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Unverified Commit b6f28ec1 authored by Francis Charette Migneault's avatar Francis Charette Migneault Committed by GitHub
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replace torch 1.5.0 items flagged with deprecation warnings (fix #1906) (#1918)

parent 6aa99ced
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Showing with 48 additions and 48 deletions
torchvision/csrc/models/densenet.cpp
View file @ b6f28ec1
......@@ -15,14 +15,14 @@ struct _DenseLayerImpl : torch::nn::SequentialImpl {
int64_t bn_size,
double drop_rate)
: drop_rate(drop_rate) {
push_back("norm1", torch::nn::BatchNorm(num_input_features));
push_back("norm1", torch::nn::BatchNorm2d(num_input_features));
push_back("relu1", torch::nn::Functional(modelsimpl::relu_));
push_back(
"conv1",
torch::nn::Conv2d(Options(num_input_features, bn_size * growth_rate, 1)
.stride(1)
.bias(false)));
push_back("norm2", torch::nn::BatchNorm(bn_size * growth_rate));
push_back("norm2", torch::nn::BatchNorm2d(bn_size * growth_rate));
push_back("relu2", torch::nn::Functional(modelsimpl::relu_));
push_back(
"conv2",
......@@ -69,7 +69,7 @@ TORCH_MODULE(_DenseBlock);
struct _TransitionImpl : torch::nn::SequentialImpl {
_TransitionImpl(int64_t num_input_features, int64_t num_output_features) {
push_back("norm", torch::nn::BatchNorm(num_input_features));
push_back("norm", torch::nn::BatchNorm2d(num_input_features));
push_back("relu ", torch::nn::Functional(modelsimpl::relu_));
push_back(
"conv",
......@@ -102,7 +102,7 @@ DenseNetImpl::DenseNetImpl(
torch::nn::Conv2d(
Options(3, num_init_features, 7).stride(2).padding(3).bias(false)));
features->push_back("norm0", torch::nn::BatchNorm(num_init_features));
features->push_back("norm0", torch::nn::BatchNorm2d(num_init_features));
features->push_back("relu0", torch::nn::Functional(modelsimpl::relu_));
features->push_back(
"pool0", torch::nn::Functional(torch::max_pool2d, 3, 2, 1, 1, false));
......@@ -125,7 +125,7 @@ DenseNetImpl::DenseNetImpl(
}
// Final batch norm
features->push_back("norm5", torch::nn::BatchNorm(num_features));
features->push_back("norm5", torch::nn::BatchNorm2d(num_features));
// Linear layer
classifier = torch::nn::Linear(num_features, num_classes);
......@@ -136,7 +136,7 @@ DenseNetImpl::DenseNetImpl(
for (auto& module : modules(/*include_self=*/false)) {
if (auto M = dynamic_cast<torch::nn::Conv2dImpl*>(module.get()))
torch::nn::init::kaiming_normal_(M->weight);
else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
torch::nn::init::constant_(M->weight, 1);
torch::nn::init::constant_(M->bias, 0);
} else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get()))
......
torchvision/csrc/models/googlenet.cpp
View file @ b6f28ec1
......@@ -11,7 +11,7 @@ namespace _googlenetimpl {
BasicConv2dImpl::BasicConv2dImpl(torch::nn::Conv2dOptions options) {
options.bias(false);
conv = torch::nn::Conv2d(options);
bn = torch::nn::BatchNorm(
bn = torch::nn::BatchNorm2d(
torch::nn::BatchNormOptions(options.out_channels()).eps(0.001));
register_module("conv", conv);
......@@ -155,7 +155,7 @@ void GoogLeNetImpl::_initialize_weights() {
else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get()))
torch::nn::init::normal_(M->weight); // Note: used instead of truncated
// normal initialization
else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
torch::nn::init::ones_(M->weight);
torch::nn::init::zeros_(M->bias);
}
......
torchvision/csrc/models/googlenet.h
View file @ b6f28ec1
......@@ -10,7 +10,7 @@ namespace models {
namespace _googlenetimpl {
struct VISION_API BasicConv2dImpl : torch::nn::Module {
torch::nn::Conv2d conv{nullptr};
torch::nn::BatchNorm bn{nullptr};
torch::nn::BatchNorm2d bn{nullptr};
BasicConv2dImpl(torch::nn::Conv2dOptions options);
......
torchvision/csrc/models/inception.cpp
View file @ b6f28ec1
......@@ -11,7 +11,7 @@ BasicConv2dImpl::BasicConv2dImpl(
double std_dev) {
options.bias(false);
conv = torch::nn::Conv2d(options);
bn = torch::nn::BatchNorm(
bn = torch::nn::BatchNorm2d(
torch::nn::BatchNormOptions(options.out_channels()).eps(0.001));
register_module("conv", conv);
......
torchvision/csrc/models/inception.h
View file @ b6f28ec1
......@@ -9,7 +9,7 @@ namespace models {
namespace _inceptionimpl {
struct VISION_API BasicConv2dImpl : torch::nn::Module {
torch::nn::Conv2d conv{nullptr};
torch::nn::BatchNorm bn{nullptr};
torch::nn::BatchNorm2d bn{nullptr};
BasicConv2dImpl(torch::nn::Conv2dOptions options, double std_dev = 0.1);
......
