/
opts.py
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/
opts.py
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"""Argument parser for genral and model / data specific options."""
import os
import argparse
import torch
import utils
class Opts():
"""All configurations and hyperparameters."""
def __init__(self):
"""Process calling arguments."""
self.parse()
self.args.data = os.path.join(self.args.rootDir, self.args.dataset)
self.args.model = os.path.join(self.args.rootDir, 'exp')
# Set torch default tensor type and random seed
torch.set_default_tensor_type('torch.FloatTensor')
torch.manual_seed(self.args.manualSeed)
# Indicate the model type. This indicates what is expected from
# the lattice objects
if self.args.lattice_type.lower() == 'grapheme':
lattice_type_tag = 'G'
elif self.args.lattice_type.lower() == 'word':
self.args.grapheme_features = 0
self.args.grapheme_hidden_size = 0
self.args.grapheme_combination = 'None'
lattice_type_tag = 'W'
else:
raise Exception('Not a valid lattice type')
# Customized parameters for the network
arch = self.args.arch.split('-')
assert len(arch) == 4, 'bad architecture input argument'
self.args.nLSTMLayers = int(arch[0])
self.args.hiddenSize = int(arch[1])
self.args.nFCLayers = int(arch[2])
self.args.linearSize = int(arch[3])
self.args.bidirectional = True
# Grapheme mergering architecture
grapheme_arch = self.args.grapheme_arch.split('-')
assert len(grapheme_arch) == 2, 'bad grapheme model architecture input argument'
self.args.grapheme_num_layers = int(grapheme_arch[0])
self.args.grapheme_hidden_size = int(grapheme_arch[1])
self.args.grapheme_bidirectional = True
if self.args.grapheme_combination == 'None':
# Won't read in the grapheme information
self.args.grapheme_features = 0
self.args.grapheme_hidden_size = 0
# Customized parameters for dataset
if 'onebest' in self.args.dataset:
# TODO: Test this input size
print('Warning: Untested code')
self.args.inputSize = 52
self.args.onebest = True
elif self.args.dataset.startswith('lattice') or self.args.dataset.endswith('-lat'):
# Is a lattice dataset
self.args.inputSize = 54
if self.args.grapheme_encoding:
self.args.inputSize += self.args.grapheme_hidden_size * 2
else:
self.args.inputSize += self.args.grapheme_features
elif self.args.dataset.startswith('confnet') or self.args.dataset.endswith('-cn'):
# Is a confusion network dataset
self.args.inputSize = 52
if 'lm' in self.args.dataset and 'am' in self.args.dataset:
# Include LM and AM
self.args.inputSize += 2
if self.args.grapheme_encoding:
self.args.inputSize += self.args.grapheme_hidden_size * 2
else:
self.args.inputSize += self.args.grapheme_features
else:
# TODO: Make cleaner
self.args.inputSize = 54 + self.args.grapheme_features
# raise ValueError('Expecting the dataset name to indicate if 1-best, lattice, or confusion network')
if self.args.forceInputSize != -1:
# Override implicit input size code above
self.args.inputSize = self.args.forceInputSize
if self.args.arc_combine_method == 'attention':
self.args.attentionLayers = 1
self.args.attentionSize = 64
# Settings for debug mode
if self.args.debug:
self.args.nEpochs = 2
self.args.nThreads = 1
# Build a useful hash key and model directory
if self.args.grapheme_encoding:
assert self.args.encoding_dropout <= 1 and self.args.encoding_dropout >= 0, \
'The dropout CLI argument must be a valid ratio'
grapheme_encoding_tag = 'E=' + str(self.args.grapheme_arch) + '-' + str(self.args.encoding_dropout) + '-' + str(self.args.encoder_type)
else:
self.args.encoding_dropout = 0
grapheme_encoding_tag = 'E=None'
# Setup model directory
self.args.hashKey = self.args.dataset \
+ '_' + self.args.arch \
+ '_' + self.args.arc_combine_method \
+ '_' + 'L='+str(self.args.LR) \
+ '_' + 'M='+str(self.args.momentum) \
+ '_' + 'S='+str(self.args.batchSize) \
+ '_' + 'O='+str(self.args.optimizer) \
+ '_' + 'D='+self.args.LRDecay \
+ '-' + str(self.args.LRDParam) \
+ '_' + str(lattice_type_tag) \
+ '_' + grapheme_encoding_tag \
+ '_' + 'F='+str(self.args.grapheme_features) \
+ '_' + 'G-C='+str(self.args.grapheme_combination) \
+ '_' + self.args.suffix
if self.args.debug:
self.args.hashKey += '_debug'
self.args.resume = os.path.join(self.args.model, self.args.hashKey)
utils.mkdir(self.args.resume)
# Display all options
utils.print_options(self.args)
def parse(self):
"""Parsing calling arguments."""
