTM',
d_model,
d_model,
num_layers=1,
dropout=0.1,
bidirectional=True)
print('lstm')
if self.args.ner_at_embedding:
model = EncoderDecoderOrg(
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
DecoderOrg(
DecoderLayerOrg(d_model, c(attn), c(attn), c(ff), dropout),
N, d_model, tgt_vocab, self.args.pointer_gen), word_embed,
word_embed, entity_encoder)
else:
if self.args.ner_last:
decoder = Decoder(
DecoderLayer(d_model, c(attn), c(attn), c(ff),
dropout), N, d_model, tgt_vocab,
self.args.pointer_gen, self.args.ner_last)
else:
decoder = Decoder(
DecoderLayer_ner(d_model, c(attn), c(attn), attn_ner,
c(ff), dropout, self.args.fusion), N,
d_model, tgt_vocab, self.args.pointer_gen,
self.args.ner_last)
model = EncoderDecoder(
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
decoder, word_embed, word_embed, entity_encoder)
# This was important from their code.
# Initialize parameters with Glorot / fan_avg.
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
# levels = 3
# num_chans = [d_model] * (args.levels)
# k_size = 5
# tcn = TCN(embed, d_model, num_channels, k_size, dropout=dropout)
return model.cuda()
def train(self):
if not self.disable_comet:
# logging
hyper_params = {
"num_layer": self.args.num_layer,
"pointer_gen": self.args.pointer_gen,
"ner_last": self.args.ner_last,
"entity_encoder_type": self.args.entity_encoder_type,
"fusion": self.args.fusion,
"dropout": self.args.dropout,
}
COMET_PROJECT_NAME = 'summarization'
COMET_WORKSPACE = 'timchen0618'
self.exp = Experiment(
api_key='mVpNOXSjW7eU0tENyeYiWZKsl',
project_name=COMET_PROJECT_NAME,
workspace=COMET_WORKSPACE,
auto_output_logging='simple',
auto_metric_logging=None,
display_summary=False,
)
self.exp.log_parameters(hyper_params)
self.exp.add_tags([
'%s entity_encoder' % self.args.entity_encoder_type,
self.args.fusion
])
if self.args.ner_last:
self.exp.add_tag('ner_last')
if self.args.ner_at_embedding:
self.exp.add_tag('ner_at_embedding')
self.exp.set_name(self.args.exp_name)
self.exp.add_tag('coreference')
print('ner_last ', self.args.ner_last)
print('ner_at_embedding', self.args.ner_at_embedding)
# dataloader & optimizer
data_yielder = self.data_utils.data_yielder(num_epoch=100)
optim = torch.optim.Adam(self.model.parameters(),
lr=1e-7,
betas=(0.9, 0.998),
eps=1e-8,
amsgrad=True) #get_std_opt(self.model)
# entity_optim = torch.optim.Adam(self.entity_encoder.parameters(), lr=1e-7, betas=(0.9, 0.998), eps=1e-8, amsgrad=True)
total_loss = []
start = time.time()
print('*' * 50)
print('Start Training...')
print('*' * 50)
start_step = 0
# if loading from checkpoint
if self.args.load_model:
state_dict = torch.load(self.args.load_model)['state_dict']
self.model.load_state_dict(state_dict)
print("Loading model from " + self.args.load_model + "...")
# encoder_state_dict = torch.load(self.args.entity_encoder)['state_dict']
# self.entity_encoder.load_state_dict(encoder_state_dict)
# print("Loading entity_encoder from %s" + self.args.entity_encoder + "...")
start_step = int(torch.load(self.args.load_model)['step'])
print('Resume training from step %d ...' % start_step)
warmup_steps = 10000
d_model = 512
lr = 1e-7
for step in range(start_step, self.args.total_steps):
self.model.train()
batch = data_yielder.__next__()
optim.zero_grad()
# entity_optim.zero_grad()
#update lr
if step % 400 == 1:
lr = (1 / (d_model**0.5)) * min(
(1 / (step / 4)**0.5), step * (1 / (warmup_steps**1.5)))
for param_group in optim.param_groups:
param_group['lr'] = lr
# for param_group in entity_optim.param_groups:
# param_group['lr'] = lr
batch['src'] = batch['src'].long()
batch['tgt'] = batch['tgt'].long()
batch['ner'] = batch['ner'].long()
batch['src_extended'] = batch['src_extended'].long()
# forward the model
if self.args.entity_encoder_type == 'albert':
d = self.model.entity_encoder.tokenizer.batch_encode_plus(
batch['ner_text'],
return_attention_masks=True,
max_length=10,
add_special_tokens=False,
pad_to_max_length=True,
return_tensors='pt')
ner_mask = d['attention_mask'].cuda().unsqueeze(1)
ner = d['input_ids'].cuda()
# print('ner', ner.size())
# print('ner_mask', ner_mask.size())
# print('src_mask', batch['src_mask'].size())
if self.args.entity_encoder_type == 'gru' or self.args.entity_encoder_type == 'lstm':
ner_feat = self.model.entity_encoder(
batch['ner'].transpose(0, 1), batch['cluster_len'])[1]
elif self.args.entity_encoder_type == 'transformer':
mask = gen_mask(batch['cluster_len'])
ner_feat = self.model.entity_encoder(batch['ner'], mask)
ner, ner_mask = self.data_utils.pad_ner_feature(
ner_feat.squeeze(), batch['num_clusters'],
batch['src'].size(0))
# print('ner', ner.size())
# print('ner_mask', ner_mask.size())
if self.args.ner_at_embedding:
out = self.model.forward(batch['src'], batch['tgt'], ner,
batch['src_mask'], batch['tgt_mask'],
batch['src_extended'],
len(batch['oov_list']))
else:
out = self.model.forward(batch['src'], batch['tgt'], ner,
batch['src_mask'], batch['tgt_mask'],
batch['src_extended'],
len(batch['oov_list']), ner_mask)
# print out info
pred = out.topk(1, dim=-1)[1].squeeze().detach().cpu().numpy()[0]
