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# Copyright 2024 Big Vision 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.
"""Segmentation-related ops."""
import functools
from big_vision.pp import registry
import numpy as np
import tensorflow as tf
from tensorflow.io import gfile
_KNOWN_MODELS = {
'oi': 'gs://big_vision/paligemma/vae-oid.npz',
}
@functools.cache
def get_checkpoint(model):
with gfile.GFile(_KNOWN_MODELS.get(model, model), 'rb') as f:
return dict(np.load(f))
@registry.Registry.register('preprocess_ops.refcoco_mask2str')
def get_refcoco_mask2str(model='oi'):
"""Returns op for tokenizing a mask."""
seg_tokens = tf.constant(['<seg%03d>' % i for i in range(128)])
loc_tokens = tf.constant(['<loc%04d>' % i for i in range(1024)])
checkpoint = get_checkpoint(model)
def refcoco_mask2str(data):
mask = data['objects/mask']
tf.ensure_shape(mask, [None, None, 3]) # requires choice()
sentence = data['objects/refs/sentence']
tf.ensure_shape(sentence, []) # requires choice()
bbox = data['objects/bbox']
tf.ensure_shape(bbox, [4]) # requires choice()
h = tf.cast(tf.shape(mask)[0], tf.float32)
w = tf.cast(tf.shape(mask)[1], tf.float32)
y1 = tf.cast(tf.round(h * bbox[0]), tf.int32)
x1 = tf.cast(tf.round(w * bbox[1]), tf.int32)
y2 = tf.cast(tf.round(h * bbox[2]), tf.int32)
x2 = tf.cast(tf.round(w * bbox[3]), tf.int32)
assert mask.dtype == tf.uint8, mask.dtype
mask = tf.image.resize(
mask[None, y1:y2, x1:x2, :1],
[64, 64],
method='bilinear',
antialias=True,
) / 255.0
mask_indices = encode_to_codebook_indices(checkpoint, mask)[0]
mask_string = tf.strings.reduce_join(tf.gather(seg_tokens, mask_indices))
binned_loc = tf.cast(tf.round(bbox * 1023), tf.int32)
binned_loc = tf.clip_by_value(binned_loc, 0, 1023)
loc_string = tf.strings.reduce_join(tf.gather(loc_tokens, binned_loc))
data['prefix'] = sentence
data['suffix'] = tf.strings.join([loc_string, mask_string])
return data
return refcoco_mask2str
# Based on https://arxiv.org/abs/2301.02229.
NUM_DOWNSAMPLE_LAYERS = 4
NUM_RES_BLOCKS = 2
def encode_to_codebook_indices(checkpoint, masks):
"""Encode a batch of binary segmentation masks into 16 tokens each.
Based on code from https://arxiv.org/abs/2301.02229
Args:
checkpoint: model weights from PyTorch model.
masks: Must be in range `[0..1]`, and of shape `[None, 64, 64, 1]`.
Returns:
A tensor of shape `[None, 16]` with elements in `range(128)`.
"""
# We require that the input masks are already resized to 64x64.
x = tf.ensure_shape(masks, [None, 64, 64, 1])
x = _norm(x)
for n in range(NUM_DOWNSAMPLE_LAYERS):
x = _conv_tf(
checkpoint, x, strides=2, padding='SAME', layer_name=f'encoder.{2*n}'
)
x = tf.nn.relu(x)
for n in range(NUM_RES_BLOCKS):
x = _resblock_tf(checkpoint, x, layer_name=f'encoder.{8+n}.net')
x = _conv_tf(
checkpoint, x, strides=1, padding='SAME', layer_name='encoder.10'
)
return _get_codebook_indices(checkpoint, x)
def _norm(x):
return 2.0 * (x - 0.5)
def _conv_tf(checkpoint, x, strides, padding, layer_name):
kernel = checkpoint[layer_name + '.weight']
kernel = np.transpose(kernel, (2, 3, 1, 0))
bias = checkpoint[layer_name + '.bias']
return tf.nn.conv2d(x, kernel, strides=strides, padding=padding) + bias
def _resblock_tf(checkpoint, x, layer_name):
"""Apply a residual block of the mask encoder."""
original_x = x
x = _conv_tf(
checkpoint, x, padding='SAME', strides=1, layer_name=layer_name + '.0'
)
x = tf.nn.relu(x)
x = _conv_tf(
checkpoint, x, padding='SAME', strides=1, layer_name=layer_name + '.2'
)
x = tf.nn.relu(x)
x = _conv_tf(
checkpoint, x, padding='SAME', strides=1, layer_name=layer_name + '.4'
)
return x + original_x
def _get_codebook_indices(checkpoint, encoder_output):
embeddings = checkpoint['_vq_vae._embedding']
flat_input = tf.reshape(encoder_output, [-1, embeddings.shape[1]])
distances = (
tf.reduce_sum(flat_input**2, axis=1, keepdims=True)
+ tf.reduce_sum(embeddings**2, axis=1)
- 2 * tf.matmul(flat_input, embeddings.T)
)
indices = tf.argmin(distances, axis=1)
return tf.reshape(indices, [-1, 16])
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