File size: 8,893 Bytes
1327f34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 | # Copyright 2025 The Scenic Authors.
#
# 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.
"""Utility functions for defining models."""
from typing import Callable, Iterable, Optional, Sequence
import flax.linen as nn
import jax
import jax.numpy as jnp
from scenic.model_lib.layers import attention_layers
from scenic.projects.baselines import vit
Initializer = Callable[[jnp.ndarray, Sequence[int], jnp.dtype], jnp.ndarray]
def shuffle_and_partition(n_batch: int,
n_tokens: int,
n_masked: int,
rng: jax.Array):
"""Implements random shuffling and partitioning necessary for MAE.
Args:
n_batch: The batch size of the sequence to generate.
n_tokens: The number of tokens.
n_masked: The number of tokens to mask. Must have 0 <= n_masked < n_tokens.
rng: The random number key.
Returns:
Two arrays. The first one contains indices of masked tokens, and has
shape [n_batch, n_masked]. The second contains indices of unmasked tokens
and has shape [n_batch, n_tokens - n_masked].
"""
if n_masked >= n_tokens or n_masked < 0:
raise ValueError(f'n_masked = {n_masked} should be >=0 and <{n_tokens}.')
ids = jnp.tile(jnp.arange(n_tokens), n_batch).reshape((n_batch, n_tokens))
n_remainder = n_tokens - n_masked
if n_masked > 0:
rng_keys = jax.random.split(rng, n_batch)
ids = jax.vmap(
lambda seq, rng: jax.random.permutation(rng, seq, independent=True))(
ids, rng_keys)
masked = jax.lax.dynamic_slice(ids, (0, 0,), (n_batch, n_masked,))
unmasked = jax.lax.dynamic_slice(ids, (0, n_masked,), (n_batch, n_remainder,))
return masked, unmasked
def get_mask_indices(n_batch: int,
n_tokens: int,
n_masked: int,
rng: jax.Array):
"""Returns indices to use for masking in MAE.
Args:
n_batch: The batch size of the sequence to generate.
n_tokens: The number of tokens.
n_masked: The number of tokens to mask. Must have 0 <= n_masked < n_tokens.
rng: The random number key.
Returns:
Three arrays. masked_indices of shape [n_batch, n_masked], unmasked_indices
of shape [n_batch, n_tokens - n_masked] and binary_mask of shape
[n_batch, n_tokens] where 1 indicates that the token is masked.
"""
batch_indices = jnp.arange(n_batch).reshape(n_batch, 1)
mask_indices, unmasked_indices = shuffle_and_partition(
n_batch, n_tokens, n_masked, rng)
binary_mask = jnp.zeros((n_batch, n_tokens)).at[batch_indices,
mask_indices].set(1.0)
return mask_indices, unmasked_indices, binary_mask
def get_tube_mask_indices(n_batch: int,
n_tokens: int,
token_mask_probability: float,
temporal_dims: int,
rng: jax.Array):
"""Returns indices to use for tube masking in VideoMAE.
The difference between the random and tube masking is that the tube masking
takes into account the temporal dimension when masking.
Args:
n_batch: The batch size of the sequence to generate.
n_tokens: The number of tokens.
token_mask_probability: Probability of dropping out the input tokens
during training.
temporal_dims: The temporal dimension.
rng: The random number key.
Returns:
Three arrays. masked_indices of shape [n_batch, n_masked], unmasked_indices
of shape [n_batch, n_tokens - n_masked] and binary_mask of shape
[n_batch, n_tokens] where 1 indicates that the token is masked.
"""
n_tokens_frame = n_tokens // temporal_dims
n_masked_frame = int(token_mask_probability * n_tokens_frame)
batch_indices = jnp.arange(n_batch).reshape(n_batch, 1)
mask_indices_frame, _ = shuffle_and_partition(n_batch, n_tokens_frame,
n_masked_frame, rng)
binary_mask_frame = jnp.zeros((n_batch, n_tokens_frame)
).at[batch_indices, mask_indices_frame].set(1.0)
# Add temporal dims
binary_mask = jnp.tile(binary_mask_frame, [1, temporal_dims])
# Apply binary_mask
n_masked_tokens = n_masked_frame * temporal_dims
n_unmasked_tokens = n_tokens - n_masked_tokens
masked_indices = jnp.nonzero(binary_mask, size=(n_batch * n_masked_tokens)
)[1].reshape(n_batch, -1)
unmasked_indices = jnp.nonzero(binary_mask - 1,
size=(n_batch * n_unmasked_tokens)
)[1].reshape(n_batch, -1)
return masked_indices, unmasked_indices, binary_mask
class AddFactorisedSpaceTimePositionEmbs(nn.Module):
"""Adds learned positional embeddings to the inputs.
