284 lines
7.9 KiB
Python
284 lines
7.9 KiB
Python
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"""
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author: Adnen Abdessaied
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maintainer: "Adnen Abdessaied"
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website: adnenabdessaied.de
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version: 1.0.1
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"""
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# --------------------------------------------------------
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# adapted from https://github.com/kexinyi/ns-vqa/blob/master/scene_parse/attr_net/options.py
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# --------------------------------------------------------
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import argparse
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import os
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import utils
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import torch
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class Options():
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def __init__(self):
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self.parser = argparse.ArgumentParser()
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self.initialized = False
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def initialize(self):
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self.parser.add_argument(
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'--mode',
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required=True,
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type=str,
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choices=['train', 'test'],
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help='The mode of the experiment')
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self.parser.add_argument(
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'--run_dir',
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required=True,
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type=str,
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help='The experiment directory')
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self.parser.add_argument(
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'--load_checkpoint_path',
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default=None,
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type=str,
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help='The path the the pretrained CaptionNet')
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self.parser.add_argument(
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'--res_path',
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required=True,
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type=str,
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help='Path where to log the predicted caption programs')
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self.parser.add_argument(
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'--gpu_ids',
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default='0',
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type=str,
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help='Id of the gpu to be used')
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self.parser.add_argument(
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'--seed',
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default=42,
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type=int,
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help='The seed used in training')
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self.parser.add_argument(
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'--dataPathTr',
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required=True,
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type=str,
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help='Path to the h5 file of the Clevr-Dialog preprocessed training data')
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self.parser.add_argument(
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'--dataPathVal',
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required=True,
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type=str,
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help='Path to the h5 file of the Clevr-Dialog preprocessed validation data')
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self.parser.add_argument(
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'--dataPathTest',
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required=True,
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type=str,
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help='Path to the h5 file of the Clevr-Dialog preprocessed test data')
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self.parser.add_argument(
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'--vocabPath',
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required=True,
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type=str,
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help='Path to the generated vocabulary')
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self.parser.add_argument(
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'--batch_size',
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default=64,
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type=int,
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help='Batch size')
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self.parser.add_argument(
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'--num_workers',
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default=0,
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type=int,
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help='Number of workers for loading')
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self.parser.add_argument(
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'--num_iters',
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default=5000,
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type=int,
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help='Total number of iterations')
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self.parser.add_argument(
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'--display_every',
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default=5,
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type=int,
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help='Display training information every N iterations')
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self.parser.add_argument(
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'--debug_every',
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default=100,
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type=int,
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help='Display debug message every N iterations')
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self.parser.add_argument(
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'--validate_every',
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default=1000,
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type=int,
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help='Validate every N iterations')
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self.parser.add_argument(
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'--shuffle_data',
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default=1,
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type=int,
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help='Activate to shuffle the training data')
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self.parser.add_argument(
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'--optim',
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default='adam',
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type=str,
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help='The name of the optimizer to be used')
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self.parser.add_argument(
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'--lr',
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default=1e-3,
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type=float,
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help='Base learning rate')
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self.parser.add_argument(
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'--betas',
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default='0.9, 0.98',
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type=str,
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help='Adam optimizer\'s betas')
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self.parser.add_argument(
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'--eps',
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default='1e-9',
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type=float,
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help='Adam optimizer\'s epsilon')
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self.parser.add_argument(
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'--lr_decay_marks',
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default='50000, 55000',
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type=str,
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help='Learing rate decay marks')
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self.parser.add_argument(
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'--lr_decay_factor',
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default=0.5,
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type=float,
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help='Learning rate decay factor')
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self.parser.add_argument(
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'--weight_decay',
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default=1e-6,
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type=float,
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help='Weight decay')
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self.parser.add_argument(
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'--embedDim',
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default=300,
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type=int,
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help='Embedding dimension')
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self.parser.add_argument(
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'--hiddenDim',
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default=512,
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type=int,
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help='LSTM hidden dimension')
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self.parser.add_argument(
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'--numLayers',
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default=2,
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type=int,
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help='Number of hidden LSTM layers')
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self.parser.add_argument(
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'--dropout',
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default=0.1,
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type=float,
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help='Dropout value')
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self.parser.add_argument(
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'--multiHead',
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default=8,
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type=int,
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help='Number of attention heads')
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self.parser.add_argument(
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'--hiddenSizeHead',
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default=64,
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type=int,
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help='Dimension of each attention head')
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self.parser.add_argument(
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'--FeedForwardSize',
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default=2048,
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type=int,
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help='Dimension of the feed forward layer')
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self.parser.add_argument(
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'--FlatMLPSize',
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default=512,
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type=int,
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help='MLP flatten size')
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self.parser.add_argument(
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'--FlatGlimpses',
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default=1,
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type=int,
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help='Number of flatten glimpses')
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self.parser.add_argument(
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'--FlatOutSize',
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default=512,
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type=int,
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help='Final attention reduction dimension')
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self.parser.add_argument(
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'--layers',
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default=6,
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type=int,
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help='Number of self attention layers')
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self.parser.add_argument(
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'--bidirectional',
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default=1,
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type=int,
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help='Activate to use bidirectional LSTMs')
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self.initialized = True
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def parse(self):
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# initialize parser
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if not self.initialized:
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self.initialize()
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self.opts = self.parser.parse_args()
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# parse gpu id list
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str_gpu_ids = self.opts.gpu_ids.split(',')
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self.opts.gpu_ids = []
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for str_id in str_gpu_ids:
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if str_id.isdigit() and int(str_id) >= 0:
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self.opts.gpu_ids.append(int(str_id))
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if len(self.opts.gpu_ids) > 0 and torch.cuda.is_available():
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print('\n[INFO] Using {} CUDA device(s) ...'.format(len(self.opts.gpu_ids)))
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else:
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print('\n[INFO] Using cpu ...')
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self.opts.gpu_ids = []
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# parse the optimizer's betas and lr decay marks
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self.opts.betas = [float(beta) for beta in self.opts.betas.split(',')]
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lr_decay_marks = [int(m) for m in self.opts.lr_decay_marks.split(',')]
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for i in range(1, len(lr_decay_marks)):
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assert lr_decay_marks[i] > lr_decay_marks[i-1]
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self.opts.lr_decay_marks = lr_decay_marks
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# print and save options
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args = vars(self.opts)
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print('\n ' + 30*'-' + 'Opts' + 30*'-')
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for k, v in args.items():
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print('%s: %s' % (str(k), str(v)))
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if not os.path.isdir(self.opts.run_dir):
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os.makedirs(self.opts.run_dir)
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filename = 'opts.txt'
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file_path = os.path.join(self.opts.run_dir, filename)
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with open(file_path, 'wt') as fout:
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fout.write('| options\n')
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for k, v in sorted(args.items()):
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fout.write('%s: %s\n' % (str(k), str(v)))
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return self.opts
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