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使用onnx进行infer逻辑，未完成

This commit is contained in:
ZaneYork 2021-12-14 22:55:03 +08:00
parent f054fed61c
commit 0cb3d040cb
6 changed files with 133 additions and 111 deletions
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32
douzero/dmc/dmc.py
View File

@ -19,6 +19,7 @@ import psutil
import shutil import shutil
mean_episode_return_buf = {p:deque(maxlen=100) for p in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']} mean_episode_return_buf = {p:deque(maxlen=100) for p in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']}
onnx_frame = mp.Value('d', -1)
def compute_loss(logits, targets): def compute_loss(logits, targets):
loss = ((logits.squeeze(-1) - targets)**2).mean() loss = ((logits.squeeze(-1) - targets)**2).mean()
@ -79,6 +80,7 @@ def train(flags):
Then it will start subprocesses as actors. Then, it will call Then it will start subprocesses as actors. Then, it will call
learning function with multiple threads. learning function with multiple threads.
""" """
global onnx_frame
if not flags.actor_device_cpu or flags.training_device != 'cpu': if not flags.actor_device_cpu or flags.training_device != 'cpu':
if not torch.cuda.is_available(): if not torch.cuda.is_available():
raise AssertionError("CUDA not available. If you have GPUs, please specify the ID after `--gpu_devices`. Otherwise, please train with CPU with `python3 train.py --actor_device_cpu --training_device cpu`") raise AssertionError("CUDA not available. If you have GPUs, please specify the ID after `--gpu_devices`. Otherwise, please train with CPU with `python3 train.py --actor_device_cpu --training_device cpu`")
@ -171,7 +173,7 @@ def train(flags):
for i in range(num_actors): for i in range(num_actors):
actor = mp.Process( actor = mp.Process(
target=act, target=act,
args=(i, device, batch_queues, models[device], flags)) args=(i, device, batch_queues, models[device], flags, onnx_frame))
actor.daemon = True actor.daemon = True
actor.start() actor.start()
actor_processes.append(actor) actor_processes.append(actor)
@ -214,6 +216,7 @@ def train(flags):
threads.append(thread) threads.append(thread)
def checkpoint(frames): def checkpoint(frames):
global onnx_frame
if flags.disable_checkpoint: if flags.disable_checkpoint:
return return
log.info('Saving checkpoint to %s', checkpointpath) log.info('Saving checkpoint to %s', checkpointpath)
@ -228,25 +231,32 @@ def train(flags):
}, checkpointpath + '.new') }, checkpointpath + '.new')
# Save the weights for evaluation purpose # Save the weights for evaluation purpose
dummy_input = (torch.tensor(np.zeros([80, 40, 108]), dtype=torch.float32), dummy_input = (
torch.tensor(np.zeros((80, 80)), dtype=torch.int8), torch.tensor(np.zeros([1, 40, 108]), dtype=torch.float32),
{ torch.tensor(np.zeros((1, 80)), dtype=torch.float32)
'return_value': False,
'flags': {'exp_epsilon':0.001}
},
) )
for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']: # ['landlord', 'landlord_up', 'landlord_front', 'landlord_down'] for position in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']:
model_weights_dir = os.path.expandvars(os.path.expanduser( model_weights_dir = os.path.expandvars(os.path.expanduser(
'%s/%s/%s' % (flags.savedir, flags.xpid, "general_" + position + '_' + str(frames) + '.ckpt'))) '%s/%s/%s' % (flags.savedir, flags.xpid, "general_" + position + '_' + str(frames) + '.ckpt')))
torch.save(learner_model.get_model(position).state_dict(), model_weights_dir) torch.save(learner_model.get_model(position).state_dict(), model_weights_dir)
if position != 'bidding': if position != 'bidding':
model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, position)
torch.onnx.export( torch.onnx.export(
learner_model.get_model(position), learner_model.get_model(position),
dummy_input, dummy_input,
'%s/model_%s.onnx' % (flags.savedir, position), model_path,
input_names=['z_batch','x_batch','flags'], input_names=['z_batch','x_batch'],
output_names=['action', 'max_value'] output_names=['values'],
dynamic_axes={
'z_batch': {
0: "legal_actions"
},
'x_batch': {
0: "legal_actions"
}
}
) )
onnx_frame = frames
shutil.move(checkpointpath + '.new', checkpointpath) shutil.move(checkpointpath + '.new', checkpointpath)

