Douzero_Resnet/model_merger.py

import os.path

import torch
from douzero.dmc.file_writer import FileWriter
from douzero.dmc.models import Model, OldModel
from douzero.dmc.utils import get_batch, log, create_env, create_optimizers, act

learner_model = Model(device="0")
# lr=flags.learning_rate,
# momentum=flags.momentum,
# eps=flags.epsilon,
# alpha=flags.alpha)
class myflags:
    learning_rate=0.0003
    momentum=0
    alpha=0.99
    epsilon=1e-5

flags = myflags
checkpointpath = "merger/model.tar"
merged_path = "merger/model_merged.tar"
optimizers = create_optimizers(flags, learner_model)
models = {}
device_iterator = ["cpu"]
for device in device_iterator:
    model = Model(device="cpu")
    model.share_memory()
    model.eval()
    models[device] = model
checkpoint_states = torch.load(
    checkpointpath
)
print("Load original weights")
for k in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']: # ['landlord', 'landlord_up', 'landlord_down']
    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])
stats = checkpoint_states["stats"]

print("Load replace weights")
for k in ['landlord']:
    if not os.path.exists("merger/resnet_" + k + ".ckpt"):
        continue
    weights = torch.load("merger/resnet_" + k + ".ckpt", map_location="cuda:0")
    learner_model.get_model(k).load_state_dict(weights)
    learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])

frames = checkpoint_states["frames"]
# frames = 3085177900
position_frames = checkpoint_states["position_frames"]
log.info(f"Resuming preempted job, current stats:\n{stats}")

log.info('Saving checkpoint to %s', checkpointpath)
_models = learner_model.get_models()

torch.save({
    'model_state_dict': {k: _models[k].state_dict() for k in _models},  # {{"general": _models["landlord"].state_dict()}
    'optimizer_state_dict': {k: optimizers[k].state_dict() for k in optimizers},  # {"general": optimizers["landlord"].state_dict()}
    "stats": stats,
    'flags': checkpoint_states["flags"],
    'frames': frames,
    'position_frames': position_frames
}, merged_path)