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# Codes are borrowed from | |
# https://github.com/ZHKKKe/MODNet/blob/master/src/trainer.py | |
# https://github.com/ZHKKKe/MODNet/blob/master/src/models/backbones/mobilenetv2.py | |
# https://github.com/ZHKKKe/MODNet/blob/master/src/models/modnet.py | |
import numpy as np | |
import scipy | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import os | |
import math | |
import torch | |
from scipy.ndimage import gaussian_filter | |
# ---------------------------------------------------------------------------------- | |
# Loss Functions | |
# ---------------------------------------------------------------------------------- | |
class GaussianBlurLayer(nn.Module): | |
""" Add Gaussian Blur to a 4D tensors | |
This layer takes a 4D tensor of {N, C, H, W} as input. | |
The Gaussian blur will be performed in given channel number (C) splitly. | |
""" | |
def __init__(self, channels, kernel_size): | |
""" | |
Arguments: | |
channels (int): Channel for input tensor | |
kernel_size (int): Size of the kernel used in blurring | |
""" | |
super(GaussianBlurLayer, self).__init__() | |
self.channels = channels | |
self.kernel_size = kernel_size | |
assert self.kernel_size % 2 != 0 | |
self.op = nn.Sequential( | |
nn.ReflectionPad2d(math.floor(self.kernel_size / 2)), | |
nn.Conv2d(channels, channels, self.kernel_size, | |
stride=1, padding=0, bias=None, groups=channels) | |
) | |
self._init_kernel() | |
def forward(self, x): | |
""" | |
Arguments: | |
x (torch.Tensor): input 4D tensor | |
Returns: | |
torch.Tensor: Blurred version of the input | |
""" | |
if not len(list(x.shape)) == 4: | |
print('\'GaussianBlurLayer\' requires a 4D tensor as input\n') | |
exit() | |
elif not x.shape[1] == self.channels: | |
print('In \'GaussianBlurLayer\', the required channel ({0}) is' | |
'not the same as input ({1})\n'.format(self.channels, x.shape[1])) | |
exit() | |
return self.op(x) | |
def _init_kernel(self): | |
sigma = 0.3 * ((self.kernel_size - 1) * 0.5 - 1) + 0.8 | |
n = np.zeros((self.kernel_size, self.kernel_size)) | |
i = math.floor(self.kernel_size / 2) | |
n[i, i] = 1 | |
kernel = gaussian_filter(n, sigma) | |
for name, param in self.named_parameters(): | |
param.data.copy_(torch.from_numpy(kernel)) | |
param.requires_grad = False | |
blurer = GaussianBlurLayer(1, 3) | |
def loss_func(pred_semantic, pred_detail, pred_matte, image, trimap, gt_matte, | |
semantic_scale=10.0, detail_scale=10.0, matte_scale=1.0): | |
""" loss of MODNet | |
Arguments: | |
blurer: GaussianBlurLayer | |
pred_semantic: model output | |
pred_detail: model output | |
pred_matte: model output | |
image : input RGB image ts pixel values should be normalized | |
trimap : trimap used to calculate the losses | |
its pixel values can be 0, 0.5, or 1 | |
(foreground=1, background=0, unknown=0.5) | |
gt_matte: ground truth alpha matte its pixel values are between [0, 1] | |
semantic_scale (float): scale of the semantic loss | |
NOTE: please adjust according to your dataset | |
detail_scale (float): scale of the detail loss | |
NOTE: please adjust according to your dataset | |
matte_scale (float): scale of the matte loss | |
NOTE: please adjust according to your dataset | |
Returns: | |
semantic_loss (torch.Tensor): loss of the semantic estimation [Low-Resolution (LR) Branch] | |
detail_loss (torch.Tensor): loss of the detail prediction [High-Resolution (HR) Branch] | |
matte_loss (torch.Tensor): loss of the semantic-detail fusion [Fusion Branch] | |
""" | |
trimap = trimap.float() | |
# calculate the boundary mask from the trimap | |
boundaries = (trimap < 0.5) + (trimap > 0.5) | |
