Bring tensorflow/models slim up to date.

Includes new gan nets.

Inclues new NasNet models.

PiperOrigin-RevId: 173772097

research/slim/nets/resnet_v2_test.py

@@ -285,6 +285,31 @@ class ResnetCompleteNetworkTest(tf.test.TestCase):
     self.assertTrue('predictions' in end_points)
     self.assertListEqual(end_points['predictions'].get_shape().as_list(),
                          [2, 1, 1, num_classes])
+    self.assertTrue('global_pool' in end_points)
+    self.assertListEqual(end_points['global_pool'].get_shape().as_list(),
+                         [2, 1, 1, 32])
+
+  def testEndpointNames(self):
+    # Like ResnetUtilsTest.testEndPointsV2(), but for the public API.
+    global_pool = True
+    num_classes = 10
+    inputs = create_test_input(2, 224, 224, 3)
+    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
+      _, end_points = self._resnet_small(inputs, num_classes,
+                                         global_pool=global_pool,
+                                         scope='resnet')
+    expected = ['resnet/conv1']
+    for block in range(1, 5):
+      for unit in range(1, 4 if block < 4 else 3):
+        for conv in range(1, 4):
+          expected.append('resnet/block%d/unit_%d/bottleneck_v2/conv%d' %
+                          (block, unit, conv))
+        expected.append('resnet/block%d/unit_%d/bottleneck_v2' % (block, unit))
+      expected.append('resnet/block%d/unit_1/bottleneck_v2/shortcut' % block)
+      expected.append('resnet/block%d' % block)
+    expected.extend(['global_pool', 'resnet/logits', 'resnet/spatial_squeeze',
+                     'predictions'])
+    self.assertItemsEqual(end_points.keys(), expected)
 
   def testClassificationShapes(self):
     global_pool = True

research/slim/nets/vgg.py

@@ -69,7 +69,8 @@ def vgg_a(inputs,
           dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_a',
-          fc_conv_padding='VALID'):
+          fc_conv_padding='VALID',
+          global_pool=False):
   """Oxford Net VGG 11-Layers version A Example.
 
   Note: All the fully_connected layers have been transformed to conv2d layers.
@@ -77,7 +78,8 @@ def vgg_a(inputs,
 
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
-    num_classes: number of predicted classes.
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
     is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
@@ -90,12 +92,17 @@ def vgg_a(inputs,
       get a prediction map downsampled by a factor of 32 as an output.
       Otherwise, the output prediction map will be (input / 32) - 6 in case of
       'VALID' padding.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original VGG architecture.)
 
   Returns:
-    the last op containing the log predictions and end_points dict.
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the input to the logits layer (if num_classes is 0 or None).
+    end_points: a dict of tensors with intermediate activations.
   """
   with tf.variable_scope(scope, 'vgg_a', [inputs]) as sc:
-    end_points_collection = sc.name + '_end_points'
+    end_points_collection = sc.original_name_scope + '_end_points'
     # Collect outputs for conv2d, fully_connected and max_pool2d.
     with slim.arg_scope([slim.conv2d, slim.max_pool2d],
                         outputs_collections=end_points_collection):
@@ -109,21 +116,26 @@ def vgg_a(inputs,
       net = slim.max_pool2d(net, [2, 2], scope='pool4')
       net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
       net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
       # Use conv2d instead of fully_connected layers.
       net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
       net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                          scope='dropout6')
       net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
-      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
-                         scope='dropout7')
-      net = slim.conv2d(net, num_classes, [1, 1],
-                        activation_fn=None,
-                        normalizer_fn=None,
-                        scope='fc8')
       # Convert end_points_collection into a end_point dict.
       end_points = slim.utils.convert_collection_to_dict(end_points_collection)
-      if spatial_squeeze:
-        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+      if global_pool:
+        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+      if num_classes:
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout7')
+        net = slim.conv2d(net, num_classes, [1, 1],
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          scope='fc8')
+        if spatial_squeeze:
+          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
         end_points[sc.name + '/fc8'] = net
       return net, end_points
 vgg_a.default_image_size = 224
@@ -135,7 +147,8 @@ def vgg_16(inputs,
            dropout_keep_prob=0.5,
            spatial_squeeze=True,
            scope='vgg_16',
-           fc_conv_padding='VALID'):
+           fc_conv_padding='VALID',
+           global_pool=False):
   """Oxford Net VGG 16-Layers version D Example.
 
