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tensor_test.go
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tensor_test.go
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package gotorch_test
import (
"io/ioutil"
"os"
"runtime"
"testing"
"time"
"unsafe"
"github.com/stretchr/testify/assert"
torch "github.com/wangkuiyi/gotorch"
"github.com/wangkuiyi/gotorch/nn/initializer"
)
func TestTensorDetach(t *testing.T) {
x := torch.RandN([]int64{1}, true)
y := x.Detach()
assert.NotNil(t, y.T)
initializer.Zeros(&y)
assert.Equal(t, float32(0.0), x.Item())
}
func TestFromBlob(t *testing.T) {
data := [2][3]float32{{1.0, 1.1, 1.2}, {2, 3, 4}}
out := torch.FromBlob(unsafe.Pointer(&data), torch.Float, []int64{2, 3})
assert.Equal(t, []int64{2, 3}, out.Shape())
}
func TestTensorString(t *testing.T) {
data := [2][3]float32{{1.0, 1.1, 1.2}, {2, 3, 4}}
out := torch.FromBlob(unsafe.Pointer(&data), torch.Float, []int64{2, 3})
g := ` 1.0000 1.1000 1.2000
2.0000 3.0000 4.0000
[ CPUFloatType{2,3} ]`
assert.Equal(t, g, out.String())
}
func TestTensorGrad(t *testing.T) {
a := torch.RandN([]int64{10, 10}, true)
assert.NotNil(t, a.Grad().T)
// According to libtorch document https://bit.ly/2QnwHrI, either a
// tensor that requires grad or not, the grad() method returns a tensor.
//
/// This function returns an undefined tensor by default and returns a
/// defined tensor the first time a call to `backward()` computes
/// gradients for this Tensor. The attribute will then contain the
/// gradients computed and future calls to `backward()` will accumulate
/// (add) gradients into it.
b := torch.RandN([]int64{10, 10}, false)
assert.NotNil(t, b.Grad().T)
}
func TestCastTo(t *testing.T) {
a := torch.NewTensor([]int64{1, 2})
b := a.CastTo(torch.Float)
assert.Equal(t, torch.Float, b.Dtype())
b = a.To(torch.NewDevice("cpu"))
assert.Equal(t, torch.Long, b.Dtype())
b = a.To(torch.NewDevice("cpu"), torch.Float)
assert.Equal(t, torch.Float, b.Dtype())
}
func TestCUDA(t *testing.T) {
a := assert.New(t)
device := getDefaultDevice()
input := torch.NewTensor([][]float32{{1, 2}, {3, 4}})
if !torch.IsCUDAAvailable() {
// CUDA should panics on CPU device
a.Panics(func() {
input.CUDA(device, false)
})
a.Panics(func() {
input.CUDA(device, true)
})
return
}
b := input.CUDA(device, false)
a.Equal(" 1 2\n 3 4\n[ CUDAFloatType{2,2} ]", b.String())
c := input.CUDA(device, true)
torch.GetCurrentCUDAStream(device).Synchronize()
a.Equal(" 1 2\n 3 4\n[ CUDAFloatType{2,2} ]", c.String())
}
func TestCopyTo(t *testing.T) {
device := torch.NewDevice("cpu")
a := torch.NewTensor([]int64{1, 2})
b := a.CopyTo(device)
assert.True(t, torch.Equal(a, b))
}
func TestDim(t *testing.T) {
a := torch.RandN([]int64{2, 3}, false)
assert.Equal(t, int64(2), a.Dim())
}
func TestShape(t *testing.T) {
a := torch.RandN([]int64{2, 3}, false)
assert.Equal(t, int64(2), a.Shape()[0])
assert.Equal(t, int64(3), a.Shape()[1])
// a.Argmax returns a 0-dim tensor
b := a.Argmax()
assert.Equal(t, 0, len(b.Shape()))
}
func TestSave(t *testing.T) {
file, e := ioutil.TempFile("", "gotroch-test-save-*")
assert.NoError(t, e)
defer os.Remove(file.Name())
a := torch.RandN([]int64{2, 3}, false)
a.Save(file.Name())
b := torch.Load(file.Name())
assert.EqualValues(t, a.Shape(), b.Shape())
assert.Equal(t, a.Dtype(), b.Dtype())
assert.Equal(t, a.String(), b.String())
}
func TestSetData(t *testing.T) {
a := torch.Full([]int64{2, 3}, 0, false)
b := torch.Ones([]int64{2, 3}, false)
assert.False(t, torch.Equal(a, b))
b.SetData(a)
assert.True(t, torch.Equal(a, b))
}
func TestTensorIndex(t *testing.T) {
a := torch.NewTensor([][]float32{{1, 2}, {3, 4}})
assert.Equal(t, float32(1), a.Index(0, 0).Item().(float32))
assert.Equal(t, float32(2), a.Index(0, 1).Item().(float32))
assert.Equal(t, float32(3), a.Index(1, 0).Item().(float32))
assert.Equal(t, float32(4), a.Index(1, 1).Item().(float32))
assert.Panics(t, func() { a.Index(0).Item() })
assert.Panics(t, func() { a.Index(0, 0, 0).Item() })
}
func TestTensorPinMemory(t *testing.T) {
a := torch.NewTensor([][]float32{{1, 2}, {3, 4}})
b := a.PinMemory()
if torch.IsCUDAAvailable() {
assert.Equal(t, " 1 2\n 3 4\n[ CUDAFloatType{2,2} ]", b.String())
} else {
assert.Equal(t, " 1 2\n 3 4\n[ CPUFloatType{2,2} ]", b.String())
}
}
func TestTensorGC(t *testing.T) {
torch.GC()
defer torch.FinishGC()
runtime.LockOSThread()
c := make(chan torch.Tensor, 0)
{
torch.NewTensor([][]float32{{1, 2}, {3, 4}})
go func() {
a := torch.NewTensor([][]float32{{1, 2}, {3, 4}})
c <- a
time.Sleep(time.Second)
runtime.KeepAlive(&a)
}()
}
<-c
assert.Eventually(t, func() bool { torch.GC(); return true }, 10*time.Millisecond, 10*time.Microsecond)
}