Internal change

PiperOrigin-RevId: 386105413

official/vision/beta/modeling/layers/nn_layers.py

@@ -26,10 +26,6 @@ from official.modeling import tf_utils
 States = Dict[str, tf.Tensor]
 Activation = Union[str, Callable]
 
-# TODO(dankondratyuk): keep legacy padding until new checkpoints are trained.
-# Otherwise, accuracy will be affected.
-LEGACY_PADDING = True
-
 
 def make_divisible(value: float,
                    divisor: int,
@@ -89,6 +85,22 @@ def hard_swish(x: tf.Tensor) -> tf.Tensor:
 tf.keras.utils.get_custom_objects().update({'hard_swish': hard_swish})
 
 
+def simple_swish(x: tf.Tensor) -> tf.Tensor:
+  """A swish/silu activation function without custom gradients.
+
+  Useful for exporting to SavedModel to avoid custom gradient warnings.
+
+  Args:
+    x: the input tensor.
+
+  Returns:
+    The activation output.
+  """
+  return x * tf.math.sigmoid(x)
+
+tf.keras.utils.get_custom_objects().update({'simple_swish': simple_swish})
+
+
 @tf.keras.utils.register_keras_serializable(package='Vision')
 class SqueezeExcitation(tf.keras.layers.Layer):
   """Creates a squeeze and excitation layer."""
@@ -752,14 +764,10 @@ class CausalConvMixin:
          (self.kernel_size[i] - 1) * (self.dilation_rate[i] - 1))
         for i in range(self.rank)
     ]
-    if LEGACY_PADDING:
-      # Apply legacy padding that does not take into account spatial strides
-      pad_total = [kernel_size_effective[i] - 1 for i in range(self.rank)]
-    else:
-      pad_total = [kernel_size_effective[0] - 1]
-      for i in range(1, self.rank):
-        overlap = (input_shape[i] - 1) % self.strides[i] + 1
-        pad_total.append(tf.maximum(kernel_size_effective[i] - overlap, 0))
+    pad_total = [kernel_size_effective[0] - 1]
+    for i in range(1, self.rank):
+      overlap = (input_shape[i] - 1) % self.strides[i] + 1
+      pad_total.append(tf.maximum(kernel_size_effective[i] - overlap, 0))
     pad_beg = [pad_total[i] // 2 for i in range(self.rank)]
     pad_end = [pad_total[i] - pad_beg[i] for i in range(self.rank)]
     padding = [[pad_beg[i], pad_end[i]] for i in range(self.rank)]

official/vision/beta/modeling/layers/nn_layers_test.py

@@ -24,10 +24,6 @@ from official.vision.beta.modeling.layers import nn_layers
 
 class NNLayersTest(parameterized.TestCase, tf.test.TestCase):
 
-  def setUp(self):
-    super().setUp()
-    nn_layers.LEGACY_PADDING = False
-
   def test_hard_swish(self):
     activation = tf.keras.layers.Activation('hard_swish')
     output = activation(tf.constant([-3, -1.5, 0, 3]))

official/vision/beta/projects/movinet/README.md

@@ -8,6 +8,8 @@ This repository is the official implementation of
 [MoViNets: Mobile Video Networks for Efficient Video
 Recognition](https://arxiv.org/abs/2103.11511).
 
+**[UPDATE 2021-07-12] Mobile Models Available via [TF Lite](#tf-lite-streaming-models)**
+
 <p align="center">
   <img src="https://storage.googleapis.com/tf_model_garden/vision/movinet/artifacts/hoverboard_stream.gif" height=500>
 </p>
@@ -53,6 +55,8 @@ approach that performs redundant computation and limits temporal scope.
 
 ## History
 
+- **2021-07-12** Add TF Lite support and replace 3D stream models with
+mobile-friendly (2+1)D stream.
 - **2021-05-30** Add streaming MoViNet checkpoints and examples.
 - **2021-05-11** Initial Commit.
 
@@ -68,6 +72,7 @@ approach that performs redundant computation and limits temporal scope.
 - [Results and Pretrained Weights](#results-and-pretrained-weights)
   - [Kinetics 600](#kinetics-600)
 - [Prediction Examples](#prediction-examples)
+- [TF Lite Example](#tf-lite-example)
 - [Training and Evaluation](#training-and-evaluation)
 - [References](#references)
 - [License](#license)
@@ -108,10 +113,14 @@ MoViNet-A5.
 
 #### Base Models
 
-Base models implement standard 3D convolutions without stream buffers.
+Base models implement standard 3D convolutions without stream buffers. Base
+models are not recommended for fast inference on CPU or mobile due to
+limited support for
+[`tf.nn.conv3d`](https://www.tensorflow.org/api_docs/python/tf/nn/conv3d).
+Instead, see the [streaming models section](#streaming-models).
 
-| Model Name | Top-1 Accuracy | Top-5 Accuracy | Input Shape | GFLOPs\* | Chekpoint | TF Hub SavedModel |
-|------------|----------------|----------------|-------------|----------|-----------|-------------------|
+| Model Name | Top-1 Accuracy | Top-5 Accuracy | Input Shape | GFLOPs\* | Checkpoint | TF Hub SavedModel |
+|------------|----------------|----------------|-------------|----------|------------|-------------------|
 | MoViNet-A0-Base | 72.28 | 90.92 | 50 x 172 x 172 | 2.7 | [checkpoint (12 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a0_base.tar.gz) | [tfhub](https://tfhub.dev/tensorflow/movinet/a0/base/kinetics-600/classification/) |
 | MoViNet-A1-Base | 76.69 | 93.40 | 50 x 172 x 172 | 6.0 | [checkpoint (18 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a1_base.tar.gz) | [tfhub](https://tfhub.dev/tensorflow/movinet/a1/base/kinetics-600/classification/) |
 | MoViNet-A2-Base | 78.62 | 94.17 | 50 x 224 x 224 | 10 | [checkpoint (20 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a2_base.tar.gz) | [tfhub](https://tfhub.dev/tensorflow/movinet/a2/base/kinetics-600/classification/) |
@@ -123,10 +132,19 @@ Base models implement standard 3D convolutions without stream buffers.
 
 #### Streaming Models
 
-Streaming models implement causal 3D convolutions with stream buffers.
+Streaming models implement causal (2+1)D convolutions with stream buffers.
+Streaming models use (2+1)D convolution instead of 3D to utilize optimized
+[`tf.nn.conv2d`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d)
+operations, which offer fast inference on CPU. Streaming models can be run on
+individual frames or on larger video clips like base models.
+
+Note: A3, A4, and A5 models use a positional encoding in the squeeze-excitation
+blocks, while A0, A1, and A2 do not. For the smaller models, accuracy is
+unaffected without positional encoding, while for the larger models accuracy is
+significantly worse without positional encoding.
 
-| Model Name | Top-1 Accuracy | Top-5 Accuracy | Input Shape\* | GFLOPs\*\* | Chekpoint | TF Hub SavedModel |
-|------------|----------------|----------------|---------------|------------|-----------|-------------------|
+| Model Name | Top-1 Accuracy | Top-5 Accuracy | Input Shape\* | GFLOPs\*\* | Checkpoint | TF Hub SavedModel |
+|------------|----------------|----------------|---------------|------------|------------|-------------------|
 | MoViNet-A0-Stream | 72.05 | 90.63 | 50 x 172 x 172 | 2.7 | [checkpoint (12 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a0_stream.tar.gz) | [tfhub](https://tfhub.dev/tensorflow/movinet/a0/stream/kinetics-600/classification/) |
 | MoViNet-A1-Stream | 76.45 | 93.25 | 50 x 172 x 172 | 6.0 | [checkpoint (18 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a1_stream.tar.gz) | [tfhub](https://tfhub.dev/tensorflow/movinet/a1/stream/kinetics-600/classification/) |
 | MoViNet-A2-Stream | 78.40 | 94.05 | 50 x 224 x 224 | 10 | [checkpoint (20 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a2_stream.tar.gz) | [tfhub](https://tfhub.dev/tensorflow/movinet/a2/stream/kinetics-600/classification/) |
@@ -139,6 +157,35 @@ duration of the 10-second clip.
 
 \*\*GFLOPs per video on Kinetics 600.
 
