# coding=utf-8 # Copyright 2022 Meta Platforms and The HuggingFace Inc. team. 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. """TF 2.0 Data2Vec Vision model.""" from __future__ import annotations import collections.abc import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import tensorflow as tf from ...activations_tf import get_tf_activation from ...modeling_tf_outputs import ( TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSemanticSegmenterOutput, TFSequenceClassifierOutput, ) from ...modeling_tf_utils import ( TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras, keras_serializable, unpack_inputs, ) from ...tf_utils import shape_list, stable_softmax from ...utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from .configuration_data2vec_vision import Data2VecVisionConfig logger = logging.get_logger(__name__) # General docstring _CONFIG_FOR_DOC = "Data2VecVisionConfig" # Base docstring _CHECKPOINT_FOR_DOC = "facebook/data2vec-vision-base" _EXPECTED_OUTPUT_SHAPE = [1, 197, 768] # Image classification docstring _IMAGE_CLASS_CHECKPOINT = "facebook/data2vec-vision-base-ft1k" _IMAGE_CLASS_EXPECTED_OUTPUT = "remote control, remote" @dataclass class TFData2VecVisionModelOutputWithPooling(TFBaseModelOutputWithPooling): """ Class for outputs of [`TFData2VecVisionModel`]. Args: last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`): Sequence of hidden-states at the output of the last layer of the model. pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`): Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token will be returned. hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `tf.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. """ last_hidden_state: Optional[tf.Tensor] = None pooler_output: Optional[tf.Tensor] = None hidden_states: Tuple[tf.Tensor] | None = None attentions: Tuple[tf.Tensor] | None = None class TFData2VecVisionDropPath(keras.layers.Layer): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). References: (1) github.com:rwightman/pytorch-image-models """ def __init__(self, drop_path, **kwargs): super().__init__(**kwargs) self.drop_path = drop_path def call(self, x, training=None): if training: keep_prob = 1 - self.drop_path shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1) random_tensor = keep_prob + tf.random.uniform(shape, 0, 1) random_tensor = tf.floor(random_tensor) return (x / keep_prob) * random_tensor return x class TFData2VecVisionEmbeddings(keras.layers.Layer): """ Construct the CLS token, position and patch embeddings. Optionally, also the mask token. """ def __init__(self, config: Data2VecVisionConfig, **kwargs): super().__init__(**kwargs) self.config = config self.patch_embeddings = TFData2VecVisionPatchEmbeddings(config, name="patch_embeddings") self.num_patches = self.patch_embeddings.num_patches self.config = config self.dropout = keras.layers.Dropout(config.hidden_dropout_prob) def build(self, input_shape=None): self.cls_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), trainable=True, name="cls_token", ) if self.config.use_mask_token: self.mask_token = self.add_weight( shape=(1, 1, self.config.hidden_size), initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), trainable=True, name="mask_token", ) else: self.mask_token = None if self.config.use_absolute_position_embeddings: self.position_embeddings = self.add_weight( shape=(1, self.num_patches + 1, self.config.hidden_size), initializer=tf.random_normal_initializer(stddev=self.config.initializer_range), trainable=True, name="position_embeddings", ) else: self.position_embeddings = None if self.built: return self.built = True if getattr(self, "patch_embeddings", None) is not None: with tf.name_scope(self.patch_embeddings.name): self.patch_embeddings.build(None) def call(self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None) -> tf.Tensor: embeddings = self.patch_embeddings(pixel_values) batch_size, seq_len, projection_dim = shape_list(embeddings) cls_tokens = tf.tile(self.cls_token, (batch_size, 1, 1)) if bool_masked_pos is not None: mask_tokens = tf.broadcast_to(self.mask_token, (batch_size, seq_len, projection_dim)) # replace the masked visual tokens by mask_tokens w = bool_masked_pos[..., None] w = tf.cast(w, mask_tokens.dtype) # since TF doesn't support eager tensor assignment embeddings = embeddings * (1 - w) + mask_tokens * w embeddings = tf.concat([cls_tokens, embeddings], axis=1) if self.position_embeddings is not None: embeddings = embeddings + self.position_embeddings embeddings = self.dropout(embeddings) return embeddings class TFData2VecVisionPatchEmbeddings(keras.layers.Layer): """ Image to Patch Embedding. """ def __init__(self, config: Data2VecVisionConfig, **kwargs): super().__init__(**kwargs) self.config = config image_size, patch_size = config.image_size, config.patch_size num_channels, hidden_size = config.num_channels, config.hidden_size image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1]) self.image_size = image_size self.patch_size = patch_size self.num_patches = num_patches self.patch_shape = patch_shape self.num_channels = num_channels self.projection = keras.layers.Conv2D( filters=hidden_size, kernel_size=patch_size, strides=patch_size, padding="valid", data_format="channels_last", kernel_initializer="glorot_uniform", # following torch.nn.Linear bias_initializer="zeros", name="projection", ) def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor: batch_size, num_channels, height, width = shape_list(pixel_values) if tf.executing_eagerly(): if num_channels != self.num_channels: raise ValueError( "Make sure that the channel dimension of the pixel values match with the one set in the" " configuration." ) if height != self.image_size[0] or width != self.image_size[1]: raise ValueError( f"Input image size ({height}*{width}) doesn't match model" f" ({self.image_size[0]}*{self.image_size[1]})." ) # When running on CPU, `keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1)) projection = self.projection(pixel_values) # Change the 2D spatial dimensions to a single temporal dimension. # shape = (batch_size, num_patches, out_channels=embed_dim) num_patches = (width // self.patch_size[1]) * (height // self.patch_size[0]) return tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1)) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "projection", None) is not None: with tf.name_scope(self.projection.name): self.projection.build([None, None, None, self.num_channels]) class TFData2VecVisionSelfAttention(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, window_size: Optional[tuple] = None, **kwargs): super().