Merge branch 'master' of https://github.com/tensorflow/models into detection_generator_pr_2

official/vision/beta/projects/simclr/heads/simclr_head.py

@@ -12,21 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Copyright 2020 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.
-# ==============================================================================
-"""Dense prediction heads."""
+"""SimCLR prediction heads."""
 
 from typing import Text, Optional
 

official/vision/beta/projects/simclr/heads/simclr_head_test.py

@@ -12,22 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-
 from absl.testing import parameterized
 
 import numpy as np

official/vision/beta/projects/simclr/losses/contrastive_losses.py

@@ -12,21 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
 """Contrastive loss functions."""
 
 import functools

official/vision/beta/projects/simclr/losses/contrastive_losses_test.py

@@ -12,22 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-
 from absl.testing import parameterized
 
 import numpy as np

official/vision/beta/projects/simclr/modeling/layers/nn_blocks.py

@@ -12,22 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-
 """Contains common building blocks for simclr neural networks."""
 from typing import Text, Optional
 

official/vision/beta/projects/simclr/modeling/layers/nn_blocks_test.py

@@ -12,22 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-
 from absl.testing import parameterized
 
 import tensorflow as tf

official/vision/beta/projects/simclr/modeling/multitask_model.py

@@ -84,7 +84,7 @@ class SimCLRMTModel(base_model.MultiTaskBaseModel):
       else:
         supervised_head = None
 
-      tasks[model_config.mode] = simclr_model.SimCLRModel(
+      tasks[model_config.task_name] = simclr_model.SimCLRModel(
           input_specs=self._input_specs,
           backbone=self._backbone,
           projection_head=projection_head,

official/vision/beta/projects/simclr/modeling/multitask_model_test.py

@@ -29,11 +29,13 @@ class MultitaskModelTest(tf.test.TestCase):
     ckpt_dir = self.get_temp_dir()
     config = multitask_config.SimCLRMTModelConfig(
         input_size=[64, 64, 3],
-        heads=(multitask_config.SimCLRMTHeadConfig(mode=simclr_model.PRETRAIN),
-               multitask_config.SimCLRMTHeadConfig(mode=simclr_model.FINETUNE)))
+        heads=(multitask_config.SimCLRMTHeadConfig(
+            mode=simclr_model.PRETRAIN, task_name='pretrain_simclr'),
+               multitask_config.SimCLRMTHeadConfig(
+                   mode=simclr_model.FINETUNE, task_name='finetune_simclr')))
     model = multitask_model.SimCLRMTModel(config)
-    self.assertIn(simclr_model.PRETRAIN, model.sub_tasks)
-    self.assertIn(simclr_model.FINETUNE, model.sub_tasks)
+    self.assertIn('pretrain_simclr', model.sub_tasks)
+    self.assertIn('finetune_simclr', model.sub_tasks)
     ckpt = tf.train.Checkpoint(backbone=model._backbone)
     ckpt.save(os.path.join(ckpt_dir, 'ckpt'))
     model.initialize()

official/vision/beta/projects/simclr/modeling/simclr_model.py

@@ -12,22 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Copyright 2020 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.
-# ==============================================================================
 """Build simclr models."""
-
 from typing import Optional
 from absl import logging
 
@@ -133,12 +118,12 @@ class SimCLRModel(tf.keras.Model):
   def checkpoint_items(self):
     """Returns a dictionary of items to be additionally checkpointed."""
     if self._supervised_head is not None:
-      items = dict(backbone=self.backbone,
+      items = dict(
+          backbone=self.backbone,
           projection_head=self.projection_head,
           supervised_head=self.supervised_head)
     else:
-      items = dict(backbone=self.backbone,
-                   projection_head=self.projection_head)
+      items = dict(backbone=self.backbone, projection_head=self.projection_head)
     return items
 
   @property

official/vision/beta/projects/simclr/modeling/simclr_model_test.py

@@ -12,22 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
-
+"""Test for SimCLR model."""
 from absl.testing import parameterized
 
 import numpy as np

official/vision/beta/projects/simclr/tasks/simclr.py

@@ -12,21 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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.
-# ==============================================================================
 """Image SimCLR task definition.
 
