企业官网建设流程全解析

昆明找工作哪个网站好,免费网页托管,网站h1标签用在哪里,网站案例代码第一步#xff1a;准备数据 印章分割-深度学习图像分割数据集 印章分割数据#xff0c;可直接应用到一些常用深度学习分割算法中#xff0c;比如FCN、Unet、SegNet、DeepLabV1、DeepLabV2、DeepLabV3、DeepLabV3、PSPNet、RefineNet、HRnet、Mask R-CNN、Segformer、DUCK-…第一步准备数据印章分割-深度学习图像分割数据集印章分割数据可直接应用到一些常用深度学习分割算法中比如FCN、Unet、SegNet、DeepLabV1、DeepLabV2、DeepLabV3、DeepLabV3、PSPNet、RefineNet、HRnet、Mask R-CNN、Segformer、DUCK-Net模型等数据集总共有2000对图片数据质量非常高甚至可应用到工业落地的项目中第二步搭建模型本文选择EGEUNet其网络结构分别如下第三步训练代码1损失函数为dice_loss focal_loss2网络代码class EGEUNet(nn.Module): def __init__(self, num_classes1, input_channels3, c_list[8, 16, 24, 32, 48, 64], bridgeTrue, gt_dsFalse): super().__init__() self.bridge bridge self.gt_ds gt_ds self.encoder1 nn.Sequential( nn.Conv2d(input_channels, c_list[0], 3, stride1, padding1), ) self.encoder2 nn.Sequential( nn.Conv2d(c_list[0], c_list[1], 3, stride1, padding1), ) self.encoder3 nn.Sequential( nn.Conv2d(c_list[1], c_list[2], 3, stride1, padding1), ) self.encoder4 nn.Sequential( Grouped_multi_axis_Hadamard_Product_Attention(c_list[2], c_list[3]), ) self.encoder5 nn.Sequential( Grouped_multi_axis_Hadamard_Product_Attention(c_list[3], c_list[4]), ) self.encoder6 nn.Sequential( Grouped_multi_axis_Hadamard_Product_Attention(c_list[4], c_list[5]), ) if bridge: self.GAB1 group_aggregation_bridge(c_list[1], c_list[0]) self.GAB2 group_aggregation_bridge(c_list[2], c_list[1]) self.GAB3 group_aggregation_bridge(c_list[3], c_list[2]) self.GAB4 group_aggregation_bridge(c_list[4], c_list[3]) self.GAB5 group_aggregation_bridge(c_list[5], c_list[4]) print(group_aggregation_bridge was used) if gt_ds: self.gt_conv1 nn.Sequential(nn.Conv2d(c_list[4], 1, 1)) self.gt_conv2 nn.Sequential(nn.Conv2d(c_list[3], 1, 1)) self.gt_conv3 nn.Sequential(nn.Conv2d(c_list[2], 1, 1)) self.gt_conv4 nn.Sequential(nn.Conv2d(c_list[1], 1, 1)) self.gt_conv5 nn.Sequential(nn.Conv2d(c_list[0], 1, 1)) print(gt deep supervision was used) self.decoder1 nn.Sequential( Grouped_multi_axis_Hadamard_Product_Attention(c_list[5], c_list[4]), ) self.decoder2 nn.Sequential( Grouped_multi_axis_Hadamard_Product_Attention(c_list[4], c_list[3]), ) self.decoder3 nn.Sequential( Grouped_multi_axis_Hadamard_Product_Attention(c_list[3], c_list[2]), ) self.decoder4 nn.Sequential( nn.Conv2d(c_list[2], c_list[1], 3, stride1, padding1), ) self.decoder5 nn.Sequential( nn.Conv2d(c_list[1], c_list[0], 3, stride1, padding1), ) self.ebn1 nn.GroupNorm(4, c_list[0]) self.ebn2 nn.GroupNorm(4, c_list[1]) self.ebn3 nn.GroupNorm(4, c_list[2]) self.ebn4 nn.GroupNorm(4, c_list[3]) self.ebn5 nn.GroupNorm(4, c_list[4]) self.dbn1 nn.GroupNorm(4, c_list[4]) self.dbn2 nn.GroupNorm(4, c_list[3]) self.dbn3 nn.GroupNorm(4, c_list[2]) self.dbn4 nn.GroupNorm(4, c_list[1]) self.dbn5 nn.GroupNorm(4, c_list[0]) self.final nn.Conv2d(c_list[0], num_classes, kernel_size1) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Conv1d): n