Merge https://github.com/tensorflow/models

im2txt/README.md

@@ -296,8 +296,12 @@ Your trained *Show and Tell* model can generate captions for any JPEG image! The
 following command line will generate captions for an image from the test set.
 
 ```shell
-# Directory containing model checkpoints.
-CHECKPOINT_DIR="${HOME}/im2txt/model/train"
+# Path to checkpoint file or a directory containing checkpoint files. Passing
+# a directory will only work if there is also a file named 'checkpoint' which
+# lists the available checkpoints in the directory. It will not work if you
+# point to a directory with just a copy of a model checkpoint: in that case,
+# you will need to pass the checkpoint path explicitly.
+CHECKPOINT_PATH="${HOME}/im2txt/model/train"
 
 # Vocabulary file generated by the preprocessing script.
 VOCAB_FILE="${HOME}/im2txt/data/mscoco/word_counts.txt"
@@ -314,7 +318,7 @@ export CUDA_VISIBLE_DEVICES=""
 
 # Run inference to generate captions.
 bazel-bin/im2txt/run_inference \
-  --checkpoint_path=${CHECKPOINT_DIR} \
+  --checkpoint_path=${CHECKPOINT_PATH} \
   --vocab_file=${VOCAB_FILE} \
   --input_files=${IMAGE_FILE}
 ```

im2txt/im2txt/run_inference.py

@@ -39,6 +39,8 @@ tf.flags.DEFINE_string("input_files", "",
                        "File pattern or comma-separated list of file patterns "
                        "of image files.")
 
+tf.logging.set_verbosity(tf.logging.INFO)
+
 
 def main(_):
   # Build the inference graph.

inception/inception/data/process_bounding_boxes.py

@@ -102,7 +102,9 @@ def GetItem(name, root, index=0):
 
 
 def GetInt(name, root, index=0):
-  return int(GetItem(name, root, index))
+  # In some XML annotation files, the point values are not integers, but floats.
+  # So we add a float function to avoid ValueError.
+  return int(float(GetItem(name, root, index)))
 
 
 def FindNumberBoundingBoxes(root):

inception/inception/slim/README.md

@@ -445,15 +445,15 @@ defined with just the following snippet:
 
 ```python
 with arg_scope([slim.ops.conv2d, slim.ops.fc], stddev=0.01, weight_decay=0.0005):
-  net = slim.ops.repeat_op(1, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1')
+  net = slim.ops.repeat_op(2, inputs, slim.ops.conv2d, 64, [3, 3], scope='conv1')
   net = slim.ops.max_pool(net, [2, 2], scope='pool1')
-  net = slim.ops.repeat_op(1, net, slim.ops.conv2d, 128, [3, 3], scope='conv2')
+  net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 128, [3, 3], scope='conv2')
   net = slim.ops.max_pool(net, [2, 2], scope='pool2')
-  net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 256, [3, 3], scope='conv3')
+  net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 256, [3, 3], scope='conv3')
   net = slim.ops.max_pool(net, [2, 2], scope='pool3')
-  net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 512, [3, 3], scope='conv4')
+  net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv4')
   net = slim.ops.max_pool(net, [2, 2], scope='pool4')
-  net = slim.ops.repeat_op(2, net, slim.ops.conv2d, 512, [3, 3], scope='conv5')
+  net = slim.ops.repeat_op(3, net, slim.ops.conv2d, 512, [3, 3], scope='conv5')
   net = slim.ops.max_pool(net, [2, 2], scope='pool5')
   net = slim.ops.flatten(net, scope='flatten5')
   net = slim.ops.fc(net, 4096, scope='fc6')

slim/README.md

@@ -13,7 +13,7 @@ converting them
 to TensorFlow's native TFRecord format and reading them in using TF-Slim's
 data reading and queueing utilities. You can easily train any model on any of
 these datasets, as we demonstrate below. We've also included a
-[jupyter notebook](https://github.com/tensorflow/models/blob/master/slim/slim_walkthough.ipynb),
+[jupyter notebook](https://github.com/tensorflow/models/blob/master/slim/slim_walkthrough.ipynb),
 which provides working examples of how to use TF-Slim for image classification.
 
 ## Contacts
@@ -303,8 +303,8 @@ $ python train_image_classifier.py \
     --dataset_split_name=train \
     --model_name=inception_v3 \
     --checkpoint_path=${CHECKPOINT_PATH} \
-    --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits/Logits \
-    --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits/Logits
+    --checkpoint_exclude_scopes=InceptionV3/Logits,InceptionV3/AuxLogits \
+    --trainable_scopes=InceptionV3/Logits,InceptionV3/AuxLogits
 ```
 