parser = argparse.ArgumentParser(description='parser for latticeRNN')
# General options
parser.add_argument('--debug', default=False, action="store_true",
help='Debug mode, only run 2 epochs and 1 thread')
parser.add_argument('--manualSeed', default=1, type=int,
help='Manual seed')
# Path options
parser.add_argument('--rootDir', type=str, required=True,
help='path to experiment root directory')
# Data options
parser.add_argument('--dataset', default='lattice_mapped_0.1_prec', type=str,
help='Name of dataset')
parser.add_argument('--target', default='target', type=str,
help='Name of target directory within the data directory')
parser.add_argument('--nThreads', default=10, type=int,
help='Number of data loading threads')
parser.add_argument('--trainPctg', default=1.0, type=float,
help='Percentage of taining data to use')
parser.add_argument('--shuffle', default=False, action="store_true",
help='Flag to shuffle the dataset before training')
parser.add_argument('--subtrain', default=False, action='store_true',
help='Run training on a subset of the dataset, but cross validation and test on the full sets')
parser.add_argument('--forceInputSize', default=-1, type=int,
help='Explicitly dictate the number of features to use')
# Grapheme data options
parser.add_argument('--lattice-type', default='word', choices=['grapheme', 'word'],
help='Indicate whether the grapheme information should be read from the lattice or not.')
parser.add_argument('--grapheme-features', default=5, type=int,
help='The number of grapheme features to consider, if any exists in the data.')
# Training/testing options
parser.add_argument('--nEpochs', default=15, type=int,
help='Number of total epochs to run')
parser.add_argument('--epochNum', default=0, type=int,
help='0=retrain|-1=latest|-2=best',
choices=[0, -1, -2])
parser.add_argument('--batchSize', default=1, type=int,
help='Mini-batch size')
parser.add_argument('--saveOne', default=False, action="store_true",
help='Only preserve one saved model')
parser.add_argument('--valOnly', default=False, action="store_true",
help='Run on validation set only')
parser.add_argument('--testOnly', default=False, action="store_true",
help='Run the test to see the performance')
parser.add_argument('--onebest', default=False, action="store_true",
help='Train on one-best path only')
# Optimization options
parser.add_argument('--LR', default=0.05, type=float,
help='Initial learning rate')
parser.add_argument('--LRDecay', default='none', type=str,
help='Learning rate decay method',
choices=['anneal', 'stepwise', 'newbob', 'none'])
parser.add_argument('--LRDParam', default=0.5, type=float,
help='Param for learning rate decay')
parser.add_argument('--momentum', default=0.5, type=float,
help='Momentum')
parser.add_argument('--weightDecay', default=1e-3, type=float,
help='Weight decay')
parser.add_argument('--clip', default=1.0, type=float,
help='Gradient clipping')
parser.add_argument('--optimizer', default='SGD', type=str,
help='Optimizer type',
choices=['SGD', 'Adam'])
# Word level model options
parser.add_argument('--init-word', default='kaiming_normal', type=str,
help='Initialisation method for linear layers',
choices=['uniform', 'normal',
'xavier_uniform', 'xavier_normal',
'kaiming_uniform', 'kaiming_normal'])
parser.add_argument('--arch', default='1-128-1-128', type=str,
help='Model architecture: '\
'nLSTMLayer-LSTMSize-nFCLayer-nFCSize')
parser.add_argument('--arc_combine-method', default='mean', type=str,
help='method for combining edges',
choices=['mean', 'max', 'posterior', 'attention'])
# Grapheme level model options
parser.add_argument('--init-grapheme', default='kaiming_normal', type=str,
help='Initialisation method for linear layers',
choices=['uniform', 'normal',
'xavier_uniform', 'xavier_normal',
'kaiming_uniform', 'kaiming_normal'])
parser.add_argument('--grapheme-combination', default='None', type=str,
help='The method to use for grapheme combination',
choices=['None', 'dot', 'mult', 'concat', 'scaled-dot', 'concat-enc-key'])
parser.add_argument('--grapheme-encoding', default=False, action="store_true",
help='Use a bidirectional recurrent structure to encode the grapheme information')
parser.add_argument('--encoder-type', default='RNN', type=str,
help='The type of bidirectional recurrent encoder to use for grapheme combination',
choices=['RNN', 'GRU', 'LSTM'])
parser.add_argument('--encoding-dropout', default=0, type=float,
help='The amount of dropout to apply in the bidirectional grapheme encoding')
parser.add_argument('--grapheme-arch', default='1-10', type=str,
help='Grapheme model architecture: num_layers-layer_size')
# Naming options
parser.add_argument('--suffix', default='LatticeRNN', type=str,
help='Suffix for saving the model')
self.args = parser.parse_args()