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Fixed sampling for GRU and reduced batch size

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Victor Mylle
2024-01-19 00:10:12 +00:00
parent e8e53ab185
commit c6fa17fa40
2 changed files with 16 additions and 5 deletions
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14
src/trainers/diffusion_trainer.py
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@@ -19,7 +19,13 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
alpha = 1. - beta alpha = 1. - beta
alpha_hat = torch.cumprod(alpha, dim=0) alpha_hat = torch.cumprod(alpha, dim=0)
inputs = inputs.repeat(n, 1).to(device) # inputs: (num_features) -> (batch_size, num_features)
# inputs: (time_steps, num_features) -> (batch_size, time_steps, num_features)
if len(inputs.shape) == 2:
inputs = inputs.repeat(n, 1)
elif len(inputs.shape) == 3:
inputs = inputs.repeat(n, 1, 1)
model.eval() model.eval()
with torch.no_grad(): with torch.no_grad():
x = torch.randn(inputs.shape[0], ts_length).to(device) x = torch.randn(inputs.shape[0], ts_length).to(device)
@@ -101,8 +107,12 @@ class DiffusionTrainer:
input_data = torch.randn(1024, 96).to(self.device) input_data = torch.randn(1024, 96).to(self.device)
time_steps = torch.randn(1024).long().to(self.device) time_steps = torch.randn(1024).long().to(self.device)
other_input_data = torch.randn(1024, self.model.other_inputs_dim).to(self.device)
if self.data_processor.lstm:
inputDim = self.data_processor.get_input_size()
other_input_data = torch.randn(1024, inputDim[1], self.model.other_inputs_dim).to(self.device)
else:
other_input_data = torch.randn(1024, self.model.other_inputs_dim).to(self.device)
task.set_configuration_object("model", str(summary(self.model, input_data=[input_data, time_steps, other_input_data]))) task.set_configuration_object("model", str(summary(self.model, input_data=[input_data, time_steps, other_input_data])))
self.data_processor = task.connect(self.data_processor, name="data_processor") self.data_processor = task.connect(self.data_processor, name="data_processor")

5
src/training_scripts/diffusion_training.py
View File

@@ -38,10 +38,11 @@ data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features") data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=True) data_processor = DataProcessor(data_config, path="", lstm=True)
data_processor.set_batch_size(8192) data_processor.set_batch_size(128)
data_processor.set_full_day_skip(True) data_processor.set_full_day_skip(True)
inputDim = data_processor.get_input_size() inputDim = data_processor.get_input_size()
print("Input dim: ", inputDim)
model_parameters = { model_parameters = {
"epochs": 5000, "epochs": 5000,
@@ -54,7 +55,7 @@ model_parameters = task.connect(model_parameters, name="model_parameters")
#### Model #### #### Model ####
# model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"]) # model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
model = GRUDiffusionModel(96, [256, 256], other_inputs_dim=inputDim[2], time_dim=64, gru_hidden_size=128) model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=256)
print("Starting training ...") print("Starting training ...")