Merge pull request #10696 from gunho1123:master

PiperOrigin-RevId: 459562542

official/projects/detr/configs/detr.py

@@ -15,44 +15,91 @@
 """DETR configurations."""
 
 import dataclasses
+import os
+from typing import List, Optional, Union
+
 from official.core import config_definitions as cfg
 from official.core import exp_factory
+from official.modeling import hyperparams
 from official.projects.detr import optimization
 from official.projects.detr.dataloaders import coco
+from official.vision.configs import backbones
+from official.vision.configs import common
 
 
 @dataclasses.dataclass
-class DetectionConfig(cfg.TaskConfig):
-  """The translation task config."""
-  train_data: cfg.DataConfig = cfg.DataConfig()
-  validation_data: cfg.DataConfig = cfg.DataConfig()
+class DataConfig(cfg.DataConfig):
+  """Input config for training."""
+  input_path: str = ''
+  tfds_name: str = ''
+  tfds_split: str = 'train'
+  global_batch_size: int = 0
+  is_training: bool = False
+  dtype: str = 'bfloat16'
+  decoder: common.DataDecoder = common.DataDecoder()
+  shuffle_buffer_size: int = 10000
+  file_type: str = 'tfrecord'
+  drop_remainder: bool = True
+
+
+@dataclasses.dataclass
+class Losses(hyperparams.Config):
+  class_offset: int = 0
   lambda_cls: float = 1.0
   lambda_box: float = 5.0
   lambda_giou: float = 2.0
-
-  init_ckpt: str = ''
-  num_classes: int = 81  # 0: background
   background_cls_weight: float = 0.1
+  l2_weight_decay: float = 1e-4
+
+
+@dataclasses.dataclass
+class Detr(hyperparams.Config):
+  num_queries: int = 100
+  hidden_size: int = 256
+  num_classes: int = 91  # 0: background
   num_encoder_layers: int = 6
   num_decoder_layers: int = 6
+  input_size: List[int] = dataclasses.field(default_factory=list)
+  backbone: backbones.Backbone = backbones.Backbone(
+      type='resnet', resnet=backbones.ResNet(model_id=50, bn_trainable=False))
+  norm_activation: common.NormActivation = common.NormActivation()
 
-  # Make DETRConfig.
-  num_queries: int = 100
-  num_hidden: int = 256
+
+@dataclasses.dataclass
+class DetrTask(cfg.TaskConfig):
+  model: Detr = Detr()
+  train_data: cfg.DataConfig = cfg.DataConfig()
+  validation_data: cfg.DataConfig = cfg.DataConfig()
+  losses: Losses = Losses()
+  init_checkpoint: Optional[str] = None
+  init_checkpoint_modules: Union[str, List[str]] = 'all'  # all, backbone
+  annotation_file: Optional[str] = None
   per_category_metrics: bool = False
 
