Fix file not found error in Deep Mask Head RCNN unit tests

PiperOrigin-RevId: 473067046

official/projects/deepmac_maskrcnn/tasks/deep_mask_head_rcnn_test.py

-# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""Tests for Mask R-CNN variant with support for deep mask heads."""
-import orbit
-import tensorflow as tf
-
-from official import vision  # pylint: disable=unused-import
-from official.core import exp_factory
-from official.modeling import optimization
-from official.projects.deepmac_maskrcnn.tasks import deep_mask_head_rcnn
-
-
-class DeepMaskHeadRCNNTaskTest(tf.test.TestCase):
-
-  def _edit_config_for_testing(self, config):
-    # modify config to suit local testing
-    config.trainer.steps_per_loop = 1
-    config.task.train_data.global_batch_size = 2
-    config.task.model.backbone.resnet.model_id = 18
-    config.task.model.decoder.fpn.num_filters = 32
-    config.task.model.detection_generator.pre_nms_top_k = 500
-    config.task.model.detection_head.fc_dims = 128
-    if config.task.model.include_mask:
-      config.task.model.mask_sampler.num_sampled_masks = 10
-      config.task.model.mask_head.num_convs = 1
-    config.task.model.roi_generator.num_proposals = 100
-    config.task.model.roi_generator.pre_nms_top_k = 150
-    config.task.model.roi_generator.test_pre_nms_top_k = 150
-    config.task.model.roi_generator.test_num_proposals = 100
-    config.task.model.rpn_head.num_filters = 32
-    config.task.model.roi_sampler.num_sampled_rois = 200
-    config.task.model.input_size = [128, 128, 3]
-    config.trainer.train_steps = 2
-    config.task.train_data.shuffle_buffer_size = 2
-    config.task.train_data.input_path = "/readahead/200M/placer/prod/home/tensorflow-performance-data/datasets/coco/train-00000-of-00256.tfrecord"
-    config.task.validation_data.global_batch_size = 2
-    config.task.validation_data.input_path = "/readahead/200M/placer/prod/home/tensorflow-performance-data/datasets/coco/val-00000-of-00032.tfrecord"
-
-  def _build_deep_mask_head_rcnn_from_config(self, config, is_training):
-    task = deep_mask_head_rcnn.DeepMaskHeadRCNNTask(config.task)
-    model = task.build_model()
-    metrics = task.build_metrics(training=is_training)
-
-    strategy = tf.distribute.get_strategy()
-
-    data_config = config.task.train_data if is_training else config.task.validation_data
-    dataset = orbit.utils.make_distributed_dataset(strategy, task.build_inputs,
-                                                   data_config)
-    iterator = iter(dataset)
-    opt_factory = optimization.OptimizerFactory(config.trainer.optimizer_config)
-    optimizer = opt_factory.build_optimizer(opt_factory.build_learning_rate())
-
-    return task, iterator, model, optimizer, metrics
-
-  def test_freeze_backbone_number_of_trainable_weights(self):
-    config = exp_factory.get_exp_config("deep_mask_head_rcnn_resnetfpn_coco")
-    self._edit_config_for_testing(config)
-
-    # Get the number of weights from a regular model
-    (task, iterator, model,
-     optimizer, metrics) = self._build_deep_mask_head_rcnn_from_config(
-         config, True)
-
-    task.train_step(next(iterator), model, optimizer, metrics=metrics)
-    default_weights = sum(len(l.trainable_weights) for l in model.layers)
-
-    # Get the number of weights from a model with backbone frozen
-    config.task.freeze_backbone = True
-    (task, iterator, model,
-     optimizer, metrics) = self._build_deep_mask_head_rcnn_from_config(
-         config, True)
-
-    task.train_step(next(iterator), model, optimizer, metrics=metrics)
-    frozen_weights = sum(len(l.trainable_weights) for l in model.layers)
-
-    # Make sure that the frozen model has less number of trainable weights
-    self.assertLess(frozen_weights, default_weights,
-                    msg="A frozen backbone should have fewer trainable weights")
-
-  def test_freeze_backbone(self):
-    config = exp_factory.get_exp_config("deep_mask_head_rcnn_resnetfpn_coco")
-    self._edit_config_for_testing(config)
-    config.task.freeze_backbone = True
-
-    (task, iterator, model,
-     optimizer, metrics) = self._build_deep_mask_head_rcnn_from_config(
-         config, True)
-
-    weights_before = [
-        w.numpy().copy() for w in model.backbone.weights]
-
-    self.assertNotEmpty(weights_before)
-    task.train_step(next(iterator), model, optimizer, metrics=metrics)
-
-    weights_after = [
-        w.numpy().copy() for w in model.backbone.weights]
-
-    self.assertAllClose(weights_after, weights_before,
-                        msg="Backbone weights should not change when frozen")
-
-
-if __name__ == "__main__":
-  tf.test.main()