移除多余的tensor封装，eval使用onnx
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zhiyang7
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7e190c2353
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eee9bce7dc
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@ -132,8 +132,8 @@ def train(flags):
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def sync_onnx_model(frames):
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for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
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if flags.enable_onnx and position:
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model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, position)
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if flags.enable_onnx:
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model_path = '%s/%s/model_%s.onnx' % (flags.onnx_model_path, flags.xpid, position)
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onnx_params = learner_model.get_model(position)\
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.get_onnx_params(torch.device('cpu') if flags.training_device == 'cpu' else torch.device('cuda:' + flags.training_device))
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torch.onnx.export(
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@ -186,7 +186,7 @@ def train(flags):
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for child in parent.children():
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child.nice(psutil.BELOW_NORMAL_PRIORITY_CLASS)
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def batch_and_learn(i, device, position, local_lock, position_lock, lock=threading.Lock()):
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def batch_and_learn(i, position, local_lock, position_lock, lock=threading.Lock()):
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"""Thread target for the learning process."""
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nonlocal frames, position_frames, stats
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while frames < flags.total_frames:
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@ -209,14 +209,13 @@ def train(flags):
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locks[device] = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
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position_locks = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_front': threading.Lock(), 'landlord_down': threading.Lock()}
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for device in device_iterator:
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for i in range(flags.num_threads):
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for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
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thread = threading.Thread(
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target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,device,position,locks[device][position],position_locks[position]))
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thread.setDaemon(True)
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thread.start()
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threads.append(thread)
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for i in range(flags.num_threads):
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for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']:
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thread = threading.Thread(
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target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,position,locks[device][position],position_locks[position]))
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thread.setDaemon(True)
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thread.start()
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threads.append(thread)
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def checkpoint(frames):
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if flags.disable_checkpoint:
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@ -6,17 +6,21 @@ the environment, we do it automatically.
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import numpy as np
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import torch
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def _format_observation(obs, device):
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def _format_observation(obs, device, flags):
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"""
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A utility function to process observations and
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move them to CUDA.
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"""
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position = obs['position']
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if not device == "cpu":
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device = 'cuda:' + str(device)
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device = torch.device(device)
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x_batch = torch.from_numpy(obs['x_batch']).to(device)
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z_batch = torch.from_numpy(obs['z_batch']).to(device)
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if flags.enable_onnx:
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x_batch = obs['x_batch']
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z_batch = obs['z_batch']
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else:
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if not device == "cpu":
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device = 'cuda:' + str(device)
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device = torch.device(device)
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x_batch = torch.from_numpy(obs['x_batch']).to(device)
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z_batch = torch.from_numpy(obs['z_batch']).to(device)
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x_no_action = torch.from_numpy(obs['x_no_action'])
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z = torch.from_numpy(obs['z'])
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obs = {'x_batch': x_batch,
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@ -35,7 +39,7 @@ class Environment:
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def initial(self, model, device, flags=None):
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obs = self.env.reset(model, device, flags=flags)
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initial_position, initial_obs, x_no_action, z = _format_observation(obs, self.device)
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initial_position, initial_obs, x_no_action, z = _format_observation(obs, self.device, flags)
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self.episode_return = torch.zeros(1, 1)
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initial_done = torch.ones(1, 1, dtype=torch.bool)
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return initial_position, initial_obs, dict(
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@ -54,7 +58,7 @@ class Environment:
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obs = self.env.reset(model, device, flags=flags)
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self.episode_return = torch.zeros(1, 1)
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position, obs, x_no_action, z = _format_observation(obs, self.device)
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position, obs, x_no_action, z = _format_observation(obs, self.device, flags)
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# reward = torch.tensor(reward).view(1, 1)
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done = torch.tensor(done).view(1, 1)
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@ -421,7 +421,7 @@ class Model:
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def set_onnx_model(self, device='cpu'):
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positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
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for position in positions:
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model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.savedir, self.flags.xpid, position))
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model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.onnx_model_path, self.flags.xpid, position))
