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Update ccv_cnnp_convolution to take dilation parameter.
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@liuliu
liuliu committed Dec 20, 2023
1 parent 46cfdad commit 7d7e335
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Showing 7 changed files with 94 additions and 87 deletions.
22 changes: 11 additions & 11 deletions bin/nnc/cifar-10.c
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Original file line number Diff line number Diff line change
Expand Up @@ -24,16 +24,16 @@ static ccv_cnnp_model_t* _building_block_new(const int filters, const int stride
ccv_cnnp_model_io_t output = ccv_cnnp_model_apply(identity, MODEL_IO_LIST(input));
if (projection_shortcut)
{
ccv_cnnp_model_t* const conv0 = ccv_cnnp_convolution(1, filters, DIM_ALLOC(1, 1), 1, HINT((strides, strides), (0, 0)), 0, 1, 0);
ccv_cnnp_model_t* const conv0 = ccv_cnnp_convolution(1, filters, DIM_ALLOC(1, 1), DIM_ALLOC(), 1, HINT((strides, strides), (0, 0)), 0, 1, 0);
shortcut = ccv_cnnp_model_apply(conv0, MODEL_IO_LIST(output));
}
ccv_cnnp_model_t* const conv1 = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), 0, HINT((strides, strides), (border, border)), 0, 1, 0),
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), DIM_ALLOC(), 0, HINT((strides, strides), (border, border)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
output = ccv_cnnp_model_apply(conv1, MODEL_IO_LIST(output));
ccv_cnnp_model_t* const conv2 = ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0);
ccv_cnnp_model_t* const conv2 = ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0);
output = ccv_cnnp_model_apply(conv2, MODEL_IO_LIST(output));
ccv_cnnp_model_t* const add = ccv_cnnp_sum(0);
output = ccv_cnnp_model_apply(add, MODEL_IO_LIST(output, shortcut));
Expand All @@ -57,7 +57,7 @@ static ccv_cnnp_model_t* _block_layer_new(const int filters, const int strides,
ccv_cnnp_model_t* _cifar_10_resnet56(void)
{
const ccv_cnnp_model_io_t input = ccv_cnnp_input();
ccv_cnnp_model_t* init_conv = ccv_cnnp_convolution(1, 16, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0);
ccv_cnnp_model_t* init_conv = ccv_cnnp_convolution(1, 16, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0);
ccv_cnnp_model_io_t output = ccv_cnnp_model_apply(init_conv, MODEL_IO_LIST(input));
output = ccv_cnnp_model_apply(_block_layer_new(16, 1, 1, 9), MODEL_IO_LIST(output));
output = ccv_cnnp_model_apply(_block_layer_new(32, 2, 1, 9), MODEL_IO_LIST(output));
Expand All @@ -78,7 +78,7 @@ static ccv_cnnp_model_t* _dawn_layer_new(const int filters, const int strides, c
{
ccv_cnnp_model_io_t input = ccv_cnnp_input();
ccv_cnnp_model_t* conv = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), DIM_ALLOC(), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
Expand All @@ -89,13 +89,13 @@ static ccv_cnnp_model_t* _dawn_layer_new(const int filters, const int strides, c
{
ccv_cnnp_model_io_t shortcut = output;
ccv_cnnp_model_t* res1 = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), DIM_ALLOC(), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
output = ccv_cnnp_model_apply(res1, MODEL_IO_LIST(output));
ccv_cnnp_model_t* res2 = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), DIM_ALLOC(), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
Expand All @@ -109,7 +109,7 @@ static ccv_cnnp_model_t* _dawn_layer_new(const int filters, const int strides, c
ccv_cnnp_model_t* _cifar_10_dawn(void)
{
ccv_cnnp_model_t* prep = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, 64, DIM_ALLOC(3, 3), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(3, 3), DIM_ALLOC(), 0, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
Expand All @@ -129,15 +129,15 @@ ccv_cnnp_model_t* _cifar_10_dawn(void)
ccv_cnnp_model_t* _cifar_10_alexnet(void)
{
return ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, 32, DIM_ALLOC(5, 5), 0, HINT((1, 1), (2, 2)), 0, 1, 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(5, 5), DIM_ALLOC(), 0, HINT((1, 1), (2, 2)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_max_pool(DIM_ALLOC(3, 3), HINT((2, 2), (0, 0)), 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(5, 5), 0, HINT((1, 1), (2, 2)), 0, 1, 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(5, 5), DIM_ALLOC(), 0, HINT((1, 1), (2, 2)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_average_pool(DIM_ALLOC(3, 3), HINT((2, 2), (0, 0)), 0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(5, 5), 0, HINT((1, 1), (2, 2)), 0, 1, 0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(5, 5), DIM_ALLOC(), 0, HINT((1, 1), (2, 2)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_average_pool(DIM_ALLOC(3, 3), HINT((2, 2), (0, 0)), 0),
Expand Down
28 changes: 14 additions & 14 deletions bin/nnc/coco.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -27,23 +27,23 @@ static ccv_cnnp_model_t* _resnet_block_new(const int filters, const int expansio
