Merge branch 'panoptic-segmentation' into panoptic-deeplab-modeling

official/projects/detr/tasks/detection_test.py

+# Copyright 2022 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.
+
+"""Tests for detection."""
+
+import numpy as np
+import tensorflow as tf
+import tensorflow_datasets as tfds
+
+from official.projects.detr import optimization
+from official.projects.detr.configs import detr as detr_cfg
+from official.projects.detr.dataloaders import coco
+from official.projects.detr.tasks import detection
+
+
+_NUM_EXAMPLES = 10
+
+
+def _gen_fn():
+  h = np.random.randint(0, 300)
+  w = np.random.randint(0, 300)
+  num_boxes = np.random.randint(0, 50)
+  return {
+      'image': np.ones(shape=(h, w, 3), dtype=np.uint8),
+      'image/id': np.random.randint(0, 100),
+      'image/filename': 'test',
+      'objects': {
+          'is_crowd': np.ones(shape=(num_boxes), dtype=np.bool),
+          'bbox': np.ones(shape=(num_boxes, 4), dtype=np.float32),
+          'label': np.ones(shape=(num_boxes), dtype=np.int64),
+          'id': np.ones(shape=(num_boxes), dtype=np.int64),
+          'area': np.ones(shape=(num_boxes), dtype=np.int64),
+      }
+  }
+
+
+def _as_dataset(self, *args, **kwargs):
+  del args
+  del kwargs
+  return tf.data.Dataset.from_generator(
+      lambda: (_gen_fn() for i in range(_NUM_EXAMPLES)),
+      output_types=self.info.features.dtype,
+      output_shapes=self.info.features.shape,
+  )
+
+
+class DetectionTest(tf.test.TestCase):
+
+  def test_train_step(self):
+    config = detr_cfg.DetectionConfig(
+        num_encoder_layers=1,
+        num_decoder_layers=1,
+        train_data=coco.COCODataConfig(
+            tfds_name='coco/2017',
+            tfds_split='validation',
+            is_training=True,
+            global_batch_size=2,
+        ))
+    with tfds.testing.mock_data(as_dataset_fn=_as_dataset):
+      task = detection.DectectionTask(config)
+      model = task.build_model()
+      dataset = task.build_inputs(config.train_data)
+      iterator = iter(dataset)
+      opt_cfg = optimization.OptimizationConfig({
+          'optimizer': {
+              'type': 'detr_adamw',
+              'detr_adamw': {
+                  'weight_decay_rate': 1e-4,
+                  'global_clipnorm': 0.1,
+              }
+          },
+          'learning_rate': {
+              'type': 'stepwise',
+              'stepwise': {
+                  'boundaries': [120000],
+                  'values': [0.0001, 1.0e-05]
+              }
+          },
+      })
+      optimizer = detection.DectectionTask.create_optimizer(opt_cfg)
+      task.train_step(next(iterator), model, optimizer)
+
+  def test_validation_step(self):
+    config = detr_cfg.DetectionConfig(
+        num_encoder_layers=1,
+        num_decoder_layers=1,
+        validation_data=coco.COCODataConfig(
+            tfds_name='coco/2017',
+            tfds_split='validation',
+            is_training=False,
+            global_batch_size=2,
+        ))
+
+    with tfds.testing.mock_data(as_dataset_fn=_as_dataset):
+      task = detection.DectectionTask(config)
+      model = task.build_model()
+      metrics = task.build_metrics(training=False)
+      dataset = task.build_inputs(config.validation_data)
+      iterator = iter(dataset)
+      logs = task.validation_step(next(iterator), model, metrics)
+      state = task.aggregate_logs(step_outputs=logs)
+      task.reduce_aggregated_logs(state)
+
+if __name__ == '__main__':
+  tf.test.main()