 SimCLR training two different modes:
@@ -39,7 +24,6 @@ the task definition:
 - training loss
 - projection_head and/or supervised_head
 """
-
 from typing import Dict, Optional
 
 from absl import logging
@@ -67,7 +51,8 @@ RuntimeConfig = config_definitions.RuntimeConfig
 class SimCLRPretrainTask(base_task.Task):
   """A task for image classification."""
 
-  def create_optimizer(self, optimizer_config: OptimizationConfig,
+  def create_optimizer(self,
+                       optimizer_config: OptimizationConfig,
                        runtime_config: Optional[RuntimeConfig] = None):
     """Creates an TF optimizer from configurations.
 
@@ -78,8 +63,8 @@ class SimCLRPretrainTask(base_task.Task):
     Returns:
       A tf.optimizers.Optimizer object.
     """
-    if (optimizer_config.optimizer.type == 'lars'
-        and self.task_config.loss.l2_weight_decay > 0.0):
+    if (optimizer_config.optimizer.type == 'lars' and
+        self.task_config.loss.l2_weight_decay > 0.0):
       raise ValueError('The l2_weight_decay cannot be used together with lars '
                        'optimizer. Please set it to 0.')
 
@@ -97,15 +82,16 @@ class SimCLRPretrainTask(base_task.Task):
 
   def build_model(self):
     model_config = self.task_config.model
-    input_specs = tf.keras.layers.InputSpec(
-        shape=[None] + model_config.input_size)
+    input_specs = tf.keras.layers.InputSpec(shape=[None] +
+                                            model_config.input_size)
 
     l2_weight_decay = self.task_config.loss.l2_weight_decay
     # Divide weight decay by 2.0 to match the implementation of tf.nn.l2_loss.
     # (https://www.tensorflow.org/api_docs/python/tf/keras/regularizers/l2)
     # (https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss)
-    l2_regularizer = (tf.keras.regularizers.l2(
-        l2_weight_decay / 2.0) if l2_weight_decay else None)
+    l2_regularizer = (
+        tf.keras.regularizers.l2(l2_weight_decay /
+                                 2.0) if l2_weight_decay else None)
 
     # Build backbone
     backbone = backbones.factory.build_backbone(
@@ -220,8 +206,7 @@ class SimCLRPretrainTask(base_task.Task):
     projection_outputs = model_outputs[simclr_model.PROJECTION_OUTPUT_KEY]
     projection1, projection2 = tf.split(projection_outputs, 2, 0)
     contrast_loss, (contrast_logits, contrast_labels) = con_losses_obj(
-        projection1=projection1,
-        projection2=projection2)
+        projection1=projection1, projection2=projection2)
 
     contrast_accuracy = tf.equal(
         tf.argmax(contrast_labels, axis=1), tf.argmax(contrast_logits, axis=1))
@@ -253,8 +238,8 @@ class SimCLRPretrainTask(base_task.Task):
                                                                         outputs)
       sup_loss = tf.reduce_mean(sup_loss)
 
-      label_acc = tf.equal(tf.argmax(labels, axis=1),
-                           tf.argmax(outputs, axis=1))
+      label_acc = tf.equal(
+          tf.argmax(labels, axis=1), tf.argmax(outputs, axis=1))
       label_acc = tf.reduce_mean(tf.cast(label_acc, tf.float32))
 
       model_loss = contrast_loss + sup_loss
@@ -278,10 +263,7 @@ class SimCLRPretrainTask(base_task.Task):
     if training:
       metrics = []
       metric_names = [
-          'total_loss',
-          'contrast_loss',
-          'contrast_accuracy',
-          'contrast_entropy'
+          'total_loss', 'contrast_loss', 'contrast_accuracy', 'contrast_entropy'
       ]
       if self.task_config.model.supervised_head:
         metric_names.extend(['supervised_loss', 'accuracy'])
@@ -293,18 +275,26 @@ class SimCLRPretrainTask(base_task.Task):
         metrics = [
             tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
             tf.keras.metrics.TopKCategoricalAccuracy(
-                k=k, name='top_{}_accuracy'.format(k))]
+                k=k, name='top_{}_accuracy'.format(k))
+        ]
       else:
         metrics = [
             tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
             tf.keras.metrics.SparseTopKCategoricalAccuracy(
-                k=k, name='top_{}_accuracy'.format(k))]
+                k=k, name='top_{}_accuracy'.format(k))
+        ]
     return metrics
 
   def train_step(self, inputs, model, optimizer, metrics=None):
     features, labels = inputs
-    if (self.task_config.model.supervised_head is not None
-        and self.task_config.evaluation.one_hot):
+
+    # To do a sanity check that we absolutely use no labels when pretraining, we
+    # can set the labels here to zero.
+    if self.task_config.train_data.input_set_label_to_zero:
+      labels *= 0
+
+    if (self.task_config.model.supervised_head is not None and
+        self.task_config.evaluation.one_hot):
       num_classes = self.task_config.model.supervised_head.num_classes
       labels = tf.one_hot(labels, num_classes)
 
@@ -313,8 +303,7 @@ class SimCLRPretrainTask(base_task.Task):
       outputs = model(features, training=True)
       # Casting output layer as float32 is necessary when mixed_precision is
       # mixed_float16 or mixed_bfloat16 to ensure output is casted as float32.
-      outputs = tf.nest.map_structure(
-          lambda x: tf.cast(x, tf.float32), outputs)
+      outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
 
       # Computes per-replica loss.
       losses = self.build_losses(
@@ -373,7 +362,8 @@ class SimCLRPretrainTask(base_task.Task):
 class SimCLRFinetuneTask(base_task.Task):
   """A task for image classification."""
 
-  def create_optimizer(self, optimizer_config: OptimizationConfig,
+  def create_optimizer(self,
+                       optimizer_config: OptimizationConfig,
                        runtime_config: Optional[RuntimeConfig] = None):
     """Creates an TF optimizer from configurations.
 
@@ -384,8 +374,8 @@ class SimCLRFinetuneTask(base_task.Task):
     Returns:
       A tf.optimizers.Optimizer object.
     """
-    if (optimizer_config.optimizer.type == 'lars'
-        and self.task_config.loss.l2_weight_decay > 0.0):
+    if (optimizer_config.optimizer.type == 'lars' and
+        self.task_config.loss.l2_weight_decay > 0.0):
       raise ValueError('The l2_weight_decay cannot be used together with lars '
                        'optimizer. Please set it to 0.')
 
@@ -403,15 +393,16 @@ class SimCLRFinetuneTask(base_task.Task):
 
   def build_model(self):
     model_config = self.task_config.model
-    input_specs = tf.keras.layers.InputSpec(
-        shape=[None] + model_config.input_size)
+    input_specs = tf.keras.layers.InputSpec(shape=[None] +
+                                            model_config.input_size)
 
     l2_weight_decay = self.task_config.loss.l2_weight_decay
     # Divide weight decay by 2.0 to match the implementation of tf.nn.l2_loss.
     # (https://www.tensorflow.org/api_docs/python/tf/keras/regularizers/l2)
     # (https://www.tensorflow.org/api_docs/python/tf/nn/l2_loss)
-    l2_regularizer = (tf.keras.regularizers.l2(
-        l2_weight_decay / 2.0) if l2_weight_decay else None)
+    l2_regularizer = (
+        tf.keras.regularizers.l2(l2_weight_decay /
+                                 2.0) if l2_weight_decay else None)
 
