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test for forward

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This commit is contained in:
Dr. Kashif Rasul
2019-11-02 16:09:10 +01:00
parent 1bd9480d86
commit 7d5e808a6c
2 changed files with 130 additions and 2 deletions
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pts/modules/feature.py
+2 -2
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@@ -71,11 +71,11 @@ class FeatureAssembler(nn.Module):
def process_static_cat(self, feature: torch.Tensor) -> torch.Tensor:
if self.embeddings['embed_static'] is not None:
feature = self.embeddings['embed_static'](feature)
return feature.unsqueeze(1).expand(-1, self.T, -1)
return feature.unsqueeze(1).expand(-1, self.T, -1).float()
def process_dynamic_cat(self, feature: torch.Tensor) -> torch.Tensor:
if self.embeddings['embed_dynamic'] is None:
return feature
return feature.float()
else:
return self.embeddings['embed_dynamic'](feature)
test/modules/test_feature.py
+128
View File
@@ -4,6 +4,7 @@ from itertools import chain, combinations
import torch
import torch.nn as nn
from torch.distributions import Uniform
from pts.modules import FeatureEmbedder, FeatureAssembler
@@ -139,4 +140,131 @@ def test_feature_assembler(config):
act_params_len = len([p for p in assemble_feature.parameters()])
assert exp_params_len == act_params_len
def test_forward_pass():
N, T = config["N"], config["T"]
inp_features = []
out_features = []
if "static_cat" not in enabled_features:
inp_features.append(torch.zeros((N, 1)))
out_features.append(torch.zeros((N, T, 1)))
elif embed_static: # and 'static_cat' in enabled_features
C = config["static_cat"]["C"]
inp_features.append(
torch.cat(
[
torch.randint(
0,
config["embed_static"]["cardinalities"][c],
(N, 1),
)
for c in range(C)
],
dim=1,
)
)
out_features.append(
torch.ones(
(
N,
T,
sum(config["embed_static"]["embedding_dims"]),
)
)
)
else: # not embed_static and 'static_cat' in enabled_features
C = config["static_cat"]["C"]
inp_features.append(
torch.cat(
[
torch.randint(
0,
config["embed_static"]["cardinalities"][c],
(N, 1),
)
for c in range(C)
],
dim=1,
)
)
out_features.append(
inp_features[-1].unsqueeze(1).expand(-1, T, -1).float()
)
if "static_real" not in enabled_features:
inp_features.append(torch.zeros((N, 1)))
out_features.append(torch.zeros((N, T, 1)))
else:
C = config["static_real"]["C"]
static_real = torch.empty((N,C)).uniform_(0,100)
inp_features.append(static_real)
out_features.append(
static_real.unsqueeze(-2).expand(-1, T, -1)
)
if "dynamic_cat" not in enabled_features:
inp_features.append(torch.zeros((N, T, 1)))
out_features.append(torch.zeros((N, T, 1)))
elif embed_dynamic: # and 'static_cat' in enabled_features
C = config["dynamic_cat"]["C"]
inp_features.append(
torch.cat(
[
torch.randint(
0,
config["embed_dynamic"]["cardinalities"][
c
],
(N, T, 1),
)
for c in range(C)
],
dim=2,
)
)
out_features.append(
torch.ones(
(
N,
T,
sum(config["embed_dynamic"]["embedding_dims"]),
)
)
)
else: # not embed_dynamic and 'dynamic_cat' in enabled_features
C = config["dynamic_cat"]["C"]
inp_features.append(
torch.cat(
[
torch.randint(
0,
config["embed_dynamic"]["cardinalities"][
c
],
(N, T, 1),
)
for c in range(C)
],
dim=2,
)
)
out_features.append(inp_features[-1].float())
if "dynamic_real" not in enabled_features:
inp_features.append(torch.zeros((N, T, 1)))
out_features.append(torch.zeros((N, T, 1)))
else:
C = config["dynamic_real"]["C"]
dynamic_real = torch.empty((N, T, C)).uniform_(0,100)
inp_features.append(dynamic_real)
out_features.append(dynamic_real)
act_output = assemble_feature(*inp_features)
exp_output = torch.cat(out_features, dim=2)
assert exp_output.shape == act_output.shape
assert torch.sum(exp_output - act_output) < 1e-20
test_parameters_length()
test_forward_pass()