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import paddle |
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import paddle.nn as nn |
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import paddle.nn.functional as F |
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from paddleseg.models import layers |
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from paddleseg import utils |
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from paddleseg.cvlibs import manager, param_init |
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from ppmatting.models.layers import GuidedCxtAtten |
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@manager.MODELS.add_component |
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class GCABaseline(nn.Layer): |
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def __init__(self, backbone, pretrained=None): |
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super().__init__() |
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self.encoder = backbone |
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self.decoder = ResShortCut_D_Dec([2, 3, 3, 2]) |
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def forward(self, inputs): |
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x = paddle.concat([inputs['img'], inputs['trimap'] / 255], axis=1) |
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embedding, mid_fea = self.encoder(x) |
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alpha_pred = self.decoder(embedding, mid_fea) |
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if self.training: |
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logit_dict = {'alpha_pred': alpha_pred, } |
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loss_dict = {} |
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alpha_gt = inputs['alpha'] |
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loss_dict["alpha"] = F.l1_loss(alpha_pred, alpha_gt) |
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loss_dict["all"] = loss_dict["alpha"] |
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return logit_dict, loss_dict |
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return alpha_pred |
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@manager.MODELS.add_component |
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class GCA(GCABaseline): |
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def __init__(self, backbone, pretrained=None): |
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super().__init__(backbone, pretrained) |
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self.decoder = ResGuidedCxtAtten_Dec([2, 3, 3, 2]) |
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def conv5x5(in_planes, out_planes, stride=1, groups=1, dilation=1): |
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"""5x5 convolution with padding""" |
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return nn.Conv2D( |
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in_planes, |
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out_planes, |
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kernel_size=5, |
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stride=stride, |
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padding=2, |
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groups=groups, |
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bias_attr=False, |
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dilation=dilation) |
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def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): |
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"""3x3 convolution with padding""" |
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return nn.Conv2D( |
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in_planes, |
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out_planes, |
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kernel_size=3, |
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stride=stride, |
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padding=dilation, |
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groups=groups, |
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bias_attr=False, |
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dilation=dilation) |
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def conv1x1(in_planes, out_planes, stride=1): |
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"""1x1 convolution""" |
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return nn.Conv2D( |
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in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False) |
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class BasicBlock(nn.Layer): |
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expansion = 1 |
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def __init__(self, |
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inplanes, |
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planes, |
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stride=1, |
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upsample=None, |
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norm_layer=None, |
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large_kernel=False): |
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super().__init__() |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm |
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self.stride = stride |
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conv = conv5x5 if large_kernel else conv3x3 |
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if self.stride > 1: |
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self.conv1 = nn.utils.spectral_norm( |
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nn.Conv2DTranspose( |
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inplanes, |
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inplanes, |
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kernel_size=4, |
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stride=2, |
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padding=1, |
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bias_attr=False)) |
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else: |
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self.conv1 = nn.utils.spectral_norm(conv(inplanes, inplanes)) |
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self.bn1 = norm_layer(inplanes) |
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self.activation = nn.LeakyReLU(0.2) |
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self.conv2 = nn.utils.spectral_norm(conv(inplanes, planes)) |
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self.bn2 = norm_layer(planes) |
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self.upsample = upsample |
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def forward(self, x): |
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identity = x |
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out = self.conv1(x) |
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out = self.bn1(out) |
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out = self.activation(out) |
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out = self.conv2(out) |
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out = self.bn2(out) |
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if self.upsample is not None: |
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identity = self.upsample(x) |
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out += identity |
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out = self.activation(out) |
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return out |
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class ResNet_D_Dec(nn.Layer): |
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def __init__(self, |
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layers=[3, 4, 4, 2], |
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norm_layer=None, |
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large_kernel=False, |
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late_downsample=False): |
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super().__init__() |
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if norm_layer is None: |
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norm_layer = nn.BatchNorm |
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self._norm_layer = norm_layer |
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self.large_kernel = large_kernel |
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self.kernel_size = 5 if self.large_kernel else 3 |
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self.inplanes = 512 if layers[0] > 0 else 256 |
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self.late_downsample = late_downsample |
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self.midplanes = 64 if late_downsample else 32 |
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self.conv1 = nn.utils.spectral_norm( |
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nn.Conv2DTranspose( |
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self.midplanes, |
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32, |
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kernel_size=4, |
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stride=2, |