     backbone = backbones.factory.build_backbone(
         input_specs=input_specs,
@@ -467,8 +458,8 @@ class SimCLRFinetuneTask(base_task.Task):
       status = ckpt.restore(ckpt_dir_or_file)
       status.assert_consumed()
     elif self.task_config.init_checkpoint_modules == 'backbone_projection':
-      ckpt = tf.train.Checkpoint(backbone=model.backbone,
-                                 projection_head=model.projection_head)
+      ckpt = tf.train.Checkpoint(
+          backbone=model.backbone, projection_head=model.projection_head)
       status = ckpt.restore(ckpt_dir_or_file)
       status.expect_partial().assert_existing_objects_matched()
     elif self.task_config.init_checkpoint_modules == 'backbone':
@@ -542,12 +533,14 @@ class SimCLRFinetuneTask(base_task.Task):
       metrics = [
           tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
           tf.keras.metrics.TopKCategoricalAccuracy(
-              k=k, name='top_{}_accuracy'.format(k))]
+              k=k, name='top_{}_accuracy'.format(k))
+      ]
     else:
       metrics = [
           tf.keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
           tf.keras.metrics.SparseTopKCategoricalAccuracy(
-              k=k, name='top_{}_accuracy'.format(k))]
+              k=k, name='top_{}_accuracy'.format(k))
+      ]
     return metrics
 
   def train_step(self, inputs, model, optimizer, metrics=None):
@@ -577,16 +570,14 @@ class SimCLRFinetuneTask(base_task.Task):
 
       # Computes per-replica loss.
       loss = self.build_losses(
-          model_outputs=outputs,
-          labels=labels, aux_losses=model.losses)
+          model_outputs=outputs, labels=labels, aux_losses=model.losses)
       # Scales loss as the default gradients allreduce performs sum inside the
       # optimizer.
       scaled_loss = loss / num_replicas
 
       # For mixed_precision policy, when LossScaleOptimizer is used, loss is
       # scaled for numerical stability.
-      if isinstance(
-          optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
+      if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
         scaled_loss = optimizer.get_scaled_loss(scaled_loss)
 
     tvars = model.trainable_variables
@@ -596,8 +587,7 @@ class SimCLRFinetuneTask(base_task.Task):
     grads = tape.gradient(scaled_loss, tvars)
     # Scales back gradient before apply_gradients when LossScaleOptimizer is
     # used.
-    if isinstance(
-        optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
+    if isinstance(optimizer, tf.keras.mixed_precision.LossScaleOptimizer):
       grads = optimizer.get_unscaled_gradients(grads)
     optimizer.apply_gradients(list(zip(grads, tvars)))
 
@@ -626,11 +616,11 @@ class SimCLRFinetuneTask(base_task.Task):
       num_classes = self.task_config.model.supervised_head.num_classes
       labels = tf.one_hot(labels, num_classes)
 
-    outputs = self.inference_step(
-        features, model)[simclr_model.SUPERVISED_OUTPUT_KEY]
+    outputs = self.inference_step(features,
+                                  model)[simclr_model.SUPERVISED_OUTPUT_KEY]
     outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
-    loss = self.build_losses(model_outputs=outputs,
-                             labels=labels, aux_losses=model.losses)
+    loss = self.build_losses(
+        model_outputs=outputs, labels=labels, aux_losses=model.losses)
 
     logs = {self.loss: loss}
     if metrics:

official/vision/beta/projects/simclr/train.py

@@ -12,22 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# Lint as: python3
-# Copyright 2020 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 SimCLR training driver."""
+"""TensorFlow Model Garden Vision SimCLR trainer."""
 from absl import app
 from absl import flags
 import gin

official/vision/beta/projects/yolo/configs/darknet_classification.py

@@ -15,9 +15,8 @@
 # Lint as: python3
 """Image classification with darknet configs."""
 
-from typing import List, Optional
-
 import dataclasses
+from typing import List, Optional
 
 from official.core import config_definitions as cfg
 from official.core import exp_factory

official/vision/beta/projects/yolo/modeling/yolo_model.py

-from official.core import registry # pylint: disable=unused-import
+# Copyright 2021 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.
+
+"""Yolo models."""
+
 import tensorflow as tf
-import tensorflow.keras as ks
-from typing import *
 
-from official.vision.beta.projects.yolo.modeling.backbones.darknet import \
-    Darknet
-from official.vision.beta.projects.yolo.modeling.decoders.yolo_decoder import \
-    YoloDecoder
-from official.vision.beta.projects.yolo.modeling.heads.yolo_head import YoloHead
 
-# static base Yolo Models that do not require configuration
+# Static base Yolo Models that do not require configuration
 # similar to a backbone model id.
 
