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"""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)
])
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