# calculate the semantic loss | |
gt_semantic = F.interpolate(gt_matte, scale_factor=1 / 16, mode='bilinear') | |
gt_semantic = blurer(gt_semantic) | |
semantic_loss = torch.mean(F.mse_loss(pred_semantic, gt_semantic)) | |
semantic_loss = semantic_scale * semantic_loss | |
# calculate the detail loss | |
pred_boundary_detail = torch.where(boundaries, trimap, pred_detail.float()) | |
gt_detail = torch.where(boundaries, trimap, gt_matte.float()) | |
detail_loss = torch.mean(F.l1_loss(pred_boundary_detail, gt_detail.float())) | |
detail_loss = detail_scale * detail_loss | |
# calculate the matte loss | |
pred_boundary_matte = torch.where(boundaries, trimap, pred_matte.float()) | |
matte_l1_loss = F.l1_loss(pred_matte, gt_matte) + 4.0 * F.l1_loss(pred_boundary_matte, gt_matte) | |
matte_compositional_loss = F.l1_loss(image * pred_matte, image * gt_matte) \ | |
+ 4.0 * F.l1_loss(image * pred_boundary_matte, image * gt_matte) | |
matte_loss = torch.mean(matte_l1_loss + matte_compositional_loss) | |
matte_loss = matte_scale * matte_loss | |
return semantic_loss, detail_loss, matte_loss | |
# ------------------------------------------------------------------------------ | |
# Useful functions | |
# ------------------------------------------------------------------------------ | |
def _make_divisible(v, divisor, min_value=None): | |
if min_value is None: | |
min_value = divisor | |
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) | |
# Make sure that round down does not go down by more than 10%. | |
if new_v < 0.9 * v: | |
new_v += divisor | |
return new_v | |
def conv_bn(inp, oup, stride): | |
return nn.Sequential( | |
nn.Conv2d(inp, oup, 3, stride, 1, bias=False), | |
nn.BatchNorm2d(oup), | |
nn.ReLU6(inplace=True) | |
) | |
def conv_1x1_bn(inp, oup): | |
return nn.Sequential( | |
nn.Conv2d(inp, oup, 1, 1, 0, bias=False), | |
nn.BatchNorm2d(oup), | |
nn.ReLU6(inplace=True) | |
) | |
# ------------------------------------------------------------------------------ | |
# Class of Inverted Residual block | |
# ------------------------------------------------------------------------------ | |
class InvertedResidual(nn.Module): | |
def __init__(self, inp, oup, stride, expansion, dilation=1): | |
super(InvertedResidual, self).__init__() | |
self.stride = stride | |
assert stride in [1, 2] | |
hidden_dim = round(inp * expansion) | |
self.use_res_connect = self.stride == 1 and inp == oup | |
if expansion == 1: | |
self.conv = nn.Sequential( | |
# dw | |
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False), | |
nn.BatchNorm2d(hidden_dim), | |
nn.ReLU6(inplace=True), | |
# pw-linear | |
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), | |
nn.BatchNorm2d(oup), | |
) | |
else: | |
self.conv = nn.Sequential( | |
# pw | |
nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), | |
nn.BatchNorm2d(hidden_dim), | |
nn.ReLU6(inplace=True), | |
# dw | |
nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False), | |
nn.BatchNorm2d(hidden_dim), | |
nn.ReLU6(inplace=True), | |
# pw-linear | |
nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), | |
nn.BatchNorm2d(oup), | |
) | |
def forward(self, x): | |
if self.use_res_connect: | |
return x + self.conv(x) | |
else: | |
return self.conv(x) | |
# ------------------------------------------------------------------------------ | |
# Class of MobileNetV2 | |
# ------------------------------------------------------------------------------ | |
class MobileNetV2(nn.Module): | |
def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000): | |
super(MobileNetV2, self).__init__() | |
self.in_channels = in_channels | |