official/projects/detr/train.py

+# Copyright 2022 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.
+
+"""TensorFlow Model Garden Vision training driver."""
+
+from absl import app
+from absl import flags
+import gin
+
+from official.common import distribute_utils
+from official.common import flags as tfm_flags
+from official.core import task_factory
+from official.core import train_lib
+from official.core import train_utils
+from official.modeling import performance
+# pylint: disable=unused-import
+from official.projects.detr.configs import detr
+from official.projects.detr.tasks import detection
+# pylint: enable=unused-import
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+  gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
+  params = train_utils.parse_configuration(FLAGS)
+  model_dir = FLAGS.model_dir
+  if 'train' in FLAGS.mode:
+    # Pure eval modes do not output yaml files. Otherwise continuous eval job
+    # may race against the train job for writing the same file.
+    train_utils.serialize_config(params, model_dir)
+
+  # Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
+  # can have significant impact on model speeds by utilizing float16 in case of
+  # GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
+  # dtype is float16
+  if params.runtime.mixed_precision_dtype:
+    performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype)
+  distribution_strategy = distribute_utils.get_distribution_strategy(
+      distribution_strategy=params.runtime.distribution_strategy,
+      all_reduce_alg=params.runtime.all_reduce_alg,
+      num_gpus=params.runtime.num_gpus,
+      tpu_address=params.runtime.tpu)
+  with distribution_strategy.scope():
+    task = task_factory.get_task(params.task, logging_dir=model_dir)
+
+  train_lib.run_experiment(
+      distribution_strategy=distribution_strategy,
+      task=task,
+      mode=FLAGS.mode,
+      params=params,
+      model_dir=model_dir)
+
+  train_utils.save_gin_config(FLAGS.mode, model_dir)
+
+if __name__ == '__main__':
+  tfm_flags.define_flags()
+  flags.mark_flags_as_required(['experiment', 'mode', 'model_dir'])
+  app.run(main)

official/projects/edgetpu/nlp/serving/export_tflite_squad.py

@@ -135,7 +135,8 @@ def main(argv: Sequence[str]) -> None:
     checkpoint = tf.train.Checkpoint(**checkpoint_dict)
     checkpoint.restore(FLAGS.model_checkpoint).assert_existing_objects_matched()
 
-  model_for_serving = build_model_for_serving(model)
+  model_for_serving = build_model_for_serving(model, FLAGS.sequence_length,
+                                              FLAGS.batch_size)
   model_for_serving.summary()
 
   # TODO(b/194449109): Need to save the model to file and then convert tflite

official/projects/movinet/configs/yaml/movinet_a0_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.2
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a0_k600_cpu_local.yaml

@@ -12,6 +12,7 @@ task:
     norm_activation:
       use_sync_bn: false
     dropout_rate: 0.5
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a0_stream_k600_8x8.yaml

@@ -24,6 +24,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.2
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a1_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a1_stream_k600_8x8.yaml

@@ -24,6 +24,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.2
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a2_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a2_stream_k600_8x8.yaml

@@ -24,6 +24,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a3_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a3_stream_k600_8x8.yaml

@@ -25,6 +25,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a4_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a4_stream_k600_8x8.yaml

@@ -25,6 +25,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a5_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_a5_stream_k600_8x8.yaml

@@ -25,6 +25,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.5
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_t0_k600_8x8.yaml

@@ -18,6 +18,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.2
+    activation: 'swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/configs/yaml/movinet_t0_stream_k600_8x8.yaml

@@ -24,6 +24,7 @@ task:
     norm_activation:
       use_sync_bn: true
     dropout_rate: 0.2
+    activation: 'hard_swish'
   train_data:
     name: kinetics600
     variant_name: rgb

official/projects/movinet/modeling/movinet.py

@@ -338,7 +338,7 @@ class Movinet(tf.keras.Model):
         3x3 followed by 5x1 conv). '3d_2plus1d' uses (2+1)D convolution with
         Conv3D and no 2D reshaping (e.g., a 5x3x3 kernel becomes 1x3x3 followed
         by 5x1x1 conv).
-      se_type: '3d', '2d', or '2plus3d'. '3d' uses the default 3D
+      se_type: '3d', '2d', '2plus3d' or 'none'. '3d' uses the default 3D
           spatiotemporal global average pooling for squeeze excitation. '2d'
           uses 2D spatial global average pooling  on each frame. '2plus3d'
           concatenates both 3D and 2D global average pooling.
@@ -369,7 +369,7 @@ class Movinet(tf.keras.Model):
 
     if conv_type not in ('3d', '2plus1d', '3d_2plus1d'):
       raise ValueError('Unknown conv type: {}'.format(conv_type))
-    if se_type not in ('3d', '2d', '2plus3d'):
+    if se_type not in ('3d', '2d', '2plus3d', 'none'):
       raise ValueError('Unknown squeeze excitation type: {}'.format(se_type))
 
     self._model_id = model_id
@@ -602,6 +602,7 @@ class Movinet(tf.keras.Model):
                 expand_filters,
             )
 
