def test_concat_two_non_singletons(self):
result = ConcatDataset([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
self.assertEqual(10, len(result))
self.assertEqual(0, result[0])
self.assertEqual(5, result[5])
return BenchmarkTasksets(train_tasks, valid_tasks, test_tasks)
if __name__ == '__main__':
tasks = get_few_shot_tasksets(dataset='cifar-fs')
tasks = get_normal_tasksets(dataset='cifar-fs')
# tasks = get_normal_tasksets(dataset='cifar-fc100')
# batch = tasks.train.sample()
# x, y = batch
# print(x.size())
# print(y.size())
# print(y)
# x, y = tasks.train[0]
# print(x.size())
# print(y.size())
from torch.utils.data import ConcatDataset
import torch.utils.data
dataset = ConcatDataset([tasks[1], tasks[0]])
# print(len(tasks[0]))
# print(len(tasks[1]))
# print(len(dataset))
# loader = torch.utils.data.DataLoader(dataset, batch_size=100)
# for x, y in loader:
# print(x.size(), y.size())
dset = l2l.data.MetaDataset(dataset)
tsk = l2l.data.TaskDataset(dset)
batch = tsk.sample()
print(batch)
#x, y, d, numPedsList, PedsList, target_ids
return seq_data, seq_num_persons_list, seq_persons_list, folder_name
def __len__(self):
# Returns sequence length
return len(self.person_to_frames)
# Test Block
from os import listdir
from os.path import isfile, join
path = '../data/dataloader/'
files_list = [f for f in listdir(path) if isfile(join(path, f))]
all_datasets = ConcatDataset([PedTrajectoryDataset(join(path, file)) for file in files_list])
train_loader = DataLoader(all_datasets, batch_size=2, shuffle=False, num_workers=0, pin_memory=False, collate_fn=lambda x: x)
#print(len(train_loader.dataset.datasets))
for i, tuple in enumerate(train_loader):
print(i)
print(tuple)
print('************************')
print("reached")
'''
d = PedTrajectoryDataset('../data/train/overfit/x.txt')
batch_size = 1
def kitti_zhou_train(resize_height, resize_width, crop_height, crop_width,
batch_size, num_workers):
"""A loader that loads image sequences for depth training from the
kitti training set.
This loader returns sequences from the left camera, as well as from the right camera.
"""
transforms_common = [
tf.RandomHorizontalFlip(),
tf.CreateScaledImage(),
tf.Resize((resize_height, resize_width),
image_types=('color', 'depth', 'camera_intrinsics', 'K')),
tf.ConvertDepth(),
tf.CreateColoraug(new_element=True),
tf.ColorJitter(brightness=0.2,
contrast=0.2,
saturation=0.2,
hue=0.1,
gamma=0.0,
fraction=0.5),
tf.RemoveOriginals(),
tf.ToTensor(),
tf.NormalizeZeroMean(),
tf.AddKeyValue('domain', 'kitti_zhou_train_depth'),
tf.AddKeyValue('purposes', ('depth', 'domain')),
]
dataset_name = 'kitti'
cfg_common = {
'dataset': dataset_name,
'trainvaltest_split': 'train',
'video_mode': 'video',
'stereo_mode': 'mono',
'split': 'zhou_split',
'video_frames': (0, -1, 1),
'disable_const_items': False
}
cfg_left = {'keys_to_load': ('color', ), 'keys_to_video': ('color', )}
cfg_right = {
'keys_to_load': ('color_right', ),
'keys_to_video': ('color_right', )
}
dataset_left = StandardDataset(data_transforms=transforms_common,
**cfg_left,
**cfg_common)
dataset_right = StandardDataset(data_transforms=[tf.ExchangeStereo()] +
transforms_common,
**cfg_right,
**cfg_common)
dataset = ConcatDataset((dataset_left, dataset_right))
loader = DataLoader(dataset,
batch_size,
True,
num_workers=num_workers,
pin_memory=True,
drop_last=True)
print(
f" - Can use {len(dataset)} images from the kitti (zhou_split) train split for depth training",
flush=True)
return loader
def train(vocabs, char_vocab, tag_vocab, train_sets, dev_sets, test_sets,
unlabeled_sets):
"""
train_sets, dev_sets, test_sets: dict[lang] -> AmazonDataset
For unlabeled langs, no train_sets are available
"""
# dataset loaders
train_loaders, unlabeled_loaders = {}, {}
train_iters, unlabeled_iters, d_unlabeled_iters = {}, {}, {}
dev_loaders, test_loaders = {}, {}
my_collate = utils.sorted_collate if opt.model == 'lstm' else utils.unsorted_collate
for lang in opt.langs:
train_loaders[lang] = DataLoader(train_sets[lang],
opt.batch_size,
shuffle=True,
collate_fn=my_collate)
train_iters[lang] = iter(train_loaders[lang])
for lang in opt.dev_langs:
dev_loaders[lang] = DataLoader(dev_sets[lang],
opt.batch_size,
shuffle=False,
collate_fn=my_collate)
test_loaders[lang] = DataLoader(test_sets[lang],
opt.batch_size,
shuffle=False,
collate_fn=my_collate)
for lang in opt.all_langs:
if lang in opt.unlabeled_langs:
uset = unlabeled_sets[lang]
else:
# for labeled langs, consider which data to use as unlabeled set
if opt.unlabeled_data == 'both':
uset = ConcatDataset([train_sets[lang], unlabeled_sets[lang]])
elif opt.unlabeled_data == 'unlabeled':
uset = unlabeled_sets[lang]
elif opt.unlabeled_data == 'train':
uset = train_sets[lang]
else:
raise Exception(
f'Unknown options for the unlabeled data usage: {opt.unlabeled_data}'
)
unlabeled_loaders[lang] = DataLoader(uset,
opt.batch_size,
shuffle=True,
collate_fn=my_collate)
unlabeled_iters[lang] = iter(unlabeled_loaders[lang])
d_unlabeled_iters[lang] = iter(unlabeled_loaders[lang])
# embeddings
emb = MultiLangWordEmb(vocabs, char_vocab, opt.use_wordemb,
opt.use_charemb).to(opt.device)
# models
F_s = None
F_p = None
C, D = None, None
num_experts = len(opt.langs) + 1 if opt.expert_sp else len(opt.langs)
if opt.model.lower() == 'lstm':
if opt.shared_hidden_size > 0:
F_s = LSTMFeatureExtractor(opt.total_emb_size, opt.F_layers,
opt.shared_hidden_size,
opt.word_dropout, opt.dropout,
opt.bdrnn)
if opt.private_hidden_size > 0:
if not opt.concat_sp:
assert opt.shared_hidden_size == opt.private_hidden_size, "shared dim != private dim when using add_sp!"
F_p = nn.Sequential(
LSTMFeatureExtractor(opt.total_emb_size, opt.F_layers,
opt.private_hidden_size, opt.word_dropout,
opt.dropout, opt.bdrnn),
MixtureOfExperts(opt.MoE_layers, opt.private_hidden_size,
len(opt.langs), opt.private_hidden_size,
opt.private_hidden_size, opt.dropout,
opt.MoE_bn, False))
else:
raise Exception(f'Unknown model architecture {opt.model}')
if opt.C_MoE:
C = SpMixtureOfExperts(
opt.C_layers, opt.shared_hidden_size, opt.private_hidden_size,
opt.concat_sp,
num_experts, opt.shared_hidden_size + opt.private_hidden_size,
len(tag_vocab), opt.mlp_dropout, opt.C_bn)
else:
C = SpMlpTagger(opt.C_layers, opt.shared_hidden_size,
opt.private_hidden_size, opt.concat_sp,
opt.shared_hidden_size + opt.private_hidden_size,
len(tag_vocab), opt.mlp_dropout, opt.C_bn)
if opt.shared_hidden_size > 0 and opt.n_critic > 0:
if opt.D_model.lower() == 'lstm':
d_args = {
'num_layers': opt.D_lstm_layers,
'input_size': opt.shared_hidden_size,
'hidden_size': opt.shared_hidden_size,
'word_dropout': opt.D_word_dropout,
'dropout': opt.D_dropout,
'bdrnn': opt.D_bdrnn,
'attn_type': opt.D_attn
}
elif opt.D_model.lower() == 'cnn':
d_args = {
'num_layers': 1,
'input_size': opt.shared_hidden_size,
'hidden_size': opt.shared_hidden_size,
'kernel_num': opt.D_kernel_num,
'kernel_sizes': opt.D_kernel_sizes,
'word_dropout': opt.D_word_dropout,
'dropout': opt.D_dropout
}
else:
d_args = None
if opt.D_model.lower() == 'mlp':
D = MLPLanguageDiscriminator(opt.D_layers, opt.shared_hidden_size,
opt.shared_hidden_size,
len(opt.all_langs), opt.loss,
opt.D_dropout, opt.D_bn)
else:
D = LanguageDiscriminator(opt.D_model, opt.D_layers,
opt.shared_hidden_size,
opt.shared_hidden_size,
len(opt.all_langs), opt.D_dropout,
opt.D_bn, d_args)
F_s, C, D = F_s.to(opt.device) if F_s else None, C.to(
opt.device), D.to(opt.device) if D else None
if F_p:
F_p = F_p.to(opt.device)
# optimizers
optimizer = optim.Adam(filter(lambda p: p.requires_grad, itertools.chain(*map(list,
[emb.parameters(), F_s.parameters() if F_s else [], \
C.parameters(), F_p.parameters() if F_p else []]))),
lr=opt.learning_rate,
weight_decay=opt.weight_decay)
if D:
optimizerD = optim.Adam(D.parameters(),
lr=opt.D_learning_rate,
weight_decay=opt.D_weight_decay)
# testing
if opt.test_only:
log.info(f'Loading model from {opt.model_save_file}...')
