research/object_detection/utils/config_util_test.py
# Copyright 2017 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 object_detection.utils.config_util."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
from six.moves import range
import tensorflow.compat.v1 as tf
from google.protobuf import text_format
from object_detection.protos import eval_pb2
from object_detection.protos import image_resizer_pb2
from object_detection.protos import input_reader_pb2
from object_detection.protos import model_pb2
from object_detection.protos import pipeline_pb2
from object_detection.protos import train_pb2
from object_detection.utils import config_util
from object_detection.utils import tf_version
# pylint: disable=g-import-not-at-top
try:
from tensorflow.contrib import training as contrib_training
except ImportError:
# TF 2.0 doesn't ship with contrib.
pass
# pylint: enable=g-import-not-at-top
def _write_config(config, config_path):
"""Writes a config object to disk."""
config_text = text_format.MessageToString(config)
with tf.gfile.Open(config_path, "wb") as f:
f.write(config_text)
def _update_optimizer_with_constant_learning_rate(optimizer, learning_rate):
"""Adds a new constant learning rate."""
constant_lr = optimizer.learning_rate.constant_learning_rate
constant_lr.learning_rate = learning_rate
def _update_optimizer_with_exponential_decay_learning_rate(
optimizer, learning_rate):
"""Adds a new exponential decay learning rate."""
exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
exponential_lr.initial_learning_rate = learning_rate
def _update_optimizer_with_manual_step_learning_rate(
optimizer, initial_learning_rate, learning_rate_scaling):
"""Adds a learning rate schedule."""
manual_lr = optimizer.learning_rate.manual_step_learning_rate
manual_lr.initial_learning_rate = initial_learning_rate
for i in range(3):
schedule = manual_lr.schedule.add()
schedule.learning_rate = initial_learning_rate * learning_rate_scaling**i
def _update_optimizer_with_cosine_decay_learning_rate(
optimizer, learning_rate, warmup_learning_rate):
"""Adds a new cosine decay learning rate."""
cosine_lr = optimizer.learning_rate.cosine_decay_learning_rate
cosine_lr.learning_rate_base = learning_rate
cosine_lr.warmup_learning_rate = warmup_learning_rate
class ConfigUtilTest(tf.test.TestCase):
def _create_and_load_test_configs(self, pipeline_config):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
return config_util.get_configs_from_pipeline_file(pipeline_config_path)
def test_get_configs_from_pipeline_file(self):
"""Test that proto configs can be read from pipeline config file."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
self.assertProtoEquals(pipeline_config.model, configs["model"])
self.assertProtoEquals(pipeline_config.train_config,
configs["train_config"])
self.assertProtoEquals(pipeline_config.train_input_reader,
configs["train_input_config"])
self.assertProtoEquals(pipeline_config.eval_config,
configs["eval_config"])
self.assertProtoEquals(pipeline_config.eval_input_reader,
configs["eval_input_configs"])
def test_create_configs_from_pipeline_proto(self):
"""Tests creating configs dictionary from pipeline proto."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
configs = config_util.create_configs_from_pipeline_proto(pipeline_config)
self.assertProtoEquals(pipeline_config.model, configs["model"])
self.assertProtoEquals(pipeline_config.train_config,
configs["train_config"])
self.assertProtoEquals(pipeline_config.train_input_reader,
configs["train_input_config"])
self.assertProtoEquals(pipeline_config.eval_config, configs["eval_config"])
self.assertProtoEquals(pipeline_config.eval_input_reader,
configs["eval_input_configs"])
def test_create_pipeline_proto_from_configs(self):
"""Tests that proto can be reconstructed from configs dictionary."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
def test_save_pipeline_config(self):
"""Tests that the pipeline config is properly saved to disk."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
configs = config_util.get_configs_from_pipeline_file(
os.path.join(self.get_temp_dir(), "pipeline.config"))
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
def test_get_configs_from_multiple_files(self):
"""Tests that proto configs can be read from multiple files."""