   Note: All the fully_connected layers have been transformed to conv2d layers.
@@ -143,7 +156,8 @@ def vgg_16(inputs,
 
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
-    num_classes: number of predicted classes.
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
     is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
@@ -156,12 +170,17 @@ def vgg_16(inputs,
       get a prediction map downsampled by a factor of 32 as an output.
       Otherwise, the output prediction map will be (input / 32) - 6 in case of
       'VALID' padding.
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original VGG architecture.)
 
   Returns:
-    the last op containing the log predictions and end_points dict.
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the input to the logits layer (if num_classes is 0 or None).
+    end_points: a dict of tensors with intermediate activations.
   """
   with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
-    end_points_collection = sc.name + '_end_points'
+    end_points_collection = sc.original_name_scope + '_end_points'
     # Collect outputs for conv2d, fully_connected and max_pool2d.
     with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                         outputs_collections=end_points_collection):
@@ -175,21 +194,26 @@ def vgg_16(inputs,
       net = slim.max_pool2d(net, [2, 2], scope='pool4')
       net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
       net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
       # Use conv2d instead of fully_connected layers.
       net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
       net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                          scope='dropout6')
       net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
-      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
-                         scope='dropout7')
-      net = slim.conv2d(net, num_classes, [1, 1],
-                        activation_fn=None,
-                        normalizer_fn=None,
-                        scope='fc8')
       # Convert end_points_collection into a end_point dict.
       end_points = slim.utils.convert_collection_to_dict(end_points_collection)
-      if spatial_squeeze:
-        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+      if global_pool:
+        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+      if num_classes:
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout7')
+        net = slim.conv2d(net, num_classes, [1, 1],
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          scope='fc8')
+        if spatial_squeeze and num_classes is not None:
+          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
         end_points[sc.name + '/fc8'] = net
       return net, end_points
 vgg_16.default_image_size = 224
@@ -201,7 +225,8 @@ def vgg_19(inputs,
            dropout_keep_prob=0.5,
            spatial_squeeze=True,
            scope='vgg_19',
-           fc_conv_padding='VALID'):
+           fc_conv_padding='VALID',
+           global_pool=False):
   """Oxford Net VGG 19-Layers version E Example.
 
   Note: All the fully_connected layers have been transformed to conv2d layers.
@@ -209,7 +234,8 @@ def vgg_19(inputs,
 
   Args:
     inputs: a tensor of size [batch_size, height, width, channels].
-    num_classes: number of predicted classes.
+    num_classes: number of predicted classes. If 0 or None, the logits layer is
+      omitted and the input features to the logits layer are returned instead.
     is_training: whether or not the model is being trained.
     dropout_keep_prob: the probability that activations are kept in the dropout
       layers during training.
@@ -222,13 +248,18 @@ def vgg_19(inputs,
       get a prediction map downsampled by a factor of 32 as an output.
       Otherwise, the output prediction map will be (input / 32) - 6 in case of
       'VALID' padding.
-
+    global_pool: Optional boolean flag. If True, the input to the classification
+      layer is avgpooled to size 1x1, for any input size. (This is not part
+      of the original VGG architecture.)
 