+Note: current streaming model checkpoints have been updated with a slightly
+different architecture. To download the old checkpoints, insert `_legacy` before
+`.tar.gz` in the URL. E.g., `movinet_a0_stream_legacy.tar.gz`.
+
+##### TF Lite Streaming Models
+
+For convenience, we provide converted TF Lite models for inference on mobile
+devices. See the [TF Lite Example](#tf-lite-example) to export and run your own
+models.
+
+For reference, MoViNet-A0-Stream runs with a similar latency to
+[MobileNetV3-Large]
+(https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/classification/)
+with +5% accuracy on Kinetics 600.
+
+| Model Name | Input Shape | Pixel 4 Latency\* | x86 Latency\* | TF Lite Binary |
+|------------|-------------|-------------------|---------------|----------------|
+| MoViNet-A0-Stream | 1 x 1 x 172 x 172 | 22 ms | 16 ms | [TF Lite (13 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a0_stream.tflite) |
+| MoViNet-A1-Stream | 1 x 1 x 172 x 172 | 42 ms | 33 ms | [TF Lite (45 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a1_stream.tflite) |
+| MoViNet-A2-Stream | 1 x 1 x 224 x 224 | 200 ms | 66 ms | [TF Lite (53 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a2_stream.tflite) |
+| MoViNet-A3-Stream | 1 x 1 x 256 x 256 | - | 120 ms | [TF Lite (73 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a3_stream.tflite) |
+| MoViNet-A4-Stream | 1 x 1 x 290 x 290 | - | 300 ms | [TF Lite (101 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a4_stream.tflite) |
+| MoViNet-A5-Stream | 1 x 1 x 320 x 320 | - | 450 ms | [TF Lite (153 MB)](https://storage.googleapis.com/tf_model_garden/vision/movinet/movinet_a5_stream.tflite) |
+
+\*Single-frame latency measured on with unaltered float32 operations on a
+single CPU core. Observed latency may differ depending on hardware
+configuration. Measured on a stock Pixel 4 (Android 11) and x86 Intel Xeon
+W-2135 CPU.
+
 ## Prediction Examples
 
 Please check out our [Colab Notebook](https://colab.research.google.com/github/tensorflow/models/tree/master/official/vision/beta/projects/movinet/movinet_tutorial.ipynb)
@@ -146,7 +193,7 @@ to get started with MoViNets.
 
 This section provides examples on how to run prediction.
 
-For base models, run the following:
+For **base models**, run the following:
 
 ```python
 import tensorflow as tf
@@ -181,7 +228,7 @@ output = model(inputs)
 prediction = tf.argmax(output, -1)
 ```
 
-For streaming models, run the following:
+For **streaming models**, run the following:
 
 ```python
 import tensorflow as tf
@@ -189,20 +236,31 @@ import tensorflow as tf
 from official.vision.beta.projects.movinet.modeling import movinet
 from official.vision.beta.projects.movinet.modeling import movinet_model
 
+model_id = 'a0'
+use_positional_encoding = model_id in {'a3', 'a4', 'a5'}
+
 # Create backbone and model.
 backbone = movinet.Movinet(
-    model_id='a0',
+    model_id=model_id,
     causal=True,
+    conv_type='2plus1d',
+    se_type='2plus3d',
+    activation='hard_swish',
+    gating_activation='hard_sigmoid',
+    use_positional_encoding=use_positional_encoding,
     use_external_states=True,
 )
+
 model = movinet_model.MovinetClassifier(
-    backbone, num_classes=600, output_states=True)
+    backbone,
+    num_classes=600,
+    output_states=True)
 
 # Create your example input here.
 # Refer to the paper for recommended input shapes.
 inputs = tf.ones([1, 8, 172, 172, 3])
 
-# [Optional] Build the model and load a pretrained checkpoint
+# [Optional] Build the model and load a pretrained checkpoint.
 model.build(inputs.shape)
 
 checkpoint_dir = '/path/to/checkpoint'
@@ -237,23 +295,89 @@ non_streaming_output, _ = model({**init_states, 'image': inputs})
 non_streaming_prediction = tf.argmax(non_streaming_output, -1)
 ```
 
