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#
# 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.
"""Text classification task with ViT."""
import dataclasses
from typing import Tuple
import numpy as np
from scipy import stats
from sklearn import metrics as sklearn_metrics
import tensorflow as tf, tf_keras
from official.core import base_task
from official.core import config_definitions as cfg
from official.core import task_factory
from official.modeling import tf_utils
from official.modeling.hyperparams import base_config
from official.nlp.data import data_loader_factory
from official.projects.pixel.modeling import pixel
@dataclasses.dataclass
class PixelModelConfig(base_config.Config):
"""The model configuration."""
filters: int = 768
num_layers: int = 12
mlp_dim: int = 3072
num_heads: int = 12
dropout_rate: float = 0.1
attention_dropout_rate: float = 0.1
init_stochastic_depth_rate: float = 0.0
@dataclasses.dataclass
class PixelConfig(cfg.TaskConfig):
"""The task configuration."""
train_data: cfg.DataConfig = cfg.DataConfig()
validation_data: cfg.DataConfig = cfg.DataConfig()
patch_h: int = 16
patch_w: int = 16
num_classes: int = 2
num_channels: int = 3
input_size: Tuple[int, int] = (16, 4096)
model: PixelModelConfig = PixelModelConfig()
@task_factory.register_task_cls(PixelConfig)
class PixelClassificationTask(base_task.Task):
"""Text classificaiton with Pixel and load checkpoint if exists."""
label_field: str = 'label'
metric_type: str = 'accuracy'
def build_model(self) -> tf_keras.Model:
encoder = pixel.VisionTransformer(
self.task_config.patch_h,
self.task_config.patch_w,
self.task_config.model.filters,
self.task_config.model.num_layers,
self.task_config.model.mlp_dim,
self.task_config.model.num_heads,
self.task_config.model.dropout_rate,
self.task_config.model.attention_dropout_rate,
self.task_config.model.init_stochastic_depth_rate,
)
model = pixel.PixelLinearClassifier(
encoder, self.task_config.num_classes, self.task_config.model.filters
)
h, w = self.task_config.input_size
positions = h // self.task_config.patch_h * w // self.task_config.patch_w
model({
'label': tf.zeros((1,)),
'pixel_values': tf.zeros((1, self.task_config.num_channels, h, w)),
'attention_mask': tf.zeros((1, positions)),
})
return model
def build_inputs(self, params, input_context=None):
return data_loader_factory.get_data_loader(params).load(input_context)
def build_losses(self, labels, model_outputs, aux_losses=None) -> tf.Tensor:
label_ids = labels[self.label_field]
if self.task_config.num_classes == 1:
loss = tf_keras.losses.mean_squared_error(label_ids, model_outputs)
else:
loss = tf_keras.losses.sparse_categorical_crossentropy(
label_ids, tf.cast(model_outputs, tf.float32), from_logits=True
)
if aux_losses:
loss += tf.add_n(aux_losses)
return tf_utils.safe_mean(loss)
def initialize(self, model: tf_keras.Model):
"""Load encoder if checkpoint exists.
Args:
model: The keras.Model built or used by this task.
"""
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)
if not ckpt_dir_or_file:
return
ckpt = tf.train.Checkpoint(encoder=model.encoder)
status = ckpt.read(ckpt_dir_or_file)
status.expect_partial().assert_existing_objects_matched()
def build_metrics(self, training=None):
del training
if self.task_config.num_classes == 1:
metrics = [tf_keras.metrics.MeanSquaredError()]
elif self.task_config.num_classes == 2:
metrics = [
tf_keras.metrics.SparseCategoricalAccuracy(name='cls_accuracy'),
tf_keras.metrics.AUC(name='auc', curve='PR'),
]
else:
metrics = [
tf_keras.metrics.SparseCategoricalAccuracy(name='cls_accuracy'),
]
return metrics
def process_metrics(self, metrics, labels, model_outputs):
for metric in metrics:
if metric.name == 'auc':
# Convert the logit to probability and extract the probability of True..
metric.update_state(
labels[self.label_field],
tf.expand_dims(tf.nn.softmax(model_outputs)[:, 1], axis=1),
)
if metric.name == 'cls_accuracy':
metric.update_state(labels[self.label_field], model_outputs)
def process_compiled_metrics(self, compiled_metrics, labels, model_outputs):
compiled_metrics.update_state(labels[self.label_field], model_outputs)
def validation_step(self, inputs, model: tf_keras.Model, metrics=None):
features, labels = inputs, inputs
outputs = self.inference_step(features, model)
loss = self.build_losses(
labels=labels, model_outputs=outputs, aux_losses=model.losses
)
logs = {self.loss: loss}
if metrics:
self.process_metrics(metrics, labels, outputs)
if model.compiled_metrics:
self.process_compiled_metrics(model.compiled_metrics, labels, outputs)
logs.update({m.name: m.result() for m in metrics or []})
logs.update({m.name: m.result() for m in model.metrics})
if self.metric_type == 'matthews_corrcoef':
logs.update({
'sentence_prediction': (
tf.expand_dims( # Ensure one prediction along batch dimension.
tf.math.argmax(outputs, axis=1), axis=1
)
),
'labels': labels[self.label_field],
})
else:
logs.update({
'sentence_prediction': outputs,
'labels': labels[self.label_field],
})
return logs
def aggregate_logs(self, state=None, step_outputs=None):
if self.metric_type == 'accuracy':
return None
if state is None:
state = {'sentence_prediction': [], 'labels': []}
state['sentence_prediction'].append(
np.concatenate(
[v.numpy() for v in step_outputs['sentence_prediction']], axis=0
)
)
state['labels'].append(
np.concatenate([v.numpy() for v in step_outputs['labels']], axis=0)
)
return state
def reduce_aggregated_logs(self, aggregated_logs, global_step=None):
if self.metric_type == 'accuracy':
return None
preds = np.concatenate(aggregated_logs['sentence_prediction'], axis=0)
labels = np.concatenate(aggregated_logs['labels'], axis=0)
if self.metric_type == 'f1':
preds = np.argmax(preds, axis=1)
return {self.metric_type: sklearn_metrics.f1_score(labels, preds)}
elif self.metric_type == 'matthews_corrcoef':
preds = np.reshape(preds, -1)
labels = np.reshape(labels, -1)
return {
self.metric_type: sklearn_metrics.matthews_corrcoef(preds, labels)
}
elif self.metric_type == 'pearson_spearman_corr':
preds = np.reshape(preds, -1)
labels = np.reshape(labels, -1)
pearson_corr = stats.pearsonr(preds, labels)[0]
spearman_corr = stats.spearmanr(preds, labels)[0]
corr_metric = (pearson_corr + spearman_corr) / 2
return {self.metric_type: corr_metric}
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