 

slim/eval_image_classifier.py

@@ -183,7 +183,7 @@ def main(_):
         checkpoint_path=checkpoint_path,
         logdir=FLAGS.eval_dir,
         num_evals=num_batches,
-        eval_op=names_to_updates.values(),
+        eval_op=list(names_to_updates.values()),
         variables_to_restore=variables_to_restore)
 
 

slim/nets/inception_resnet_v2.py

@@ -171,7 +171,7 @@ def inception_resnet_v2(inputs, num_classes=1001, is_training=True,
         end_points['Mixed_5b'] = net
         net = slim.repeat(net, 10, block35, scale=0.17)
 
-        # 17 x 17 x 1024
+        # 17 x 17 x 1088
         with tf.variable_scope('Mixed_6a'):
           with tf.variable_scope('Branch_0'):
             tower_conv = slim.conv2d(net, 384, 3, stride=2, padding='VALID',

slim/nets/inception_resnet_v2_test.py

@@ -65,9 +65,9 @@ class InceptionTest(tf.test.TestCase):
         inception.inception_resnet_v2(inputs, num_classes)
       with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
         inception.inception_resnet_v2(inputs, num_classes)
-      for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_cpu'):
+      for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
         self.assertDeviceEqual(v.device, '/cpu:0')
-      for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_gpu'):
+      for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
         self.assertDeviceEqual(v.device, '/gpu:0')
 
   def testHalfSizeImages(self):

slim/nets/inception_v1.py

@@ -270,7 +270,7 @@ def inception_v1(inputs,
     is_training: whether is training or not.
     dropout_keep_prob: the percentage of activation values that are retained.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/inception_v2.py

@@ -443,7 +443,7 @@ def inception_v2(inputs,
       usage will be to set this value in (0, 1) to reduce the number of
       parameters or computation cost of the model.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/inception_v3.py

@@ -453,7 +453,7 @@ def inception_v3(inputs,
       usage will be to set this value in (0, 1) to reduce the number of
       parameters or computation cost of the model.
     prediction_fn: a function to get predictions out of logits.
-    spatial_squeeze: if True, logits is of shape is [B, C], if false logits is
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
         of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.

slim/nets/inception_v4_test.py

@@ -146,9 +146,9 @@ class InceptionTest(tf.test.TestCase):
       inception.inception_v4(inputs, num_classes)
     with tf.variable_scope('on_gpu'), tf.device('/gpu:0'):
       inception.inception_v4(inputs, num_classes)
-    for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_cpu'):
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'):
       self.assertDeviceEqual(v.device, '/cpu:0')
-    for v in tf.get_collection(tf.GraphKeys.VARIABLES, scope='on_gpu'):
+    for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'):
       self.assertDeviceEqual(v.device, '/gpu:0')
 
   def testHalfSizeImages(self):

slim/nets/resnet_v1.py

@@ -119,6 +119,7 @@ def resnet_v1(inputs,
               global_pool=True,
               output_stride=None,
               include_root_block=True,
+              spatial_squeeze=True,
               reuse=None,
               scope=None):
   """Generator for v1 ResNet models.
@@ -158,6 +159,8 @@ def resnet_v1(inputs,
       ratio of input to output spatial resolution.
     include_root_block: If True, include the initial convolution followed by
       max-pooling, if False excludes it.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.
     scope: Optional variable_scope.
@@ -197,11 +200,13 @@ def resnet_v1(inputs,
         if num_classes is not None:
           net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                             normalizer_fn=None, scope='logits')
+        if spatial_squeeze:
+          logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
         # Convert end_points_collection into a dictionary of end_points.
         end_points = slim.utils.convert_collection_to_dict(end_points_collection)
         if num_classes is not None:
-          end_points['predictions'] = slim.softmax(net, scope='predictions')
-        return net, end_points
+          end_points['predictions'] = slim.softmax(logits, scope='predictions')
+        return logits, end_points
 resnet_v1.default_image_size = 224
 
