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Fix black errors

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This commit is contained in:
D-X-Y 2021-03-21 05:59:56 -07:00
parent b8c173eb76
commit 1acd1e9f9b
3 changed files with 12 additions and 4 deletions
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2
exps/trading/baselines.py
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@ -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

7
lib/trade_models/quant_transformer.py
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@ -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)))

7
lib/trade_models/transformers.py
View File

@ -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)