self.num_classes = num_classes | |
input_channel = 32 | |
last_channel = 1280 | |
interverted_residual_setting = [ | |
# t, c, n, s | |
[1, 16, 1, 1], | |
[expansion, 24, 2, 2], | |
[expansion, 32, 3, 2], | |
[expansion, 64, 4, 2], | |
[expansion, 96, 3, 1], | |
[expansion, 160, 3, 2], | |
[expansion, 320, 1, 1], | |
] | |
# building first layer | |
input_channel = _make_divisible(input_channel * alpha, 8) | |
self.last_channel = _make_divisible(last_channel * alpha, 8) if alpha > 1.0 else last_channel | |
self.features = [conv_bn(self.in_channels, input_channel, 2)] | |
# building inverted residual blocks | |
for t, c, n, s in interverted_residual_setting: | |
output_channel = _make_divisible(int(c * alpha), 8) | |
for i in range(n): | |
if i == 0: | |
self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t)) | |
else: | |
self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t)) | |
input_channel = output_channel | |
# building last several layers | |
self.features.append(conv_1x1_bn(input_channel, self.last_channel)) | |
# make it nn.Sequential | |
self.features = nn.Sequential(*self.features) | |
# building classifier | |
if self.num_classes is not None: | |
self.classifier = nn.Sequential( | |
nn.Dropout(0.2), | |
nn.Linear(self.last_channel, num_classes), | |
) | |
# Initialize weights | |
self._init_weights() | |
def forward(self, x): | |
# Stage1 | |
x = self.features[0](x) | |
x = self.features[1](x) | |
# Stage2 | |
x = self.features[2](x) | |
x = self.features[3](x) | |
# Stage3 | |
x = self.features[4](x) | |
x = self.features[5](x) | |
x = self.features[6](x) | |
# Stage4 | |
x = self.features[7](x) | |
x = self.features[8](x) | |
x = self.features[9](x) | |
x = self.features[10](x) | |
x = self.features[11](x) | |
x = self.features[12](x) | |
x = self.features[13](x) | |
# Stage5 | |
x = self.features[14](x) | |
x = self.features[15](x) | |
x = self.features[16](x) | |
x = self.features[17](x) | |
x = self.features[18](x) | |
# Classification | |
if self.num_classes is not None: | |
x = x.mean(dim=(2, 3)) | |
x = self.classifier(x) | |
# Output | |
return x | |
def _load_pretrained_model(self, pretrained_file): | |
pretrain_dict = torch.load(pretrained_file, map_location='cpu') | |
model_dict = {} | |
state_dict = self.state_dict() | |
print("[MobileNetV2] Loading pretrained model...") | |
for k, v in pretrain_dict.items(): | |
if k in state_dict: | |
model_dict[k] = v | |
else: | |
print(k, "is ignored") | |
state_dict.update(model_dict) | |
self.load_state_dict(state_dict) | |
def _init_weights(self): | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d): | |
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
m.weight.data.normal_(0, math.sqrt(2. / n)) | |
if m.bias is not None: | |
m.bias.data.zero_() | |
elif isinstance(m, nn.BatchNorm2d): | |
m.weight.data.fill_(1) | |
m.bias.data.zero_() | |
elif isinstance(m, nn.Linear): | |
n = m.weight.size(1) | |
m.weight.data.normal_(0, 0.01) | |
m.bias.data.zero_() | |
class BaseBackbone(nn.Module): | |
""" Superclass of Replaceable Backbone Model for Semantic Estimation | |
""" | |
def __init__(self, in_channels): | |
super(BaseBackbone, self).__init__() | |
self.in_channels = in_channels | |
self.model = None | |
self.enc_channels = [] | |
def forward(self, x): | |
raise NotImplementedError | |
def load_pretrained_ckpt(self): | |
raise NotImplementedError | |
class MobileNetV2Backbone(BaseBackbone): | |
""" MobileNetV2 Backbone | |
""" | |
def __init__(self, in_channels): | |
super(MobileNetV2Backbone, self).__init__(in_channels) | |