 
@@ -226,6 +231,7 @@ def resnet_v1_50(inputs,
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
+resnet_v1_50.default_image_size = resnet_v1.default_image_size
 
 
 def resnet_v1_101(inputs,
@@ -249,6 +255,7 @@ def resnet_v1_101(inputs,
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
+resnet_v1_101.default_image_size = resnet_v1.default_image_size
 
 
 def resnet_v1_152(inputs,
@@ -271,6 +278,7 @@ def resnet_v1_152(inputs,
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
+resnet_v1_152.default_image_size = resnet_v1.default_image_size
 
 
 def resnet_v1_200(inputs,
@@ -293,3 +301,4 @@ def resnet_v1_200(inputs,
   return resnet_v1(inputs, blocks, num_classes, is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
+resnet_v1_200.default_image_size = resnet_v1.default_image_size

slim/nets/resnet_v2.py

@@ -117,6 +117,7 @@ def resnet_v2(inputs,
               global_pool=True,
               output_stride=None,
               include_root_block=True,
+              spatial_squeeze=True,
               reuse=None,
               scope=None):
   """Generator for v2 (preactivation) ResNet models.
@@ -157,6 +158,8 @@ def resnet_v2(inputs,
     include_root_block: If True, include the initial convolution followed by
       max-pooling, if False excludes it. If excluded, `inputs` should be the
       results of an activation-less convolution.
+    spatial_squeeze: if True, logits is of shape [B, C], if false logits is
+        of shape [B, 1, 1, C], where B is batch_size and C is number of classes.
     reuse: whether or not the network and its variables should be reused. To be
       able to reuse 'scope' must be given.
     scope: Optional variable_scope.
@@ -206,12 +209,14 @@ def resnet_v2(inputs,
         if num_classes is not None:
           net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                             normalizer_fn=None, scope='logits')
-        logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
+        if spatial_squeeze:
+          logits = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
         # Convert end_points_collection into a dictionary of end_points.
         end_points = slim.utils.convert_collection_to_dict(end_points_collection)
         if num_classes is not None:
-          end_points['predictions'] = slim.softmax(net, scope='predictions')
+          end_points['predictions'] = slim.softmax(logits, scope='predictions')
         return logits, end_points
+resnet_v2.default_image_size = 224
 
 
 def resnet_v2_50(inputs,
@@ -234,7 +239,8 @@ def resnet_v2_50(inputs,
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
-resnet_v2_50.default_image_size = 224
+resnet_v2_50.default_image_size = resnet_v2.default_image_size
+
 
 def resnet_v2_101(inputs,
                   num_classes=None,
@@ -256,7 +262,7 @@ def resnet_v2_101(inputs,
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
-resnet_v2_101.default_image_size = 224
+resnet_v2_101.default_image_size = resnet_v2.default_image_size
 
 
 def resnet_v2_152(inputs,
@@ -279,7 +285,7 @@ def resnet_v2_152(inputs,
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
-resnet_v2_152.default_image_size = 224
+resnet_v2_152.default_image_size = resnet_v2.default_image_size
 
 
 def resnet_v2_200(inputs,
@@ -302,4 +308,4 @@ def resnet_v2_200(inputs,
   return resnet_v2(inputs, blocks, num_classes, is_training=is_training,
                    global_pool=global_pool, output_stride=output_stride,
                    include_root_block=True, reuse=reuse, scope=scope)
-resnet_v2_200.default_image_size = 224
+resnet_v2_200.default_image_size = resnet_v2.default_image_size

