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import cv2
import torch
from torch import nn
from torch.nn import functional as F
# from nnMorpho.binary_operators import erosion
from detectron2.layers.batch_norm import NaiveSyncBatchNorm
class GenTrimapTorch(object):
def __init__(self, max_kernal=200):
self.max_kernal = max_kernal
self.erosion_kernels = [None] + [torch.from_numpy(cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size))).float().cuda() for size in range(1, self.max_kernal)]
def __call__(self, mask, kernel_size):
fg_width = kernel_size
bg_width = kernel_size
fg_mask = mask
bg_mask = 1 - mask
fg_mask = erosion(fg_mask, self.erosion_kernels[fg_width], border='a')
bg_mask = erosion(bg_mask, self.erosion_kernels[bg_width], border='a')
trimap = torch.ones_like(mask) * 0.5
trimap[fg_mask == 1] = 1.0
trimap[bg_mask == 1] = 0.0
return trimap
class LayerNorm2d(nn.Module):
def __init__(self, num_channels: int, eps: float = 1e-6) -> None:
super().__init__()
self.weight = nn.Parameter(torch.ones(num_channels))
self.bias = nn.Parameter(torch.zeros(num_channels))
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class BasicDownBlock(nn.Module):
def __init__(self, in_channel, out_channel, res = True, norm=LayerNorm2d, block_num=1, kernel_size=3):
super().__init__()
self.res = res
self.basic_layer = nn.ModuleList()
for i in range(block_num):
if i == 0:
basic_layer_in_ch = in_channel
stride = 2
else:
basic_layer_in_ch = out_channel
stride = 1
self.basic_layer.append(nn.GELU())
self.basic_layer.append(nn.Sequential(
nn.Conv2d(basic_layer_in_ch, out_channel, kernel_size, stride, kernel_size // 2),
norm(out_channel),
nn.GELU(),
nn.Conv2d(out_channel, out_channel, kernel_size, 1, kernel_size // 2),
norm(out_channel),
))
self.act = nn.GELU()
if self.res:
self.res_layer = nn.Conv2d(in_channel, out_channel, kernel_size, 2, kernel_size // 2)
def forward(self, x):
if self.res:
identity = self.res_layer(x)
else:
identity = F.interpolate(x, size=(out.shape[-2], out.shape[-1]), mode='bilinear', align_corners=False)
out = x
for layer in self.basic_layer:
out = layer(out)
out = out + identity
out = self.act(out)
return out
class BasicUpBlock(nn.Module):
def __init__( self, in_channel, out_channel, res = True, skip_connect = 'concat', norm=LayerNorm2d, block_num=1, kernel_size=3):
super().__init__()
assert skip_connect in {'sum', 'concat'}
self.res = res
self.skip_connect = skip_connect
self.basic_layer = nn.ModuleList()
for i in range(block_num):
if i == 0:
basic_layer_in_ch = in_channel
first_conv = nn.ConvTranspose2d(basic_layer_in_ch, out_channel, 2, 2)
else:
basic_layer_in_ch = out_channel
first_conv = nn.Conv2d(out_channel, out_channel, kernel_size, 1, kernel_size // 2)
self.basic_layer.append(nn.GELU())
self.basic_layer.append(nn.Sequential(
first_conv,
norm(out_channel),
nn.GELU(),
nn.Conv2d(out_channel, out_channel, kernel_size, 1, kernel_size // 2),
norm(out_channel),
))
self.act = nn.GELU()
if self.res:
self.res_layer = nn.Conv2d(in_channel, out_channel, kernel_size, 1, kernel_size // 2)
def forward(self, x, skip_feat, concat_feat=None):
