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修复训练vanilla模型的BUG

This commit is contained in:
zhiyang7 2021-12-22 09:04:56 +08:00
parent 42f28b1aa9
commit a4d2a93afc
2 changed files with 37 additions and 19 deletions
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4
douzero/dmc/dmc.py
View File

@ -104,7 +104,7 @@ def train(flags):
# Initialize queues
actor_processes = []
ctx = mp.get_context('spawn')
batch_queues = {"landlord": ctx.Queue(flags.unroll_length * 4), "landlord_up": ctx.Queue(flags.unroll_length * 4), 'landlord_front': ctx.Queue(flags.unroll_length * 4), "landlord_down": ctx.Queue(flags.unroll_length * 4)}
batch_queues = {"landlord": ctx.SimpleQueue(), "landlord_up": ctx.SimpleQueue(), 'landlord_front': ctx.SimpleQueue(), "landlord_down": ctx.SimpleQueue()}
onnx_frame = ctx.Value('d', -1)
# Learner model for training
@ -268,7 +268,7 @@ def train(flags):
fps = (frames - start_frames) / (end_time - start_time)
fps_log.append(fps)
if len(fps_log) > 24:
if len(fps_log) > 240:
fps_log = fps_log[1:]
fps_avg = np.mean(fps_log)

52
douzero/dmc/models.py
View File

@ -330,22 +330,34 @@ class OldModel:
"""
def __init__(self, device=0, flags=None):
self.models = {}
self.onnx_models = {}
self.flags = flags
if not device == "cpu":
device = 'cuda:' + str(device)
self.device = torch.device(device)
self.models['landlord'] = LandlordLstmModel().to(self.device)
self.models['landlord_up'] = FarmerLstmModel().to(self.device)
self.models['landlord_front'] = FarmerLstmModel().to(self.device)
self.models['landlord_down'] = FarmerLstmModel().to(self.device)
self.onnx_models = {
'landlord': None,
'landlord_up': None,
'landlord_front': None,
'landlord_down': None
}
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
if flags is not None and flags.enable_onnx:
for position in positions:
self.models[position] = None
else:
for position in positions[1:]:
self.models[position] = FarmerLstmModel().to(self.device)
self.models['landlord'] = LandlordLstmModel().to(self.device)
self.onnx_models = {
'landlord': None,
'landlord_up': None,
'landlord_front': None,
'landlord_down': None
}
def set_onnx_model(self, position, model_path):
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path))
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.onnx_model_path, self.flags.xpid, position))
if device == 'cpu':
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
else:
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
def get_onnx_params(self, position):
self.models[position].get_onnx_params(self.device)
@ -356,16 +368,22 @@ class OldModel:
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)})
onnx_out = model.run(None, {'z_batch': z, 'x_batch': 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:
action = torch.argmax(values,dim=0)[0]
return dict(action=action)
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)
def share_memory(self):
if self.models['landlord'] is not None: