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import torch
import torch.nn as nn
import torch.nn.functional as F
from climategan.blocks import BaseDecoder, Conv2dBlock, InterpolateNearest2d
from climategan.utils import find_target_size
def create_depth_decoder(opts, no_init=False, verbose=0):
if opts.gen.d.architecture == "base":
decoder = BaseDepthDecoder(opts)
if "s" in opts.task:
assert opts.gen.s.use_dada is False
if "m" in opts.tasks:
assert opts.gen.m.use_dada is False
else:
decoder = DADADepthDecoder(opts)
if verbose > 0:
print(f" - Add {decoder.__class__.__name__}")
return decoder
class DADADepthDecoder(nn.Module):
"""
Depth decoder based on depth auxiliary task in DADA paper
"""
def __init__(self, opts):
super().__init__()
if (
opts.gen.encoder.architecture == "deeplabv3"
and opts.gen.deeplabv3.backbone == "mobilenet"
):
res_dim = 320
else:
res_dim = 2048
mid_dim = 512
self.do_feat_fusion = False
if opts.gen.m.use_dada or ("s" in opts.tasks and opts.gen.s.use_dada):
self.do_feat_fusion = True
self.dec4 = Conv2dBlock(
128,
res_dim,
1,
stride=1,
padding=0,
bias=True,
activation="lrelu",
norm="none",
)
self.relu = nn.ReLU(inplace=True)
self.enc4_1 = Conv2dBlock(
res_dim,
mid_dim,
1,
stride=1,
padding=0,
bias=False,
activation="lrelu",
pad_type="reflect",
norm="batch",
)
self.enc4_2 = Conv2dBlock(
mid_dim,
mid_dim,
3,
stride=1,
padding=1,
bias=False,
activation="lrelu",
pad_type="reflect",
norm="batch",
)
self.enc4_3 = Conv2dBlock(
mid_dim,
128,
1,
stride=1,
padding=0,
bias=False,
activation="lrelu",
pad_type="reflect",
norm="batch",
)
self.upsample = None
if opts.gen.d.upsample_featuremaps:
self.upsample = nn.Sequential(
*[
InterpolateNearest2d(),
Conv2dBlock(
128,
32,
3,
stride=1,
padding=1,
bias=False,
activation="lrelu",
pad_type="reflect",
norm="batch",
),
nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
]
)
self._target_size = find_target_size(opts, "d")
print(
" - {}: setting target size to {}".format(
self.__class__.__name__, self._target_size
)
)
def set_target_size(self, size):
"""
Set final interpolation's target size
Args:
size (int, list, tuple): target size (h, w). If int, target will be (i, i)
"""
if isinstance(size, (list, tuple)):
self._target_size = size[:2]
else:
self._target_size = (size, size)
def forward(self, z):
if isinstance(z, (list, tuple)):
z = z[0]
z4_enc = self.enc4_1(z)
z4_enc = self.enc4_2(z4_enc)
z4_enc = self.enc4_3(z4_enc)
z_depth = None
if self.do_feat_fusion:
z_depth = self.dec4(z4_enc)
if self.upsample is not None:
z4_enc = self.upsample(z4_enc)
depth = torch.mean(z4_enc, dim=1, keepdim=True) # DADA paper decoder
if depth.shape[-1] != self._target_size:
depth = F.interpolate(
depth,
size=(384, 384), # size used in MiDaS inference
mode="bicubic", # what MiDaS uses
align_corners=False,
)
depth = F.interpolate(
depth, (self._target_size, self._target_size), mode="nearest"
) # what we used in the transforms to resize input
return depth, z_depth
def __str__(self):
return "DADA Depth Decoder"
class BaseDepthDecoder(BaseDecoder):
def __init__(self, opts):
low_level_feats_dim = -1
use_v3 = opts.gen.encoder.architecture == "deeplabv3"
use_mobile_net = opts.gen.deeplabv3.backbone == "mobilenet"
use_low = opts.gen.d.use_low_level_feats
if use_v3 and use_mobile_net:
input_dim = 320
if use_low:
low_level_feats_dim = 24
elif use_v3:
input_dim = 2048
if use_low:
low_level_feats_dim = 256
else:
input_dim = 2048
n_upsample = 1 if opts.gen.d.upsample_featuremaps else 0
output_dim = (
1
if not opts.gen.d.classify.enable
else opts.gen.d.classify.linspace.buckets
)
self._target_size = find_target_size(opts, "d")
print(
" - {}: setting target size to {}".format(
self.__class__.__name__, self._target_size
)
)
super().__init__(
n_upsample=n_upsample,
n_res=opts.gen.d.n_res,
input_dim=input_dim,
proj_dim=opts.gen.d.proj_dim,
output_dim=output_dim,
norm=opts.gen.d.norm,
activ=opts.gen.d.activ,
pad_type=opts.gen.d.pad_type,
output_activ="none",
low_level_feats_dim=low_level_feats_dim,
)
def set_target_size(self, size):
"""
Set final interpolation's target size
Args:
size (int, list, tuple): target size (h, w). If int, target will be (i, i)
"""
if isinstance(size, (list, tuple)):
self._target_size = size[:2]
else:
self._target_size = (size, size)
def forward(self, z, cond=None):
if self._target_size is None:
error = "self._target_size should be set with self.set_target_size()"
error += "to interpolate depth to the target depth map's size"
raise ValueError(error)
d = super().forward(z)
preds = F.interpolate(
d, size=self._target_size, mode="bilinear", align_corners=True
)
return preds, None
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