Fix black errors

exps/trading/baselines.py

@@ -13,6 +13,8 @@
 # python exps/trading/baselines.py --alg LightGBM   #
 # python exps/trading/baselines.py --alg DoubleE    #
 # python exps/trading/baselines.py --alg TabNet     #
+#                                                   #
+# python exps/trading/baselines.py --alg Transformer#
 #####################################################
 import sys
 import argparse

lib/trade_models/quant_transformer.py

@@ -27,8 +27,10 @@ import torch.utils.data as th_data
 
 from log_utils import AverageMeter
 from utils import count_parameters
-from trade_models.transformers import DEFAULT_NET_CONFIG
-from trade_models.transformers import get_transformer
+
+from xlayers import super_core
+from .transformers import DEFAULT_NET_CONFIG
+from .transformers import get_transformer
 
 
 from qlib.model.base import Model
@@ -90,6 +92,7 @@ class QuantTransformer(Model):
                 torch.cuda.manual_seed_all(self.seed)
 
         self.model = get_transformer(self.net_config)
+        self.model.set_super_run_type(super_core.SuperRunMode.FullModel)
         self.logger.info("model: {:}".format(self.model))
         self.logger.info("model size: {:.3f} MB".format(count_parameters(self.model)))
 

lib/trade_models/transformers.py

@@ -17,7 +17,7 @@ from xlayers import trunc_normal_
 from xlayers import super_core
 
 
-__all__ = ["DefaultSearchSpace"]
+__all__ = ["DefaultSearchSpace", "DEFAULT_NET_CONFIG", "get_transformer"]
 
 
 def _get_mul_specs(candidates, num):
@@ -41,6 +41,7 @@ def _assert_types(x, expected_types):
         )
 
 
+DEFAULT_NET_CONFIG = None
 _default_max_depth = 5
 DefaultSearchSpace = dict(
     d_feat=6,
@@ -163,7 +164,9 @@ class SuperTransformer(super_core.SuperModule):
         else:
             stem_dim = spaces.get_determined_value(self._stem_dim)
         cls_tokens = self.cls_token.expand(batch, -1, -1)
-        cls_tokens = F.interpolate(cls_tokens, size=(stem_dim), mode="linear", align_corners=True)
+        cls_tokens = F.interpolate(
+            cls_tokens, size=(stem_dim), mode="linear", align_corners=True
+        )
         feats_w_ct = torch.cat((cls_tokens, feats), dim=1)
         feats_w_tp = self.pos_embed(feats_w_ct)
         xfeats = self.backbone(feats_w_tp)