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Implement InvalidShapeException

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This commit is contained in:
Matúš Námešný
2025-11-21 18:54:45 +01:00
parent 6685aa6629
commit c83e1f0c45
6 changed files with 77 additions and 91 deletions
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24
src/layers/avg_pooling.cpp
View File

@@ -18,35 +18,19 @@ AvgPool2d::AvgPool2d(
padding_shape(padding_shape),
backend(backend) {
if (in_shape.size() != 3) {
throw std::runtime_error(
std::format(
"Invalid input shape. Expected 3 dims, got {}", input_shape.size()
)
);
throw InvalidShapeException("input", 3, in_shape.size());
}
if (pool_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid pool shape. Expected 2 dims, got {}", pool_shape.size()
)
);
throw InvalidShapeException("pool", 2, pool_shape.size());
}
if (stride_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid stride shape. Expected 2 dims, got {}", stride_shape.size()
)
);
throw InvalidShapeException("stride", 2, stride_shape.size());
}
if (padding_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid padding shape. Expected 2 dims, got {}", padding_shape.size()
)
);
throw InvalidShapeException("padding", 2, padding_shape.size());
}
out_shape = {

59
src/layers/conv2d.cpp
View File

@@ -21,47 +21,31 @@ Conv2d::Conv2d(
padding_shape(padding_shape),
backend(backend) {
if (in_shape.size() != 3) {
throw std::runtime_error(
std::format(
"Invalid input shape. Expected 3 dims, got {}", in_shape.size()
)
);
throw InvalidShapeException("input", 3, in_shape.size());
}
if (kernel_shape.size() != 3) {
throw std::runtime_error(
std::format(
"Invalid kernel shape. Expected 3 dims, got {}", kernel_shape.size()
)
);
throw InvalidShapeException("kernel", 3, kernel_shape.size());
}
if (stride_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid stride shape. Expected 2 dims, got {}", stride_shape.size()
)
);
throw InvalidShapeException("stride", 3, stride_shape.size());
}
if (padding_shape.size() != 2) {
throw std::runtime_error(
std::format(
"Invalid padding shape. Expected 2 dims, got {}", padding_shape.size()
)
);
throw InvalidShapeException("padding", 3, padding_shape.size());
}
size_t out_h = (in_shape[0] - kernel_shape[0] + 2 * padding_shape[0]) /
stride_shape[0] +
1;
size_t out_w = (in_shape[1] - kernel_shape[1] + 2 * padding_shape[1]) /
stride_shape[1] +
1;
out_shape.resize(3);
out_shape[0] = out_h;
out_shape[1] = out_w;
out_shape[2] = kernel_shape[2];
out_shape = {
(in_shape[0] - kernel_shape[0] + 2 * padding_shape[0]) /
stride_shape[0] +
1,
(in_shape[1] - kernel_shape[1] + 2 * padding_shape[1]) /
stride_shape[1] +
1,
kernel_shape[2]
};
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[3]},
CUDANet::DType::FLOAT32, backend
@@ -69,7 +53,7 @@ Conv2d::Conv2d(
weights = CUDANet::Tensor(
Shape{
kernel_shape[0] * kernel_shape[1] * kernel_shape[2] * in_shape[2]
kernel_shape[0], kernel_shape[1], kernel_shape[2], in_shape[2]
},
CUDANet::DType::FLOAT32, backend
);
@@ -83,18 +67,11 @@ Conv2d::Conv2d(
Conv2d::~Conv2d() {}
CUDANet::Tensor& Conv2d::forward( CUDANet::Tensor& input) {
CUDANet::Tensor& Conv2d::forward(CUDANet::Tensor& input) {
output.zero();
backend->conv2d(
weights,
biases,
input,
output,
in_shape,
padding_shape,
kernel_shape,
stride_shape,
out_shape
weights, biases, input, output, in_shape, padding_shape, kernel_shape,
stride_shape, out_shape
);
return output;
}