 
+COCO_INPUT_PATH_BASE = 'coco'
+COCO_TRAIN_EXAMPLES = 118287
+COCO_VAL_EXAMPLES = 5000
+
+
 @exp_factory.register_config_factory('detr_coco')
 def detr_coco() -> cfg.ExperimentConfig:
   """Config to get results that matches the paper."""
   train_batch_size = 64
   eval_batch_size = 64
-  num_train_data = 118287
+  num_train_data = COCO_TRAIN_EXAMPLES
   num_steps_per_epoch = num_train_data // train_batch_size
   train_steps = 500 * num_steps_per_epoch  # 500 epochs
   decay_at = train_steps - 100 * num_steps_per_epoch  # 400 epochs
   config = cfg.ExperimentConfig(
-      task=DetectionConfig(
+      task=DetrTask(
+          init_checkpoint='',
+          init_checkpoint_modules='backbone',
+          model=Detr(
+              num_classes=81,
+              input_size=[1333, 1333, 3],
+              norm_activation=common.NormActivation()),
+          losses=Losses(),
           train_data=coco.COCODataConfig(
               tfds_name='coco/2017',
               tfds_split='train',
@@ -65,9 +112,7 @@ def detr_coco() -> cfg.ExperimentConfig:
               tfds_split='validation',
               is_training=False,
               global_batch_size=eval_batch_size,
-              drop_remainder=False
-          )
-      ),
+              drop_remainder=False)),
       trainer=cfg.TrainerConfig(
           train_steps=train_steps,
           validation_steps=-1,
@@ -95,8 +140,135 @@ def detr_coco() -> cfg.ExperimentConfig:
                       'values': [0.0001, 1.0e-05]
                   }
               },
-              })
+          })),
+      restrictions=[
+          'task.train_data.is_training != None',
+      ])
+  return config
+
+
+@exp_factory.register_config_factory('detr_coco_tfrecord')
+def detr_coco_tfrecord() -> cfg.ExperimentConfig:
+  """Config to get results that matches the paper."""
+  train_batch_size = 64
+  eval_batch_size = 64
+  steps_per_epoch = COCO_TRAIN_EXAMPLES // train_batch_size
+  train_steps = 300 * steps_per_epoch  # 300 epochs
+  decay_at = train_steps - 100 * steps_per_epoch  # 200 epochs
+  config = cfg.ExperimentConfig(
+      task=DetrTask(
+          init_checkpoint='',
+          init_checkpoint_modules='backbone',
+          annotation_file=os.path.join(COCO_INPUT_PATH_BASE,
+                                       'instances_val2017.json'),
+          model=Detr(
+              input_size=[1333, 1333, 3],
+              norm_activation=common.NormActivation()),
+          losses=Losses(),
+          train_data=DataConfig(
+              input_path=os.path.join(COCO_INPUT_PATH_BASE, 'train*'),
+              is_training=True,
+              global_batch_size=train_batch_size,
+              shuffle_buffer_size=1000,
+          ),
+          validation_data=DataConfig(
+              input_path=os.path.join(COCO_INPUT_PATH_BASE, 'val*'),
+              is_training=False,
+              global_batch_size=eval_batch_size,
+              drop_remainder=False,
+          )),
+      trainer=cfg.TrainerConfig(
+          train_steps=train_steps,
+          validation_steps=COCO_VAL_EXAMPLES // eval_batch_size,
+          steps_per_loop=steps_per_epoch,
+          summary_interval=steps_per_epoch,
+          checkpoint_interval=steps_per_epoch,
+          validation_interval=5 * steps_per_epoch,
+          max_to_keep=1,
+          best_checkpoint_export_subdir='best_ckpt',
+          best_checkpoint_eval_metric='AP',
+          optimizer_config=optimization.OptimizationConfig({
+              'optimizer': {
+                  'type': 'detr_adamw',
+                  'detr_adamw': {
+                      'weight_decay_rate': 1e-4,
+                      'global_clipnorm': 0.1,
+                      # Avoid AdamW legacy behavior.
+                      'gradient_clip_norm': 0.0
+                  }
+              },
+              'learning_rate': {
+                  'type': 'stepwise',
+                  'stepwise': {
+                      'boundaries': [decay_at],
+                      'values': [0.0001, 1.0e-05]
+                  }
+              },
+          })),
+      restrictions=[
+          'task.train_data.is_training != None',
+      ])
+  return config
+
+
+@exp_factory.register_config_factory('detr_coco_tfds')
+def detr_coco_tfds() -> cfg.ExperimentConfig:
+  """Config to get results that matches the paper."""
+  train_batch_size = 64
+  eval_batch_size = 64
+  steps_per_epoch = COCO_TRAIN_EXAMPLES // train_batch_size
+  train_steps = 300 * steps_per_epoch  # 300 epochs
+  decay_at = train_steps - 100 * steps_per_epoch  # 200 epochs
+  config = cfg.ExperimentConfig(
+      task=DetrTask(
+          init_checkpoint='',
+          init_checkpoint_modules='backbone',
+          model=Detr(
+              num_classes=81,
+              input_size=[1333, 1333, 3],
+              norm_activation=common.NormActivation()),
+          losses=Losses(class_offset=1),
+          train_data=DataConfig(
+              tfds_name='coco/2017',
+              tfds_split='train',
+              is_training=True,
+              global_batch_size=train_batch_size,
+              shuffle_buffer_size=1000,
           ),
+          validation_data=DataConfig(
+              tfds_name='coco/2017',
+              tfds_split='validation',
+              is_training=False,
+              global_batch_size=eval_batch_size,
+              drop_remainder=False)),
+      trainer=cfg.TrainerConfig(
+          train_steps=train_steps,
+          validation_steps=COCO_VAL_EXAMPLES // eval_batch_size,
+          steps_per_loop=steps_per_epoch,
+          summary_interval=steps_per_epoch,
+          checkpoint_interval=steps_per_epoch,
+          validation_interval=5 * steps_per_epoch,
+          max_to_keep=1,
+          best_checkpoint_export_subdir='best_ckpt',
+          best_checkpoint_eval_metric='AP',
+          optimizer_config=optimization.OptimizationConfig({
+              'optimizer': {
+                  'type': 'detr_adamw',
+                  'detr_adamw': {
+                      'weight_decay_rate': 1e-4,
+                      'global_clipnorm': 0.1,
+                      # Avoid AdamW legacy behavior.
+                      'gradient_clip_norm': 0.0
+                  }
+              },
+              'learning_rate': {
+                  'type': 'stepwise',
+                  'stepwise': {
+                      'boundaries': [decay_at],
+                      'values': [0.0001, 1.0e-05]
+                  }
+              },
+          })),
       restrictions=[
           'task.train_data.is_training != None',
       ])