 # this is done greatly simplify the model config
 # the structure is as follows. model version, {v3, v4, v#, ... etc}
 # the model config type {regular, tiny, small, large, ... etc}
 YOLO_MODELS = {
-  "v4" : dict(
-    regular = dict(
+    "v4":
+        dict(
+            regular=dict(
                 embed_spp=False,
                 use_fpn=True,
                 max_level_process_len=None,
                 path_process_len=6),
-    tiny = dict(
+            tiny=dict(
                 embed_spp=False,
                 use_fpn=False,
                 max_level_process_len=2,
                 path_process_len=1),
-    csp = dict(
+            csp=dict(
                 embed_spp=False,
                 use_fpn=True,
                 max_level_process_len=None,
@@ -43,18 +52,19 @@ YOLO_MODELS = {
                 path_process_len=8,
                 fpn_filter_scale=2),
         ),
-  "v3" : dict(
-    regular = dict(
+    "v3":
+        dict(
+            regular=dict(
                 embed_spp=False,
                 use_fpn=False,
                 max_level_process_len=None,
                 path_process_len=6),
-    tiny = dict(
+            tiny=dict(
                 embed_spp=False,
                 use_fpn=False,
                 max_level_process_len=2,
                 path_process_len=1),
-    spp = dict(
+            spp=dict(
                 embed_spp=True,
                 use_fpn=False,
                 max_level_process_len=2,
@@ -63,16 +73,17 @@ YOLO_MODELS = {
 }
 
 
-class Yolo(ks.Model):
+class Yolo(tf.keras.Model):
   """The YOLO model class."""
 
   def __init__(self,
                backbone=None,
                decoder=None,
                head=None,
-               filter=None,
+               detection_generator=None,
                **kwargs):
     """Detection initialization function.
+
     Args:
       backbone: `tf.keras.Model`, a backbone network.
       decoder: `tf.keras.Model`, a decoder network.
@@ -83,10 +94,10 @@ class Yolo(ks.Model):
     super().__init__(**kwargs)
 
     self._config_dict = {
-        'backbone': backbone,
-        'decoder': decoder,
-        'head': head,
-        'detection_generator': detection_generator
+        "backbone": backbone,
+        "decoder": decoder,
+        "head": head,
+        "detection_generator": detection_generator
     }
 
     # model components

official/vision/beta/projects/yolo/ops/box_ops.py

+# Copyright 2021 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.
+
+"""Yolo box ops."""
+import math
 import tensorflow as tf
 from official.vision.beta.projects.yolo.ops import math_ops
-import math
 
 
 def yxyx_to_xcycwh(box: tf.Tensor):
-  """Converts boxes from ymin, xmin, ymax, xmax to x_center, y_center, width,
-  height.
+  """Converts boxes from yxyx to x_center, y_center, width, height.
 
   Args:
     box: any `Tensor` whose last dimension is 4 representing the coordinates of
@@ -26,9 +40,7 @@ def yxyx_to_xcycwh(box: tf.Tensor):
 
 @tf.custom_gradient
 def _xcycwh_to_yxyx(box: tf.Tensor, scale):
-  """Private function called by xcycwh_to_yxyx to allow custom gradients
-  with defaults.
-  """
+  """Private function to allow custom gradients with defaults."""
   with tf.name_scope('xcycwh_to_yxyx'):
     xy, wh = tf.split(box, 2, axis=-1)
     xy_min = xy - wh / 2
@@ -38,7 +50,7 @@ def _xcycwh_to_yxyx(box: tf.Tensor, scale):
     box = tf.concat([y_min, x_min, y_max, x_max], axis=-1)
 
     def delta(dbox):
-      #y_min = top, x_min = left, y_max = bottom, x_max = right
+      # y_min = top, x_min = left, y_max = bottom, x_max = right
       dt, dl, db, dr = tf.split(dbox, 4, axis=-1)
       dx = dl + dr
       dy = dt + db
@@ -52,12 +64,12 @@ def _xcycwh_to_yxyx(box: tf.Tensor, scale):
 
 
 def xcycwh_to_yxyx(box: tf.Tensor, darknet=False):
-  """Converts boxes from x_center, y_center, width, height to ymin, xmin, ymax,
-  xmax.
+  """Converts boxes from x_center, y_center, width, height to yxyx format.
 
   Args:
     box: any `Tensor` whose last dimension is 4 representing the coordinates of
       boxes in x_center, y_center, width, height.
+    darknet: `bool`, if True a scale of 1.0 is used.
 
   Returns:
     box: a `Tensor` whose shape is the same as `box` in new format.
@@ -105,8 +117,7 @@ def intersect_and_union(box1, box2, yxyx=False):
 
 
 def smallest_encompassing_box(box1, box2, yxyx=False):
-  """Calculates the smallest box that encompasses both that encomapasses both
-  box1 and box2.
+  """Calculates the smallest box that encompasses box1 and box2.
 
   Args:
     box1: any `Tensor` whose last dimension is 4 representing the coordinates of
@@ -194,7 +205,7 @@ def compute_giou(box1, box2, yxyx=False, darknet=False):
     boxc = smallest_encompassing_box(box1, box2, yxyx=yxyx)
     if yxyx:
       boxc = yxyx_to_xcycwh(boxc)
-    cxcy, cwch = tf.split(boxc, 2, axis=-1)
+    _, cwch = tf.split(boxc, 2, axis=-1)
     c = tf.math.reduce_prod(cwch, axis=-1)
 
     # compute giou
@@ -239,9 +250,9 @@ def compute_diou(box1, box2, beta=1.0, yxyx=False, darknet=False):
       box1 = yxyx_to_xcycwh(box1)
       box2 = yxyx_to_xcycwh(box2)
 
-    b1xy, b1wh = tf.split(box1, 2, axis=-1)
-    b2xy, b2wh = tf.split(box2, 2, axis=-1)
-    bcxy, bcwh = tf.split(boxc, 2, axis=-1)
+    b1xy, _ = tf.split(box1, 2, axis=-1)
+    b2xy, _ = tf.split(box2, 2, axis=-1)
+    _, bcwh = tf.split(boxc, 2, axis=-1)
 
     center_dist = tf.reduce_sum((b1xy - b2xy)**2, axis=-1)
     c_diag = tf.reduce_sum(bcwh**2, axis=-1)
@@ -276,8 +287,8 @@ def compute_ciou(box1, box2, yxyx=False, darknet=False):
       box1 = yxyx_to_xcycwh(box1)
       box2 = yxyx_to_xcycwh(box2)
 
-    b1x, b1y, b1w, b1h = tf.split(box1, 4, axis=-1)
-    b2x, b2y, b2w, b2h = tf.split(box1, 4, axis=-1)
+    _, _, b1w, b1h = tf.split(box1, 4, axis=-1)
+    _, _, b2w, b2h = tf.split(box1, 4, axis=-1)
 
     # computer aspect ratio consistency
     terma = tf.cast(math_ops.divide_no_nan(b1w, b1h), tf.float32)
@@ -292,13 +303,13 @@ def compute_ciou(box1, box2, yxyx=False, darknet=False):
   return iou, ciou
 
 
-# equal to bbox_overlap but far more versitile
 def aggregated_comparitive_iou(boxes1,
                                boxes2=None,
                                iou_type=0,
                                beta=0.6):
-  """Calculates the intersection over union between every box in boxes1 and
-  every box in boxes2.
+  """Calculates the IOU between two set of boxes.
+
+  Similar to bbox_overlap but far more versitile.
 
