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"""Model definitions"""
import tensorflow as tf
from transformers import TFAutoModel, TFViTModel
from kapre.augmentation import SpecAugment
class FixMatchTune(tf.keras.Model):
"""fixmatch"""
def __init__(
self,
encoder_name="readerbench/RoBERT-base",
num_classes=4,
**kwargs
):
super(FixMatchTune,self).__init__(**kwargs)
self.bert = TFAutoModel.from_pretrained(encoder_name)
self.num_classes = num_classes
self.weak_augment = tf.keras.layers.GaussianNoise(stddev=0.5)
self.strong_augment = tf.keras.layers.GaussianNoise(stddev=5)
self.cls_head = tf.keras.Sequential([
tf.keras.layers.Dense(256,activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(self.num_classes, activation="softmax")
])
def call(self, inputs, training):
ids, mask = inputs
embeds = self.bert(input_ids=ids, attention_mask=mask,training=training).pooler_output
strongs = self.strong_augment(embeds,training=training)
weaks = self.weak_augment(embeds,training=training)
strong_preds = self.cls_head(strongs,training=training)
weak_preds = self.cls_head(weaks,training=training)
return weak_preds, strong_preds
class MixMatch(tf.keras.Model):
"""mixmatch"""
def __init__(self,bert_model="readerbench/RoBERT-base",num_classes=4,**kwargs):
super(MixMatch,self).__init__(**kwargs)
self.bert = TFAutoModel.from_pretrained(bert_model)
self.num_classes = num_classes
self.cls_head = tf.keras.Sequential([
tf.keras.layers.Dense(256,activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(self.num_classes, activation="softmax")
])
self.augment = tf.keras.layers.GaussianNoise(stddev=2)
def call(self, inputs, training):
ids, mask = inputs
embeds = self.bert(input_ids=ids, attention_mask=mask,training=training).pooler_output
augs = self.augment(embeds,training=training)
return self.cls_head(augs,training=training)
class LPModel(tf.keras.Model):
"""label propagation"""
def __init__(self,bert_model="readerbench/RoBERT-base",num_classes=4,**kwargs):
super(LPModel,self).__init__(**kwargs)
self.bert = TFAutoModel.from_pretrained(bert_model)
self.num_classes = num_classes
self.cls_head = tf.keras.Sequential([
tf.keras.layers.Dense(256,activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(self.num_classes, activation="softmax")
])
def call(self, inputs, training):
ids, mask = inputs
embeds = self.bert(input_ids=ids, attention_mask=mask,training=training).pooler_output
return self.cls_head(embeds, training=training)
class AudioFixMatch(tf.keras.Model):
def __init__(self, encoder_name='google/vit-base-patch16-224', num_classes=6, **kwargs):
super(AudioFixMatch, self).__init__(**kwargs)
self.vit = TFViTModel.from_pretrained(encoder_name)
self.num_classes = num_classes
self.cls_head = tf.keras.Sequential([
tf.keras.layers.Dense(256,activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(self.num_classes, activation="softmax")
])
self.strong_augment = SpecAugment(
freq_mask_param=8,
time_mask_param=8,
n_freq_masks=2,
n_time_masks=2,
mask_value=0.0,
data_format="channels_first"
)
self.weak_augment = SpecAugment(
freq_mask_param=2,
time_mask_param=2,
n_freq_masks=2,
n_time_masks=2,
mask_value=0.0,
data_format="channels_first"
)
def call(self, inputs, training):
strong = self.strong_augment(inputs[:,0,:,:][:,tf.newaxis,:,:],training=training)
weak = self.weak_augment(inputs[:,0,:,:][:,tf.newaxis,:,:],training=training)
embeds_strong = self.vit(pixel_values=tf.repeat(strong,3,axis=1),training=training).pooler_output
embeds_weak = self.vit(pixel_values=tf.repeat(weak,3,axis=1),training=training).pooler_output
return self.cls_head(embeds_weak), self.cls_head(embeds_strong)
class AudioMixMatch(tf.keras.Model):
def __init__(self, encoder_name='google/vit-base-patch16-224', num_classes=6, **kwargs):
super(AudioMixMatch, self).__init__(**kwargs)
self.vit = TFViTModel.from_pretrained(encoder_name)
self.num_classes = num_classes
self.cls_head = tf.keras.Sequential([
tf.keras.layers.Dense(256,activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(64,activation="relu"),
tf.keras.layers.Dense(self.num_classes, activation="softmax")
])
self.augment = SpecAugment(
freq_mask_param=3,
time_mask_param=3,
n_freq_masks=2,
n_time_masks=2,
mask_value=0.0,
data_format="channels_first"
)
def aug_features(self, inputs, training):
aug = self.augment(inputs[:,0,:,:][:,tf.newaxis,:,:],training=training)
embeds = self.vit(pixel_values=tf.repeat(aug,3,axis=1),training=training).pooler_output
return embeds
def call(self, inputs, training):
aug = self.augment(inputs[:,0,:,:][:,tf.newaxis,:,:],training=training)
embeds = self.vit(pixel_values=tf.repeat(aug,3,axis=1),training=training).pooler_output
return self.cls_head(embeds) |