official/vision/tasks/semantic_segmentation.py
# Copyright 2024 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 segmentation task definition."""
from typing import Any, List, Mapping, Optional, Tuple, Union
from absl import logging
import tensorflow as tf, tf_keras
from official.common import dataset_fn
from official.core import base_task
from official.core import task_factory
from official.vision.configs import semantic_segmentation as exp_cfg
from official.vision.dataloaders import input_reader
from official.vision.dataloaders import input_reader_factory
from official.vision.dataloaders import segmentation_input
from official.vision.dataloaders import tfds_factory
from official.vision.evaluation import segmentation_metrics
from official.vision.losses import segmentation_losses
from official.vision.modeling import factory
from official.vision.utils.object_detection import visualization_utils
@task_factory.register_task_cls(exp_cfg.SemanticSegmentationTask)
class SemanticSegmentationTask(base_task.Task):
"""A task for semantic segmentation."""
def build_model(self):
"""Builds segmentation model."""
input_specs = tf_keras.layers.InputSpec(shape=[None] +
self.task_config.model.input_size)
l2_weight_decay = self.task_config.losses.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)
model = factory.build_segmentation_model(
input_specs=input_specs,
model_config=self.task_config.model,
l2_regularizer=l2_regularizer)
# Builds the model
dummy_inputs = tf_keras.Input(self.task_config.model.input_size)
_ = model(dummy_inputs, training=False)
return model
def initialize(self, model: tf_keras.Model):
"""Loads pretrained checkpoint."""
if not self.task_config.init_checkpoint:
return
ckpt_dir_or_file = self.task_config.init_checkpoint
if tf.io.gfile.isdir(ckpt_dir_or_file):
ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
# Restoring checkpoint.
if 'all' in self.task_config.init_checkpoint_modules:
ckpt = tf.train.Checkpoint(**model.checkpoint_items)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
else:
ckpt_items = {}
if 'backbone' in self.task_config.init_checkpoint_modules:
ckpt_items.update(backbone=model.backbone)
if 'decoder' in self.task_config.init_checkpoint_modules:
ckpt_items.update(decoder=model.decoder)
ckpt = tf.train.Checkpoint(**ckpt_items)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
logging.info('Finished loading pretrained checkpoint from %s',
ckpt_dir_or_file)
def build_inputs(self,
params: exp_cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None):
"""Builds classification input."""
ignore_label = self.task_config.losses.ignore_label
gt_is_matting_map = self.task_config.losses.gt_is_matting_map
if params.tfds_name:
decoder = tfds_factory.get_segmentation_decoder(params.tfds_name)
else:
decoder = segmentation_input.Decoder(
image_feature=params.image_feature,
additional_dense_features=params.additional_dense_features)
parser = segmentation_input.Parser(
output_size=params.output_size,
crop_size=params.crop_size,
ignore_label=ignore_label,
resize_eval_groundtruth=params.resize_eval_groundtruth,
gt_is_matting_map=gt_is_matting_map,
groundtruth_padded_size=params.groundtruth_padded_size,
aug_scale_min=params.aug_scale_min,
aug_scale_max=params.aug_scale_max,
aug_rand_hflip=params.aug_rand_hflip,
preserve_aspect_ratio=params.preserve_aspect_ratio,
dtype=params.dtype,
image_feature=params.image_feature,
additional_dense_features=params.additional_dense_features,
centered_crop=params.centered_crop)
reader = input_reader_factory.input_reader_generator(
params,
dataset_fn=dataset_fn.pick_dataset_fn(params.file_type),
decoder_fn=decoder.decode,
combine_fn=input_reader.create_combine_fn(params),
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read(input_context=input_context)
return dataset
def build_losses(self,
labels: Mapping[str, tf.Tensor],
model_outputs: Union[Mapping[str, tf.Tensor], tf.Tensor],
aux_losses: Optional[Any] = None):
"""Segmentation loss.
Args:
labels: labels.
model_outputs: Output logits of the classifier.
aux_losses: auxiliarly loss tensors, i.e. `losses` in keras.Model.
Returns:
The total loss tensor.
"""
loss_params = self._task_config.losses
segmentation_loss_fn = segmentation_losses.SegmentationLoss(
loss_params.label_smoothing,
loss_params.class_weights,
loss_params.ignore_label,
use_groundtruth_dimension=loss_params.use_groundtruth_dimension,
use_binary_cross_entropy=loss_params.use_binary_cross_entropy,
top_k_percent_pixels=loss_params.top_k_percent_pixels,
gt_is_matting_map=loss_params.gt_is_matting_map)
total_loss = segmentation_loss_fn(model_outputs['logits'], labels['masks'])
if 'mask_scores' in model_outputs:
mask_scoring_loss_fn = segmentation_losses.MaskScoringLoss(
loss_params.ignore_label)
total_loss += loss_params.mask_scoring_weight * mask_scoring_loss_fn(
model_outputs['mask_scores'],
model_outputs['logits'],
labels['masks'])
if aux_losses:
total_loss += tf.add_n(aux_losses)
total_loss = loss_params.loss_weight * total_loss
return total_loss
def process_metrics(self, metrics, labels, model_outputs, **kwargs):
"""Process and update metrics.
Called when using custom training loop API.
Args:
metrics: a nested structure of metrics objects. The return of function
self.build_metrics.
labels: a tensor or a nested structure of tensors.
model_outputs: a tensor or a nested structure of tensors. For example,
output of the keras model built by self.build_model.