gg = batch['src_extended'].long().detach().cpu().numpy()[0][:100]
tt = batch['tgt'].long().detach().cpu().numpy()[0]
yy = batch['y'].long().detach().cpu().numpy()[0]
#compute loss & update
loss = self.model.loss_compute(out, batch['y'].long())
loss.backward()
optim.step()
# entity_optim.step()
total_loss.append(loss.detach().cpu().numpy())
# logging information
if step % self.args.print_every_steps == 1:
elapsed = time.time() - start
print("Epoch Step: %d Loss: %f Time: %f lr: %6.6f" %
(step, np.mean(total_loss), elapsed,
optim.param_groups[0]['lr']))
self.outfile.write("Epoch Step: %d Loss: %f Time: %f\n" %
(step, np.mean(total_loss), elapsed))
print(
'src:\n',
self.data_utils.id2sent(gg, False, False,
batch['oov_list']))
print(
'tgt:\n',
self.data_utils.id2sent(yy, False, False,
batch['oov_list']))
print(
'pred:\n',
self.data_utils.id2sent(pred, False, False,
batch['oov_list']))
print('oov_list:\n', batch['oov_list'])
if ner_mask != None and not self.args.ner_at_embedding:
pp = self.model.greedy_decode(
batch['src_extended'].long()[:1], ner[:1],
batch['src_mask'][:1], 100, self.data_utils.bos,
len(batch['oov_list']), self.data_utils.vocab_size,
True, ner_mask[:1])
else:
pp = self.model.greedy_decode(
batch['src_extended'].long()[:1], ner[:1],
batch['src_mask'][:1], 100, self.data_utils.bos,
len(batch['oov_list']), self.data_utils.vocab_size,
True)
pp = pp.detach().cpu().numpy()
print(
'pred_greedy:\n',
self.data_utils.id2sent(pp[0], False, False,
batch['oov_list']))
print()
start = time.time()
if not self.disable_comet:
# self.log.add_scalar('Loss/train', np.mean(total_loss), step)
self.exp.log_metric('Train Loss',
np.mean(total_loss),
step=step)
self.exp.log_metric('Learning Rate',
optim.param_groups[0]['lr'],
step=step)
self.exp.log_text('Src: ' + self.data_utils.id2sent(
gg, False, False, batch['oov_list']))
self.exp.log_text('Tgt:' + self.data_utils.id2sent(
yy, False, False, batch['oov_list']))
self.exp.log_text('Pred:' + self.data_utils.id2sent(
pred, False, False, batch['oov_list']))
self.exp.log_text('Pred Greedy:' + self.data_utils.id2sent(
pp[0], False, False, batch['oov_list']))
self.exp.log_text('OOV:' + ' '.join(batch['oov_list']))
total_loss = []
##########################
# validation
##########################
if step % self.args.valid_every_steps == 2:
print('*' * 50)
print('Start Validation...')
print('*' * 50)
self.model.eval()
val_yielder = self.data_utils.data_yielder(1, valid=True)
total_loss = []
fw = open(self.w_valid_file, 'w')
for batch in val_yielder:
with torch.no_grad():
batch['src'] = batch['src'].long()
batch['tgt'] = batch['tgt'].long()
batch['ner'] = batch['ner'].long()
batch['src_extended'] = batch['src_extended'].long()
### ner ######
if self.args.entity_encoder_type == 'albert':
d = self.model.entity_encoder.tokenizer.batch_encode_plus(
batch['ner_text'],
return_attention_masks=True,
max_length=10,
add_special_tokens=False,
pad_to_max_length=True,
return_tensors='pt')
ner_mask = d['attention_mask'].cuda().unsqueeze(1)
ner = d['input_ids'].cuda()
if self.args.entity_encoder_type == 'gru' or self.args.entity_encoder_type == 'lstm':
ner_feat = self.model.entity_encoder(
batch['ner'].transpose(0, 1),
batch['cluster_len'])[1]
elif self.args.entity_encoder_type == 'transformer':
mask = gen_mask(batch['cluster_len'])
ner_feat = self.model.entity_encoder(
batch['ner'], mask)
ner, ner_mask = self.data_utils.pad_ner_feature(
ner_feat.squeeze(), batch['num_clusters'],
batch['src'].size(0))
### ner ######
if self.args.ner_at_embedding:
out = self.model.forward(batch['src'],
batch['tgt'], ner,
batch['src_mask'],
batch['tgt_mask'],
batch['src_extended'],
len(batch['oov_list']))
else:
out = self.model.forward(batch['src'],
batch['tgt'], ner,
batch['src_mask'],
batch['tgt_mask'],
batch['src_extended'],
len(batch['oov_list']),
ner_mask)
loss = self.model.loss_compute(out, batch['y'].long())
total_loss.append(loss.item())
if self.args.ner_at_embedding:
pred = self.model.greedy_decode(
batch['src_extended'].long(), ner,
batch['src_mask'], self.args.max_len,
self.data_utils.bos, len(batch['oov_list']),
self.data_utils.vocab_size)
else:
pred = self.model.greedy_decode(
batch['src_extended'].long(),
ner,
batch['src_mask'],
self.args.max_len,
self.data_utils.bos,
len(batch['oov_list']),
self.data_utils.vocab_size,
ner_mask=ner_mask)
for l in pred:
sentence = self.data_utils.id2sent(
l[1:], True, self.args.beam_size != 1,
batch['oov_list'])
fw.write(sentence)
fw.write("\n")
fw.close()
# files_rouge = FilesRouge()
# scores = files_rouge.get_scores(self.w_valid_file, self.args.valid_tgt_file, avg=True)
scores = cal_rouge_score(self.w_valid_file,
self.args.valid_ref_file)
r1_score = scores['rouge1']
r2_score = scores['rouge2']
print('=============================================')
print('Validation Result -> Loss : %6.6f' %
(sum(total_loss) / len(total_loss)))
print(scores)
print('=============================================')
self.outfile.write(
'=============================================\n')