temp_dir = self.get_temp_dir()
# Write model config file.
model_config_path = os.path.join(temp_dir, "model.config")
model = model_pb2.DetectionModel()
model.faster_rcnn.num_classes = 10
_write_config(model, model_config_path)
# Write train config file.
train_config_path = os.path.join(temp_dir, "train.config")
train_config = train_config = train_pb2.TrainConfig()
train_config.batch_size = 32
_write_config(train_config, train_config_path)
# Write train input config file.
train_input_config_path = os.path.join(temp_dir, "train_input.config")
train_input_config = input_reader_pb2.InputReader()
train_input_config.label_map_path = "path/to/label_map"
_write_config(train_input_config, train_input_config_path)
# Write eval config file.
eval_config_path = os.path.join(temp_dir, "eval.config")
eval_config = eval_pb2.EvalConfig()
eval_config.num_examples = 20
_write_config(eval_config, eval_config_path)
# Write eval input config file.
eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
eval_input_config = input_reader_pb2.InputReader()
eval_input_config.label_map_path = "path/to/another/label_map"
_write_config(eval_input_config, eval_input_config_path)
configs = config_util.get_configs_from_multiple_files(
model_config_path=model_config_path,
train_config_path=train_config_path,
train_input_config_path=train_input_config_path,
eval_config_path=eval_config_path,
eval_input_config_path=eval_input_config_path)
self.assertProtoEquals(model, configs["model"])
self.assertProtoEquals(train_config, configs["train_config"])
self.assertProtoEquals(train_input_config,
configs["train_input_config"])
self.assertProtoEquals(eval_config, configs["eval_config"])
self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0])
def _assertOptimizerWithNewLearningRate(self, optimizer_name):
"""Asserts successful updating of all learning rate schemes."""
original_learning_rate = 0.7
learning_rate_scaling = 0.1
warmup_learning_rate = 0.07
hparams = contrib_training.HParams(learning_rate=0.15)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
# Constant learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_constant_learning_rate(optimizer,
original_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
constant_lr = optimizer.learning_rate.constant_learning_rate
self.assertAlmostEqual(hparams.learning_rate, constant_lr.learning_rate)
# Exponential decay learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_exponential_decay_learning_rate(
optimizer, original_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
exponential_lr = optimizer.learning_rate.exponential_decay_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
exponential_lr.initial_learning_rate)
# Manual step learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_manual_step_learning_rate(
optimizer, original_learning_rate, learning_rate_scaling)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
manual_lr = optimizer.learning_rate.manual_step_learning_rate
self.assertAlmostEqual(hparams.learning_rate,
manual_lr.initial_learning_rate)
for i, schedule in enumerate(manual_lr.schedule):
self.assertAlmostEqual(hparams.learning_rate * learning_rate_scaling**i,
schedule.learning_rate)
# Cosine decay learning rate.
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer = getattr(pipeline_config.train_config.optimizer, optimizer_name)
_update_optimizer_with_cosine_decay_learning_rate(optimizer,
original_learning_rate,
warmup_learning_rate)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer = getattr(configs["train_config"].optimizer, optimizer_name)
cosine_lr = optimizer.learning_rate.cosine_decay_learning_rate
self.assertAlmostEqual(hparams.learning_rate, cosine_lr.learning_rate_base)
warmup_scale_factor = warmup_learning_rate / original_learning_rate
self.assertAlmostEqual(hparams.learning_rate * warmup_scale_factor,
cosine_lr.warmup_learning_rate)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testRMSPropWithNewLearingRate(self):
"""Tests new learning rates for RMSProp Optimizer."""
self._assertOptimizerWithNewLearningRate("rms_prop_optimizer")
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testMomentumOptimizerWithNewLearningRate(self):
"""Tests new learning rates for Momentum Optimizer."""
self._assertOptimizerWithNewLearningRate("momentum_optimizer")
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testAdamOptimizerWithNewLearningRate(self):
"""Tests new learning rates for Adam Optimizer."""
self._assertOptimizerWithNewLearningRate("adam_optimizer")
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testGenericConfigOverride(self):
"""Tests generic config overrides for all top-level configs."""