if F_s:
F_s.load_state_dict(
torch.load(os.path.join(opt.model_save_file, f'netF_s.pth')))
for lang in opt.all_langs:
F_p.load_state_dict(
torch.load(os.path.join(opt.model_save_file, f'net_F_p.pth')))
C.load_state_dict(
torch.load(os.path.join(opt.model_save_file, f'netC.pth')))
if D:
D.load_state_dict(
torch.load(os.path.join(opt.model_save_file, f'netD.pth')))
log.info('Evaluating validation sets:')
acc = {}
log.info(dev_loaders)
log.info(vocabs)
for lang in opt.all_langs:
acc[lang] = evaluate(f'{lang}_dev', dev_loaders[lang],
vocabs[lang], tag_vocab, emb, lang, F_s, F_p,
C)
avg_acc = sum([acc[d] for d in opt.dev_langs]) / len(opt.dev_langs)
log.info(f'Average validation accuracy: {avg_acc}')
log.info('Evaluating test sets:')
test_acc = {}
for lang in opt.all_langs:
test_acc[lang] = evaluate(f'{lang}_test', test_loaders[lang],
vocabs[lang], tag_vocab, emb, lang, F_s,
F_p, C)
avg_test_acc = sum([test_acc[d]
for d in opt.dev_langs]) / len(opt.dev_langs)
log.info(f'Average test accuracy: {avg_test_acc}')
return {'valid': acc, 'test': test_acc}
# training
best_acc, best_avg_acc = defaultdict(float), 0.0
epochs_since_decay = 0
# lambda scheduling
if opt.lambd > 0 and opt.lambd_schedule:
opt.lambd_orig = opt.lambd
num_iter = int(utils.gmean([len(train_loaders[l]) for l in opt.langs]))
# adapt max_epoch
if opt.max_epoch > 0 and num_iter * opt.max_epoch < 15000:
opt.max_epoch = 15000 // num_iter
log.info(f"Setting max_epoch to {opt.max_epoch}")
for epoch in range(opt.max_epoch):
emb.train()
if F_s:
F_s.train()
C.train()
if D:
D.train()
if F_p:
F_p.train()
# lambda scheduling
if hasattr(opt, 'lambd_orig') and opt.lambd_schedule:
if epoch == 0:
opt.lambd = opt.lambd_orig
elif epoch == 5:
opt.lambd = 10 * opt.lambd_orig
elif epoch == 15:
opt.lambd = 100 * opt.lambd_orig
log.info(f'Scheduling lambda = {opt.lambd}')
# training accuracy
correct, total = defaultdict(int), defaultdict(int)
gate_correct = defaultdict(int)
c_gate_correct = defaultdict(int)
# D accuracy
d_correct, d_total = 0, 0
for i in tqdm(range(num_iter), ascii=True):
# D iterations
if opt.shared_hidden_size > 0:
utils.freeze_net(emb)
utils.freeze_net(F_s)
utils.freeze_net(F_p)
utils.freeze_net(C)
utils.unfreeze_net(D)
# WGAN n_critic trick since D trains slower
n_critic = opt.n_critic
if opt.wgan_trick:
if opt.n_critic > 0 and ((epoch == 0 and i < 25)
or i % 500 == 0):
n_critic = 100
for _ in range(n_critic):
D.zero_grad()
loss_d = {}
lang_features = {}
# train on both labeled and unlabeled langs
for lang in opt.all_langs:
# targets not used
d_inputs, _ = utils.endless_get_next_batch(
unlabeled_loaders, d_unlabeled_iters, lang)
d_inputs, d_lengths, mask, d_chars, d_char_lengths = d_inputs
d_embeds = emb(lang, d_inputs, d_chars, d_char_lengths)
shared_feat = F_s((d_embeds, d_lengths))
if opt.grad_penalty != 'none':
lang_features[lang] = shared_feat.detach()
if opt.D_model.lower() == 'mlp':
d_outputs = D(shared_feat)
# if token-level D, we can reuse the gate label generator
d_targets = utils.get_gate_label(d_outputs,
lang,
mask,
False,
all_langs=True)
d_total += torch.sum(d_lengths).item()
else:
d_outputs = D((shared_feat, d_lengths))
d_targets = utils.get_lang_label(
opt.loss, lang, len(d_lengths))
d_total += len(d_lengths)
# D accuracy
_, pred = torch.max(d_outputs, -1)
# d_total += len(d_lengths)
d_correct += (pred == d_targets).sum().item()
l_d = functional.nll_loss(d_outputs.view(
-1, D.num_langs),
d_targets.view(-1),
ignore_index=-1)
l_d.backward()
loss_d[lang] = l_d.item()
# gradient penalty
if opt.grad_penalty != 'none':
gp = utils.calc_gradient_penalty(
D,
lang_features,
onesided=opt.onesided_gp,
interpolate=(opt.grad_penalty == 'wgan'))
gp.backward()
optimizerD.step()
# F&C iteration
utils.unfreeze_net(emb)
if opt.use_wordemb and opt.fix_emb:
for lang in emb.langs:
emb.wordembs[lang].weight.requires_grad = False
if opt.use_charemb and opt.fix_charemb:
emb.charemb.weight.requires_grad = False
utils.unfreeze_net(F_s)
utils.unfreeze_net(F_p)
utils.unfreeze_net(C)
utils.freeze_net(D)
emb.zero_grad()
if F_s:
F_s.zero_grad()
if F_p:
F_p.zero_grad()
C.zero_grad()
# optimizer.zero_grad()
for lang in opt.langs:
inputs, targets = utils.endless_get_next_batch(
train_loaders, train_iters, lang)
inputs, lengths, mask, chars, char_lengths = inputs
bs, seq_len = inputs.size()
embeds = emb(lang, inputs, chars, char_lengths)
shared_feat, private_feat = None, None
if opt.shared_hidden_size > 0:
shared_feat = F_s((embeds, lengths))
if opt.private_hidden_size > 0:
private_feat, gate_outputs = F_p((embeds, lengths))
if opt.C_MoE:
c_outputs, c_gate_outputs = C((shared_feat, private_feat))
else:
c_outputs = C((shared_feat, private_feat))
# targets are padded with -1
l_c = functional.nll_loss(c_outputs.view(bs * seq_len, -1),
targets.view(-1),
ignore_index=-1)
# gate loss
if F_p:
gate_targets = utils.get_gate_label(
gate_outputs, lang, mask, False)
l_gate = functional.cross_entropy(gate_outputs.view(
bs * seq_len, -1),
gate_targets.view(-1),
ignore_index=-1)
l_c += opt.gate_loss_weight * l_gate
_, gate_pred = torch.max(
gate_outputs.view(bs * seq_len, -1), -1)
gate_correct[lang] += (
gate_pred == gate_targets.view(-1)).sum().item()
if opt.C_MoE and opt.C_gate_loss_weight > 0:
c_gate_targets = utils.get_gate_label(
c_gate_outputs, lang, mask, opt.expert_sp)
_, c_gate_pred = torch.max(
c_gate_outputs.view(bs * seq_len, -1), -1)
if opt.expert_sp:
l_c_gate = functional.binary_cross_entropy_with_logits(
mask.unsqueeze(-1) * c_gate_outputs,
c_gate_targets)
c_gate_correct[lang] += torch.index_select(
c_gate_targets.view(bs * seq_len, -1), -1,
c_gate_pred.view(bs * seq_len)).sum().item()
else:
l_c_gate = functional.cross_entropy(
c_gate_outputs.view(bs * seq_len, -1),
c_gate_targets.view(-1),
ignore_index=-1)
c_gate_correct[lang] += (c_gate_pred == c_gate_targets.
view(-1)).sum().item()
l_c += opt.C_gate_loss_weight * l_c_gate
l_c.backward()
_, pred = torch.max(c_outputs, -1)
total[lang] += torch.sum(lengths).item()
correct[lang] += (pred == targets).sum().item()
# update F with D gradients on all langs
if D:
for lang in opt.all_langs:
inputs, _ = utils.endless_get_next_batch(
unlabeled_loaders, unlabeled_iters, lang)
inputs, lengths, mask, chars, char_lengths = inputs
embeds = emb(lang, inputs, chars, char_lengths)
shared_feat = F_s((embeds, lengths))
# d_outputs = D((shared_feat, lengths))
if opt.D_model.lower() == 'mlp':
d_outputs = D(shared_feat)
# if token-level D, we can reuse the gate label generator
d_targets = utils.get_gate_label(d_outputs,
lang,
mask,
False,
all_langs=True)
else:
d_outputs = D((shared_feat, lengths))
d_targets = utils.get_lang_label(
opt.loss, lang, len(lengths))
l_d = functional.nll_loss(d_outputs.view(-1, D.num_langs),
d_targets.view(-1),
ignore_index=-1)
if opt.lambd > 0:
l_d *= -opt.lambd
l_d.backward()
optimizer.step()
# end of epoch
log.info('Ending epoch {}'.format(epoch + 1))
if d_total > 0:
log.info('D Training Accuracy: {}%'.format(100.0 * d_correct /
d_total))
log.info('Training accuracy:')
log.info('\t'.join(opt.langs))
log.info('\t'.join(
[str(100.0 * correct[d] / total[d]) for d in opt.langs]))
log.info('Gate accuracy:')
log.info('\t'.join(
[str(100.0 * gate_correct[d] / total[d]) for d in opt.langs]))
log.info('Tagger Gate accuracy:')
log.info('\t'.join(
[str(100.0 * c_gate_correct[d] / total[d]) for d in opt.langs]))
log.info('Evaluating validation sets:')
acc = {}
for lang in opt.dev_langs:
acc[lang] = evaluate(f'{lang}_dev', dev_loaders[lang],
vocabs[lang], tag_vocab, emb, lang, F_s, F_p,
C)
avg_acc = sum([acc[d] for d in opt.dev_langs]) / len(opt.dev_langs)
log.info(f'Average validation accuracy: {avg_acc}')
log.info('Evaluating test sets:')
test_acc = {}
for lang in opt.dev_langs:
test_acc[lang] = evaluate(f'{lang}_test', test_loaders[lang],
vocabs[lang], tag_vocab, emb, lang, F_s,
F_p, C)
avg_test_acc = sum([test_acc[d]
for d in opt.dev_langs]) / len(opt.dev_langs)
log.info(f'Average test accuracy: {avg_test_acc}')
if avg_acc > best_avg_acc:
epochs_since_decay = 0
log.info(f'New best average validation accuracy: {avg_acc}')
best_acc['valid'] = acc
best_acc['test'] = test_acc
best_avg_acc = avg_acc
with open(os.path.join(opt.model_save_file, 'options.pkl'),
'wb') as ouf:
pickle.dump(opt, ouf)
if F_s:
torch.save(F_s.state_dict(),
'{}/netF_s.pth'.format(opt.model_save_file))
torch.save(emb.state_dict(),
'{}/net_emb.pth'.format(opt.model_save_file))
if F_p:
torch.save(F_p.state_dict(),
'{}/net_F_p.pth'.format(opt.model_save_file))
torch.save(C.state_dict(),
'{}/netC.pth'.format(opt.model_save_file))
if D:
torch.save(D.state_dict(),
'{}/netD.pth'.format(opt.model_save_file))
else:
epochs_since_decay += 1
if opt.lr_decay < 1 and epochs_since_decay >= opt.lr_decay_epochs:
epochs_since_decay = 0
old_lr = optimizer.param_groups[0]['lr']
optimizer.param_groups[0]['lr'] = old_lr * opt.lr_decay
log.info(f'Decreasing LR to {old_lr * opt.lr_decay}')
# end of training
log.info(f'Best average validation accuracy: {best_avg_acc}')
return best_acc
def train(self, net, samples, optimizer, e):
alpha = 2 * max(0, ((50 - e) / 50))
criterion = losses.ELULovaszFocalWithLogitsLoss(alpha, 2 - alpha)
transforms = generator.TransformationsGenerator([
random.RandomFlipLr(),
random.RandomAffine(image_size=101,
translation=lambda rs:
(rs.randint(-20, 20), rs.randint(-20, 20)),
scale=lambda rs: (rs.uniform(0.85, 1.15), 1),
**utils.transformations_options)
])
samples_aux = list(
set(samples).intersection(set(utils.get_aux_samples())))
dataset_aux = datasets.ImageDataset(samples_aux, settings.train,
transforms)
dataset_pseudo = datasets.SemiSupervisedImageDataset(
samples_test,
settings.test,
transforms,
size=len(samples_test),
test_predictions=self.test_predictions,
momentum=0.0)
dataset = datasets.ImageDataset(samples, settings.train, transforms)
weight_train = len(dataset_pseudo) / len(dataset) * 2
weight_aux = weight_train / 2
weights = [weight_train] * len(dataset) + [weight_aux] * len(
dataset_aux) + [1] * len(dataset_pseudo)
dataloader = DataLoader(
ConcatDataset([dataset, dataset_aux, dataset_pseudo]),
num_workers=10,
batch_size=16,
sampler=WeightedRandomSampler(weights=weights, num_samples=3200))
average_meter_train = meters.AverageMeter()
with tqdm(total=len(dataloader), leave=False,
ascii=True) as pbar, torch.enable_grad():
net.train()
padding = tta.Pad((13, 14, 13, 14))
for images, masks_targets in dataloader:
masks_targets = masks_targets.to(gpu)
masks_predictions = padding.transform_backward(
net(padding.transform_forward(images))).contiguous()
loss = criterion(masks_predictions, masks_targets)
loss.backward()
optimizer.step()
optimizer.zero_grad()
average_meter_train.add('loss', loss.item())
self.update_pbar(torch.sigmoid(masks_predictions),
masks_targets, pbar, average_meter_train,
'Training epoch {}'.format(e))
train_stats = {
'train_' + k: v
for k, v in average_meter_train.get_all().items()
}
return train_stats
set1 = subDataSet(wav_filename,
meta_filename,
crop_duration_s=crop_duration_s,
transform=composed)
specific_name = "split1_ir0_ov1_2"
wav_filename = os.path.join(wav_dir, specific_name + ".wav")
meta_filename = os.path.join(meta_dir, specific_name + ".csv")
set2 = subDataSet(wav_filename,
meta_filename,
crop_duration_s=crop_duration_s,
transform=composed)
datasets.append(set1)
datasets.append(set2)
dataset = ConcatDataset(datasets)
print(dataset[0]['specgram'].size())
print(dataset[23]['specgram'].size())
print(os.listdir(wav_dir).__len__())
# end region
# region fulltest
wav_dir = "C:\\Users\\jgohj\\PycharmProjects\\Jon\\data\\mic_dev_test"
meta_dir = "C:\\Users\\jgohj\\PycharmProjects\\Jon\\data\\metadata_dev"
wav_list = os.listdir((wav_dir))
composed = transforms.Compose([Spectrogram1(), Binarize()])
melcomposed = transforms.Compose([MelSpectrogram(), Binarize()])
datasets = []
meldatasets = []
print("Creating Full Data set...")
