Delete assets/model.py

assets/model.py

@@ -1,283 +0,0 @@
-#!/usr/bin/python
-# -*- encoding: utf-8 -*-
-
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from .resnet import Resnet18
-# from modules.bn import InPlaceABNSync as BatchNorm2d
-
-
-class ConvBNReLU(nn.Module):
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan,
-                out_chan,
-                kernel_size = ks,
-                stride = stride,
-                padding = padding,
-                bias = False)
-        self.bn = nn.BatchNorm2d(out_chan)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-class BiSeNetOutput(nn.Module):
-    def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
-        self.init_weight()
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.conv_out(x)
-        return x
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class AttentionRefinementModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-
-class ContextPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(ContextPath, self).__init__()
-        self.resnet = Resnet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-        self.init_weight()
-
-    def forward(self, x):
-        H0, W0 = x.size()[2:]
-        feat8, feat16, feat32 = self.resnet(x)
-        H8, W8 = feat8.size()[2:]
-        H16, W16 = feat16.size()[2:]
-        H32, W32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, (nn.Linear, nn.Conv2d)):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-### This is not used, since I replace this with the resnet feature with the same size
-class SpatialPath(nn.Module):
-    def __init__(self, *args, **kwargs):
-        super(SpatialPath, self).__init__()
-        self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
-        self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
-        self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
-        self.init_weight()
-
-    def forward(self, x):
-        feat = self.conv1(x)
-        feat = self.conv2(feat)
-        feat = self.conv3(feat)
-        feat = self.conv_out(feat)
-        return feat
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class FeatureFusionModule(nn.Module):
-    def __init__(self, in_chan, out_chan, *args, **kwargs):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan,
-                out_chan//4,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.conv2 = nn.Conv2d(out_chan//4,
-                out_chan,
-                kernel_size = 1,
-                stride = 1,
-                padding = 0,
-                bias = False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-        self.init_weight()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params = [], []
-        for name, module in self.named_modules():
-            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
-                wd_params.append(module.weight)
-                if not module.bias is None:
-                    nowd_params.append(module.bias)
-            elif isinstance(module, nn.BatchNorm2d):
-                nowd_params += list(module.parameters())
-        return wd_params, nowd_params
-
-
-class BiSeNet(nn.Module):
-    def __init__(self, n_classes, *args, **kwargs):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        ## here self.sp is deleted
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, n_classes)
-        self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
-        self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
-        self.init_weight()
-
-    def forward(self, x):
-        H, W = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # here return res3b1 feature
-        feat_sp = feat_res8  # use res3b1 feature to replace spatial path feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        feat_out = self.conv_out(feat_fuse)
-        feat_out16 = self.conv_out16(feat_cp8)
-        feat_out32 = self.conv_out32(feat_cp16)
-
-        feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
-        feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
-        feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
-        return feat_out, feat_out16, feat_out32
-
-    def init_weight(self):
-        for ly in self.children():
-            if isinstance(ly, nn.Conv2d):
-                nn.init.kaiming_normal_(ly.weight, a=1)
-                if not ly.bias is None: nn.init.constant_(ly.bias, 0)
-
-    def get_params(self):
-        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
-        for name, child in self.named_children():
-            child_wd_params, child_nowd_params = child.get_params()
-            if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
-                lr_mul_wd_params += child_wd_params
-                lr_mul_nowd_params += child_nowd_params
-            else:
-                wd_params += child_wd_params
-                nowd_params += child_nowd_params
-        return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
-
-
-if __name__ == "__main__":
-    net = BiSeNet(19)
-    net.cuda()
-    net.eval()
-    in_ten = torch.randn(16, 3, 640, 480).cuda()
-    out, out16, out32 = net(in_ten)
-    print(out.shape)
-
-    net.get_params()