torchvision/csrc/models/mnasnet.cpp
View file @ b6f28ec1
......@@ -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).bias(false)));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
torch::nn::BatchNormOptions(mid).momentum(bn_momentum)));
layers->push_back(
torch::nn::Functional(torch::nn::Functional(modelsimpl::relu_)));
......@@ -34,12 +34,12 @@ struct MNASNetInvertedResidualImpl : torch::nn::Module {
.stride(stride)
.groups(mid)
.bias(false))));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
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).bias(false)));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
torch::nn::BatchNormOptions(output).momentum(bn_momentum)));
register_module("layers", layers);
......@@ -109,9 +109,9 @@ void MNASNetImpl::_initialize_weights() {
torch::nn::init::kaiming_normal_(
M->weight,
0,
torch::nn::init::FanMode::FanOut,
torch::nn::init::Nonlinearity::ReLU);
else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
torch::kFanOut,
torch::kReLU);
else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
torch::nn::init::ones_(M->weight);
torch::nn::init::zeros_(M->bias);
} else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get())) {
......@@ -128,17 +128,17 @@ MNASNetImpl::MNASNetImpl(double alpha, int64_t num_classes, double dropout) {
layers->push_back(
torch::nn::Conv2d(Options(3, 32, 3).padding(1).stride(2).bias(false)));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
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).bias(false)));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
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).bias(false)));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
torch::nn::BatchNormOptions(16).momentum(BN_MOMENTUM)));
layers->push_back(stack(16, depths[0], 3, 2, 3, 3, BN_MOMENTUM));
......@@ -150,7 +150,7 @@ MNASNetImpl::MNASNetImpl(double alpha, int64_t num_classes, double dropout) {
layers->push_back(torch::nn::Conv2d(
Options(depths[5], 1280, 1).padding(0).stride(1).bias(false)));
layers->push_back(torch::nn::BatchNorm(
layers->push_back(torch::nn::BatchNorm2d(
torch::nn::BatchNormOptions(1280).momentum(BN_MOMENTUM)));
layers->push_back(torch::nn::Functional(modelsimpl::relu_));
......
torchvision/csrc/models/mobilenet.cpp
View file @ b6f28ec1
......@@ -33,7 +33,7 @@ struct ConvBNReLUImpl : torch::nn::SequentialImpl {
.padding(padding)
.groups(groups)
.bias(false)));
push_back(torch::nn::BatchNorm(out_planes));
push_back(torch::nn::BatchNorm2d(out_planes));
push_back(torch::nn::Functional(modelsimpl::relu6_));
}
......@@ -68,7 +68,7 @@ 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).bias(false)));
conv->push_back(torch::nn::BatchNorm(output));
conv->push_back(torch::nn::BatchNorm2d(output));
register_module("conv", conv);
}
......@@ -135,10 +135,10 @@ MobileNetV2Impl::MobileNetV2Impl(
for (auto& module : modules(/*include_self=*/false)) {
if (auto M = dynamic_cast<torch::nn::Conv2dImpl*>(module.get())) {
torch::nn::init::kaiming_normal_(
M->weight, 0, torch::nn::init::FanMode::FanOut);
M->weight, 0, torch::kFanOut);
if (M->options.bias())
torch::nn::init::zeros_(M->bias);
} else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
} else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
torch::nn::init::ones_(M->weight);
torch::nn::init::zeros_(M->bias);
} else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get())) {
......
torchvision/csrc/models/resnet.cpp
View file @ b6f28ec1
......@@ -40,8 +40,8 @@ BasicBlock::BasicBlock(
conv1 = conv3x3(inplanes, planes, stride);
conv2 = conv3x3(planes, planes);
bn1 = torch::nn::BatchNorm(planes);
bn2 = torch::nn::BatchNorm(planes);
bn1 = torch::nn::BatchNorm2d(planes);
bn2 = torch::nn::BatchNorm2d(planes);
register_module("conv1", conv1);
register_module("conv2", conv2);
......@@ -68,9 +68,9 @@ Bottleneck::Bottleneck(
conv2 = conv3x3(width, width, stride, groups);
conv3 = conv1x1(width, planes * expansion);
bn1 = torch::nn::BatchNorm(width);
bn2 = torch::nn::BatchNorm(width);
bn3 = torch::nn::BatchNorm(planes * expansion);
bn1 = torch::nn::BatchNorm2d(width);
bn2 = torch::nn::BatchNorm2d(width);
bn3 = torch::nn::BatchNorm2d(planes * expansion);
register_module("conv1", conv1);
register_module("conv2", conv2);
......
