Rename with_bias() to bias(), and output_channels() to out_channels() in C++...

Rename with_bias() to bias(), and output_channels() to out_channels() in C++ conv layer options usage (#1576)

torchvision/csrc/models/densenet.cpp

@@ -21,7 +21,7 @@ struct _DenseLayerImpl : torch::nn::SequentialImpl {
         "conv1",
         torch::nn::Conv2d(Options(num_input_features, bn_size * growth_rate, 1)
                               .stride(1)
-                              .with_bias(false)));
+                              .bias(false)));
     push_back("norm2", torch::nn::BatchNorm(bn_size * growth_rate));
     push_back("relu2", torch::nn::Functional(modelsimpl::relu_));
     push_back(
@@ -29,7 +29,7 @@ struct _DenseLayerImpl : torch::nn::SequentialImpl {
         torch::nn::Conv2d(Options(bn_size * growth_rate, growth_rate, 3)
                               .stride(1)
                               .padding(1)
-                              .with_bias(false)));
+                              .bias(false)));
   }
 
   torch::Tensor forward(torch::Tensor x) {
@@ -75,7 +75,7 @@ struct _TransitionImpl : torch::nn::SequentialImpl {
         "conv",
         torch::nn::Conv2d(Options(num_input_features, num_output_features, 1)
                               .stride(1)
-                              .with_bias(false)));
+                              .bias(false)));
     push_back("pool", torch::nn::Functional([](torch::Tensor input) {
                 return torch::avg_pool2d(input, 2, 2, 0, false, true);
               }));
@@ -102,7 +102,7 @@ DenseNetImpl::DenseNetImpl(
       torch::nn::Conv2d(Options(3, num_init_features, 7)
                             .stride(2)
                             .padding(3)
-                            .with_bias(false)));
+                            .bias(false)));
 
   features->push_back("norm0", torch::nn::BatchNorm(num_init_features));
   features->push_back("relu0", torch::nn::Functional(modelsimpl::relu_));

torchvision/csrc/models/googlenet.cpp

@@ -9,10 +9,10 @@ using Options = torch::nn::Conv2dOptions;
 
 namespace _googlenetimpl {
 BasicConv2dImpl::BasicConv2dImpl(torch::nn::Conv2dOptions options) {
-  options.with_bias(false);
+  options.bias(false);
   conv = torch::nn::Conv2d(options);
   bn = torch::nn::BatchNorm(
-      torch::nn::BatchNormOptions(options.output_channels()).eps(0.001));
+      torch::nn::BatchNormOptions(options.out_channels()).eps(0.001));
 
   register_module("conv", conv);
   register_module("bn", bn);

torchvision/csrc/models/inception.cpp

@@ -9,10 +9,10 @@ namespace _inceptionimpl {
 BasicConv2dImpl::BasicConv2dImpl(
     torch::nn::Conv2dOptions options,
     double std_dev) {
-  options.with_bias(false);
+  options.bias(false);
   conv = torch::nn::Conv2d(options);
   bn = torch::nn::BatchNorm(
-      torch::nn::BatchNormOptions(options.output_channels()).eps(0.001));
+      torch::nn::BatchNormOptions(options.out_channels()).eps(0.001));
 
   register_module("conv", conv);
   register_module("bn", bn);

torchvision/csrc/models/mnasnet.cpp

@@ -24,7 +24,7 @@ struct MNASNetInvertedResidualImpl : torch::nn::Module {
     apply_residual = input == output && stride == 1;
 