def _get_dataset(self, filename, dicts=None):
if not filename and not dicts:
raise ValueError("You must either supply `filename` or `dicts`")
# loading dicts from file (default)
if dicts is None:
dicts = list(self.processor.file_to_dicts(filename))
#shuffle list of dicts here if we later want to have a random dev set splitted from train set
if str(self.processor.train_filename) in str(filename):
if not self.processor.dev_filename:
if self.processor.dev_split > 0.0:
random.shuffle(dicts)
num_dicts = len(dicts)
multiprocessing_chunk_size, num_cpus_used = calc_chunksize(
num_dicts=num_dicts,
max_processes=self.max_processes,
max_chunksize=self.max_multiprocessing_chunksize,
)
with ExitStack() as stack:
if self.max_processes > 1: # use multiprocessing only when max_processes > 1
p = stack.enter_context(mp.Pool(processes=num_cpus_used))
logger.info(
f"Got ya {num_cpus_used} parallel workers to convert {num_dicts} dictionaries "
f"to pytorch datasets (chunksize = {multiprocessing_chunk_size})..."
)
log_ascii_workers(num_cpus_used, logger)
results = p.imap(
partial(self._dataset_from_chunk,
processor=self.processor),
grouper(dicts, multiprocessing_chunk_size),
chunksize=1,
)
else:
logger.info(
f"Multiprocessing disabled, using a single worker to convert {num_dicts}"
f"dictionaries to pytorch datasets.")
results = map(
partial(self._dataset_from_chunk,
processor=self.processor),
grouper(dicts, num_dicts))
datasets = []
desc = f"Preprocessing Dataset"
if filename:
desc += f" {filename}"
with tqdm(total=len(dicts), unit=' Dicts', desc=desc) as pbar:
for dataset, tensor_names in results:
datasets.append(dataset)
# update progress bar (last step can have less dicts than actual chunk_size)
pbar.update(
min(multiprocessing_chunk_size, pbar.total - pbar.n))
# _dataset_from_chunk can return a None in cases where downsampling has occurred
datasets = [d for d in datasets if d]
concat_datasets = ConcatDataset(datasets)
return concat_datasets, tensor_names
def _make_question_answering(cls,
datasilo,
sets=["train", "dev", "test"],
n_splits=5,
shuffle=True,
random_state=None,
n_neg_answers_per_question=1):
"""
Create number of folds data-silo-like objects which can be used for training from the
original data silo passed on. This function takes into account the characteristics of the
data for question-answering-
:param datasilo: the data silo that contains the original data
:type datasilo: DataSilo
:param sets: which sets to use to create the xval folds (strings)
:type sets: list
:param n_splits: number of folds to create
:type n_splits: int
:param shuffle: shuffle each class' samples before splitting
:type shuffle: bool
:param random_state: random state for shuffling
:type random_state: int
:param n_neg_answers_per_question: number of negative answers per question to include for training
:type n_neg_answers_per_question: int
"""
assert "id" in datasilo.tensor_names, f"Expected tensor 'id' in tensor names, found {datasilo.tensor_names}"
assert "labels" in datasilo.tensor_names, f"Expected tensor 'labels' in tensor names, found {datasilo.tensor_names}"
id_index = datasilo.tensor_names.index("id")
label_index = datasilo.tensor_names.index("labels")
sets_to_concat = []
for setname in sets:
if datasilo.data[setname]:
sets_to_concat.extend(datasilo.data[setname])
all_data = ConcatDataset(sets_to_concat)
documents = []
keyfunc = lambda x: x[id_index][0]
all_data = sorted(all_data.datasets, key=keyfunc)
for key, document in groupby(all_data, key=keyfunc):
documents.append(list(document))
xval_split = cls._split_for_qa(
documents=documents,
id_index=id_index,
n_splits=n_splits,
shuffle=shuffle,
random_state=random_state,
)
silos = []
for train_set, test_set in xval_split:
# Each training set is further divided into actual train and dev set
if datasilo.processor.dev_split > 0:
dev_split = datasilo.processor.dev_split
n_dev = int(np.ceil(dev_split * len(train_set)))
assert n_dev > 0, f"dev split of {dev_split} is not large enough to split away a development set"
n_actual_train = len(train_set) - n_dev
actual_train_set = train_set[:n_actual_train]
dev_set = train_set[n_actual_train:]
ds_dev = [
sample for document in dev_set for sample in document
]
else:
ds_dev = None
actual_train_set = train_set
train_samples = []
for doc in actual_train_set:
keyfunc = lambda x: x[id_index][1]
doc = sorted(doc, key=keyfunc)
for key, question in groupby(doc, key=keyfunc):
# add all available answrs to train set
sample_list = list(question)
neg_answer_idx = []
for index, sample in enumerate(sample_list):
if sample[label_index][0][0] or sample[label_index][0][
1]:
train_samples.append(sample)
else:
neg_answer_idx.append(index)
# add random n_neg_answers_per_question samples to train set
if len(neg_answer_idx) <= n_neg_answers_per_question:
train_samples.extend(
[sample_list[idx] for idx in neg_answer_idx])
else:
neg_answer_idx = random.sample(
neg_answer_idx, n_neg_answers_per_question)
train_samples.extend(
[sample_list[idx] for idx in neg_answer_idx])
ds_train = train_samples
ds_test = [sample for document in test_set for sample in document]
silos.append(
DataSiloForCrossVal(datasilo, ds_train, ds_dev, ds_test))
return silos
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(
"--pkl_dir",
default=None,
type=str,
help="The pkl data dir for training set logits.",
)
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS),
)
parser.add_argument(
"--data_cache_name",
default=None,
type=str,
help="The name of cached data",
)
parser.add_argument(
"--language",
default=None,
type=str,
required=True,
help=
"Evaluation language. Also train language if `train_language` is set to None.",
)
parser.add_argument("--benchmark",
default='xtreme',
type=str,
help="benchmark, xglue/xtreme")
parser.add_argument(
"--train_language",
default=None,
type=str,
help="Train language if is different of the evaluation language.")
parser.add_argument("--sample_ratio",
default=0.0,
type=float,
help="The training sample ratio of each language")
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written.",
)
# Other parameters
parser.add_argument("--log_dir",
default=None,
type=str,
help="The output log dir.")
# Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--gpu_id", default=None, type=str, help="GPU id")
parser.add_argument("--filter_k", type=int, default=0)
parser.add_argument("--filter_m", type=int, default=0)
parser.add_argument("--first_loss_only", action='store_true')
parser.add_argument("--use_eng_logits",
action='store_true',
help='use english soft logits for other language')
parser.add_argument("--alpha",
type=float,
default=0,
help='alpha for kd loss')
parser.add_argument("--temperature",
type=float,
default=0.1,
help="temprature to soft logits")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--hidden_dropout_prob",
default=0.1,
type=float,
help=
"When splitting up a long document into chunks, how much stride to take between chunks.",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the test set.")
parser.add_argument("--eval_checkpoints",
type=str,
default=None,
help="evaluation checkpoints")
parser.add_argument("--eval_splits",
default='valid',
type=str,
help="eval splits")
parser.add_argument("--eval_train",
action='store_true',
help="eval splits")
parser.add_argument("--pkl_index",
default="0",
type=str,
help="pickle index for dumping training logits")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Rul evaluation during training at each logging step.")
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
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("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay 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("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--use_all_samples_per_epoch",
type=boolean_string,
default='true',
help="Use all samples for per epoch training")
parser.add_argument("--max_train_samples_per_epoch",
default=None,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps",
type=int,
default=-1,
help="Log every X updates steps.")
parser.add_argument("--logging_each_epoch",
action="store_true",
help="Whether to log after each epoch.")
parser.add_argument("--logging_steps_in_sample",
type=int,
default=-1,
help="log every X samples.")
parser.add_argument("--save_steps",
type=int,
default=-1,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda",
action="store_true",
help="Avoid using CUDA when available")
parser.add_argument("--overwrite_output_dir",
action="store_true",
help="Overwrite the content of the output directory")
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--seed",
type=int,
default=52,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
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("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument("--server_ip",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--server_port",
type=str,
default="",
help="For distant debugging.")
args = parser.parse_args()
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 distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.gpu_id:
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
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")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
if args.pkl_dir is None:
args.pkl_dir = args.data_dir
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
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,
)
logger.info("Training/evaluation parameters %s", args)
# preprocess args
assert not (args.logging_steps != -1 and args.logging_steps_in_sample != -1
), "these two parameters can't both be setted"
if args.logging_steps == -1 and args.logging_steps_in_sample != -1:
total_batch_size = args.n_gpu * args.per_gpu_train_batch_size * args.gradient_accumulation_steps
args.logging_steps = args.logging_steps_in_sample // total_batch_size
# Set seed
set_seed(args)
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name](language=args.language,
train_language=args.train_language,
benchmark=args.benchmark)
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
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,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.filter_k > 0: # there is cross attention layer
config.first_loss_only = args.first_loss_only
config.alpha = args.alpha
config.temperature = args.temperature
config.filter_m = args.filter_m
config.hidden_dropout_prob = args.hidden_dropout_prob
config.output_hidden_states = True
config.filter_k = min(args.filter_k,
config.num_hidden_layers - args.filter_m)
config.num_hidden_layers = args.filter_m
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,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
filter_m=args.filter_m,
filter_k=args.filter_k,
)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
# Training
if args.do_train:
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
if args.filter_k > 0: # FILTER
train_langs = [
"en-{}".format(lang) for lang in args.language.split(',')
]
else:
train_langs = args.train_language.split(',')
dataset_list = []
for lang in train_langs:
lg_train_dataset, guids = load_and_cache_examples(args,
args.task_name,
tokenizer,
lang,
split="train")
dataset_list.append(lg_train_dataset)
if args.filter_k > 0:
train_dataset = AlignDataset(
dataset_list,
train_langs.index('en-en'),
is_training=True,
use_all_samples=args.use_all_samples_per_epoch)
else:
train_dataset = ConcatDataset(dataset_list)
global_step, tr_loss = train(args, train_dataset, label_list, 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 and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Evaluation
if args.do_eval and args.local_rank in [-1, 0]:
results = {}
if args.eval_checkpoints:
checkpoints = [
os.path.join(args.output_dir, ckpt)
for ckpt in args.eval_checkpoints.split(',')
]
else:
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("transformers.modeling_utils").setLevel(
logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
best_avg, best_checkpoint = 0, None
task_metric = "acc" if args.task_name != "rel" else "ndcg"
for checkpoint in checkpoints:
prefix = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
tokenizer = tokenizer_class.from_pretrained(
checkpoint, do_lower_case=args.do_lower_case)
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args,
model,
tokenizer,
label_list,
prefix=prefix,
splits=args.eval_splits.split(','))
results[os.path.basename(checkpoint)] = result
logger.info("{}\t{}".format(checkpoint, result))
if best_avg < result["valid_avg"][task_metric]:
best_avg = result["valid_avg"][task_metric]
best_checkpoint = checkpoint
with open(os.path.join(args.output_dir, "eval_logs.txt"),
'w') as log_writer:
for key, val in results.items():
log_writer.write("{}\t{}\n".format(key, json.dumps(val)))
if args.eval_train and args.local_rank in [-1, 0]:
if args.eval_checkpoints:
# use the first one
checkpoint = [
os.path.join(args.output_dir, ckpt)
for ckpt in args.eval_checkpoints.split(',')
][0]
else:
checkpoint = os.path.join(args.output_dir, 'checkpoint-best')
assert os.path.exists(checkpoint)
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
evaluate(args,
model,
tokenizer,
label_list,
prefix="",
splits=['train'])
logger.info("Task {0} finished!".format(args.task_name))
os.makedirs(args.checkpoint_folder)
logging.info("Prepare training datasets.")