# Set one parameter for each of the top-level pipeline configs:
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.num_classes = 1
pipeline_config.train_config.batch_size = 1
pipeline_config.eval_config.num_visualizations = 1
pipeline_config.train_input_reader.label_map_path = "/some/path"
pipeline_config.eval_input_reader.add().label_map_path = "/some/path"
pipeline_config.graph_rewriter.quantization.weight_bits = 1
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Override each of the parameters:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
hparams = contrib_training.HParams(
**{
"model.ssd.num_classes": 2,
"train_config.batch_size": 2,
"train_input_config.label_map_path": "/some/other/path",
"eval_config.num_visualizations": 2,
"graph_rewriter_config.quantization.weight_bits": 2
})
configs = config_util.merge_external_params_with_configs(configs, hparams)
# Ensure that the parameters have the overridden values:
self.assertEqual(2, configs["model"].ssd.num_classes)
self.assertEqual(2, configs["train_config"].batch_size)
self.assertEqual("/some/other/path",
configs["train_input_config"].label_map_path)
self.assertEqual(2, configs["eval_config"].num_visualizations)
self.assertEqual(2,
configs["graph_rewriter_config"].quantization.weight_bits)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewBatchSize(self):
"""Tests that batch size is updated appropriately."""
original_batch_size = 2
hparams = contrib_training.HParams(batch_size=16)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = original_batch_size
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(16, new_batch_size)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewBatchSizeWithClipping(self):
"""Tests that batch size is clipped to 1 from below."""
original_batch_size = 2
hparams = contrib_training.HParams(batch_size=0.5)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = original_batch_size
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(1, new_batch_size) # Clipped to 1.0.
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteBatchSizeWithKeyValue(self):
"""Tests that batch size is overwritten based on key/value."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
hparams = contrib_training.HParams(**{"train_config.batch_size": 10})
configs = config_util.merge_external_params_with_configs(configs, hparams)
new_batch_size = configs["train_config"].batch_size
self.assertEqual(10, new_batch_size)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteSampleFromDatasetWeights(self):
"""Tests config override for sample_from_datasets_weights."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.sample_from_datasets_weights.extend(
[1, 2])
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Override parameters:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
hparams = contrib_training.HParams(sample_from_datasets_weights=[0.5, 0.5])
configs = config_util.merge_external_params_with_configs(configs, hparams)
# Ensure that the parameters have the overridden values:
self.assertListEqual(
[0.5, 0.5],
list(configs["train_input_config"].sample_from_datasets_weights))
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteSampleFromDatasetWeightsWrongLength(self):
"""Tests config override for sample_from_datasets_weights."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.sample_from_datasets_weights.extend(
[1, 2])
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
_write_config(pipeline_config, pipeline_config_path)
# Try to override parameter with too many weights:
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
hparams = contrib_training.HParams(
sample_from_datasets_weights=[0.5, 0.5, 0.5])
with self.assertRaises(
ValueError,
msg="sample_from_datasets_weights override has a different number of"
" values (3) than the configured dataset weights (2)."
):
config_util.merge_external_params_with_configs(configs, hparams)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testKeyValueOverrideBadKey(self):
"""Tests that overwriting with a bad key causes an exception."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
configs = self._create_and_load_test_configs(pipeline_config)
hparams = contrib_training.HParams(**{"train_config.no_such_field": 10})
with self.assertRaises(ValueError):
config_util.merge_external_params_with_configs(configs, hparams)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testOverwriteBatchSizeWithBadValueType(self):
"""Tests that overwriting with a bad valuye type causes an exception."""
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.batch_size = 2
configs = self._create_and_load_test_configs(pipeline_config)
# Type should be an integer, but we're passing a string "10".
hparams = contrib_training.HParams(**{"train_config.batch_size": "10"})
with self.assertRaises(TypeError):
config_util.merge_external_params_with_configs(configs, hparams)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewMomentumOptimizerValue(self):
"""Tests that new momentum value is updated appropriately."""
original_momentum_value = 0.4
hparams = contrib_training.HParams(momentum_optimizer_value=1.1)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
optimizer_config.momentum_optimizer_value = original_momentum_value
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
new_momentum_value = optimizer_config.momentum_optimizer_value
self.assertAlmostEqual(1.0, new_momentum_value) # Clipped to 1.0.
