def test_T(HalfTensorMock):
tensor = torch.ones([1, 2])
assert core.T(tensor) is tensor
array = np.arange(0, 5)
assert core.T(array.astype(np.int)).type() == "torch.LongTensor"
assert core.T(array.astype(np.float)).type() == "torch.FloatTensor"
core.T(array.astype(np.float), half=True)
HalfTensorMock.assert_called_once()
with pytest.raises(NotImplementedError):
assert core.T(array.astype(np.object))
def test_T():
tensor = torch.ones([1, 2])
np.testing.assert_equal(core.to_np(core.T(tensor)), [[1, 1]])
array = np.arange(0, 5)
assert core.T(array.astype(np.int)).type() == "torch.LongTensor"
assert core.T(array.astype(np.float)).type() == "torch.FloatTensor"
with mock.patch("fastai.core.to_half") as to_half_mock:
core.T(array.astype(np.float), half=True)
to_half_mock.assert_called_once()
with pytest.raises(NotImplementedError):
assert core.T(array.astype(np.object))
def test_create_variable_passing_Variable_object():
v = torch.autograd.Variable(core.T(np.arange(0, 3)))
cv = core.create_variable(v, volatile=True)
if core.IS_TORCH_04:
assert (cv == v).all()
else:
assert cv is v
def test_to_np():
array = np.arange(0, 3).astype(np.float)
assert core.to_np(array) is array
tensor = core.T(array)
result = core.to_np([tensor, tensor])
np.testing.assert_equal(result[0], array)
np.testing.assert_equal(result[1], array)
variable = core.V(array)
np.testing.assert_equal(core.to_np(variable), array)
with mock.patch("torch.cuda") as cuda_mock:
tensor_long = core.T(array.astype(np.int))
cuda_mock.is_available(return_value=True)
cuda_mock.HalfTensor = torch.LongTensor
array = core.to_np(tensor_long)
np.testing.assert_equal(array, [0., 1., 2.])
assert array.dtype in (np.float32, np.float64)
def test_to_np(): array = np.arange(0, 3).astype(np.float) assert core.to_np(array) is array tensor = core.T(array) result = core.to_np([tensor, tensor]) np.testing.assert_equal(result[0], array) np.testing.assert_equal(result[1], array) variable = core.V(array) np.testing.assert_equal(core.to_np(variable), array)
def test_to_np():
array = np.arange(0, 3).astype(np.float)
assert core.to_np(array) is array
tensor = core.T(array)
result = core.to_np([tensor, tensor])
np.testing.assert_equal(result[0], array)
np.testing.assert_equal(result[1], array)
variable = core.V(array)
np.testing.assert_equal(core.to_np(variable), array)
with mock.patch("torch.cuda.is_available") as is_available_mock:
with mock.patch("fastai.core.is_half_tensor") as is_half_tensor_mock:
is_available_mock.return_value = True
is_half_tensor_mock.return_value = True
tensor = core.T(array.astype(np.int))
array = core.to_np(tensor)
np.testing.assert_equal(array, [0., 1., 2.])
assert array.dtype in (np.float32, np.float64)
def test_create_variable_passing_Variable_object():
v = torch.autograd.Variable(core.T(np.arange(0, 3)))
assert core.create_variable(v, volatile=True) is v
def run(constant_overwrites):
dir_path = os.path.dirname(os.path.realpath(__file__))
config_path = os.path.join(dir_path, 'hyperparams.yml')
constants = merge_dict(load_hyperparams(config_path), constant_overwrites)
# data subdir expected to exist
LM_PATH.mkdir(exist_ok=True)
(LM_PATH / 'tmp').mkdir(exist_ok=True)
CLAS_PATH.mkdir(exist_ok=True)
(CLAS_PATH / 'tmp').mkdir(exist_ok=True)
data_path = dir_path + '/train.csv'
if not os.path.exists(data_path):
train_df, val_df, test_df, x_train, y_train, x_val, y_val, x_test, y_test, classes = preprocess_csv(
)
else:
train_df = pd.read_csv(dir_path + '/train.csv',
header=None,
chunksize=CHUNKSIZE)
# x_train, y_train = train_df[0].values, train_df[1].values
val_df = pd.read_csv(dir_path + '/val.csv',
header=None,
chunksize=CHUNKSIZE)
# x_val, y_val = val_df[0].values, val_df[1].values
# test_df = pd.read_csv(dir_path + '/test.csv', header=None, chunksize=CHUNKSIZE)
# x_test, y_test = test_df[0].values, test_df[1].values
# classes = np.genfromtxt(dir_path + '/classes.txt', dtype=str)
# print('Counts x_train: {}, y_train: {}, x_val: {}, y_val: {}, x_test: {}, y_test: {}, classes: {}'
# .format(len(x_train), len(y_train), len(x_val), len(y_val), len(x_test), len(y_test), len(classes)))
if constants['train_lm']:
logging.info('Training LM...')
