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ctcmain.py
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ctcmain.py
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from data_utils_torch import *
from ctcmodel import *
from warpctc_pytorch import CTCLoss
import os
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
if __name__ == '__main__':
BATCH_SIZE = 64
HEIGHT = 48
WIDTH = 128
HIDDEN_SIZE = 128
NUM_RNN_LAYERS = 1
DROPOUT = 0.2
LR = 0.0003
CLIP = 10.
NUM_EPOCHS = 100
PRINT_EVERY_N_ITER = 100
SAVE_DIR = 'CTC128_lr0.0003cp10'
if not os.path.exists("results"):
os.mkdir("results")
SAVE_DIR = os.path.join("results", SAVE_DIR)
if not os.path.exists(SAVE_DIR):
os.mkdir(SAVE_DIR)
TOTAL_SIZE = None
TEST_SIZE = 8192
USE_CUDA = torch.cuda.is_available()
dl_train, dl_test, vocab = load_dataset(batch_size=BATCH_SIZE, test_size=TEST_SIZE,total_size=TOTAL_SIZE)
END = vocab['token2id']['$']
VOCAB_SIZE = len(vocab['token2id'])-2 #exclude "$" and " ", and use "^" as <BLANK>
ctc = CTCModel(output_size=VOCAB_SIZE,num_rnn_layers=NUM_RNN_LAYERS,
rnn_hidden_size=HIDDEN_SIZE,
dropout=DROPOUT)
if USE_CUDA:
ctc.cuda()
ctc_params = list(filter(lambda p: p.requires_grad, ctc.parameters()))
ctc_optimizer = optim.Adam(ctc_params, lr=LR)
criterion = CTCLoss()
epoch_train_loss = []
epoch_train_accuracy = []
batch_train_loss = []
batch_train_accuracy = []
test_loss = []
test_accuracy = []
for epoch in range(1, NUM_EPOCHS + 1):
loss = accuracy = 0
batches_loss = batches_acc = 0
for num_iter, (x, y) in enumerate(dl_train):
vx = Variable(x)
y = y[:,1:].type(torch.IntTensor)
vy = Variable(y[y<END-1].contiguous())
lens = Variable(torch.from_numpy(
(np.where(y.numpy()==END)[1]).astype(np.int32)))
a_loss, a_accuracy = CTCtrain(vx,vy,lens,ctc, ctc_optimizer,
criterion, CLIP, use_cuda=USE_CUDA)
loss += a_loss
accuracy += a_accuracy
batches_loss += a_loss
batches_acc += a_accuracy
if (num_iter + 1) % PRINT_EVERY_N_ITER == 0:
batches_loss /= PRINT_EVERY_N_ITER
batches_acc /= PRINT_EVERY_N_ITER
print("Iteration: {}/{} Epoch: {}/{}".format(
num_iter + 1, len(dl_train), epoch, NUM_EPOCHS))
print("recent batches:\n"
"loss {}\n"
"accuracy {}".format(batches_loss, batches_acc))
batch_train_loss.append(batches_loss)
batch_train_accuracy.append(batches_acc)
batches_loss = batches_acc = 0
epoch_train_loss.append(loss / len(dl_train))
epoch_train_accuracy.append(accuracy / len(dl_train))
print("epoch train loss: {}\n epoch train accuracy: {}".format(epoch_train_loss[-1], epoch_train_accuracy[-1]))
# test
loss = accuracy = 0
for num_iter, (x, y) in enumerate(dl_test):
vx = Variable(x)
y = y[:,1:].type(torch.IntTensor)
vy = Variable(y[y<END-1].contiguous())
lens = Variable(torch.from_numpy(
(np.where(y.numpy()==END)[1]).astype(np.int32)))
a_loss, a_accuracy, outputs = CTCevaluate(vx, vy, lens,ctc, criterion,CLIP,
use_cuda=USE_CUDA)
loss += a_loss
accuracy += a_accuracy
test_loss.append(loss / len(dl_test))
test_accuracy.append(accuracy / len(dl_test))
print("test loss: {}\n"
"test accuracy: {}".format(test_loss[-1], test_accuracy[-1]))
c = np.random.choice(BATCH_SIZE)
print(''.join(vocab['id2token'][i] for i in outputs[c]) + '|' + ''.join(
vocab['id2token'][i] for i in y[c]) + '|')
print("Training over")
# save figures
fig = plt.figure(figsize=(20, 10))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.plot(batch_train_loss, 'r', label='loss')
ax1.legend()
ax2.plot(batch_train_accuracy, label='acc')
ax2.legend()
fig.savefig(os.path.join(SAVE_DIR, "sampled_batch_error.png"))
print("A figure is saved.")
fig = plt.figure(figsize=(20, 10))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
ax1.plot(epoch_train_loss, 'r', label='train_loss')
ax1.plot(test_loss, 'b', label='test_loss')
ax1.legend()
ax2.plot(epoch_train_accuracy, 'r', label='train_acc')
ax2.plot(test_accuracy, 'b', label='test_acc')
ax2.legend()
fig.savefig(os.path.join(SAVE_DIR, "epoch_error.png"))
print("Another fig is saved.")
# plt.show()
np.savetxt(os.path.join(SAVE_DIR, "acc.txt"),
np.vstack([epoch_train_accuracy, test_accuracy]).T)