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test_memory_repr.py
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import cupy as cp
import cv2
import numpy as np
import pytest
import torch
from savant.utils.memory_repr import (
cupy_array_as_opencv_gpu_mat,
opencv_gpu_mat_as_cupy_array,
)
from savant.utils.memory_repr_pytorch import (
opencv_gpu_mat_as_pytorch_tensor,
pytorch_tensor_as_opencv_gpu_mat,
)
TORCH_TYPE = [torch.int8, torch.uint8, torch.float32]
NUMPY_TYPE = [np.int8, np.uint8, np.float32]
CUPY_TYPE = [cp.int8, cp.uint8, cp.float32]
class TestAsOpenCV:
@pytest.mark.parametrize('input_type', TORCH_TYPE)
@pytest.mark.parametrize('channels', [1, 3, 4])
@pytest.mark.parametrize('memory_format', ['channels_first', 'channels_last'])
def test_pytorch_3d(self, input_type, channels, memory_format):
"""Test for pytorch 3d tensors emulate color image"""
if memory_format == 'channels_first':
# shape - [channels, height, width]
pytorch_tensor = (
torch.randint(0, 255, size=(channels, 10, 20), device='cuda')
.to(input_type)
.permute(1, 2, 0)
)
elif memory_format == 'channels_last':
# shape - [height, width, channels]
pytorch_tensor = torch.randint(
0, 255, size=(10, 20, channels), device='cuda'
).to(input_type)
else:
raise ValueError(f'Unsupported memory format {memory_format}')
if memory_format == 'channels_first' and channels != 1:
with pytest.raises(
AssertionError,
match='Array must be in C-contiguous layout.',
):
opencv_gpu_mat = pytorch_tensor_as_opencv_gpu_mat(
pytorch_tensor.permute(1, 2, 0)
)
else:
opencv_gpu_mat = pytorch_tensor_as_opencv_gpu_mat(pytorch_tensor)
np.testing.assert_almost_equal(
opencv_gpu_mat.download(),
(
pytorch_tensor.squeeze(2).cpu().numpy()
if channels == 1
else pytorch_tensor.cpu().numpy()
),
)
assert opencv_gpu_mat.cudaPtr() == pytorch_tensor.data_ptr()
@pytest.mark.parametrize('input_type', TORCH_TYPE)
def test_pytorch_2d(self, input_type):
"""Test for pytorch tensors with grayscale image"""
# shape - [height, width]
pytorch_tensor = torch.randint(0, 255, (10, 20), device='cuda').to(input_type)
opencv_gpu_mat = pytorch_tensor_as_opencv_gpu_mat(pytorch_tensor)
np.testing.assert_almost_equal(
opencv_gpu_mat.download(),
pytorch_tensor.cpu().numpy(),
)
assert opencv_gpu_mat.cudaPtr() == pytorch_tensor.data_ptr()
@pytest.mark.parametrize('input_type', CUPY_TYPE)
@pytest.mark.parametrize('channels', [1, 3, 4])
@pytest.mark.parametrize('memory_format', ['channels_first', 'channels_last'])
def test_cupy_3d(self, input_type, channels, memory_format):
"""Test for cupy tensors"""
if memory_format == 'channels_last':
cupy_array = cp.random.randint(0, 255, (10, 20, channels)).astype(
input_type
)
elif memory_format == 'channels_first':
cupy_array = (
cp.random.randint(0, 255, (channels, 10, 20))
.astype(input_type)
.transpose(1, 2, 0)
)
else:
raise ValueError(f'Unsupported memory format {memory_format}')
if memory_format == 'channels_first' and channels != 1:
with pytest.raises(
AssertionError,
match='Array must be in C-contiguous layout.',
):
opencv_mat = cupy_array_as_opencv_gpu_mat(
np.transpose(cupy_array, (1, 2, 0))
)
else:
opencv_mat = cupy_array_as_opencv_gpu_mat(cupy_array)
np.testing.assert_almost_equal(
opencv_mat.download(),
cupy_array.squeeze(2).get() if channels == 1 else cupy_array.get(),
)
assert opencv_mat.cudaPtr() == cupy_array.data.ptr
@pytest.mark.parametrize('input_type', CUPY_TYPE)
def test_cupy_2d(self, input_type):
"""Test for pytorch tensors with grayscale image"""
cupy_array = cp.random.randint(0, 255, (10, 20)).astype(input_type)
opencv_gpu_mat = cupy_array_as_opencv_gpu_mat(cupy_array)
np.testing.assert_almost_equal(
opencv_gpu_mat.download(),
cupy_array.get(),
)
assert opencv_gpu_mat.cudaPtr() == cupy_array.data.ptr
class TestToTorch:
@pytest.mark.parametrize('input_type', NUMPY_TYPE)
@pytest.mark.parametrize('channels', [1, 3, 4])
def test_opencv(self, input_type, channels):
opencv_gpu_mat = cv2.cuda_GpuMat()
opencv_gpu_mat.upload(
np.random.randint(0, 255, (10, 20, channels)).astype(input_type)
)
torch_tensor = opencv_gpu_mat_as_pytorch_tensor(opencv_gpu_mat)
np.testing.assert_almost_equal(
opencv_gpu_mat.download() if channels == 1 else opencv_gpu_mat.download(),
torch_tensor.cpu().numpy(),
)
assert opencv_gpu_mat.cudaPtr() == torch_tensor.data_ptr()
@pytest.mark.parametrize('input_type', NUMPY_TYPE)
def test_opencv_grayscale(self, input_type):
opencv_gpu_mat = cv2.cuda_GpuMat()
opencv_gpu_mat.upload(np.random.randint(0, 255, (10, 20)).astype(input_type))
torch_tensor = opencv_gpu_mat_as_pytorch_tensor(opencv_gpu_mat)
np.testing.assert_almost_equal(
opencv_gpu_mat.download(),
torch_tensor.cpu().numpy(),
)
assert opencv_gpu_mat.cudaPtr() == torch_tensor.data_ptr()
class TestToCUPY:
@pytest.mark.parametrize('input_type', NUMPY_TYPE)
@pytest.mark.parametrize('channels', [1, 3, 4])
def test_opencv(self, input_type, channels):
opencv_gpu_mat = cv2.cuda_GpuMat()
opencv_gpu_mat.upload(
np.random.randint(0, 255, (10, 20, channels)).astype(input_type)
)
cupy_array = opencv_gpu_mat_as_cupy_array(opencv_gpu_mat)
np.testing.assert_almost_equal(
opencv_gpu_mat.download(),
cupy_array.get(),
)
assert opencv_gpu_mat.cudaPtr() == cupy_array.data.ptr
@pytest.mark.parametrize('input_type', NUMPY_TYPE)
def test_opencv_grayscale(self, input_type):
opencv_gpu_mat = cv2.cuda_GpuMat()
opencv_gpu_mat.upload(np.random.randint(0, 255, (10, 20)).astype(input_type))
cupy_array = opencv_gpu_mat_as_cupy_array(opencv_gpu_mat)
np.testing.assert_almost_equal(
opencv_gpu_mat.download(),
cupy_array.get(),
)
assert opencv_gpu_mat.cudaPtr() == cupy_array.data.ptr