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#
# 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.
r"""Sam mask decoder.
Pytorch reference:
https://github.com/facebookresearch/segment-anything/blob/HEAD/\
segment_anything/modeling/mask_decoder.py
"""
import flax.linen as nn
import jax.numpy as jnp
from scenic.projects.baselines.segment_anything.modeling import transformer
class MaskDecoder(nn.Module):
"""Sam mask decoder."""
transformer_dim: int = 256
num_multimask_outputs: int = 3
iou_head_depth: int = 3
iou_head_hidden_dim: int = 256
def setup(self):
self.iou_token = self.param(
'iou_token.weight',
nn.initializers.normal(stddev=1.),
(1, self.transformer_dim))
self.mask_tokens = self.param(
'mask_tokens.weight',
nn.initializers.normal(stddev=1.),
(self.num_multimask_outputs + 1, self.transformer_dim))
self.output_upscaling = OutputScaling(
transformer_dim=self.transformer_dim, name='output_upscaling')
self.output_hypernework_mlps = [
MLP(hidden_dim=self.iou_head_hidden_dim,
output_dim=self.transformer_dim // 8, num_layers=3,
name=f'output_hypernetworks_mlps.{i}',
) for i in range(self.num_multimask_outputs + 1)]
self.iou_prediction_head = MLP(
hidden_dim=self.iou_head_hidden_dim,
output_dim=self.num_multimask_outputs + 1,
num_layers=self.iou_head_depth,
name='iou_prediction_head')
self.transformer = transformer.TwoWayTransformer(name='transformer')
def predict_masks(
self, image_embeddings, image_pe,
sparse_prompt_embeddings, dense_prompt_embeddings):
"""Predict masks for a single image.
Args:
image_embeddings: (H, W, embed_dim)
image_pe: (H, W, embed_dim)
sparse_prompt_embeddings: (num_prompts, num_points, embed_dim)
dense_prompt_embeddings: (num_prompts, H, W, embed_dim)
Returns:
masks: (num_prompts, num_multimask_outputs + 1, h', w')
iou_pred: (num_prompts, num_multimask_outputs + 1)
"""
output_tokens = jnp.concatenate(
[self.iou_token, self.mask_tokens],
axis=0) # (num_multimask_outputs + 2, transformer_dim)
num_prompts = sparse_prompt_embeddings.shape[0]
output_tokens = jnp.broadcast_to(
output_tokens[None],
(num_prompts, self.num_multimask_outputs + 2, self.transformer_dim))
tokens = jnp.concatenate(
[output_tokens, sparse_prompt_embeddings], axis=1,
) # (num_prompts, num_multimask_outputs + 2 + num_points, embed_dim)
src = jnp.repeat(
image_embeddings[None], tokens.shape[0],
axis=0) # (num_prompts, H, W, D)
src = src + dense_prompt_embeddings
pos_src = jnp.repeat(
image_pe[None], tokens.shape[0], axis=0) # (num_prompts, H, W, D)
num_prompts, h, w, d = src.shape
hs, src = self.transformer(src, pos_src, tokens)
iou_token_out = hs[:, 0, :]
mask_tokens_out = hs[:, 1: (1 + self.num_multimask_outputs + 1), :]
src = src.reshape(num_prompts, h, w, d)
upscaled_embedding = self.output_upscaling(src) # (num_prompts, h', w', d)
hyper_in_list = []
for i in range(self.num_multimask_outputs + 1):
hyper_in_list.append(
self.output_hypernework_mlps[i](
mask_tokens_out[:, i, :]) # (num_prompts, d)
)
hyper_in = jnp.stack(hyper_in_list, axis=1) # (num_prompts, num_masks, d)
num_prompts, h, w, d = upscaled_embedding.shape
masks = hyper_in @ upscaled_embedding.reshape(
num_prompts, h * w, d).transpose(
0, 2, 1) # (num_prompts, num_masks, h'w')
masks = masks.reshape(num_prompts, self.num_multimask_outputs + 1, h, w)
iou_pred = self.iou_prediction_head(iou_token_out)
return masks, iou_pred
@nn.compact
def __call__(
self, image_embeddings, image_pe,
sparse_prompt_embeddings, dense_prompt_embeddings,
multimask_output: bool = True):
"""Forward model for a single image.
Args:
image_embeddings: (H, W, 3)
image_pe: (H, W, D)
sparse_prompt_embeddings: (num_prompts, num_points, embed_dim)
dense_prompt_embeddings: (num_prompts, H, W, embed_dim)
multimask_output: bool
Returns:
masks: (num_prompts, num_multimask_outputs, h', w'),
num_multimask_outputs = 3 if multimask_output is True, otherwise 1.
iou_pred: (num_prompts, num_multimask_outputs)
"""
masks, iou_pred = self.predict_masks(
image_embeddings=image_embeddings,
image_pe=image_pe,
sparse_prompt_embeddings=sparse_prompt_embeddings,
dense_prompt_embeddings=dense_prompt_embeddings,
)
if multimask_output:
return masks[:, 1:], iou_pred[:, 1:]
else:
return masks[:, :1], iou_pred[:, :1]
class MLP(nn.Module):
hidden_dim: int
output_dim: int
num_layers: int
@nn.compact
def __call__(self, x):
for i in range(self.num_layers - 1):
x = nn.Dense(self.hidden_dim, name=f'layers.{i}')(x)
x = nn.relu(x)
x = nn.Dense(self.output_dim, name=f'layers.{self.num_layers - 1}')(x)
return x
class OutputScaling(nn.Module):
"""Output scaling."""
transformer_dim: int
@nn.compact
def __call__(self, x):
x = nn.ConvTranspose(
self.transformer_dim // 4, kernel_size=(2, 2), strides=(2, 2),
transpose_kernel=True,
name='0')(x)
x = nn.LayerNorm(name='1')(x)
x = nn.gelu(x, approximate=False)
x = nn.ConvTranspose(
self.transformer_dim // 8, kernel_size=(2, 2), strides=(2, 2),
transpose_kernel=True,
name='3')(x)
x = nn.gelu(x, approximate=False)
return x
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