88 lines
4.0 KiB
Python
88 lines
4.0 KiB
Python
import argparse
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import json
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import os
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import sys
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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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parser.add_argument('--batch_size', nargs="?", type=int, default=100, help='Batch_size for experiment')
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parser.add_argument('--continue_from_epoch', nargs="?", type=int, default=-1, help='Batch_size for experiment')
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parser.add_argument('--dataset_name', type=str, help='Dataset on which the system will train/eval our model')
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parser.add_argument('--seed', nargs="?", type=int, default=7112018,
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help='Seed to use for random number generator for experiment')
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parser.add_argument('--image_num_channels', nargs="?", type=int, default=1,
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help='The channel dimensionality of our image-data')
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parser.add_argument('--image_height', nargs="?", type=int, default=28, help='Height of image data')
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parser.add_argument('--image_width', nargs="?", type=int, default=28, help='Width of image data')
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parser.add_argument('--dim_reduction_type', nargs="?", type=str, default='strided_convolution',
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help='One of [strided_convolution, dilated_convolution, max_pooling, avg_pooling]')
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parser.add_argument('--num_layers', nargs="?", type=int, default=4,
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help='Number of convolutional layers in the network (excluding '
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'dimensionality reduction layers)')
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parser.add_argument('--num_filters', nargs="?", type=int, default=64,
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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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parser.add_argument('--num_epochs', nargs="?", type=int, default=100, help='The experiment\'s epoch budget')
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parser.add_argument('--experiment_name', nargs="?", type=str, default="exp_1",
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help='Experiment name - to be used for building the experiment folder')
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parser.add_argument('--use_gpu', nargs="?", type=str2bool, default=False,
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help='A flag indicating whether we will use GPU acceleration or not')
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parser.add_argument('--weight_decay_coefficient', nargs="?", type=float, default=1e-05,
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help='Weight decay to use for Adam')
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parser.add_argument('--filepath_to_arguments_json_file', nargs="?", type=str, default=None,
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help='')
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args = parser.parse_args()
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if args.filepath_to_arguments_json_file is not None:
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args = extract_args_from_json(json_file_path=args.filepath_to_arguments_json_file, existing_args_dict=args)
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arg_str = [(str(key), str(value)) for (key, value) in vars(args).items()]
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print(arg_str)
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import torch
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if torch.cuda.is_available(): # checks whether a cuda gpu is available and whether the gpu flag is True
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device = torch.cuda.current_device()
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print("use {} GPU(s)".format(torch.cuda.device_count()), file=sys.stderr)
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else:
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print("use CPU", file=sys.stderr)
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device = torch.device('cpu') # sets the device to be CPU
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return args, device
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class AttributeAccessibleDict(object):
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def __init__(self, adict):
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self.__dict__.update(adict)
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def extract_args_from_json(json_file_path, existing_args_dict=None):
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summary_filename = json_file_path
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with open(summary_filename) as f:
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arguments_dict = json.load(fp=f)
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for key, value in vars(existing_args_dict).items():
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if key not in arguments_dict:
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arguments_dict[key] = value
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arguments_dict = AttributeAccessibleDict(arguments_dict)
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return arguments_dict
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