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# Copyright 2023 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.
"""Mock task for testing."""
import dataclasses
import numpy as np
import tensorflow as tf, tf_keras
from official.core import base_task
from official.core import config_definitions as cfg
from official.core import exp_factory
from official.modeling.hyperparams import base_config
class MockModel(tf_keras.Model):
def __init__(self, network):
super().__init__()
self.network = network
def call(self, inputs): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
outputs = self.network(inputs)
self.add_loss(tf.reduce_mean(outputs))
return outputs
@dataclasses.dataclass
class MockTaskConfig(cfg.TaskConfig):
pass
@base_config.bind(MockTaskConfig)
class MockTask(base_task.Task):
"""Mock task object for testing."""
def __init__(self, params=None, logging_dir=None, name=None):
super().__init__(params=params, logging_dir=logging_dir, name=name)
def build_model(self, *arg, **kwargs):
inputs = tf_keras.layers.Input(shape=(2,), name="random", dtype=tf.float32)
outputs = tf_keras.layers.Dense(
1, bias_initializer=tf_keras.initializers.Ones(), name="dense_0")(
inputs)
network = tf_keras.Model(inputs=inputs, outputs=outputs)
return MockModel(network)
def build_metrics(self, training: bool = True):
del training
return [tf_keras.metrics.Accuracy(name="acc")]
def validation_step(self, inputs, model: tf_keras.Model, metrics=None):
logs = super().validation_step(inputs, model, metrics)
logs["counter"] = tf.constant(1, dtype=tf.float32)
return logs
def build_inputs(self, params):
def generate_data(_):
x = tf.zeros(shape=(2,), dtype=tf.float32)
label = tf.zeros([1], dtype=tf.int32)
return x, label
dataset = tf.data.Dataset.range(1)
dataset = dataset.repeat()
dataset = dataset.map(
generate_data, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return dataset.prefetch(buffer_size=1).batch(2, drop_remainder=True)
def aggregate_logs(self, state, step_outputs):
if state is None:
state = {}
for key, value in step_outputs.items():
if key not in state:
state[key] = []
state[key].append(
np.concatenate([np.expand_dims(v.numpy(), axis=0) for v in value]))
return state
def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
for k, v in aggregated_logs.items():
aggregated_logs[k] = np.sum(np.stack(v, axis=0))
return aggregated_logs
@exp_factory.register_config_factory("mock")
def mock_experiment() -> cfg.ExperimentConfig:
config = cfg.ExperimentConfig(
task=MockTaskConfig(), trainer=cfg.TrainerConfig())
return config
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