if (LM_PATH / 'tmp' / 'tok_train.npy').exists():
logging.info('Loading tokens...')
tok_train = np.load(LM_PATH / 'tmp' / 'tok_train.npy')
tok_val = np.load(LM_PATH / 'tmp' / 'tok_val.npy')
else:
logging.info('Get tokens...')
tok_train, labels_train = get_all(train_df, 1)
tok_val, labels_val = get_all(val_df, 1)
np.save(LM_PATH / 'tmp' / 'tok_train.npy', tok_train)
np.save(LM_PATH / 'tmp' / 'tok_val.npy', tok_val)
if (LM_PATH / 'tmp' / 'itos.pkl').exists():
train_ids = np.load(LM_PATH / 'tmp' / 'train_ids.npy')
val_ids = np.load(LM_PATH / 'tmp' / 'val_ids.npy')
itos = pickle.load(open(LM_PATH / 'tmp' / 'itos.pkl', 'rb'))
else:
freq = collections.Counter(t for ts in tok_train for t in ts)
itos = [t for t, k in freq.most_common(MAX_VOCAB)
if k > MIN_FREQ] # int idx to str token
itos.insert(0, '_pad_')
itos.insert(1, '_unk_')
stoi = collections.defaultdict(
lambda: 0,
{t: i
for i, t in enumerate(itos)}) # str token to int idx
train_ids = np.array([[stoi[t] for t in ts] for ts in tok_train])
val_ids = np.array([[stoi[t] for t in ts] for ts in tok_val])
np.save(LM_PATH / 'tmp' / 'train_ids.npy', train_ids)
np.save(LM_PATH / 'tmp' / 'val_ids.npy', val_ids)
pickle.dump(itos, open(LM_PATH / 'tmp' / 'itos.pkl', 'wb'))
vocab_size = len(itos)
emb_dim, n_hidden, n_layers = 400, 1150, 3
pre_path = PATH / 'models' / 'wt103'
pre_lm_path = pre_path / 'fwd_wt103.h5'
w = torch.load(pre_lm_path, map_location=lambda storage, loc: storage)
enc_w = fastai.to_np(w['0.encoder.weight'])
row_mean = enc_w.mean(0)
itos_model = pickle.load((pre_path / 'itos_wt103.pkl').open('rb'))
stoi_model = collections.defaultdict(
lambda: -1, {t: i
for i, t in enumerate(itos_model)})
new_w = np.zeros((vocab_size, emb_dim), dtype=np.float32)
for i, t in enumerate(itos):
j = stoi_model[t]
new_w[i] = enc_w[j] if j >= 0 else row_mean
w['0.encoder.weight'] = fastai.T(new_w)
w['0.encoder_with_dropout.embed.weight'] = fastai.T(np.copy(new_w))
w['1.decoder.weight'] = fastai.T(np.copy(new_w))
wd = 1e-7 # weight decay
bptt = 70 # backpropagation through time, a.k.a. ngrams
batch_size = 52
optimizer_fn = functools.partial(torch.optim.Adam, betas=(0.8, 0.99))
dl_train = ftext.LanguageModelLoader(np.concatenate(train_ids),
batch_size, bptt) # data loader
dl_val = ftext.LanguageModelLoader(np.concatenate(val_ids), batch_size,
bptt)
md = ftext.LanguageModelData(PATH,
1,
vocab_size,
dl_train,
dl_val,
batch_size=batch_size,
bptt=bptt)
drops = np.array([0.25, 0.1, 0.2, 0.02, 0.15]) * 0.7
learner = md.get_model(optimizer_fn,
emb_dim,
n_hidden,
n_layers,
dropouti=drops[0],
dropout=drops[1],
wdrop=drops[2],
dropoute=drops[3],
dropouth=drops[4])
learner.metrics = [fmetrics.accuracy]
learner.freeze_to(-1)
learner.model.load_state_dict(w)
lr = 1e-3
lrs = lr
learner.fit(lrs / 2, 1, wds=wd, use_clr=(32, 2), cycle_len=1)
learner.save('lm_last_ft')
learner.lr_find(start_lr=lrs / 10, end_lr=lrs * 10, linear=True)
learner.sched.plot()
learner.fit(lrs, 1, wds=wd, use_clr=(20, 10), cycle_len=15)
learner.save('lm1')
learner.save_encoder('lm1_enc')
learner.sched.plot_loss()
if (CLAS_PATH / 'tmp' / 'tok_train.npy').exists():
tok_train = np.load(CLAS_PATH / 'tmp' / 'tok_train.npy')
tok_val = np.load(CLAS_PATH / 'tmp' / 'tok_val.npy')
