Merge branch 'master' into fixes

benchmark.py

@@ -55,15 +55,15 @@ def iteration(input_data):
  target_size = torch.IntTensor(batch_size).fill_(int((seconds * 100) / 2))
  input_percentages = torch.IntTensor(batch_size).fill_(1)
 
- inputs = Variable(input_data)
- target_sizes = Variable(target_size)
- targets = Variable(target)
+ inputs = Variable(input_data, requires_grad=False)
+ target_sizes = Variable(target_size requires_grad=False)
+ targets = Variable(target requires_grad=False)
  start = time.time()
  out = model(inputs)
  out = out.transpose(0, 1) # TxNxH
 
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int() requires_grad=False)
  loss = criterion(out, targets, sizes, target_sizes)
  loss = loss / inputs.size(0) # average the loss by minibatch
  # compute gradient

logger.py

@@ -0,0 +1,73 @@
+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+import tensorflow as tf
+import numpy as np
+import scipy.misc 
+try:
+ from StringIO import StringIO # Python 2.7
+except ImportError:
+ from io import BytesIO # Python 3.x
+
+
+class Logger(object):
+
+ def __init__(self, log_dir):
+ """Create a summary writer logging to log_dir."""
+ self.writer = tf.summary.FileWriter(log_dir)
+
+ def scalar_summary(self, tag, value, step):
+ """Log a scalar variable."""
+ summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
+ self.writer.add_summary(summary, step)
+ self.writer.flush()
+
+ def image_summary(self, tag, images, step):
+ """Log a list of images."""
+
+ img_summaries = []
+ for i, img in enumerate(images):
+ # Write the image to a string
+ try:
+ s = StringIO()
+ except:
+ s = BytesIO()
+ scipy.misc.toimage(img).save(s, format="png")
+
+ # Create an Image object
+ img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
+ height=img.shape[0],
+ width=img.shape[1])
+ # Create a Summary value
+ img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))
+
+ # Create and write Summary
+ summary = tf.Summary(value=img_summaries)
+ self.writer.add_summary(summary, step)
+ self.writer.flush()
+
+ def histo_summary(self, tag, values, step, bins=1000):
+ """Log a histogram of the tensor of values."""
+
+ # Create a histogram using numpy
+ counts, bin_edges = np.histogram(values, bins=bins)
+
+ # Fill the fields of the histogram proto
+ hist = tf.HistogramProto()
+ hist.min = float(np.min(values))
+ hist.max = float(np.max(values))
+ hist.num = int(np.prod(values.shape))
+ hist.sum = float(np.sum(values))
+ hist.sum_squares = float(np.sum(values**2))
+
+ # Drop the start of the first bin
+ bin_edges = bin_edges[1:]
+
+ # Add bin edges and counts
+ for edge in bin_edges:
+ hist.bucket_limit.append(edge)
+ for c in counts:
+ hist.bucket.append(c)
+
+ # Create and write Summary
+ summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+ self.writer.add_summary(summary, step)
+ self.writer.flush()

predict.py

@@ -26,7 +26,7 @@
  parser = SpectrogramParser(audio_conf, normalize=True)
  spect = parser.parse_audio(args.audio_path).contiguous()
  spect = spect.view(1, 1, spect.size(0), spect.size(1))
- out = model(Variable(spect))
+ out = model(Variable(spect, volatile=True))
  out = out.transpose(0, 1) # TxNxH
  decoded_output = decoder.decode(out.data)
  print(decoded_output[0])

test.py

@@ -34,7 +34,7 @@
  for i, (data) in enumerate(test_loader):
  inputs, targets, input_percentages, target_sizes = data
 
- inputs = Variable(inputs)
+ inputs = Variable(inputs, volatile=True)
 
  # unflatten targets
  split_targets = []
@@ -49,7 +49,7 @@
  out = model(inputs)
  out = out.transpose(0, 1) # TxNxH
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int(), volatile=True)
 
  decoded_output = decoder.decode(out.data, sizes)
  target_strings = decoder.process_strings(decoder.convert_to_strings(split_targets))

train.py

@@ -49,8 +49,13 @@
  help='Minimum noise level to sample from. (1.0 means all noise, not original signal)', type=float)
 parser.add_argument('--noise_max', default=0.5,
  help='Maximum noise levels to sample from. Maximum 1.0', type=float)
-parser.set_defaults(cuda=False, silent=False, checkpoint=False, visdom=False, augment=False)
+parser.add_argument('--tensorboard', dest='tensorboard', action='store_true', help='Turn on tensorboard graphing')
+parser.add_argument('--log_dir', default='visualize/deepspeech_final', help='Location of tensorboard log')
+parser.add_argument('--log_params', dest='log_params', action='store_true', help='Log parameter values and gradients')
+parser.set_defaults(cuda=False, silent=False, checkpoint=False, visdom=False, augment=False, tensorboard=False, log_params=False)
 