if self.skip_connect == 'sum':
x = x + skip_feat
else:
x = torch.concat((x, skip_feat), dim=1)
if concat_feat is not None:
x = torch.concat((x, concat_feat), dim=1)
out = x
for layer in self.basic_layer:
out = layer(out)
# out = self.basic_layer(x)
identity = F.interpolate(x, size=(out.shape[-2], out.shape[-1]), mode='bilinear', align_corners=False)
if self.res:
identity = self.res_layer(identity)
out = out + identity
out = self.act(out)
return out
class DetailUNet(nn.Module):
def __init__(
self,
img_feat_in = 4,
vit_early_feat_in = 768,
matting_feat_in = 5,
downsample_in_out = [(4, 32), (32, 64), (64, 128), (128, 256)],
upsample_in_out = [(256, 128), (128, 64), (64, 32), (32, 16)],
matting_head_in = 16,
skip_connect = 'sum',
norm_type = 'LN',
):
super().__init__()
assert len(downsample_in_out) == len(upsample_in_out)
downsample_in_out[0] = (img_feat_in, downsample_in_out[0][1])
assert norm_type in {'BN', 'LN', 'SyncBN'}
if norm_type == 'BN':
self.norm = torch.nn.BatchNorm2d
elif norm_type == 'SyncBN':
self.norm = NaiveSyncBatchNorm
else:
self.norm = LayerNorm2d
self.down_blks = nn.ModuleList()
for in_ch, out_ch in downsample_in_out:
self.down_blks.append(
BasicDownBlock(in_ch, out_ch, norm=self.norm)
)
self.mid_layer = nn.Sequential(
nn.Conv2d(vit_early_feat_in, downsample_in_out[-1][1], 1, 1),
self.norm(downsample_in_out[-1][1]),
nn.GELU(),
)
self.up_blks = nn.ModuleList()
for i, (in_ch, out_ch) in enumerate(upsample_in_out):
if i == 2:
in_ch += matting_feat_in
self.up_blks.append(
BasicUpBlock(in_ch, out_ch, skip_connect=skip_connect, norm=self.norm)
)
self.matting_head = nn.Conv2d(matting_head_in, 1, 3, 1, 1)
def forward(self, x, vit_early_feat, matting_feat, return_alpha_logits=False):
details = []
dfeatures = x
for i in range(len(self.down_blks)):
dfeatures = self.down_blks[i](dfeatures)
details.append(dfeatures)
out = self.mid_layer(vit_early_feat)
for i in range(len(self.up_blks)):
if i == 2:
out = self.up_blks[i](out, details[-i - 1], matting_feat)
else:
out = self.up_blks[i](out, details[-i - 1])
alpha = self.matting_head(out)
if return_alpha_logits:
return alpha, out
else:
return alpha
class MattingDetailDecoder(nn.Module):
def __init__(
self,
img_feat_in = 4,
vit_intern_feat_in = 1024,
vit_intern_feat_index = [0, 1, 2, 3],
downsample_in_out = [(4, 32), (32, 64), (64, 128), (128, 256)],
upsample_in_out = [(256, 128), (128, 64), (64, 32), (32, 16)],
matting_head_in = 16,
skip_connect = 'sum',
norm_type = 'BN',
norm_mask_logits = 6.5,
with_trimap = False,
min_kernel_size = 20,
kernel_div = 10,
concat_gen_trimap = False,
wo_hq_features = False,
block_num = 1,
wo_big_kernel = False,
sam2_multi_scale_feates = False,
):
super().__init__()
assert len(downsample_in_out) == len(upsample_in_out)
assert skip_connect in {'sum', 'concat'}
downsample_in_out[0] = (img_feat_in, downsample_in_out[0][1])
self.vit_intern_feat_in = vit_intern_feat_in
self.vit_intern_feat_index = vit_intern_feat_index
self.norm_mask_logits = norm_mask_logits
self.with_trimap = with_trimap
self.min_kernel_size = min_kernel_size