labels_train = np.load(CLAS_PATH / 'tmp' / 'labels_train.npy')
labels_val = np.load(CLAS_PATH / 'tmp' / 'labels_val.npy')
else:
tok_train, labels_train = get_all(train_df, 1)
tok_val, labels_val = get_all(val_df, 1)
np.save(CLAS_PATH / 'tmp' / 'tok_train.npy', tok_train)
np.save(CLAS_PATH / 'tmp' / 'tok_val.npy', tok_val)
np.save(CLAS_PATH / 'tmp' / 'labels_train.npy', labels_train)
np.save(CLAS_PATH / 'tmp' / 'labels_val.npy', labels_val)
if (CLAS_PATH / 'tmp' / 'itos.pkl').exists():
train_ids = np.load(CLAS_PATH / 'tmp' / 'train_ids.npy')
val_ids = np.load(CLAS_PATH / 'tmp' / 'val_ids.npy')
itos = pickle.load(open(CLAS_PATH / 'tmp' / 'itos.pkl', 'rb'))
else:
freq = collections.Counter(t for ts in tok_train for t in ts)
itos = [t for t, k in freq.most_common(MAX_VOCAB)
if k > MIN_FREQ] # int idx to str token
itos.insert(0, '_pad_')
itos.insert(1, '_unk_')
stoi = collections.defaultdict(
lambda: 0, {t: i
for i, t in enumerate(itos)}) # str token to int idx
train_ids = np.array([[stoi[t] for t in ts] for ts in tok_train])
val_ids = np.array([[stoi[t] for t in ts] for ts in tok_val])
np.save(CLAS_PATH / 'tmp' / 'train_ids.npy', train_ids)
np.save(CLAS_PATH / 'tmp' / 'val_ids.npy', val_ids)
pickle.dump(itos, open(CLAS_PATH / 'tmp' / 'itos.pkl', 'wb'))
vocab_size = len(itos)
bptt = 70 # backpropagation through time, a.k.a. ngrams
emb_dim, n_hidden, n_layers = 400, 1150, 3
# optimizer_fn = functools.partial(optim.Adam, betas=(0.8, 0.99))
batch_size = 48
min_label = min(labels_train)
labels_train -= min_label
labels_val -= min_label
k = int(max(labels_train)) + 1
ds_train = ftext.TextDataset(train_ids, labels_train)
ds_val = ftext.TextDataset(val_ids, labels_val)
sampler_train = ftext.SortishSampler(train_ids,
key=lambda x: len(train_ids[x]),
bs=batch_size // 2)
sampler_val = ftext.SortSampler(val_ids, key=lambda x: len(val_ids[x]))
dl_train = dataloader.DataLoader(ds_train,
batch_size // 2,
transpose=True,
num_workers=1,
pad_idx=1,
sampler=sampler_train)
dl_val = dataloader.DataLoader(ds_val,
batch_size // 2,
transpose=True,
num_workers=1,
pad_idx=1,
sampler=sampler_val)
md = fdata.ModelData(PATH, dl_train, dl_val)
# drops = np.array([0.4, 0.5, 0.05, 0.3, 0.1])
drops = np.array([0.4, 0.5, 0.05, 0.3, 0.4]) * 0.5
model = lm_rnn.get_rnn_classifier(bptt,
20 * 70,
k,
vocab_size,
emb_sz=emb_dim,
n_hid=n_hidden,
n_layers=n_layers,
pad_token=1,
layers=[emb_dim * 3, 50, k],
drops=[drops[4], 0.1],
dropouti=drops[0],
wdrop=drops[1],
dropoute=drops[2],
dropouth=drops[3])
optimizer_fn = functools.partial(torch.optim.Adam, betas=(0.7, 0.99))
# learner = RNN_Learner(md, TextModel(to_gpu(model)), opt_fn=optimizer_fn)
learner = ftext.RNN_Learner(md,
ftext.TextModel(model),
opt_fn=optimizer_fn)
learner.reg_fn = functools.partial(lm_rnn.seq2seq_reg, alpha=2, beta=1)
learner.clip = 25.0
learner.metrics = [fmetrics.accuracy]
# lr = 3e-3
# lrm = 2.6
# lrs = np.array([lr / lrm**4, lr / lrm**3, lr / lrm**2, lr / lrm, lr])
lrs = np.array([1e-4, 1e-4, 1e-4, 1e-3, 1e-2])
# wd = 1e-7 # weight decay
wd = 0
learner.load_encoder('lm1_enc')
learner.freeze_to(-1)
learner.lr_find(lrs / 1000)
learner.sched.plot()
learner.fit(lrs, 1, wds=wd, cycle_len=1, use_clr=(8, 3))
learner.save('clas_0')