self.model = MobileNetV2(self.in_channels, alpha=1.0, expansion=6, num_classes=None) | |
self.enc_channels = [16, 24, 32, 96, 1280] | |
def forward(self, x): | |
# x = reduce(lambda x, n: self.model.features[n](x), list(range(0, 2)), x) | |
x = self.model.features[0](x) | |
x = self.model.features[1](x) | |
enc2x = x | |
# x = reduce(lambda x, n: self.model.features[n](x), list(range(2, 4)), x) | |
x = self.model.features[2](x) | |
x = self.model.features[3](x) | |
enc4x = x | |
# x = reduce(lambda x, n: self.model.features[n](x), list(range(4, 7)), x) | |
x = self.model.features[4](x) | |
x = self.model.features[5](x) | |
x = self.model.features[6](x) | |
enc8x = x | |
# x = reduce(lambda x, n: self.model.features[n](x), list(range(7, 14)), x) | |
x = self.model.features[7](x) | |
x = self.model.features[8](x) | |
x = self.model.features[9](x) | |
x = self.model.features[10](x) | |
x = self.model.features[11](x) | |
x = self.model.features[12](x) | |
x = self.model.features[13](x) | |
enc16x = x | |
# x = reduce(lambda x, n: self.model.features[n](x), list(range(14, 19)), x) | |
x = self.model.features[14](x) | |
x = self.model.features[15](x) | |
x = self.model.features[16](x) | |
x = self.model.features[17](x) | |
x = self.model.features[18](x) | |
enc32x = x | |
return [enc2x, enc4x, enc8x, enc16x, enc32x] | |
def load_pretrained_ckpt(self): | |
# the pre-trained model is provided by https://github.com/thuyngch/Human-Segmentation-PyTorch | |
ckpt_path = './pretrained/mobilenetv2_human_seg.ckpt' | |
if not os.path.exists(ckpt_path): | |
print('cannot find the pretrained mobilenetv2 backbone') | |
exit() | |
ckpt = torch.load(ckpt_path) | |
self.model.load_state_dict(ckpt) | |
SUPPORTED_BACKBONES = { | |
'mobilenetv2': MobileNetV2Backbone, | |
} | |
# ------------------------------------------------------------------------------ | |
# MODNet Basic Modules | |
# ------------------------------------------------------------------------------ | |
class IBNorm(nn.Module): | |
""" Combine Instance Norm and Batch Norm into One Layer | |
""" | |
def __init__(self, in_channels): | |
super(IBNorm, self).__init__() | |
in_channels = in_channels | |
self.bnorm_channels = int(in_channels / 2) | |
self.inorm_channels = in_channels - self.bnorm_channels | |
self.bnorm = nn.BatchNorm2d(self.bnorm_channels, affine=True) | |
self.inorm = nn.InstanceNorm2d(self.inorm_channels, affine=False) | |
def forward(self, x): | |
bn_x = self.bnorm(x[:, :self.bnorm_channels, ...].contiguous()) | |
in_x = self.inorm(x[:, self.bnorm_channels:, ...].contiguous()) | |
return torch.cat((bn_x, in_x), 1) | |
class Conv2dIBNormRelu(nn.Module): | |
""" Convolution + IBNorm + ReLu | |
""" | |
def __init__(self, in_channels, out_channels, kernel_size, | |
stride=1, padding=0, dilation=1, groups=1, bias=True, | |
with_ibn=True, with_relu=True): | |
super(Conv2dIBNormRelu, self).__init__() | |
layers = [ | |
nn.Conv2d(in_channels, out_channels, kernel_size, | |
stride=stride, padding=padding, dilation=dilation, | |
groups=groups, bias=bias) | |
] | |
if with_ibn: | |
layers.append(IBNorm(out_channels)) | |
if with_relu: | |
layers.append(nn.ReLU(inplace=True)) | |
self.layers = nn.Sequential(*layers) | |
def forward(self, x): | |
return self.layers(x) | |
class SEBlock(nn.Module): | |
""" SE Block Proposed in https://arxiv.org/pdf/1709.01507.pdf | |
""" | |
def __init__(self, in_channels, out_channels, reduction=1): | |
super(SEBlock, self).__init__() | |
self.pool = nn.AdaptiveAvgPool2d(1) | |
self.fc = nn.Sequential( | |
nn.Linear(in_channels, int(in_channels // reduction), bias=False), | |
nn.ReLU(inplace=True), | |