official/projects/detr/configs/detr_test.py

@@ -27,15 +27,25 @@ from official.projects.detr.dataloaders import coco
 class DetrTest(tf.test.TestCase, parameterized.TestCase):
 
   @parameterized.parameters(('detr_coco',))
-  def test_detr_configs(self, config_name):
+  def test_detr_configs_tfds(self, config_name):
     config = exp_factory.get_exp_config(config_name)
     self.assertIsInstance(config, cfg.ExperimentConfig)
-    self.assertIsInstance(config.task, exp_cfg.DetectionConfig)
+    self.assertIsInstance(config.task, exp_cfg.DetrTask)
     self.assertIsInstance(config.task.train_data, coco.COCODataConfig)
     config.task.train_data.is_training = None
     with self.assertRaises(KeyError):
       config.validate()
 
+  @parameterized.parameters(('detr_coco_tfrecord'), ('detr_coco_tfds'))
+  def test_detr_configs(self, config_name):
+    config = exp_factory.get_exp_config(config_name)
+    self.assertIsInstance(config, cfg.ExperimentConfig)
+    self.assertIsInstance(config.task, exp_cfg.DetrTask)
+    self.assertIsInstance(config.task.train_data, cfg.DataConfig)
+    config.task.train_data.is_training = None
+    with self.assertRaises(KeyError):
+      config.validate()
+
 
 if __name__ == '__main__':
   tf.test.main()

official/projects/detr/dataloaders/coco.py

@@ -145,7 +145,7 @@ class COCODataLoader():
         self._params.global_batch_size
     ) if input_context else self._params.global_batch_size
     dataset = dataset.batch(
-        per_replica_batch_size, drop_remainder=self._params.is_training)
+        per_replica_batch_size, drop_remainder=self._params.drop_remainder)
     return dataset
 
   def load(self, input_context: Optional[tf.distribute.InputContext] = None):