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padding=1, |
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bias_attr=False)) |
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self.bn1 = norm_layer(32) |
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self.leaky_relu = nn.LeakyReLU(0.2) |
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self.conv2 = nn.Conv2D( |
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32, |
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1, |
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kernel_size=self.kernel_size, |
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stride=1, |
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padding=self.kernel_size // 2) |
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self.upsample = nn.UpsamplingNearest2D(scale_factor=2) |
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self.tanh = nn.Tanh() |
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self.layer1 = self._make_layer(BasicBlock, 256, layers[0], stride=2) |
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self.layer2 = self._make_layer(BasicBlock, 128, layers[1], stride=2) |
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self.layer3 = self._make_layer(BasicBlock, 64, layers[2], stride=2) |
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self.layer4 = self._make_layer( |
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BasicBlock, self.midplanes, layers[3], stride=2) |
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self.init_weight() |
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def _make_layer(self, block, planes, blocks, stride=1): |
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if blocks == 0: |
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return nn.Sequential(nn.Identity()) |
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norm_layer = self._norm_layer |
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upsample = None |
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if stride != 1: |
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upsample = nn.Sequential( |
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nn.UpsamplingNearest2D(scale_factor=2), |
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nn.utils.spectral_norm( |
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conv1x1(self.inplanes, planes * block.expansion)), |
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norm_layer(planes * block.expansion), ) |
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elif self.inplanes != planes * block.expansion: |
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upsample = nn.Sequential( |
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nn.utils.spectral_norm( |
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conv1x1(self.inplanes, planes * block.expansion)), |
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norm_layer(planes * block.expansion), ) |
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layers = [ |
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block(self.inplanes, planes, stride, upsample, norm_layer, |
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self.large_kernel) |
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] |
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self.inplanes = planes * block.expansion |
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for _ in range(1, blocks): |
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layers.append( |
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block( |
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self.inplanes, |
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planes, |
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norm_layer=norm_layer, |
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large_kernel=self.large_kernel)) |
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return nn.Sequential(*layers) |
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def forward(self, x, mid_fea): |
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x = self.layer1(x) |
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print(x.shape) |
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x = self.layer2(x) |
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print(x.shape) |
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x = self.layer3(x) |
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print(x.shape) |
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x = self.layer4(x) |
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print(x.shape) |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.leaky_relu(x) |
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x = self.conv2(x) |
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alpha = (self.tanh(x) + 1.0) / 2.0 |
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return alpha |
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def init_weight(self): |
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for layer in self.sublayers(): |
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if isinstance(layer, nn.Conv2D): |
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if hasattr(layer, "weight_orig"): |
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param = layer.weight_orig |
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else: |
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param = layer.weight |
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param_init.xavier_uniform(param) |
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elif isinstance(layer, (nn.BatchNorm, nn.SyncBatchNorm)): |
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param_init.constant_init(layer.weight, value=1.0) |
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param_init.constant_init(layer.bias, value=0.0) |
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elif isinstance(layer, BasicBlock): |
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param_init.constant_init(layer.bn2.weight, value=0.0) |
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class ResShortCut_D_Dec(ResNet_D_Dec): |
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def __init__(self, |
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layers, |
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norm_layer=None, |
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large_kernel=False, |
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late_downsample=False): |
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super().__init__( |
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layers, norm_layer, large_kernel, late_downsample=late_downsample) |
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def forward(self, x, mid_fea): |
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fea1, fea2, fea3, fea4, fea5 = mid_fea['shortcut'] |
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x = self.layer1(x) + fea5 |
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x = self.layer2(x) + fea4 |
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x = self.layer3(x) + fea3 |
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x = self.layer4(x) + fea2 |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.leaky_relu(x) + fea1 |
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x = self.conv2(x) |
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alpha = (self.tanh(x) + 1.0) / 2.0 |
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return alpha |
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class ResGuidedCxtAtten_Dec(ResNet_D_Dec): |
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def __init__(self, |
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layers, |
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norm_layer=None, |
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large_kernel=False, |
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late_downsample=False): |
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super().__init__( |
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layers, norm_layer, large_kernel, late_downsample=late_downsample) |
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self.gca = GuidedCxtAtten(128, 128) |
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def forward(self, x, mid_fea): |
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fea1, fea2, fea3, fea4, fea5 = mid_fea['shortcut'] |
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im = mid_fea['image_fea'] |
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x = self.layer1(x) + fea5 |
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x = self.layer2(x) + fea4 |
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x = self.gca(im, x, mid_fea['unknown']) |
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x = self.layer3(x) + fea3 |
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x = self.layer4(x) + fea2 |
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x = self.conv1(x) |
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x = self.bn1(x) |
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x = self.leaky_relu(x) + fea1 |
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x = self.conv2(x) |
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alpha = (self.tanh(x) + 1.0) / 2.0 |
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return alpha |
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