+## TF Lite Example
+
+This section outlines an example on how to export a model to run on mobile
+devices with [TF Lite](https://www.tensorflow.org/lite).
+
+First, convert to [TF SavedModel](https://www.tensorflow.org/guide/saved_model)
+by running `export_saved_model.py`. For example, for `MoViNet-A0-Stream`, run:
+
+```shell
+python3 export_saved_model.py \
+  --model_id=a0 \
+  --causal=True \
+  --conv_type=2plus1d \
+  --se_type=2plus3d \
+  --activation=hard_swish \
+  --gating_activation=hard_sigmoid \
+  --use_positional_encoding=False \
+  --num_classes=600 \
+  --batch_size=1 \
+  --num_frames=1 \
+  --image_size=172 \
+  --bundle_input_init_states_fn=False \
+  --checkpoint_path=/path/to/checkpoint \
+  --export_path=/tmp/movinet_a0_stream
+```
+
+Then the SavedModel can be converted to TF Lite using the [`TFLiteConverter`](https://www.tensorflow.org/lite/convert):
+
+```python
+saved_model_dir = '/tmp/movinet_a0_stream'
+converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
+tflite_model = converter.convert()
+
+with open('/tmp/movinet_a0_stream.tflite', 'wb') as f:
+  f.write(tflite_model)
+```
+
+To run with TF Lite using [tf.lite.Interpreter](https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_python)
+with the Python API:
+
+```python
+# Create the interpreter and signature runner
+interpreter = tf.lite.Interpreter('/tmp/movinet_a0_stream.tflite')
+signature = interpreter.get_signature_runner()
+
+# Extract state names and create the initial (zero) states
+def state_name(name: str) -> str:
+  return name[len('serving_default_'):-len(':0')]
+
+init_states = {
+    state_name(x['name']): tf.zeros(x['shape'], dtype=x['dtype'])
+    for x in interpreter.get_input_details()
+}
+del init_states['image']
+
+# Insert your video clip here
+video = tf.ones([1, 8, 172, 172, 3])
+clips = tf.split(video, video.shape[1], axis=1)
+
+# To run on a video, pass in one frame at a time
+states = init_states
+for clip in clips:
+  # Input shape: [1, 1, 172, 172, 3]
+  outputs = signature(**states, image=clip)
+  logits = outputs.pop('logits')
+  states = outputs
+```
+
+Follow the [official guide](https://www.tensorflow.org/lite/guide) to run a
+model with TF Lite on your mobile device.
+
 ## Training and Evaluation
 
 Run this command line for continuous training and evaluation.
 
 ```shell
-MODE=train_and_eval  # Can also be 'train'
+MODE=train_and_eval  # Can also be 'train' if using a separate evaluator job
 CONFIG_FILE=official/vision/beta/projects/movinet/configs/yaml/movinet_a0_k600_8x8.yaml
 python3 official/vision/beta/projects/movinet/train.py \
     --experiment=movinet_kinetics600 \
     --mode=${MODE} \
-    --model_dir=/tmp/movinet/ \
-    --config_file=${CONFIG_FILE} \
-    --params_override="" \
-    --gin_file="" \
-    --gin_params="" \
-    --tpu="" \
-    --tf_data_service=""
+    --model_dir=/tmp/movinet_a0_base/ \
+    --config_file=${CONFIG_FILE}
 ```
 
 Run this command line for evaluation.
@@ -264,13 +388,8 @@ CONFIG_FILE=official/vision/beta/projects/movinet/configs/yaml/movinet_a0_k600_8
 python3 official/vision/beta/projects/movinet/train.py \
     --experiment=movinet_kinetics600 \
     --mode=${MODE} \
-    --model_dir=/tmp/movinet/ \
-    --config_file=${CONFIG_FILE} \
-    --params_override="" \
-    --gin_file="" \
-    --gin_params="" \
-    --tpu="" \
-    --tf_data_service=""
+    --model_dir=/tmp/movinet_a0_base/ \
+    --config_file=${CONFIG_FILE}
 ```
 
 ## License

official/vision/beta/projects/movinet/configs/movinet.py

@@ -130,6 +130,7 @@ class MovinetModel(video_classification.VideoClassificationModel):
       norm_momentum=0.99,
       norm_epsilon=1e-3,
       use_sync_bn=True)
+  activation: str = 'swish'
   output_states: bool = False
 
 

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

@@ -15,6 +15,11 @@ task:
       movinet:
         model_id: 'a0'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         stochastic_depth_drop_rate: 0.2
     norm_activation:
       use_sync_bn: true

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

@@ -15,6 +15,11 @@ task:
       movinet:
         model_id: 'a1'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         stochastic_depth_drop_rate: 0.2
     norm_activation:
       use_sync_bn: true

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

@@ -15,10 +15,15 @@ task:
       movinet:
         model_id: 'a2'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         stochastic_depth_drop_rate: 0.2
     norm_activation:
       use_sync_bn: true
-    dropout_rate: 0.2
+    dropout_rate: 0.5
   train_data:
     name: kinetics600
     variant_name: rgb

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

@@ -15,6 +15,11 @@ task:
       movinet:
         model_id: 'a3'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         use_positional_encoding: true
         stochastic_depth_drop_rate: 0.2
     norm_activation:

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

@@ -15,6 +15,11 @@ task:
       movinet:
         model_id: 'a4'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         use_positional_encoding: true
         stochastic_depth_drop_rate: 0.2
     norm_activation:

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

@@ -15,6 +15,11 @@ task:
       movinet:
         model_id: 'a5'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         use_positional_encoding: true
         stochastic_depth_drop_rate: 0.2
     norm_activation:
@@ -42,7 +47,8 @@ task:
   validation_data:
     name: kinetics600
     feature_shape: !!python/tuple
-    - 120
+    # Evaluate on 115 frames instead of 120, as the model will get OOM on TPU
+    - 115
     - 320
     - 320
     - 3

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

@@ -15,6 +15,11 @@ task:
       movinet:
         model_id: 't0'
         causal: true
+        # Note: we train with '3d_2plus1d', but convert to '2plus1d' for inference
+        conv_type: '3d_2plus1d'
+        se_type: '2plus3d'
+        activation: 'hard_swish'
+        gating_activation: 'hard_sigmoid'
         stochastic_depth_drop_rate: 0.2
     norm_activation:
       use_sync_bn: true

official/vision/beta/projects/movinet/export_saved_model.py

@@ -28,6 +28,26 @@ python3 export_saved_model.py \
   --checkpoint_path=""
 ```
 