official/projects/detr/dataloaders/detr_input.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.
+
+"""COCO data loader for DETR."""
+
+from typing import Tuple
+import tensorflow as tf
+
+from official.vision.dataloaders import parser
+
+from official.vision.ops import box_ops
+from official.vision.ops import preprocess_ops
+
+RESIZE_SCALES = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+
+
+class Parser(parser.Parser):
+  """Parse an image and its annotations into a dictionary of tensors."""
+
+  def __init__(self,
+               class_offset: int = 0,
+               output_size: Tuple[int, int] = (1333, 1333),
+               max_num_boxes: int = 100,
+               resize_scales: Tuple[int, ...] = RESIZE_SCALES,
+               aug_rand_hflip=True):
+    self._class_offset = class_offset
+    self._output_size = output_size
+    self._max_num_boxes = max_num_boxes
+    self._resize_scales = resize_scales
+    self._aug_rand_hflip = aug_rand_hflip
+
+  def _parse_train_data(self, data):
+    """Parses data for training and evaluation."""
+    classes = data['groundtruth_classes'] + self._class_offset
+    boxes = data['groundtruth_boxes']
+    is_crowd = data['groundtruth_is_crowd']
+
+    # Gets original image.
+    image = data['image']
+
+    # Normalizes image with mean and std pixel values.
+    image = preprocess_ops.normalize_image(image)
+    image, boxes, _ = preprocess_ops.random_horizontal_flip(image, boxes)
+
+    do_crop = tf.greater(tf.random.uniform([]), 0.5)
+    if do_crop:
+      # Rescale
+      boxes = box_ops.denormalize_boxes(boxes, tf.shape(image)[:2])
+      index = tf.random.categorical(tf.zeros([1, 3]), 1)[0]
+      scales = tf.gather([400.0, 500.0, 600.0], index, axis=0)
+      short_side = scales[0]
+      image, image_info = preprocess_ops.resize_image(image, short_side)
+      boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_info[2, :],
+                                                   image_info[1, :],
+                                                   image_info[3, :])
+      boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
+
+      # Do croping
+      shape = tf.cast(image_info[1], dtype=tf.int32)
+      h = tf.random.uniform([],
+                            384,
+                            tf.math.minimum(shape[0], 600),
+                            dtype=tf.int32)
+      w = tf.random.uniform([],
+                            384,
+                            tf.math.minimum(shape[1], 600),
+                            dtype=tf.int32)
+      i = tf.random.uniform([], 0, shape[0] - h + 1, dtype=tf.int32)
+      j = tf.random.uniform([], 0, shape[1] - w + 1, dtype=tf.int32)
+      image = tf.image.crop_to_bounding_box(image, i, j, h, w)
+      boxes = tf.clip_by_value(
+          (boxes[..., :] * tf.cast(
+              tf.stack([shape[0], shape[1], shape[0], shape[1]]),
+              dtype=tf.float32) -
+           tf.cast(tf.stack([i, j, i, j]), dtype=tf.float32)) /
+          tf.cast(tf.stack([h, w, h, w]), dtype=tf.float32), 0.0, 1.0)
+    scales = tf.constant(self._resize_scales, dtype=tf.float32)
+    index = tf.random.categorical(tf.zeros([1, 11]), 1)[0]
+    scales = tf.gather(scales, index, axis=0)
+
+    image_shape = tf.shape(image)[:2]
+    boxes = box_ops.denormalize_boxes(boxes, image_shape)
+    short_side = scales[0]
+    image, image_info = preprocess_ops.resize_image(image, short_side,
+                                                    max(self._output_size))
+    boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_info[2, :],
+                                                 image_info[1, :],
+                                                 image_info[3, :])
+    boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
+
+    # Filters out ground truth boxes that are all zeros.
+    indices = box_ops.get_non_empty_box_indices(boxes)
+    boxes = tf.gather(boxes, indices)
+    classes = tf.gather(classes, indices)
+    is_crowd = tf.gather(is_crowd, indices)
+    boxes = box_ops.yxyx_to_cycxhw(boxes)
+
+    image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
+                                         self._output_size[1])
+    labels = {
+        'classes':
+            preprocess_ops.clip_or_pad_to_fixed_size(classes,
+                                                     self._max_num_boxes),
+        'boxes':
+            preprocess_ops.clip_or_pad_to_fixed_size(boxes, self._max_num_boxes)
+    }
+
+    return image, labels
+
+  def _parse_eval_data(self, data):
+    """Parses data for training and evaluation."""
+    classes = data['groundtruth_classes']
+    boxes = data['groundtruth_boxes']
+    is_crowd = data['groundtruth_is_crowd']
+
+    # Gets original image and its size.
+    image = data['image']
+
+    # Normalizes image with mean and std pixel values.
+    image = preprocess_ops.normalize_image(image)
+
+    scales = tf.constant([self._resize_scales[-1]], tf.float32)
+
+    image_shape = tf.shape(image)[:2]
+    boxes = box_ops.denormalize_boxes(boxes, image_shape)
+    gt_boxes = boxes
+    short_side = scales[0]
+    image, image_info = preprocess_ops.resize_image(image, short_side,
+                                                    max(self._output_size))
+    boxes = preprocess_ops.resize_and_crop_boxes(boxes, image_info[2, :],
+                                                 image_info[1, :],
+                                                 image_info[3, :])
+    boxes = box_ops.normalize_boxes(boxes, image_info[1, :])
+
+    # Filters out ground truth boxes that are all zeros.
+    indices = box_ops.get_non_empty_box_indices(boxes)
+    boxes = tf.gather(boxes, indices)
+    classes = tf.gather(classes, indices)
+    is_crowd = tf.gather(is_crowd, indices)
+    boxes = box_ops.yxyx_to_cycxhw(boxes)
+
+    image = tf.image.pad_to_bounding_box(image, 0, 0, self._output_size[0],
+                                         self._output_size[1])
+    labels = {
+        'classes':
+            preprocess_ops.clip_or_pad_to_fixed_size(classes,
+                                                     self._max_num_boxes),
+        'boxes':
+            preprocess_ops.clip_or_pad_to_fixed_size(boxes, self._max_num_boxes)
+    }
+    labels.update({
+        'id':
+            int(data['source_id']),
+        'image_info':
+            image_info,
+        'is_crowd':
+            preprocess_ops.clip_or_pad_to_fixed_size(is_crowd,
+                                                     self._max_num_boxes),
+        'gt_boxes':
+            preprocess_ops.clip_or_pad_to_fixed_size(gt_boxes,
+                                                     self._max_num_boxes),
+    })
+
+    return image, labels