ccv_cnnp_model_t* const avgdown = ccv_cnnp_average_pool(DIM_ALLOC(strides, strides), HINT((strides, strides), (0, 0)), 0);
shortcut = ccv_cnnp_model_apply(avgdown, MODEL_IO_LIST(input));
}
ccv_cnnp_model_t* const conv0 = ccv_cnnp_convolution(1, filters * expansion, DIM_ALLOC(1, 1), 1, HINT((1, 1), (0, 0)), 0, 1, 0);
ccv_cnnp_model_t* const conv0 = ccv_cnnp_convolution(1, filters * expansion, DIM_ALLOC(1, 1), DIM_ALLOC(), 1, HINT((1, 1), (0, 0)), 0, 1, 0);
shortcut = ccv_cnnp_model_apply(conv0, MODEL_IO_LIST(shortcut));
}
ccv_cnnp_model_t* const conv1 = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters, DIM_ALLOC(1, 1), 0, HINT((1, 1), (0, 0)), 0, 1, 0),
ccv_cnnp_convolution(1, filters, DIM_ALLOC(1, 1), DIM_ALLOC(), 0, HINT((1, 1), (0, 0)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
ccv_cnnp_model_io_t output = ccv_cnnp_model_apply(conv1, MODEL_IO_LIST(input));
ccv_cnnp_model_t* const conv2 = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), 0, HINT((strides, strides), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, filters, DIM_ALLOC(3, 3), DIM_ALLOC(), 0, HINT((strides, strides), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0)
), 1, 0);
output = ccv_cnnp_model_apply(conv2, MODEL_IO_LIST(output));
ccv_cnnp_model_t* const conv3 = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, filters * expansion, DIM_ALLOC(1, 1), 0, HINT((1, 1), (0, 0)), 0, 1, 0),
ccv_cnnp_convolution(1, filters * expansion, DIM_ALLOC(1, 1), DIM_ALLOC(), 0, HINT((1, 1), (0, 0)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0)
), 1, 0);
output = ccv_cnnp_model_apply(conv3, MODEL_IO_LIST(output));
Expand Down Expand Up @@ -72,13 +72,13 @@ ccv_array_t* _imagenet_resnet50_v1d(void)
{
const ccv_cnnp_model_io_t input = ccv_cnnp_input();
ccv_cnnp_model_t* init_conv = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), 1, HINT((2, 2), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((2, 2), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_max_pool(DIM_ALLOC(3, 3), HINT((2, 2), (1, 1)), 0)
Expand Down Expand Up @@ -110,14 +110,14 @@ ccv_array_t* _imagenet_resnet50_v1d(void)
static void _fpn(const int d, const ccv_cnnp_model_io_t* const c, const int c_size, ccv_cnnp_model_io_t* const p)
{
int i;
ccv_cnnp_model_io_t output = ccv_cnnp_model_apply(ccv_cnnp_convolution(1, d, DIM_ALLOC(1, 1), 0, HINT((1, 1), (0, 0)), 0, 1, 0), MODEL_IO_LIST(c[c_size - 1]));
ccv_cnnp_model_io_t output = ccv_cnnp_model_apply(ccv_cnnp_convolution(1, d, DIM_ALLOC(1, 1), DIM_ALLOC(), 0, HINT((1, 1), (0, 0)), 0, 1, 0), MODEL_IO_LIST(c[c_size - 1]));
p[c_size - 1] = output;
for (i = c_size - 2; i >= 0; i--)
{
const ccv_cnnp_model_io_t lateral = ccv_cnnp_model_apply(ccv_cnnp_convolution(1, d, DIM_ALLOC(1, 1), 0, HINT((1, 1), (0, 0)), 0, 1, 0), MODEL_IO_LIST(c[i]));
const ccv_cnnp_model_io_t lateral = ccv_cnnp_model_apply(ccv_cnnp_convolution(1, d, DIM_ALLOC(1, 1), DIM_ALLOC(), 0, HINT((1, 1), (0, 0)), 0, 1, 0), MODEL_IO_LIST(c[i]));
const ccv_cnnp_model_io_t up = ccv_cnnp_model_apply(ccv_cnnp_upsample(CCV_NNC_UPSAMPLE_BILINEAR, 2, 2, 0), MODEL_IO_LIST(output));
const ccv_cnnp_model_io_t sum = ccv_cnnp_model_apply(ccv_cnnp_sum(0), MODEL_IO_LIST(lateral, up));
output = ccv_cnnp_model_apply(ccv_cnnp_convolution(1, d, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0), MODEL_IO_LIST(sum));
output = ccv_cnnp_model_apply(ccv_cnnp_convolution(1, d, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0), MODEL_IO_LIST(sum));
p[i] = output;
}
}
Expand All @@ -126,13 +126,13 @@ ccv_array_t* _imagenet_resnet50_v1d_fpn(void)
{
const ccv_cnnp_model_io_t input = ccv_cnnp_input();
ccv_cnnp_model_t* init_conv = ccv_cnnp_sequential_new(MODEL_LIST(
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), 1, HINT((2, 2), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((2, 2), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 32, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(3, 3), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_convolution(1, 64, DIM_ALLOC(3, 3), DIM_ALLOC(), 1, HINT((1, 1), (1, 1)), 0, 1, 0),
ccv_cnnp_batch_norm(0.9, 1e-4, 1, 0),
ccv_cnnp_relu(0),
ccv_cnnp_max_pool(DIM_ALLOC(3, 3), HINT((2, 2), (1, 1)), 0)
Expand Down Expand Up @@ -171,7 +171,7 @@ ccv_cnnp_model_t* _coco_resnet50_v1d_rpn(void)
ccv_cnnp_model_io_t proposals[5];
int i;
// 3 aspect ratios (1:2, 1:1, 2:1). Each has 4 + 1 (x, y, w, h, objectness), total 15.
ccv_cnnp_model_t* const rpn_proposals = ccv_cnnp_convolution(1, 15, DIM_ALLOC(1, 1), 0, HINT((1, 1), (0, 0)), 0, 1, "rpn");
ccv_cnnp_model_t* const rpn_proposals = ccv_cnnp_convolution(1, 15, DIM_ALLOC(1, 1), DIM_ALLOC(), 0, HINT((1, 1), (0, 0)), 0, 1, "rpn");
for (i = 0; i < 5; i++)
{
p[i] = ccv_cnnp_input();
Expand Down
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