datasets = []
for dataset_path in args.datasets:
if args.dataset_type == 'voc':
dataset = VOCDataset(dataset_path, transform=train_transform,
target_transform=target_transform)
label_file = os.path.join(args.checkpoint_folder, "voc-model-labels.txt")
store_labels(label_file, dataset.class_names)
num_classes = len(dataset.class_names)
else:
raise ValueError(f"Dataset tpye {args.dataset_type} is not supported.")
datasets.append(dataset)
logging.info(f"Stored labels into file {label_file}.")
train_dataset = ConcatDataset(datasets)
logging.info("Train dataset size: {}".format(len(train_dataset)))
train_loader = DataLoader(train_dataset, args.batch_size,
num_workers=args.num_workers,
shuffle=True)
logging.info("Prepare Validation datasets.")
if args.dataset_type == "voc":
val_dataset = VOCDataset(args.validation_dataset, transform=test_transform,
target_transform=target_transform, is_test=True)
logging.info("validation dataset size: {}".format(len(val_dataset)))
val_loader = DataLoader(val_dataset, args.batch_size,
num_workers=args.num_workers,
shuffle=False)
logging.info("Build network.")
net = create_net(num_classes)
def train_cl(model, train_datasets, replay_mode="none", scenario="class",classes_per_task=None,iters=2000,batch_size=32,
generator=None, gen_iters=0, gen_loss_cbs=list(), loss_cbs=list(), eval_cbs=list(), sample_cbs=list(),
use_exemplars=True, add_exemplars=False, eval_cbs_exemplars=list()):
'''Train a model (with a "train_a_batch" method) on multiple tasks, with replay-strategy specified by [replay_mode].
[model] <nn.Module> main model to optimize across all tasks
[train_datasets] <list> with for each task the training <DataSet>
[replay_mode] <str>, choice from "generative", "exact", "current", "offline" and "none"
[scenario] <str>, choice from "task", "domain" and "class"
[classes_per_task] <int>, # of classes per task
[iters] <int>, # of optimization-steps (i.e., # of batches) per task
[generator] None or <nn.Module>, if a seperate generative model should be trained (for [gen_iters] per task)
[*_cbs] <list> of call-back functions to evaluate training-progress'''
# Set model in training-mode
model.train()
# Use cuda?
cuda = model._is_on_cuda()
device = model._device()
# Initiate possible sources for replay (no replay for 1st task)
Exact = Generative = Current = False
previous_model = None
# Register starting param-values (needed for "intelligent synapses").
if isinstance(model, ContinualLearner) and (model.si_c>0):
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
model.register_buffer('{}_SI_prev_task'.format(n), p.data.clone())
# Loop over all tasks.
for task, train_dataset in enumerate(train_datasets, 1):
# If offline replay-setting, create large database of all tasks so far
if replay_mode=="offline" and (not scenario=="task"):
train_dataset = ConcatDataset(train_datasets[:task])
# -but if "offline"+"task"-scenario: all tasks so far included in 'exact replay' & no current batch
if replay_mode=="offline" and scenario == "task":
Exact = True
previous_datasets = train_datasets
# Add exemplars (if available) to current dataset (if requested)
if add_exemplars and task > 1:
# ---------- ADHOC SOLUTION: permMNIST needs transform to tensor, while splitMNIST does not ---------- #
if len(train_datasets) > 6:
target_transform = (lambda y, x=classes_per_task: torch.tensor(y % x)) if (
scenario == "domain"
) else (lambda y: torch.tensor(y))
else:
target_transform = (lambda y, x=classes_per_task: y % x) if scenario == "domain" else None
# ---------------------------------------------------------------------------------------------------- #
exemplar_dataset = ExemplarDataset(model.exemplar_sets, target_transform=target_transform)
training_dataset = ConcatDataset([train_dataset, exemplar_dataset])
else:
training_dataset = train_dataset
# Prepare <dicts> to store running importance estimates and param-values before update ("Synaptic Intelligence")
if isinstance(model, ContinualLearner) and (model.si_c>0):
W = {}
p_old = {}
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
W[n] = p.data.clone().zero_()
p_old[n] = p.data.clone()
# Find [active_classes]
active_classes = None # -> for Domain-IL scenario, always all classes are active
if scenario == "task":
# -for Task-IL scenario, create <list> with for all tasks so far a <list> with the active classes
active_classes = [list(range(classes_per_task * i, classes_per_task * (i + 1))) for i in range(task)]
elif scenario == "class":
# -for Class-IL scenario, create one <list> with active classes of all tasks so far
active_classes = list(range(classes_per_task * task))
# Reset state of optimizer(s) for every task (if requested)
if model.optim_type == "adam_reset":
model.optimizer = optim.Adam(model.optim_list, betas=(0.9, 0.999))
if (generator is not None) and generator.optim_type == "adam_reset":
generator.optimizer = optim.Adam(model.optim_list, betas=(0.9, 0.999))
# Initialize # iters left on current data-loader(s)
iters_left = iters_left_previous = 1
if scenario == "task":
up_to_task = task if replay_mode == "offline" else task-1
iters_left_previous = [1]*up_to_task
data_loader_previous = [None]*up_to_task
# Define tqdm progress bar(s)
progress = tqdm.tqdm(range(1, iters+1))
if generator is not None:
progress_gen = tqdm.tqdm(range(1, gen_iters+1))
# Loop over all iterations
iters_to_use = iters if (generator is None) else max(iters, gen_iters)
for batch_index in range(1, iters_to_use+1):
# Update # iters left on current data-loader(s) and, if needed, create new one(s)
iters_left -= 1
if iters_left==0:
data_loader = iter(utils.get_data_loader(training_dataset, batch_size, cuda=cuda, drop_last=True))
# NOTE: [train_dataset] is training-set of current task
# [training_dataset] is training-set of current task with stored exemplars added (if requested)
iters_left = len(data_loader)
if Exact:
if scenario == "task":
up_to_task = task if replay_mode == "offline" else task-1
batch_size_replay = int(np.ceil(batch_size/up_to_task)) if (up_to_task>1) else batch_size
# -in Task-IL scenario, need separate replay for each task
for task_id in range(up_to_task):
batch_size_to_use = min(batch_size_replay, len(previous_datasets[task_id]))
iters_left_previous[task_id] -= 1
if iters_left_previous[task_id]==0:
data_loader_previous[task_id] = iter(utils.get_data_loader(
train_datasets[task_id], batch_size_to_use, cuda=cuda, drop_last=True
))
iters_left_previous[task_id] = len(data_loader_previous[task_id])
else:
iters_left_previous -= 1
if iters_left_previous == 0:
batch_size_to_use = min(batch_size, len(ConcatDataset(previous_datasets)))
data_loader_previous = iter(utils.get_data_loader(ConcatDataset(previous_datasets),
batch_size_to_use, cuda=cuda, drop_last=True))
iters_left_previous = len(data_loader_previous)
# -----------------Collect data------------------#
# -----CURRENT BATCH----- #
if replay_mode == "offline" and scenario == "task":
x = y = scores = None
else:
x, y = next(data_loader) # --> sample training data of current task
y = y-classes_per_task*(task-1) if scenario == "task" else y # --> ITL: adjust y-targets to 'active range'
x, y = x.to(device), y.to(device) # --> transfer them to correct device
# If --bce, --bce-distill & scenario=="class", calculate scores of current batch with previous model
binary_distillation = hasattr(model, "binaryCE") and model.binaryCE and model.binaryCE_distill
if binary_distillation and scenario == "class" and (previous_model is not None):
with torch.no_grad():
scores = previous_model(x)[:, :(classes_per_task * (task - 1))]
else:
scores = None
# -----REPLAYED BATCH----- #
if not Exact and not Generative and not Current:
x_ = y_ = scores_ = None # -> if no replay
# -->> Exact Replay <<-- ##
if Exact:
scores_ = None
if scenario in ("domain", "class"):
# Sample replayed training data, move to correct device
x_, y_ = next(data_loader_previous)
x_ = x_.to(device)
y_ = y_.to(device) if (model.replay_targets == "hard") else None
# If required, get target scores (i.e, [scores_] -- using previous model, with no_grad()
if model.replay_targets == "soft":
with torch.no_grad():
scores_ = previous_model(x_)
scores_ = scores_[:, :(classes_per_task*(task-1))] if scenario == "class" else scores_
# -> when scenario=="class", zero probabilities will be added in the [utils.loss_fn_kd]-function
elif scenario == "task":
# Sample replayed training data, wrap in (cuda-)Variables and store in lists
x_ = list()
y_ = list()
up_to_task = task if replay_mode=="offline" else task-1
for task_id in range(up_to_task):
x_temp, y_temp = next(data_loader_previous[task_id])
x_.append(x_temp.to(device))
# -only keep [y_] if required (as otherwise unnecessary computations will be done)
if model.replay_targets == "hard":
y_temp = y_temp - (classes_per_task*task_id) #-> adjust y-targets to 'active range'
y_.append(y_temp.to(device))
else:
y_.append(None)
# If required, get target scores (i.e, [scores_] -- using previous model
if (model.replay_targets == "soft") and (previous_model is not None):
scores_ = list()
for task_id in range(up_to_task):
with torch.no_grad():
scores_temp = previous_model(x_[task_id])
scores_temp = scores_temp[:, (classes_per_task*task_id):(classes_per_task*(task_id+1))]
scores_.append(scores_temp)
# -->> Generative / Current Replay <<--##
if Generative or Current:
# Get replayed data (i.e., [x_]) -- either current data or use previous generator
x_ = x if Current else previous_generator.sample(batch_size)
# Get target scores and labels (i.e., [scores_] / [y_]) -- using previous model, with no_grad()
# -if there are no task-specific mask, obtain all predicted scores at once
if (not hasattr(previous_model, "mask_dict")) or (previous_model.mask_dict is None):
with torch.no_grad():
all_scores_ = previous_model(x_)
# -depending on chosen scenario, collect relevant predicted scores (per task, if required)
if scenario in ("domain", "class") and (
(not hasattr(previous_model, "mask_dict")) or (previous_model.mask_dict is None)
):
scores_ = all_scores_[:,:(classes_per_task * (task - 1))] if scenario == "class" else all_scores_
_, y_ = torch.max(scores_, dim=1)
else:
# NOTE: it's possible to have scenario=domain with task-mask (so actually it's the Task-IL scenario)
# -[x_] needs to be evaluated according to each previous task, so make list with entry per task
scores_ = list()
y_ = list()
for task_id in range(task - 1):
# -if there is a task-mask (i.e., XdG is used), obtain predicted scores for each task separately
if hasattr(previous_model, "mask_dict") and previous_model.mask_dict is not None:
previous_model.apply_XdGmask(task=task_id + 1)
with torch.no_grad():
all_scores_ = previous_model(x_)
if scenario == "domain":
temp_scores_ = all_scores_
else:
temp_scores_ = all_scores_[:,
(classes_per_task * task_id):(classes_per_task * (task_id + 1))]
_, temp_y_ = torch.max(temp_scores_, dim=1)
scores_.append(temp_scores_)
y_.append(temp_y_)
# Only keep predicted y/scores if required (as otherwise unnecessary computations will be done)
y_ = y_ if (model.replay_targets == "hard") else None
scores_ = scores_ if (model.replay_targets == "soft") else None
# ---> Train MAIN MODEL
if batch_index <= iters:
# Train the main model with this batch
loss_dict = model.train_a_batch(x, y, x_=x_, y_=y_, scores=scores, scores_=scores_,
active_classes=active_classes, task=task, rnt=1./task)
# Update running parameter importance estimates in W
if isinstance(model, ContinualLearner) and (model.si_c>0):
for n, p in model.named_parameters():
if p.requires_grad:
n = n.replace('.', '__')
if p.grad is not None:
W[n].add_(-p.grad*(p.detach()-p_old[n]))
p_old[n] = p.detach().clone()
# Fire callbacks (for visualization of training-progress / evaluating performance after each task)
for loss_cb in loss_cbs:
if loss_cb is not None:
loss_cb(progress, batch_index, loss_dict, task=task)
for eval_cb in eval_cbs:
if eval_cb is not None:
eval_cb(model, batch_index, task=task)
if model.label == "VAE":
for sample_cb in sample_cbs:
if sample_cb is not None:
sample_cb(model, batch_index, task=task)
#---> Train GENERATOR
if generator is not None and batch_index <= gen_iters:
# Train the generator with this batch
loss_dict = generator.train_a_batch(x, y, x_=x_, y_=y_, scores_=scores_, active_classes=active_classes,
task=task, rnt=1./task)
# Fire callbacks on each iteration
for loss_cb in gen_loss_cbs:
if loss_cb is not None:
loss_cb(progress_gen, batch_index, loss_dict, task=task)
for sample_cb in sample_cbs:
if sample_cb is not None:
sample_cb(generator, batch_index, task=task)
##----------> UPON FINISHING EACH TASK...