m.kernel_size[0] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) elif isinstance(m, nn.Conv2d): fan_out m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out // m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x): out F.gelu(F.max_pool2d(self.ebn1(self.encoder1(x)), 2, 2)) t1 out # b, c0, H/2, W/2 out F.gelu(F.max_pool2d(self.ebn2(self.encoder2(out)), 2, 2)) t2 out # b, c1, H/4, W/4 out F.gelu(F.max_pool2d(self.ebn3(self.encoder3(out)), 2, 2)) t3 out # b, c2, H/8, W/8 out F.gelu(F.max_pool2d(self.ebn4(self.encoder4(out)), 2, 2)) t4 out # b, c3, H/16, W/16 out F.gelu(F.max_pool2d(self.ebn5(self.encoder5(out)), 2, 2)) t5 out # b, c4, H/32, W/32 out F.gelu(self.encoder6(out)) # b, c5, H/32, W/32 t6 out out5 F.gelu(self.dbn1(self.decoder1(out))) # b, c4, H/32, W/32 if self.gt_ds: gt_pre5 self.gt_conv1(out5) t5 self.GAB5(t6, t5, gt_pre5) gt_pre5 F.interpolate(gt_pre5, scale_factor32, modebilinear, align_cornersTrue) else: t5 self.GAB5(t6, t5) out5 torch.add(out5, t5) # b, c4, H/32, W/32 out4 F.gelu(F.interpolate(self.dbn2(self.decoder2(out5)), scale_factor(2, 2), modebilinear, align_cornersTrue)) # b, c3, H/16, W/16 if self.gt_ds: gt_pre4 self.gt_conv2(out4) t4 self.GAB4(t5, t4, gt_pre4) gt_pre4 F.interpolate(gt_pre4, scale_factor16, modebilinear, align_cornersTrue) else: t4 self.GAB4(t5, t4) out4 torch.add(out4, t4) # b, c3, H/16, W/16 out3 F.gelu(F.interpolate(self.dbn3(self.decoder3(out4)), scale_factor(2, 2), modebilinear, align_cornersTrue)) # b, c2, H/8, W/8 if self.gt_ds: gt_pre3 self.gt_conv3(out3) t3 self.GAB3(t4, t3, gt_pre3) gt_pre3 F.interpolate(gt_pre3, scale_factor8, modebilinear, align_cornersTrue) else: t3 self.GAB3(t4, t3) out3 torch.add(out3, t3) # b, c2, H/8, W/8 out2 F.gelu(F.interpolate(self.dbn4(self.decoder4(out3)), scale_factor(2, 2), modebilinear, align_cornersTrue)) # b, c1, H/4, W/4 if self.gt_ds: gt_pre2 self.gt_conv4(out2) t2 self.GAB2(t3, t2, gt_pre2) gt_pre2 F.interpolate(gt_pre2, scale_factor4, modebilinear, align_cornersTrue) else: t2 self.GAB2(t3, t2) out2 torch.add(out2, t2) # b, c1, H/4, W/4 out1 F.gelu(F.interpolate(self.dbn5(self.decoder5(out2)), scale_factor(2, 2), modebilinear, align_cornersTrue)) # b, c0, H/2, W/2 if self.gt_ds: gt_pre1 self.gt_conv5(out1) t1 self.GAB1(t2, t1, gt_pre1) gt_pre1 F.interpolate(gt_pre1, scale_factor2, modebilinear, align_cornersTrue) else: t1 self.GAB1(t2, t1) out1 torch.add(out1, t1) # b, c0, H/2, W/2 out0 F.interpolate(self.final(out1), scale_factor(2, 2), modebilinear, align_cornersTrue) # b, num_class, H, W if self.gt_ds: return (torch.sigmoid(gt_pre5), torch.sigmoid(gt_pre4), torch.sigmoid(gt_pre3), torch.sigmoid(gt_pre2), torch.sigmoid(gt_pre1)), torch.sigmoid(out0) else: return torch.sigmoid(out0)第四步统计一些指标训练过程中的loss和miou第五步搭建GUI界面第六步整个工程的内容项目完整文件下载请见演示与介绍视频的简介处给出➷➷➷https://www.bilibili.com/video/BV1Qpz4BfECo/​