self.outfile.write('Validation Result -> Loss : %6.6f\n' %
(sum(total_loss) / len(total_loss)))
self.outfile.write(
'=============================================\n')
# self.model.train()
# self.log.add_scalar('Loss/valid', sum(total_loss)/len(total_loss), step)
# self.log.add_scalar('Score/valid', r1_score, step)
if not self.disable_comet:
self.exp.log_metric('Valid Loss',
sum(total_loss) / len(total_loss),
step=step)
self.exp.log_metric('R1 Score', r1_score, step=step)
self.exp.log_metric('R2 Score', r2_score, step=step)
#Saving Checkpoint
w_step = int(step / 10000)
print('Saving ' + str(w_step) + 'w_model.pth!\n')
self.outfile.write('Saving ' + str(w_step) + 'w_model.pth\n')
model_name = str(w_step) + 'w_' + '%6.6f' % (
sum(total_loss) / len(total_loss)
) + '%2.3f_' % r1_score + '%2.3f_' % r2_score + 'model.pth'
state = {'step': step, 'state_dict': self.model.state_dict()}
torch.save(state, os.path.join(self.model_dir, model_name))
# entity_encoder_name = str(w_step) + '0w_' + '%6.6f'%(sum(total_loss)/len(total_loss)) + '%2.3f_'%r1_score + 'entity_encoder.pth'
# state = {'step': step, 'state_dict': self.entity_encoder.state_dict()}
# torch.save(state, os.path.join(self.model_dir, entity_encoder_name))
def test(self):
#prepare model
path = self.args.load_model
# entity_encoder_path = self.args.entity_encoder
state_dict = torch.load(path)['state_dict']
max_len = self.args.max_len
model = self.model
model.load_state_dict(state_dict)
# entity_encoder_dict = torch.load(entity_encoder_path)['state_dict']
# self.entity_encoder.load_state_dict(entity_encoder_dict)
pred_dir = make_save_dir(self.args.pred_dir)
filename = self.args.filename
#start decoding
data_yielder = self.data_utils.data_yielder(num_epoch=1)
total_loss = []
start = time.time()
#file
f = open(os.path.join(pred_dir, filename), 'w')
self.model.eval()
# decode_strategy = BeamSearch(
# self.beam_size,
# batch_size=batch.batch_size,
# pad=self._tgt_pad_idx,
# bos=self._tgt_bos_idx,
# eos=self._tgt_eos_idx,
# n_best=self.n_best,
# global_scorer=self.global_scorer,
# min_length=self.min_length, max_length=self.max_length,
# return_attention=attn_debug or self.replace_unk,
# block_ngram_repeat=self.block_ngram_repeat,
# exclusion_tokens=self._exclusion_idxs,
# stepwise_penalty=self.stepwise_penalty,
# ratio=self.ratio)
step = 0
for batch in data_yielder:
#print(batch['src'].data.size())
step += 1
if step % 100 == 0:
print('%d batch processed. Time elapsed: %f min.' %
(step, (time.time() - start) / 60.0))
start = time.time()
### ner ###
if self.args.entity_encoder_type == 'albert':
d = self.model.entity_encoder.tokenizer.batch_encode_plus(
batch['ner_text'],
return_attention_masks=True,
max_length=10,
add_special_tokens=False,
pad_to_max_length=True,
return_tensors='pt')
ner_mask = d['attention_mask'].cuda().unsqueeze(1)
ner = d['input_ids'].cuda()
else:
ner_mask = None
ner = batch['ner'].long()
with torch.no_grad():
if self.args.beam_size == 1:
if self.args.ner_at_embedding:
out = self.model.greedy_decode(
batch['src_extended'].long(),
self.model.entity_encoder(ner),
batch['src_mask'], max_len, self.data_utils.bos,
len(batch['oov_list']), self.data_utils.vocab_size)
else:
out = self.model.greedy_decode(
batch['src_extended'].long(),
self.model.entity_encoder(ner),
batch['src_mask'],
max_len,
self.data_utils.bos,
len(batch['oov_list']),
self.data_utils.vocab_size,
ner_mask=ner_mask)
else:
ret = self.beam_decode(batch, max_len,
len(batch['oov_list']))
out = ret['predictions']
for l in out:
sentence = self.data_utils.id2sent(l[1:], True,
self.args.beam_size != 1,
batch['oov_list'])
#print(l[1:])
f.write(sentence)
f.write("\n")
def beam_decode(self, batch, max_len, oov_nums):
src = batch['src'].long()
src_mask = batch['src_mask']
src_extended = batch['src_extended'].long()
bos_token = self.data_utils.bos
beam_size = self.args.beam_size
vocab_size = self.data_utils.vocab_size
batch_size = src.size(0)
def rvar(a):
return a.repeat(beam_size, 1, 1)
def rvar2(a):
return a.repeat(beam_size, 1)
def bottle(m):
return m.view(batch_size * beam_size, -1)
def unbottle(m):
return m.view(beam_size, batch_size, -1)
### ner ###
if self.args.entity_encoder_type == 'albert':
d = self.model.entity_encoder.tokenizer.batch_encode_plus(
batch['ner_text'],
return_attention_masks=True,
max_length=10,
add_special_tokens=False,
pad_to_max_length=True,
return_tensors='pt')
ner_mask = d['attention_mask'].cuda().unsqueeze(1)
ner = d['input_ids'].cuda()
else:
ner_mask = None
ner = batch['ner'].long()
ner = self.model.entity_encoder(ner)
if self.args.ner_at_embedding:
memory = self.model.encode(src, src_mask, ner)
else:
memory = self.model.encode(src, src_mask)
assert batch_size == 1
beam = [
Beam(beam_size,
self.data_utils.pad,
bos_token,
self.data_utils.eos,
min_length=self.args.min_length) for i in range(batch_size)
]
memory = rvar(memory)
ner = rvar(ner)
src_mask = rvar(src_mask)
src_extended = rvar2(src_extended)
for i in range(self.args.max_len):
if all((b.done() for b in beam)):