# Close progres-bar(s)
progress.close()
if generator is not None:
progress_gen.close()
# EWC: estimate Fisher Information matrix (FIM) and update term for quadratic penalty
if isinstance(model, ContinualLearner) and (model.ewc_lambda>0):
# -find allowed classes
allowed_classes = list(
range(classes_per_task*(task-1), classes_per_task*task)
) if scenario=="task" else (list(range(classes_per_task*task)) if scenario=="class" else None)
# -if needed, apply correct task-specific mask
if model.mask_dict is not None:
model.apply_XdGmask(task=task)
# -estimate FI-matrix
model.estimate_fisher(training_dataset, allowed_classes=allowed_classes)
# SI: calculate and update the normalized path integral
if isinstance(model, ContinualLearner) and (model.si_c>0):
model.update_omega(W, model.epsilon)
# EXEMPLARS: update exemplar sets
if (add_exemplars or use_exemplars) or replay_mode=="exemplars":
exemplars_per_class = int(np.floor(model.memory_budget / (classes_per_task*task)))
# reduce examplar-sets
model.reduce_exemplar_sets(exemplars_per_class)
# for each new class trained on, construct examplar-set
new_classes = list(range(classes_per_task)) if scenario=="domain" else list(range(classes_per_task*(task-1),
classes_per_task*task))
for class_id in new_classes:
start = time.time()
# create new dataset containing only all examples of this class
class_dataset = SubDataset(original_dataset=train_dataset, sub_labels=[class_id])
# based on this dataset, construct new exemplar-set for this class
model.construct_exemplar_set(dataset=class_dataset, n=exemplars_per_class)
print("Constructed exemplar-set for class {}: {} seconds".format(class_id, round(time.time()-start)))
model.compute_means = True
# evaluate this way of classifying on test set
for eval_cb in eval_cbs_exemplars:
if eval_cb is not None:
eval_cb(model, iters, task=task)
# REPLAY: update source for replay
previous_model = copy.deepcopy(model).eval()
if replay_mode == 'generative':
Generative = True
previous_generator = copy.deepcopy(generator).eval() if generator is not None else previous_model
elif replay_mode == 'current':
Current = True
elif replay_mode in ('exemplars', 'exact'):
Exact = True
if replay_mode == "exact":
previous_datasets = train_datasets[:task]
else:
if scenario == "task":
previous_datasets = []
for task_id in range(task):
previous_datasets.append(
ExemplarDataset(
model.exemplar_sets[
(classes_per_task * task_id):(classes_per_task * (task_id + 1))],
target_transform=lambda y, x=classes_per_task * task_id: y + x)
)
else:
target_transform = (lambda y, x=classes_per_task: y % x) if scenario == "domain" else None
previous_datasets = [
ExemplarDataset(model.exemplar_sets, target_transform=target_transform)]
def create_pretokenized_dataset(paths):
datasets = [PreTokenizedFileDataset(p) for p in paths]
dataset = ConcatDataset(datasets)
return dataset
def test_concat_raises_index_error(self):
result = ConcatDataset([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
with self.assertRaises(IndexError):
# this one goes to 11
result[11]
verbose=False,
)
val_dataset = CSGODataset(
transform=transform_multichannel,
dataset_split='val',
verbose=False,
)
test_dataset = CSGODataset(
transform=transform_multichannel,
dataset_split='test',
verbose=False,
)
train_val_dataset = ConcatDataset([train_dataset, val_dataset])
# implicit else
train_loader = torch.utils.data.DataLoader(
train_val_dataset,
batch_size=64,
shuffle=True,
num_workers=0,
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=64,
shuffle=False,
num_workers=0,
)
csv_file = filenames[0].split('/')[-1]
#Créer data set pour un csv file en particulier
# essai=DoodlesDataset(csv_file, path,nrows=select_nrows, size=size_image,skiprows=range(1,10))
# loader=DataLoader(essai,batch_size=10)
# for image, label in loader:
# print(image)
# t1=image[0,0,:,:]
# #imshow(t1)
# print(label)
doodles = ConcatDataset([
DoodlesDataset(fn.split('/')[-1],
path,
nrows=select_nrows,
size=size_image) for fn in filenames
])
loader = DataLoader(doodles, batch_size=2, shuffle=True)
i = 0
for image, label in loader:
# print(image)
t1 = image[0, 0, :, :]
t2 = image[1, 0, :, :]
# imshow(t1)
# imshow(t2)
i += 2
print(i)
print(label)
def get_train_loaders(config):
"""
Returns dictionary containing the training and validation loaders
(torch.utils.data.DataLoader) backed by the datasets.hdf5.HDF5Dataset.
:param config: a top level configuration object containing the 'loaders' key
:return: dict {
'train': <train_loader>
'val': <val_loader>
}
"""
assert 'loaders' in config, 'Could not find data loaders configuration'
loaders_config = config['loaders']
logger = get_logger('HDF5Dataset')
logger.info('Creating training and validation set loaders...')
# get train and validation files
train_paths = loaders_config['train_path']
val_paths = loaders_config['val_path']
assert isinstance(train_paths, list)
assert isinstance(val_paths, list)
# get h5 internal paths for raw and label
raw_internal_path = loaders_config['raw_internal_path']
label_internal_path = loaders_config['label_internal_path']
weight_internal_path = loaders_config.get('weight_internal_path', None)
# get train/validation patch size and stride
train_patch = tuple(loaders_config['train_patch'])
train_stride = tuple(loaders_config['train_stride'])
val_patch = tuple(loaders_config['val_patch'])
val_stride = tuple(loaders_config['val_stride'])
# get slice_builder_cls
slice_builder_str = loaders_config.get('slice_builder', 'SliceBuilder')
logger.info(f'Slice builder class: {slice_builder_str}')
slice_builder_cls = _get_slice_builder_cls(slice_builder_str)
train_datasets = []
for train_path in train_paths:
try:
logger.info(f'Loading training set from: {train_path}...')
# create H5 backed training and validation dataset with data augmentation
train_dataset = HDF5Dataset(
train_path,
train_patch,
train_stride,
phase='train',
transformer_config=loaders_config['transformer'],
raw_internal_path=raw_internal_path,
label_internal_path=label_internal_path,
weight_internal_path=weight_internal_path,
slice_builder_cls=slice_builder_cls)
train_datasets.append(train_dataset)
except Exception:
logger.info(f'Skipping training set: {train_path}', exc_info=True)
val_datasets = []
for val_path in val_paths:
try:
logger.info(f'Loading validation set from: {val_path}...')