   Returns:
-    the last op containing the log predictions and end_points dict.
+    net: the output of the logits layer (if num_classes is a non-zero integer),
+      or the non-dropped-out input to the logits layer (if num_classes is 0 or
+      None).
+    end_points: a dict of tensors with intermediate activations.
   """
   with tf.variable_scope(scope, 'vgg_19', [inputs]) as sc:
-    end_points_collection = sc.name + '_end_points'
+    end_points_collection = sc.original_name_scope + '_end_points'
     # Collect outputs for conv2d, fully_connected and max_pool2d.
     with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                         outputs_collections=end_points_collection):
@@ -242,21 +273,26 @@ def vgg_19(inputs,
       net = slim.max_pool2d(net, [2, 2], scope='pool4')
       net = slim.repeat(net, 4, slim.conv2d, 512, [3, 3], scope='conv5')
       net = slim.max_pool2d(net, [2, 2], scope='pool5')
+
       # Use conv2d instead of fully_connected layers.
       net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
       net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                          scope='dropout6')
       net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
-      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
-                         scope='dropout7')
-      net = slim.conv2d(net, num_classes, [1, 1],
-                        activation_fn=None,
-                        normalizer_fn=None,
-                        scope='fc8')
       # Convert end_points_collection into a end_point dict.
       end_points = slim.utils.convert_collection_to_dict(end_points_collection)
-      if spatial_squeeze:
-        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
+      if global_pool:
+        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
+        end_points['global_pool'] = net
+      if num_classes:
+        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
+                           scope='dropout7')
+        net = slim.conv2d(net, num_classes, [1, 1],
+                          activation_fn=None,
+                          normalizer_fn=None,
+                          scope='fc8')
+        if spatial_squeeze:
+          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
         end_points[sc.name + '/fc8'] = net
       return net, end_points
 vgg_19.default_image_size = 224

research/slim/nets/vgg_test.py

@@ -48,6 +48,18 @@ class VGGATest(tf.test.TestCase):
       self.assertListEqual(logits.get_shape().as_list(),
                            [batch_size, 2, 2, num_classes])
 
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False,
+                            global_pool=True)
+      self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
   def testEndPoints(self):
     batch_size = 5
     height, width = 224, 224
@@ -74,6 +86,32 @@ class VGGATest(tf.test.TestCase):
                        ]
       self.assertSetEqual(set(end_points.keys()), set(expected_names))
 
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = vgg.vgg_a(inputs, num_classes)
+      expected_names = ['vgg_a/conv1/conv1_1',
+                        'vgg_a/pool1',
+                        'vgg_a/conv2/conv2_1',
+                        'vgg_a/pool2',
+                        'vgg_a/conv3/conv3_1',
+                        'vgg_a/conv3/conv3_2',
+                        'vgg_a/pool3',
+                        'vgg_a/conv4/conv4_1',
+                        'vgg_a/conv4/conv4_2',
+                        'vgg_a/pool4',
+                        'vgg_a/conv5/conv5_1',
+                        'vgg_a/conv5/conv5_2',
+                        'vgg_a/pool5',
+                        'vgg_a/fc6',
+                        'vgg_a/fc7',
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('vgg_a/fc7'))
+
   def testModelVariables(self):
     batch_size = 5
     height, width = 224, 224
@@ -177,6 +215,18 @@ class VGG16Test(tf.test.TestCase):
       self.assertListEqual(logits.get_shape().as_list(),
                            [batch_size, 2, 2, num_classes])
 
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False,
+                             global_pool=True)
+      self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
   def testEndPoints(self):
     batch_size = 5
     height, width = 224, 224
@@ -208,6 +258,37 @@ class VGG16Test(tf.test.TestCase):
                        ]
       self.assertSetEqual(set(end_points.keys()), set(expected_names))
 