break
# Construct batch x beam_size nxt words.
# Get all the pending current beam words and arrange for forward.
inp = torch.stack([b.get_current_state()
for b in beam]).t().contiguous().view(-1, 1)
#inp -> [1, 3]
inp_mask = inp < self.data_utils.vocab_size
inp = inp * inp_mask.long()
decoder_input = inp
if self.args.ner_at_embedding:
final_dist = self.model.decode(memory, ner, src_mask,
decoder_input, None,
src_extended, oov_nums)
else:
final_dist = self.model.decode(memory,
ner,
src_mask,
decoder_input,
None,
src_extended,
oov_nums,
ner_mask=ner_mask)
# final_dist, decoder_hidden, attn_dist_p, p_gen = self.seq2seq_model.model_copy.decoder(
# decoder_input, decoder_hidden,
# post_encoder_outputs, post_enc_padding_mask,
# extra_zeros, post_enc_batch_extend_vocab
# )
# # Run one step.
# print('inp', inp.size())
# decoder_outputs: beam x rnn_size
# (b) Compute a vector of batch*beam word scores.
out = unbottle(final_dist)
out[:, :, 2] = 0 #no unk
# out.size -> [3, 1, vocab]
# (c) Advance each beam.
for j, b in enumerate(beam):
b.advance(out[:, j])
# decoder_hidden = self.beam_update(j, b.get_current_origin(), beam_size, decoder_hidden)
# (4) Extract sentences from beam.
ret = self._from_beam(beam)
return ret
def _from_beam(self, beam):
ret = {"predictions": [], "scores": []}
for b in beam:
n_best = self.args.n_best
scores, ks = b.sort_finished(minimum=n_best)
hyps = []
for i, (times, k) in enumerate(ks[:n_best]):
hyp = b.get_hyp(times, k)
hyps.append(hyp)
ret["predictions"].append(hyps)
ret["scores"].append(scores)
return ret
'weight_decay':
1e-6
}, {
'params': [
p for n, p in model_caption.named_parameters()
if n in word_embedding_param_names
],
'weight_decay':
0.0
}],
lr=1e-3)
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch_bak = checkpoint['epoch'] + 1
experiment.log_text("MOBILENET captions")
val_loss_old = 0.0
val_loss = 0.0
step_count = 0
with experiment.train():
for epoch in range(0, params['epochs']):
if step_count <= 3:
temporal_loss = 0.0
caption_loss = 0.0
running_loss = 0.0
total_batches = 0.0
for i, data in enumerate(tqdm(train_dl, file=sys.stdout)):
# zero the parameter gradients
class experiment_logger:
'''
Interface for logging experiments on neptune, comet, or both.
Args: log_backend, project_name)
Other backends may also be added in the future
Currently defined methods:
add_params:
add_tags:
log_text: strings
log_metrics: numerical values
log_figure: pyplot figures
stop: end logging and close connection
'''
def __init__(self, log_backend, project_name):
'''
Parameters
----------
log_backend : STR
One of 'comet', 'neptune', 'all'
project_name : STR
one of available proyects ('yeast', 'jersey', 'wheat', 'debug', etc)
Returns
-------
None.
'''
self.proj_name = project_name
self.backend = log_backend
#Bool indicating wether neptune logging is enabled
self.neptune = log_backend=='neptune' or log_backend=='all'
#Bool indicating wether comet logging is enabled
self.comet = log_backend=='comet' or log_backend=='all'
if self.neptune:
neptune.init("dna-i/"+project_name,
api_token='eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5haSIsImFwaV91cmwiOiJodHRwczovL3VpLm5lcHR1bmUuYWkiLCJhcGlfa2V5IjoiMWYzMzhjMjItYjczNC00NzZhLWFlZTYtOTI2NzE5MzUwZmNkIn0=')
print("logging experiments on neptune project "+project_name)
neptune.create_experiment()
if self.comet:
self.comet_experiment = Experiment(api_key="V0OXnWOi4KVNS4OkwLjdnxSgK",
project_name=project_name, workspace="dna-i")
print("logging experiments on comet project "+project_name)
if not (self.neptune or self.comet):
raise ValueError('Logging Backend NOT Available')
def add_params(self, params, step=None ):
'''
Adds parameters to experiment log
Parameters
----------
params : Dict
Key-Value pairs
Returns
-------
None.
'''
if self.neptune:
for key, value in params.items():
neptune.set_property(key, value)
if step is not None:
neptune.set_property('step', step)
if self.comet:
self.comet_experiment.log_parameters(params,step=step)
def add_tags(self, tags):
'''
Adds parameters to experiment log
Parameters
----------
params : tags
list of tags (strings)
e.g.: ['tag1', 'tag2']
Returns
-------
None.
'''
if self.neptune:
neptune.append_tag(tags)
if self.comet:
self.comet_experiment.add_tags(tags)
def log_metrics(self, name, value, epoch=None):
'''
Logging pointwise metrics
Parameters
----------
name : STR
Metric key
value : Float/Integer/(Boolean/String)
Comet also allows Boolean/string
Tuples are lallowed
epoch: (OPT) INT
Epoch - or anything used as x axis when plotting metrics
Returns
-------
None.
