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Use buffers #4

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67398a8
Use buffers
kashif May 22, 2020
5fa71aa
fix typo
kashif May 22, 2020
f8e7dc8
get rid of the pesky dtypes
kashif May 22, 2020
76a665e
no need for instance variable
kashif May 23, 2020
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34 changes: 14 additions & 20 deletions models/nbeats.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,13 +94,12 @@ class TrendBasis(t.nn.Module):
def __init__(self, degree_of_polynomial: int, backcast_size: int, forecast_size: int):
super().__init__()
self.polynomial_size = degree_of_polynomial + 1 # degree of polynomial with constant term
self.backcast_time = t.nn.Parameter(
t.tensor(np.concatenate([np.power(np.arange(backcast_size, dtype=np.float) / backcast_size, i)[None, :]
for i in range(self.polynomial_size)]), dtype=t.float32),
requires_grad=False)
self.forecast_time = t.nn.Parameter(
t.tensor(np.concatenate([np.power(np.arange(forecast_size, dtype=np.float) / forecast_size, i)[None, :]
for i in range(self.polynomial_size)]), dtype=t.float32), requires_grad=False)
self.register_buffer('backcast_time',
t.Tensor(np.concatenate([np.power(np.arange(backcast_size) / backcast_size, i)[None, :]
for i in range(self.polynomial_size)])))
self.register_buffer('forecast_time',
t.Tensor(np.concatenate([np.power(np.arange(forecast_size) / forecast_size, i)[None, :]
for i in range(self.polynomial_size)])))

def forward(self, theta: t.Tensor):
backcast = t.einsum('bp,pt->bt', theta[:, self.polynomial_size:], self.backcast_time)
Expand All @@ -114,21 +113,16 @@ class SeasonalityBasis(t.nn.Module):
"""
def __init__(self, harmonics: int, backcast_size: int, forecast_size: int):
super().__init__()
self.frequency = np.append(np.zeros(1, dtype=np.float32),
np.arange(harmonics, harmonics / 2 * forecast_size,
dtype=np.float32) / harmonics)[None, :]
frequency = np.append(np.zeros(1),
np.arange(harmonics, harmonics / 2 * forecast_size) / harmonics)[None, :]
backcast_grid = -2 * np.pi * (
np.arange(backcast_size, dtype=np.float32)[:, None] / forecast_size) * self.frequency
np.arange(backcast_size)[:, None] / forecast_size) * frequency
forecast_grid = 2 * np.pi * (
np.arange(forecast_size, dtype=np.float32)[:, None] / forecast_size) * self.frequency
self.backcast_cos_template = t.nn.Parameter(t.tensor(np.transpose(np.cos(backcast_grid)), dtype=t.float32),
requires_grad=False)
self.backcast_sin_template = t.nn.Parameter(t.tensor(np.transpose(np.sin(backcast_grid)), dtype=t.float32),
requires_grad=False)
self.forecast_cos_template = t.nn.Parameter(t.tensor(np.transpose(np.cos(forecast_grid)), dtype=t.float32),
requires_grad=False)
self.forecast_sin_template = t.nn.Parameter(t.tensor(np.transpose(np.sin(forecast_grid)), dtype=t.float32),
requires_grad=False)
np.arange(forecast_size)[:, None] / forecast_size) * frequency
self.register_buffer('backcast_cos_template', t.Tensor(np.transpose(np.cos(backcast_grid))))
self.register_buffer('backcast_sin_template', t.Tensor(np.transpose(np.sin(backcast_grid))))
self.register_buffer('forecast_cos_template', t.Tensor(np.transpose(np.cos(forecast_grid))))
self.register_buffer('forecast_sin_template', t.Tensor(np.transpose(np.sin(forecast_grid))))

def forward(self, theta: t.Tensor):
params_per_harmonic = theta.shape[1] // 4
Expand Down