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import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from siclib.models import BaseModel
from siclib.models.utils.modules import ConvModule, FeatureFusionBlock
logger = logging.getLogger(__name__)
# flake8: noqa
# mypy: ignore-errors
class DecoderBlock(nn.Module):
def __init__(
self,
previous,
out,
ksize=3,
num_convs=1,
norm_str="BatchNorm2d",
padding="zeros",
fusion="sum",
):
super().__init__()
self.fusion = fusion
if self.fusion == "sum":
self.fusion_layers = nn.Identity()
elif self.fusion == "glu":
self.fusion_layers = nn.Sequential(
nn.Conv2d(2 * out, 2 * out, 1, padding=0, bias=True),
nn.GLU(dim=1),
)
elif self.fusion == "ff":
self.fusion_layers = FeatureFusionBlock(out, upsample=False)
else:
raise ValueError(f"Unknown fusion: {self.fusion}")
if norm_str is not None:
norm = getattr(nn, norm_str, None)
layers = []
for i in range(num_convs):
conv = nn.Conv2d(
previous if i == 0 else out,
out,
kernel_size=ksize,
padding=ksize // 2,
bias=norm_str is None,
padding_mode=padding,
)
layers.append(conv)
if norm_str is not None:
layers.append(norm(out))
layers.append(nn.ReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, previous, skip):
_, _, hp, wp = previous.shape
_, _, hs, ws = skip.shape
scale = 2 ** np.round(np.log2(np.array([hs / hp, ws / wp])))
upsampled = nn.functional.interpolate(
previous, scale_factor=scale.tolist(), mode="bilinear", align_corners=False
)
# If the shape of the input map `skip` is not a multiple of 2,
# it will not match the shape of the upsampled map `upsampled`.
# If the downsampling uses ceil_mode=False, we need to crop `skip`.
# If it uses ceil_mode=True (not supported here), we should pad it.
_, _, hu, wu = upsampled.shape
_, _, hs, ws = skip.shape
if (hu <= hs) and (wu <= ws):
skip = skip[:, :, :hu, :wu]
elif (hu >= hs) and (wu >= ws):
skip = nn.functional.pad(skip, [0, wu - ws, 0, hu - hs])
else:
raise ValueError(f"Inconsistent skip vs upsampled shapes: {(hs, ws)}, {(hu, wu)}")
skip = skip.clone()
feats_skip = self.layers(skip)
if self.fusion == "sum":
return self.fusion_layers(feats_skip + upsampled)
elif self.fusion == "glu":
x = torch.cat([feats_skip, upsampled], dim=1)
return self.fusion_layers(x)
elif self.fusion == "ff":
return self.fusion_layers(feats_skip, upsampled)
else:
raise ValueError(f"Unknown fusion: {self.fusion}")
class FPN(BaseModel):
default_conf = {
"predict_uncertainty": True,
"in_channels_list": [64, 128, 256, 512],
"out_channels": 64,
"num_convs": 1,
"norm": None,
"padding": "zeros",
"fusion": "sum",
"with_low_level": True,
}
required_data_keys = ["hl"]
def _init(self, conf):
self.in_channels_list = conf.in_channels_list
self.out_channels = conf.out_channels
self.num_convs = conf.num_convs
self.norm = conf.norm
self.padding = conf.padding
self.fusion = conf.fusion
self.first = nn.Conv2d(
self.in_channels_list[-1], self.out_channels, 1, padding=0, bias=True
)
self.blocks = nn.ModuleList(
[
DecoderBlock(
c,
self.out_channels,
ksize=1,
num_convs=self.num_convs,
norm_str=self.norm,
padding=self.padding,
fusion=self.fusion,
)
for c in self.in_channels_list[::-1][1:]
]
)
self.out = nn.Sequential(
ConvModule(
in_channels=self.out_channels,
out_channels=self.out_channels,
kernel_size=3,
padding=1,
bias=False,
),
ConvModule(
in_channels=self.out_channels,
out_channels=self.out_channels,
kernel_size=3,
padding=1,
bias=False,
),
)
self.predict_uncertainty = conf.predict_uncertainty
if self.predict_uncertainty:
self.linear_pred_uncertainty = nn.Sequential(
ConvModule(
in_channels=self.out_channels,
out_channels=self.out_channels,
kernel_size=3,
padding=1,
bias=False,
),
nn.Conv2d(in_channels=self.out_channels, out_channels=1, kernel_size=1),
)
self.with_ll = conf.with_low_level
if self.with_ll:
self.out_conv = ConvModule(self.out_channels, self.out_channels, 3, padding=1)
self.ll_fusion = FeatureFusionBlock(self.out_channels, upsample=False)
def _forward(self, features):
layers = features["hl"]
feats = None
for idx, x in enumerate(reversed(layers)):
feats = self.first(x) if feats is None else self.blocks[idx - 1](feats, x)
feats = self.out(feats)
feats = F.interpolate(feats, scale_factor=2, mode="bilinear", align_corners=False)
feats = self.out_conv(feats)
if self.with_ll:
assert "ll" in features, "Low-level features are required for this model"
feats_ll = features["ll"].clone()
feats = self.ll_fusion(feats, feats_ll)
uncertainty = (
self.linear_pred_uncertainty(feats).squeeze(1) if self.predict_uncertainty else None
)
return feats, uncertainty
def loss(self, pred, data):
raise NotImplementedError
def metrics(self, pred, data):
raise NotImplementedError
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