Attributes:
posemb_init_space: Positional embedding initializer. Default value is taken
from BERT.
posemb_init_time: Positional embedding initializer. Default value is taken
from BERT.
Returns:
Output with same shape as input.
"""
posemb_init_space: Initializer = nn.initializers.normal(stddev=0.02)
posemb_init_time: Initializer = nn.initializers.normal(stddev=0.02)
@nn.compact
def __call__(self, inputs: jnp.ndarray) -> jnp.ndarray:
# Inputs.shape is [batch_size, time, space, hidden_dim].
assert inputs.ndim == 4, ('Number of dimensions should be 4,'
' but it is: %d' % inputs.ndim)
_, time, space, hidden_dim = inputs.shape
pos_emb_shape_space = (1, 1, space, hidden_dim)
pos_emb_shape_time = (1, time, 1, hidden_dim)
pe_spatial = self.param('pos_embedding_space', self.posemb_init_space,
pos_emb_shape_space, inputs.dtype)
pe_temporal = self.param('pos_embedding_time', self.posemb_init_time,
pos_emb_shape_time, inputs.dtype)
return inputs + pe_spatial + pe_temporal
def add_positional_embeddings(
inputs: jnp.ndarray,
posemb_type: str,
input_shape: Optional[Iterable[int]] = None,
layer_name: str = 'posembed_input') -> jnp.ndarray:
"""Adds positional embeddings to an input sequence.
Args:
inputs: Tokens of shape [batch, num_tokens, hidden_size].
posemb_type: The type of positional encoding. Must be one of
{sinusoidal_1d, sinusoidal_2d, sinusoidal_3d, learned_1d}.
input_shape: Used for "sinusoidal_2d" and "sinusoidal_3d". In this case,
the input is reshaped to this size ie [batch, height, width, hidden_size],
before applying the positional encodings and then reshaping back.
layer_name: The layer name for learned embedddings.
Returns:
The input tokens with the positional encodings added. The shape is
[batch, num_tokens, hidden_size].
"""
if posemb_type == 'learned_1d':
x_posemb = vit.AddPositionEmbs(
posemb_init=nn.initializers.normal(stddev=0.02), # from BERT.
name=layer_name)(inputs)
elif posemb_type == 'learned_space_time':
x_reshape = inputs.reshape(input_shape)
x_posemb = AddFactorisedSpaceTimePositionEmbs(
posemb_init_space=nn.initializers.normal(stddev=0.02), # from BERT.
posemb_init_time=nn.initializers.normal(stddev=0.02),
name=layer_name)(x_reshape)
x_posemb = jnp.reshape(x_posemb, inputs.shape)
elif posemb_type == 'sinusoidal_1d':
x_posemb = attention_layers.Add1DPositionEmbedding(
posemb_init=None)(inputs)
elif posemb_type in {'sinusoidal_2d', 'sinusoidal_3d'}:
x_reshape = inputs.reshape(input_shape)
x_posemb = attention_layers.AddFixedSinCosPositionEmbedding()(x_reshape)
x_posemb = jnp.reshape(x_posemb, inputs.shape)
elif posemb_type == 'none':
x_posemb = inputs
else:
raise ValueError(f'Unknown positional embedding {posemb_type}')
return x_posemb
def embed_2d_patch(x, patches, embedding_dim, return_1d=True, name='embedding'):
"""Embedding input patches with 2D conv."""
assert patches.get('size') is not None, ('patches.size is now the only way'
'to define the patches')
assert embedding_dim, 'embedding_dim must be specified'
fh = patches.size[0]
fw = patches.size[1]
x = nn.Conv(
embedding_dim, (fh, fw),
strides=(fh, fw),
padding='VALID',
name=name)(x)
if return_1d:
batch_size = x.shape[0]
x = jnp.reshape(x, [batch_size, -1, embedding_dim])
return x
|