127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
import argparse
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def str2bool(v):
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if v.lower() in ("yes", "true", "t", "y", "1"):
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return True
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elif v.lower() in ("no", "false", "f", "n", "0"):
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return False
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else:
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raise argparse.ArgumentTypeError("Boolean value expected.")
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def get_args():
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"""
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Returns a namedtuple with arguments extracted from the command line.
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:return: A namedtuple with arguments
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"""
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parser = argparse.ArgumentParser(
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description="Welcome to the MLP course's Pytorch training and inference helper script"
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)
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parser.add_argument(
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"--batch_size",
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nargs="?",
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type=int,
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default=100,
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help="Batch_size for experiment",
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)
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parser.add_argument(
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"--continue_from_epoch",
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nargs="?",
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type=int,
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default=-1,
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help="Epoch you want to continue training from while restarting an experiment",
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)
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parser.add_argument(
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"--seed",
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nargs="?",
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type=int,
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default=7112018,
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help="Seed to use for random number generator for experiment",
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)
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parser.add_argument(
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"--image_num_channels",
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nargs="?",
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type=int,
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default=3,
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help="The channel dimensionality of our image-data",
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)
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parser.add_argument(
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"--image_height", nargs="?", type=int, default=32, help="Height of image data"
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)
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parser.add_argument(
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"--image_width", nargs="?", type=int, default=32, help="Width of image data"
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)
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parser.add_argument(
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"--num_stages",
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nargs="?",
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type=int,
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default=3,
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help="Number of convolutional stages in the network. A stage is considered a sequence of "
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"convolutional layers where the input volume remains the same in the spacial dimension and"
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" is always terminated by a dimensionality reduction stage",
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)
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parser.add_argument(
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"--num_blocks_per_stage",
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nargs="?",
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type=int,
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default=5,
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help="Number of convolutional blocks in each stage, not including the reduction stage."
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" A convolutional block is made up of two convolutional layers activated using the "
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" leaky-relu non-linearity",
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)
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parser.add_argument(
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"--num_filters",
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nargs="?",
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type=int,
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default=16,
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help="Number of convolutional filters per convolutional layer in the network (excluding "
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"dimensionality reduction layers)",
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)
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parser.add_argument(
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"--num_epochs",
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nargs="?",
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type=int,
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default=100,
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help="Total number of epochs for model training",
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)
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parser.add_argument(
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"--num_classes",
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nargs="?",
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type=int,
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default=100,
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help="Number of classes in the dataset",
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)
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parser.add_argument(
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"--experiment_name",
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nargs="?",
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type=str,
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default="exp_1",
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help="Experiment name - to be used for building the experiment folder",
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)
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parser.add_argument(
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"--use_gpu",
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nargs="?",
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type=str2bool,
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default=True,
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help="A flag indicating whether we will use GPU acceleration or not",
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)
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parser.add_argument(
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"--weight_decay_coefficient",
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nargs="?",
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type=float,
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default=0,
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help="Weight decay to use for Adam",
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)
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parser.add_argument(
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"--block_type",
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type=str,
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default="conv_block",
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help="Type of convolutional blocks to use in our network "
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"(This argument will be useful in running experiments to debug your network)",
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)
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args = parser.parse_args()
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print(args)
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return args
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