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imdb_main_WAG_full_classifier.py
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###############################################################################
# Author: Wasi Ahmad
# Project: Biattentive Classification Network for Sentence Classification
# Date Created: 01/06/2018
#
# File Description: This script is the entry point of the entire pipeline.
###############################################################################
import util, helper, data, os, sys, numpy, torch, pickle, json
import imdb_train_WAG_full_classifier as train
from torch import optim
from model import BCN
from selector_model import Selector
from torch.autograd import Variable
from os import listdir
from os.path import isfile, join
args = util.get_args()
# if output directory doesn't exist, create it
if not os.path.exists(args.output_base_path):
os.makedirs(args.output_base_path)
### selected versions
sparsity_list_coherent2 = [ 0.015, 0.01, 0.00075, 0.0005]#, 0.0001 ]
sparsity_list_coherent1 = [ 0.00075, 0.0005]
# Set the random seed manually for reproducibility.
numpy.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
if not args.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
else:
torch.cuda.manual_seed(args.seed)
print('\ncommand-line params : {0}\n'.format(sys.argv[1:]))
print('{0}\n'.format(args))
sparsity_list = [0.00075]
coherent_list = [1.0]
###############################################################################
# Load data
###############################################################################
# load train and dev dataset
train_corpus = data.Corpus(args.tokenize)
train_corpus_temp = data.Corpus(args.tokenize)
dev_corpus = data.Corpus(args.tokenize)
test_corpus = data.Corpus(args.tokenize)
ori_train_size = -1
task_names = ['snli', 'multinli'] if args.task == 'allnli' else [args.task]
for task in task_names:
if 'IMDB' in task:
###############################################################################
# Load Learning to Skim paper's Pickle file
###############################################################################
train_d, dev_d, test_d = helper.get_splited_imdb_data(args.output_base_path+task+'/'+'imdb.p', SAG = args.SAG)
train_corpus_temp.parse(train_d, task, args.max_example)
dev_corpus.parse(dev_d, task, args.max_example)
test_corpus.parse(test_d, task, args.max_example)
ori_train_size = len(train_corpus_temp.data)
else:
train_corpus_temp.parse(args.output_base_path + task + '/train.txt', task, args.max_example)
ori_train_size = len(train_corpus_temp.data)
if task == 'multinli':
dev_corpus.parse(args.output_base_path + task + '/dev_matched.txt', task, args.tokenize)
test_corpus.parse(args.output_base_path + task + '/test_matched.txt', task, args.tokenize)
else:
dev_corpus.parse(args.output_base_path + task + '/dev.txt', task, args.tokenize)
test_corpus.parse(args.output_base_path + task + '/test.txt', task, args.tokenize)
dir_path = args.output_base_path+args.task+'/'+'selected/'
file_prefix ='train_selected_'
file_suffix = '.txt'
onlyfiles = [f for f in listdir(dir_path) if isfile(join(dir_path, f))]
for file_name in onlyfiles:
if file_name.startswith(file_prefix) and file_name.endswith(file_suffix):
file_name = join(dir_path, file_name)
train_d_temp, _, _ = helper.get_splited_imdb_data(file_name)
print('original train set size = ', len(train_corpus_temp.data))
train_corpus_temp.parse(train_d_temp, 'IMDB', args.max_example) # Although RT but selected is saved in IMDB format
print('augmented train set size = ', len(train_corpus_temp.data))
ncorpus = len(train_corpus_temp.data)//ori_train_size
for i in range(ori_train_size):
for j in range(ncorpus):
train_corpus.data.append(train_corpus_temp.data[j*ori_train_size+i])
print('final train corpus size: ', len(train_corpus.data))