nn.Linear(int(in_channels // reduction), out_channels, bias=False), | |
nn.Sigmoid() | |
) | |
def forward(self, x): | |
b, c, _, _ = x.size() | |
w = self.pool(x).view(b, c) | |
w = self.fc(w).view(b, c, 1, 1) | |
return x * w.expand_as(x) | |
# ------------------------------------------------------------------------------ | |
# MODNet Branches | |
# ------------------------------------------------------------------------------ | |
class LRBranch(nn.Module): | |
""" Low Resolution Branch of MODNet | |
""" | |
def __init__(self, backbone): | |
super(LRBranch, self).__init__() | |
enc_channels = backbone.enc_channels | |
self.backbone = backbone | |
self.se_block = SEBlock(enc_channels[4], enc_channels[4], reduction=4) | |
self.conv_lr16x = Conv2dIBNormRelu(enc_channels[4], enc_channels[3], 5, stride=1, padding=2) | |
self.conv_lr8x = Conv2dIBNormRelu(enc_channels[3], enc_channels[2], 5, stride=1, padding=2) | |
self.conv_lr = Conv2dIBNormRelu(enc_channels[2], 1, kernel_size=3, stride=2, padding=1, with_ibn=False, | |
with_relu=False) | |
def forward(self, img, inference): | |
enc_features = self.backbone.forward(img) | |
enc2x, enc4x, enc32x = enc_features[0], enc_features[1], enc_features[4] | |
enc32x = self.se_block(enc32x) | |
lr16x = F.interpolate(enc32x, scale_factor=2, mode='bilinear', align_corners=False) | |
lr16x = self.conv_lr16x(lr16x) | |
lr8x = F.interpolate(lr16x, scale_factor=2, mode='bilinear', align_corners=False) | |
lr8x = self.conv_lr8x(lr8x) | |
pred_semantic = None | |
if not inference: | |
lr = self.conv_lr(lr8x) | |
pred_semantic = torch.sigmoid(lr) | |
return pred_semantic, lr8x, [enc2x, enc4x] | |
class HRBranch(nn.Module): | |
""" High Resolution Branch of MODNet | |
""" | |
def __init__(self, hr_channels, enc_channels): | |
super(HRBranch, self).__init__() | |
self.tohr_enc2x = Conv2dIBNormRelu(enc_channels[0], hr_channels, 1, stride=1, padding=0) | |
self.conv_enc2x = Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=2, padding=1) | |
self.tohr_enc4x = Conv2dIBNormRelu(enc_channels[1], hr_channels, 1, stride=1, padding=0) | |
self.conv_enc4x = Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1) | |
self.conv_hr4x = nn.Sequential( | |
Conv2dIBNormRelu(3 * hr_channels + 3, 2 * hr_channels, 3, stride=1, padding=1), | |
Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), | |
Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), | |
) | |
self.conv_hr2x = nn.Sequential( | |
Conv2dIBNormRelu(2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1), | |
Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1), | |
Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), | |
Conv2dIBNormRelu(hr_channels, hr_channels, 3, stride=1, padding=1), | |
) | |
self.conv_hr = nn.Sequential( | |
Conv2dIBNormRelu(hr_channels + 3, hr_channels, 3, stride=1, padding=1), | |
Conv2dIBNormRelu(hr_channels, 1, kernel_size=1, stride=1, padding=0, with_ibn=False, with_relu=False), | |
) | |
def forward(self, img, enc2x, enc4x, lr8x, inference): | |
img2x = F.interpolate(img, scale_factor=1 / 2, mode='bilinear', align_corners=False) | |
img4x = F.interpolate(img, scale_factor=1 / 4, mode='bilinear', align_corners=False) | |
enc2x = self.tohr_enc2x(enc2x) | |
hr4x = self.conv_enc2x(torch.cat((img2x, enc2x), dim=1)) | |
enc4x = self.tohr_enc4x(enc4x) | |
hr4x = self.conv_enc4x(torch.cat((hr4x, enc4x), dim=1)) | |
lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False) | |
hr4x = self.conv_hr4x(torch.cat((hr4x, lr4x, img4x), dim=1)) | |
hr2x = F.interpolate(hr4x, scale_factor=2, mode='bilinear', align_corners=False) | |
hr2x = self.conv_hr2x(torch.cat((hr2x, enc2x), dim=1)) | |