'''
if self.neptune:
try:
if epoch is not None:
if type(value) is tuple:
print("Logging tuple as r and p-value")
for val, n in zip(value, [" (r)", " (p-val)"]):
neptune.log_metric(name + n,epoch,y=val)
else:
neptune.log_metric(name, epoch, y=value)
else:
if type(value) is tuple:
print("Logging tuple as r and p-value")
for val, n in zip(value, [" (r)", " (p-val)"]):
neptune.log_metric(name+n, val)
else:
neptune.log_metric(name, value)
except:
print("Metric type {} not supported by neptune.".format(type(value)))
print("logging as text")
self.log_text( "{}".format(value), key=name)
if self.comet:
try:
if epoch is not None:
if type(value) is tuple:
print("Logging tuple as r and p-value")
for val, n in zip(value, [" (r)", " (p-val)"]):
self.comet_experiment.log_metric(name+n, val, step=int(epoch))
else:
self.comet_experiment.log_metric(name, value, epoch=epoch)
else:
if type(value) is tuple:
print("Logging tuple as r and p-value")
for val, n in zip(value, [" (r)", " (p-val)"]):
self.comet_experiment.log_metric(name+n, val)
else:
self.comet_experiment.log_metric(name, value)
except:
print("Metric type {} not supported by comet.".format(type(value)))
if type(value) is tuple:
print("Logging tuple as x-y pairs")
for idx, val in enumerate(value):
self.comet_experiment.log_metric(name, val, epoch=idx)
else:
print("Logging as other.")
self.comet_experiment.log_other(name, value)
def log_text(self, string, key=None, epoch=None):
'''
Logs text strings
Parameters
----------
string : STR
text to log
key: STR
log_name needed for Neptune strings
epoch: INT
epoch or any other index
Returns
-------
None.
'''
if self.neptune:
if type(string) is str:
if key is None:
print('Neptune log_name needed for logging text')
print('Using a dummy name: text')
neptune.log_text('text', string)
if epoch is None:
neptune.log_text(key, string)
else:
neptune.log_text(key, epoch, y=string)
else:
print("Wrong type: logging text must be a string")
if self.comet:
if type(string) is str:
if key is not None:
print("Commet text logging does not support keys, prepending it to text")
string = key+ ', '+string
if epoch is None:
self.comet_experiment.log_text(string)
else:
self.comet_experiment.log_text(string, step=epoch)
else:
print("Wrong type: logging text must be a string")
def log_figure(self, figure=None, figure_name=None, step=None):
'''
Logs pyplot figure
Parameters
----------
figure : pyplot figure, optional in comet mandatory in neptune.
The default is None, uses global pyplot figure.
figure_name : STR, optional in comet mandatory in neptune.
The default is None.
step : INT, optional
An index. The default is None.
Returns
-------
None.
'''
if self.neptune:
if figure is not None:
if figure_name is None:
print("Figure name must be given to neptune logger")
print("Using dummy name: figure")
figure_name = 'figure'
if step is None:
neptune.log_image(figure_name, figure)
else:
neptune.log_image(figure_name, step, y=figure)
else:
print("A figure must be passed to neptune logger")
if self.comet:
self.comet_experiment.log_figure(figure_name=figure_name, figure=figure, step=step)
def stop(self):
if self.neptune:
neptune.stop()
if self.comet:
self.comet_experiment.end()
def add_table(self, filename, tabular_data=None, headers=False):
self.comet_experiment.log_table(filename, tabular_data, headers)
def log_image(self, image=None, figure_name=None, step=None):
'''
Logs pyplot figure
Parameters
----------
figure : pyplot figure, optional in comet mandatory in neptune.
The default is None, uses global pyplot figure.
figure_name : STR, optional in comet mandatory in neptune.
The default is None.
step : INT, optional
An index. The default is None.
Returns
-------
None.
'''
self.log_image(image, name=figure_name, overwrite=False, image_format="png", image_scale=1.0, \
image_shape=None, image_colormap=None, image_minmax=None, image_channels="last", \
copy_to_tmp=True, step=step)
def log_hist3d(self, values=None, figure_name=None, step=None):
'''
Logs pyplot figure
Parameters
----------
figure : pyplot figure, optional in comet mandatory in neptune.
The default is None, uses global pyplot figure.
figure_name : STR, optional in comet mandatory in neptune.
The default is None.
step : INT, optional
An index. The default is None.
Returns
-------
None.
'''
if self.neptune:
print("not implemented")
if self.comet:
self.comet_experiment.log_histogram_3d(values, name=figure_name, step=step)
def log_table(self, name=None, data=None, headers=False):
'''
Parameters
----------
name : str
Table name
data : array, list
headers : TYPE, optional
wether to use headers
Returns
-------
None.