# for sp in sparsity_list_coherent2:
# coherent_list = [2.0]
# for ch in coherent_list:
# file_name = dir_path+file_prefix+'sparsity_'+str(sp)+'_coherent_'+str(ch)+file_suffix
# if 'train' in file_prefix:
# train_d_temp, _, _ = helper.get_splited_imdb_data(file_name)
# print('original train set size = ', len(train_corpus.data))
# train_corpus.parse(train_d_temp, task, args.max_example)
# print('augmented train set size = ', len(train_corpus.data))
# for sp in sparsity_list_coherent1:
# coherent_list = [1.0]
# for ch in coherent_list:
# file_name = dir_path+file_prefix+'sparsity_'+str(sp)+'_coherent_'+str(ch)+file_suffix
# if 'train' in file_prefix:
# train_d_temp, _, _ = helper.get_splited_imdb_data(file_name)
# print('original train set size = ', len(train_corpus.data))
# train_corpus.parse(train_d_temp, task, args.max_example)
# print('augmented train set size = ', len(train_corpus.data))
if args.debug:
threshold_examples = 20
mid_train = int(len(train_corpus.data)/2)
mid_dev = int(len(dev_corpus.data)/2)
mid_test = int(len(test_corpus.data)/2)
train_corpus.data = train_corpus.data[mid_train-threshold_examples:mid_train+threshold_examples]
dev_corpus.data = dev_corpus.data[mid_dev-threshold_examples:mid_dev+threshold_examples]
test_corpus.data = test_corpus.data[mid_test-threshold_examples:mid_test+threshold_examples]
print('train set size = ', len(train_corpus.data))
print('development set size = ', len(dev_corpus.data))
print('test set size = ', len(test_corpus.data))
# save the dictionary object to use during testing
if os.path.exists(args.output_base_path + args.task+'/'+ 'dictionary.p'):
print('loading dictionary')
dictionary = helper.load_object(args.output_base_path + args.task+'/'+ 'dictionary.p')
else:
dictionary = data.Dictionary()
dictionary.build_dict(train_corpus.data + dev_corpus.data + test_corpus.data, args.max_words)
helper.save_object(dictionary, args.output_base_path + args.task+'/'+'dictionary.p')
print('vocabulary size = ', len(dictionary))
embeddings_index = helper.load_word_embeddings(args.word_vectors_directory, args.word_vectors_file, dictionary.word2idx)
print('number of OOV words = ', len(dictionary) - len(embeddings_index))
# ###############################################################################
# # Build the model
# ###############################################################################
model = BCN(dictionary, embeddings_index, args)
selector = Selector(dictionary, embeddings_index, args)
print (selector)
print (model)
optim_fn_selector, optim_params_selector = helper.get_optimizer(args.optimizer)
optimizer_selector = optim_fn_selector(filter(lambda p: p.requires_grad, selector.parameters()), **optim_params_selector)
optim_fn, optim_params = helper.get_optimizer(args.optimizer)
optimizer = optim_fn(filter(lambda p: p.requires_grad, model.parameters()), **optim_params)
best_acc = 0
param_dict_selector = helper.count_parameters(selector)
param_dict = helper.count_parameters(model)
print('number of trainable parameters = ', numpy.sum(list(param_dict_selector.values())), numpy.sum(list(param_dict.values())), numpy.sum(list(param_dict.values())) + numpy.sum(list(param_dict_selector.values())) )
if args.cuda:
torch.cuda.set_device(args.gpu)
selector = selector.cuda()
model = model.cuda()
if args.load_model == 0 or args.load_model==2:
print('loading selector')
helper.load_model(selector, args.output_base_path+args.task+'/'+ args.selector_file_name, 'selector', args.cuda)
if args.load_model == 1 or args.load_model==2:
print('loading classifier')
helper.load_model(model, args.output_base_path+args.task+'/'+args.classifier_file_name, 'state_dict', args.cuda)
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = helper.load_checkpoint(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc = checkpoint['best_acc']
selector.load_state_dict(checkpoint['selector'])
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> Both Selector and BCN classifier aare loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# ###############################################################################
# # Train the model
# ###############################################################################
# train = train.Train(model, optimizer, selector, optimizer_selector, dictionary, args, best_acc)
train = train.Train(model, optimizer, dictionary, args, best_acc)
train.train_epochs(train_corpus, dev_corpus, test_corpus, args.start_epoch, args.epochs)