+def to_np(x):
+ return x.data.cpu().numpy()
 
 class AverageMeter(object):
  """Computes and stores the average and current value"""
@@ -87,6 +92,25 @@ def main():
  loss_results, cer_results, wer_results = torch.Tensor(args.epochs), torch.Tensor(args.epochs), torch.Tensor(
  args.epochs)
  epochs = torch.arange(1, args.epochs + 1)
+ if args.tensorboard:
+ from logger import Logger
+ try:
+ os.makedirs(args.log_dir)
+ except OSError as e:
+ if e.errno == errno.EEXIST:
+ print('Directory already exists.')
+ for file in os.listdir(args.log_dir):
+ file_path = os.path.join(args.log_dir, file)
+ try:
+ if os.path.isfile(file_path):
+ os.unlink(file_path)
+ except Exception as e:
+ raise
+ else:
+ raise
+ loss_results, cer_results, wer_results = torch.Tensor(args.epochs), torch.Tensor(args.epochs), torch.Tensor(
+ args.epochs)
+ logger = Logger(args.log_dir)
 
  try:
  os.makedirs(save_folder)
@@ -146,7 +170,7 @@ def main():
  if args.visdom and \
  package['loss_results'] is not None and start_epoch > 0: # Add previous scores to visdom graph
  epoch = start_epoch
- loss_results, cer_results, wer_results = package['loss_results'], package['cer_results'], package[
+ loss_results[0:epoch], cer_results[0:epoch], wer_results[0:epoch] = package['loss_results'], package['cer_results'], package[
  'wer_results']
  x_axis = epochs[0:epoch]
  y_axis = [loss_results[0:epoch], wer_results[0:epoch], cer_results[0:epoch]]
@@ -156,6 +180,18 @@ def main():
  Y=y_axis[x],
  opts=opts[x],
  )
+ if args.tensorboard and package['loss_results'] is not None and start_epoch > 0: # Add previous scores to tensorboard logs
+ epoch = start_epoch
+ loss_results, cer_results, wer_results = package['loss_results'], package['cer_results'], package[
+ 'wer_results']
+ for i in range(len(loss_results)):
+ info = {
+ 'Avg Train Loss': loss_results[i],
+ 'Avg WER': wer_results[i],
+ 'Avg CER': cer_results[i]
+ }
+ for tag, val in info.items():
+ logger.scalar_summary(tag, val, i+1)
  else:
  avg_loss = 0
  start_epoch = 0
@@ -177,9 +213,9 @@ def main():
  inputs, targets, input_percentages, target_sizes = data
  # measure data loading time
  data_time.update(time.time() - end)
- inputs = Variable(inputs)
- target_sizes = Variable(target_sizes)
- targets = Variable(targets)
+ inputs = Variable(inputs, requires_grad=False)
+ target_sizes = Variable(target_sizes, requires_grad=False)
+ targets = Variable(targets, requires_grad=False)
 
  if args.cuda:
  inputs = inputs.cuda()
@@ -188,7 +224,7 @@ def main():
  out = out.transpose(0, 1) # TxNxH
 
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int(), requires_grad=False)
 
  loss = criterion(out, targets, sizes, target_sizes)
  loss = loss / inputs.size(0) # average the loss by minibatch
@@ -238,12 +274,12 @@ def main():
  epoch + 1, loss=avg_loss))
 
  start_iter = 0 # Reset start iteration for next epoch
- total_cer, total_wer = 0, 0
+ total_cer, total_wer= 0, 0
  model.eval()
  for i, (data) in enumerate(test_loader): # test
  inputs, targets, input_percentages, target_sizes = data
 
- inputs = Variable(inputs)
+ inputs = Variable(inputs, volatile=True)
 
  # unflatten targets
  split_targets = []
@@ -258,7 +294,7 @@ def main():
  out = model(inputs)
  out = out.transpose(0, 1) # TxNxH
  seq_length = out.size(0)
- sizes = Variable(input_percentages.mul_(int(seq_length)).int())
+ sizes = Variable(input_percentages.mul_(int(seq_length)).int(), volatile=True)
 
  decoded_output = decoder.decode(out.data, sizes)
  target_strings = decoder.process_strings(decoder.convert_to_strings(split_targets))
@@ -271,7 +307,6 @@ def main():
 
  if args.cuda:
  torch.cuda.synchronize()
-
  wer = total_wer / len(test_loader.dataset)
  cer = total_cer / len(test_loader.dataset)
  wer *= 100
@@ -286,9 +321,9 @@ def main():
  loss_results[epoch] = avg_loss
  wer_results[epoch] = wer
  cer_results[epoch] = cer
- epoch += 1
- x_axis = epochs[0:epoch]
- y_axis = [loss_results[0:epoch], wer_results[0:epoch], cer_results[0:epoch]]
+ # epoch += 1
+ x_axis = epochs[0:epoch+1]
+ y_axis = [loss_results[0:epoch+1], wer_results[0:epoch+1], cer_results[0:epoch+1]]
  for x in range(len(viz_windows)):
  if viz_windows[x] is None:
  viz_windows[x] = viz.line(
@@ -303,6 +338,23 @@ def main():
  win=viz_windows[x],
  update='replace',
  )
+ if args.tensorboard:
+ loss_results[epoch] = avg_loss
+ wer_results[epoch] = wer
+ cer_results[epoch] = cer
+ info = {
+ 'Avg Train Loss': avg_loss,
+ 'Avg WER': wer,
+ 'Avg CER': cer
+ }
+ for tag, val in info.items():
+ logger.scalar_summary(tag, val, epoch+1)
+ if args.log_params:
+ for tag, value in model.named_parameters():
+ tag = tag.replace('.', '/')
+ logger.histo_summary(tag, to_np(value), epoch+1)
+ if value.grad is not None: # Condition inserted because batch_norm RNN_0 weights.grad and bias.grad are None. Check why
+ logger.histo_summary(tag+'/grad', to_np(value.grad), epoch+1)
  if args.checkpoint:
  file_path = '%s/deepspeech_%d.pth.tar' % (save_folder, epoch + 1)
  torch.save(DeepSpeech.serialize(model, optimizer=optimizer, epoch=epoch, loss_results=loss_results,