val_dataset = HDF5Dataset(
val_path,
val_patch,
val_stride,
phase='val',
transformer_config=loaders_config['transformer'],
raw_internal_path=raw_internal_path,
label_internal_path=label_internal_path,
weight_internal_path=weight_internal_path)
val_datasets.append(val_dataset)
except Exception:
logger.info(f'Skipping validation set: {val_path}', exc_info=True)
num_workers = loaders_config.get('num_workers', 1)
logger.info(f'Number of workers for train/val datasets: {num_workers}')
# when training with volumetric data use batch_size of 1 due to GPU memory constraints
return {
'train':
DataLoader(ConcatDataset(train_datasets),
batch_size=1,
shuffle=True,
num_workers=num_workers),
'val':
DataLoader(ConcatDataset(val_datasets),
batch_size=1,
shuffle=True,
num_workers=num_workers)
}
def generate_random_dataset(path,
nb_row_valid,
nb_rows_test,
nb_rows,
dict_nb_lignes,
size_image=224,
encoding_dict=None,
filenames=None,
use_acc_proportionate_sampling=False):
'''
Pour chaque classe dans filenames, on prend nb_rows données aléatoire dans le fichier
:param path:
:param nb_row_valid:
:param nb_rows_test:
:param nb_rows:
:param size_image:
:param encoding_dict:
:param filenames:
:return:
'''
if filenames == None:
filenames = os.listdir(path)
if use_acc_proportionate_sampling:
if os.path.isfile("saves_obj/dict_acc_per_class_valid.pk"):
dict_acc_class = load_object(
"saves_obj/dict_acc_per_class_valid.pk")
else:
print(
"Aucun dictionnaire d'accuracy par classe trouvé; sampling uniforme utilisé"
)
use_acc_proportionate_sampling = False
nb_lignes_skip = nb_row_valid + nb_rows_test
list_dataset = []
dict_nb_row_used_per_class = {}
for fn in filenames:
n = dict_nb_lignes[fn]
skip = list(range(1, nb_lignes_skip)) + sorted(
random.sample(range(nb_lignes_skip, n),
n - nb_rows - nb_lignes_skip))
if use_acc_proportionate_sampling:
acc = dict_acc_class[fn[:-4]]
new_rows = round((1.1 - acc) * nb_rows)
else:
new_rows = nb_rows
dict_nb_row_used_per_class[fn] = new_rows
data_set = DoodlesDataset(fn,
path,
nrows=new_rows,
size=size_image,
skiprows=skip,
encoding_dict=encoding_dict,
mode="train")
list_dataset.append(data_set)
doodles = ConcatDataset(list_dataset)
print(
"Nombre de données d'entraînement (total:{}):".format(
sum(dict_nb_row_used_per_class.values())),
dict_nb_row_used_per_class)
return doodles
def get_loaders(train_paths,
val_paths,
raw_internal_path,
label_internal_path,
label_dtype,
train_patch,
train_stride,
val_patch,
val_stride,
transformer,
pixel_wise_weight=False,
curriculum_learning=False,
ignore_index=None):
"""
Returns dictionary containing the training and validation loaders
(torch.utils.data.DataLoader) backed by the datasets.hdf5.HDF5Dataset
:param train_path: path to the H5 file containing the training set
:param val_path: path to the H5 file containing the validation set
:param raw_internal_path:
:param label_internal_path:
:param label_dtype: target type of the label dataset
:param train_patch:
:param train_stride:
:param val_path:
:param val_stride:
:param transformer:
:return: dict {
'train': <train_loader>
'val': <val_loader>
}
"""
transformers = {
'LabelToBoundaryTransformer': LabelToBoundaryTransformer,
'RandomLabelToBoundaryTransformer': RandomLabelToBoundaryTransformer,
'AnisotropicRotationTransformer': AnisotropicRotationTransformer,
'IsotropicRotationTransformer': IsotropicRotationTransformer,
'StandardTransformer': StandardTransformer,
'BaseTransformer': BaseTransformer
}
assert transformer in transformers
if curriculum_learning:
slice_builder_cls = CurriculumLearningSliceBuilder
else:
slice_builder_cls = SliceBuilder
train_datasets = []
for train_path in train_paths:
# create H5 backed training and validation dataset with data augmentation
train_dataset = HDF5Dataset(train_path,
train_patch,
train_stride,
phase='train',
label_dtype=label_dtype,
raw_internal_path=raw_internal_path,
label_internal_path=label_internal_path,
transformer=transformers[transformer],
weighted=pixel_wise_weight,
ignore_index=ignore_index,
slice_builder_cls=slice_builder_cls)
train_datasets.append(train_dataset)
val_datasets = []
for val_path in val_paths:
val_dataset = HDF5Dataset(val_path,
val_patch,
val_stride,
phase='val',
label_dtype=label_dtype,
raw_internal_path=raw_internal_path,
label_internal_path=label_internal_path,
transformer=transformers[transformer],
weighted=pixel_wise_weight,
ignore_index=ignore_index)
val_datasets.append(val_dataset)
# shuffle only if curriculum_learning scheme is not used
return {
'train':
DataLoader(ConcatDataset(train_datasets),
batch_size=1,
shuffle=not curriculum_learning),
'val':
DataLoader(ConcatDataset(val_datasets),
batch_size=1,
shuffle=not curriculum_learning)
}
#transforms.RandomAffine(180, translate=(10, 10)),
#transforms.Normalize((0.1307,), (0.3081,))
]))
#false data augumentation
tf_combinations = get_transform_combination2()
for tf in tf_combinations:
tf1 = []
tf1.extend(tf)
tf1.append(transforms.CenterCrop(IMG_SIZE))
tf1.append(transforms.ToTensor())
false_aug = ImageDataset(input_file_path,
DATA_ROOT_DIR,
0,
transform=transforms.Compose(tf1))
false_img_dataset = ConcatDataset([false_img_dataset, false_aug])
kfold = KFold(n_splits=KFOLD)
true_dataset_fold = kfold.split(true_img_dataset)
false_dataset_fold = kfold.split(false_img_dataset)
accuracy = []
#model training and test prediction with k fold cross validation
for fold_idx, ( (true_train_idx, true_test_idx), (false_train_idx, false_test_idx) ) in\
enumerate( zip(true_dataset_fold, false_dataset_fold) ):
true_train_data = [true_img_dataset[i] for i in true_train_idx]
true_test_data = [true_img_dataset[i] for i in true_test_idx]
false_train_data = [false_img_dataset[i] for i in false_train_idx]
false_test_data = [false_img_dataset[i] for i in false_test_idx]
def load_data(data_root,
dataset,
phase,
batch_size,
sampler_dic=None,
num_workers=8,
cifar_imb_ratio=None,
test_open=False,
shuffle=True):
txt = './data/%s/%s_%s.txt' % (dataset, dataset, (
phase if phase not in ['train_plain', 'tail'] else 'train'))
if dataset != 'iNaturalist18':
key = 'default'
else:
key = 'iNaturalist18'
rgb_mean, rgb_std = RGB_statistics[key]['mean'], RGB_statistics[key]['std']
if phase not in ['train', 'val']:
transform = get_data_transform('test', rgb_mean, rgb_std, key)
else:
transform = get_data_transform(phase, rgb_mean, rgb_std, key)
# print('Use data transformation:', transform)
if dataset == 'CIFAR10_LT':
print('====> CIFAR10 Imbalance Ratio: ', cifar_imb_ratio)
set_ = IMBALANCECIFAR10(phase,
imbalance_ratio=cifar_imb_ratio,
root=data_root)
elif dataset == 'CIFAR100_LT':
print('====> CIFAR100 Imbalance Ratio: ', cifar_imb_ratio)
set_ = IMBALANCECIFAR100(phase,
imbalance_ratio=cifar_imb_ratio,
root=data_root)
else:
print('Loading data from %s' % (txt))
set_ = LT_Dataset(data_root, txt, transform, phase)
if phase == 'test' and test_open:
open_txt = './data/%s/%s_open.txt' % (dataset, dataset)
print('Testing with opensets from %s' % (open_txt))
open_set_ = LT_Dataset('./data/%s/%s_open' % (dataset, dataset),
open_txt, transform)
set_ = ConcatDataset([set_, open_set_])
if sampler_dic and (phase == 'train' or phase == 'train_drw'):
print('Using sampler.')
# print('Sample %s samples per-class.' % sampler_dic['num_samples_cls'])
print('Sampler parameters: ', sampler_dic['params'])
return DataLoader(dataset=set_,
batch_size=batch_size,
sampler=sampler_dic['sampler'](
set_, **sampler_dic['params']),
num_workers=num_workers)
else:
print('No sampler.')
print('Shuffle is %s.' % (shuffle))
return DataLoader(dataset=set_,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
type=str,
default='gpu',
help='For cpu: \'cpu\', for gpu: \'gpu\'')
parser.add_argument('--chunk_size',
type=int,
default=36,
help='chunk size(sequence length)')
parser.add_argument('--step_size',
type=int,
default=1,
help='sequence split step')
parser.add_argument('--lr', type=float, default=5e-4, help='learning rate')
parser.add_argument('--weight_decay',
type=argtype.check_float,
default='1e-2',
help='weight_decay')
parser.add_argument('--epoch',
type=argtype.epoch,
default='inf',
help='the number of epoch for training')
parser.add_argument('--batch_size',
type=int,
default=256,
help='size of batches for training')
parser.add_argument('--val_ratio',
type=float,
default=.3,
help='validation set ratio')
parser.add_argument('--model_name',
type=str,
default='main_model',
help='model name to save')
parser.add_argument('--transfer',
type=argtype.boolean,
default=False,
help='whether fine tuning or not')
parser.add_argument('--oversample_times',
type=int,
default=30,
help='the times oversampling times for fine tuning')
parser.add_argument('--patience',
type=int,
default=20,
help='patience for early stopping')
parser.add_argument('--c_loss',
type=argtype.boolean,
default=True,
help='whether using custom loss or not')
parser.add_argument('--predict',
type=argtype.boolean,
default=False,
help='predict and save csv file or not')
parser.add_argument('--filename',
type=str,
default='submission',
help='csv file name to save predict result')
parser.add_argument('--Y_list',
type=argtype.str_to_list,
default='Y12,Y15',
help='target Y for pre-training')
parser.add_argument('--window_size',
type=int,
default=1,
help='window size for moving average')
parser.add_argument('--attention',
type=argtype.boolean,
default=True,
help='select model using attention mechanism')
args = parser.parse_args()
data_dir = './data'
if args.device == 'gpu':
args.device = 'cuda'
device = torch.device(args.device)
chunk_size = args.chunk_size
step_size = args.step_size
lr = args.lr
weight_decay = args.weight_decay
EPOCH = args.epoch
batch_size = args.batch_size
val_ratio = args.val_ratio
model_name = args.model_name
transfer_learning = args.transfer
times = args.oversample_times
patience = args.patience
c_loss = args.c_loss
pred = args.predict
filename = args.filename
Y_list = args.Y_list
window_size = args.window_size
attention = args.attention
params = {
'chunk_size': chunk_size,
'step_size': step_size,
'learning_rate': lr,
'weight_decay': weight_decay,
'epoch size': EPOCH,
'batch_size': batch_size,