torchvision/csrc/models/resnet.h
View file @ b6f28ec1
......@@ -28,7 +28,7 @@ struct VISION_API BasicBlock : torch::nn::Module {
torch::nn::Sequential downsample;
torch::nn::Conv2d conv1{nullptr}, conv2{nullptr};
torch::nn::BatchNorm bn1{nullptr}, bn2{nullptr};
torch::nn::BatchNorm2d bn1{nullptr}, bn2{nullptr};
static int expansion;
......@@ -51,7 +51,7 @@ struct VISION_API Bottleneck : torch::nn::Module {
torch::nn::Sequential downsample;
torch::nn::Conv2d conv1{nullptr}, conv2{nullptr}, conv3{nullptr};
torch::nn::BatchNorm bn1{nullptr}, bn2{nullptr}, bn3{nullptr};
torch::nn::BatchNorm2d bn1{nullptr}, bn2{nullptr}, bn3{nullptr};
static int expansion;
......@@ -71,7 +71,7 @@ template <typename Block>
struct ResNetImpl : torch::nn::Module {
int64_t groups, base_width, inplanes;
torch::nn::Conv2d conv1;
torch::nn::BatchNorm bn1;
torch::nn::BatchNorm2d bn1;
torch::nn::Sequential layer1, layer2, layer3, layer4;
torch::nn::Linear fc;
......@@ -99,7 +99,7 @@ torch::nn::Sequential ResNetImpl<Block>::_make_layer(
if (stride != 1 || inplanes != planes * Block::expansion) {
downsample = torch::nn::Sequential(
_resnetimpl::conv1x1(inplanes, planes * Block::expansion, stride),
torch::nn::BatchNorm(planes * Block::expansion));
torch::nn::BatchNorm2d(planes * Block::expansion));
}
torch::nn::Sequential layers;
......@@ -146,9 +146,9 @@ ResNetImpl<Block>::ResNetImpl(
torch::nn::init::kaiming_normal_(
M->weight,
/*a=*/0,
torch::nn::init::FanMode::FanOut,
torch::nn::init::Nonlinearity::ReLU);
else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
torch::kFanOut,
torch::kReLU);
else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
torch::nn::init::constant_(M->weight, 1);
torch::nn::init::constant_(M->bias, 0);
}
......
torchvision/csrc/models/shufflenetv2.cpp
View file @ b6f28ec1
......@@ -49,20 +49,20 @@ struct ShuffleNetV2InvertedResidualImpl : torch::nn::Module {
if (stride > 1) {
branch1 = torch::nn::Sequential(
conv33(inp, inp, stride),
torch::nn::BatchNorm(inp),
torch::nn::BatchNorm2d(inp),
conv11(inp, branch_features),
torch::nn::BatchNorm(branch_features),
torch::nn::BatchNorm2d(branch_features),
torch::nn::Functional(modelsimpl::relu_));
}
branch2 = torch::nn::Sequential(
conv11(stride > 1 ? inp : branch_features, branch_features),
torch::nn::BatchNorm(branch_features),
torch::nn::BatchNorm2d(branch_features),
torch::nn::Functional(modelsimpl::relu_),
conv33(branch_features, branch_features, stride),
torch::nn::BatchNorm(branch_features),
torch::nn::BatchNorm2d(branch_features),
conv11(branch_features, branch_features),
torch::nn::BatchNorm(branch_features),
torch::nn::BatchNorm2d(branch_features),
torch::nn::Functional(modelsimpl::relu_));
if (!branch1.is_empty())
......@@ -108,7 +108,7 @@ ShuffleNetV2Impl::ShuffleNetV2Impl(
.stride(2)
.padding(1)
.bias(false)),
torch::nn::BatchNorm(output_channels),
torch::nn::BatchNorm2d(output_channels),
torch::nn::Functional(modelsimpl::relu_));
input_channels = output_channels;
......@@ -135,7 +135,7 @@ ShuffleNetV2Impl::ShuffleNetV2Impl(
.stride(1)
.padding(0)
.bias(false)),
torch::nn::BatchNorm(output_channels),
torch::nn::BatchNorm2d(output_channels),
torch::nn::Functional(modelsimpl::relu_));
fc = torch::nn::Linear(output_channels, num_classes);
......
torchvision/csrc/models/vgg.cpp
View file @ b6f28ec1
......@@ -19,7 +19,7 @@ torch::nn::Sequential makeLayers(
torch::nn::Conv2dOptions(channels, V, 3).padding(1)));
if (batch_norm)
seq->push_back(torch::nn::BatchNorm(V));
seq->push_back(torch::nn::BatchNorm2d(V));
seq->push_back(torch::nn::Functional(modelsimpl::relu_));
channels = V;
......@@ -35,10 +35,10 @@ void VGGImpl::_initialize_weights() {
torch::nn::init::kaiming_normal_(
M->weight,
/*a=*/0,
torch::nn::init::FanMode::FanOut,
torch::nn::init::Nonlinearity::ReLU);
torch::kFanOut,
torch::kReLU);
torch::nn::init::constant_(M->bias, 0);
} else if (auto M = dynamic_cast<torch::nn::BatchNormImpl*>(module.get())) {
} else if (auto M = dynamic_cast<torch::nn::BatchNorm2dImpl*>(module.get())) {
torch::nn::init::constant_(M->weight, 1);
torch::nn::init::constant_(M->bias, 0);
} else if (auto M = dynamic_cast<torch::nn::LinearImpl*>(module.get())) {
......
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