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewClassificationLocalizationWeightRatio(self):
"""Tests that the loss weight ratio is updated appropriately."""
original_localization_weight = 0.1
original_classification_weight = 0.2
new_weight_ratio = 5.0
hparams = contrib_training.HParams(
classification_localization_weight_ratio=new_weight_ratio)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.loss.localization_weight = (
original_localization_weight)
pipeline_config.model.ssd.loss.classification_weight = (
original_classification_weight)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
loss = configs["model"].ssd.loss
self.assertAlmostEqual(1.0, loss.localization_weight)
self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
@unittest.skipIf(tf_version.is_tf2(), "Skipping TF1.X only test.")
def testNewFocalLossParameters(self):
"""Tests that the loss weight ratio is updated appropriately."""
original_alpha = 1.0
original_gamma = 1.0
new_alpha = 0.3
new_gamma = 2.0
hparams = contrib_training.HParams(
focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
classification_loss = pipeline_config.model.ssd.loss.classification_loss
classification_loss.weighted_sigmoid_focal.alpha = original_alpha
classification_loss.weighted_sigmoid_focal.gamma = original_gamma
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
configs = config_util.merge_external_params_with_configs(configs, hparams)
classification_loss = configs["model"].ssd.loss.classification_loss
self.assertAlmostEqual(new_alpha,
classification_loss.weighted_sigmoid_focal.alpha)
self.assertAlmostEqual(new_gamma,
classification_loss.weighted_sigmoid_focal.gamma)
def testMergingKeywordArguments(self):
"""Tests that keyword arguments get merged as do hyperparameters."""
original_num_train_steps = 100
desired_num_train_steps = 10
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_config.num_steps = original_num_train_steps
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_steps": desired_num_train_steps}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
train_steps = configs["train_config"].num_steps
self.assertEqual(desired_num_train_steps, train_steps)
def testGetNumberOfClasses(self):
"""Tests that number of classes can be retrieved."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 20
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
number_of_classes = config_util.get_number_of_classes(configs["model"])
self.assertEqual(20, number_of_classes)
def testNewTrainInputPath(self):
"""Tests that train input path can be overwritten with single file."""
original_train_path = ["path/to/data"]
new_train_path = "another/path/to/data"
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
reader_config = pipeline_config.train_input_reader.tf_record_input_reader
reader_config.input_path.extend(original_train_path)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_input_path": new_train_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
reader_config = configs["train_input_config"].tf_record_input_reader
final_path = reader_config.input_path
self.assertEqual([new_train_path], final_path)
def testNewTrainInputPathList(self):
"""Tests that train input path can be overwritten with multiple files."""
original_train_path = ["path/to/data"]
new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
reader_config = pipeline_config.train_input_reader.tf_record_input_reader
reader_config.input_path.extend(original_train_path)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_input_path": new_train_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
reader_config = configs["train_input_config"].tf_record_input_reader
final_path = reader_config.input_path
self.assertEqual(new_train_path, final_path)
def testNewLabelMapPath(self):
"""Tests that label map path can be overwritten in input readers."""
original_label_map_path = "path/to/original/label_map"
new_label_map_path = "path//to/new/label_map"
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.label_map_path = original_label_map_path
eval_input_reader = pipeline_config.eval_input_reader.add()
eval_input_reader.label_map_path = original_label_map_path
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"label_map_path": new_label_map_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(new_label_map_path,
configs["train_input_config"].label_map_path)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_label_map_path, eval_input_config.label_map_path)
def testDontOverwriteEmptyLabelMapPath(self):
"""Tests that label map path will not by overwritten with empty string."""
original_label_map_path = "path/to/original/label_map"
new_label_map_path = ""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.label_map_path = original_label_map_path
eval_input_reader = pipeline_config.eval_input_reader.add()
eval_input_reader.label_map_path = original_label_map_path
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"label_map_path": new_label_map_path}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(original_label_map_path,
configs["train_input_config"].label_map_path)
self.assertEqual(original_label_map_path,
configs["eval_input_configs"][0].label_map_path)
def testNewMaskType(self):
"""Tests that mask type can be overwritten in input readers."""
original_mask_type = input_reader_pb2.NUMERICAL_MASKS
new_mask_type = input_reader_pb2.PNG_MASKS
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_input_reader = pipeline_config.train_input_reader
train_input_reader.mask_type = original_mask_type
eval_input_reader = pipeline_config.eval_input_reader.add()
eval_input_reader.mask_type = original_mask_type
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"mask_type": new_mask_type}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(new_mask_type, configs["train_input_config"].mask_type)
self.assertEqual(new_mask_type, configs["eval_input_configs"][0].mask_type)
def testUseMovingAverageForEval(self):
use_moving_averages_orig = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.use_moving_averages = use_moving_averages_orig
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_with_moving_averages": True}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(True, configs["eval_config"].use_moving_averages)
def testGetImageResizerConfig(self):
"""Tests that number of classes can be retrieved."""
model_config = model_pb2.DetectionModel()
model_config.faster_rcnn.image_resizer.fixed_shape_resizer.height = 100
model_config.faster_rcnn.image_resizer.fixed_shape_resizer.width = 300
image_resizer_config = config_util.get_image_resizer_config(model_config)
self.assertEqual(image_resizer_config.fixed_shape_resizer.height, 100)
self.assertEqual(image_resizer_config.fixed_shape_resizer.width, 300)
def testGetSpatialImageSizeFromFixedShapeResizerConfig(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.fixed_shape_resizer.height = 100
image_resizer_config.fixed_shape_resizer.width = 200
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [100, 200])
def testGetSpatialImageSizeFromAspectPreservingResizerConfig(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension = True
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [600, 600])
def testGetSpatialImageSizeFromAspectPreservingResizerDynamic(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [-1, -1])
def testGetSpatialImageSizeFromConditionalShapeResizer(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.conditional_shape_resizer.size_threshold = 100
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [-1, -1])
def testGetMaxNumContextFeaturesFromModelConfig(self):
model_config = model_pb2.DetectionModel()
model_config.faster_rcnn.context_config.max_num_context_features = 10
max_num_context_features = config_util.get_max_num_context_features(
model_config)
self.assertAllEqual(max_num_context_features, 10)
def testGetContextFeatureLengthFromModelConfig(self):
model_config = model_pb2.DetectionModel()
model_config.faster_rcnn.context_config.context_feature_length = 100
context_feature_length = config_util.get_context_feature_length(
model_config)
self.assertAllEqual(context_feature_length, 100)
def testEvalShuffle(self):
"""Tests that `eval_shuffle` keyword arguments are applied correctly."""
original_shuffle = True
desired_shuffle = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_shuffle": desired_shuffle}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(desired_shuffle, configs["eval_input_configs"][0].shuffle)
def testTrainShuffle(self):
"""Tests that `train_shuffle` keyword arguments are applied correctly."""
original_shuffle = True
desired_shuffle = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"train_shuffle": desired_shuffle}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
train_shuffle = configs["train_input_config"].shuffle
self.assertEqual(desired_shuffle, train_shuffle)
def testOverWriteRetainOriginalImages(self):
"""Tests that `train_shuffle` keyword arguments are applied correctly."""