'valid_ratio': val_ratio,
'model_name': model_name,
'transfer_learning': transfer_learning,
'oversample_times': times,
'early_stopping_patience': patience,
'c_loss': c_loss,
'pred': pred,
'filename': filename,
'Y_list': Y_list,
'window_size': window_size,
'attention': attention
}
Y = ''
for y in Y_list:
Y += y
model_name = f'{model_name}/{Y}'
Dataframe = dataframe.Dataframe(data_dir=data_dir)
input_size = len(Dataframe.feature_cols)
if attention:
model = regressor.Attention_Regressor(input_size).to(device)
else:
model = regressor.BiLSTM_Regressor().to(device)
checkpoint = Checkpoint(model_name=model_name,
transfer_learning=transfer_learning)
early_stopping = Early_stopping(patience=patience)
vis = Custom_Visdom(model_name, transfer_learning)
vis.print_params(params)
if transfer_learning:
dataset_list = []
if attention:
pre_df = Dataframe.get_pretrain_df()\
.iloc[-chunk_size+1:][Dataframe.feature_cols]
df = Dataframe.get_y18_df()
df = pd.concat([pre_df, df], axis=0)
else:
df = Dataframe.get_y18_df()
train_dataset = datasets.CustomSequenceDataset(chunk_size=chunk_size,
df=df,
Y='Y18',
step_size=step_size,
noise=True,
times=times)
dataset_list.append(train_dataset)
dataset = ConcatDataset(dataset_list)
train_loader, valid_loader = datasets.split_dataset(
dataset=dataset,
batch_size=batch_size,
val_ratio=val_ratio,
shuffle=True)
checkpoint.load_model(model)
else:
dataset_list = []
for y in Y_list:
df = Dataframe.get_pretrain_df()
df[y] = df[y].rolling(window=window_size, min_periods=1).mean()
dataset = datasets.CustomSequenceDataset(chunk_size=chunk_size,
df=df,
Y=y,
step_size=step_size,
noise=False,
times=1)
dataset_list.append(dataset)
dataset = ConcatDataset(dataset_list)
train_loader, valid_loader = datasets.split_dataset(
dataset=dataset,
batch_size=batch_size,
val_ratio=val_ratio,
shuffle=True)
optimizer = Adam(model.parameters(),
lr=lr,
weight_decay=float(weight_decay))
if c_loss:
criterion = custom_loss.mse_AIFrenz_torch
else:
criterion = nn.MSELoss()
training_time = time.time()
epoch = 0
y_df = Dataframe.get_pretrain_df()[Y_list]
y18_df = Dataframe.get_y18_df()[['Y18']]
while epoch < EPOCH:
print(f'\r Y: {Y} \
chunk size: {chunk_size} \
transfer: {transfer_learning}')
epoch += 1
train_loss_per_epoch, train_loss_list_per_batch, batch_list = train(
model=model,
train_loader=train_loader,
criterion=criterion,
optimizer=optimizer,
epoch=epoch,
transfer_learning=transfer_learning,
attention=attention,
freeze_name='transfer_layer')
valid_loss = valid(model=model,
valid_loader=valid_loader,
criterion=criterion,
attention=attention)
iter_time = time.time() - training_time
print(
f'\r Epoch: {epoch:3d}/{str(EPOCH):3s}\t',
f'train time: {int(iter_time//60):2d}m {iter_time%60:5.2f}s\t'
f'avg train loss: {train_loss_per_epoch:7.3f}\t'
f'valid loss: {valid_loss:7.3f}')
checkpoint.save_log(batch_list, epoch, train_loss_list_per_batch,
train_loss_per_epoch, valid_loss)
early_stop, is_best = early_stopping(valid_loss)
checkpoint.save_checkpoint(model, optimizer, is_best)
vis.print_training(EPOCH, epoch, training_time, train_loss_per_epoch,
valid_loss, patience, early_stopping.counter)
vis.loss_plot(checkpoint)
print('-----' * 17)
y_true, y_pred, y_idx = predict.trainset_predict(
model=model,
data_dir=data_dir,
Y=Y_list[0],
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y18_true, y18_pred, y18_idx = predict.trainset_predict(
model=model,
data_dir=data_dir,
Y='Y18',
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y_df['pred'] = y_pred
y18_df['pred'] = y18_pred
vis.predict_plot(y_df, 'pre')
vis.predict_plot(y18_df, 'trans')
vis.print_error()
if early_stop:
break
if transfer_learning:
checkpoint.load_model(model, transfer_learningd=True)
else:
checkpoint.load_model(model, transfer_learningd=False)
y_true, y_pred, y_idx = predict.trainset_predict(model=model,
data_dir=data_dir,
Y=Y_list[0],
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y18_true, y18_pred, y18_idx = predict.trainset_predict(
model=model,
data_dir=data_dir,
Y='Y18',
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y_df['pred'] = y_pred
y18_df['pred'] = y18_pred
vis.predict_plot(y_df, 'pre')
vis.predict_plot(y18_df, 'trans')
vis.print_error()
if pred:
predict.test_predict(model=model,
chunk_size=chunk_size,
filename=filename,
attention=attention)
whole_corpus = datasets.TableFeatures(corpus,
sherlock_feature_groups,
topic_feature=topic,
label_enc=label_enc,
id_filter=None,
max_col_count=MAX_COL_COUNT)
if args.mode!='eval':
train = copy.copy(whole_corpus).set_filter(train_ids)
train_list.append(train)
test = copy.copy(whole_corpus).set_filter(test_ids)
test_list.append(test)
if args.mode!='eval':
training_data = ConcatDataset(train_list)
testing_data = ConcatDataset(test_list)
print('----------------------------------')
end_loading = time.time()
print("Loading done:", end_loading - start_loading)
time_record['Load'] = end_loading - start_loading
model = CRF(len(valid_types) , batch_first=True).to(device)
####################
# Training
def __init__(self, config):
print('Batch size: ', config.batch_size)
print('read background_dataset!' + '\n')
background_dataset = BackgroundDataset(
[config.PRW_img_path, config.CUHK_SYSU_path])
self.background_dataloader = DataLoader(dataset=background_dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.worker_num,
drop_last=True)
print('read surreal_dataset dataset!' + '\n')
# 读取真实的uvmap
surreal_dataset = RealTextureDataset(pkl_path=config.texture_pkl_path)
self.surreal_dataloader = DataLoader(dataset=surreal_dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.worker_num,
drop_last=True)
print('read reid_dataset dataset!' + '\n')
print('read market_dataset dataset!' + '\n')
dataset = Market1501Dataset()
if config.triplet:
print('4*4!')
trainloader = DataLoader(ImageData(dataset.train),
sampler=RandomIdentitySampler(
dataset.train, config.num_instance),
batch_size=config.batch_size,
num_workers=config.worker_num,
drop_last=True)
queryloader = DataLoader(ImageData(dataset.query),
sampler=RandomIdentitySampler(
dataset.query, config.num_instance),
batch_size=config.batch_size,
num_workers=config.worker_num,
drop_last=True)
galleryloader = DataLoader(ImageData(dataset.gallery),
sampler=RandomIdentitySampler(
dataset.gallery,
config.num_instance),
batch_size=config.batch_size,
num_workers=config.worker_num,
drop_last=True)
self.reid_dataloader = [trainloader, queryloader, galleryloader]
'''
prw_dataset = PRWDataset(pkl_path = config.frames_mat_pkl_path,num_instance=4)
market_dataset = Market1501Dataset(pkl_path = config.Market_all_pkl,num_instance=4)
reid_dataset = ConcatDataset([market_dataset, prw_dataset])
#market_dataset = Market1501Dataset(pkl_path = '/unsullied/sharefs/zhongyunshan/isilon-home/datasets/Texture/market_1501_train.pkl',num_instance=4)
market_dataset = Market1501Dataset(pkl_path = config.Market_all_pkl,num_instance=4)
reid_dataset = market_dataset
self.reid_dataloader = DataLoader(dataset=reid_dataset, batch_size=int(config.batch_size/config.num_instance),
shuffle=True, num_workers=config.worker_num, drop_last=True)
'''
else:
print('16*1!')
prw_dataset = PRWDataset(pkl_path=config.frames_mat_pkl_path,
num_instance=1)
market_dataset = Market1501Dataset(pkl_path=config.Market_all_pkl,
num_instance=1)
reid_dataset = ConcatDataset([market_dataset, prw_dataset])
self.reid_dataloader = DataLoader(dataset=reid_dataset,
batch_size=config.batch_size,
shuffle=True,
num_workers=config.worker_num,
drop_last=True)
# read the mask of face and hand
texture_mask = TextureMask(size=64) # 设定读取64*64大小的mask
self.face_mask = texture_mask.get_mask('face')
self.hand_mask = texture_mask.get_mask('hand')
self.mask = self.face_mask + self.hand_mask
self.gpu_available = torch.cuda.is_available()
if self.gpu_available:
print('Use GPU! GPU num: ', config.gpu_nums)
gpu_ids = [i for i in range(config.gpu_nums)]
# 读取pretrained model
if config.pretrained_model_path is None:
print('No resume train model!')
self.generator = UNet(input_channels=3,
output_channels=3,
gpu_ids=gpu_ids)
else:
print('resume train model!')
print(config.epoch_now)
self.generator = torch.load(config.pretrained_model_path)
if config.reid_model == 'reid_loss_market1501':
print('origin model!')
from loss.reid_loss_market1501 import ReIDLoss
config.num_classes = 1501
self.reid_loss = ReIDLoss(model_path=config.reid_weight_path,
num_classes=config.num_classes,
gpu_ids=gpu_ids,
margin=config.margin)
elif config.reid_model == 'PCB_intern_loss':
print('PCB_intern_loss!')
from loss.PCB_intern_loss import ReIDLoss
self.reid_loss = ReIDLoss(model_path=config.reid_weight_path,
num_classes=config.num_classes,
gpu_ids=gpu_ids,
margin=config.margin)
elif config.reid_model == 'ImageNet_Resnet':
print('ImageNet_Resnet!')
print('layer: ', config.layer)
from loss.ImageNet_Resnet import ReIDLoss
self.reid_loss = ReIDLoss(gpu_ids=gpu_ids)
elif config.reid_model == 'PCB_MiddleFeature':
print('PCB_MiddleFeature!')
print('layer: ', config.layer)
from loss.PCB_MiddleFeature import ReIDLoss
self.reid_loss = ReIDLoss(model_path=config.reid_weight_path,
num_classes=config.num_classes,
gpu_ids=gpu_ids,
margin=config.margin,
layer=config.layer)
elif config.reid_model == 'NoPCB_Resnet':
print('NoPCB_Resnet!')
print('layer: ', config.layer)
from loss.NoPCB_Resnet import ReIDLoss
self.reid_loss = ReIDLoss(gpu_ids=gpu_ids)
elif config.reid_model == 'NoPCB_Resnet_deepfashion':
print('NoPCB_Resnet_deepfashion!')
print('layer: ', config.layer)
from loss.NoPCB_Resnet_deepfashion import ReIDLoss
self.reid_loss = ReIDLoss(gpu_ids=gpu_ids)
elif config.reid_model == 'PCB_softmax':
print('PCB_softmax!')
from loss.PCB_softmax_loss import ReIDLoss
config.num_classes = 1501
self.reid_loss = ReIDLoss(model_path=config.reid_weight_path,
num_classes=config.num_classes,
gpu_ids=gpu_ids,
margin=config.margin)
elif config.reid_model == 'PCB_PerLoss':
print('PCB_PerLoss!')
from loss.PCB_PerLoss import ReIDLoss
self.reid_loss = ReIDLoss(model_path=config.reid_weight_path,
num_classes=config.num_classes,
gpu_ids=gpu_ids)
elif config.reid_model == 'PCB_AllCat':
print('PCB_AllCat!')