133
douzero/dmc/models.py
View File

@ -6,9 +6,14 @@ models into one class for convenience.
import numpy as np import numpy as np
import torch import torch
import onnxruntime
from onnxruntime.datasets import get_example
from torch import nn from torch import nn
import torch.nn.functional as F import torch.nn.functional as F
def to_numpy(tensor):
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
class LandlordLstmModel(nn.Module): class LandlordLstmModel(nn.Module):
def __init__(self): def __init__(self):
super().__init__() super().__init__()
@ -20,7 +25,7 @@ class LandlordLstmModel(nn.Module):
self.dense5 = nn.Linear(512, 256) self.dense5 = nn.Linear(512, 256)
self.dense6 = nn.Linear(256, 1) self.dense6 = nn.Linear(256, 1)
def forward(self, z, x, return_value=False, flags=None): def forward(self, z, x):
lstm_out, (h_n, _) = self.lstm(z) lstm_out, (h_n, _) = self.lstm(z)
lstm_out = lstm_out[:,-1,:] lstm_out = lstm_out[:,-1,:]
x = torch.cat([lstm_out,x], dim=-1) x = torch.cat([lstm_out,x], dim=-1)
@ -35,14 +40,7 @@ class LandlordLstmModel(nn.Module):
x = self.dense5(x) x = self.dense5(x)
x = torch.relu(x) x = torch.relu(x)
x = self.dense6(x) x = self.dense6(x)
if return_value:
return dict(values=x) return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action)
class FarmerLstmModel(nn.Module): class FarmerLstmModel(nn.Module):
def __init__(self): def __init__(self):
@ -55,7 +53,7 @@ class FarmerLstmModel(nn.Module):
self.dense5 = nn.Linear(512, 256) self.dense5 = nn.Linear(512, 256)
self.dense6 = nn.Linear(256, 1) self.dense6 = nn.Linear(256, 1)
def forward(self, z, x, return_value=False, flags=None): def forward(self, z, x):
lstm_out, (h_n, _) = self.lstm(z) lstm_out, (h_n, _) = self.lstm(z)
lstm_out = lstm_out[:,-1,:] lstm_out = lstm_out[:,-1,:]
x = torch.cat([lstm_out,x], dim=-1) x = torch.cat([lstm_out,x], dim=-1)
@ -70,14 +68,7 @@ class FarmerLstmModel(nn.Module):
x = self.dense5(x) x = self.dense5(x)
x = torch.relu(x) x = torch.relu(x)
x = self.dense6(x) x = self.dense6(x)
if return_value:
return dict(values=x) return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action)
class LandlordLstmModelLegacy(nn.Module): class LandlordLstmModelLegacy(nn.Module):
def __init__(self): def __init__(self):
@ -90,7 +81,7 @@ class LandlordLstmModelLegacy(nn.Module):
self.dense5 = nn.Linear(512, 256) self.dense5 = nn.Linear(512, 256)
self.dense6 = nn.Linear(256, 1) self.dense6 = nn.Linear(256, 1)
def forward(self, z, x, return_value=False, flags=None): def forward(self, z, x):
lstm_out, (h_n, _) = self.lstm(z) lstm_out, (h_n, _) = self.lstm(z)
lstm_out = lstm_out[:,-1,:] lstm_out = lstm_out[:,-1,:]
x = torch.cat([lstm_out,x], dim=-1) x = torch.cat([lstm_out,x], dim=-1)
@ -105,14 +96,7 @@ class LandlordLstmModelLegacy(nn.Module):
x = self.dense5(x) x = self.dense5(x)
x = torch.relu(x) x = torch.relu(x)
x = self.dense6(x) x = self.dense6(x)
if return_value:
return dict(values=x) return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action)
class FarmerLstmModelLegacy(nn.Module): class FarmerLstmModelLegacy(nn.Module):
def __init__(self): def __init__(self):
@ -125,7 +109,7 @@ class FarmerLstmModelLegacy(nn.Module):
self.dense5 = nn.Linear(512, 256) self.dense5 = nn.Linear(512, 256)
self.dense6 = nn.Linear(256, 1) self.dense6 = nn.Linear(256, 1)
def forward(self, z, x, return_value=False, flags=None): def forward(self, z, x):
lstm_out, (h_n, _) = self.lstm(z) lstm_out, (h_n, _) = self.lstm(z)
lstm_out = lstm_out[:,-1,:] lstm_out = lstm_out[:,-1,:]
x = torch.cat([lstm_out,x], dim=-1) x = torch.cat([lstm_out,x], dim=-1)
@ -140,14 +124,7 @@ class FarmerLstmModelLegacy(nn.Module):
x = self.dense5(x) x = self.dense5(x)
