official/projects/simclr/tasks/simclr.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,
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# See the License for the specific language governing permissions and
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"""Image SimCLR task definition.
SimCLR training two different modes:
- pretrain
- fine-tuning
For the above two different modes, the following components are different in
the task definition:
- training data format
- training loss
- projection_head and/or supervised_head
"""
from typing import Dict, Optional
from absl import logging
import tensorflow as tf, tf_keras
from official.core import base_task
from official.core import config_definitions
from official.core import input_reader
from official.core import task_factory
from official.modeling import optimization
from official.modeling import performance
from official.modeling import tf_utils
from official.projects.simclr.configs import simclr as exp_cfg
from official.projects.simclr.dataloaders import simclr_input
from official.projects.simclr.heads import simclr_head
from official.projects.simclr.losses import contrastive_losses
from official.projects.simclr.modeling import simclr_model
from official.vision.modeling import backbones
OptimizationConfig = optimization.OptimizationConfig
RuntimeConfig = config_definitions.RuntimeConfig
@task_factory.register_task_cls(exp_cfg.SimCLRPretrainTask)
class SimCLRPretrainTask(base_task.Task):
"""A task for image classification."""
def create_optimizer(self,
optimizer_config: OptimizationConfig,
runtime_config: Optional[RuntimeConfig] = None):
"""Creates an TF optimizer from configurations.
Args:
optimizer_config: the parameters of the Optimization settings.
runtime_config: the parameters of the runtime.
Returns:
A tf.optimizers.Optimizer object.
"""
if (optimizer_config.optimizer.type == 'lars' and
self.task_config.loss.l2_weight_decay > 0.0):
raise ValueError('The l2_weight_decay cannot be used together with lars '
'optimizer. Please set it to 0.')
opt_factory = optimization.OptimizerFactory(optimizer_config)
optimizer = opt_factory.build_optimizer(opt_factory.build_learning_rate())
# Configuring optimizer when loss_scale is set in runtime config. This helps
# avoiding overflow/underflow for float16 computations.
if runtime_config and runtime_config.loss_scale:
optimizer = performance.configure_optimizer(
optimizer,
use_float16=runtime_config.mixed_precision_dtype == 'float16',
loss_scale=runtime_config.loss_scale)
return optimizer
def build_model(self):
model_config = self.task_config.model
input_specs = tf_keras.layers.InputSpec(shape=[None] +
model_config.input_size)
l2_weight_decay = self.task_config.loss.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)
# Build backbone
backbone = backbones.factory.build_backbone(
input_specs=input_specs,
backbone_config=model_config.backbone,
norm_activation_config=model_config.norm_activation,
l2_regularizer=l2_regularizer)
# Build projection head
norm_activation_config = model_config.norm_activation
projection_head_config = model_config.projection_head
projection_head = simclr_head.ProjectionHead(
proj_output_dim=projection_head_config.proj_output_dim,
num_proj_layers=projection_head_config.num_proj_layers,
ft_proj_idx=projection_head_config.ft_proj_idx,
kernel_regularizer=l2_regularizer,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon)
# Build supervised head
supervised_head_config = model_config.supervised_head
if supervised_head_config:
if supervised_head_config.zero_init:
s_kernel_initializer = 'zeros'
else:
s_kernel_initializer = 'random_uniform'
supervised_head = simclr_head.ClassificationHead(
num_classes=supervised_head_config.num_classes,
kernel_initializer=s_kernel_initializer,
kernel_regularizer=l2_regularizer)
else:
supervised_head = None
model = simclr_model.SimCLRModel(
input_specs=input_specs,
backbone=backbone,
projection_head=projection_head,
supervised_head=supervised_head,
mode=model_config.mode,
backbone_trainable=model_config.backbone_trainable)
logging.info(model.get_config())
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
if tf.io.gfile.isdir(ckpt_dir_or_file):
ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
# Restoring checkpoint.
if self.task_config.init_checkpoint_modules == 'all':
ckpt = tf.train.Checkpoint(**model.checkpoint_items)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
elif self.task_config.init_checkpoint_modules == 'backbone':
ckpt = tf.train.Checkpoint(backbone=model.backbone)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
else:
raise ValueError(
"Only 'all' or 'backbone' can be used to initialize the model.")