from loss.PCB_AllCat import ReIDLoss
self.reid_loss = ReIDLoss(model_path=config.reid_weight_path,
num_classes=config.num_classes,
gpu_ids=gpu_ids,
margin=config.margin)
else:
raise KeyError('{} not in keys!'.format(config.reid_model))
if self.gpu_available:
self.generator = nn.DataParallel(self.generator) # multi-GPU
self.generator = self.generator.cuda()
self.reid_loss = self.reid_loss.cuda()
self.mask = self.mask.cuda()
self.texture2img = TextureToImage(action_npz=config.action_npz,
batch_size=config.batch_size,
use_gpu=self.gpu_available)
# 计算face and hand 的共同 loss, 均方损失函数
self.face_loss = nn.MSELoss()
# Unet optimizer
self.generator_optimizer = Adam(params=self.generator.parameters(),
lr=config.learning_rate,
weight_decay=config.weight_decay)
configure(
os.path.join(
config.runs_log_path, config.log_name +
str(datetime.datetime.now()).replace(' ', '_')))
self.model_save_dir = os.path.join(
config.model_log_path,
config.log_name + str(datetime.datetime.now()).replace(' ', '_'))
if not os.path.exists(self.model_save_dir):
os.mkdir(self.model_save_dir)
def _create_dataloaders(config,
dataset_class,
tf1,
tf2,
partitions,
target_transform=None,
shuffle=False):
train_imgs_list = []
for train_partition in partitions:
if "STL10" == config.dataset:
train_imgs_curr = dataset_class(root=config.dataset_root,
transform=tf1,
split=train_partition,
target_transform=target_transform)
else:
train_imgs_curr = dataset_class(download=True,
root=config.dataset_root,
transform=tf1,
train=train_partition,
target_transform=target_transform)
if hasattr(config, "mix_train"):
if config.mix_train and (train_partition == "train+unlabeled"):
train_imgs_curr = reorder_train_deterministic(train_imgs_curr)
train_imgs_list.append(train_imgs_curr)
train_imgs = ConcatDataset(train_imgs_list)
train_dataloader = torch.utils.data.DataLoader(
train_imgs,
batch_size=int(config.dataloader_batch_sz),
shuffle=shuffle,
num_workers=6,
drop_last=False,
persistent_workers=True)
if not shuffle:
assert (isinstance(train_dataloader.sampler,
torch.utils.data.sampler.SequentialSampler))
dataloaders = [train_dataloader]
for d_i in range(config.num_dataloaders):
print("Creating auxiliary dataloader ind %d out of %d time %s" %
(d_i, config.num_dataloaders, datetime.now()))
sys.stdout.flush()
train_tf_imgs_list = []
for train_partition in partitions:
if "STL10" == config.dataset:
train_imgs_tf_curr = dataset_class(
download=True,
root=config.dataset_root,
transform=tf2, # random per call
split=train_partition,
target_transform=target_transform)
else:
train_imgs_tf_curr = dataset_class(
download=True,
root=config.dataset_root,
transform=tf2,
train=train_partition,
target_transform=target_transform)
if hasattr(config, "mix_train"):
if config.mix_train and (train_partition == "train+unlabeled"):
train_imgs_tf_curr = reorder_train_deterministic(
train_imgs_tf_curr)
train_tf_imgs_list.append(train_imgs_tf_curr)
train_imgs_tf = ConcatDataset(train_tf_imgs_list)
train_tf_dataloader = \
torch.utils.data.DataLoader(train_imgs_tf,
batch_size=int(config.dataloader_batch_sz),
shuffle=shuffle,
num_workers=6,
drop_last=False, persistent_workers=True)
if not shuffle:
assert (isinstance(train_tf_dataloader.sampler,
torch.utils.data.sampler.SequentialSampler))
assert (len(train_dataloader) == len(train_tf_dataloader))
dataloaders.append(train_tf_dataloader)
num_train_batches = len(dataloaders[0])
print("Length of datasets vector %d" % len(dataloaders))
print("Number of batches per epoch: %d" % num_train_batches)
sys.stdout.flush()
return dataloaders
def dataset_loader(dataset_name,
data_dir,
categories,
raw_input_dims,
split,
text_dim,
text_feat,
max_text_words,
max_expert_tokens,
vocab,
attr_vocab,
use_val=False):
dataset_classes = {"CE": CE}
if len(categories) > 1 and split == 'train':
dataset_list = []
for cat in categories:
dataset = dataset_classes[dataset_name](
data_dir=data_dir,
text_dim=text_dim,
category=cat,
raw_input_dims=raw_input_dims,
split=split,
text_feat=text_feat,
max_text_words=max_text_words,
max_expert_tokens=max_expert_tokens,
vocab=vocab,
attr_vocab=attr_vocab,
transforms=transforms.Compose([
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
]))
dataset_list.append(dataset)
if use_val:
for cat in categories:
dataset = dataset_classes[dataset_name](
data_dir=data_dir,
text_dim=text_dim,
category=cat,
raw_input_dims=raw_input_dims,
split='val',
text_feat=text_feat,
max_text_words=max_text_words,
max_expert_tokens=max_expert_tokens,
vocab=vocab,
attr_vocab=attr_vocab,
transforms=transforms.Compose([
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
]))
dataset_list.append(dataset)
dataset = ConcatDataset(dataset_list)
# elif len(categories) > 1 and (split in ['val', 'val_trg', 'test', 'test_trg']):
elif split in ['val', 'val_trg', 'test', 'test_trg']:
dataset_list = []
for cat in categories:
dataset = dataset_classes[dataset_name](
data_dir=data_dir,
text_dim=text_dim,
category=cat,
raw_input_dims=raw_input_dims,
split=split,
text_feat=text_feat,
max_text_words=max_text_words,
max_expert_tokens=max_expert_tokens,
vocab=vocab,
attr_vocab=attr_vocab,
transforms=transforms.Compose([
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
]))
dataset_list.append(dataset)
dataset = dataset_list
else:
dataset = dataset_classes[dataset_name](
data_dir=data_dir,
text_dim=text_dim,
category=categories[0],
raw_input_dims=raw_input_dims,
split=split,
text_feat=text_feat,
max_text_words=max_text_words,
max_expert_tokens=max_expert_tokens,
vocab=vocab,
attr_vocab=attr_vocab,
transforms=transforms.Compose([
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))
]))
return dataset
cancer_targets = np.ones((cancer_set.shape[0])).astype(np.int64)
not_cancer_targets = np.zeros((not_cancer_set.shape[0])).astype(np.int64)
not_cancer_dataset = TensorDataset(
torch.from_numpy(not_cancer_set.swapaxes(1, 3).swapaxes(2, 3)).float(),
torch.from_numpy(not_cancer_targets), torch.from_numpy(seg_set))
del not_cancer_set
del seg_set
cancer_dataset = TensorDataset(
torch.from_numpy(cancer_set.swapaxes(1, 3).swapaxes(2, 2)).float(),
torch.from_numpy(cancer_targets),
torch.from_numpy(np.zeros((len(cancer_set), 299, 299), dtype=np.bool)))
del cancer_set
gc.collect()
complete_dataset = ConcatDataset((not_cancer_dataset, cancer_dataset))
num_total = len(complete_dataset)
num_train = int(0.8 * num_total)
num_val = int(0.1 * num_total)
num_test = num_total - num_train - num_val
torch.manual_seed(0)
train_dataset, test_dataset, val_dataset = torch.utils.data.random_split(
complete_dataset, [num_train, num_test, num_val])
datasets = {'train': train_dataset, 'test': test_dataset, 'val': val_dataset}
dataset_sizes = {
'train': len(train_dataset),
'test': len(test_dataset),
'val': len(val_dataset)
}
dataloaders = {
x: torch.utils.data.DataLoader(datasets[x],
def build_dataset(cfg, transforms, split='train', num_tta=0):
assert split in ['train', 'valid', 'test']
dataset_config = cfg['dataset']
num_class = dataset_config['num_class']
fold = dataset_config['fold']
batch_size = dataset_config['batch_size']
num_workers = dataset_config['num_workers']
use_upsampling = dataset_config['upsampling']
is_test = split == 'test'
if split == 'test':
df = pd.read_csv(dataset_map['test'])
image_dir = dataset_map['test_images']
else:
df = pd.read_csv(dataset_map['fold'])
image_dir = dataset_map['train_images']
if dataset_config['use_original']:
if split == 'train':
df = df[df['fold'] != fold]
if use_upsampling: df = upsampling(df)
elif split == 'valid':
df = df[df['fold'] == fold]
if split == 'valid':
if not dataset_config['valid_with_both']:
if dataset_config['valid_with_large']:
df = df[df['large']]
elif dataset_config['valid_with_small']:
df = df[~df['large']]
sampler_df = [df]
dataset = BlindDataset(image_dir=image_dir,
df=df,
transforms=transforms,
num_class=num_class,
is_test=is_test,
num_tta=num_tta)
if split == 'train' and dataset_config['use_diabetic_retinopathy']:
diabetic_df = pd.read_csv(diabetic_retinopathy_map['train'],
index_col='Unnamed: 0')
del diabetic_df['Unnamed: 0.1']
if use_upsampling:
diabetic_df = upsampling(diabetic_df) # up sampling for diabetic
diabetic_dataset = BlindDataset(
image_dir=diabetic_retinopathy_map['train_images'],
df=diabetic_df,
transforms=transforms,
num_class=num_class,
is_test=is_test)
if not dataset_config['use_original']:
dataset = diabetic_dataset
sampler_df = [diabetic_df]
else:
sampler_df += [diabetic_df]
dataset = ConcatDataset([dataset, diabetic_dataset])
if split == 'train' and \
(dataset_config['use_class_ratio'] or dataset_config['use_dataset_ratio']):
sampler = get_sampler(dataset_config['use_class_ratio'],
dataset_config['use_dataset_ratio'],
dataset_config['class_ratio'],
dataset_config['dataset_ratio'], sampler_df)
else:
sampler = None
data_loader = DataLoader(
dataset,
shuffle=True if sampler is None else False,
batch_size=batch_size,
num_workers=num_workers,
sampler=sampler,
)
return data_loader
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(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " +
", ".join(MODEL_CLASSES.keys()),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list: "
+ ", ".join(ALL_MODELS),
)
parser.add_argument(
"--data_cache_name",
default=None,
type=str,
help="The name of cached data",
)
parser.add_argument(
"--language",
default=None,
type=str,
required=True,
help=
"Evaluation language. Also train language if `train_language` is set to None.",
)
parser.add_argument(
"--train_language",
default=None,
type=str,
help="Train language if is different of the evaluation language.")
parser.add_argument("--train_tasks",
default=None,
type=str,
help="Training tasks in together finetuning.")
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()),
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written.",
)
# Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--gpu_id", default="", type=str, help="GPU id")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3",
)
parser.add_argument(
"--task_ratio",
default=1.0,
type=float,
help="ratio of tasks between 0-1",
)
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.",
)
parser.add_argument("--do_train",
action="store_true",
help="Whether to run training.")
parser.add_argument("--do_eval",
action="store_true",
help="Whether to run eval on the test set.")
parser.add_argument(
"--evaluate_during_training",
action="store_true",
help="Rul evaluation during training at each logging step.")
parser.add_argument(
"--do_lower_case",
action="store_true",
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
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("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay 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("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument(
"--break_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps",
type=int,
default=50,
help="Log every X updates steps.")
parser.add_argument("--logging_each_epoch",
action="store_true",
help="Whether to log after each epoch.")
parser.add_argument("--logging_steps_in_sample",
type=int,
default=-1,
help="log every X samples.")
parser.add_argument("--save_steps",
type=int,
default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda",
action="store_true",
help="Avoid using CUDA when available")
parser.add_argument("--overwrite_output_dir",
action="store_true",
help="Overwrite the content of the output directory")
parser.add_argument(
"--overwrite_cache",
action="store_true",
help="Overwrite the cached training and evaluation sets")
parser.add_argument("--seed",
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
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("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument("--server_ip",
type=str,
default="",
help="For distant debugging.")
parser.add_argument("--server_port",
type=str,
default="",
help="For distant debugging.")
args = parser.parse_args()
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 distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_id
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")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
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 if args.local_rank in [-1, 0] else logging.WARN,
)
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,
)
# preprocess args
if args.train_language is None or args.train_language == "all":
args.train_language = args.language
assert not (args.logging_steps != -1 and args.logging_steps_in_sample != -1
), "these two parameters can't both be setted"
if args.logging_steps == -1 and args.logging_steps_in_sample != -1:
total_batch_size = args.n_gpu * args.per_gpu_train_batch_size * args.gradient_accumulation_steps
args.logging_steps = args.logging_steps_in_sample // total_batch_size
# Set seed
set_seed(args)
# Prepare XNLI task
# args.task_name = "xnli"
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
num_labels = []
for task_name in args.train_tasks.split(","):
processor = processors[task_name](language=TASK_LANGS[task_name],
train_language=args.train_language)
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels.append(len(label_list))
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
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,
cache_dir=args.cache_dir if args.cache_dir else None,
)
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,
cache_dir=args.cache_dir if args.cache_dir else None,
)
model = model_class.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
task_dataset_list = []
train_tasks = args.train_tasks.split(",")
for task_name in train_tasks:
train_langs = args.train_language.split(',')
dataset_list = []
for lang in train_langs:
lg_train_dataset, guids = load_and_cache_examples(
args, task_name, tokenizer, lang, split="train")
dataset_list.append(lg_train_dataset)
train_dataset = ConcatDataset(dataset_list)
task_dataset_list.append(train_dataset)
# train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, args.train_language, split="train")
global_step, tr_loss = train(args, task_dataset_list, 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 and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = (model.module if hasattr(model, "module") else model
) # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
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)
model.to(args.device)
# Evaluation
results = {}
return results
def test_concat_two_singletons(self):
result = ConcatDataset([[0], [1]])
self.assertEqual(2, len(result))
self.assertEqual(0, result[0])
self.assertEqual(1, result[1])