original_retain_original_images = True
desired_retain_original_images = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.retain_original_images = (
original_retain_original_images)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {
"retain_original_images_in_eval": desired_retain_original_images
}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
retain_original_images = configs["eval_config"].retain_original_images
self.assertEqual(desired_retain_original_images, retain_original_images)
def testOverwriteAllEvalSampling(self):
original_num_eval_examples = 1
new_num_eval_examples = 10
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
original_num_eval_examples)
pipeline_config.eval_input_reader.add().sample_1_of_n_examples = (
original_num_eval_examples)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"sample_1_of_n_eval_examples": new_num_eval_examples}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_num_eval_examples,
eval_input_config.sample_1_of_n_examples)
def testOverwriteAllEvalNumEpochs(self):
original_num_epochs = 10
new_num_epochs = 1
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().num_epochs = original_num_epochs
pipeline_config.eval_input_reader.add().num_epochs = original_num_epochs
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_num_epochs": new_num_epochs}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(new_num_epochs, eval_input_config.num_epochs)
def testUpdateMaskTypeForAllInputConfigs(self):
original_mask_type = input_reader_pb2.NUMERICAL_MASKS
new_mask_type = input_reader_pb2.PNG_MASKS
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
train_config = pipeline_config.train_input_reader
train_config.mask_type = original_mask_type
eval_1 = pipeline_config.eval_input_reader.add()
eval_1.mask_type = original_mask_type
eval_1.name = "eval_1"
eval_2 = pipeline_config.eval_input_reader.add()
eval_2.mask_type = original_mask_type
eval_2.name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"mask_type": new_mask_type}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
self.assertEqual(configs["train_input_config"].mask_type, new_mask_type)
for eval_input_config in configs["eval_input_configs"]:
self.assertEqual(eval_input_config.mask_type, new_mask_type)
def testErrorOverwritingMultipleInputConfig(self):
original_shuffle = False
new_shuffle = True
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
eval_1 = pipeline_config.eval_input_reader.add()
eval_1.shuffle = original_shuffle
eval_1.name = "eval_1"
eval_2 = pipeline_config.eval_input_reader.add()
eval_2.shuffle = original_shuffle
eval_2.name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {"eval_shuffle": new_shuffle}
with self.assertRaises(ValueError):
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
def testCheckAndParseInputConfigKey(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().name = "eval_1"
pipeline_config.eval_input_reader.add().name = "eval_2"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
specific_shuffle_update_key = "eval_input_configs:eval_2:shuffle"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, specific_shuffle_update_key))
self.assertTrue(is_valid_input_config_key)
self.assertEqual(key_name, "eval_input_configs")
self.assertEqual(input_name, "eval_2")
self.assertEqual(field_name, "shuffle")
legacy_shuffle_update_key = "eval_shuffle"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(configs,
legacy_shuffle_update_key))
self.assertTrue(is_valid_input_config_key)
self.assertEqual(key_name, "eval_input_configs")
self.assertEqual(input_name, None)
self.assertEqual(field_name, "shuffle")
non_input_config_update_key = "label_map_path"
is_valid_input_config_key, key_name, input_name, field_name = (
config_util.check_and_parse_input_config_key(
configs, non_input_config_update_key))
self.assertFalse(is_valid_input_config_key)
self.assertEqual(key_name, None)
self.assertEqual(input_name, None)
self.assertEqual(field_name, "label_map_path")
with self.assertRaisesRegexp(ValueError,
"Invalid key format when overriding configs."):
config_util.check_and_parse_input_config_key(
configs, "train_input_config:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid key_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "invalid_key_name:train_name:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid input_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "eval_input_configs:unknown_eval_name:shuffle")
with self.assertRaisesRegexp(
ValueError, "Invalid field_name when overriding input config."):
config_util.check_and_parse_input_config_key(
configs, "eval_input_configs:eval_2:unknown_field_name")
def testUpdateInputReaderConfigSuccess(self):
original_shuffle = False
new_shuffle = True
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.train_input_reader.shuffle = original_shuffle
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
config_util.update_input_reader_config(
configs,
key_name="train_input_config",
input_name=None,
field_name="shuffle",
value=new_shuffle)
self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
config_util.update_input_reader_config(
configs,
key_name="train_input_config",
input_name=None,
field_name="shuffle",
value=new_shuffle)
self.assertEqual(configs["train_input_config"].shuffle, new_shuffle)
def testUpdateInputReaderConfigErrors(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_input_reader.add().name = "same_eval_name"
pipeline_config.eval_input_reader.add().name = "same_eval_name"
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
with self.assertRaisesRegexp(ValueError,
"Duplicate input name found when overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name="same_eval_name",
field_name="shuffle",
value=False)
with self.assertRaisesRegexp(
ValueError, "Input name name_not_exist not found when overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name="name_not_exist",
field_name="shuffle",
value=False)
with self.assertRaisesRegexp(ValueError,
"Unknown input config overriding."):
config_util.update_input_reader_config(
configs,
key_name="eval_input_configs",
input_name=None,
field_name="shuffle",
value=False)
def testOverWriteRetainOriginalImageAdditionalChannels(self):
"""Tests that keyword arguments are applied correctly."""
