DouZero
/
Douzero_Resnet
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

移除多余的tensor封装，eval使用onnx

This commit is contained in:
zhiyang7 2021-12-21 10:46:24 +08:00
parent 7e190c2353
commit eee9bce7dc
5 changed files with 61 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
douzero/dmc/dmc.py
View File

@ -132,8 +132,8 @@ def train(flags):
def sync_onnx_model(frames):
for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
if flags.enable_onnx and position:
model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, position)
if flags.enable_onnx:
model_path = '%s/%s/model_%s.onnx' % (flags.onnx_model_path, flags.xpid, position)
onnx_params = learner_model.get_model(position)\
.get_onnx_params(torch.device('cpu') if flags.training_device == 'cpu' else torch.device('cuda:' + flags.training_device))
torch.onnx.export(
@ -186,7 +186,7 @@ def train(flags):
for child in parent.children():
child.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS)
def batch_and_learn(i, device, position, local_lock, position_lock, lock=threading.Lock()):
def batch_and_learn(i, position, local_lock, position_lock, lock=threading.Lock()):
"""Thread target for the learning process."""
nonlocal frames, position_frames, stats
while frames < flags.total_frames:
@ -209,11 +209,10 @@ def train(flags):
locks[device] = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
position_locks = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
for device in device_iterator:
for i in range(flags.num_threads):
for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
thread = threading.Thread(
target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,device,position,locks[device][position],position_locks[position]))
target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,position,locks[device][position],position_locks[position]))
thread.setDaemon(True)
thread.start()
threads.append(thread)

10
douzero/dmc/env_utils.py
View File

@ -6,12 +6,16 @@ the environment, we do it automatically.
import numpy as np
import torch
def _format_observation(obs, device):
def _format_observation(obs, device, flags):
"""
A utility function to process observations and
move them to CUDA.
"""
position = obs['position']
if flags.enable_onnx:
x_batch = obs['x_batch']
z_batch = obs['z_batch']
else:
if not device == "cpu":
device = 'cuda:' + str(device)
device = torch.device(device)
@ -35,7 +39,7 @@ class Environment:
def initial(self, model, device, flags=None):
obs = self.env.reset(model, device, flags=flags)
initial_position, initial_obs, x_no_action, z = _format_observation(obs, self.device)
initial_position, initial_obs, x_no_action, z = _format_observation(obs, self.device, flags)
self.episode_return = torch.zeros(1, 1)
initial_done = torch.ones(1, 1, dtype=torch.bool)
return initial_position, initial_obs, dict(
@ -54,7 +58,7 @@ class Environment:
obs = self.env.reset(model, device, flags=flags)
self.episode_return = torch.zeros(1, 1)
position, obs, x_no_action, z = _format_observation(obs, self.device)
position, obs, x_no_action, z = _format_observation(obs, self.device, flags)
# reward = torch.tensor(reward).view(1, 1)
done = torch.tensor(done).view(1, 1)

22
douzero/dmc/models.py
View File

@ -421,7 +421,7 @@ class Model:
def set_onnx_model(self, device='cpu'):
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
for position in positions:
model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.savedir, self.flags.xpid, position))
model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.onnx_model_path, self.flags.xpid, position))
if device == 'cpu':
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
else:
@ -431,21 +431,27 @@ class Model:
self.models[position].get_onnx_params(self.device)
def forward(self, position, z, x, return_value=False, flags=None, debug=False):
if flags.enable_onnx:
model = self.onnx_models[position]
if model is None:
onnx_out = model.run(None, {'z_batch': z, 'x_batch': x})
values = torch.tensor(onnx_out[0])
else:
model = self.models[position]
values = model.forward(z, x)['values']
else:
onnx_out = model.run(None, {'z_batch': to_numpy(z), 'x_batch': to_numpy(x)})
values = torch.tensor(onnx_out[0])
if return_value:
return dict(values=values)
return dict(values = values if flags.enable_onnx else values.cpu().detach().numpy())
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(values.shape[0], (1,))[0]
if flags.enable_onnx:
action = np.random.randint(0, values.shape[0], (1,))[0]
else:
action = torch.randint(values.shape[0], (1,))[0].cpu().detach().numpy()
else:
if flags.enable_onnx:
action = np.argmax(values, axis=0)[0].cpu().detach().numpy()
else:
action = torch.argmax(values,dim=0)[0]
return dict(action=action)
return dict(action = action)
def share_memory(self):
if self.models['landlord'] is not None:

2
douzero/dmc/utils.py
View File

@ -118,7 +118,7 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
if len(obs['legal_actions']) > 1:
with torch.no_grad():
agent_output = model.forward(position, obs['z_batch'], obs['x_batch'], flags=flags)
_action_idx = int(agent_output['action'].cpu().detach().numpy())
_action_idx = int(agent_output['action'])
action = obs['legal_actions'][_action_idx]
else:
action = obs['legal_actions'][0]

24
douzero/evaluation/deep_agent.py
View File

@ -1,5 +1,8 @@
import torch
import numpy as np
import os
import onnxruntime
from onnxruntime.datasets import get_example
from douzero.env.env import get_obs
@ -25,6 +28,21 @@ def _load_model(position, model_path, model_type, use_legacy):
if torch.cuda.is_available():
model.cuda()
model.eval()
onnx_params = model.get_onnx_params(torch.device('cpu'))
model_path = model_path + '.onnx'
if not os.path.exists(model_path):
torch.onnx.export(
model,
onnx_params['args'],
model_path,
export_params=True,
opset_version=10,
do_constant_folding=True,
input_names=onnx_params['input_names'],
output_names=onnx_params['output_names'],
dynamic_axes=onnx_params['dynamic_axes']
)
return model
class DeepAgent:
@ -33,6 +51,7 @@ class DeepAgent:
self.use_legacy = True if "legacy" 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.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',
13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
@ -46,8 +65,9 @@ class DeepAgent:
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.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]
best_action_index = np.argmax(y_pred, axis=0)[0]
best_action = infoset.legal_actions[best_action_index]