mirror of
https://github.com/hexastack/hexabot
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110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
"""Generic script to run any method in a TensorFlow model."""
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from argparse import ArgumentParser
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import json
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import os
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import sys
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import boilerplate as tfbp
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if __name__ == "__main__":
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if len(sys.argv) < 3:
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print(
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"Usage:\n New run: python run.py [method] [save_dir] [model] [data_loader]"
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" [hyperparameters...]\n Existing run: python run.py [method] [save_dir] "
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"[data_loader]? [hyperparameters...]",
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file=sys.stderr,
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)
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exit(1)
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# Avoid errors due to a missing `experiments` directory.
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if not os.path.exists("experiments"):
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os.makedirs("experiments")
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# Dynamically parse arguments from the command line depending on the model and data
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# loader provided. The `method` and `save_dir` arguments are always required.
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parser = ArgumentParser()
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parser.add_argument("method", type=str)
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parser.add_argument("save_dir", type=str)
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# If modules.json exists, the model and the data loader modules can be inferred from
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# `save_dir`, and the data loader can be optionally changed from its default.
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#
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# Note that we need to use `sys` because we need to read the command line args to
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# determine what to parse with argparse.
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modules_json_path = os.path.join("experiments", sys.argv[2], "modules.json")
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if os.path.exists(modules_json_path):
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with open(modules_json_path) as f:
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classes = json.load(f)
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Model = tfbp.get_model(classes["model"])
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else:
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Model = tfbp.get_model(sys.argv[3])
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parser.add_argument("model", type=str)
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if not os.path.exists(os.path.join("experiments", sys.argv[2])):
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os.makedirs(os.path.join("experiments", sys.argv[2]))
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with open(modules_json_path, "w") as f:
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json.dump(
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{"model": sys.argv[3]},
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f,
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indent=4,
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sort_keys=True,
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)
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args = {}
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saved_hparams = {}
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hparams_json_path = os.path.join("experiments", sys.argv[2], "hparams.json")
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if os.path.exists(hparams_json_path):
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with open(hparams_json_path) as f:
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saved_hparams = json.load(f)
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for name, value in Model.default_hparams.items():
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if name in saved_hparams:
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value = saved_hparams[name]
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args[name] = value
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# Add a keyword argument to the argument parser for each hyperparameter.
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for name, value in args.items():
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# Make sure to correctly parse hyperparameters whose values are lists/tuples.
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if type(value) in [list, tuple]:
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if not len(value):
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raise ValueError(
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f"Cannot infer type of hyperparameter `{name}`. Please provide a "
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"default value with nonzero length."
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)
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parser.add_argument(
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f"--{name}", f"--{name}_", nargs="+", type=type(value[0]), default=value
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)
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else:
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parser.add_argument(f"--{name}", type=type(value), default=value)
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# Collect parsed hyperparameters.
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FLAGS = parser.parse_args()
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kwargs = {k: v for k, v in FLAGS._get_kwargs()}
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for k in ["model", "save_dir"]:
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if k in kwargs:
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del kwargs[k]
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# Instantiate model and data loader.
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model = Model(os.path.join("experiments", FLAGS.save_dir), **kwargs)
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# Restore the model's weights, or save them for a new run.
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if os.path.isfile(os.path.join(model.save_dir, "checkpoint")):
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model.restore()
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else:
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model.save()
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# Run the specified model method.
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if FLAGS.method not in Model._methods:
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methods_str = "\n ".join(Model._methods.keys())
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raise ValueError(
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f"Model does not have a runnable method `{FLAGS.method}`. Methods available:"
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f"\n {methods_str}"
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)
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Model._methods[FLAGS.method](model)
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