+Export for TF Lite example:
+
+```shell
+python3 export_saved_model.py \
+  --model_id=a0 \
+  --causal=True \
+  --conv_type=2plus1d \
+  --se_type=2plus3d \
+  --activation=hard_swish \
+  --gating_activation=hard_sigmoid \
+  --use_positional_encoding=False \
+  --num_classes=600 \
+  --batch_size=1 \
+  --num_frames=1 \  # Use a single frame for streaming mode
+  --image_size=172 \  # Input resolution for the model
+  --bundle_input_init_states_fn=False \
+  --checkpoint_path=/path/to/checkpoint \
+  --export_path=/tmp/movinet_a0_stream
+```
+
 To use an exported saved_model, refer to export_saved_model_test.py.
 """
 
@@ -79,6 +99,10 @@ flags.DEFINE_integer(
 flags.DEFINE_integer(
     'image_size', None,
     'The resolution of the input. Set to None for dynamic input.')
+flags.DEFINE_bool(
+    'bundle_input_init_states_fn', True,
+    'Add init_states as a function signature to the saved model.'
+    'This is not necessary if the input shape is static (e.g., for TF Lite).')
 flags.DEFINE_string(
     'checkpoint_path', '',
     'Checkpoint path to load. Leave blank for default initialization.')
@@ -97,24 +121,33 @@ def main(_) -> None:
 
   # Use dimensions of 1 except the channels to export faster,
   # since we only really need the last dimension to build and get the output
-  # states. These dimensions will be set to `None` once the model is built.
+  # states. These dimensions can be set to `None` once the model is built.
   input_shape = [1 if s is None else s for s in input_specs.shape]
 
+  activation = FLAGS.activation
+  if activation == 'swish':
+    # Override swish activation implementation to remove custom gradients
+    activation = 'simple_swish'
+
   backbone = movinet.Movinet(
-      FLAGS.model_id,
+      model_id=FLAGS.model_id,
       causal=FLAGS.causal,
+      use_positional_encoding=FLAGS.use_positional_encoding,
       conv_type=FLAGS.conv_type,
-      use_external_states=FLAGS.causal,
+      se_type=FLAGS.se_type,
       input_specs=input_specs,
-      activation=FLAGS.activation,
+      activation=activation,
       gating_activation=FLAGS.gating_activation,
-      se_type=FLAGS.se_type,
-      use_positional_encoding=FLAGS.use_positional_encoding)
+      use_sync_bn=False,
+      use_external_states=FLAGS.causal)
   model = movinet_model.MovinetClassifier(
       backbone,
       num_classes=FLAGS.num_classes,
       output_states=FLAGS.causal,
-      input_specs=dict(image=input_specs))
+      input_specs=dict(image=input_specs),
+      # TODO(dankondratyuk): currently set to swish, but will need to
+      # re-train to use other activations.
+      activation='simple_swish')
   model.build(input_shape)
 
   # Compile model to generate some internal Keras variables.
@@ -131,7 +164,7 @@ def main(_) -> None:
     # with the full output state shapes.
     input_image = tf.ones(input_shape)
     _, states = model({**model.init_states(input_shape), 'image': input_image})
-    _, states = model({**states, 'image': input_image})
+    _ = model({**states, 'image': input_image})
 