__init__(**kwargs) if config.hidden_size % config.num_attention_heads != 0: raise ValueError( f"The hidden size ({config.hidden_size}) is not a multiple of the number " f"of attention heads ({config.num_attention_heads})" ) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int(config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.sqrt_att_head_size = math.sqrt(self.attention_head_size) self.query = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" ) self.key = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key", use_bias=False, ) self.value = keras.layers.Dense( units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" ) self.dropout = keras.layers.Dropout(rate=config.attention_probs_dropout_prob) if window_size: self.relative_position_bias = TFData2VecVisionRelativePositionBias( config, window_size=window_size, name="relative_position_bias" ) else: self.relative_position_bias = None self.config = config def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] return tf.transpose(tensor, perm=[0, 2, 1, 3]) def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, relative_position_bias: Optional["TFData2VecVisionRelativePositionBias"] = None, training: bool = False, ) -> Tuple[tf.Tensor]: batch_size = shape_list(hidden_states)[0] mixed_query_layer = self.query(inputs=hidden_states) mixed_key_layer = self.key(inputs=hidden_states) mixed_value_layer = self.value(inputs=hidden_states) query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) key_layer = self.transpose_for_scores(mixed_key_layer, batch_size) value_layer = self.transpose_for_scores(mixed_value_layer, batch_size) # Take the dot product between "query" and "key" to get the raw attention scores. # (batch size, num_heads, seq_len_q, seq_len_k) attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) attention_scores = attention_scores / self.sqrt_att_head_size # Add relative position bias if present. if self.relative_position_bias is not None: # Passing `0.0` to the `relative_position_bias()` layer because otherwise Keras # might complain about `Layer.call()` not being invoked properly. In this case this input # i.e., 0.0 is not going to be used in any calculations so we're safe. attention_scores = attention_scores + self.relative_position_bias(0.0)[None, ...] # Add shared relative position bias if provided. if relative_position_bias is not None: attention_scores = attention_scores + relative_position_bias # Normalize the attention scores to probabilities. attention_probs = stable_softmax(logits=attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(inputs=attention_probs, training=training) # Mask heads if we want to if head_mask is not None: attention_probs = tf.multiply(attention_probs, head_mask) attention_output = tf.matmul(attention_probs, value_layer) attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, all_head_size) attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "query", None) is not None: with tf.name_scope(self.query.name): self.query.build([None, None, self.config.hidden_size]) if getattr(self, "key", None) is not None: with tf.name_scope(self.key.name): self.key.build([None, None, self.config.hidden_size]) if getattr(self, "value", None) is not None: with tf.name_scope(self.value.name): self.value.build([None, None, self.config.hidden_size]) if getattr(self, "relative_position_bias", None) is not None: with tf.name_scope(self.relative_position_bias.name): self.relative_position_bias.build(None) class TFData2VecVisionSelfOutput(keras.layers.Layer): """ The residual connection is defined in TFData2VecVisionLayer instead of here (as is the case with other models), due to the layernorm applied before each block. """ def __init__(self, config: Data2VecVisionConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, gamma=None, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) class TFData2VecVisionAttention(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, window_size: Optional[tuple] = None, **kwargs): super().__init__(**kwargs) self.attention = TFData2VecVisionSelfAttention(config, window_size=window_size, name="attention") self.dense_output = TFData2VecVisionSelfOutput(config, name="output") def prune_heads(self, heads): raise NotImplementedError def call( self, input_tensor: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, relative_position_bias: Optional["TFData2VecVisionRelativePositionBias"] = None, training: bool = False, ) -> Tuple[tf.Tensor]: self_outputs = self.attention( hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, relative_position_bias=relative_position_bias, training=training, ) attention_output = self.dense_output( hidden_states=self_outputs[0], input_tensor=input_tensor, training=training ) outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "dense_output", None) is not None: with tf.name_scope(self.dense_output.name): self.dense_output.build(None) # Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->Data2VecVision class TFData2VecVisionIntermediate(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) if isinstance(config.hidden_act, str): self.intermediate_act_fn = get_tf_activation(config.hidden_act) else: self.intermediate_act_fn = config.hidden_act self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.hidden_size]) class TFData2VecVisionOutput(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, **kwargs): super().