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seq2seq.py
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seq2seq.py
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import random
from utils import *
import time
import math
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import pdb
import os
import numpy as np
import argparse
parser = argparse.ArgumentParser(description='Train on SCAN splits.')
parser.add_argument('-v', '--variant', dest='variant', default='default',
help='type of split. One of default, inter, inter2, copy, copy_out')
parser.add_argument('-s', '--split', type=str, dest='split',
default='scan', help='desired split name, example: \'scan\' or \'jump_around_right\'')
parser.add_argument('-m', '--model', type=str, dest='model',
default='GRU', help='Model to train with. Choices: \'GRU\', \'GRU_A\', \'LSTM\'')
parser.add_argument('-g', '--gpu', type=int, dest='gpu',
default='0', help='gpu to train on')
parser.add_argument('-i', '--iters', type=int, dest='iters',
default='100000', help='Number of iterations to train')
parser.add_argument('-t', '--tag', type=str, dest='tag',
default=None, help='Optional tag to append to saved results file name, which is by default just the split name, model, and iters')
args = parser.parse_args()
assert args.variant in ['default', 'inter', 'inter2', 'copy', 'copy_out'], 'invalid argument'
def data_suffix(variant):
if variant == 'default':
return '.txt'
elif variant == 'inter':
return '_inter.txt'
elif variant == 'inter2':
return '_inter2.txt'
elif variant == 'copy':
return '_copy.txt'
elif variant == 'copy_out':
return '_copy_out.txt'
else:
assert False, 'invalid variant'
# use full data for determining input/output language, in case splits somehow change it
full_data = SCAN_DIR + 'tasks' + data_suffix(args.variant)
INPUT_LANG, OUTPUT_LANG, full_pairs = prepareData('scan_in', 'scan_out', full_data, False)
# used for train as well as test splits
# I add one for the <EOS> tag. Not sure if necessary but can't hurt.
# MAX_LENGTH needs to account for input length too!
MAX_LENGTH = max(max(len(pair[1].split(' ')), len(pair[0].split(' '))) for pair in full_pairs) + 1
if not torch.cuda.is_available():
print('GPU unavailable, training with CPU')
DEVICE = torch.device('cpu')
else:
DEVICE = torch.device('cuda:{}'.format(args.gpu))
print('Training on {}'.format(DEVICE))
"""
Helpers
"""
def asMinutes(s):
m = math.floor(s / 60)
s -= m * 60
return '%dm %ds' % (m, s)
def timeSince(since, percent):
now = time.time()
s = now - since
es = s / (percent)
rs = es - s
return '%s (- %s)' % (asMinutes(s), asMinutes(rs))
# Get Sentences
def indicesFromSentence(lang, sentence):
return [lang.word2index[word] for word in sentence.split(' ')]
def tensorFromSentence(lang, sentence, device):
indices = indicesFromSentence(lang, sentence)
indices.append(EOS_token)
return torch.tensor(indices, dtype=torch.long, device=device).view(-1, 1)
def tensorsFromPair(input_lang, output_lang, pair, device):
input_tensor = tensorFromSentence(input_lang, pair[0], device)
target_tensor = tensorFromSentence(output_lang, pair[1], device)
return (input_tensor, target_tensor)
"""
Model Architecture
"""
class EncoderRNN(nn.Module):
def __init__(self, device=None, input_size=None, hidden_size=None, dropout=.5):
super(EncoderRNN, self).__init__()
self.hidden_size = hidden_size
self.device = device
self.embedding = nn.Embedding(input_size, hidden_size)
self.rnn = nn.GRU(hidden_size, hidden_size)
self.dropout = nn.Dropout(dropout)
def forward(self, input, hidden):
# pdb.set_trace()
embedded = self.dropout(self.embedding(input).view(1, 1, -1))
output = embedded
output, hidden = self.rnn(output, hidden)
return output, hidden
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device=self.device)
class DecoderRNN(nn.Module):
def __init__(self, device, hidden_size, output_size, dropout=.5):
super(DecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.device = device
self.embedding = nn.Embedding(output_size, hidden_size)
self.rnn = nn.GRU(hidden_size, hidden_size)
self.out = nn.Linear(hidden_size, output_size)
self.softmax = nn.LogSoftmax(dim=1)
self.dropout = nn.Dropout(dropout)
def forward(self, input, hidden):
output = self.dropout(self.embedding(input).view(1, 1, -1))
output = F.relu(output)
output, hidden = self.rnn(output, hidden)
output = self.softmax(self.out(output[0]))