official/projects/detr/experiments/detr_r50_300epochs.sh

@@ -3,4 +3,4 @@ python3 official/projects/detr/train.py \
   --experiment=detr_coco \
   --mode=train_and_eval \
   --model_dir=/tmp/logging_dir/ \
-  --params_override=task.init_ckpt='gs://tf_model_garden/vision/resnet50_imagenet/ckpt-62400',trainer.train_steps=554400
+  --params_override=task.init_checkpoint='gs://tf_model_garden/vision/resnet50_imagenet/ckpt-62400',trainer.train_steps=554400

official/projects/detr/experiments/detr_r50_500epochs.sh

@@ -3,4 +3,4 @@ python3 official/projects/detr/train.py \
   --experiment=detr_coco \
   --mode=train_and_eval \
   --model_dir=/tmp/logging_dir/ \
-  --params_override=task.init_ckpt='gs://tf_model_garden/vision/resnet50_imagenet/ckpt-62400'
+  --params_override=task.init_checkpoint='gs://tf_model_garden/vision/resnet50_imagenet/ckpt-62400'

official/projects/detr/modeling/detr.py

@@ -24,7 +24,6 @@ import tensorflow as tf
 
 from official.modeling import tf_utils
 from official.projects.detr.modeling import transformer
-from official.vision.modeling.backbones import resnet
 
 
 def position_embedding_sine(attention_mask,
@@ -100,7 +99,11 @@ class DETR(tf.keras.Model):
   class and box heads.
   """
 
-  def __init__(self, num_queries, hidden_size, num_classes,
+  def __init__(self,
+               backbone,
+               num_queries,
+               hidden_size,
+               num_classes,
                num_encoder_layers=6,
                num_decoder_layers=6,
                dropout_rate=0.1,
@@ -114,9 +117,7 @@ class DETR(tf.keras.Model):
     self._dropout_rate = dropout_rate
     if hidden_size % 2 != 0:
       raise ValueError("hidden_size must be a multiple of 2.")
-    # TODO(frederickliu): Consider using the backbone factory.
-    # TODO(frederickliu): Add to factory once we get skeleton code in.
-    self._backbone = resnet.ResNet(50, bn_trainable=False)
+    self._backbone = backbone
 
   def build(self, input_shape=None):
     self._input_proj = tf.keras.layers.Conv2D(
@@ -159,6 +160,7 @@ class DETR(tf.keras.Model):
 
   def get_config(self):
     return {
+        "backbone": self._backbone,
         "num_queries": self._num_queries,
         "hidden_size": self._hidden_size,
         "num_classes": self._num_classes,

official/projects/detr/modeling/detr_test.py

@@ -15,6 +15,7 @@
 """Tests for tensorflow_models.official.projects.detr.detr."""
 import tensorflow as tf
 from official.projects.detr.modeling import detr
+from official.vision.modeling.backbones import resnet
 