__init__(**kwargs) self.dense = keras.layers.Dense( units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" ) self.dropout = keras.layers.Dropout(rate=config.hidden_dropout_prob) self.config = config def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: hidden_states = self.dense(inputs=hidden_states) hidden_states = self.dropout(inputs=hidden_states, training=training) return hidden_states def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "dense", None) is not None: with tf.name_scope(self.dense.name): self.dense.build([None, None, self.config.intermediate_size]) class TFData2VecVisionLayer(keras.layers.Layer): """This corresponds to the Block class in the timm implementation.""" def __init__( self, config: Data2VecVisionConfig, window_size: Optional[tuple] = None, drop_path_rate: float = 0.0, **kwargs ): super().__init__(**kwargs) self.config = config self.attention = TFData2VecVisionAttention(config, window_size=window_size, name="attention") self.intermediate = TFData2VecVisionIntermediate(config, name="intermediate") self.data2vec_output = TFData2VecVisionOutput(config, name="output") self.layernorm_before = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_before") self.layernorm_after = keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm_after") # Using `layers.Activation` instead of `tf.identity` to better control `training` # behaviour. self.drop_path = ( TFData2VecVisionDropPath(drop_path_rate, name="drop_path") if drop_path_rate > 0.0 else keras.layers.Activation("linear", name="drop_path") ) self.init_values = config.layer_scale_init_value def build(self, input_shape: tf.TensorShape = None): if self.init_values > 0: self.lambda_1 = self.add_weight( shape=(self.config.hidden_size), initializer="ones", trainable=True, name="lambda_1", ) self.lambda_2 = self.add_weight( shape=(self.config.hidden_size), initializer="ones", trainable=True, name="lambda_2", ) self.lambda_1.assign(self.init_values * tf.ones((self.config.hidden_size))) self.lambda_2.assign(self.init_values * tf.ones((self.config.hidden_size))) else: self.lambda_1, self.lambda_2 = None, None if self.built: return self.built = True if getattr(self, "attention", None) is not None: with tf.name_scope(self.attention.name): self.attention.build(None) if getattr(self, "intermediate", None) is not None: with tf.name_scope(self.intermediate.name): self.intermediate.build(None) if getattr(self, "data2vec_output", None) is not None: with tf.name_scope(self.data2vec_output.name): self.data2vec_output.build(None) if getattr(self, "layernorm_before", None) is not None: with tf.name_scope(self.layernorm_before.name): self.layernorm_before.build([None, None, self.config.hidden_size]) if getattr(self, "layernorm_after", None) is not None: with tf.name_scope(self.layernorm_after.name): self.layernorm_after.build([None, None, self.config.hidden_size]) if getattr(self, "drop_path", None) is not None: with tf.name_scope(self.drop_path.name): self.drop_path.build(None) def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor, output_attentions: bool, relative_position_bias: Optional["TFData2VecVisionRelativePositionBias"] = None, training: bool = False, ) -> Tuple[tf.Tensor]: self_attention_outputs = self.attention( # in Data2VecVision, layernorm is applied before self-attention input_tensor=self.layernorm_before(inputs=hidden_states), head_mask=head_mask, output_attentions=output_attentions, relative_position_bias=relative_position_bias, training=training, ) attention_output = self_attention_outputs[0] outputs = self_attention_outputs[1:] # add self attentions if we output attention weights # apply lambda_1 if present if self.lambda_1 is not None: attention_output = self.lambda_1 * attention_output # first residual connection hidden_states = self.drop_path(attention_output) + hidden_states # in Data2VecVision, layernorm is also applied after self-attention layer_output = self.layernorm_after(hidden_states) layer_output = self.intermediate(layer_output) layer_output = self.data2vec_output(layer_output) if self.lambda_2 is not None: layer_output = self.lambda_2 * layer_output # second residual connection layer_output = self.drop_path(layer_output) + hidden_states outputs = (layer_output,) + outputs return outputs # Taken and modified from here: # https://github.com/leondgarse/keras_cv_attention_models/blob/main/keras_cv_attention_models/beit/beit.py#L28 class TFData2VecVisionRelativePositionBias(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, window_size: tuple, **kwargs) -> None: super().__init__(**kwargs) self.config = config self.window_size = window_size # +3 for cls_token_pos_len # window_size can be something like (14, 14) self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 self.relative_position_index = self.get_position_index() def build(self, input_shape): self.relative_position_bias_table = self.add_weight( shape=(self.num_relative_distance, self.config.num_attention_heads), initializer="zeros", trainable=True, name="relative_position_bias_table", ) # [2*Wh-1 * 2*Ww-1, nH] # cls to token & token 2 cls & cls to cls super().build(input_shape) def get_position_index(self): # get pair-wise relative position index for each token inside the window xx, yy = tf.meshgrid(range(self.window_size[0]), range(self.window_size[1])) coords = tf.stack([yy, xx], axis=0) # [2, Wh, Ww] coords_flatten = tf.reshape(coords, [2, -1]) # [2, Wh*Ww] relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # [2, Wh*Ww, Wh*Ww] relative_coords = tf.transpose(relative_coords, perm=[1, 2, 0]) # [Wh*Ww, Wh*Ww, 2] xx = (relative_coords[:, :, 0] + self.window_size[0] - 1) * (2 * self.window_size[1] - 1) yy = relative_coords[:, :, 1] + self.window_size[1] - 1 relative_coords = tf.stack([xx, yy], axis=-1) relative_position_index = tf.reduce_sum(relative_coords, axis=-1) # [Wh*Ww, Wh*Ww] top = tf.ones((1, relative_position_index.shape[1]), dtype=relative_position_index.dtype) * ( self.num_relative_distance - 3 ) left = tf.ones((relative_position_index.shape[0], 1), dtype=relative_position_index.dtype) * ( self.num_relative_distance - 2 ) corner = tf.ones((1, 1), dtype=relative_position_index.dtype) * (self.num_relative_distance - 1) left_corner = tf.concat([corner, left], axis=0) relative_position_index = tf.concat([top, relative_position_index], axis=0) relative_position_index = tf.concat([left_corner, relative_position_index], axis=1) # [Wh*Ww + 1, Wh*Ww + 1] return relative_position_index def call(self, inputs=None) -> tf.Tensor: relative_position_bias = tf.gather(self.relative_position_bias_table, self.relative_position_index, axis=0) return tf.transpose(relative_position_bias, [2, 0, 1]) class TFData2VecVisionEncoder(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, window_size: Optional[tuple] = None, **kwargs): super().