Make inception to work with tf1.0. (#204)

datalab/mlalpha/_dataset.py

@@ -61,15 +61,15 @@ def __init__(self, file_pattern, schema=None, schema_file=None):
  with ml.util._file.open_local_or_gcs(schema_file, 'r') as f:
  self._schema = json.load(f)
 
- if isinstance(files, basestring):
- files = [files]
- self._input_files = files
+ if isinstance(file_pattern, basestring):
+ file_pattern = [file_pattern]
+ self._input_files = file_pattern
 
  self._glob_files = []
 
 
  @property
- def _input_files(self):
+ def input_files(self):
  """Returns the file list that was given to this class without globing files."""
  return self._input_files
 

solutionbox/inception/datalab_solutions/inception/_cloud.py

@@ -31,8 +31,7 @@
 from . import _trainer
 from . import _util
 
-
-_TF_GS_URL= 'gs://cloud-datalab/deploy/tf/tensorflow-0.12.0rc1-cp27-none-linux_x86_64.whl'
+_TF_GS_URL = 'gs://cloud-datalab/deploy/tf/tensorflow-1.0.0rc1-cp27-none-linux_x86_64.whl'
 # Keep in sync with "data_files" in package's setup.py
 _SETUP_PY = '/datalab/packages_setup/inception/setup.py'
 
@@ -68,7 +67,7 @@ def preprocess(self, train_dataset, eval_dataset, output_dir, pipeline_option):
  'temp_location': os.path.join(output_dir, 'tmp'),
  'job_name': job_name,
  'project': _util.default_project(),
- 'extra_packages': [ml.sdk_location, staging_package_url, _TF_GS_URL],
+ 'extra_packages': [_TF_GS_URL, ml.version.nodeps_sdk_location, staging_package_url],
  'teardown_policy': 'TEARDOWN_ALWAYS',
  'no_save_main_session': True
  }
@@ -96,7 +95,7 @@ def train(self, input_dir, batch_size, max_steps, output_path, cloud_train_confi
  'checkpoint': self._checkpoint
  }
  job_request = {
- 'package_uris': staging_package_url,
+ 'package_uris': [_TF_GS_URL, staging_package_url],
  'python_module': 'datalab_solutions.inception.task',
  'args': job_args
  }
@@ -147,7 +146,7 @@ def batch_predict(self, dataset, model_dir, gcs_staging_location, output_csv,
  'temp_location': os.path.join(gcs_staging_location, 'tmp'),
  'job_name': job_name,
  'project': _util.default_project(),
- 'extra_packages': [ml.sdk_location, staging_package_url, _TF_GS_URL],
+ 'extra_packages': [_TF_GS_URL, ml.version.nodeps_sdk_location, staging_package_url],
  'teardown_policy': 'TEARDOWN_ALWAYS',
  'no_save_main_session': True
  }

solutionbox/inception/datalab_solutions/inception/_inceptionlib.py

@@ -158,7 +158,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -182,7 +182,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -205,7 +205,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -224,7 +224,7 @@ def inception_v3_base(inputs,
  with tf.variable_scope('Branch_2'):
  branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
  scope='MaxPool_1a_3x3')
- net = tf.concat(3, [branch_0, branch_1, branch_2])
+ net = tf.concat([branch_0, branch_1, branch_2], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -253,7 +253,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -282,7 +282,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
  # mixed_6: 17 x 17 x 768.
@@ -310,7 +310,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -339,7 +339,7 @@ def inception_v3_base(inputs,
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
  scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -361,7 +361,7 @@ def inception_v3_base(inputs,
  with tf.variable_scope('Branch_2'):
  branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
  scope='MaxPool_1a_3x3')
- net = tf.concat(3, [branch_0, branch_1, branch_2])
+ net = tf.concat([branch_0, branch_1, branch_2], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
  # mixed_9: 8 x 8 x 2048.
@@ -371,21 +371,21 @@ def inception_v3_base(inputs,
  branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
  with tf.variable_scope('Branch_1'):
  branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
- branch_1 = tf.concat(3, [
+ branch_1 = tf.concat([
  slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
- slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
+ slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')], 3)
  with tf.variable_scope('Branch_2'):
  branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
  branch_2 = slim.conv2d(
  branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
- branch_2 = tf.concat(3, [
+ branch_2 = tf.concat([
  slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
- slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
+ slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')], 3)
  with tf.variable_scope('Branch_3'):
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(
  branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
 
@@ -396,21 +396,21 @@ def inception_v3_base(inputs,
  branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
  with tf.variable_scope('Branch_1'):
  branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
- branch_1 = tf.concat(3, [
+ branch_1 = tf.concat([
  slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
- slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
+ slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')], 3)
  with tf.variable_scope('Branch_2'):
  branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
  branch_2 = slim.conv2d(
  branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
- branch_2 = tf.concat(3, [
+ branch_2 = tf.concat([
  slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
- slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
+ slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')], 3)
  with tf.variable_scope('Branch_3'):
  branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
  branch_3 = slim.conv2d(
  branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
- net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
+ net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
  end_points[end_point] = net
  if end_point == final_endpoint: return net, end_points
  raise ValueError('Unknown final endpoint %s' % final_endpoint)

solutionbox/inception/datalab_solutions/inception/_model.py

@@ -156,8 +156,8 @@ def decode_and_resize(image_str_tensor):
  image = tf.image.convert_image_dtype(image, dtype=tf.float32)
 
  # Then shift images to [-1, 1) for Inception.
- image = tf.sub(image, 0.5)
- image = tf.mul(image, 2.0)
+ image = tf.subtract(image, 0.5)
+ image = tf.multiply(image, 2.0)
 
  # Build Inception layers, which expect A tensor of type float from [-1, 1)
  # and shape [batch_size, height, width, channels].
@@ -325,7 +325,7 @@ def build_prediction_graph(self):
  # The labels are needed for client to match class scores list.
  labels_tensor = tf.expand_dims(tf.constant(labels), 0)
  num_instance = tf.shape(keys)
- labels_tensors_n = tf.tile(labels_tensor, tf.concat(0, [num_instance, [1]]))
+ labels_tensors_n = tf.tile(labels_tensor, tf.concat(axis=0, values=[num_instance, [1]]))
 
  outputs = {
  'key': keys.name,

solutionbox/inception/datalab_solutions/inception/_preprocess.py

@@ -160,8 +160,8 @@ def build_graph(self):
  image, [self.HEIGHT, self.WIDTH], align_corners=False)
 
  # Then rescale range to [-1, 1) for Inception.
- image = tf.sub(image, 0.5)
- inception_input = tf.mul(image, 2.0)
+ image = tf.subtract(image, 0.5)
+ inception_input = tf.multiply(image, 2.0)
 
  # Build Inception layers, which expect a tensor of type float from [-1, 1)
  # and shape [batch_size, height, width, channels].