self.kernel_div = kernel_div
self.concat_gen_trimap = concat_gen_trimap
self.wo_hq_features = wo_hq_features
self.block_num = block_num
self.wo_big_kernel = wo_big_kernel
self.sam2_multi_scale_feates = sam2_multi_scale_feates
if self.sam2_multi_scale_feates:
assert downsample_in_out[0][0] == 6
downsample_in_out = [(4, 32), (32, 64), (64 + 32, 128), (128 + 64, 256)]
upsample_in_out = [(256, 128), (128, 64), (64, 32), (32, 16)]
if self.with_trimap and not self.concat_gen_trimap:
self.gen_trimap = GenTrimapTorch()
assert norm_type in {'BN', 'LN', 'SyncBN'}
if norm_type == 'BN':
self.norm = torch.nn.BatchNorm2d
elif norm_type == 'SyncBN':
self.norm = NaiveSyncBatchNorm
else:
self.norm = LayerNorm2d
if self.block_num >= 2 and not self.wo_big_kernel:
self.big_kernel_process = nn.Sequential(
nn.Conv2d(img_feat_in, 16, kernel_size=13, stride=1, padding=6),
self.norm(16),
nn.GELU(),
nn.Conv2d(16, 32, kernel_size=13, stride=1, padding=6),
self.norm(32),
nn.GELU(),
)
downsample_in_out[0] = (32, downsample_in_out[0][1])
if not self.sam2_multi_scale_feates:
self.vit_feat_proj = nn.ModuleDict()
for idx in self.vit_intern_feat_index:
self.vit_feat_proj[str(idx)] = nn.Conv2d(self.vit_intern_feat_in, self.vit_intern_feat_in // len(self.vit_intern_feat_index), 1, 1)
self.vit_feat_aggregation = nn.Sequential(
nn.Conv2d(self.vit_intern_feat_in // len(self.vit_intern_feat_index) * len(self.vit_intern_feat_index), downsample_in_out[-1][1], 3, 1, 1),
self.norm(downsample_in_out[-1][1]),
nn.GELU(),
)
self.down_blks = nn.ModuleList()
for in_ch, out_ch in downsample_in_out:
self.down_blks.append(
BasicDownBlock(in_ch, out_ch, norm=self.norm, block_num=self.block_num, kernel_size=5 if self.block_num >= 2 else 3)
)
if self.sam2_multi_scale_feates:
self.mid_layer = nn.ModuleList([
nn.Sequential(
nn.Conv2d(32, 32, 1, 1),
self.norm(32),
nn.GELU(),
),
nn.Sequential(
nn.Conv2d(64, 64, 1, 1),
self.norm(64),
nn.GELU(),
),
nn.Sequential(
nn.Conv2d(256, 256, 1, 1),
self.norm(256),
nn.GELU(),
),
nn.Sequential(
nn.Conv2d(512, 256, 3, 1, 1),
self.norm(256),
nn.GELU(),
),
])
else:
self.mid_layer = nn.Sequential(
nn.Conv2d(downsample_in_out[-1][1] * 2, downsample_in_out[-1][1], 1, 1),
self.norm(downsample_in_out[-1][1]),
nn.GELU(),
)
self.up_blks = nn.ModuleList()
for _, (in_ch, out_ch) in enumerate(upsample_in_out):
if skip_connect == 'concat':
self.up_blks.append(BasicUpBlock(in_ch * 2, out_ch, skip_connect=skip_connect, norm=self.norm, block_num=self.block_num))
else:
self.up_blks.append(BasicUpBlock(in_ch, out_ch, skip_connect=skip_connect, norm=self.norm, block_num=self.block_num))
self.matting_head = nn.Conv2d(matting_head_in, 1, 3, 1, 1)
if self.norm_mask_logits == 'BN':
self.logits_norm = self.norm(1)
def preprocess_inputs(self, images, hq_features, pred_trimap):
if self.wo_hq_features:
return images
if isinstance(self.norm_mask_logits, float):
norm_hq_features = hq_features / self.norm_mask_logits
elif self.norm_mask_logits == 'BN':
norm_hq_features = self.logits_norm(hq_features)
elif self.norm_mask_logits == 'Sigmoid':
if hq_features.shape[1] == 1:
norm_hq_features = torch.sigmoid(hq_features)
else:
norm_hq_features = torch.softmax(hq_features, dim=1)
elif self.norm_mask_logits:
norm_hq_features = hq_features / torch.std(hq_features, dim=(1, 2, 3), keepdim=True)
else:
norm_hq_features = hq_features
if self.concat_gen_trimap:
pred_trimap = F.interpolate(pred_trimap, size=(images.shape[-2], images.shape[-1]), mode='bilinear', align_corners=False)
pred_trimap = torch.argmax(pred_trimap, dim=1, keepdim=True).float() / 2.0
norm_hq_features = torch.concat((norm_hq_features, pred_trimap.detach()), dim=1)
elif self.with_trimap:
mask = (norm_hq_features > 0).float()
for i_batch in range(images.shape[0]):
mask_area = torch.sum(mask[i_batch])
kernel_size = max(self.min_kernel_size, int((mask_area ** 0.5) / self.kernel_div))
kernel_size = min(kernel_size, self.gen_trimap.max_kernal - 1)
mask[i_batch, 0] = self.gen_trimap(mask[i_batch, 0], kernel_size=kernel_size)
trimaps = mask
norm_hq_features = torch.concat((norm_hq_features, trimaps), dim=1)
conditional_images = torch.concatenate((images, norm_hq_features), dim=1)
return conditional_images
def forward(self, images, hq_features, vit_intern_feat, return_alpha_logits=False, pred_trimap=None):
condition_input = self.preprocess_inputs(images, hq_features, pred_trimap)
if not self.sam2_multi_scale_feates:
# aggregate 4 vit_intern_feat
# assert len(vit_intern_feat) == self.vit_intern_feat_num
vit_feats = []
for idx in self.vit_intern_feat_index:
vit_feats.append(self.vit_feat_proj[str(idx)](vit_intern_feat[idx].permute(0, 3, 1, 2)))
vit_feats = torch.concat(vit_feats, dim=1)
vit_aggregation_feats = self.vit_feat_aggregation(vit_feats)
details = []
dfeatures = condition_input
if hasattr(self, 'big_kernel_process'):
dfeatures = self.big_kernel_process(dfeatures)
for i in range(len(self.down_blks)):
if self.sam2_multi_scale_feates:
if i == 2:
dfeatures = torch.concat((dfeatures, self.mid_layer[0](vit_intern_feat['high_res_feats'][0])), dim=1)
elif i == 3:
dfeatures = torch.concat((dfeatures, self.mid_layer[1](vit_intern_feat['high_res_feats'][1])), dim=1)
dfeatures = self.down_blks[i](dfeatures)
details.append(dfeatures)
if self.sam2_multi_scale_feates:
out = torch.concat((details[-1], self.mid_layer[2](vit_intern_feat['image_embed'])), dim=1)
out = self.mid_layer[3](out)
else:
out = self.mid_layer(torch.concat((details[-1], vit_aggregation_feats), dim=1))
for i in range(len(self.up_blks)):
out = self.up_blks[i](out, details[-i - 1])
alpha = torch.sigmoid(self.matting_head(out))
if return_alpha_logits:
return alpha, out
else:
return alpha
if __name__ == '__main__':
from engine.mattingtrainer import parameter_count_table
model = MattingDetailDecoder(img_feat_in = 5, vit_intern_feat_index=[0])
x = torch.randn((2, 5, 1024, 1024))
hq_features = torch.randn((2, 1, 1024, 1024))
vit_feat = [torch.randn((2, 64, 64, 1024)) for _ in range(4)]
out = model(x, hq_features, vit_feat)
print(out.shape)
print("Trainable parameters: \n" + parameter_count_table(model, trainable_only=True, max_depth=5))
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