'''
self.comet_experiment.log_table(name+'.csv', tabular_data= data, headers = headers )
class ModelTrainer:
def __init__(self, model, dataloader, args):
self.model = model
self.args = args
self.data = dataloader
self.metric = args.metric
if (dataloader is not None):
self.frq_log = len(dataloader['train']) // args.frq_log
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
model.to(self.device)
if args.optimizer == 'sgd':
self.optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optimizer == 'adam':
self.optimizer = optim.Adam(model.parameters(),
lr=args.lr,
betas=(args.beta1, 0.999),
weight_decay=args.weight_decay)
else:
raise Exception('--optimizer should be one of {sgd, adam}')
if args.scheduler == 'set':
self.scheduler = optim.lr_scheduler.LambdaLR(
self.optimizer,
lambda epoch: 10**(epoch / args.scheduler_factor))
elif args.scheduler == 'auto':
self.scheduler = optim.lr_scheduler.ReduceLROnPlateau(
self.optimizer,
mode='min',
factor=args.scheduler_factor,
patience=5,
verbose=True,
threshold=0.0001,
threshold_mode='rel',
cooldown=0,
min_lr=0,
eps=1e-08)
self.experiment = Experiment(api_key=args.comet_key,
project_name=args.comet_project,
workspace=args.comet_workspace,
auto_weight_logging=True,
auto_metric_logging=False,
auto_param_logging=False)
self.experiment.set_name(args.name)
self.experiment.log_parameters(vars(args))
self.experiment.set_model_graph(str(self.model))
def train_one_epoch(self, epoch):
self.model.train()
train_loader = self.data['train']
train_loss = 0
correct = 0
comet_offset = epoch * len(train_loader)
for batch_idx, (data, target) in tqdm(enumerate(train_loader),
leave=True,
total=len(train_loader)):
data, target = data.to(self.device), target.to(self.device)
self.optimizer.zero_grad()
output = self.model(data)
loss = F.cross_entropy(output, target, reduction='sum')
loss.backward()
self.optimizer.step()
pred = output.argmax(dim=1, keepdim=True)
acc = pred.eq(target.view_as(pred)).sum().item()
train_loss += loss.item()
correct += acc
loss = loss.item() / len(data)
acc = 100. * acc / len(data)
comet_step = comet_offset + batch_idx
self.experiment.log_metric('batch_loss', loss, comet_step, epoch)
self.experiment.log_metric('batch_acc', acc, comet_step, epoch)
if (batch_idx + 1) % self.frq_log == 0:
self.experiment.log_metric('log_loss', loss, comet_step, epoch)
self.experiment.log_metric('log_acc', acc, comet_step, epoch)
print('Epoch: {} [{}/{}]\tLoss: {:.6f}\tAcc: {:.2f}%'.format(
epoch + 1, (batch_idx + 1) * len(data),
len(train_loader.dataset), loss, acc))
train_loss /= len(train_loader.dataset)
acc = 100. * correct / len(train_loader.dataset)
comet_step = comet_offset + len(train_loader) - 1
self.experiment.log_metric('loss', train_loss, comet_step, epoch)
self.experiment.log_metric('acc', acc, comet_step, epoch)
print(
'Epoch: {} [Done]\tLoss: {:.4f}\tAccuracy: {}/{} ({:.2f}%)'.format(
epoch + 1, train_loss, correct, len(train_loader.dataset),
acc))
return {'loss': train_loss, 'acc': acc}
def train(self):
self.log_cmd()
best = -1
history = {'lr': [], 'train_loss': []}
try:
print(">> Training %s" % self.model.name)
for epoch in range(self.args.nepoch):
with self.experiment.train():
train_res = self.train_one_epoch(epoch)
with self.experiment.validate():
print("\nvalidation...")
comet_offset = (epoch + 1) * len(self.data['train']) - 1
res = self.val(self.data['val'], comet_offset, epoch)
if res[self.metric] > best:
best = res[self.metric]
self.save_weights(epoch)
if self.args.scheduler == 'set':
lr = self.optimizer.param_groups[0]['lr']
history['lr'].append(lr)
history['train_loss'].append(train_res['loss'])
self.scheduler.step(epoch + 1)
lr = self.optimizer.param_groups[0]['lr']
print('learning rate changed to: %.10f' % lr)
elif self.args.scheduler == 'auto':
self.scheduler.step(train_res['loss'])
finally:
print(">> Training model %s. [Stopped]" % self.model.name)
self.experiment.log_asset_folder(os.path.join(
self.args.outf, self.args.name, 'weights'),
step=None,
log_file_name=False,
recursive=False)
if self.args.scheduler == 'set':
plt.semilogx(history['lr'], history['train_loss'])
plt.grid(True)
self.experiment.log_figure(figure=plt)
plt.show()
def val(self, val_loader, comet_offset=-1, epoch=-1):
self.model.eval()
test_loss = 0
correct = 0
labels = list(range(self.args.nclass))
cm = np.zeros((len(labels), len(labels)))
with torch.no_grad():
for data, target in tqdm(val_loader,
leave=True,
total=len(val_loader)):
data, target = data.to(self.device), target.to(self.device)
output = self.model(data)
test_loss += F.cross_entropy(output, target,
reduction='sum').item()
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
pred = pred.view_as(target).data.cpu().numpy()
target = target.data.cpu().numpy()
cm += confusion_matrix(target, pred, labels=labels)
test_loss /= len(val_loader.dataset)
accuracy = 100. * correct / len(val_loader.dataset)
print('Evaluation: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.
format(test_loss, correct, len(val_loader.dataset), accuracy))
res = {'loss': test_loss, 'acc': accuracy}
self.experiment.log_metrics(res, step=comet_offset, epoch=epoch)
self.experiment.log_confusion_matrix(
matrix=cm,
labels=[ClassDict.getName(x) for x in labels],
title='confusion matrix after epoch %03d' % epoch,
file_name="confusion_matrix_%03d.json" % epoch)
return res
def test(self):
self.load_weights()
with self.experiment.test():
print('\ntesting....')
res = self.val(self.data['test'])
def log_cmd(self):
d = vars(self.args)
cmd = '!python main.py \\\n'
tab = ' '
for k, v in d.items():
if v is None or v == '' or (isinstance(v, bool) and v is False):
continue
if isinstance(v, bool):
arg = '--{} \\\n'.format(k)
else:
arg = '--{} {} \\\n'.format(k, v)
cmd = cmd + tab + arg
# print(cmd);
self.experiment.log_text(cmd)
def save_weights(self, epoch: int):
weight_dir = os.path.join(self.args.outf, self.args.name, 'weights')
if not os.path.exists(weight_dir):
os.makedirs(weight_dir)
torch.save({
'epoch': epoch,
'state_dict': self.model.state_dict()
}, os.path.join(weight_dir, 'model.pth'))
def load_weights(self):
path_g = self.args.weights_path
if path_g is None:
weight_dir = os.path.join(self.args.outf, self.args.name,
'weights')
path_g = os.path.join(weight_dir, 'model.pth')
print('>> Loading weights...')