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if device == 'cpu':
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self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
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else:
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@ -431,21 +431,27 @@ class Model:
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self.models[position].get_onnx_params(self.device)
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def forward(self, position, z, x, return_value=False, flags=None, debug=False):
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model = self.onnx_models[position]
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if model is None:
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if flags.enable_onnx:
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model = self.onnx_models[position]
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onnx_out = model.run(None, {'z_batch': z, 'x_batch': x})
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values = torch.tensor(onnx_out[0])
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else:
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model = self.models[position]
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values = model.forward(z, x)['values']
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else:
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onnx_out = model.run(None, {'z_batch': to_numpy(z), 'x_batch': to_numpy(x)})
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values = torch.tensor(onnx_out[0])
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if return_value:
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return dict(values=values)
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return dict(values = values if flags.enable_onnx else values.cpu().detach().numpy())
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else:
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if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
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action = torch.randint(values.shape[0], (1,))[0]
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if flags.enable_onnx:
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action = np.random.randint(0, values.shape[0], (1,))[0]
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else:
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action = torch.randint(values.shape[0], (1,))[0].cpu().detach().numpy()
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else:
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action = torch.argmax(values,dim=0)[0]
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return dict(action=action)
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if flags.enable_onnx:
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action = np.argmax(values, axis=0)[0].cpu().detach().numpy()
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else:
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action = torch.argmax(values,dim=0)[0]
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return dict(action = action)
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def share_memory(self):
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if self.models['landlord'] is not None:
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@ -118,7 +118,7 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
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if len(obs['legal_actions']) > 1:
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with torch.no_grad():
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agent_output = model.forward(position, obs['z_batch'], obs['x_batch'], flags=flags)
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_action_idx = int(agent_output['action'].cpu().detach().numpy())
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_action_idx = int(agent_output['action'])
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action = obs['legal_actions'][_action_idx]
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else:
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action = obs['legal_actions'][0]
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@ -1,5 +1,8 @@
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import torch
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import numpy as np
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import os
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import onnxruntime
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from onnxruntime.datasets import get_example
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from douzero.env.env import get_obs
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@ -25,6 +28,21 @@ def _load_model(position, model_path, model_type, use_legacy):
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if torch.cuda.is_available():
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model.cuda()
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model.eval()
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onnx_params = model.get_onnx_params(torch.device('cpu'))
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model_path = model_path + '.onnx'
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if not os.path.exists(model_path):
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torch.onnx.export(
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model,
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onnx_params['args'],
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model_path,
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export_params=True,
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opset_version=10,
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do_constant_folding=True,
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input_names=onnx_params['input_names'],
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output_names=onnx_params['output_names'],
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dynamic_axes=onnx_params['dynamic_axes']
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)
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return model
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class DeepAgent:
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@ -33,6 +51,7 @@ class DeepAgent:
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self.use_legacy = True if "legacy" in model_path else False
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self.model_type = "general" if "resnet" in model_path else "old"
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self.model = _load_model(position, model_path, self.model_type, self.use_legacy)
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self.onnx_model = onnxruntime.InferenceSession(get_example(os.path.abspath(model_path + '.onnx')), providers=['CPUExecutionProvider'])
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self.EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
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8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
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13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
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@ -46,8 +65,9 @@ class DeepAgent:
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x_batch = torch.from_numpy(obs['x_batch']).float()
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if torch.cuda.is_available():
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z_batch, x_batch = z_batch.cuda(), x_batch.cuda()
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y_pred = self.model.forward(z_batch, x_batch)['values']
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y_pred = y_pred.detach().cpu().numpy()
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# y_pred = self.model.forward(z_batch, x_batch)['values']
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# y_pred = y_pred.detach().cpu().numpy()
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y_pred = self.onnx_model.run(None, {'z_batch': obs['z_batch'], 'x_batch': obs['x_batch']})[0]
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best_action_index = np.argmax(y_pred, axis=0)[0]
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best_action = infoset.legal_actions[best_action_index]
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