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Zero
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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import Mask2FormerForUniversalSegmentation
from transformers.models.mask2former.configuration_mask2former import Mask2FormerConfig
class StyleMatte(nn.Module):
def __init__(self):
super(StyleMatte, self).__init__()
self.fpn = FPN_fuse(feature_channels=[256, 256, 256, 256], fpn_out=256)
config = Mask2FormerConfig.from_json_file('./configs/stylematte_config.json')
self.pixel_decoder = Mask2FormerForUniversalSegmentation(config).base_model.pixel_level_module
self.fgf = FastGuidedFilter(eps=1e-4)
self.conv = nn.Conv2d(256, 1, kernel_size=3, padding=1)
def forward(self, image, normalize=False):
decoder_out = self.pixel_decoder(image)
decoder_states = list(decoder_out.decoder_hidden_states)
decoder_states.append(decoder_out.decoder_last_hidden_state)
out_pure = self.fpn(decoder_states)
image_lr = nn.functional.interpolate(image.mean(1, keepdim=True),
scale_factor=0.25,
mode='bicubic',
align_corners=True
)
out = self.conv(out_pure)
out = self.fgf(image_lr, out, image.mean(1, keepdim=True))
return torch.sigmoid(out)
def get_training_params(self):
return list(self.fpn.parameters())+list(self.conv.parameters())
def conv2d_relu(input_filters, output_filters, kernel_size=3, bias=True):
return nn.Sequential(
nn.Conv2d(input_filters, output_filters,
kernel_size=kernel_size, padding=kernel_size//2, bias=bias),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(output_filters)
)
def up_and_add(x, y):
return F.interpolate(x, size=(y.size(2), y.size(3)), mode='bilinear', align_corners=True) + y
class FPN_fuse(nn.Module):
def __init__(self, feature_channels=[256, 512, 1024, 2048], fpn_out=256):
super(FPN_fuse, self).__init__()
assert feature_channels[0] == fpn_out
self.conv1x1 = nn.ModuleList([nn.Conv2d(ft_size, fpn_out, kernel_size=1)
for ft_size in feature_channels[1:]])
self.smooth_conv = nn.ModuleList([nn.Conv2d(fpn_out, fpn_out, kernel_size=3, padding=1)]
* (len(feature_channels)-1))
self.conv_fusion = nn.Sequential(
nn.Conv2d(2*fpn_out, fpn_out, kernel_size=3,
padding=1, bias=False),
nn.BatchNorm2d(fpn_out),
nn.ReLU(inplace=True),
)
def forward(self, features):
features[:-1] = [conv1x1(feature) for feature,
conv1x1 in zip(features[:-1], self.conv1x1)]
feature = up_and_add(self.smooth_conv[0](features[0]), features[1])
feature = up_and_add(self.smooth_conv[1](feature), features[2])
feature = up_and_add(self.smooth_conv[2](feature), features[3])
H, W = features[-1].size(2), features[-1].size(3)
x = [feature, features[-1]]
x = [F.interpolate(x_el, size=(H, W), mode='bilinear',
align_corners=True) for x_el in x]
x = self.conv_fusion(torch.cat(x, dim=1))
return x
class PSPModule(nn.Module):
# In the original inmplementation they use precise RoI pooling
# Instead of using adaptative average pooling
def __init__(self, in_channels, bin_sizes=[1, 2, 4, 6]):
super(PSPModule, self).__init__()
out_channels = in_channels // len(bin_sizes)
self.stages = nn.ModuleList([self._make_stages(in_channels, out_channels, b_s)
for b_s in bin_sizes])
self.bottleneck = nn.Sequential(
nn.Conv2d(in_channels+(out_channels * len(bin_sizes)), in_channels,
kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.Dropout2d(0.1)
)
def _make_stages(self, in_channels, out_channels, bin_sz):
prior = nn.AdaptiveAvgPool2d(output_size=bin_sz)
conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
bn = nn.BatchNorm2d(out_channels)
relu = nn.ReLU(inplace=True)
return nn.Sequential(prior, conv, bn, relu)
def forward(self, features):
h, w = features.size()[2], features.size()[3]
pyramids = [features]
pyramids.extend([F.interpolate(stage(features), size=(h, w), mode='bilinear',
align_corners=True) for stage in self.stages])
output = self.bottleneck(torch.cat(pyramids, dim=1))
return output
class GuidedFilter(nn.Module):
def __init__(self, r, eps=1e-8):
