62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
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import os.path
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import torch
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from douzero.dmc.file_writer import FileWriter
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from douzero.dmc.models import Model, OldModel
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from douzero.dmc.utils import get_batch, log, create_env, create_optimizers, act
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learner_model = Model(device="0")
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# lr=flags.learning_rate,
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# momentum=flags.momentum,
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# eps=flags.epsilon,
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# alpha=flags.alpha)
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class myflags:
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learning_rate=0.0003
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momentum=0
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alpha=0.99
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epsilon=1e-5
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flags = myflags
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checkpointpath = "merger/model.tar"
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merged_path = "merger/model_merged.tar"
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optimizers = create_optimizers(flags, learner_model)
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models = {}
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device_iterator = ["cpu"]
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for device in device_iterator:
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model = Model(device="cpu")
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model.share_memory()
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model.eval()
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models[device] = model
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checkpoint_states = torch.load(
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checkpointpath
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)
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print("Load original weights")
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for k in ['landlord', 'landlord_up', 'landlord_front', 'landlord_down', 'bidding']: # ['landlord', 'landlord_up', 'landlord_down']
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learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
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optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k])
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stats = checkpoint_states["stats"]
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print("Load replace weights")
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for k in ['landlord']:
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if not os.path.exists("merger/resnet_" + k + ".ckpt"):
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continue
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weights = torch.load("merger/resnet_" + k + ".ckpt", map_location="cuda:0")
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learner_model.get_model(k).load_state_dict(weights)
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learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k])
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frames = checkpoint_states["frames"]
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# frames = 3085177900
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position_frames = checkpoint_states["position_frames"]
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log.info(f"Resuming preempted job, current stats:\n{stats}")
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log.info('Saving checkpoint to %s', checkpointpath)
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_models = learner_model.get_models()
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torch.save({
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'model_state_dict': {k: _models[k].state_dict() for k in _models}, # {{"general": _models["landlord"].state_dict()}
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'optimizer_state_dict': {k: optimizers[k].state_dict() for k in optimizers}, # {"general": optimizers["landlord"].state_dict()}
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"stats": stats,
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'flags': checkpoint_states["flags"],
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'frames': frames,
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'position_frames': position_frames
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}, merged_path)
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