 
 class DetrTest(tf.test.TestCase):
@@ -25,7 +26,8 @@ class DetrTest(tf.test.TestCase):
     num_classes = 10
     image_size = 640
     batch_size = 2
-    model = detr.DETR(num_queries, hidden_size, num_classes)
+    backbone = resnet.ResNet(50, bn_trainable=False)
+    model = detr.DETR(backbone, num_queries, hidden_size, num_classes)
     outs = model(tf.ones((batch_size, image_size, image_size, 3)))
     self.assertLen(outs, 6)  # intermediate decoded outputs.
     for out in outs:
@@ -47,6 +49,7 @@ class DetrTest(tf.test.TestCase):
 
   def test_get_from_config_detr(self):
     config = {
+        'backbone': resnet.ResNet(50, bn_trainable=False),
         'num_queries': 2,
         'hidden_size': 4,
         'num_classes': 10,

official/projects/detr/tasks/detection.py

@@ -13,20 +13,29 @@
 # limitations under the License.
 
 """DETR detection task definition."""
+from typing import Optional
 
+from absl import logging
 import tensorflow as tf
 
+from official.common import dataset_fn
 from official.core import base_task
 from official.core import task_factory
 from official.projects.detr.configs import detr as detr_cfg
 from official.projects.detr.dataloaders import coco
+from official.projects.detr.dataloaders import detr_input
 from official.projects.detr.modeling import detr
 from official.projects.detr.ops import matchers
+from official.vision.dataloaders import input_reader_factory
+from official.vision.dataloaders import tf_example_decoder
+from official.vision.dataloaders import tfds_factory
+from official.vision.dataloaders import tf_example_label_map_decoder
 from official.vision.evaluation import coco_evaluator
+from official.vision.modeling import backbones
 from official.vision.ops import box_ops
 
 
-@task_factory.register_task_cls(detr_cfg.DetectionConfig)
+@task_factory.register_task_cls(detr_cfg.DetrTask)
 class DectectionTask(base_task.Task):
   """A single-replica view of training procedure.
 
@@ -37,46 +46,103 @@ class DectectionTask(base_task.Task):
 
   def build_model(self):
     """Build DETR model."""
-    model = detr.DETR(
-        self._task_config.num_queries,
-        self._task_config.num_hidden,
-        self._task_config.num_classes,
-        self._task_config.num_encoder_layers,
-        self._task_config.num_decoder_layers)
+
+    input_specs = tf.keras.layers.InputSpec(shape=[None] +
+                                            self._task_config.model.input_size)
+
+    backbone = backbones.factory.build_backbone(
+        input_specs=input_specs,
+        backbone_config=self._task_config.model.backbone,
+        norm_activation_config=self._task_config.model.norm_activation)
+    model = detr.DETR(backbone, self._task_config.model.num_queries,
+                      self._task_config.model.hidden_size,
+                      self._task_config.model.num_classes,
+                      self._task_config.model.num_encoder_layers,
+                      self._task_config.model.num_decoder_layers)
     return model
 
   def initialize(self, model: tf.keras.Model):
     """Loading pretrained checkpoint."""
+    if not self._task_config.init_checkpoint:
+      return
+
+    ckpt_dir_or_file = self._task_config.init_checkpoint
+
+    # Restoring checkpoint.
+    if tf.io.gfile.isdir(ckpt_dir_or_file):
+      ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
+
+    if self._task_config.init_checkpoint_modules == 'all':
+      ckpt = tf.train.Checkpoint(**model.checkpoint_items)
+      status = ckpt.restore(ckpt_dir_or_file)
+      status.assert_consumed()
+    elif self._task_config.init_checkpoint_modules == 'backbone':
       ckpt = tf.train.Checkpoint(backbone=model.backbone)
-    status = ckpt.read(self._task_config.init_ckpt)
+      status = ckpt.restore(ckpt_dir_or_file)
       status.expect_partial().assert_existing_objects_matched()
 