slim/scripts/finetune_resnet_v1_50_on_flowers.sh

+#!/bin/bash
+#
+# This script performs the following operations:
+# 1. Downloads the Flowers dataset
+# 2. Fine-tunes a ResNetV1-50 model on the Flowers training set.
+# 3. Evaluates the model on the Flowers validation set.
+#
+# Usage:
+# cd slim
+# ./slim/scripts/finetune_resnet_v1_50_on_flowers.sh
+
+# Where the pre-trained ResNetV1-50 checkpoint is saved to.
+PRETRAINED_CHECKPOINT_DIR=/tmp/checkpoints
+
+# Where the training (fine-tuned) checkpoint and logs will be saved to.
+TRAIN_DIR=/tmp/flowers-models/resnet_v1_50
+
+# Where the dataset is saved to.
+DATASET_DIR=/tmp/flowers
+
+# Download the pre-trained checkpoint.
+if [ ! -d "$PRETRAINED_CHECKPOINT_DIR" ]; then
+  mkdir ${PRETRAINED_CHECKPOINT_DIR}
+fi
+if [ ! -f ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt ]; then
+  wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
+  tar -xvf resnet_v1_50_2016_08_28.tar.gz
+  mv resnet_v1_50.ckpt ${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt
+  rm resnet_v1_50_2016_08_28.tar.gz
+fi
+
+# Download the dataset
+python download_and_convert_data.py \
+  --dataset_name=flowers \
+  --dataset_dir=${DATASET_DIR}
+
+# Fine-tune only the new layers for 3000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=resnet_v1_50 \
+  --checkpoint_path=${PRETRAINED_CHECKPOINT_DIR}/resnet_v1_50.ckpt \
+  --checkpoint_exclude_scopes=resnet_v1_50/logits \
+  --trainable_scopes=resnet_v1_50/logits \
+  --max_number_of_steps=3000 \
+  --batch_size=32 \
+  --learning_rate=0.01 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR} \
+  --eval_dir=${TRAIN_DIR} \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=resnet_v1_50
+
+# Fine-tune all the new layers for 1000 steps.
+python train_image_classifier.py \
+  --train_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=train \
+  --dataset_dir=${DATASET_DIR} \
+  --checkpoint_path=${TRAIN_DIR} \
+  --model_name=resnet_v1_50 \
+  --max_number_of_steps=1000 \
+  --batch_size=32 \
+  --learning_rate=0.001 \
+  --save_interval_secs=60 \
+  --save_summaries_secs=60 \
+  --log_every_n_steps=100 \
+  --optimizer=rmsprop \
+  --weight_decay=0.00004
+
+# Run evaluation.
+python eval_image_classifier.py \
+  --checkpoint_path=${TRAIN_DIR}/all \
+  --eval_dir=${TRAIN_DIR}/all \
+  --dataset_name=flowers \
+  --dataset_split_name=validation \
+  --dataset_dir=${DATASET_DIR} \
+  --model_name=resnet_v1_50

slim/slim_walkthough.ipynb → slim/slim_walkthrough.ipynb

@@ -232,7 +232,7 @@
    },
    "outputs": [],
    "source": [
-    "# The following snippet trains the regression model using a sum_of_squares loss.\n",
+    "# The following snippet trains the regression model using a mean_squared_error loss.\n",
     "ckpt_dir = '/tmp/regression_model/'\n",
     "\n",
     "with tf.Graph().as_default():\n",
@@ -244,7 +244,7 @@
     "    predictions, nodes = regression_model(inputs, is_training=True)\n",
     "\n",
     "    # Add the loss function to the graph.\n",
-    "    loss = slim.losses.sum_of_squares(predictions, targets)\n",
+    "    loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n",
     "    \n",
     "    # The total loss is the uers's loss plus any regularization losses.\n",
     "    total_loss = slim.losses.get_total_loss()\n",
@@ -289,12 +289,12 @@
     "    predictions, end_points = regression_model(inputs, is_training=True)\n",
     "\n",
     "    # Add multiple loss nodes.\n",
-    "    sum_of_squares_loss = slim.losses.sum_of_squares(predictions, targets)\n",
+    "    mean_squared_error_loss = tf.losses.mean_squared_error(labels=targets, predictions=predictions)\n",
     "    absolute_difference_loss = slim.losses.absolute_difference(predictions, targets)\n",
     "\n",
     "    # The following two ways to compute the total loss are equivalent\n",
     "    regularization_loss = tf.add_n(slim.losses.get_regularization_losses())\n",
-    "    total_loss1 = sum_of_squares_loss + absolute_difference_loss + regularization_loss\n",
+    "    total_loss1 = mean_squared_error_loss + absolute_difference_loss + regularization_loss\n",
     "\n",
     "    # Regularization Loss is included in the total loss by default.\n",
     "    # This is good for training, but not for testing.\n",

