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Zero
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from sam2.modeling.sam2_utils import LayerNorm2d
class ImageEncoder(nn.Module):
def __init__(
self,
trunk: nn.Module,
neck: nn.Module,
scalp: int = 0,
):
super().__init__()
self.trunk = trunk
self.neck = neck
self.scalp = scalp
assert (
self.trunk.channel_list == self.neck.backbone_channel_list
), f"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}"
def forward(self, sample: torch.Tensor):
# Forward through backbone
features, pos = self.neck(self.trunk(sample))
if self.scalp > 0:
# Discard the lowest resolution features
features, pos = features[: -self.scalp], pos[: -self.scalp]
src = features[-1]
output = {
"vision_features": src,
"vision_pos_enc": pos,
"backbone_fpn": features,
}
return output
class FpnNeck(nn.Module):
"""
A modified variant of Feature Pyramid Network (FPN) neck
(we remove output conv and also do bicubic interpolation similar to ViT
pos embed interpolation)
"""
def __init__(
self,
position_encoding: nn.Module,
d_model: int,
backbone_channel_list: List[int],
kernel_size: int = 1,
stride: int = 1,
padding: int = 0,
fpn_interp_model: str = "bilinear",
fuse_type: str = "sum",
fpn_top_down_levels: Optional[List[int]] = None,
):
"""Initialize the neck
:param trunk: the backbone
:param position_encoding: the positional encoding to use
:param d_model: the dimension of the model
:param neck_norm: the normalization to use
"""
super().__init__()
self.position_encoding = position_encoding
self.convs = nn.ModuleList()
self.backbone_channel_list = backbone_channel_list
for dim in backbone_channel_list:
current = nn.Sequential()
current.add_module(
"conv",
nn.Conv2d(
in_channels=dim,
out_channels=d_model,
kernel_size=kernel_size,
stride=stride,
padding=padding,
),
)
self.convs.append(current)
self.fpn_interp_model = fpn_interp_model
assert fuse_type in ["sum", "avg"]
self.fuse_type = fuse_type
# levels to have top-down features in its outputs
# e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3
# have top-down propagation, while outputs of level 0 and level 1 have only
# lateral features from the same backbone level.
if fpn_top_down_levels is None:
# default is to have top-down features on all levels
fpn_top_down_levels = range(len(self.convs))
self.fpn_top_down_levels = list(fpn_top_down_levels)
def forward(self, xs: List[torch.Tensor]):
out = [None] * len(self.convs)
pos = [None] * len(self.convs)
assert len(xs) == len(self.convs)
# fpn forward pass
# see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py
prev_features = None
# forward in top-down order (from low to high resolution)
n = len(self.convs) - 1
for i in range(n, -1, -1):
x = xs[i]
lateral_features = self.convs[n - i](x)
if i in self.fpn_top_down_levels and prev_features is not None:
top_down_features = F.interpolate(
prev_features.to(dtype=torch.float32),
scale_factor=2.0,
mode=self.fpn_interp_model,
align_corners=(
None if self.fpn_interp_model == "nearest" else False
),
antialias=False,
)
prev_features = lateral_features + top_down_features
if self.fuse_type == "avg":
prev_features /= 2
else:
prev_features = lateral_features
x_out = prev_features
out[i] = x_out
pos[i] = self.position_encoding(x_out).to(x_out.dtype)
return out, pos
class ViTDetNeck(nn.Module):
def __init__(
self,
position_encoding: nn.Module,
d_model: int,
backbone_channel_list: List[int],
kernel_size: int = 1,
stride: int = 1,
padding: int = 0,
neck_norm=None,
):
"""Initialize the neck
:param trunk: the backbone
:param position_encoding: the positional encoding to use
:param d_model: the dimension of the model
:param neck_norm: the normalization to use
"""
super().__init__()
self.backbone_channel_list = backbone_channel_list
self.position_encoding = position_encoding
self.convs = nn.ModuleList()
use_bias = neck_norm is None
for dim in self.backbone_channel_list:
current = nn.Sequential()
current.add_module(
"conv_1x1",
nn.Conv2d(
in_channels=dim,
out_channels=d_model,
kernel_size=1,
bias=use_bias,
),
)
if neck_norm is not None:
current.add_module("norm_0", LayerNorm2d(d_model))
current.add_module(
"conv_3x3",
nn.Conv2d(
in_channels=d_model,
out_channels=d_model,
kernel_size=3,
padding=1,
bias=use_bias,
),
)
if neck_norm is not None:
current.add_module("norm_1", LayerNorm2d(d_model))
self.convs.append(current)
def forward(self, xs: List[torch.Tensor]):
out = [None] * len(self.convs)
pos = [None] * len(self.convs)
assert len(xs) == len(self.convs)
x = xs[0]
x_out = self.convs[0](x)
out[0] = x_out
pos[0] = self.position_encoding(x_out).to(x_out.dtype)
return out, pos
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