__init__(**kwargs) self.config = config if config.use_shared_relative_position_bias: self.relative_position_bias = TFData2VecVisionRelativePositionBias( config, window_size=window_size, name="relative_position_bias" ) else: self.relative_position_bias = None # stochastic depth decay rule dpr = list(tf.linspace(0.0, config.drop_path_rate, config.num_hidden_layers)) self.layer = [ TFData2VecVisionLayer( config, window_size=window_size if config.use_relative_position_bias else None, drop_path_rate=dpr[i], name=f"layer_._{i}", ) for i in range(config.num_hidden_layers) ] def call( self, hidden_states: tf.Tensor, head_mask: tf.Tensor | None = None, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ) -> Union[tuple, TFBaseModelOutput]: all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None for i, layer_module in enumerate(self.layer): if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[i] if head_mask is not None else None # Passing `0.0` to the `relative_position_bias()` layer because otherwise Keras # might complain about `Layer.call()` not being invoked properly. In this case this input # i.e., 0.0 is not going to be used in any calculations so we're safe. relative_position_bias = ( self.relative_position_bias(0.0) if self.relative_position_bias is not None else None ) layer_outputs = layer_module(hidden_states, layer_head_mask, output_attentions, relative_position_bias) hidden_states = layer_outputs[0] if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) return TFBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "relative_position_bias", None) is not None: with tf.name_scope(self.relative_position_bias.name): self.relative_position_bias.build(None) if getattr(self, "layer", None) is not None: for layer in self.layer: with tf.name_scope(layer.name): layer.build(None) @keras_serializable class TFData2VecVisionMainLayer(keras.layers.Layer): config_class = Data2VecVisionConfig def __init__(self, config: Data2VecVisionConfig, add_pooling_layer: bool = True, **kwargs): super().__init__(**kwargs) self.config = config self.add_pooling_layer = add_pooling_layer self.embeddings = TFData2VecVisionEmbeddings(config, name="embeddings") self.encoder = TFData2VecVisionEncoder( config, window_size=self.embeddings.patch_embeddings.patch_shape, name="encoder" ) self.layernorm = ( tf.identity if config.use_mean_pooling else keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") ) # We are setting the `data_format` like so because from here on we will revert to the # NCHW output format self.pooler = TFData2VecVisionPooler(config, name="pooler") if add_pooling_layer else None def get_input_embeddings(self) -> keras.layers.Layer: return self.embeddings.patch_embeddings def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ raise NotImplementedError @unpack_inputs def call( self, pixel_values: tf.Tensor | None = None, bool_masked_pos: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[tuple, TFData2VecVisionModelOutputWithPooling]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("You have to specify pixel_values") # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] if head_mask is not None: raise NotImplementedError else: head_mask = [None] * self.config.num_hidden_layers embedding_output = self.embeddings(pixel_values, bool_masked_pos, training=training) encoder_outputs = self.encoder( embedding_output, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) sequence_output = encoder_outputs[0] sequence_output = self.layernorm(sequence_output) pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,) return head_outputs + encoder_outputs[1:] return TFData2VecVisionModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "embeddings", None) is not None: with tf.name_scope(self.embeddings.name): self.embeddings.build(None) if getattr(self, "encoder", None) is not None: with tf.name_scope(self.encoder.name): self.encoder.build(None) if getattr(self, "layernorm", None) is not None: if hasattr(self.layernorm, "name"): with tf.name_scope(self.layernorm.name): self.layernorm.build((None, self.config.hidden_size)) if getattr(self, "pooler", None) is not None: with tf.name_scope(self.pooler.name): self.pooler.build(None) class TFData2VecVisionPooler(keras.layers.Layer): def __init__(self, config: Data2VecVisionConfig, **kwargs): super().