File size: 5,892 Bytes
dea4744 |
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 |
"""Parses PaliGemma output."""
import functools
import re
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
import PIL.Image
EXAMPLE_STRING = '<loc0000><loc0000><loc0930><loc1012> <seg114><seg074><seg106><seg044><seg030><seg027><seg119><seg119><seg120><seg117><seg082><seg082><seg051><seg005><seg125><seg097> wall ; <loc0722><loc0047><loc0895><loc0378> <seg068><seg114><seg014><seg037><seg029><seg063><seg048><seg104><seg010><seg056><seg021><seg056><seg019><seg017><seg102><seg121> car ; <loc0180><loc0596><loc0782><loc0961> <seg026><seg028><seg028><seg026><seg104><seg026><seg029><seg022><seg000><seg068><seg092><seg125><seg003><seg127><seg121><seg043> david bowie ; <loc0234><loc0043><loc0736><loc0289> <seg068><seg008><seg091><seg064><seg007><seg055><seg017><seg090><seg042><seg052><seg068><seg086><seg001><seg014><seg093><seg052> david bowie ; <loc0230><loc0300><loc0736><loc0499> <seg073><seg011><seg114><seg059><seg048><seg097><seg091><seg022><seg007><seg036><seg091><seg022><seg016><seg009><seg003><seg036> david bowie' # pylint: disable=line-too-long
_MODEL_PATH = 'vae-oid.npz'
_SEGMENT_DETECT_RE = re.compile(
r'(.*?)' +
r'<loc(\d{4})>' * 4 + r'\s*' +
'(?:%s)?' % (r'<seg(\d{3})>' * 16) +
r'\s*([^;<>]+)? ?(?:; )?',
)
def _get_params(checkpoint):
"""Converts PyTorch checkpoint to Flax params."""
def transp(kernel):
return np.transpose(kernel, (2, 3, 1, 0))
def conv(name):
return {
'bias': checkpoint[name + '.bias'],
'kernel': transp(checkpoint[name + '.weight']),
}
def resblock(name):
return {
'Conv_0': conv(name + '.0'),
'Conv_1': conv(name + '.2'),
'Conv_2': conv(name + '.4'),
}
return {
'_embeddings': checkpoint['_vq_vae._embedding'],
'Conv_0': conv('decoder.0'),
'ResBlock_0': resblock('decoder.2.net'),
'ResBlock_1': resblock('decoder.3.net'),
'ConvTranspose_0': conv('decoder.4'),
'ConvTranspose_1': conv('decoder.6'),
'ConvTranspose_2': conv('decoder.8'),
'ConvTranspose_3': conv('decoder.10'),
'Conv_1': conv('decoder.12'),
}
def _quantized_values_from_codebook_indices(codebook_indices, embeddings):
batch_size, num_tokens = codebook_indices.shape
assert num_tokens == 16, codebook_indices.shape
unused_num_embeddings, embedding_dim = embeddings.shape
encodings = jnp.take(embeddings, codebook_indices.reshape((-1)), axis=0)
encodings = encodings.reshape((batch_size, 4, 4, embedding_dim))
return encodings
@functools.cache
def _get_reconstruct_masks():
"""Reconstructs masks from codebook indices.
Returns:
A function that expects indices shaped `[B, 16]` of dtype int32, each
ranging from 0 to 127 (inclusive), and that returns a decoded masks sized
`[B, 64, 64, 1]`, of dtype float32, in range [-1, 1].
"""
class ResBlock(nn.Module):
features: int
@nn.compact
def __call__(self, x):
original_x = x
x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
x = nn.relu(x)
x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
x = nn.relu(x)
x = nn.Conv(features=self.features, kernel_size=(1, 1), padding=0)(x)
return x + original_x
class Decoder(nn.Module):
"""Upscales quantized vectors to mask."""
@nn.compact
def __call__(self, x):
num_res_blocks = 2
dim = 128
num_upsample_layers = 4
x = nn.Conv(features=dim, kernel_size=(1, 1), padding=0)(x)
x = nn.relu(x)
for _ in range(num_res_blocks):
x = ResBlock(features=dim)(x)
for _ in range(num_upsample_layers):
x = nn.ConvTranspose(
features=dim,
kernel_size=(4, 4),
strides=(2, 2),
padding=2,
transpose_kernel=True,
)(x)
x = nn.relu(x)
dim //= 2
x = nn.Conv(features=1, kernel_size=(1, 1), padding=0)(x)
return x
def reconstruct_masks(codebook_indices):
quantized = _quantized_values_from_codebook_indices(
codebook_indices, params['_embeddings']
)
return Decoder().apply({'params': params}, quantized)
with open(_MODEL_PATH, 'rb') as f:
params = _get_params(dict(np.load(f)))
return jax.jit(reconstruct_masks, backend='cpu')
def extract_objs(text, width, height, unique_labels=False):
"""Returns objs for a string with "<loc>" and "<seg>" tokens."""
objs = []
seen = set()
while text:
m = _SEGMENT_DETECT_RE.match(text)
if not m:
break
gs = list(m.groups())
before = gs.pop(0)
name = gs.pop()
y1, x1, y2, x2 = [int(x) / 1024 for x in gs[:4]]
y1, x1, y2, x2 = map(round, (y1*height, x1*width, y2*height, x2*width))
seg_indices = gs[4:20]
if seg_indices[0] is None:
mask = None
else:
seg_indices = np.array([int(x) for x in seg_indices], dtype=np.int32)
m64, = _get_reconstruct_masks()(seg_indices[None])[..., 0]
m64 = np.clip(np.array(m64) * 0.5 + 0.5, 0, 1)
m64 = PIL.Image.fromarray((m64 * 255).astype('uint8'))
mask = np.zeros([height, width])
if y2 > y1 and x2 > x1:
mask[y1:y2, x1:x2] = np.array(m64.resize([x2 - x1, y2 - y1])) / 255.0
content = m.group()
if before:
objs.append(dict(content=before))
content = content[len(before):]
while unique_labels and name in seen:
name = (name or '') + "'"
seen.add(name)
objs.append(dict(
content=content, xyxy=(x1, y1, x2, y2), mask=mask, name=name))
text = text[len(before) + len(content):]
if text:
objs.append(dict(content=text))
return objs
if __name__ == '__main__':
# Simple test.
print([
{
k: (v.shape, v.mean()) if isinstance(v, np.ndarray) else v
for k, v in obj.items()
}
for obj in extract_objs(EXAMPLE_STRING, 100, 200)
])
|