**kwargs: other args.
"""
for metric in metrics:
if 'mask_scores_mse' == metric.name:
actual_mask_scores = segmentation_losses.get_actual_mask_scores(
model_outputs['logits'], labels['masks'],
self.task_config.losses.ignore_label)
metric.update_state(actual_mask_scores, model_outputs['mask_scores'])
else:
metric.update_state(labels, model_outputs['logits'])
def build_metrics(self, training: bool = True):
"""Gets streaming metrics for training/validation."""
metrics = []
self.iou_metric = None
if training and self.task_config.evaluation.report_train_mean_iou:
metrics.append(
segmentation_metrics.MeanIoU(
name='mean_iou',
num_classes=self.task_config.model.num_classes,
rescale_predictions=False,
dtype=tf.float32))
if self.task_config.model.get('mask_scoring_head'):
metrics.append(
tf_keras.metrics.MeanSquaredError(name='mask_scores_mse'))
if not training:
self.iou_metric = segmentation_metrics.PerClassIoU(
name='per_class_iou',
num_classes=self.task_config.model.num_classes,
rescale_predictions=(
not self.task_config.validation_data.resize_eval_groundtruth),
dtype=tf.float32)
if (self.task_config.validation_data.resize_eval_groundtruth and
self.task_config.model.get('mask_scoring_head')):
# Masks scores metric can only be computed if labels are scaled to match
# preticted mask scores.
metrics.append(
tf_keras.metrics.MeanSquaredError(name='mask_scores_mse'))
return metrics
def train_step(self,
inputs: Tuple[Any, Any],
model: tf_keras.Model,
optimizer: tf_keras.optimizers.Optimizer,
metrics: Optional[List[Any]] = None):
"""Does forward and backward.
Args:
inputs: a dictionary of input tensors.
model: the model, forward pass definition.
optimizer: the optimizer for this training step.
metrics: a nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
features, labels = inputs
input_partition_dims = self.task_config.train_input_partition_dims
if input_partition_dims:
strategy = tf.distribute.get_strategy()
features = strategy.experimental_split_to_logical_devices(
features, input_partition_dims)
num_replicas = tf.distribute.get_strategy().num_replicas_in_sync
with tf.GradientTape() as tape:
outputs = model(features, training=True)
if isinstance(outputs, tf.Tensor):
outputs = {'logits': outputs}
# 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)
# Computes per-replica loss.
loss = self.build_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):
scaled_loss = optimizer.get_scaled_loss(scaled_loss)
tvars = model.trainable_variables
grads = tape.gradient(scaled_loss, tvars)
# Scales back gradient before apply_gradients when LossScaleOptimizer is
# used.
if isinstance(optimizer, tf_keras.mixed_precision.LossScaleOptimizer):
grads = optimizer.get_unscaled_gradients(grads)
optimizer.apply_gradients(list(zip(grads, tvars)))
logs = {self.loss: loss}
if metrics:
self.process_metrics(metrics, labels, outputs)
logs.update({m.name: m.result() for m in metrics})
return logs
def validation_step(self,
inputs: Tuple[Any, Any],
model: tf_keras.Model,
metrics: Optional[List[Any]] = None):
"""Validatation step.
Args:
inputs: a dictionary of input tensors.
model: the keras.Model.
metrics: a nested structure of metrics objects.
Returns:
A dictionary of logs.
"""
features, labels = inputs
input_partition_dims = self.task_config.eval_input_partition_dims
if input_partition_dims:
strategy = tf.distribute.get_strategy()
features = strategy.experimental_split_to_logical_devices(
features, input_partition_dims)
outputs = self.inference_step(features, model)
if isinstance(outputs, tf.Tensor):
outputs = {'logits': outputs}
outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
if self.task_config.validation_data.resize_eval_groundtruth:
loss = self.build_losses(
model_outputs=outputs, labels=labels, aux_losses=model.losses)
else:
loss = 0
logs = {self.loss: loss}
if self.iou_metric is not None:
self.iou_metric.update_state(labels, outputs['logits'])
if metrics:
self.process_metrics(metrics, labels, outputs)
if (
hasattr(self.task_config, 'allow_image_summary')
and self.task_config.allow_image_summary
):
logs.update(
{'visualization': (tf.cast(features, dtype=tf.float32), outputs)}
)
return logs
def inference_step(self, inputs: tf.Tensor, model: tf_keras.Model):
"""Performs the forward step."""
return model(inputs, training=False)
def aggregate_logs(self, state=None, step_outputs=None):
if state is None and self.iou_metric is not None:
self.iou_metric.reset_states()
if 'visualization' in step_outputs:
# Update segmentation state for writing summary if there are artifacts for
# visualization.
if state is None:
state = {}
state.update(visualization_utils.update_segmentation_state(step_outputs))
if state is None:
# Create an arbitrary state to indicate it's not the first step in the
# following calls to this function.
state = True
return state
def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
logs = {}
if self.iou_metric is not None:
ious = self.iou_metric.result()
# TODO(arashwan): support loading class name from a label map file.
if self.task_config.evaluation.report_per_class_iou:
for i, value in enumerate(ious.numpy()):
logs.update({'iou/{}'.format(i): value})
# Computes mean IoU
logs.update({'mean_iou': tf.reduce_mean(ious)})
# Add visualization for summary.
if isinstance(aggregated_logs, dict) and 'image' in aggregated_logs:
validation_outputs = visualization_utils.visualize_segmentation_outputs(
logs=aggregated_logs, task_config=self.task_config
)
logs.update(validation_outputs)
return logs