logging.info('Finished loading pretrained checkpoint from %s',
ckpt_dir_or_file)
def build_inputs(self, params, input_context=None):
input_size = self.task_config.model.input_size
if params.tfds_name:
decoder = simclr_input.TFDSDecoder(params.decoder.decode_label)
else:
decoder = simclr_input.Decoder(params.decoder.decode_label)
parser = simclr_input.Parser(
output_size=input_size[:2],
aug_rand_crop=params.parser.aug_rand_crop,
aug_rand_hflip=params.parser.aug_rand_hflip,
aug_color_distort=params.parser.aug_color_distort,
aug_color_jitter_strength=params.parser.aug_color_jitter_strength,
aug_color_jitter_impl=params.parser.aug_color_jitter_impl,
aug_rand_blur=params.parser.aug_rand_blur,
parse_label=params.parser.parse_label,
test_crop=params.parser.test_crop,
mode=params.parser.mode,
dtype=params.dtype)
reader = input_reader.InputReader(
params,
dataset_fn=tf.data.TFRecordDataset,
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read(input_context=input_context)
return dataset
def build_losses(self,
labels,
model_outputs,
aux_losses=None) -> Dict[str, tf.Tensor]:
# Compute contrastive relative loss
con_losses_obj = contrastive_losses.ContrastiveLoss(
projection_norm=self.task_config.loss.projection_norm,
temperature=self.task_config.loss.temperature)
# The projection outputs from model has the size of
# (2 * bsz, project_dim)
projection_outputs = model_outputs[simclr_model.PROJECTION_OUTPUT_KEY]
projection1, projection2 = tf.split(projection_outputs, 2, 0)
contrast_loss, (contrast_logits, contrast_labels) = con_losses_obj(
projection1=projection1, projection2=projection2)
contrast_accuracy = tf.equal(
tf.argmax(contrast_labels, axis=1), tf.argmax(contrast_logits, axis=1))
contrast_accuracy = tf.reduce_mean(tf.cast(contrast_accuracy, tf.float32))
contrast_prob = tf.nn.softmax(contrast_logits)
contrast_entropy = -tf.reduce_mean(
tf.reduce_sum(contrast_prob * tf.math.log(contrast_prob + 1e-8), -1))
model_loss = contrast_loss
losses = {
'contrast_loss': contrast_loss,
'contrast_accuracy': contrast_accuracy,
'contrast_entropy': contrast_entropy
}
if self.task_config.model.supervised_head is not None:
outputs = model_outputs[simclr_model.SUPERVISED_OUTPUT_KEY]
labels = tf.concat([labels, labels], 0)
if self.task_config.evaluation.one_hot:
sup_loss = tf_keras.losses.CategoricalCrossentropy(
from_logits=True, reduction=tf_keras.losses.Reduction.NONE)(labels,
outputs)
else:
sup_loss = tf_keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf_keras.losses.Reduction.NONE)(labels,
outputs)
sup_loss = tf.reduce_mean(sup_loss)
label_acc = tf.equal(
tf.argmax(labels, axis=1), tf.argmax(outputs, axis=1))
label_acc = tf.reduce_mean(tf.cast(label_acc, tf.float32))
model_loss = contrast_loss + sup_loss
losses.update({
'accuracy': label_acc,
'supervised_loss': sup_loss,
})
total_loss = model_loss
if aux_losses:
reg_loss = tf.reduce_sum(aux_losses)
total_loss = model_loss + reg_loss
losses['total_loss'] = total_loss
return losses
def build_metrics(self, training=True):
if training:
metrics = []
metric_names = [
'total_loss', 'contrast_loss', 'contrast_accuracy', 'contrast_entropy'
]
if self.task_config.model.supervised_head:
metric_names.extend(['supervised_loss', 'accuracy'])
for name in metric_names:
metrics.append(tf_keras.metrics.Mean(name, dtype=tf.float32))
else:
k = self.task_config.evaluation.top_k
if self.task_config.evaluation.one_hot:
metrics = [
tf_keras.metrics.CategoricalAccuracy(name='accuracy'),
tf_keras.metrics.TopKCategoricalAccuracy(
k=k, name='top_{}_accuracy'.format(k))
]
else:
metrics = [
tf_keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
tf_keras.metrics.SparseTopKCategoricalAccuracy(
k=k, name='top_{}_accuracy'.format(k))
]
return metrics
def train_step(self, inputs, model, optimizer, metrics=None):