     # Create a function to explicitly set the names of the outputs
     def predict(inputs):
@@ -153,7 +186,10 @@ def main(_) -> None:
     init_states_fn = init_states_fn.get_concrete_function(
         tf.TensorSpec([5], dtype=tf.int32))
 
-    signatures = {'call': predict_fn, 'init_states': init_states_fn}
+    if FLAGS.bundle_input_init_states_fn:
+      signatures = {'call': predict_fn, 'init_states': init_states_fn}
+    else:
+      signatures = predict_fn
 
     tf.keras.models.save_model(
         model, FLAGS.export_path, signatures=signatures)

official/vision/beta/projects/movinet/export_saved_model_test.py

@@ -48,7 +48,7 @@ class ExportSavedModelTest(tf.test.TestCase):
     example_input = tf.ones([1, 8, 172, 172, 3])
     outputs = model(example_input)
 
-    self.assertEqual(outputs.shape, [1, 600])
+    self.assertAllEqual(outputs.shape, [1, 600])
 
   def test_movinet_export_a0_stream_with_tfhub(self):
     saved_model_path = self.get_temp_dir()
@@ -94,9 +94,55 @@ class ExportSavedModelTest(tf.test.TestCase):
     for frame in frames:
       outputs, states = model({**states, 'image': frame})
 
-    self.assertEqual(outputs.shape, [1, 600])
+    self.assertAllEqual(outputs.shape, [1, 600])
     self.assertNotEmpty(states)
     self.assertAllClose(outputs, expected_outputs, 1e-5, 1e-5)
 
+  def test_movinet_export_a0_stream_with_tflite(self):
+    saved_model_path = self.get_temp_dir()
+
+    FLAGS.export_path = saved_model_path
+    FLAGS.model_id = 'a0'
+    FLAGS.causal = True
+    FLAGS.conv_type = '2plus1d'
+    FLAGS.se_type = '2plus3d'
+    FLAGS.activation = 'hard_swish'
+    FLAGS.gating_activation = 'hard_sigmoid'
+    FLAGS.use_positional_encoding = False
+    FLAGS.num_classes = 600
+    FLAGS.batch_size = 1
+    FLAGS.num_frames = 1
+    FLAGS.image_size = 172
+    FLAGS.bundle_input_init_states_fn = False
+
+    export_saved_model.main('unused_args')
+
+    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
+    tflite_model = converter.convert()
+
+    interpreter = tf.lite.Interpreter(model_content=tflite_model)
+    signature = interpreter.get_signature_runner()
+
+    def state_name(name: str) -> str:
+      return name[len('serving_default_'):-len(':0')]
+
+    init_states = {
+        state_name(x['name']): tf.zeros(x['shape'], dtype=x['dtype'])
+        for x in interpreter.get_input_details()
+    }
+    del init_states['image']
+
+    video = tf.ones([1, 8, 172, 172, 3])
+    clips = tf.split(video, video.shape[1], axis=1)
+
+    states = init_states
+    for clip in clips:
+      outputs = signature(**states, image=clip)
+      logits = outputs.pop('logits')
+      states = outputs
+
+    self.assertAllEqual(logits.shape, [1, 600])
+    self.assertNotEmpty(states)
+
 if __name__ == '__main__':
   tf.test.main()

official/vision/beta/projects/movinet/modeling/movinet.py

@@ -43,6 +43,9 @@ S12: KernelSize = (1, 2, 2)
 S22: KernelSize = (2, 2, 2)
 S21: KernelSize = (2, 1, 1)
 
+# Type for a state container (map)
+TensorMap = Mapping[str, tf.Tensor]
+
 
 @dataclasses.dataclass
 class BlockSpec:
@@ -319,6 +322,7 @@ class Movinet(tf.keras.Model):
                bias_regularizer: Optional[str] = None,
                stochastic_depth_drop_rate: float = 0.,
                use_external_states: bool = False,
+               output_states: bool = True,
                **kwargs):
     """MoViNet initialization function.
 
