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seq_len and other fixes

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wassname
2022-11-23 12:02:22 +08:00
parent fc1a605b01
commit cb27d2ded3
10 changed files with 488 additions and 374 deletions
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models/modules/metareghead.py
+17 -10
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@@ -7,9 +7,16 @@ from models.modules.regressors import RidgeRegressor
from models.modules.inr import INR, INRLayer
class SumHead(nn.Module):
def __init__(self, d, c_out=1, ):
def __init__(self, d, c_out=1, dropout=0):
super().__init__()
self.l = nn.Linear(d, c_out) # init a random transform
# self.conv = nn.Sequential(
# CausalConv1d(head_nf, c_out, 1, bias=False, norm="Spectral"),
# )
self.l = nn.Sequential(
INRLayer(d, d, dropout=dropout),
# INRLayer(d, d, dropout=dropout),
nn.Linear(d, c_out)
) # nn.Linear(d, c_out) # init a random transform
def forward(self, query, support, support_labels):
return self.l(query)
@@ -27,7 +34,7 @@ class TransformerHead(nn.Module):
INRLayer(c_out, hidden_dim, dropout=0),
nn.Linear(hidden_dim, hidden_dim)
)
self.l = nn.MultiheadAttention(embed_dim=d, num_heads=num_heads, batch_first=True, kdim=d, vdim=hidden_dim, add_bias_kv=True, bias=True)
self.l = nn.MultiheadAttention(embed_dim=d, num_heads=num_heads, batch_first=True, kdim=d, vdim=hidden_dim, add_bias_kv=True, bias=True, dropout=0)
# after using attention let's decode it
self.decoder = nn.Sequential(
INRLayer(d, d, dropout=dropout),
@@ -40,12 +47,12 @@ class TransformerHead(nn.Module):
returns the classification score on the query set.
Parameters:
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
lambda_reg: a scalar. Represents the strength of L2 regularization.
query: a (tasks_per_batch, n_query, d) Tensor.
support: a (tasks_per_batch, n_support, d) Tensor.
support_labels: a (tasks_per_batch, n_support) Tensor.
n_way: a scalar. Represents the number of classes in a few-shot classification task.
n_shot: a scalar. Represents the number of support examples given per class.
lambda_reg: a scalar. Represents the strength of L2 regularization.
Returns: a (tasks_per_batch, n_query, n_way) Tensor.
"""
# should be (batch, seq, feature)
@@ -62,7 +69,7 @@ class RegressionHead(nn.Module):
# the regular DeepTime one
self.head = RidgeRegressor()
elif ("None" in base_learner):
self.head = SumHead(d=d)
self.head = SumHead(d=d, dropout=dropout)
elif ("Transformer" in base_learner):
self.head = TransformerHead(d=d, dropout=dropout, num_heads=num_heads)
else: