使用onnx-gpu进行infer逻辑,未完成

This commit is contained in:
ZaneYork 2021-12-15 22:09:18 +08:00
parent 601dafb008
commit 3369b491e2
5 changed files with 67 additions and 48 deletions

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@ -1 +1 @@
python train.py --load_model --batch_size 8 --learning_rate 0.0003 python train.py --load_model --batch_size 8 --learning_rate 0.0003 --enable_onnx

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@ -68,6 +68,7 @@ def learn(position, actor_models, model, batch, optimizer, flags, lock):
nn.utils.clip_grad_norm_(model.parameters(), flags.max_grad_norm) nn.utils.clip_grad_norm_(model.parameters(), flags.max_grad_norm)
optimizer.step() optimizer.step()
if not flags.enable_onnx:
for actor_model in actor_models.values(): for actor_model in actor_models.values():
actor_model.get_model(position).load_state_dict(model.state_dict()) actor_model.get_model(position).load_state_dict(model.state_dict())
return stats return stats
@ -103,9 +104,9 @@ def train(flags):
models = {} models = {}
for device in device_iterator: for device in device_iterator:
if flags.old_model: if flags.old_model:
model = OldModel(device="cpu") model = OldModel(device="cpu", flags = flags)
else: else:
model = Model(device="cpu") model = Model(device="cpu", flags = flags)
model.share_memory() model.share_memory()
model.eval() model.eval()
models[device] = model models[device] = model
@ -149,6 +150,7 @@ def train(flags):
for k in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']: # ['landlord', 'landlord_up', 'landlord_down'] for k in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']: # ['landlord', 'landlord_up', 'landlord_down']
learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k]) learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k]) optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k])
if not flags.enable_onnx:
for device in device_iterator: for device in device_iterator:
models[device].get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k]) models[device].get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
stats = checkpoint_states["stats"] stats = checkpoint_states["stats"]