-  def build_inputs(self, params, input_context=None):
+    logging.info('Finished loading pretrained checkpoint from %s',
+                 ckpt_dir_or_file)
+
+  def build_inputs(self,
+                   params,
+                   input_context: Optional[tf.distribute.InputContext] = None):
     """Build input dataset."""
-    return coco.COCODataLoader(params).load(input_context)
+    if isinstance(params, coco.COCODataConfig):
+      dataset = coco.COCODataLoader(params).load(input_context)
+    else:
+      if params.tfds_name:
+        decoder = tfds_factory.get_detection_decoder(params.tfds_name)
+      else:
+        decoder_cfg = params.decoder.get()
+        if params.decoder.type == 'simple_decoder':
+          decoder = tf_example_decoder.TfExampleDecoder(
+              regenerate_source_id=decoder_cfg.regenerate_source_id)
+        elif params.decoder.type == 'label_map_decoder':
+          decoder = tf_example_label_map_decoder.TfExampleDecoderLabelMap(
+              label_map=decoder_cfg.label_map,
+              regenerate_source_id=decoder_cfg.regenerate_source_id)
+        else:
+          raise ValueError('Unknown decoder type: {}!'.format(
+              params.decoder.type))
+
+      parser = detr_input.Parser(
+          class_offset=self._task_config.losses.class_offset,
+          output_size=self._task_config.model.input_size[:2],
+      )
+
+      reader = input_reader_factory.input_reader_generator(
+          params,
+          dataset_fn=dataset_fn.pick_dataset_fn(params.file_type),
+          decoder_fn=decoder.decode,
+          parser_fn=parser.parse_fn(params.is_training))
+      dataset = reader.read(input_context=input_context)
+
+    return dataset
 
   def _compute_cost(self, cls_outputs, box_outputs, cls_targets, box_targets):
     # Approximate classification cost with 1 - prob[target class].
     # The 1 is a constant that doesn't change the matching, it can be ommitted.
     # background: 0
-    cls_cost = self._task_config.lambda_cls * tf.gather(
+    cls_cost = self._task_config.losses.lambda_cls * tf.gather(
         -tf.nn.softmax(cls_outputs), cls_targets, batch_dims=1, axis=-1)
 
     # Compute the L1 cost between boxes,
-    paired_differences = self._task_config.lambda_box * tf.abs(
+    paired_differences = self._task_config.losses.lambda_box * tf.abs(
         tf.expand_dims(box_outputs, 2) - tf.expand_dims(box_targets, 1))
     box_cost = tf.reduce_sum(paired_differences, axis=-1)
 
     # Compute the giou cost betwen boxes
-    giou_cost = self._task_config.lambda_giou * -box_ops.bbox_generalized_overlap(
+    giou_cost = self._task_config.losses.lambda_giou * -box_ops.bbox_generalized_overlap(
         box_ops.cycxhw_to_yxyx(box_outputs),
         box_ops.cycxhw_to_yxyx(box_targets))
 
     total_cost = cls_cost + box_cost + giou_cost
 
     max_cost = (
-        self._task_config.lambda_cls * 0.0 + self._task_config.lambda_box * 4. +
-        self._task_config.lambda_giou * 0.0)
+        self._task_config.losses.lambda_cls * 0.0 +
+        self._task_config.losses.lambda_box * 4. +
+        self._task_config.losses.lambda_giou * 0.0)
 
     # Set pads to large constant
     valid = tf.expand_dims(
@@ -115,35 +181,26 @@ class DectectionTask(base_task.Task):
     # Down-weight background to account for class imbalance.
     xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
         labels=cls_targets, logits=cls_assigned)
-    cls_loss = self._task_config.lambda_cls * tf.where(
-        background,
-        self._task_config.background_cls_weight * xentropy,
-        xentropy
-        )
+    cls_loss = self._task_config.losses.lambda_cls * tf.where(
+        background, self._task_config.losses.background_cls_weight * xentropy,
+        xentropy)
     cls_weights = tf.where(
         background,
-        self._task_config.background_cls_weight * tf.ones_like(cls_loss),
-        tf.ones_like(cls_loss)
-        )
+        self._task_config.losses.background_cls_weight * tf.ones_like(cls_loss),
+        tf.ones_like(cls_loss))
 
     # Box loss is only calculated on non-background class.
     l_1 = tf.reduce_sum(tf.abs(box_assigned - box_targets), axis=-1)
-    box_loss = self._task_config.lambda_box * tf.where(
-        background,
-        tf.zeros_like(l_1),
-        l_1
-        )
+    box_loss = self._task_config.losses.lambda_box * tf.where(
+        background, tf.zeros_like(l_1), l_1)
 
