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Embed.py
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Embed.py
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import paddle
import math
def compared_version(ver1, ver2):
"""
:param ver1
:param ver2
:return: ver1< = >ver2 False/True
"""
list1 = str(ver1).split('.')
list2 = str(ver2).split('.')
for i in (range(len(list1)) if len(list1) < len(list2) else range(len(
list2))):
if int(list1[i]) == int(list2[i]):
pass
elif int(list1[i]) < int(list2[i]):
return -1
else:
return 1
if len(list1) == len(list2):
return True
elif len(list1) < len(list2):
return False
else:
return True
class PositionalEmbedding(paddle.nn.Layer):
def __init__(self, d_model, max_len=5000):
super(PositionalEmbedding, self).__init__()
pe = paddle.zeros(shape=[max_len, d_model]).astype(dtype='float32')
pe.require_grad = False
position = paddle.arange(start=0, end=max_len).astype(dtype='float32'
).unsqueeze(axis=1)
div_term = (paddle.arange(start=0, end=d_model, step=2).astype(
dtype='float32') * -(math.log(10000.0) / d_model)).exp()
pe[:, 0::2] = paddle.sin(x=position * div_term)
pe[:, 1::2] = paddle.cos(x=position * div_term)
pe = pe.unsqueeze(axis=0)
self.register_buffer(name='pe', tensor=pe)
def forward(self, x):
return self.pe[:, :x.shape[1]]
class TokenEmbedding(paddle.nn.Layer):
def __init__(self, c_in, d_model):
super(TokenEmbedding, self).__init__()
padding = 1 if compared_version(paddle.__version__, '1.5.0') else 2
self.tokenConv = paddle.nn.Conv1D(in_channels=c_in, out_channels=
d_model, kernel_size=3, padding=padding, padding_mode=
'circular', bias_attr=False)
for m in self.sublayers():
if isinstance(m, paddle.nn.Conv1D):
init_KaimingNormal = paddle.nn.initializer.KaimingNormal(
nonlinearity='leaky_relu')
init_KaimingNormal(m.weight)
def forward(self, x):
x = self.tokenConv(x.transpose(perm=[0, 2, 1]))
perm_13 = list(range(x.ndim))
perm_13[1] = 2
perm_13[2] = 1
x = x.transpose(perm=perm_13)
return x
class FixedEmbedding(paddle.nn.Layer):
def __init__(self, c_in, d_model):
super(FixedEmbedding, self).__init__()
w = paddle.zeros(shape=[c_in, d_model]).astype(dtype='float32')
w.require_grad = False
position = paddle.arange(start=0, end=c_in).astype(dtype='float32'
).unsqueeze(axis=1)
div_term = (paddle.arange(start=0, end=d_model, step=2).astype(
dtype='float32') * -(math.log(10000.0) / d_model)).exp()
w[:, 0::2] = paddle.sin(x=position * div_term)
w[:, 1::2] = paddle.cos(x=position * div_term)
self.emb = paddle.nn.Embedding(num_embeddings=c_in, embedding_dim=
d_model)
out_3 = paddle.create_parameter(shape=w.shape, dtype=w.numpy().
dtype, default_initializer=paddle.nn.initializer.Assign(w))
out_3.stop_gradient = not False
self.emb.weight = out_3
def forward(self, x):
return self.emb(x).detach()
class TemporalEmbedding(paddle.nn.Layer):
def __init__(self, d_model, embed_type='fixed', freq='h'):
super(TemporalEmbedding, self).__init__()
minute_size = 4
hour_size = 24
weeknum_size = 53
weekday_size = 7
day_size = 32
month_size = 13
Embed = (FixedEmbedding if embed_type == 'fixed' else paddle.nn.
Embedding)
if freq == 't':
self.minute_embed = Embed(minute_size, d_model)
self.hour_embed = Embed(hour_size, d_model)
self.weekday_embed = Embed(weekday_size, d_model)
self.weeknum_embed = Embed(weeknum_size, d_model)
self.day_embed = Embed(day_size, d_model)
self.month_embed = Embed(month_size, d_model)
self.Temporal_feature = ['month', 'day', 'week', 'weekday', 'hour']
def forward(self, x):
x = x.astype(dtype='int64')
for idx, freq in enumerate(self.Temporal_feature):
if freq == 'year':
pass
elif freq == 'month':
month_x = self.month_embed(x[:, :, idx])
elif freq == 'day':
day_x = self.day_embed(x[:, :, idx])
elif freq == 'week':
weeknum_x = self.weeknum_embed(x[:, :, idx])
elif freq == 'weekday':
weekday_x = self.weekday_embed(x[:, :, idx])
elif freq == 'hour':
hour_x = self.hour_embed(x[:, :, idx])
return hour_x + weekday_x + weeknum_x + day_x + month_x
class TimeFeatureEmbedding(paddle.nn.Layer):
def __init__(self, d_model, embed_type='timeF', freq='h'):
super(TimeFeatureEmbedding, self).__init__()
freq_map = {'h': 4, 't': 5, 's': 6, 'm': 1, 'a': 1, 'w': 2, 'd': 3,
'b': 3}
d_inp = freq_map[freq]
self.embed = paddle.nn.Linear(in_features=d_inp, out_features=
d_model, bias_attr=False)
def forward(self, x):
return self.embed(x)
class DataEmbedding(paddle.nn.Layer):
def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1
):
super(DataEmbedding, self).__init__()
self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
self.position_embedding = PositionalEmbedding(d_model=d_model)
self.temporal_embedding = TemporalEmbedding(d_model=d_model,
embed_type=embed_type, freq=freq
) if embed_type != 'timeF' else TimeFeatureEmbedding(d_model=
d_model, embed_type=embed_type, freq=freq)
self.dropout = paddle.nn.Dropout(p=dropout)
def forward(self, x, x_mark):
x = self.value_embedding(x) + self.temporal_embedding(x_mark
) + self.position_embedding(x)
return self.dropout(x)
class DataEmbedding_wo_pos(paddle.nn.Layer):
def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1
):
super(DataEmbedding_wo_pos, self).__init__()
self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model)
self.position_embedding = PositionalEmbedding(d_model=d_model)
self.temporal_embedding = TemporalEmbedding(d_model=d_model,
embed_type=embed_type, freq=freq
) if embed_type != 'timeF' else TimeFeatureEmbedding(d_model=
d_model, embed_type=embed_type, freq=freq)
self.dropout = paddle.nn.Dropout(p=dropout)
def forward(self, x, x_mark):
x = self.value_embedding(x) + self.temporal_embedding(x_mark)
return self.dropout(x)