__init__(**kwargs) self.layernorm = ( keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm") if config.use_mean_pooling else None ) self.config = config def call(self, hidden_states: tf.Tensor) -> tf.Tensor: if self.layernorm is not None: # Mean pool the final hidden states of the patch tokens patch_tokens = hidden_states[:, 1:, :] pooled_output = self.layernorm(tf.reduce_mean(patch_tokens, axis=1)) else: # Pool by simply taking the final hidden state of the [CLS] token pooled_output = hidden_states[:, 0] return pooled_output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "layernorm", None) is not None: if hasattr(self.layernorm, "name"): with tf.name_scope(self.layernorm.name): self.layernorm.build((None, self.config.hidden_size)) class TFData2VecVisionPreTrainedModel(TFPreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Data2VecVisionConfig base_model_prefix = "data2vec_vision" main_input_name = "pixel_values" _keys_to_ignore_on_load_unexpected = [r"relative_position_index"] DATA2VEC_VISION_START_DOCSTRING = r""" This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.). This model is also a [keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior. TensorFlow models and layers in `transformers` accept two formats as input: - having all inputs as keyword arguments (like PyTorch models), or - having all inputs as a list, tuple or dict in the first positional argument. The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first positional argument: - a single Tensor with `pixel_values` only and nothing else: `model(pixel_values)` - a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: `model([pixel_values, attention_mask])` or `model([pixel_values, attention_mask, token_type_ids])` - a dictionary with one or several input Tensors associated to the input names given in the docstring: `model({"pixel_values": pixel_values, "token_type_ids": token_type_ids})` Note that when creating models and layers with [subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry about any of this, as you can just pass inputs like you would to any other Python function! Args: config ([`Data2VecVisionConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. """ DATA2VEC_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`BeitImageProcessor.__call__`] for details. head_mask (`np.ndarray` or `tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. training (`bool`, *optional*, defaults to `False``): Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). """ @add_start_docstrings( "The bare Data2VecVision Model transformer outputting raw hidden-states without any specific head on top.", DATA2VEC_VISION_START_DOCSTRING, ) class TFData2VecVisionModel(TFData2VecVisionPreTrainedModel): def __init__(self, config: Data2VecVisionConfig, add_pooling_layer: bool = False, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.config = config self.data2vec_vision = TFData2VecVisionMainLayer( config, add_pooling_layer=add_pooling_layer, name="data2vec_vision" ) def get_input_embeddings(self): return self.data2vec_vision.get_input_embeddings() @unpack_inputs @add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFData2VecVisionModelOutputWithPooling, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, ) def call( self, pixel_values: TFModelInputType | None = None, bool_masked_pos: tf.Tensor | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, training: bool = False, ) -> Union[tuple, TFData2VecVisionModelOutputWithPooling]: r""" bool_masked_pos (`tf.Tensor` of shape `(batch_size, num_patches)`, *optional*): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). """ outputs = self.data2vec_vision( pixel_values=pixel_values, bool_masked_pos=bool_masked_pos, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) return outputs def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "data2vec_vision", None) is not None: with tf.name_scope(self.data2vec_vision.name): self.data2vec_vision.build(None) @add_start_docstrings( """ Data2VecVision Model transformer with an image classification head on top (a linear layer on top of the average of the final hidden states of the patch tokens) e.g. for ImageNet. """, DATA2VEC_VISION_START_DOCSTRING, ) class TFData2VecVisionForImageClassification(TFData2VecVisionPreTrainedModel, TFSequenceClassificationLoss): def __init__(self, config: Data2VecVisionConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.data2vec_vision = TFData2VecVisionMainLayer(config, add_pooling_layer=True, name="data2vec_vision") # Classifier head self.classifier = keras.layers.Dense( units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier", ) self.config = config @unpack_inputs @add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, ) def call( self, pixel_values: TFModelInputType | None = None, head_mask: np.ndarray | tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: np.ndarray | tf.Tensor | None = None, training: Optional[bool] = False, ) -> Union[TFSequenceClassifierOutput, tuple]: r""" labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = self.data2vec_vision( pixel_values=pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, training=training, ) pooled_output = outputs.pooler_output if return_dict else outputs[1] logits = self.classifier(pooled_output) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "data2vec_vision", None) is not None: with tf.name_scope(self.data2vec_vision.name): self.data2vec_vision.build(None) if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, self.config.hidden_size]) class TFData2VecVisionConvModule(keras.layers.Layer): """ A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation. """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], padding: str = "valid", bias: bool = False, dilation: Union[int, Tuple[int, int]] = 1, **kwargs, ) -> None: super().__init__(**kwargs) self.conv = keras.layers.Conv2D( filters=out_channels, kernel_size=kernel_size, padding=padding, use_bias=bias, dilation_rate=dilation, name="conv", ) self.bn = keras.layers.BatchNormalization(name="bn", momentum=0.9, epsilon=1e-5) self.activation = tf.nn.relu self.in_channels = in_channels self.out_channels = out_channels def call(self, input: tf.Tensor) -> tf.Tensor: output = self.conv(input) output = self.bn(output) output = self.activation(output) return output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "conv", None) is not None: with tf.name_scope(self.conv.name): self.conv.build([None, None, None, self.in_channels]) if getattr(self, "bn", None) is not None: with tf.name_scope(self.bn.name): self.bn.build((None, None, None, self.out_channels)) class TFAdaptiveAvgPool2D(keras.layers.Layer): def __init__(self, output_dims: Tuple[int, int], input_ordering: str = "NHWC", **kwargs): super().