     layers->push_back(
-        torch::nn::Conv2d(Options(input, mid, 1).with_bias(false)));
+        torch::nn::Conv2d(Options(input, mid, 1).bias(false)));
     layers->push_back(torch::nn::BatchNorm(
         torch::nn::BatchNormOptions(mid).momentum(bn_momentum)));
     layers->push_back(
@@ -34,13 +34,13 @@ struct MNASNetInvertedResidualImpl : torch::nn::Module {
                                                 .padding(kernel / 2)
                                                 .stride(stride)
                                                 .groups(mid)
-                                                .with_bias(false))));
+                                                .bias(false))));
     layers->push_back(torch::nn::BatchNorm(
         torch::nn::BatchNormOptions(mid).momentum(bn_momentum)));
     layers->push_back(
         torch::nn::Functional(torch::nn::Functional(modelsimpl::relu_)));
     layers->push_back(
-        torch::nn::Conv2d(Options(mid, output, 1).with_bias(false)));
+        torch::nn::Conv2d(Options(mid, output, 1).bias(false)));
     layers->push_back(torch::nn::BatchNorm(
         torch::nn::BatchNormOptions(output).momentum(bn_momentum)));
 
@@ -129,17 +129,17 @@ MNASNetImpl::MNASNetImpl(double alpha, int64_t num_classes, double dropout) {
   auto depths = scale_depths({24, 40, 80, 96, 192, 320}, alpha);
 
   layers->push_back(torch::nn::Conv2d(
-      Options(3, 32, 3).padding(1).stride(2).with_bias(false)));
+      Options(3, 32, 3).padding(1).stride(2).bias(false)));
   layers->push_back(torch::nn::BatchNorm(
       torch::nn::BatchNormOptions(32).momentum(BN_MOMENTUM)));
   layers->push_back(torch::nn::Functional(modelsimpl::relu_));
   layers->push_back(torch::nn::Conv2d(
-      Options(32, 32, 3).padding(1).stride(1).groups(32).with_bias(false)));
+      Options(32, 32, 3).padding(1).stride(1).groups(32).bias(false)));
   layers->push_back(torch::nn::BatchNorm(
       torch::nn::BatchNormOptions(32).momentum(BN_MOMENTUM)));
   layers->push_back(torch::nn::Functional(modelsimpl::relu_));
   layers->push_back(torch::nn::Conv2d(
-      Options(32, 16, 1).padding(0).stride(1).with_bias(false)));
+      Options(32, 16, 1).padding(0).stride(1).bias(false)));
   layers->push_back(torch::nn::BatchNorm(
       torch::nn::BatchNormOptions(16).momentum(BN_MOMENTUM)));
 
@@ -151,7 +151,7 @@ MNASNetImpl::MNASNetImpl(double alpha, int64_t num_classes, double dropout) {
   layers->push_back(stack(depths[4], depths[5], 3, 1, 6, 1, BN_MOMENTUM));
 
   layers->push_back(torch::nn::Conv2d(
-      Options(depths[5], 1280, 1).padding(0).stride(1).with_bias(false)));
+      Options(depths[5], 1280, 1).padding(0).stride(1).bias(false)));
   layers->push_back(torch::nn::BatchNorm(
       torch::nn::BatchNormOptions(1280).momentum(BN_MOMENTUM)));
   layers->push_back(torch::nn::Functional(modelsimpl::relu_));

torchvision/csrc/models/mobilenet.cpp

@@ -32,7 +32,7 @@ struct ConvBNReLUImpl : torch::nn::SequentialImpl {
                                     .stride(stride)
                                     .padding(padding)
                                     .groups(groups)
-                                    .with_bias(false)));
+                                    .bias(false)));
     push_back(torch::nn::BatchNorm(out_planes));
     push_back(torch::nn::Functional(modelsimpl::relu6_));
   }
@@ -67,7 +67,7 @@ struct MobileNetInvertedResidualImpl : torch::nn::Module {
 
     conv->push_back(ConvBNReLU(hidden_dim, hidden_dim, 3, stride, hidden_dim));
     conv->push_back(torch::nn::Conv2d(
-        Options(hidden_dim, output, 1).stride(1).padding(0).with_bias(false)));
+        Options(hidden_dim, output, 1).stride(1).padding(0).bias(false)));
     conv->push_back(torch::nn::BatchNorm(output));
 