   Args:
     boxes1: a `Tensor` of shape [batch size, N, 4] representing the coordinates
@@ -322,11 +333,11 @@ def aggregated_comparitive_iou(boxes1,
   else:
     boxes2 = tf.transpose(boxes1, perm=(0, 2, 1, 3))
 
-  if iou_type == 0:  #diou
+  if iou_type == 0:  # diou
     _, iou = compute_diou(boxes1, boxes2, beta=beta, yxyx=True)
-  elif iou_type == 1:  #giou
+  elif iou_type == 1:  # giou
     _, iou = compute_giou(boxes1, boxes2, yxyx=True)
-  elif iou_type == 2:  #ciou
+  elif iou_type == 2:  # ciou
     _, iou = compute_ciou(boxes1, boxes2, yxyx=True)
   else:
     iou = compute_iou(boxes1, boxes2, yxyx=True)

official/vision/beta/projects/yolo/ops/box_ops_test.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""box_ops tests."""
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf

official/vision/beta/projects/yolo/ops/math_ops.py

+# Copyright 2021 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.
+
 """A set of private math operations used to safely implement the YOLO loss."""
 import tensorflow as tf
 
@@ -9,7 +23,7 @@ def rm_nan_inf(x, val=0.0):
     x: any `Tensor` of any type.
     val: value to replace nan and infinity with.
 
-  Return:
+  Returns:
     a `Tensor` with nan and infinity removed.
   """
   cond = tf.math.logical_or(tf.math.is_nan(x), tf.math.is_inf(x))
@@ -25,7 +39,7 @@ def rm_nan(x, val=0.0):
     x: any `Tensor` of any type.
     val: value to replace nan.
 
-  Return:
+  Returns:
     a `Tensor` with nan removed.
   """
   cond = tf.math.is_nan(x)
@@ -41,7 +55,7 @@ def divide_no_nan(a, b):
     a: any `Tensor` of any type.
     b: any `Tensor` of any type with the same shape as tensor a.
 
-  Return:
+  Returns:
     a `Tensor` representing a divided by b, with all nan values removed.
   """
   zero = tf.cast(0.0, b.dtype)
@@ -49,7 +63,9 @@ def divide_no_nan(a, b):
 
 
 def mul_no_nan(x, y):
-  """Nan safe multiply operation built to allow model compilation in tflite and
+  """Nan safe multiply operation.
+
+  Built to allow model compilation in tflite and
   to allow one tensor to mask another. Where ever x is zero the
   multiplication is not computed and the value is replaced with a zero. This is
   required because 0 * nan = nan. This can make computation unstable in some
@@ -59,7 +75,7 @@ def mul_no_nan(x, y):
     x: any `Tensor` of any type.
     y: any `Tensor` of any type with the same shape as tensor x.
 
-  Return:
+  Returns:
     a `Tensor` representing x times y, where x is used to safely mask the
     tensor y.
   """

official/vision/beta/projects/yolo/ops/preprocess_ops_test.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""preprocess_ops tests."""
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf

official/vision/beta/serving/detection.py

@@ -36,8 +36,6 @@ class DetectionModule(export_base.ExportModule):
 
     if self._batch_size is None:
       raise ValueError('batch_size cannot be None for detection models.')
-    if not self.params.task.model.detection_generator.use_batched_nms:
-      raise ValueError('Only batched_nms is supported.')
     input_specs = tf.keras.layers.InputSpec(shape=[self._batch_size] +
                                             self._input_image_size + [3])
 

official/vision/beta/serving/detection_test.py

@@ -125,13 +125,6 @@ class DetectionExportTest(tf.test.TestCase, parameterized.TestCase):
       detection.DetectionModule(
           params, batch_size=None, input_image_size=[640, 640])
 
-  def test_build_model_fail_with_batched_nms_false(self):
-    params = exp_factory.get_exp_config('retinanet_resnetfpn_coco')
-    params.task.model.detection_generator.use_batched_nms = False
-    with self.assertRaisesRegex(ValueError, 'Only batched_nms is supported.'):
-      detection.DetectionModule(
-          params, batch_size=1, input_image_size=[640, 640])
-
 
 if __name__ == '__main__':
   tf.test.main()