DouZero
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Douzero_Resnet
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修复BUG

This commit is contained in:
zhiyang7 2021-12-23 09:22:38 +08:00
parent b5982b7195
commit 016d77aeb0
2 changed files with 16 additions and 8 deletions
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5
douzero/dmc/models.py
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@ -385,6 +385,11 @@ model_dict_new['landlord'] = GeneralModel
model_dict_new['landlord_up'] = GeneralModel
model_dict_new['landlord_front'] = GeneralModel
model_dict_new['landlord_down'] = GeneralModel
model_dict_new_lite = {}
model_dict_new_lite['landlord'] = GeneralModelLite
model_dict_new_lite['landlord_up'] = GeneralModelLite
model_dict_new_lite['landlord_front'] = GeneralModelLite
model_dict_new_lite['landlord_down'] = GeneralModelLite
class OldModel:
"""

17
douzero/evaluation/deep_agent.py
View File

@ -6,10 +6,13 @@ from onnxruntime.datasets import get_example
from douzero.env.env import get_obs
def _load_model(position, model_path, model_type, use_legacy):
from douzero.dmc.models import model_dict_new, model_dict, model_dict_legacy
def _load_model(position, model_path, model_type, use_legacy, use_lite):
from douzero.dmc.models import model_dict_new, model_dict_new_lite, model_dict, model_dict_legacy
model = None
if model_type == "general":
if use_lite:
model = model_dict_new_lite[position]()
else:
model = model_dict_new[position]()
else:
if use_legacy:
@ -51,7 +54,7 @@ class DeepAgent:
self.use_legacy = True if "legacy" in model_path else False
self.lite_model = True if "lite" in model_path else False
self.model_type = "general" if "resnet" in model_path else "old"
self.model = _load_model(position, model_path, self.model_type, self.use_legacy)
self.model = _load_model(position, model_path, self.model_type, self.use_legacy, self.lite_model)
self.onnx_model = onnxruntime.InferenceSession(get_example(os.path.abspath(model_path + '.onnx')), providers=['CPUExecutionProvider'])
self.EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
@ -62,10 +65,10 @@ class DeepAgent:
obs = get_obs(infoset, self.model_type == "general", self.use_legacy, self.lite_model)
z_batch = torch.from_numpy(obs['z_batch']).float()
x_batch = torch.from_numpy(obs['x_batch']).float()
if torch.cuda.is_available():
z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
# z_batch = torch.from_numpy(obs['z_batch']).float()
# x_batch = torch.from_numpy(obs['x_batch']).float()
# if torch.cuda.is_available():
# z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
# y_pred = self.model.forward(z_batch, x_batch)['values']
# y_pred = y_pred.detach().cpu().numpy()
y_pred = self.onnx_model.run(None, {'z_batch': obs['z_batch'], 'x_batch': obs['x_batch']})[0]