x = torch.relu(x) x = torch.relu(x)
x = self.dense6(x) x = self.dense6(x)
if return_value:
return dict(values=x) return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action)
class GeneralModel1(nn.Module): class GeneralModel1(nn.Module):
def __init__(self): def __init__(self):
@ -185,7 +162,7 @@ class GeneralModel1(nn.Module):
self.dense5 = nn.Linear(512, 512) self.dense5 = nn.Linear(512, 512)
self.dense6 = nn.Linear(512, 1) self.dense6 = nn.Linear(512, 1)
def forward(self, z, x, return_value=False, flags=None, debug=False): def forward(self, z, x):
z = z.unsqueeze(1) z = z.unsqueeze(1)
z = self.conv_z_1(z) z = self.conv_z_1(z)
z = z.squeeze(-1) z = z.squeeze(-1)
@ -209,14 +186,7 @@ class GeneralModel1(nn.Module):
x = self.dense5(x) x = self.dense5(x)
x = torch.relu(x) x = torch.relu(x)
x = self.dense6(x) x = self.dense6(x)
if return_value:
return dict(values=x) return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action, max_value=torch.max(x))
# 用于ResNet18和34的残差块，用的是2个3x3的卷积 # 用于ResNet18和34的残差块，用的是2个3x3的卷积
@ -278,7 +248,7 @@ class GeneralModelLegacy(nn.Module):
self.in_planes = planes * block.expansion self.in_planes = planes * block.expansion
return nn.Sequential(*layers) return nn.Sequential(*layers)
def forward(self, z, x, return_value=False, flags=None, debug=False): def forward(self, z, x):
out = F.relu(self.bn1(self.conv1(z))) out = F.relu(self.bn1(self.conv1(z)))
out = self.layer1(out) out = self.layer1(out)
out = self.layer2(out) out = self.layer2(out)
@ -291,14 +261,7 @@ class GeneralModelLegacy(nn.Module):
out = F.leaky_relu_(self.linear3(out)) out = F.leaky_relu_(self.linear3(out))
out = F.leaky_relu_(self.linear4(out)) out = F.leaky_relu_(self.linear4(out))
out = F.leaky_relu_(self.linear5(out)) out = F.leaky_relu_(self.linear5(out))
if return_value:
return dict(values=out) return dict(values=out)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(out.shape[0], (1,))[0]
else:
action = torch.argmax(out,dim=0)[0]
return dict(action=action, max_value=torch.max(out))
class GeneralModel(nn.Module): class GeneralModel(nn.Module):
def __init__(self): def __init__(self):
@ -329,7 +292,7 @@ class GeneralModel(nn.Module):
self.in_planes = planes * block.expansion self.in_planes = planes * block.expansion
return nn.Sequential(*layers) return nn.Sequential(*layers)
def forward(self, z, x, return_value=False, flags=None, debug=False): def forward(self, z, x):
out = F.relu(self.bn1(self.conv1(z))) out = F.relu(self.bn1(self.conv1(z)))
out = self.layer1(out) out = self.layer1(out)
out = self.layer2(out) out = self.layer2(out)
@ -342,17 +305,7 @@ class GeneralModel(nn.Module):
out = F.leaky_relu_(self.linear3(out)) out = F.leaky_relu_(self.linear3(out))
out = F.leaky_relu_(self.linear4(out)) out = F.leaky_relu_(self.linear4(out))
out = F.leaky_relu_(self.linear5(out)) out = F.leaky_relu_(self.linear5(out))
if return_value:
return dict(values=out) return dict(values=out)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(out.shape[0], (1,))[0]
else:
action = torch.argmax(out,dim=0)[0]
return dict(action=action, max_value=torch.max(out))
class BidModel(nn.Module): class BidModel(nn.Module):
@ -366,7 +319,7 @@ class BidModel(nn.Module):
self.dense5 = nn.Linear(512, 512) self.dense5 = nn.Linear(512, 512)
self.dense6 = nn.Linear(512, 1) self.dense6 = nn.Linear(512, 1)
def forward(self, z, x, return_value=False, flags=None, debug=False): def forward(self, z, x):
x = self.dense1(x) x = self.dense1(x)
x = F.leaky_relu(x) x = F.leaky_relu(x)
# x = F.relu(x) # x = F.relu(x)
@ -383,14 +336,7 @@ class BidModel(nn.Module):
# x = F.relu(x) # x = F.relu(x)
x = F.leaky_relu(x) x = F.leaky_relu(x)
x = self.dense6(x) x = self.dense6(x)
if return_value:
return dict(values=x) return dict(values=x)
else:
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
action = torch.randint(x.shape[0], (1,))[0]
else:
action = torch.argmax(x,dim=0)[0]
return dict(action=action, max_value=torch.max(x))
# Model dict is only used in evaluation but not training # Model dict is only used in evaluation but not training
@ -438,9 +384,17 @@ class General_Model:
self.models['landlord_down'] = GeneralModel1().to(torch.device(device)) self.models['landlord_down'] = GeneralModel1().to(torch.device(device))
self.models['bidding'] = BidModel().to(torch.device(device)) self.models['bidding'] = BidModel().to(torch.device(device))
def forward(self, position, z, x, training=False, flags=None, debug=False): def forward(self, position, z, x, return_value=False, flags=None, debug=False):
model = self.models[position] model = self.models[position]
return model.forward(z, x, training, flags, debug) values = model.forward(z, x)['values']
if return_value:
return dict(values=values)
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]
else:
action = torch.argmax(values,dim=0)[0]
return dict(action=action, max_value=torch.max(values))
def share_memory(self): def share_memory(self):
self.models['landlord'].share_memory() self.models['landlord'].share_memory()
@ -480,9 +434,17 @@ class OldModel:
self.models['landlord_down'] = FarmerLstmModel().to(torch.device(device)) self.models['landlord_down'] = FarmerLstmModel().to(torch.device(device))
self.models['bidding'] = BidModel().to(torch.device(device)) self.models['bidding'] = BidModel().to(torch.device(device))
def forward(self, position, z, x, training=False, flags=None): def forward(self, position, z, x, return_value=False, flags=None):
model = self.models[position] model = self.models[position]
return model.forward(z, x, training, flags) values = model.forward(z, x)['values']
if return_value:
return dict(values=values)
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]
else:
action = torch.argmax(values,dim=0)[0]
return dict(action=action, max_value=torch.max(values))
def share_memory(self): def share_memory(self):
self.models['landlord'].share_memory() self.models['landlord'].share_memory()
@ -523,10 +485,35 @@ class Model:
self.models['landlord_front'] = GeneralModel().to(torch.device(device)) self.models['landlord_front'] = GeneralModel().to(torch.device(device))
self.models['landlord_down'] = GeneralModel().to(torch.device(device)) self.models['landlord_down'] = GeneralModel().to(torch.device(device))
self.models['bidding'] = BidModel().to(torch.device(device)) self.models['bidding'] = BidModel().to(torch.device(device))
self.onnx_models = {
'landlord': None,
'landlord_up': None,
'landlord_front': None,
'landlord_down': None,
'bidding': None
}
self.models['bidding'] = BidModel().to(torch.device(device))
def forward(self, position, z, x, training=False, flags=None, debug=False): def set_onnx_model(self, position, model_path):
self.onnx_models[position] = get_example(model_path)
def forward(self, position, z, x, return_value=False, flags=None, debug=False):
model = self.onnx_models[position]
if model is None:
model = self.models[position] model = self.models[position]
return model.forward(z, x, training, flags, debug) values = model.forward(z, x)['values']
else:
sess = onnxruntime.InferenceSession(model)
onnx_out = sess.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)
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]
else:
action = torch.argmax(values,dim=0)[0]
return dict(action=action, max_value=torch.max(values))
def share_memory(self): def share_memory(self):
self.models['landlord'].share_memory() self.models['landlord'].share_memory()

