mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
af71fb882f
* Dump autogradpp into PyTorch * Fixed up CMake for autogradpp/C++ API * Made cereal a submodule * Change search location of autogradpps mnist directory * Add test_api to CI * Download MNIST from the internet instead of storing in repo * Fix warnings
259 lines
7.0 KiB
C++
259 lines
7.0 KiB
C++
#include "test.h"
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AUTOGRAD_CONTAINER_CLASS(TestModel) {
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public:
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void initialize_containers() override {
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add(Linear(10, 3).make(), "l1");
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add(Linear(3, 5).make(), "l2");
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add(Linear(5, 100).make(), "l3");
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}
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variable_list forward(variable_list input) override { return input; };
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};
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AUTOGRAD_CONTAINER_CLASS(NestedModel) {
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public:
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void initialize_containers() override {
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add(Linear(5, 20).make(), "l1");
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add(TestModel().make(), "test");
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}
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void initialize_parameters() override {
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add(Var(DefaultTensor(at::kFloat).tensor({3, 2, 21}), false), "param");
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}
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variable_list forward(variable_list input) override { return input; };
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};
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CASE("containers/conv2d/even") {
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auto model = Conv2d(3, 2, 3).stride(2).make();
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auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5, 5}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 4);
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EXPECT(s.ndimension() == 0);
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for (auto i = 0; i < 4; i++) {
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EXPECT(y.size(i) == 2);
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}
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EXPECT(model->parameters()["weight"].grad().numel() == 3 * 2 * 3 * 3);
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};
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CASE("containers/conv2d/uneven") {
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auto model = Conv2d(3, 2, IntVec({3, 2})).stride(2).make();
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auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5, 4}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 4);
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EXPECT(s.ndimension() == 0);
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for (auto i = 0; i < 4; i++) {
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EXPECT(y.size(i) == 2);
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}
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EXPECT(model->parameters()["weight"].grad().numel() == 3 * 2 * 3 * 2);
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};
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CASE("containers/conv1d/even") {
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auto model = Conv1d(3, 2, 3).stride(2).make();
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auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 4);
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EXPECT(s.ndimension() == 0);
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for (auto i = 0; i < 3; i++) {
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EXPECT(y.size(i) == 2);
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}
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EXPECT(model->parameters()["weight"].grad().numel() == 3 * 2 * 3);
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};
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CASE("containers/conv3d/even") {
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auto model = Conv3d(3, 2, 3).stride(2).make();
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auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5, 5, 5}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 5);
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EXPECT(s.ndimension() == 0);
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for (auto i = 0; i < 5; i++) {
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EXPECT(y.size(i) == 2);
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}
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EXPECT(model->parameters()["weight"].grad().numel() == 3 * 2 * 3 * 3 * 3);
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};
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CASE("containers/linear/basic1") {
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auto model = Linear(5, 2).make();
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auto x = Var(at::CPU(at::kFloat).randn({10, 5}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 2);
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EXPECT(s.ndimension() == 0);
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EXPECT(y.size(0) == 10);
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EXPECT(y.size(1) == 2);
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EXPECT(model->parameters()["weight"].grad().numel() == 2 * 5);
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};
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CASE("containers/linear/sequential") {
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auto model = ContainerList()
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.append(Linear(10, 3).make())
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.append(Linear(3, 5).make())
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.append(Linear(5, 100).make())
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.make();
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auto x = Var(at::CPU(at::kFloat).randn({1000, 10}));
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for (auto layer : *model) {
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x = layer->forward({x})[0];
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x = x.clamp_min(0); // relu
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}
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backward(x);
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EXPECT(x.ndimension() == 2);
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EXPECT(x.size(0) == 1000);
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EXPECT(x.size(1) == 100);
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EXPECT(x.data().min().toCFloat() == 0);
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};
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CASE("containers/linear/simple") {
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auto model = SimpleContainer().make();
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auto l1 = model->add(Linear(10, 3).make(), "l1");
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auto l2 = model->add(Linear(3, 5).make(), "l2");
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auto l3 = model->add(Linear(5, 100).make(), "l3");
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auto x = Var(at::CPU(at::kFloat).randn({1000, 10}));
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x = l1->forward({x})[0].clamp_min(0);
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x = l2->forward({x})[0].clamp_min(0);