return output, hidden
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device=self.device)
class EncoderLSTM(nn.Module):
def __init__(self, device, input_size, hidden_size, n_layers=1, dropout=.5):
super(EncoderLSTM, self).__init__()
self.hidden_size = hidden_size
self.device = device
self.n_layers = n_layers
self.embedding = nn.Embedding(input_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, n_layers)
self.dropout = nn.Dropout(dropout)
def forward(self, input, hidden, cell):
# pdb.set_trace()
embedded = self.dropout(self.embedding(input).view(1, 1, -1))
output = embedded
output, (hidden, cell) = self.rnn(output, (hidden, cell))
return output, (hidden, cell)
def initHidden(self):
return (torch.zeros(self.n_layers, 1, self.hidden_size, device=self.device), torch.zeros(self.n_layers, 1, self.hidden_size, device=self.device))
class DecoderLSTM(nn.Module):
def __init__(self, device, hidden_size, output_size, n_layers=1, dropout=.5):
super(DecoderLSTM, self).__init__()
self.hidden_size = hidden_size
self.device = device
self.embedding = nn.Embedding(output_size, hidden_size)
self.rnn = nn.LSTM(hidden_size, hidden_size, n_layers)
self.out = nn.Linear(hidden_size, output_size)
# self.softmax = nn.LogSoftmax(dim=1)
self.dropout = nn.Dropout(dropout)
def forward(self, input, hidden, cell):
output = self.dropout(self.embedding(input).view(1, 1, -1))
output = F.relu(output)
output, (hidden, cell) = self.rnn(output, (hidden, cell))
output = self.out(output[0])
return output, (hidden, cell)
def initHidden(self):
return (torch.zeros(self.n_layers, 1, self.hidden_size, device=self.device), torch.zeros(self.n_layers, 1, self.hidden_size, device=self.device))
class AttnDecoderRNN(nn.Module):
def __init__(self, device, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):
super(AttnDecoderRNN, self).__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.dropout_p = dropout_p
self.max_length = max_length
self.device = device
self.embedding = nn.Embedding(self.output_size, self.hidden_size)
self.attn = nn.Linear(self.hidden_size * 2, self.max_length)
self.attn_combine = nn.Linear(self.hidden_size * 2, self.hidden_size)
self.dropout = nn.Dropout(self.dropout_p)
self.gru = nn.GRU(self.hidden_size, self.hidden_size)
self.out = nn.Linear(self.hidden_size, self.output_size)
def forward(self, input, hidden, encoder_outputs):
embedded = self.embedding(input).view(1, 1, -1)
embedded = self.dropout(embedded)
attn_weights = F.softmax(
self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)
attn_applied = torch.bmm(attn_weights.unsqueeze(0),
encoder_outputs.unsqueeze(0))
output = torch.cat((embedded[0], attn_applied[0]), 1)
output = self.attn_combine(output).unsqueeze(0)
output = F.relu(output)
output, hidden = self.gru(output, hidden)
output = F.log_softmax(self.out(output[0]), dim=1)
return output, hidden, attn_weights
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device=self.device)
"""
Training
"""
def train(device, input_tensor, target_tensor, encoder, decoder, model, encoder_optimizer,
decoder_optimizer, criterion):
gradient_clip = 5
teacher_forcing_ratio = 0.5
encoder_hidden = encoder.initHidden()
encoder_optimizer.zero_grad()
decoder_optimizer.zero_grad()
input_length = input_tensor.size(0)
target_length = target_tensor.size(0)
encoder_outputs = torch.zeros(MAX_LENGTH, encoder.hidden_size, device=device)
loss = 0
for ei in range(input_length):
if model == "LSTM":
encoder_output, encoder_hidden = encoder(
input_tensor[ei], *encoder_hidden)
else:
encoder_output, encoder_hidden = encoder(
input_tensor[ei], encoder_hidden)
encoder_outputs[ei] = encoder_output[0, 0]
decoder_input = torch.tensor([[SOS_token]], device=device)
decoder_hidden = encoder_hidden
use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
if use_teacher_forcing:
# Teacher forcing: Feed the target as the next input
for di in range(target_length):
if model == "LSTM":
decoder_output, decoder_hidden = decoder(
decoder_input, *decoder_hidden)
elif model == "GRU_A":
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
else:
decoder_output, decoder_hidden = decoder(
decoder_input, decoder_hidden)
loss += criterion(decoder_output, target_tensor[di])
decoder_input = target_tensor[di] # Teacher forcing
else:
# Without teacher forcing: use its own predictions as the next input
for di in range(target_length):
if model == "LSTM":