pred_detail = None | |
if not inference: | |
hr = F.interpolate(hr2x, scale_factor=2, mode='bilinear', align_corners=False) | |
hr = self.conv_hr(torch.cat((hr, img), dim=1)) | |
pred_detail = torch.sigmoid(hr) | |
return pred_detail, hr2x | |
class FusionBranch(nn.Module): | |
""" Fusion Branch of MODNet | |
""" | |
def __init__(self, hr_channels, enc_channels): | |
super(FusionBranch, self).__init__() | |
self.conv_lr4x = Conv2dIBNormRelu(enc_channels[2], hr_channels, 5, stride=1, padding=2) | |
self.conv_f2x = Conv2dIBNormRelu(2 * hr_channels, hr_channels, 3, stride=1, padding=1) | |
self.conv_f = nn.Sequential( | |
Conv2dIBNormRelu(hr_channels + 3, int(hr_channels / 2), 3, stride=1, padding=1), | |
Conv2dIBNormRelu(int(hr_channels / 2), 1, 1, stride=1, padding=0, with_ibn=False, with_relu=False), | |
) | |
def forward(self, img, lr8x, hr2x): | |
lr4x = F.interpolate(lr8x, scale_factor=2, mode='bilinear', align_corners=False) | |
lr4x = self.conv_lr4x(lr4x) | |
lr2x = F.interpolate(lr4x, scale_factor=2, mode='bilinear', align_corners=False) | |
f2x = self.conv_f2x(torch.cat((lr2x, hr2x), dim=1)) | |
f = F.interpolate(f2x, scale_factor=2, mode='bilinear', align_corners=False) | |
f = self.conv_f(torch.cat((f, img), dim=1)) | |
pred_matte = torch.sigmoid(f) | |
return pred_matte | |
# ------------------------------------------------------------------------------ | |
# MODNet | |
# ------------------------------------------------------------------------------ | |
class MODNet(nn.Module): | |
""" Architecture of MODNet | |
""" | |
def __init__(self, in_channels=3, hr_channels=32, backbone_arch='mobilenetv2', backbone_pretrained=False): | |
super(MODNet, self).__init__() | |
self.in_channels = in_channels | |
self.hr_channels = hr_channels | |
self.backbone_arch = backbone_arch | |
self.backbone_pretrained = backbone_pretrained | |
self.backbone = SUPPORTED_BACKBONES[self.backbone_arch](self.in_channels) | |
self.lr_branch = LRBranch(self.backbone) | |
self.hr_branch = HRBranch(self.hr_channels, self.backbone.enc_channels) | |
self.f_branch = FusionBranch(self.hr_channels, self.backbone.enc_channels) | |
for m in self.modules(): | |
if isinstance(m, nn.Conv2d): | |
self._init_conv(m) | |
elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.InstanceNorm2d): | |
self._init_norm(m) | |
if self.backbone_pretrained: | |
self.backbone.load_pretrained_ckpt() | |
def forward(self, img, inference): | |
pred_semantic, lr8x, [enc2x, enc4x] = self.lr_branch(img, inference) | |
pred_detail, hr2x = self.hr_branch(img, enc2x, enc4x, lr8x, inference) | |
pred_matte = self.f_branch(img, lr8x, hr2x) | |
return pred_semantic, pred_detail, pred_matte | |
def compute_loss(args): | |
pred_semantic, pred_detail, pred_matte, image, trimap, gt_matte = args | |
semantic_loss, detail_loss, matte_loss = loss_func(pred_semantic, pred_detail, pred_matte, | |
image, trimap, gt_matte) | |
loss = semantic_loss + detail_loss + matte_loss | |
return matte_loss, loss | |
def freeze_norm(self): | |
norm_types = [nn.BatchNorm2d, nn.InstanceNorm2d] | |
for m in self.modules(): | |
for n in norm_types: | |
if isinstance(m, n): | |
m.eval() | |
continue | |
def _init_conv(self, conv): | |
nn.init.kaiming_uniform_( | |
conv.weight, a=0, mode='fan_in', nonlinearity='relu') | |
if conv.bias is not None: | |
nn.init.constant_(conv.bias, 0) | |
def _init_norm(self, norm): | |
if norm.weight is not None: | |
nn.init.constant_(norm.weight, 1) | |
nn.init.constant_(norm.bias, 0) | |
def _apply(self, fn): | |
super(MODNet, self)._apply(fn) | |
blurer._apply(fn) # let blurer's device same as modnet | |
return self | |