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@ -2,6 +2,7 @@
This file includes the torch models. We wrap the three This file includes the torch models. We wrap the three
models into one class for convenience. models into one class for convenience.
""" """
import os
import numpy as np import numpy as np
@ -331,8 +332,8 @@ class GeneralModel(nn.Module):
def get_onnx_params(self): def get_onnx_params(self):
return { return {
'args': ( 'args': (
torch.tensor(np.zeros([1, 40, 108]), dtype=torch.float32), torch.tensor(np.zeros([1, 40, 108]), dtype=torch.float32, device='cuda:0'),
torch.tensor(np.zeros((1, 80)), dtype=torch.float32) torch.tensor(np.zeros((1, 80)), dtype=torch.float32, device='cuda:0')
), ),
'input_names': ['z_batch','x_batch'], 'input_names': ['z_batch','x_batch'],
'output_names': ['values'], 'output_names': ['values'],
@ -478,7 +479,7 @@ class OldModel:
The wrapper for the three models. We also wrap several The wrapper for the three models. We also wrap several
interfaces such as share_memory, eval, etc. interfaces such as share_memory, eval, etc.
""" """
def __init__(self, device=0): def __init__(self, device=0, flags=None):
self.models = {} self.models = {}
if not device == "cpu": if not device == "cpu":
device = 'cuda:' + str(device) device = 'cuda:' + str(device)
@ -519,6 +520,7 @@ class OldModel:
return dict(action=action) return dict(action=action)
def share_memory(self): def share_memory(self):
if self.models['landlord'] is not None:
self.models['landlord'].share_memory() self.models['landlord'].share_memory()
self.models['landlord_up'].share_memory() self.models['landlord_up'].share_memory()
self.models['landlord_front'].share_memory() self.models['landlord_front'].share_memory()
@ -526,6 +528,7 @@ class OldModel:
self.models['bidding'].share_memory() self.models['bidding'].share_memory()
def eval(self): def eval(self):
if self.models['landlord'] is not None:
self.models['landlord'].eval() self.models['landlord'].eval()
self.models['landlord_up'].eval() self.models['landlord_up'].eval()
self.models['landlord_front'].eval() self.models['landlord_front'].eval()
@ -547,15 +550,21 @@ class Model:
The wrapper for the three models. We also wrap several The wrapper for the three models. We also wrap several
interfaces such as share_memory, eval, etc. interfaces such as share_memory, eval, etc.
""" """
def __init__(self, device=0): def __init__(self, device=0, flags=None):
self.models = {} self.models = {}
self.onnx_models = {}
self.flags = flags
if not device == "cpu": if not device == "cpu":
device = 'cuda:' + str(device) device = 'cuda:' + str(device)
# model = GeneralModel().to(torch.device(device)) # model = GeneralModel().to(torch.device(device))
self.models['landlord'] = GeneralModel().to(torch.device(device)) positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
self.models['landlord_up'] = GeneralModel().to(torch.device(device)) if flags is not None and flags.enable_onnx:
self.models['landlord_front'] = GeneralModel().to(torch.device(device)) self.models['bidding'] = BidModel().to(torch.device(device))
self.models['landlord_down'] = GeneralModel().to(torch.device(device)) for position in positions:
self.models[position] = None
else:
for position in positions:
self.models[position] = GeneralModel().to(torch.device(device))
self.models['bidding'] = BidModel().to(torch.device(device)) self.models['bidding'] = BidModel().to(torch.device(device))
self.onnx_models = { self.onnx_models = {
'landlord': None, 'landlord': None,
@ -564,15 +573,20 @@ class Model:
'landlord_down': None, 'landlord_down': None,
'bidding': None 'bidding': None
} }
self.models['bidding'] = BidModel().to(torch.device(device))
def set_onnx_model(self, position, model_path): def set_onnx_model(self):
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path)) 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))
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
self.onnx_models['bidding'] = None
def get_onnx_params(self, position): def get_onnx_params(self, position):
self.models[position].get_onnx_params() self.models[position].get_onnx_params()
def forward(self, position, z, x, return_value=False, flags=None, debug=False): def forward(self, position, z, x, return_value=False, flags=None, debug=False):
if self.flags.enable_onnx and len(self.onnx_models) == 0:
self.set_onnx_model()
model = self.onnx_models[position] model = self.onnx_models[position]
if model is None: if model is None:
model = self.models[position] model = self.models[position]
@ -590,6 +604,7 @@ class Model:
return dict(action=action) return dict(action=action)
def share_memory(self): def share_memory(self):
if self.models['landlord'] is not None:
self.models['landlord'].share_memory() self.models['landlord'].share_memory()
self.models['landlord_up'].share_memory() self.models['landlord_up'].share_memory()
self.models['landlord_front'].share_memory() self.models['landlord_front'].share_memory()
@ -597,6 +612,7 @@ class Model:
self.models['bidding'].share_memory() self.models['bidding'].share_memory()
def eval(self): def eval(self):
if self.models['landlord'] is not None:
self.models['landlord'].eval() self.models['landlord'].eval()
self.models['landlord_up'].eval() self.models['landlord_up'].eval()
self.models['landlord_front'].eval() self.models['landlord_front'].eval()

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@ -83,8 +83,11 @@ def create_optimizers(flags, learner_model):
def act(i, device, batch_queues, model, flags, onnx_frame): 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']
if not flags.enable_onnx:
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))))
else:
model.models['bidding'].to(torch.device(device if device == "cpu" else ("cuda:" + str(device))))
try: try:
T = flags.unroll_length T = flags.unroll_length
log.info('Device %s Actor %i started.', str(device), i) log.info('Device %s Actor %i started.', str(device), i)
@ -117,9 +120,7 @@ def act(i, device, batch_queues, model, flags, onnx_frame):
last_onnx_frame = onnx_frame.value last_onnx_frame = onnx_frame.value
for p in positions: for p in positions:
if p != 'bidding': if p != 'bidding':
model_path = '%s/%s/model_%s.onnx' % (flags.savedir, flags.xpid, p) model.set_onnx_model()
if os.path.exists(model_path):
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'])

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@ -4,4 +4,4 @@ gitdb2
rlcard rlcard
psutil psutil
onnx onnx
onnxruntime onnxruntime-gpu