     register_module("conv", conv);
@@ -136,7 +136,7 @@ MobileNetV2Impl::MobileNetV2Impl(
     if (auto M = dynamic_cast<torch::nn::Conv2dImpl*>(module.get())) {
       torch::nn::init::kaiming_normal_(
           M->weight, 0, torch::nn::init::FanMode::FanOut);
-      if (M->options.with_bias())
+      if (M->options.bias())
         torch::nn::init::zeros_(M->bias);
     } else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
       torch::nn::init::ones_(M->weight);

torchvision/csrc/models/resnet.cpp

@@ -11,13 +11,13 @@ torch::nn::Conv2d conv3x3(
     int64_t stride,
     int64_t groups) {
   torch::nn::Conv2dOptions O(in, out, 3);
-  O.padding(1).stride(stride).groups(groups).with_bias(false);
+  O.padding(1).stride(stride).groups(groups).bias(false);
   return torch::nn::Conv2d(O);
 }
 
 torch::nn::Conv2d conv1x1(int64_t in, int64_t out, int64_t stride) {
   torch::nn::Conv2dOptions O(in, out, 1);
-  O.stride(stride).with_bias(false);
+  O.stride(stride).bias(false);
   return torch::nn::Conv2d(O);
 }
 

torchvision/csrc/models/resnet.h

@@ -124,7 +124,7 @@ ResNetImpl<Block>::ResNetImpl(
     : groups(groups),
       base_width(width_per_group),
       inplanes(64),
-      conv1(torch::nn::Conv2dOptions(3, 64, 7).stride(2).padding(3).with_bias(
+      conv1(torch::nn::Conv2dOptions(3, 64, 7).stride(2).padding(3).bias(
           false)),
       bn1(64),
       layer1(_make_layer(64, layers[0])),

torchvision/csrc/models/shufflenetv2.cpp

@@ -25,13 +25,13 @@ torch::Tensor channel_shuffle(torch::Tensor x, int64_t groups) {
 
 torch::nn::Conv2d conv11(int64_t input, int64_t output) {
   Options opts(input, output, 1);
-  opts = opts.stride(1).padding(0).with_bias(false);
+  opts = opts.stride(1).padding(0).bias(false);
   return torch::nn::Conv2d(opts);
 }
 
 torch::nn::Conv2d conv33(int64_t input, int64_t output, int64_t stride) {
   Options opts(input, output, 3);
-  opts = opts.stride(stride).padding(1).with_bias(false).groups(input);
+  opts = opts.stride(stride).padding(1).bias(false).groups(input);
   return torch::nn::Conv2d(opts);
 }
 
@@ -107,7 +107,7 @@ ShuffleNetV2Impl::ShuffleNetV2Impl(
       torch::nn::Conv2d(Options(input_channels, output_channels, 3)
                             .stride(2)
                             .padding(1)
-                            .with_bias(false)),
+                            .bias(false)),
       torch::nn::BatchNorm(output_channels),
       torch::nn::Functional(modelsimpl::relu_));
 
@@ -134,7 +134,7 @@ ShuffleNetV2Impl::ShuffleNetV2Impl(
       torch::nn::Conv2d(Options(input_channels, output_channels, 1)
                             .stride(1)
                             .padding(0)
-                            .with_bias(false)),
+                            .bias(false)),
       torch::nn::BatchNorm(output_channels),
       torch::nn::Functional(modelsimpl::relu_));
 

torchvision/csrc/models/squeezenet.cpp

@@ -91,7 +91,7 @@ SqueezeNetImpl::SqueezeNetImpl(double version, int64_t num_classes)
       else
         torch::nn::init::kaiming_uniform_(M->weight);
 
-      if (M->options.with_bias())
+      if (M->options.bias())
         torch::nn::init::constant_(M->bias, 0);
     }
 }