__init__(**kwargs) self.output_dims = output_dims self.input_ordering = input_ordering if input_ordering not in ("NCHW", "NHWC"): raise ValueError("Unrecognized input_ordering, should be 'NCHW' or 'NHWC'!") self.h_axis = input_ordering.index("H") self.w_axis = input_ordering.index("W") def pseudo_1d_pool(self, inputs: tf.Tensor, h_pooling: bool): # Figure out which axis we're pooling on if h_pooling: axis = self.h_axis output_dim = self.output_dims[0] else: axis = self.w_axis output_dim = self.output_dims[1] input_dim = inputs.shape[axis] # Figure out the potential pooling windows # This is the key idea - the torch op always uses only two # consecutive pooling window sizes, like 3 and 4. Therefore, # if we pool with both possible sizes, we simply need to gather # the 'correct' pool at each position to reimplement the torch op. small_window = math.ceil(input_dim / output_dim) big_window = small_window + 1 if h_pooling: output_dim = self.output_dims[0] small_window_shape = (small_window, 1) big_window_shape = (big_window, 1) else: output_dim = self.output_dims[1] small_window_shape = (1, small_window) big_window_shape = (1, big_window) # For resizes to 1, or integer resizes, we can take quick shortcuts if output_dim == input_dim: return inputs elif output_dim == 1: return tf.reduce_mean(inputs, axis=axis, keepdims=True) elif input_dim % output_dim == 0: return tf.nn.avg_pool2d( inputs, ksize=small_window_shape, strides=small_window_shape, padding="VALID", data_format=self.input_ordering, ) # When upscaling by an integer factor we can also take a quick shortcut elif output_dim > input_dim and output_dim % input_dim == 0: return tf.repeat(inputs, repeats=output_dim // input_dim, axis=axis) # For non-integer resizes, we pool with both possible window sizes and concatenate them if output_dim < input_dim: small_pool = tf.nn.avg_pool2d( inputs, ksize=small_window_shape, strides=1, padding="VALID", data_format=self.input_ordering ) big_pool = tf.nn.avg_pool2d( inputs, ksize=big_window_shape, strides=1, padding="VALID", data_format=self.input_ordering ) both_pool = tf.concat([small_pool, big_pool], axis=axis) else: # When we're actually upscaling instead, then we build the pools a bit differently small_pool = inputs big_pool = tf.nn.avg_pool2d( inputs, ksize=big_window_shape, strides=1, padding="VALID", data_format=self.input_ordering ) both_pool = tf.concat([small_pool, big_pool], axis=axis) # We compute vectors of the start and end positions for each pooling window # Each (start, end) pair here corresponds to a single output position window_starts = tf.math.floor((tf.range(output_dim, dtype=tf.float32) * input_dim) / output_dim) window_starts = tf.cast(window_starts, tf.int64) window_ends = tf.math.ceil((tf.range(1, output_dim + 1, dtype=tf.float32) * input_dim) / output_dim) window_ends = tf.cast(window_ends, tf.int64) # pool_selector is a boolean array of shape (output_dim,) where 1 indicates that output position # has a big receptive field and 0 indicates that that output position has a small receptive field pool_selector = tf.cast(window_ends - window_starts - small_window, tf.bool) # Since we concatenated the small and big pools, we need to do a bit of # pointer arithmetic to get the indices of the big pools small_indices = window_starts big_indices = window_starts + small_pool.shape[axis] # Finally, we use the pool_selector to generate a list of indices, one per output position gather_indices = tf.where(pool_selector, big_indices, small_indices) # Gathering from those indices yields the final, correct pooling return tf.gather(both_pool, gather_indices, axis=axis) def call(self, inputs: tf.Tensor): if self.input_ordering == "NHWC": input_shape = inputs.shape[1:3] else: input_shape = inputs.shape[2:] # We break the task down into each possible case # Firstly, if we're resizing down to 1, it's just tf.reduce_mean if self.output_dims[0] == self.output_dims[1] == 1: if self.input_ordering == "NHWC": reduce_dims = [1, 2] else: reduce_dims = [2, 3] return tf.reduce_mean(inputs, axis=reduce_dims, keepdims=True) # Secondly, if we're resizing by an integer factor on both dimensions, we can take a quick shortcut elif input_shape[0] % self.output_dims[0] == 0 and input_shape[1] % self.output_dims[1] == 0: h_resize = int(input_shape[0] // self.output_dims[0]) w_resize = int(input_shape[1] // self.output_dims[1]) return tf.nn.avg_pool2d( inputs, ksize=(h_resize, w_resize), strides=(h_resize, w_resize), padding="VALID", data_format=self.input_ordering, ) else: # Finally, if we can't take the shortcut, we do a 1D pool on each axis. pseudo_1d_pool will take a shortcut # for dimensions where an integer resize is possible. It can also handle upscaling. h_pooled = self.pseudo_1d_pool(inputs, h_pooling=True) return self.pseudo_1d_pool(h_pooled, h_pooling=False) class TFData2VecVisionPyramidPoolingModule(keras.layers.Layer): """ Pyramid Pooling Module (PPM) used in PSPNet. Args: pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid Module. channels (int): Channels after modules, before conv_seg. Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation. """ def __init__(self, pool_scales: Tuple[int, ...], in_channels: int, out_channels: int, **kwargs) -> None: super().__init__(**kwargs) self.pool_scales = pool_scales self.in_channels = in_channels self.out_channels = out_channels self.layer_list = [] for idx, pool_scale in enumerate(pool_scales): pool_scale = pool_scale if isinstance(pool_scale, collections.abc.Iterable) else (pool_scale, pool_scale) self.layer_list.append( [ TFAdaptiveAvgPool2D(output_dims=pool_scale), TFData2VecVisionConvModule( in_channels=in_channels, out_channels=self.out_channels, kernel_size=1, name=f"{idx}.1" ), ] ) def call(self, x: tf.Tensor) -> List[tf.Tensor]: ppm_outs = [] inputs = x for ppm in self.layer_list: for layer_module in ppm: ppm_out = layer_module(x) x = ppm_out upsampled_ppm_out = tf.image.resize(ppm_out, size=shape_list(inputs)[1:-1], method="bilinear") ppm_outs.append(upsampled_ppm_out) return ppm_outs def build(self, input_shape=None): for layer in self.layer_list: for layer_module in layer: with tf.name_scope(layer_module.name): layer_module.build(None) class TFData2VecVisionUperHead(keras.layers.Layer): """ Unified Perceptual Parsing for Scene Understanding. This head is the implementation of [UPerNet](https://arxiv.org/abs/1807.10221). Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation. """ def __init__(self, config: Data2VecVisionConfig, **kwargs) -> None: super().__init__(**kwargs) self.pool_scales = config.pool_scales # e.g. (1, 2, 3, 6) self.in_channels = [config.hidden_size] * 4 # e.g. [768, 768, 768, 768] self.channels = config.hidden_size self.classifier = keras.layers.Conv2D(config.num_labels, kernel_size=1, name="classifier") # PSP Module self.psp_modules = TFData2VecVisionPyramidPoolingModule( self.pool_scales, self.in_channels[-1], self.channels, name="psp_modules" ) self.bottleneck = TFData2VecVisionConvModule( self.in_channels[-1] + len(self.pool_scales) * self.channels, self.channels, kernel_size=3, padding="same", name="bottleneck", ) # FPN Module self.lateral_convs = [] self.fpn_convs = [] for idx, in_channels in enumerate(self.in_channels[:-1]): # skip the top layer l_conv = TFData2VecVisionConvModule( in_channels, out_channels=self.channels, kernel_size=1, name=f"lateral_convs.{idx}" ) fpn_conv = TFData2VecVisionConvModule( in_channels=self.channels, out_channels=self.channels, kernel_size=3, padding="same", name=f"fpn_convs.{idx}", ) self.lateral_convs.append(l_conv) self.fpn_convs.append(fpn_conv) self.fpn_bottleneck = TFData2VecVisionConvModule( in_channels=len(self.in_channels) * self.channels, out_channels=self.channels, kernel_size=3, padding="same", name="fpn_bottleneck", ) def psp_forward(self, inputs): x = inputs[-1] psp_outs = [x] psp_outs.extend(self.psp_modules(x)) psp_outs = tf.concat(psp_outs, axis=-1) output = self.bottleneck(psp_outs) return output def call(self, encoder_hidden_states: tf.Tensor) -> tf.Tensor: # build laterals laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)] laterals.append(self.psp_forward(encoder_hidden_states)) # build top-down path used_backbone_levels = len(laterals) for i in range(used_backbone_levels - 1, 0, -1): prev_shape = shape_list(laterals[i - 1])[1:-1] laterals[i - 1] = laterals[i - 1] + tf.image.resize(laterals[i], size=prev_shape, method="bilinear") # build outputs fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)] # append psp feature fpn_outs.append(laterals[-1]) for i in range(used_backbone_levels - 1, 0, -1): fpn_outs[i] = tf.image.resize(fpn_outs[i], size=shape_list(fpn_outs[0])[1:-1], method="bilinear") fpn_outs = tf.concat(fpn_outs, axis=-1) output = self.fpn_bottleneck(fpn_outs) output = self.classifier(output) return output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, None, self.channels]) if getattr(self, "psp_modules", None) is not None: with tf.name_scope(self.psp_modules.name): self.psp_modules.build(None) if getattr(self, "bottleneck", None) is not None: with tf.name_scope(self.bottleneck.name): self.bottleneck.build(None) if getattr(self, "fpn_bottleneck", None) is not None: with tf.name_scope(self.fpn_bottleneck.name): self.fpn_bottleneck.build(None) for layer in self.lateral_convs: with tf.name_scope(layer.name): layer.build(None) for layer in self.fpn_convs: with tf.name_scope(layer.name): layer.build(None) class TFData2VecVisionFCNHead(keras.layers.Layer): """ Fully Convolution Networks for Semantic Segmentation. This head is implemented from [FCNNet](https://arxiv.org/abs/1411.4038). Args: config (Data2VecVisionConfig): Configuration. kernel_size (int): The kernel size for convs in the head. Default: 3. dilation (int): The dilation rate for convs in the head. Default: 1. Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation. """ def __init__( self, config: Data2VecVisionConfig, in_index: int = 2, kernel_size: int = 3, dilation: Union[int, Tuple[int, int]] = 1, **kwargs, ) -> None: super().__init__(**kwargs) self.in_channels = config.hidden_size self.channels = config.auxiliary_channels self.num_convs = config.auxiliary_num_convs self.concat_input = config.auxiliary_concat_input self.in_index = in_index convs = [] convs.append( TFData2VecVisionConvModule( in_channels=self.in_channels, out_channels=self.channels, kernel_size=kernel_size, padding="same", dilation=dilation, name="convs.0", ) ) for i in range(self.num_convs - 1): convs.append( TFData2VecVisionConvModule( in_channels=self.channels, out_channels=self.channels, kernel_size=kernel_size, padding="same", dilation=dilation, name=f"conv_module_{i + 2}", ) ) if self.num_convs == 0: self.convs = [tf.identity] else: self.convs = convs if self.concat_input: self.conv_cat = TFData2VecVisionConvModule( self.in_channels + self.channels, out_channels=self.channels, kernel_size=kernel_size, padding="same", name="conv_cat", ) self.classifier = keras.layers.Conv2D(config.num_labels, kernel_size=1, name="classifier") def call(self, encoder_hidden_states: tf.Tensor) -> tf.Tensor: # just take the relevant feature maps hidden_states = encoder_hidden_states[self.in_index] output = hidden_states for layer_module in self.convs: output = layer_module(output) if self.concat_input: output = self.conv_cat(tf.concat([hidden_states, output], axis=-1)) output = self.classifier(output) return output def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "classifier", None) is not None: with tf.name_scope(self.classifier.name): self.classifier.build([None, None, None, self.channels]) if getattr(self, "conv_cat", None) is not None: with tf.name_scope(self.conv_cat.name): self.conv_cat.build(None) @add_start_docstrings( """ Data2VecVision Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes. """, DATA2VEC_VISION_START_DOCSTRING, ) class TFData2VecVisionForSemanticSegmentation(TFData2VecVisionPreTrainedModel): def __init__(self, config: Data2VecVisionConfig, *inputs, **kwargs) -> None: super().