original_retain_original_image_additional_channels = True
desired_retain_original_image_additional_channels = False
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.eval_config.retain_original_image_additional_channels = (
original_retain_original_image_additional_channels)
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
override_dict = {
"retain_original_image_additional_channels_in_eval":
desired_retain_original_image_additional_channels
}
configs = config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
retain_original_image_additional_channels = configs[
"eval_config"].retain_original_image_additional_channels
self.assertEqual(desired_retain_original_image_additional_channels,
retain_original_image_additional_channels)
def testUpdateNumClasses(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.faster_rcnn.num_classes = 10
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
self.assertEqual(config_util.get_number_of_classes(configs["model"]), 10)
config_util.merge_external_params_with_configs(
configs, kwargs_dict={"num_classes": 2})
self.assertEqual(config_util.get_number_of_classes(configs["model"]), 2)
def testRemoveUnnecessaryEma(self):
input_dict = {
"expanded_conv_10/project/act_quant/min":
1,
"FeatureExtractor/MobilenetV2_2/expanded_conv_5/expand/act_quant/min":
2,
"expanded_conv_10/expand/BatchNorm/gamma/min/ExponentialMovingAverage":
3,
"expanded_conv_3/depthwise/BatchNorm/beta/max/ExponentialMovingAverage":
4,
"BoxPredictor_1/ClassPredictor_depthwise/act_quant":
5
}
no_ema_collection = ["/min", "/max"]
output_dict = {
"expanded_conv_10/project/act_quant/min":
1,
"FeatureExtractor/MobilenetV2_2/expanded_conv_5/expand/act_quant/min":
2,
"expanded_conv_10/expand/BatchNorm/gamma/min":
3,
"expanded_conv_3/depthwise/BatchNorm/beta/max":
4,
"BoxPredictor_1/ClassPredictor_depthwise/act_quant":
5
}
self.assertEqual(
output_dict,
config_util.remove_unnecessary_ema(input_dict, no_ema_collection))
def testUpdateRescoreInstances(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
kpt_task = pipeline_config.model.center_net.keypoint_estimation_task.add()
kpt_task.rescore_instances = True
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
cn_config = configs["model"].center_net
self.assertEqual(
True, cn_config.keypoint_estimation_task[0].rescore_instances)
config_util.merge_external_params_with_configs(
configs, kwargs_dict={"rescore_instances": False})
cn_config = configs["model"].center_net
self.assertEqual(
False, cn_config.keypoint_estimation_task[0].rescore_instances)
def testUpdateRescoreInstancesWithBooleanString(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
kpt_task = pipeline_config.model.center_net.keypoint_estimation_task.add()
kpt_task.rescore_instances = True
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
cn_config = configs["model"].center_net
self.assertEqual(
True, cn_config.keypoint_estimation_task[0].rescore_instances)
config_util.merge_external_params_with_configs(
configs, kwargs_dict={"rescore_instances": "False"})
cn_config = configs["model"].center_net
self.assertEqual(
False, cn_config.keypoint_estimation_task[0].rescore_instances)
def testUpdateRescoreInstancesWithMultipleTasks(self):
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
kpt_task = pipeline_config.model.center_net.keypoint_estimation_task.add()
kpt_task.rescore_instances = True
kpt_task = pipeline_config.model.center_net.keypoint_estimation_task.add()
kpt_task.rescore_instances = True
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
cn_config = configs["model"].center_net
self.assertEqual(
True, cn_config.keypoint_estimation_task[0].rescore_instances)
config_util.merge_external_params_with_configs(
configs, kwargs_dict={"rescore_instances": False})
cn_config = configs["model"].center_net
self.assertEqual(
True, cn_config.keypoint_estimation_task[0].rescore_instances)
self.assertEqual(
True, cn_config.keypoint_estimation_task[1].rescore_instances)
if __name__ == "__main__":
tf.test.main()