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = vgg.vgg_16(inputs, num_classes)
+      expected_names = ['vgg_16/conv1/conv1_1',
+                        'vgg_16/conv1/conv1_2',
+                        'vgg_16/pool1',
+                        'vgg_16/conv2/conv2_1',
+                        'vgg_16/conv2/conv2_2',
+                        'vgg_16/pool2',
+                        'vgg_16/conv3/conv3_1',
+                        'vgg_16/conv3/conv3_2',
+                        'vgg_16/conv3/conv3_3',
+                        'vgg_16/pool3',
+                        'vgg_16/conv4/conv4_1',
+                        'vgg_16/conv4/conv4_2',
+                        'vgg_16/conv4/conv4_3',
+                        'vgg_16/pool4',
+                        'vgg_16/conv5/conv5_1',
+                        'vgg_16/conv5/conv5_2',
+                        'vgg_16/conv5/conv5_3',
+                        'vgg_16/pool5',
+                        'vgg_16/fc6',
+                        'vgg_16/fc7',
+                       ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('vgg_16/fc7'))
+
   def testModelVariables(self):
     batch_size = 5
     height, width = 224, 224
@@ -321,6 +402,18 @@ class VGG19Test(tf.test.TestCase):
       self.assertListEqual(logits.get_shape().as_list(),
                            [batch_size, 2, 2, num_classes])
 
+  def testGlobalPool(self):
+    batch_size = 1
+    height, width = 256, 256
+    num_classes = 1000
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False,
+                             global_pool=True)
+      self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
+      self.assertListEqual(logits.get_shape().as_list(),
+                           [batch_size, 1, 1, num_classes])
+
   def testEndPoints(self):
     batch_size = 5
     height, width = 224, 224
@@ -356,6 +449,41 @@ class VGG19Test(tf.test.TestCase):
       ]
       self.assertSetEqual(set(end_points.keys()), set(expected_names))
 
+  def testNoClasses(self):
+    batch_size = 5
+    height, width = 224, 224
+    num_classes = None
+    with self.test_session():
+      inputs = tf.random_uniform((batch_size, height, width, 3))
+      net, end_points = vgg.vgg_19(inputs, num_classes)
+      expected_names = [
+          'vgg_19/conv1/conv1_1',
+          'vgg_19/conv1/conv1_2',
+          'vgg_19/pool1',
+          'vgg_19/conv2/conv2_1',
+          'vgg_19/conv2/conv2_2',
+          'vgg_19/pool2',
+          'vgg_19/conv3/conv3_1',
+          'vgg_19/conv3/conv3_2',
+          'vgg_19/conv3/conv3_3',
+          'vgg_19/conv3/conv3_4',
+          'vgg_19/pool3',
+          'vgg_19/conv4/conv4_1',
+          'vgg_19/conv4/conv4_2',
+          'vgg_19/conv4/conv4_3',
+          'vgg_19/conv4/conv4_4',
+          'vgg_19/pool4',
+          'vgg_19/conv5/conv5_1',
+          'vgg_19/conv5/conv5_2',
+          'vgg_19/conv5/conv5_3',
+          'vgg_19/conv5/conv5_4',
+          'vgg_19/pool5',
+          'vgg_19/fc6',
+          'vgg_19/fc7',
+      ]
+      self.assertSetEqual(set(end_points.keys()), set(expected_names))
+      self.assertTrue(net.op.name.startswith('vgg_19/fc7'))
+
   def testModelVariables(self):
     batch_size = 5
     height, width = 224, 224

research/slim/preprocessing/cifarnet_preprocessing.py

@@ -30,7 +30,8 @@ slim = tf.contrib.slim
 def preprocess_for_train(image,
                          output_height,
                          output_width,
-                         padding=_PADDING):
+                         padding=_PADDING,
+                         add_image_summaries=True):
   """Preprocesses the given image for training.
 
   Note that the actual resizing scale is sampled from
@@ -41,11 +42,13 @@ def preprocess_for_train(image,
     output_height: The height of the image after preprocessing.
     output_width: The width of the image after preprocessing.
     padding: The amound of padding before and after each dimension of the image.
+    add_image_summaries: Enable image summaries.
 