__init__(config, *inputs, **kwargs) self.num_labels = config.num_labels self.data2vec_vision = TFData2VecVisionMainLayer(config, add_pooling_layer=False, name="data2vec_vision") # FPNs self.fpn1 = [ keras.layers.Conv2DTranspose(config.hidden_size, kernel_size=2, strides=2, name="fpn1.0"), keras.layers.BatchNormalization(name="fpn1.1", momentum=0.9, epsilon=1e-5), keras.layers.Activation("gelu"), keras.layers.Conv2DTranspose(config.hidden_size, kernel_size=2, strides=2, name="fpn1.3"), ] self.fpn2 = [keras.layers.Conv2DTranspose(config.hidden_size, kernel_size=2, strides=2, name="fpn2.0")] self.fpn3 = tf.identity self.fpn4 = keras.layers.MaxPool2D(pool_size=2, strides=2) # Semantic segmentation head(s) self.decode_head = TFData2VecVisionUperHead(config, name="decode_head") self.auxiliary_head = ( TFData2VecVisionFCNHead(config, name="auxiliary_head") if config.use_auxiliary_head else None ) def compute_loss(self, logits, auxiliary_logits, labels): # upsample logits to the images' original size if len(shape_list(labels)) > 3: label_interp_shape = shape_list(labels)[1:-1] else: label_interp_shape = shape_list(labels)[-2:] upsampled_logits = tf.image.resize(logits, size=label_interp_shape, method="bilinear") if auxiliary_logits is not None: upsampled_auxiliary_logits = tf.image.resize(auxiliary_logits, size=label_interp_shape, method="bilinear") # compute weighted loss loss_fct = keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction="none") # Copied from https://www.tensorflow.org/text/tutorials/transformer#loss_and_metrics. # Utility to mask the index to ignore during computing the loss. def masked_loss(real, pred): mask = tf.math.logical_not(tf.math.equal(real, self.config.semantic_loss_ignore_index)) loss_ = loss_fct(real, pred) mask = tf.cast(mask, dtype=loss_.dtype) loss_ *= mask reduced_masked_loss = tf.reduce_sum(loss_) / tf.reduce_sum(mask) return tf.reshape(reduced_masked_loss, (1,)) main_loss = masked_loss(labels, upsampled_logits) auxiliary_loss = masked_loss(labels, upsampled_auxiliary_logits) loss = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss return loss @unpack_inputs @add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=TFSemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) def call( self, pixel_values: tf.Tensor | None = None, head_mask: tf.Tensor | None = None, labels: tf.Tensor | None = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[tuple, TFSemanticSegmenterOutput]: r""" labels (`tf.Tensor` of shape `(batch_size, height, width)`, *optional*): Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). Returns: Examples: ```python >>> from transformers import AutoImageProcessor, TFData2VecVisionForSemanticSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> image_processor = AutoImageProcessor.from_pretrained("facebook/data2vec-vision-base") >>> model = TFData2VecVisionForSemanticSegmentation.from_pretrained("facebook/data2vec-vision-base") >>> inputs = image_processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> # logits are of shape (batch_size, num_labels, height, width) >>> logits = outputs.logits ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) if labels is not None and self.config.num_labels == 1: raise ValueError("The number of labels should be greater than one") outputs = self.data2vec_vision( pixel_values, head_mask=head_mask, output_attentions=output_attentions, output_hidden_states=True, # we need the intermediate hidden states return_dict=return_dict, ) encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] # only keep certain features, and reshape # note that we do +1 as the encoder_hidden_states also includes the initial embeddings features = [feature for idx, feature in enumerate(encoder_hidden_states) if idx + 1 in self.config.out_indices] patch_resolution = self.config.image_size // self.config.patch_size def reshape_features(x): # We do it this way so TF can always infer the non-batch dims at compile time x = tf.reshape(x, (-1, patch_resolution, patch_resolution, self.config.hidden_size)) return x features = [reshape_features(x[:, 1:, :]) for x in features] # apply FPNs ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4] for module in ops[0]: features[0] = module(features[0]) features[1] = ops[1][0](features[1]) for i in range(len(features[2:])): features[i + 2] = ops[i + 2](features[i + 2]) logits = self.decode_head(features) # Tranpose the logits to maintain consistency in the output formats. transposed_logits = tf.transpose(logits, perm=[0, 3, 1, 2]) auxiliary_logits = None if self.auxiliary_head is not None: auxiliary_logits = self.auxiliary_head(features) loss = None if labels is not None: loss = self.compute_loss(logits, auxiliary_logits, labels) if not return_dict: if output_hidden_states: output = (logits,) + outputs[1:] else: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSemanticSegmenterOutput( loss=loss, logits=transposed_logits, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=outputs.attentions, ) def build(self, input_shape=None): if self.built: return self.built = True if getattr(self, "data2vec_vision", None) is not None: with tf.name_scope(self.data2vec_vision.name): self.data2vec_vision.build(None) if getattr(self, "decode_head", None) is not None: with tf.name_scope(self.decode_head.name): self.decode_head.build(None) if getattr(self, "auxiliary_head", None) is not None: with tf.name_scope(self.auxiliary_head.name): self.auxiliary_head.build(None) if getattr(self, "fpn1", None) is not None: with tf.name_scope(self.fpn1[0].name): self.fpn1[0].build([None, None, None, self.config.hidden_size]) with tf.name_scope(self.fpn1[1].name): self.fpn1[1].build((None, None, None, self.config.hidden_size)) with tf.name_scope(self.fpn1[3].name): self.fpn1[3].build([None, None, None, self.config.hidden_size]) if getattr(self, "fpn2", None) is not None: with tf.name_scope(self.fpn2[0].name): self.fpn2[0].build([None, None, None, self.config.hidden_size]) __all__ = [ "TFData2VecVisionForImageClassification", "TFData2VecVisionForSemanticSegmentation", "TFData2VecVisionModel", "TFData2VecVisionPreTrainedModel", ]