@@ -353,6 +357,10 @@ class Movinet(tf.keras.Model):
       stochastic_depth_drop_rate: the base rate for stochastic depth.
       use_external_states: if True, expects states to be passed as additional
         input.
+      output_states: if True, output intermediate states that can be used to run
+          the model in streaming mode. Inputting the output states of the
+          previous input clip with the current input clip will utilize a stream
+          buffer for streaming video.
       **kwargs: keyword arguments to be passed.
     """
     block_specs = BLOCK_SPECS[model_id]
@@ -385,6 +393,7 @@ class Movinet(tf.keras.Model):
     self._bias_regularizer = bias_regularizer
     self._stochastic_depth_drop_rate = stochastic_depth_drop_rate
     self._use_external_states = use_external_states
+    self._output_states = output_states
 
     if self._use_external_states and not self._causal:
       raise ValueError('External states should be used with causal mode.')
@@ -411,8 +420,7 @@ class Movinet(tf.keras.Model):
       self,
       input_specs: tf.keras.layers.InputSpec,
       state_specs: Optional[Mapping[str, tf.keras.layers.InputSpec]] = None,
-  ) -> Tuple[Mapping[str, tf.keras.Input], Tuple[Mapping[str, tf.Tensor],
-                                                 Mapping[str, tf.Tensor]]]:
+  ) -> Tuple[TensorMap, Union[TensorMap, Tuple[TensorMap, TensorMap]]]:
     """Builds the model network.
 
     Args:
@@ -423,7 +431,7 @@ class Movinet(tf.keras.Model):
     Returns:
       Inputs and outputs as a tuple. Inputs are expected to be a dict with
       base input and states. Outputs are expected to be a dict of endpoints
-      and output states.
+      and (optional) output states.
     """
     state_specs = state_specs if state_specs is not None else {}
 
@@ -519,7 +527,7 @@ class Movinet(tf.keras.Model):
       else:
         raise ValueError('Unknown block type {}'.format(block))
 
-    outputs = (endpoints, states)
+    outputs = (endpoints, states) if self._output_states else endpoints
 
     return inputs, outputs
 
@@ -679,6 +687,8 @@ class Movinet(tf.keras.Model):
         'kernel_regularizer': self._kernel_regularizer,
         'bias_regularizer': self._bias_regularizer,
         'stochastic_depth_drop_rate': self._stochastic_depth_drop_rate,
+        'use_external_states': self._use_external_states,
+        'output_states': self._output_states,
     }
     return config_dict
 