weights_g = torch.load(path_g, map_location=self.device)['state_dict']
self.model.load_state_dict(weights_g)
print(' Done.')
def predict(self, x):
x = x / 2**15
self.model.eval()
with torch.no_grad():
x = torch.from_numpy(x).float()
x = self.transform(x)
x = x.unsqueeze(0)
x = self.model(x)
x = F.softmax(x, dim=1)
x = x.numpy()
return x
def get_params():
parser = argparse.ArgumentParser(description='Perm')
# Hparams
padd = parser.add_argument
padd('--batch-size',
type=int,
default=64,
metavar='N',
help='input batch size for training (default: 64)')
padd('--latent_dim',
type=int,
default=20,
metavar='N',
help='Latent dim for VAE')
padd('--lr',
type=float,
default=0.01,
metavar='LR',
help='learning rate (default: 0.01)')
padd('--momentum',
type=float,
default=0.5,
metavar='M',
help='SGD momentum (default: 0.5)')
padd('--latent_size',
type=int,
default=50,
metavar='N',
help='Size of latent distribution (default: 50)')
padd('--estimator',
default='reinforce',
const='reinforce',
nargs='?',
choices=['reinforce', 'lax'],
help='Grad estimator for noise (default: %(default)s)')
padd('--reward',
default='soft',
const='soft',
nargs='?',
choices=['soft', 'hard'],
help='Reward for grad estimator (default: %(default)s)')
# Training
padd('--epochs',
type=int,
default=10,
metavar='N',
help='number of epochs to train (default: 10)')
padd('--PGD_steps',
type=int,
default=40,
metavar='N',
help='max gradient steps (default: 30)')
padd('--max_iter',
type=int,
default=20,
metavar='N',
help='max gradient steps (default: 30)')
padd('--max_batches',
type=int,
default=None,
metavar='N',
help=
'max number of batches per epoch, used for debugging (default: None)')
padd('--epsilon',
type=float,
default=0.5,
metavar='M',
help='Epsilon for Delta (default: 0.1)')
padd('--LAMBDA',
type=float,
default=100,
metavar='M',
help='Lambda for L2 lagrange penalty (default: 0.1)')
padd('--nn_temp',
type=float,
default=1.0,
metavar='M',
help='Starting diff. nearest neighbour temp (default: 1.0)')
padd('--temp_decay_rate',
type=float,
default=0.9,
metavar='M',
help='Nearest neighbour temp decay rate (default: 0.9)')
padd('--temp_decay_schedule',
type=float,
default=100,
metavar='M',
help='How many batches before decay (default: 100)')
padd('--bb_steps',
type=int,
default=2000,
metavar='N',
help='Max black box steps per sample(default: 1000)')
padd('--attack_epochs',
type=int,
default=10,
metavar='N',
help='Max numbe of epochs to train G')
padd('--seed',
type=int,
default=1,
metavar='S',
help='random seed (default: 1)')
padd('--batch_size', type=int, default=256, metavar='S', help='Batch size')
padd('--embedding_dim', type=int, default=300, help='embedding_dim')
padd('--embedding_type',
type=str,
default="non-static",
help='embedding_type')
padd('--test_batch_size',
type=int,
default=128,
metavar='N',
help='Test Batch size. 256 requires 12GB GPU memory')
padd('--test',
default=False,
action='store_true',
help='just test model and print accuracy')
padd('--deterministic_G',
default=False,
action='store_true',
help='Auto-encoder, no VAE')
padd('--resample_test',
default=False,
action='store_true',
help='Load model and test resampling capability')
padd('--resample_iterations',
type=int,
default=100,
metavar='N',
help='How many times to resample (default: 100)')
padd('--clip_grad',
default=True,
action='store_true',
help='Clip grad norm')
padd('--train_vae', default=False, action='store_true', help='Train VAE')
padd('--train_ae', default=False, action='store_true', help='Train AE')
padd('--use_flow',
default=False,
action='store_true',
help='Add A NF to Generator')
padd('--carlini_loss',
default=False,
action='store_true',
help='Use CW loss function')
padd('--vanilla_G',
default=False,
action='store_true',
help='Vanilla G White Box')
padd('--prepared_data',
default='dataloader/prepared_data.pickle',
help='Test on a single data')
# Imported Model Params
padd('--emsize', type=int, default=300, help='size of word embeddings')
padd('--nhidden',
type=int,
default=300,
help='number of hidden units per layer in LSTM')
padd('--nlayers', type=int, default=2, help='number of layers')
padd('--noise_radius',
type=float,
default=0.2,
help='stdev of noise for autoencoder (regularizer)')
padd('--noise_anneal',
type=float,
default=0.995,
help='anneal noise_radius exponentially by this every 100 iterations')
padd('--hidden_init',
action='store_true',
help="initialize decoder hidden state with encoder's")
padd('--arch_i',
type=str,
default='300-300',
help='inverter architecture (MLP)')
padd('--arch_g',
type=str,
default='300-300',
help='generator architecture (MLP)')
padd('--arch_d',
type=str,
default='300-300',
help='critic/discriminator architecture (MLP)')
padd('--arch_conv_filters',
type=str,
default='500-700-1000',
help='encoder filter sizes for different convolutional layers')
padd('--arch_conv_strides',
type=str,
default='1-2-2',
help='encoder strides for different convolutional layers')
padd('--arch_conv_windows',
type=str,
default='3-3-3',
help='encoder window sizes for different convolutional layers')
padd('--z_size',
type=int,
default=100,
help='dimension of random noise z to feed into generator')
padd('--temp',
type=float,
default=1,
help='softmax temperature (lower --> more discrete)')
padd('--enc_grad_norm',
type=bool,
default=True,
help='norm code gradient from critic->encoder')
padd('--train_emb', type=bool, default=True, help='Train Glove Embeddings')