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x = l3->forward({x})[0].clamp_min(0);
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backward(x);
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EXPECT(x.ndimension() == 2);
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EXPECT(x.size(0) == 1000);
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EXPECT(x.size(1) == 100);
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EXPECT(x.data().min().toCFloat() == 0);
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};
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CASE("containers/clone") {
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auto model = TestModel().make();
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auto model2 = model->clone();
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auto m1param = model->parameters();
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auto m2param = model2->parameters();
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for (auto& param : m1param) {
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EXPECT(param.second.allclose(m2param[param.first]));
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param.second.data().mul_(2);
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}
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for (auto& param : m1param) {
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EXPECT(!param.second.allclose(m2param[param.first]));
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}
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};
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CASE("containers/embedding/basic") {
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int dict_size = 10;
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auto model = Embedding(dict_size, 2).make();
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// Cannot get gradients to change indices (input) - only for embedding params
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auto x = Var(at::CPU(at::kLong).tensor({10}).fill_(dict_size - 1), false);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 2);
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EXPECT(s.ndimension() == 0);
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EXPECT(y.size(0) == 10);
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EXPECT(y.size(1) == 2);
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EXPECT(model->parameters()["weight"].grad().numel() == 2 * dict_size);
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};
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CASE("containers/embedding/list") {
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auto model = Embedding(6, 4).make();
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auto x = Var(at::CPU(at::kLong).tensor({2, 3}).fill_(5), false);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 3);
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EXPECT(y.size(0) == 2);
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EXPECT(y.size(1) == 3);
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EXPECT(y.size(2) == 4);
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};
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CASE("containers/cuda/1") {
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CUDA_GUARD;
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auto model = Linear(5, 2).make();
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model->cuda();
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auto x = Var(at::CUDA(at::kFloat).randn({10, 5}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 2);
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EXPECT(s.ndimension() == 0);
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EXPECT(y.size(0) == 10);
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EXPECT(y.size(1) == 2);
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EXPECT(model->parameters()["weight"].grad().numel() == 2 * 5);
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};
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CASE("containers/cuda/2") {
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CUDA_GUARD;
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auto model = Linear(5, 2).make();
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model->cuda();
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model->cpu();
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auto x = Var(at::CPU(at::kFloat).randn({10, 5}), true);
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auto y = model->forward({x})[0];
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Variable s = y.sum();
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backward(s);
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EXPECT(y.ndimension() == 2);
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EXPECT(s.ndimension() == 0);
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EXPECT(y.size(0) == 10);
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EXPECT(y.size(1) == 2);
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EXPECT(model->parameters()["weight"].grad().numel() == 2 * 5);
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};
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CASE("containers/dropout/1") {
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auto dropout = Dropout(0.5).make();
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Variable x = Var(at::CPU(at::kFloat).ones(100));
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Variable y = dropout->forward({x})[0];
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backward(y);
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EXPECT(y.ndimension() == 1);
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EXPECT(y.size(0) == 100);
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EXPECT(y.sum().toCFloat() < 130); // Probably
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EXPECT(y.sum().toCFloat() > 70); // Probably
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dropout->eval();
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y = dropout->forward({x})[0];
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EXPECT(y.data().sum().toCFloat() == 100);
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};
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CASE("containers/param") {
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auto model = NestedModel().make();
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EXPECT(model->param("param").size(0) == 3);
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EXPECT(model->param("param").size(1) == 2);
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EXPECT(model->param("param").size(2) == 21);
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EXPECT(model->param("l1.bias").size(0) == 20);
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EXPECT(model->param("l1.weight").size(0) == 20);
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EXPECT(model->param("l1.weight").size(1) == 5);
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EXPECT(model->param("test.l1.bias").size(0) == 3);
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EXPECT(model->param("test.l1.weight").size(0) == 3);
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EXPECT(model->param("test.l1.weight").size(1) == 10);
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EXPECT(model->param("test.l2.bias").size(0) == 5);
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EXPECT(model->param("test.l2.weight").size(0) == 5);
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EXPECT(model->param("test.l2.weight").size(1) == 3);
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EXPECT(model->param("test.l3.bias").size(0) == 100);
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EXPECT(model->param("test.l3.weight").size(0) == 100);
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EXPECT(model->param("test.l3.weight").size(1) == 5);
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}
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