tutorials/embedding/README.md

@@ -28,7 +28,7 @@ g++ -std=c++11 -shared word2vec_ops.cc word2vec_kernels.cc -o word2vec_ops.so -f
 
 On Mac, add `-undefined dynamic_lookup` to the g++ command.
 
-(For an explanation of what this is doing, see the tutorial on [Adding a New Op to TensorFlow](https://www.tensorflow.org/how_tos/adding_an_op/#building_the_op_library). The flag `-D_GLIBCXX_USE_CXX11_ABI=0` is included to support newer versions of g++.)
+(For an explanation of what this is doing, see the tutorial on [Adding a New Op to TensorFlow](https://www.tensorflow.org/how_tos/adding_an_op/#building_the_op_library). The flag `-D_GLIBCXX_USE_CXX11_ABI=0` is included to support newer versions of gcc. However, if you compiled TensorFlow from source using gcc 5 or later, you may need to exclude the flag.)
 Then run using:
 
 ```shell

tutorials/embedding/word2vec.py

@@ -210,7 +210,7 @@ class Word2Vec(object):
         tf.zeros([opts.vocab_size, opts.emb_dim]),
         name="sm_w_t")
 
-    # Softmax bias: [emb_dim].
+    # Softmax bias: [vocab_size].
     sm_b = tf.Variable(tf.zeros([opts.vocab_size]), name="sm_b")
 
     # Global step: scalar, i.e., shape [].

tutorials/image/cifar10/cifar10_train.py

@@ -52,6 +52,8 @@ tf.app.flags.DEFINE_integer('max_steps', 1000000,
                             """Number of batches to run.""")
 tf.app.flags.DEFINE_boolean('log_device_placement', False,
                             """Whether to log device placement.""")
+tf.app.flags.DEFINE_integer('log_frequency', 10,
+                            """How often to log results to the console.""")
 
 
 def train():
@@ -78,19 +80,21 @@ def train():
 
       def begin(self):
         self._step = -1
+        self._start_time = time.time()
 
       def before_run(self, run_context):
         self._step += 1
-        self._start_time = time.time()
         return tf.train.SessionRunArgs(loss)  # Asks for loss value.
 
       def after_run(self, run_context, run_values):
-        duration = time.time() - self._start_time
-        loss_value = run_values.results
-        if self._step % 10 == 0:
-          num_examples_per_step = FLAGS.batch_size
-          examples_per_sec = num_examples_per_step / duration
-          sec_per_batch = float(duration)
+        if self._step % FLAGS.log_frequency == 0:
+          current_time = time.time()
+          duration = current_time - self._start_time
+          self._start_time = current_time
+
+          loss_value = run_values.results
+          examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
+          sec_per_batch = float(duration / FLAGS.log_frequency)
 
           format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
                         'sec/batch)')

tutorials/rnn/translate/seq2seq_model.py

@@ -100,7 +100,7 @@ class Seq2SeqModel(object):
       b = tf.get_variable("proj_b", [self.target_vocab_size], dtype=dtype)
       output_projection = (w, b)
 
-      def sampled_loss(labels, inputs):
+      def sampled_loss(inputs, labels):
         labels = tf.reshape(labels, [-1, 1])
         # We need to compute the sampled_softmax_loss using 32bit floats to
         # avoid numerical instabilities.