padd('--gan_toenc',
type=float,
default=-0.01,
help='weight factor passing gradient from gan to encoder')
padd('--dropout',
type=float,
default=0.0,
help='dropout applied to layers (0 = no dropout)')
padd('--useJS',
type=bool,
default=True,
help='use Jenson Shannon distance')
padd('--perturb_z',
type=bool,
default=True,
help='perturb noise space z instead of hidden c')
padd('--max_seq_len', type=int, default=200, help='max_seq_len')
padd('--gamma', type=float, default=0.95, help='Discount Factor')
padd('--model',
type=str,
default="lstm_arch",
help='classification model name')
padd('--distance_func',
type=str,
default="cosine",
help='NN distance function')
padd('--hidden_dim', type=int, default=128, help='hidden_dim')
padd('--burn_in', type=int, default=500, help='Train VAE burnin')
padd('--beta', type=float, default=0., help='Entropy reg')
padd('--embedding_training',
type=bool,
default=False,
help='embedding_training')
padd('--seqgan_reward',
action='store_true',
default=False,
help='use seq gan reward')
padd('--train_classifier',
action='store_true',
default=False,
help='Train Classifier from scratch')
padd('--diff_nn',
action='store_true',
default=False,
help='Backprop through Nearest Neighbors')
# Bells
padd('--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
padd('--data_parallel',
action='store_true',
default=False,
help="Use multiple GPUs")
padd('--save_adv_samples',
action='store_true',
default=False,
help='Write adversarial samples to disk')
padd('--nearest_neigh_all',
action='store_true',
default=False,
help='Evaluate near. neig. for whole evaluation set')
padd("--comet",
action="store_true",
default=False,
help='Use comet for logging')
padd(
"--offline_comet",
action="store_true",
default=False,
help=
'Use comet offline. To upload, after training run: comet-upload file.zip'
)
padd("--comet_username",
type=str,
default="joeybose",
help='Username for comet logging')
padd("--comet_apikey", type=str,\
default="Ht9lkWvTm58fRo9ccgpabq5zV",help='Api for comet logging')
padd('--debug', default=False, action='store_true', help='Debug')
padd('--debug_neighbour',
default=False,
action='store_true',
help='Debug nearest neighbour training')
padd('--load_model',
default=False,
action='store_true',
help='Whether to load a checkpointed model')
padd('--save_model',
default=False,
action='store_true',
help='Whether to checkpointed model')
padd('--model_path', type=str, default="saved_models/lstm_torchtext2.pt",\
help='where to save/load target model')
padd('--adv_model_path', type=str, default="saved_models/adv_model.pt",\
help='where to save/load adversarial')
padd('--no_load_embedding',
action='store_false',
default=True,
help='load Glove embeddings')
padd('--namestr', type=str, default='BMD Text', \
help='additional info in output filename to describe experiments')
padd('--dataset', type=str, default="imdb", help='dataset')
padd('--clip', type=float, default=1, help='gradient clipping, max norm')
padd('--use_glove', type=str, default="true", help='gpu number')
args = parser.parse_args()
args.classes = 2
args.sample_file = "temp/adv_samples.txt"
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
# Check if settings file
if os.path.isfile("settings.json"):
with open('settings.json') as f:
data = json.load(f)
args.comet_apikey = data["apikey"]
args.comet_username = data["username"]
# Prep file to save adversarial samples
if args.save_adv_samples:
now = datetime.datetime.now()
if os.path.exists(args.sample_file):
os.remove(args.sample_file)
with open(args.sample_file, 'w') as f:
f.write("Adversarial samples starting:\n{}\n".format(now))
# Comet logging
args.device = torch.device("cuda" if use_cuda else "cpu")
if args.comet and not args.offline_comet:
experiment = Experiment(api_key=args.comet_apikey,
project_name="black-magic-design",
workspace=args.comet_username)
elif args.offline_comet:
offline_path = "temp/offline_comet"
if not os.path.exists(offline_path):
os.makedirs(offline_path)
from comet_ml import OfflineExperiment
experiment = OfflineExperiment(project_name="black-magic-design",
workspace=args.comet_username,
offline_directory=offline_path)
# To upload offline comet, run: comet-upload file.zip
if args.comet or args.offline_comet:
experiment.set_name(args.namestr)
def log_text(self, msg):
# Change line breaks for html breaks
msg = msg.replace('\n', '<br>')
self.log_html("<p>{}</p>".format(msg))
experiment.log_text = MethodType(log_text, experiment)
args.experiment = experiment
return args
ipdb.set_trace = lambda: None
# Comet logging
args.device = torch.device("cuda" if use_cuda else "cpu")
if args.comet and not args.offline_comet:
experiment = Experiment(api_key=args.comet_apikey,
project_name="black-magic-design",
workspace=args.comet_username)
elif args.offline_comet:
offline_path = "temp/offline_comet"
if not os.path.exists(offline_path):
os.makedirs(offline_path)
from comet_ml import OfflineExperiment
experiment = OfflineExperiment(project_name="black-magic-design",
workspace=args.comet_username,
offline_directory=offline_path)
# To upload offline comet, run: comet-upload file.zip
if args.comet or args.offline_comet:
experiment.set_name(args.namestr)
def log_text(self, msg):
# Change line breaks for html breaks
msg = msg.replace('\n', '<br>')
self.log_html("<p>{}</p>".format(msg))
experiment.log_text = MethodType(log_text, experiment)
args.experiment = experiment
main(args)