10
douzero/dmc/utils.py
View File

@ -81,7 +81,7 @@ def create_optimizers(flags, learner_model):
return optimizers return optimizers
def act(i, device, batch_queues, model, flags): def act(i, device, batch_queues, model, flags, onnx_frame):
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding'] positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']
for pos in positions: for pos in positions:
model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device)))) model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
@ -110,8 +110,16 @@ def act(i, device, batch_queues, model, flags):
position, obs, env_output = env.initial(model, device, flags=flags) position, obs, env_output = env.initial(model, device, flags=flags)
bid_obs_buffer = env_output["begin_buf"]["bid_obs_buffer"] bid_obs_buffer = env_output["begin_buf"]["bid_obs_buffer"]
multiply_obs_buffer = env_output["begin_buf"]["multiply_obs_buffer"] multiply_obs_buffer = env_output["begin_buf"]["multiply_obs_buffer"]
last_onnx_frame = -1
while True: while True:
# print("posi", position) # print("posi", position)
if onnx_frame != last_onnx_frame:
last_onnx_frame = onnx_frame
for p in positions:
if p != 'bidding':
model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, p)
model.set_onnx_model(p, os.path.abspath(model_path))
for bid_obs in bid_obs_buffer: for bid_obs in bid_obs_buffer:
obs_z_buf["bidding"].append(bid_obs['z_batch']) obs_z_buf["bidding"].append(bid_obs['z_batch'])
obs_x_batch_buf["bidding"].append(bid_obs["x_batch"]) obs_x_batch_buf["bidding"].append(bid_obs["x_batch"])

31
evaluate.py
View File

@ -61,8 +61,8 @@ if __name__ == '__main__':
parser.add_argument('--bid', type=bool, default=True) parser.add_argument('--bid', type=bool, default=True)
parser.add_argument('--title', type=str, default='New') parser.add_argument('--title', type=str, default='New')
args = parser.parse_args() args = parser.parse_args()
# args.output = True args.output = True
args.output = False # args.output = False
args.bid = False args.bid = False
if args.output or args.bid: if args.output or args.bid:
args.num_workers = 1 args.num_workers = 1
@ -72,15 +72,30 @@ if __name__ == '__main__':
eval_list = [ eval_list = [
# { # {
# 'landlord': { 'folder': 'baselines', 'prefix': 'legacy_general', 'frame': 48545600}, # 'landlord': {'folder': 'baselines', 'prefix': 'legacy_general', 'frame': 143539200},
# 'farmer': {'folder': 'baselines', 'prefix': 'legacy_general', 'frame': 143539200},
# 'two_way': False
# },
{
'farmer': { 'folder': 'baselines', 'prefix': 'legacy_general', 'frame': 143539200},
'landlord': { 'folder': 'baselines', 'prefix': 'resnet', 'frame': 23358800},
'two_way': True
},
# {
# 'landlord': {'folder': 'baselines', 'prefix': 'resnet', 'frame': 11534400},
# 'farmer': {'folder': 'baselines', 'prefix': 'resnet', 'frame': 11534400},
# 'two_way': False
# },
# {
# 'landlord': {'folder': 'baselines', 'prefix': 'legacy_resnet', 'frame': 11754400},
# 'farmer': {'folder': 'baselines', 'prefix': 'legacy_resnet', 'frame': 11754400},
# 'two_way': False
# },
# {
# 'landlord': {'folder': 'baselines', 'prefix': 'legacy_resnet', 'frame': 11754400},
# 'farmer': {'folder': 'baselines', 'prefix': 'resnet', 'frame': 11534400}, # 'farmer': {'folder': 'baselines', 'prefix': 'resnet', 'frame': 11534400},
# 'two_way': True # 'two_way': True
# }, # },
{
'landlord': {'folder': 'baselines', 'prefix': 'legacy_resnet', 'frame': 11754400},
'farmer': {'folder': 'baselines', 'prefix': 'resnet', 'frame': 11534400},
'two_way': True
}
] ]
for vs in reversed(eval_list): for vs in reversed(eval_list):

4
generate_eval_data_with_bid.py
View File

@ -15,8 +15,8 @@ deck.extend([20, 20, 30, 30])
def get_parser(): def get_parser():
parser = argparse.ArgumentParser(description='DouZero: random data generator') parser = argparse.ArgumentParser(description='DouZero: random data generator')
parser.add_argument('--output', default='eval_data_200', type=str) parser.add_argument('--output', default='eval_data_500', type=str)
parser.add_argument('--path', default='baselines/resnet_bidding_27853600.ckpt', type=str) parser.add_argument('--path', default='baselines/resnet_landlord_23358800.ckpt', type=str)
parser.add_argument('--num_games', default=200, type=int) parser.add_argument('--num_games', default=200, type=int)
parser.add_argument('--exp_epsilon', default=0.01, type=float) parser.add_argument('--exp_epsilon', default=0.01, type=float)
return parser return parser

2
requirements.txt
View File

@ -3,3 +3,5 @@ GitPython
gitdb2 gitdb2
rlcard rlcard
psutil psutil
onnx
onnxruntime