decoder_output, decoder_hidden = decoder(
decoder_input, *decoder_hidden)
elif model == "GRU_A":
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
else:
decoder_output, decoder_hidden = decoder(
decoder_input, decoder_hidden)
topv, topi = decoder_output.topk(1)
decoder_input = topi.squeeze().detach() # detach from history as input
loss += criterion(decoder_output, target_tensor[di])
if decoder_input.item() == EOS_token:
break
torch.nn.utils.clip_grad_norm_(encoder.parameters(), gradient_clip)
torch.nn.utils.clip_grad_norm_(decoder.parameters(), gradient_clip)
loss.backward()
encoder_optimizer.step()
decoder_optimizer.step()
return loss.item() / target_length
def trainIters(device, encoder, decoder, model, pairs, n_iters, print_every=1000, plot_every=100,
learning_rate=0.001):
print("Starting training: {} iterations".format(n_iters))
start = time.time()
plot_losses = []
print_loss_total = 0 # Reset every print_every
plot_loss_total = 0 # Reset every plot_every
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)
training_pairs = [tensorsFromPair(INPUT_LANG, OUTPUT_LANG, random.choice(pairs), device)
for i in range(n_iters)]
criterion = nn.NLLLoss()
evaluateRandomly(device, encoder, decoder, model, pairs, n = 100)
for i in range(1, n_iters + 1):
training_pair = training_pairs[i - 1]
input_tensor = training_pair[0]
target_tensor = training_pair[1]
loss = train(device, input_tensor, target_tensor, encoder,
decoder, model, encoder_optimizer, decoder_optimizer, criterion)
print_loss_total += loss
plot_loss_total += loss
if i % print_every == 0:
print_loss_avg = print_loss_total / print_every
print_loss_total = 0
print('Duration (Remaining): %s Iters: (%d %d%%) Loss avg: %.4f' % (timeSince(start, i / n_iters),
i, i / n_iters * 100, print_loss_avg))
evaluateRandomly(device, encoder, decoder, model, pairs, n = 100)
if i % plot_every == 0:
plot_loss_avg = plot_loss_total / plot_every
plot_losses.append(plot_loss_avg)
plot_loss_total = 0
# showPlot(plot_losses)
return plot_losses
"""
Evaluation
"""
def evaluate(device, encoder, decoder, model, sentence):
with torch.no_grad():
input_tensor = tensorFromSentence(INPUT_LANG, sentence, device)
input_length = input_tensor.size()[0]
encoder_hidden = encoder.initHidden()
encoder_outputs = torch.zeros(MAX_LENGTH, encoder.hidden_size, device=device)
for ei in range(input_length):
if model == "LSTM":
encoder_output, encoder_hidden = encoder(input_tensor[ei],
*encoder_hidden)
else:
encoder_output, encoder_hidden = encoder(input_tensor[ei],
encoder_hidden)
encoder_outputs[ei] += encoder_output[0, 0]
decoder_input = torch.tensor([[SOS_token]], device=device) # SOS
decoder_hidden = encoder_hidden
decoded_words = []
decoder_attentions = torch.zeros(MAX_LENGTH, MAX_LENGTH, device=device)
for di in range(MAX_LENGTH):
if model == "GRU_A":
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
decoder_attentions[di] = decoder_attention.data
elif model == "LSTM":
decoder_output, decoder_hidden = decoder(
decoder_input, *decoder_hidden)
else:
decoder_output, decoder_hidden = decoder(
decoder_input, decoder_hidden)
topv, topi = decoder_output.topk(1)
decoder_input = topi.squeeze().detach() # detach from history as input
if topi.item() == EOS_token:
decoded_words.append('<EOS>')
break
else:
decoded_words.append(OUTPUT_LANG.index2word[topi.item()])
decoder_input = topi.squeeze().detach()
return decoded_words
def evaluateTestSet(device, encoder, decoder, model, pairs):
encoder.eval()
decoder.eval()
with torch.no_grad():
hits = 0
for pair in pairs:
output_words = evaluate(device, encoder, decoder, model, pair[0])
output_sentence = ' '.join(output_words)
if output_words[-1] == '<EOS>':
output_sentence = ' '.join(output_words[:-1])
if pair[1] == output_sentence:
hits += 1
else:
assert len(output_words) == MAX_LENGTH, str.format(
'unexpected length: {} but max is {}',
len(output_words), MAX_LENGTH)
print('Evaluation accuracy: {}/{} = {:.2f}%'.format(hits, len(pairs),
hits/len(pairs)))
encoder.train()
decoder.train()
return hits/len(pairs)
def evaluateRandomly(device, encoder, decoder, model, pairs, n=10, verbose=False):
encoder.eval()
decoder.eval()
with torch.no_grad():
hits = 0
for i in range(n):
pair = random.choice(pairs)
if verbose:
print('>', pair[0])
print('=', pair[1])
output_words = evaluate(device, encoder, decoder, model, pair[0])
output_sentence = ' '.join(output_words)