   Returns:
     A preprocessed image.
   """
-  tf.summary.image('image', tf.expand_dims(image, 0))
+  if add_image_summaries:
+    tf.summary.image('image', tf.expand_dims(image, 0))
 
   # Transform the image to floats.
   image = tf.to_float(image)
@@ -58,7 +61,8 @@ def preprocess_for_train(image,
   # Randomly flip the image horizontally.
   distorted_image = tf.image.random_flip_left_right(distorted_image)
 
-  tf.summary.image('distorted_image', tf.expand_dims(distorted_image, 0))
+  if add_image_summaries:
+    tf.summary.image('distorted_image', tf.expand_dims(distorted_image, 0))
 
   # Because these operations are not commutative, consider randomizing
   # the order their operation.
@@ -70,18 +74,21 @@ def preprocess_for_train(image,
   return tf.image.per_image_standardization(distorted_image)
 
 
-def preprocess_for_eval(image, output_height, output_width):
+def preprocess_for_eval(image, output_height, output_width,
+                        add_image_summaries=True):
   """Preprocesses the given image for evaluation.
 
   Args:
     image: A `Tensor` representing an image of arbitrary size.
     output_height: The height of the image after preprocessing.
     output_width: The width of the image after preprocessing.
+    add_image_summaries: Enable image summaries.
 
   Returns:
     A preprocessed image.
   """
-  tf.summary.image('image', tf.expand_dims(image, 0))
+  if add_image_summaries:
+    tf.summary.image('image', tf.expand_dims(image, 0))
   # Transform the image to floats.
   image = tf.to_float(image)
 
@@ -89,13 +96,15 @@ def preprocess_for_eval(image, output_height, output_width):
   resized_image = tf.image.resize_image_with_crop_or_pad(image,
                                                          output_width,
                                                          output_height)
-  tf.summary.image('resized_image', tf.expand_dims(resized_image, 0))
+  if add_image_summaries:
+    tf.summary.image('resized_image', tf.expand_dims(resized_image, 0))
 
   # Subtract off the mean and divide by the variance of the pixels.
   return tf.image.per_image_standardization(resized_image)
 
 
-def preprocess_image(image, output_height, output_width, is_training=False):
+def preprocess_image(image, output_height, output_width, is_training=False,
+                     add_image_summaries=True):
   """Preprocesses the given image.
 
   Args:
@@ -104,11 +113,16 @@ def preprocess_image(image, output_height, output_width, is_training=False):
     output_width: The width of the image after preprocessing.
     is_training: `True` if we're preprocessing the image for training and
       `False` otherwise.
+    add_image_summaries: Enable image summaries.
 
   Returns:
     A preprocessed image.
   """
   if is_training:
-    return preprocess_for_train(image, output_height, output_width)
+    return preprocess_for_train(
+        image, output_height, output_width,
+        add_image_summaries=add_image_summaries)
   else:
-    return preprocess_for_eval(image, output_height, output_width)
+    return preprocess_for_eval(
+        image, output_height, output_width,
+        add_image_summaries=add_image_summaries)

research/slim/preprocessing/preprocessing_factory.py

@@ -54,6 +54,8 @@ def get_preprocessing(name, is_training=False):
       'inception_resnet_v2': inception_preprocessing,
       'lenet': lenet_preprocessing,
       'mobilenet_v1': inception_preprocessing,
+      'nasnet_mobile': inception_preprocessing,
+      'nasnet_large': inception_preprocessing,
       'resnet_v1_50': vgg_preprocessing,
       'resnet_v1_101': vgg_preprocessing,
       'resnet_v1_152': vgg_preprocessing,

research/slim/scripts/export_mobilenet.sh

 #!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
 # This script prepares the various different versions of MobileNet models for
 # use in a mobile application. If you don't specify your own trained checkpoint
 # file, it will download pretrained checkpoints for ImageNet. You'll also need