features, labels = inputs
# To do a sanity check that we absolutely use no labels when pretraining, we
# can set the labels here to zero.
if self.task_config.train_data.input_set_label_to_zero:
labels *= 0
if (self.task_config.model.supervised_head is not None and
self.task_config.evaluation.one_hot):
num_classes = self.task_config.model.supervised_head.num_classes
labels = tf.one_hot(labels, num_classes)
num_replicas = tf.distribute.get_strategy().num_replicas_in_sync
with tf.GradientTape() as tape:
outputs = model(features, training=True)
# 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.
losses = self.build_losses(
model_outputs=outputs, labels=labels, aux_losses=model.losses)
scaled_loss = losses['total_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
logging.info('Trainable variables:')
for var in tvars:
logging.info(var.name)
grads = tape.gradient(scaled_loss, tvars)
# Scales back gradient 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: losses['total_loss']}
for m in metrics:
m.update_state(losses[m.name])
logs.update({m.name: m.result()})
return logs
def validation_step(self, inputs, model, metrics=None):
if self.task_config.model.supervised_head is None:
raise ValueError(
'Skipping eval during pretraining without supervised head.')
features, labels = inputs
if self.task_config.evaluation.one_hot:
num_classes = self.task_config.model.supervised_head.num_classes
labels = tf.one_hot(labels, num_classes)
outputs = model(
features, training=False)[simclr_model.SUPERVISED_OUTPUT_KEY]
outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
logs = {self.loss: 0}
if metrics:
self.process_metrics(metrics, labels, outputs)
logs.update({m.name: m.result() for m in metrics})
elif model.compiled_metrics:
self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
logs.update({m.name: m.result() for m in model.metrics})
return logs
@task_factory.register_task_cls(exp_cfg.SimCLRFinetuneTask)
class SimCLRFinetuneTask(base_task.Task):
"""A task for image classification."""
def create_optimizer(self,
optimizer_config: OptimizationConfig,
runtime_config: Optional[RuntimeConfig] = None):
"""Creates an TF optimizer from configurations.
Args:
optimizer_config: the parameters of the Optimization settings.
runtime_config: the parameters of the runtime.
Returns:
A tf.optimizers.Optimizer object.
"""
if (optimizer_config.optimizer.type == 'lars' and
self.task_config.loss.l2_weight_decay > 0.0):
raise ValueError('The l2_weight_decay cannot be used together with lars '
'optimizer. Please set it to 0.')
opt_factory = optimization.OptimizerFactory(optimizer_config)
optimizer = opt_factory.build_optimizer(opt_factory.build_learning_rate())
# Configuring optimizer when loss_scale is set in runtime config. This helps
# avoiding overflow/underflow for float16 computations.
if runtime_config and runtime_config.loss_scale:
optimizer = performance.configure_optimizer(
optimizer,
use_float16=runtime_config.mixed_precision_dtype == 'float16',
loss_scale=runtime_config.loss_scale)
return optimizer
def build_model(self):
model_config = self.task_config.model
input_specs = tf_keras.layers.InputSpec(shape=[None] +
model_config.input_size)
l2_weight_decay = self.task_config.loss.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)
backbone = backbones.factory.build_backbone(
input_specs=input_specs,
backbone_config=model_config.backbone,
norm_activation_config=model_config.norm_activation,
l2_regularizer=l2_regularizer)
norm_activation_config = model_config.norm_activation
projection_head_config = model_config.projection_head
projection_head = simclr_head.ProjectionHead(
proj_output_dim=projection_head_config.proj_output_dim,
num_proj_layers=projection_head_config.num_proj_layers,
ft_proj_idx=projection_head_config.ft_proj_idx,
kernel_regularizer=l2_regularizer,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon)
supervised_head_config = model_config.supervised_head
if supervised_head_config.zero_init:
s_kernel_initializer = 'zeros'
else:
s_kernel_initializer = 'random_uniform'
supervised_head = simclr_head.ClassificationHead(
num_classes=supervised_head_config.num_classes,
kernel_initializer=s_kernel_initializer,
kernel_regularizer=l2_regularizer)
model = simclr_model.SimCLRModel(
input_specs=input_specs,
backbone=backbone,
projection_head=projection_head,
supervised_head=supervised_head,
mode=model_config.mode,
backbone_trainable=model_config.backbone_trainable)
logging.info(model.get_config())
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
if tf.io.gfile.isdir(ckpt_dir_or_file):
ckpt_dir_or_file = tf.train.latest_checkpoint(ckpt_dir_or_file)