super(GuidedFilter, self).__init__()
self.r = r
self.eps = eps
self.boxfilter = BoxFilter(r)
def forward(self, x, y):
n_x, c_x, h_x, w_x = x.size()
n_y, c_y, h_y, w_y = y.size()
assert n_x == n_y
assert c_x == 1 or c_x == c_y
assert h_x == h_y and w_x == w_y
assert h_x > 2 * self.r + 1 and w_x > 2 * self.r + 1
# N
N = self.boxfilter((x.data.new().resize_((1, 1, h_x, w_x)).fill_(1.0)))
# mean_x
mean_x = self.boxfilter(x) / N
# mean_y
mean_y = self.boxfilter(y) / N
# cov_xy
cov_xy = self.boxfilter(x * y) / N - mean_x * mean_y
# var_x
var_x = self.boxfilter(x * x) / N - mean_x * mean_x
# A
A = cov_xy / (var_x + self.eps)
# b
b = mean_y - A * mean_x
# mean_A; mean_b
mean_A = self.boxfilter(A) / N
mean_b = self.boxfilter(b) / N
return mean_A * x + mean_b
class FastGuidedFilter(nn.Module):
def __init__(self, r=1, eps=1e-8):
super(FastGuidedFilter, self).__init__()
self.r = r
self.eps = eps
self.boxfilter = BoxFilter(r)
def forward(self, lr_x, lr_y, hr_x):
n_lrx, c_lrx, h_lrx, w_lrx = lr_x.size()
n_lry, c_lry, h_lry, w_lry = lr_y.size()
n_hrx, c_hrx, h_hrx, w_hrx = hr_x.size()
assert n_lrx == n_lry and n_lry == n_hrx
assert c_lrx == c_hrx and (c_lrx == 1 or c_lrx == c_lry)
assert h_lrx == h_lry and w_lrx == w_lry
assert h_lrx > 2*self.r+1 and w_lrx > 2*self.r+1
# N
N = self.boxfilter(lr_x.new().resize_((1, 1, h_lrx, w_lrx)).fill_(1.0))
# mean_x
mean_x = self.boxfilter(lr_x) / N
# mean_y
mean_y = self.boxfilter(lr_y) / N
# cov_xy
cov_xy = self.boxfilter(lr_x * lr_y) / N - mean_x * mean_y
# var_x
var_x = self.boxfilter(lr_x * lr_x) / N - mean_x * mean_x
# A
A = cov_xy / (var_x + self.eps)
# b
b = mean_y - A * mean_x
# mean_A; mean_b
mean_A = F.interpolate(
A, (h_hrx, w_hrx), mode='bilinear', align_corners=True)
mean_b = F.interpolate(
b, (h_hrx, w_hrx), mode='bilinear', align_corners=True)
return mean_A*hr_x+mean_b
class DeepGuidedFilterRefiner(nn.Module):
def __init__(self, hid_channels=16):
super().__init__()
self.box_filter = nn.Conv2d(
4, 4, kernel_size=3, padding=1, bias=False, groups=4)
self.box_filter.weight.data[...] = 1 / 9
self.conv = nn.Sequential(
nn.Conv2d(4 * 2 + hid_channels, hid_channels,
kernel_size=1, bias=False),
nn.BatchNorm2d(hid_channels),
nn.ReLU(True),
nn.Conv2d(hid_channels, hid_channels, kernel_size=1, bias=False),
nn.BatchNorm2d(hid_channels),
nn.ReLU(True),
nn.Conv2d(hid_channels, 4, kernel_size=1, bias=True)
)
def forward(self, fine_src, base_src, base_fgr, base_pha, base_hid):
fine_x = torch.cat([fine_src, fine_src.mean(1, keepdim=True)], dim=1)
base_x = torch.cat([base_src, base_src.mean(1, keepdim=True)], dim=1)
base_y = torch.cat([base_fgr, base_pha], dim=1)
mean_x = self.box_filter(base_x)
mean_y = self.box_filter(base_y)
cov_xy = self.box_filter(base_x * base_y) - mean_x * mean_y
var_x = self.box_filter(base_x * base_x) - mean_x * mean_x
A = self.conv(torch.cat([cov_xy, var_x, base_hid], dim=1))
b = mean_y - A * mean_x
H, W = fine_src.shape[2:]
A = F.interpolate(A, (H, W), mode='bilinear', align_corners=False)
b = F.interpolate(b, (H, W), mode='bilinear', align_corners=False)
out = A * fine_x + b
fgr, pha = out.split([3, 1], dim=1)
return fgr, pha
def diff_x(input, r):
assert input.dim() == 4
left = input[:, :, r:2 * r + 1]
middle = input[:, :, 2 * r + 1:] - input[:, :, :-2 * r - 1]
right = input[:, :, -1:] - input[:, :, -2 * r - 1: -r - 1]
output = torch.cat([left, middle, right], dim=2)
return output
def diff_y(input, r):
assert input.dim() == 4
left = input[:, :, :, r:2 * r + 1]
middle = input[:, :, :, 2 * r + 1:] - input[:, :, :, :-2 * r - 1]
right = input[:, :, :, -1:] - input[:, :, :, -2 * r - 1: -r - 1]
output = torch.cat([left, middle, right], dim=3)
return output
class BoxFilter(nn.Module):
def __init__(self, r):
super(BoxFilter, self).__init__()
self.r = r
def forward(self, x):
assert x.dim() == 4
return diff_y(diff_x(x.cumsum(dim=2), self.r).cumsum(dim=3), self.r)
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