+          if '3d' in self._se_type:
             states[f'{prefix}_pool_buffer'] = (
                 input_shape[0], 1, 1, 1, expand_filters,
             )

official/projects/movinet/modeling/movinet_layers.py

@@ -93,10 +93,9 @@ class MobileConv2D(tf.keras.layers.Layer):
       data_format: Optional[str] = None,
       dilation_rate: Union[int, Sequence[int]] = (1, 1),
       groups: int = 1,
-      activation: Optional[nn_layers.Activation] = None,
       use_bias: bool = True,
-      kernel_initializer: tf.keras.initializers.Initializer = 'glorot_uniform',
-      bias_initializer: tf.keras.initializers.Initializer = 'zeros',
+      kernel_initializer: str = 'glorot_uniform',
+      bias_initializer: str = 'zeros',
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
       bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
       activity_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
@@ -105,6 +104,8 @@ class MobileConv2D(tf.keras.layers.Layer):
       use_depthwise: bool = False,
       use_temporal: bool = False,
       use_buffered_input: bool = False,  # pytype: disable=annotation-type-mismatch  # typed-keras
+      batch_norm_op: Optional[Any] = None,
+      activation_op: Optional[Any] = None,
       **kwargs):  # pylint: disable=g-doc-args
     """Initializes mobile conv2d.
 
@@ -117,6 +118,10 @@ class MobileConv2D(tf.keras.layers.Layer):
       use_buffered_input: if True, the input is expected to be padded
           beforehand. In effect, calling this layer will use 'valid' padding on
           the temporal dimension to simulate 'causal' padding.
+      batch_norm_op: A callable object of batch norm layer. If None, no batch
+        norm will be applied after the convolution.
+      activation_op: A callabel object of activation layer. If None, no
+        activation will be applied after the convolution.
       **kwargs: keyword arguments to be passed to this layer.
 
     Returns:
@@ -130,7 +135,6 @@ class MobileConv2D(tf.keras.layers.Layer):
     self._data_format = data_format
     self._dilation_rate = dilation_rate
     self._groups = groups
-    self._activation = activation
     self._use_bias = use_bias
     self._kernel_initializer = kernel_initializer
     self._bias_initializer = bias_initializer
@@ -142,6 +146,8 @@ class MobileConv2D(tf.keras.layers.Layer):
     self._use_depthwise = use_depthwise
     self._use_temporal = use_temporal
     self._use_buffered_input = use_buffered_input
+    self._batch_norm_op = batch_norm_op
+    self._activation_op = activation_op
 
     kernel_size = normalize_tuple(kernel_size, 2, 'kernel_size')
 
@@ -156,7 +162,6 @@ class MobileConv2D(tf.keras.layers.Layer):
           depth_multiplier=1,
           data_format=data_format,
           dilation_rate=dilation_rate,
-          activation=activation,
           use_bias=use_bias,
           depthwise_initializer=kernel_initializer,
           bias_initializer=bias_initializer,
@@ -175,7 +180,6 @@ class MobileConv2D(tf.keras.layers.Layer):
           data_format=data_format,
           dilation_rate=dilation_rate,
           groups=groups,
-          activation=activation,
           use_bias=use_bias,
           kernel_initializer=kernel_initializer,
           bias_initializer=bias_initializer,
@@ -196,7 +200,6 @@ class MobileConv2D(tf.keras.layers.Layer):
         'data_format': self._data_format,
         'dilation_rate': self._dilation_rate,
         'groups': self._groups,
-        'activation': self._activation,
         'use_bias': self._use_bias,
         'kernel_initializer': self._kernel_initializer,
         'bias_initializer': self._bias_initializer,
@@ -229,6 +232,10 @@ class MobileConv2D(tf.keras.layers.Layer):
     x = tf.reshape(inputs, input_shape)
 
     x = self._conv(x)
+    if self._batch_norm_op is not None:
+      x = self._batch_norm_op(x)
+    if self._activation_op is not None:
+      x = self._activation_op(x)
 
     if self._use_temporal:
       output_shape = [
@@ -357,8 +364,20 @@ class ConvBlock(tf.keras.layers.Layer):
     padding = 'causal' if self._causal else 'same'
     self._groups = input_shape[-1] if self._depthwise else 1
 
-    self._conv_temporal = None
+    self._batch_norm = None
+    self._batch_norm_temporal = None
+    if self._use_batch_norm:
+      self._batch_norm = self._batch_norm_layer(
+          momentum=self._batch_norm_momentum,
+          epsilon=self._batch_norm_epsilon,
+          name='bn')
+      if self._conv_type != '3d' and self._kernel_size[0] > 1:
+        self._batch_norm_temporal = self._batch_norm_layer(
+            momentum=self._batch_norm_momentum,
+            epsilon=self._batch_norm_epsilon,
+            name='bn_temporal')
 