24
src/layers/dense.cpp
View File

@@ -5,26 +5,22 @@
using namespace CUDANet::Layers;
Dense::Dense(CUDANet::Shape in, CUDANet::Shape out, CUDANet::Backend* backend)
Dense::Dense(CUDANet::Shape in_shape, CUDANet::Shape out_shape, CUDANet::Backend* backend)
: backend(backend),
in_shape(in),
out_shape(out) {
in_shape(in_shape),
out_shape(out_shape) {
if (in.size() != 1) {
throw std::runtime_error(
std::format("Invalid shape. Expected [1], got {}", in_shape)
);
if (in_shape.size() != 1) {
throw InvalidShapeException("input", 1, in_shape.size());
}
if (out.size() != 1) {
throw std::runtime_error(
std::format("Invalid shape. Expected [1], got {}", out_shape)
);
if (out_shape.size() != 1) {
throw InvalidShapeException("output", 1, out_shape.size());
}
weights = CUDANet::Tensor(Shape{in[0] * out[0]}, CUDANet::DType::FLOAT32, backend);
biases = CUDANet::Tensor(Shape{out[0]}, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(Shape{out[0]}, CUDANet::DType::FLOAT32, backend);
weights = CUDANet::Tensor(Shape{out_shape[0], in_shape[0]}, CUDANet::DType::FLOAT32, backend);
biases = CUDANet::Tensor(Shape{out_shape[0]}, CUDANet::DType::FLOAT32, backend);
output = CUDANet::Tensor(Shape{out_shape[0]}, CUDANet::DType::FLOAT32, backend);
weights.zero();
biases.zero();

38
src/layers/max_pool.cpp
View File

@@ -6,27 +6,39 @@ using namespace CUDANet::Layers;
MaxPool2d::MaxPool2d(
CUDANet::Shape input_shape,
CUDANet::Shape pooling_shape,
CUDANet::Shape pool_shape,
CUDANet::Shape stride_shape,
CUDANet::Shape padding_shape,
CUDANet::Backend* backend
)
: in_shape(input_shape),
pooling_shape(pooling_shape),
pool_shape(pool_shape),
stride_shape(stride_shape),
padding_shape(padding_shape),
backend(backend) {
size_t out_h = (in_shape[0] + 2 * padding_shape[0] - pooling_shape[0]) /
stride_shape[0] +
1;
size_t out_w = (in_shape[1] + 2 * padding_shape[1] - pooling_shape[1]) /
stride_shape[1] +
1;
if (in_shape.size() != 3) {
throw InvalidShapeException("input", 3, in_shape.size());
}
out_shape.resize(3);
out_shape[0] = out_h;
out_shape[1] = out_w;
out_shape[2] = in_shape[2];
if (pool_shape.size() != 2) {
throw InvalidShapeException("pool", 2, pool_shape.size());
}
if (stride_shape.size() != 2) {
throw InvalidShapeException("stride", 2, stride_shape.size());
}
if (padding_shape.size() != 2) {
throw InvalidShapeException("padding", 2, padding_shape.size());
}
out_shape = {
(in_shape[0] + 2 * padding_shape[0] - pool_shape[0]) / stride_shape[0] +
1,
(in_shape[1] + 2 * padding_shape[1] - pool_shape[1]) / stride_shape[1] +
1,
in_shape[2]
};
output = CUDANet::Tensor(
Shape{out_shape[0] * out_shape[1] * out_shape[3]},
@@ -39,7 +51,7 @@ MaxPool2d::~MaxPool2d() {}
CUDANet::Tensor& MaxPool2d::forward(CUDANet::Tensor& input) {
output.zero();
backend->maxPool2d(
input, output, in_shape, pooling_shape, stride_shape, padding_shape,
input, output, in_shape, pool_shape, stride_shape, padding_shape,
out_shape
);
return output;