official/vision/beta/projects/movinet/modeling/movinet_layers.py

@@ -265,7 +265,7 @@ class ConvBlock(tf.keras.layers.Layer):
       tf.keras.regularizers.L2(KERNEL_WEIGHT_DECAY),
       use_batch_norm: bool = True,
       batch_norm_layer: tf.keras.layers.Layer =
-      tf.keras.layers.experimental.SyncBatchNormalization,
+      tf.keras.layers.BatchNormalization,
       batch_norm_momentum: float = 0.99,
       batch_norm_epsilon: float = 1e-3,
       activation: Optional[Any] = None,
@@ -547,8 +547,8 @@ class StreamConvBlock(ConvBlock):
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = tf.keras
       .regularizers.L2(KERNEL_WEIGHT_DECAY),
       use_batch_norm: bool = True,
-      batch_norm_layer: tf.keras.layers.Layer = tf.keras.layers.experimental
-      .SyncBatchNormalization,
+      batch_norm_layer: tf.keras.layers.Layer =
+      tf.keras.layers.BatchNormalization,
       batch_norm_momentum: float = 0.99,
       batch_norm_epsilon: float = 1e-3,
       activation: Optional[Any] = None,
@@ -915,7 +915,7 @@ class SkipBlock(tf.keras.layers.Layer):
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] =
       tf.keras.regularizers.L2(KERNEL_WEIGHT_DECAY),
       batch_norm_layer: tf.keras.layers.Layer =
-      tf.keras.layers.experimental.SyncBatchNormalization,
+      tf.keras.layers.BatchNormalization,
       batch_norm_momentum: float = 0.99,
       batch_norm_epsilon: float = 1e-3,
       **kwargs):
@@ -1031,8 +1031,8 @@ class MovinetBlock(tf.keras.layers.Layer):
       kernel_initializer: tf.keras.initializers.Initializer = 'HeNormal',
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = tf.keras
       .regularizers.L2(KERNEL_WEIGHT_DECAY),
-      batch_norm_layer: tf.keras.layers.Layer = tf.keras.layers.experimental
-      .SyncBatchNormalization,
+      batch_norm_layer: tf.keras.layers.Layer =
+      tf.keras.layers.BatchNormalization,
       batch_norm_momentum: float = 0.99,
       batch_norm_epsilon: float = 1e-3,
       state_prefix: Optional[str] = None,
@@ -1232,8 +1232,8 @@ class Stem(tf.keras.layers.Layer):
       kernel_initializer: tf.keras.initializers.Initializer = 'HeNormal',
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = tf.keras
       .regularizers.L2(KERNEL_WEIGHT_DECAY),
-      batch_norm_layer: tf.keras.layers.Layer = tf.keras.layers.experimental
-      .SyncBatchNormalization,
+      batch_norm_layer: tf.keras.layers.Layer =
+      tf.keras.layers.BatchNormalization,
       batch_norm_momentum: float = 0.99,
       batch_norm_epsilon: float = 1e-3,
       state_prefix: Optional[str] = None,
@@ -1340,8 +1340,8 @@ class Head(tf.keras.layers.Layer):
       kernel_initializer: tf.keras.initializers.Initializer = 'HeNormal',
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = tf.keras
       .regularizers.L2(KERNEL_WEIGHT_DECAY),
-      batch_norm_layer: tf.keras.layers.Layer = tf.keras.layers.experimental
-      .SyncBatchNormalization,
+      batch_norm_layer: tf.keras.layers.Layer =
+      tf.keras.layers.BatchNormalization,
       batch_norm_momentum: float = 0.99,
       batch_norm_epsilon: float = 1e-3,
       state_prefix: Optional[str] = None,
@@ -1470,6 +1470,7 @@ class ClassifierHead(tf.keras.layers.Layer):
     self._num_classes = num_classes
     self._dropout_rate = dropout_rate
     self._conv_type = conv_type
+    self._activation = activation
     self._output_activation = output_activation
     self._max_pool_predictions = max_pool_predictions
     self._kernel_initializer = kernel_initializer
@@ -1509,6 +1510,7 @@ class ClassifierHead(tf.keras.layers.Layer):
         'num_classes': self._num_classes,
         'dropout_rate': self._dropout_rate,
         'conv_type': self._conv_type,
+        'activation': self._activation,
         'output_activation': self._output_activation,
         'max_pool_predictions': self._max_pool_predictions,
         'kernel_initializer': self._kernel_initializer,

official/vision/beta/projects/movinet/modeling/movinet_model.py

@@ -36,6 +36,7 @@ class MovinetClassifier(tf.keras.Model):
       backbone: tf.keras.Model,
       num_classes: int,
       input_specs: Optional[Mapping[str, tf.keras.layers.InputSpec]] = None,
+      activation: str = 'swish',
       dropout_rate: float = 0.0,
       kernel_initializer: str = 'HeNormal',
       kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
@@ -48,6 +49,7 @@ class MovinetClassifier(tf.keras.Model):
       backbone: A 3d backbone network.
       num_classes: Number of classes in classification task.
       input_specs: Specs of the input tensor.
+      activation: name of the main activation function.
       dropout_rate: Rate for dropout regularization.
       kernel_initializer: Kernel initializer for the final dense layer.
       kernel_regularizer: Kernel regularizer.
@@ -65,6 +67,7 @@ class MovinetClassifier(tf.keras.Model):
 
     self._num_classes = num_classes
     self._input_specs = input_specs
+    self._activation = activation
     self._dropout_rate = dropout_rate
     self._kernel_initializer = kernel_initializer
     self._kernel_regularizer = kernel_regularizer
@@ -151,7 +154,8 @@ class MovinetClassifier(tf.keras.Model):
         dropout_rate=self._dropout_rate,
         kernel_initializer=self._kernel_initializer,
         kernel_regularizer=self._kernel_regularizer,
-        conv_type=backbone.conv_type)(
+        conv_type=backbone.conv_type,
+        activation=self._activation)(
             x)
 
     outputs = (x, states) if self._output_states else x
@@ -180,6 +184,7 @@ class MovinetClassifier(tf.keras.Model):
   def get_config(self):
     config = {
         'backbone': self._backbone,
+        'activation': self._activation,
         'num_classes': self._num_classes,
         'input_specs': self._input_specs,
         'dropout_rate': self._dropout_rate,
@@ -226,6 +231,7 @@ def build_movinet_model(
       num_classes=num_classes,
       kernel_regularizer=l2_regularizer,
       input_specs=input_specs_dict,
+      activation=model_config.activation,
       dropout_rate=model_config.dropout_rate,
       output_states=model_config.output_states)