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https://github.com/microsoft/qlib.git
synced 2026-06-30 17:41:18 +08:00
Add return for use_gpu..
This commit is contained in:
@@ -93,7 +93,7 @@ class ALSTM(Model):
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\ndevice : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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@@ -107,7 +107,7 @@ class ALSTM(Model):
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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self.device,
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self.use_gpu,
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seed,
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)
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@@ -138,7 +138,7 @@ class ALSTM(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -96,7 +96,7 @@ class ALSTM(Model):
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\ndevice : {}"
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"\nn_jobs : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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@@ -111,7 +111,7 @@ class ALSTM(Model):
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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self.device,
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n_jobs,
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self.use_gpu,
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seed,
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@@ -143,7 +143,7 @@ class ALSTM(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -103,7 +103,7 @@ class GATs(Model):
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"\nbase_model : {}"
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"\nwith_pretrain : {}"
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"\nmodel_path : {}"
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"\nvisible_GPU : {}"
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"\ndevice : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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@@ -119,7 +119,7 @@ class GATs(Model):
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base_model,
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with_pretrain,
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model_path,
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GPU,
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self.device,
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self.use_gpu,
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seed,
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)
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@@ -151,7 +151,7 @@ class GATs(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -172,7 +172,7 @@ class GATs(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -138,7 +138,7 @@ class GRU(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -96,7 +96,7 @@ class GRU(Model):
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\ndevice : {}"
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"\nn_jobs : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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@@ -111,7 +111,7 @@ class GRU(Model):
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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self.device,
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n_jobs,
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self.use_gpu,
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seed,
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@@ -143,7 +143,7 @@ class GRU(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -134,7 +134,7 @@ class LSTM(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -95,7 +95,7 @@ class LSTM(Model):
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"\nearly_stop : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\ndevice : {}"
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"\nn_jobs : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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@@ -110,7 +110,7 @@ class LSTM(Model):
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early_stop,
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optimizer.lower(),
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loss,
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GPU,
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self.device,
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n_jobs,
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self.use_gpu,
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seed,
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@@ -139,7 +139,7 @@ class LSTM(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def mse(self, pred, label):
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loss = (pred - label) ** 2
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@@ -99,8 +99,8 @@ class DNNModelPytorch(Model):
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"\nloss_type : {}"
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"\neval_steps : {}"
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"\nseed : {}"
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"\nvisible_GPU : {}"
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"\nuse_gpu : {}"
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"\ndevice : {}"
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"\nuse_GPU : {}"
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"\nweight_decay : {}".format(
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layers,
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lr,
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@@ -114,7 +114,7 @@ class DNNModelPytorch(Model):
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loss,
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eval_steps,
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seed,
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GPU,
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self.device,
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self.use_gpu,
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weight_decay,
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)
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@@ -158,7 +158,7 @@ class DNNModelPytorch(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def fit(
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self,
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@@ -241,7 +241,6 @@ class SFM(Model):
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self.optimizer = optimizer.lower()
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self.loss = loss
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self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
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@@ -260,7 +259,7 @@ class SFM(Model):
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"\neval_steps : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\ndevice : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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@@ -277,7 +276,7 @@ class SFM(Model):
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eval_steps,
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optimizer.lower(),
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loss,
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GPU,
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self.device,
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self.use_gpu,
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seed,
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)
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@@ -309,6 +308,10 @@ class SFM(Model):
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self.fitted = False
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self.sfm_model.to(self.device)
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@property
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def use_gpu(self):
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return self.device != torch.device("cpu")
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def test_epoch(self, data_x, data_y):
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# prepare training data
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@@ -86,8 +86,8 @@ class TabnetModel(Model):
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"TabNet:"
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"\nbatch_size : {}"
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"\nvirtual bs : {}"
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"\nGPU : {}"
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"\npretrain: {}".format(self.batch_size, vbs, GPU, pretrain)
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"\ndevice : {}"
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"\npretrain: {}".format(self.batch_size, vbs, self.device, self.pretrain)
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)
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self.fitted = False
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np.random.seed(self.seed)
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@@ -118,7 +118,7 @@ class TabnetModel(Model):
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@property
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def use_gpu(self):
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self.device != torch.device("cpu")
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return self.device != torch.device("cpu")
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def pretrain_fn(self, dataset=DatasetH, pretrain_file="./pretrain/best.model"):
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get_or_create_path(pretrain_file)
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