Layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid))) Generate_multi_grid = lambda index, grids: grids if isinstance(grids, tuple) else 1 Kernel_size=1, stride=stride, bias=False), Nn.Conv2d(self.inplanes, planes * block.expansion, If stride != 1 or self.inplanes != planes * block.expansion: Nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True))ĭef _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1): Self.head = nn.Sequential(ASPPModule(2048), Self.layer3 = self._make_layer(block, 256, layers, stride=1, dilation=2) Self.layer2 = self._make_layer(block, 128, layers, stride=2) Self.layer1 = self._make_layer(block, 64, layers) Self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change Self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.relu = nn.ReLU(inplace=False) Out = torch.cat(, 1)ĭef init(self, block, layers, num_classes): Inter_x = nv1_batchnorm1d(inter_x)įeat1 = F.interpolate(inter_x, size=(129, 257), mode='bilinear', align_corners=True) Nn.Conv2d(inner_features * 5, out_features, padding=0, kernel_size=1, dilation=1, bias=False), Nn.BatchNorm2d(inner_features)) self.bottleneck = nn.Sequential(
nv5 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations, dilation=dilations, bias=False), nv4 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations, dilation=dilations, bias=False), nv3 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations, dilation=dilations, bias=False), nv2 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False), Nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False), “Rethinking Atrous Convolution for Semantic Image Segmentation.”ĭef init(self, features, inner_features=256, out_features=512, dilations=(12, 24, 36)): Self.stride = stride def forward(self, x):Ĭhen, Liang-Chieh, et al. Self.relu_inplace = nn.ReLU(inplace=True) nv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) Padding=dilation multi_grid, dilation=dilationmulti_grid, bias=False) nv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, nv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) Return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,ĭef init(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1): Import _zoo as model_zooįrom tograd import Variable affine_par = Trueĭef conv3x3(in_planes, out_planes, stride=1): (Github repo, Google Drive, Dropbox, etc.) Please attach or include links to any models, data, files, or scripts necessary to reproduce your issue. Environmentīaremetal or Container (if container which image + tag): Ok ( _tensorrt git(url: GitHub - NVIDIA/TensorRT: TensorRT is a C++ library for high performance inference on NVIDIA GPUs and deep learning accelerators.)) Relevant Files In addition, please tell me if you need to let me know. There was no answer through Google search, so I left this article. Terminate called after throwing an instance of ‘std :: out_of_range’
However, an error occurred in output.onnx (deeplabv3) with trtexec. The export to pytorch-> onnx was successful. Then, using trtexec, we want to test the performance (speed) in TensorAlti.
0 kommentar(er)