if verbose:
print('<', output_sentence)
if output_words[-1] == '<EOS>':
output_sentence = ' '.join(output_words[:-1])
if pair[1] == output_sentence:
hits += 1
if verbose:
print('')
encoder.train()
decoder.train()
print('Hits {}/{} test samples'.format(hits, n))
def saveModel(encoder, decoder, checkpoint, path):
checkpoint['encoder_state_dict'] = encoder.state_dict()
checkpoint['decoder_state_dict'] = decoder.state_dict()
checkpoint['hidden_size'] = encoder.hidden_size
checkpoint['dropout'] = encoder.dropout.p
try:
checkpoint['n_layers'] = encoder.n_layers
except AttributeError:
pass
torch.save(checkpoint, path)
del checkpoint['encoder_state_dict']
del checkpoint['decoder_state_dict']
with open(path[:-3] + '.txt', 'w+') as f:
f.write(str(checkpoint))
print('Saved model at {}'.format(path))
def loadParameters(encoder, decoder, path, device):
checkpoint = torch.load(path, map_location=device)
encoder.load_state_dict(checkpoint['encoder_state_dict'])
decoder.load_state_dict(checkpoint['decoder_state_dict'])
return checkpoint
def scanData(path):
_, _, pairs = prepareData('scan_in', 'scan_out', path, False)
print('Loaded data from {}'.format(path))
print('{} examples. Sample pair: {}'.format(len(pairs), random.choice(pairs)))
return pairs
def trainTestSplit(device, encoder, decoder, model, train_path, test_path, iters=100000):
train_path = SCAN_DIR + train_path
test_path = SCAN_DIR + test_path
train_pairs = scanData(train_path)
test_pairs = scanData(test_path)
train_losses = trainIters(device, encoder, decoder, model, train_pairs, iters)
print('Evaluating training split accuracy')
train_acc = evaluateTestSet(device, encoder, decoder, model, train_pairs)
print('Evaluating test split accuracy')
test_acc = evaluateTestSet(device, encoder, decoder, model, test_pairs)
checkpoint = {'train_accuracy': train_acc,
'test_accuracy': test_acc,
'train_losses': train_losses}
return checkpoint
def initModel(model, device, hidden_size=200, dropout=0.5, n_layers=2):
input_size = INPUT_LANG.n_words
output_size = OUTPUT_LANG.n_words
if model == 'LSTM':
encoder = EncoderLSTM(device, input_size, hidden_size, n_layers, dropout)
decoder = DecoderLSTM(device, hidden_size, output_size, n_layers, dropout)
else: # GRU
encoder = EncoderRNN(device=device, input_size=input_size, hidden_size=hidden_size, dropout=dropout).to(device)
if model == 'GRU_A':
decoder = AttnDecoderRNN(device, hidden_size, output_size, dropout)
else:
decoder = DecoderRNN(device, hidden_size, output_size, dropout).to(device)
encoder = encoder.to(device)
decoder = decoder.to(device)
print('Initialized model {}'.format(model))
return encoder, decoder
def evalSplit(device, encoder, decoder, model, split_path):
split_path = SCAN_DIR + split_path
split_pairs = scanData(split_path)
pairs = scanData(split_path)
accuracy = evaluateTestSet(device, encoder, decoder, model, pairs)
return accuracy
if __name__ == '__main__':
assert args.split in ['jump', 'turn_left', 'jump_around_right', 'around_right', 'opposite_right', 'length', 'mcd', 'scan']
if args.split == 'scan':
train_path = 'tasks' + data_suffix(args.variant)
test_path = train_path
else:
train_path = args.split + '_train' + data_suffix(args.variant)
test_path = args.split + '_test' + data_suffix(args.variant)
if args.model == 'GRU':
encoder, decoder= initModel('GRU', DEVICE, hidden_size=100, dropout=0.1)
elif args.model == 'GRU_A':
encoder, decoder = initModel('GRU_A', DEVICE, hidden_size=100, dropout=0.1)
else:
assert args.model == 'LSTM'
encoder, decoder = initModel('LSTM', DEVICE, hidden_size=200, dropout=0.5, n_layers=2)
# checkpoint_path = ''
# loadParameters(encoder, decoder, checkpoint_path)
print('Training SCAN model {} on split \'{}\' for {} iterations on device {}, variant {}, tag {}'.format(args.model, args.split, args.iters, DEVICE, args.variant, args.tag))
checkpoint = trainTestSplit(DEVICE, encoder, decoder, args.model, train_path, test_path, iters=args.iters)
save_path = 'saved/{}_{}_{}{}.pt'.format(args.split, args.variant,
args.iters, '_' + args.tag if args.tag else '')
i = 0
while os.path.isfile(save_path):
save_path = save_path[:-3] + '_({}).pt'.format(i)
saveModel(encoder, decoder, checkpoint, save_path)
# checkpoint = loadParameters(encoder, decoder, 'saved/jump_inter_150000.pt', DEVICE)
# print('Evaluating with loaded model')
# evalSplit(DEVICE, encoder, decoder, args.model, test_path)