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This commit is contained in:
Dr. Kashif Rasul
2020-02-03 16:20:01 +01:00
parent f0b4fc988d
commit 8566973f8f
1 changed files with 2 additions and 53 deletions
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pts/model/transformer_tempflow/transformer_tempflow_network.py
+2 -53
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@@ -198,18 +198,13 @@ class TransformerTempFlowTrainingNetwork(nn.Module):
)
if future_time_feat is None or future_target_cdf is None:
time_feat = past_time_feat[
:, - self.context_length :, ...
]
time_feat = past_time_feat[:, -self.context_length :, ...]
sequence = past_target_cdf
sequence_length = self.history_length
subsequences_length = self.context_length
else:
time_feat = torch.cat(
(
past_time_feat[:, - self.context_length :, ...],
future_time_feat,
),
(past_time_feat[:, -self.context_length :, ...], future_time_feat,),
dim=1,
)
sequence = torch.cat((past_target_cdf, future_target_cdf), dim=1)
@@ -257,16 +252,6 @@ class TransformerTempFlowTrainingNetwork(nn.Module):
inputs = torch.cat((input_lags, repeated_index_embeddings, time_feat), dim=-1)
return inputs, scale, index_embeddings
# outputs, states, lags_scaled, inputs = self.unroll(
# lags=lags,
# scale=scale,
# time_feat=time_feat,
# target_dimension_indicator=target_dimension_indicator,
# unroll_length=subsequences_length,
# begin_state=None,
# )
# return outputs, states, scale, lags_scaled, inputs
def distr_args(self, decoder_output: torch.Tensor):
"""
@@ -350,15 +335,6 @@ class TransformerTempFlowTrainingNetwork(nn.Module):
# unroll the decoder in "training mode", i.e. by providing future data
# as well
# rnn_outputs, _, scale, _, _ = self.unroll_encoder(
# past_time_feat=past_time_feat,
# past_target_cdf=past_target_cdf,
# past_observed_values=past_observed_values,
# past_is_pad=past_is_pad,
# future_time_feat=future_time_feat,
# future_target_cdf=future_target_cdf,
# target_dimension_indicator=target_dimension_indicator,
# )
inputs, scale, _ = self.create_network_input(
past_time_feat=past_time_feat,
past_target_cdf=past_target_cdf,
@@ -382,14 +358,6 @@ class TransformerTempFlowTrainingNetwork(nn.Module):
tgt_mask=self.tgt_mask,
)
# # put together target sequence
# # (batch_size, seq_len, target_dim)
# target = torch.cat(
# (past_target_cdf[:, -self.context_length :, ...], future_target_cdf), dim=1,
# )
# # assert_shape(target, (-1, seq_len, self.target_dim))
if self.scaling:
self.flow.scale = scale
@@ -532,15 +500,6 @@ class TransformerTempFlowPredictionNetwork(TransformerTempFlowTrainingNetwork):
self.decoder_input(dec_input).permute(1, 0, 2), repeated_enc_out
)
# rnn_outputs, repeated_states, _, _ = self.unroll(
# begin_state=repeated_states,
# lags=lags,
# scale=repeated_scale,
# time_feat=repeated_time_feat[:, k : k + 1, ...],
# target_dimension_indicator=repeated_target_dimension_indicator,
# unroll_length=1,
# )
distr_args = self.distr_args(decoder_output=dec_output.permute(1, 0, 2))
# (batch_size, 1, target_dim)
@@ -614,16 +573,6 @@ class TransformerTempFlowPredictionNetwork(TransformerTempFlowTrainingNetwork):
future_target_cdf=None,
target_dimension_indicator=target_dimension_indicator,
)
# unroll the decoder in "prediction mode", i.e. with past data only
# _, begin_states, scale, _, _ = self.unroll_encoder(
# past_time_feat=past_time_feat,
# past_target_cdf=past_target_cdf,
# past_observed_values=past_observed_values,
# past_is_pad=past_is_pad,
# future_time_feat=None,
# future_target_cdf=None,
# target_dimension_indicator=target_dimension_indicator,
# )
enc_out = self.transformer.encoder(self.encoder_input(inputs).permute(1, 0, 2))