mirror of
https://github.com/wassname/pytorch-ts.git
synced 2026-07-10 22:34:02 +08:00
renamed flag to dequantize
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@@ -55,7 +55,7 @@ class TempFlowEstimator(PTSEstimator):
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hidden_size=100,
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n_hidden=2,
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conditioning_length: int = 200,
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quantize: bool = False,
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dequantize: bool = False,
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scaling: bool = True,
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pick_incomplete: bool = False,
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@@ -86,7 +86,7 @@ class TempFlowEstimator(PTSEstimator):
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self.hidden_size = hidden_size
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self.n_hidden = n_hidden
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self.conditioning_length = conditioning_length
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self.quantize = quantize
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self.dequantize = dequantize
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self.lags_seq = (
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lags_seq
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@@ -175,7 +175,7 @@ class TempFlowEstimator(PTSEstimator):
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hidden_size=self.hidden_size,
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n_hidden=self.n_hidden,
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conditioning_length=self.conditioning_length,
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quantize=self.quantize,
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dequantize=self.dequantize,
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).to(device)
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def create_predictor(
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@@ -203,7 +203,7 @@ class TempFlowEstimator(PTSEstimator):
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hidden_size=self.hidden_size,
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n_hidden=self.n_hidden,
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conditioning_length=self.conditioning_length,
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quantize=self.quantize,
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dequantize=self.dequantize,
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num_parallel_samples=self.num_parallel_samples,
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).to(device)
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@@ -27,7 +27,7 @@ class TempFlowTrainingNetwork(nn.Module):
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n_blocks: int,
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hidden_size: int,
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n_hidden: int,
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quantize: bool,
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dequantize: bool,
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cardinality: List[int] = [1],
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embedding_dimension: int = 1,
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scaling: bool = True,
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@@ -62,7 +62,7 @@ class TempFlowTrainingNetwork(nn.Module):
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hidden_size=hidden_size,
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cond_label_size=conditioning_length,
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)
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self.quantize = quantize
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self.dequantize = dequantize
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self.distr_output = FlowOutput(
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self.flow, input_size=input_size, cond_size=conditioning_length
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@@ -384,7 +384,7 @@ class TempFlowTrainingNetwork(nn.Module):
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# we sum the last axis to have the same shape for all likelihoods
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# (batch_size, subseq_length, 1)
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if self.quantize:
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if self.dequantize:
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target += torch.rand_like(target)
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likelihoods = -self.flow.log_prob(target, distr_args).unsqueeze(-1)
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