     # Giou loss is only calculated on non-background class.
     giou = tf.linalg.diag_part(1.0 - box_ops.bbox_generalized_overlap(
         box_ops.cycxhw_to_yxyx(box_assigned),
         box_ops.cycxhw_to_yxyx(box_targets)
         ))
-    giou_loss = self._task_config.lambda_giou * tf.where(
-        background,
-        tf.zeros_like(giou),
-        giou
-        )
+    giou_loss = self._task_config.losses.lambda_giou * tf.where(
+        background, tf.zeros_like(giou), giou)
 
     # Consider doing all reduce once in train_step to speed up.
     num_boxes_per_replica = tf.reduce_sum(num_boxes)
@@ -160,6 +217,7 @@ class DectectionTask(base_task.Task):
         tf.reduce_sum(giou_loss), num_boxes_sum)
 
     aux_losses = tf.add_n(aux_losses) if aux_losses else 0.0
+
     total_loss = cls_loss + box_loss + giou_loss + aux_losses
     return total_loss, cls_loss, box_loss, giou_loss
 
@@ -172,7 +230,7 @@ class DectectionTask(base_task.Task):
 
     if not training:
       self.coco_metric = coco_evaluator.COCOEvaluator(
-          annotation_file='',
+          annotation_file=self._task_config.annotation_file,
           include_mask=False,
           need_rescale_bboxes=True,
           per_category_metrics=self._task_config.per_category_metrics)

official/projects/detr/tasks/detection_test.py

@@ -22,6 +22,7 @@ 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
+from official.vision.configs import backbones
 
 
 _NUM_EXAMPLES = 10
@@ -58,9 +59,16 @@ def _as_dataset(self, *args, **kwargs):
 class DetectionTest(tf.test.TestCase):
 
   def test_train_step(self):
-    config = detr_cfg.DetectionConfig(
+    config = detr_cfg.DetrTask(
+        model=detr_cfg.Detr(
+            input_size=[1333, 1333, 3],
             num_encoder_layers=1,
             num_decoder_layers=1,
+            num_classes=81,
+            backbone=backbones.Backbone(
+                type='resnet',
+                resnet=backbones.ResNet(model_id=10, bn_trainable=False))
+        ),
         train_data=coco.COCODataConfig(
             tfds_name='coco/2017',
             tfds_split='validation',
@@ -92,9 +100,16 @@ class DetectionTest(tf.test.TestCase):
       task.train_step(next(iterator), model, optimizer)
 
   def test_validation_step(self):
-    config = detr_cfg.DetectionConfig(
+    config = detr_cfg.DetrTask(
+        model=detr_cfg.Detr(
+            input_size=[1333, 1333, 3],
             num_encoder_layers=1,
             num_decoder_layers=1,
+            num_classes=81,
+            backbone=backbones.Backbone(
+                type='resnet',
+                resnet=backbones.ResNet(model_id=10, bn_trainable=False))
+        ),
         validation_data=coco.COCODataConfig(
             tfds_name='coco/2017',
             tfds_split='validation',
@@ -112,5 +127,77 @@ class DetectionTest(tf.test.TestCase):
       state = task.aggregate_logs(step_outputs=logs)
       task.reduce_aggregated_logs(state)
 
+
+class DetectionTFDSTest(tf.test.TestCase):
+
+  def test_train_step(self):
+    config = detr_cfg.DetrTask(
+        model=detr_cfg.Detr(
+            input_size=[1333, 1333, 3],
+            num_encoder_layers=1,
+            num_decoder_layers=1,
+            backbone=backbones.Backbone(
+                type='resnet',
+                resnet=backbones.ResNet(model_id=10, bn_trainable=False))
+        ),
+        losses=detr_cfg.Losses(class_offset=1),
+        train_data=detr_cfg.DataConfig(
+            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.DetrTask(
+        model=detr_cfg.Detr(
+            input_size=[1333, 1333, 3],
+            num_encoder_layers=1,
+            num_decoder_layers=1,
+            backbone=backbones.Backbone(
+                type='resnet',
+                resnet=backbones.ResNet(model_id=10, bn_trainable=False))
+        ),
+        losses=detr_cfg.Losses(class_offset=1),
+        validation_data=detr_cfg.DataConfig(
+            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()