# Restoring checkpoint.
if self.task_config.init_checkpoint_modules == 'all':
ckpt = tf.train.Checkpoint(**model.checkpoint_items)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
elif self.task_config.init_checkpoint_modules == 'backbone_projection':
ckpt = tf.train.Checkpoint(
backbone=model.backbone, projection_head=model.projection_head)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
elif self.task_config.init_checkpoint_modules == 'backbone':
ckpt = tf.train.Checkpoint(backbone=model.backbone)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
else:
raise ValueError(
"Only 'all' or 'backbone' can be used to initialize the model.")
# If the checkpoint is from pretraining, reset the following parameters
model.backbone_trainable = self.task_config.model.backbone_trainable
logging.info('Finished loading pretrained checkpoint from %s',
ckpt_dir_or_file)
def build_inputs(self, params, input_context=None):
input_size = self.task_config.model.input_size
if params.tfds_name:
decoder = simclr_input.TFDSDecoder(params.decoder.decode_label)
else:
decoder = simclr_input.Decoder(params.decoder.decode_label)
parser = simclr_input.Parser(
output_size=input_size[:2],
parse_label=params.parser.parse_label,
test_crop=params.parser.test_crop,
mode=params.parser.mode,
dtype=params.dtype)
reader = input_reader.InputReader(
params,
dataset_fn=tf.data.TFRecordDataset,
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read(input_context=input_context)
return dataset
def build_losses(self, labels, model_outputs, aux_losses=None):
"""Sparse categorical cross entropy 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.
"""
losses_config = self.task_config.loss
if losses_config.one_hot:
total_loss = tf_keras.losses.categorical_crossentropy(
labels,
model_outputs,
from_logits=True,
label_smoothing=losses_config.label_smoothing)
else:
total_loss = tf_keras.losses.sparse_categorical_crossentropy(
labels, model_outputs, from_logits=True)
total_loss = tf_utils.safe_mean(total_loss)
if aux_losses:
total_loss += tf.add_n(aux_losses)
return total_loss
def build_metrics(self, training=True):
"""Gets streaming metrics for training/validation."""
k = self.task_config.evaluation.top_k
if self.task_config.evaluation.one_hot:
metrics = [
tf_keras.metrics.CategoricalAccuracy(name='accuracy'),
tf_keras.metrics.TopKCategoricalAccuracy(
k=k, name='top_{}_accuracy'.format(k))
]
else:
metrics = [
tf_keras.metrics.SparseCategoricalAccuracy(name='accuracy'),
tf_keras.metrics.SparseTopKCategoricalAccuracy(
k=k, name='top_{}_accuracy'.format(k))
]
return metrics
def train_step(self, inputs, model, optimizer, metrics=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
if self.task_config.loss.one_hot:
num_classes = self.task_config.model.supervised_head.num_classes
labels = tf.one_hot(labels, num_classes)
num_replicas = tf.distribute.get_strategy().num_replicas_in_sync
with tf.GradientTape() as tape:
outputs = model(
features, training=True)[simclr_model.SUPERVISED_OUTPUT_KEY]
# 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
logging.info('Trainable variables:')
for var in tvars:
logging.info(var.name)
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})
elif model.compiled_metrics:
self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
logs.update({m.name: m.result() for m in model.metrics})
return logs
def validation_step(self, inputs, model, metrics=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
if self.task_config.loss.one_hot:
num_classes = self.task_config.model.supervised_head.num_classes
labels = tf.one_hot(labels, num_classes)
outputs = self.inference_step(features,
model)[simclr_model.SUPERVISED_OUTPUT_KEY]
outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
loss = self.build_losses(
model_outputs=outputs, labels=labels, aux_losses=model.losses)
logs = {self.loss: loss}
if metrics:
self.process_metrics(metrics, labels, outputs)
logs.update({m.name: m.result() for m in metrics})
elif model.compiled_metrics:
self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
logs.update({m.name: m.result() for m in model.metrics})
return logs