+    self._conv_temporal = None
     if self._conv_type == '3d_2plus1d' and self._kernel_size[0] > 1:
       self._conv = nn_layers.Conv3D(
           self._filters,
@@ -394,6 +413,8 @@ class ConvBlock(tf.keras.layers.Layer):
           kernel_initializer=self._kernel_initializer,
           kernel_regularizer=self._kernel_regularizer,
           use_buffered_input=False,
+          batch_norm_op=self._batch_norm,
+          activation_op=self._activation_layer,
           name='conv2d')
       if self._kernel_size[0] > 1:
         self._conv_temporal = MobileConv2D(
@@ -408,6 +429,8 @@ class ConvBlock(tf.keras.layers.Layer):
             kernel_initializer=self._kernel_initializer,
             kernel_regularizer=self._kernel_regularizer,
             use_buffered_input=self._use_buffered_input,
+            batch_norm_op=self._batch_norm_temporal,
+            activation_op=self._activation_layer,
             name='conv2d_temporal')
     else:
       self._conv = nn_layers.Conv3D(
@@ -422,37 +445,26 @@ class ConvBlock(tf.keras.layers.Layer):
           use_buffered_input=self._use_buffered_input,
           name='conv3d')
 
-    self._batch_norm = None
-    self._batch_norm_temporal = None
-
-    if self._use_batch_norm:
-      self._batch_norm = self._batch_norm_layer(
-          momentum=self._batch_norm_momentum,
-          epsilon=self._batch_norm_epsilon,
-          name='bn')
-      if self._conv_type != '3d' and self._conv_temporal is not None:
-        self._batch_norm_temporal = self._batch_norm_layer(
-            momentum=self._batch_norm_momentum,
-            epsilon=self._batch_norm_epsilon,
-            name='bn_temporal')
-
     super(ConvBlock, self).build(input_shape)
 
   def call(self, inputs):
     """Calls the layer with the given inputs."""
     x = inputs
 
+    # bn_op and activation_op are folded into the '2plus1d' conv layer so that
+    # we do not explicitly call them here.
+    # TODO(lzyuan): clean the conv layers api once the models are re-trained.
     x = self._conv(x)
-    if self._batch_norm is not None:
+    if self._batch_norm is not None and self._conv_type != '2plus1d':
       x = self._batch_norm(x)
-    if self._activation_layer is not None:
+    if self._activation_layer is not None and self._conv_type != '2plus1d':
       x = self._activation_layer(x)
 
     if self._conv_temporal is not None:
       x = self._conv_temporal(x)
-      if self._batch_norm_temporal is not None:
+      if self._batch_norm_temporal is not None and self._conv_type != '2plus1d':
         x = self._batch_norm_temporal(x)
-      if self._activation_layer is not None:
+      if self._activation_layer is not None and self._conv_type != '2plus1d':
         x = self._activation_layer(x)
 
     return x
@@ -640,10 +652,13 @@ class StreamConvBlock(ConvBlock):
     if self._conv_temporal is None and self._stream_buffer is not None:
       x, states = self._stream_buffer(x, states=states)
 
+    # bn_op and activation_op are folded into the '2plus1d' conv layer so that
+    # we do not explicitly call them here.
+    # TODO(lzyuan): clean the conv layers api once the models are re-trained.
     x = self._conv(x)
-    if self._batch_norm is not None:
+    if self._batch_norm is not None and self._conv_type != '2plus1d':
       x = self._batch_norm(x)
-    if self._activation_layer is not None:
+    if self._activation_layer is not None and self._conv_type != '2plus1d':
       x = self._activation_layer(x)
 
     if self._conv_temporal is not None:
@@ -653,9 +668,9 @@ class StreamConvBlock(ConvBlock):
         x, states = self._stream_buffer(x, states=states)
 
       x = self._conv_temporal(x)
-      if self._batch_norm_temporal is not None:
+      if self._batch_norm_temporal is not None and self._conv_type != '2plus1d':
         x = self._batch_norm_temporal(x)
-      if self._activation_layer is not None:
+      if self._activation_layer is not None and self._conv_type != '2plus1d':
         x = self._activation_layer(x)
 
     return x, states
@@ -885,6 +900,7 @@ class MobileBottleneck(tf.keras.layers.Layer):
 
     x = self._expansion_layer(inputs)
     x, states = self._feature_layer(x, states=states)
+    if self._attention_layer is not None:
       x, states = self._attention_layer(x, states=states)
     x = self._projection_layer(x)
 
@@ -1136,6 +1152,8 @@ class MovinetBlock(tf.keras.layers.Layer):
         batch_norm_momentum=self._batch_norm_momentum,
         batch_norm_epsilon=self._batch_norm_epsilon,
         name='projection')
+    self._attention = None
+    if se_type != 'none':
       self._attention = StreamSqueezeExcitation(
           se_hidden_filters,
           se_type=se_type,