Coverage Report

Created: 2021-09-30 20:21

/home/liu/buildslave/linux-x64-runtests/build/lib/inc/ccv_convnet_internal.h
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#ifndef GUARD_ccv_convnet_internal_h
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#define GUARD_ccv_convnet_internal_h
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inline static void ccv_convnet_make_output(ccv_convnet_layer_t* layer, int input_rows, int input_cols, int* rows, int* cols, int* partition)
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3.26k
{
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  assert(rows != 0 && cols != 0);
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  switch(layer->type)
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  {
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    case CCV_CONVNET_CONVOLUTIONAL:
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      assert(layer->net.convolutional.rows % 2); // as of now, don't support even number of kernel size
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      assert(layer->net.convolutional.cols % 2);
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      assert((input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows) % layer->net.convolutional.strides == 0);
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      assert((input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols) % layer->net.convolutional.strides == 0);
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      *rows = (input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *cols = (input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_FULL_CONNECT:
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      *rows = layer->net.full_connect.count;
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      *cols = 1;
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      *partition = 1;
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      break;
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    case CCV_CONVNET_LOCAL_RESPONSE_NORM:
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      *rows = input_rows;
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      *cols = input_cols;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_MAX_POOL:
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    case CCV_CONVNET_AVERAGE_POOL:
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      assert((input_rows + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      assert((input_cols + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      *rows = (input_rows + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *cols = (input_cols + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    default:
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      assert(0);
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      break;
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  }
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}
convnet.tests.c:ccv_convnet_make_output
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Source
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{
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  assert(rows != 0 && cols != 0);
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  switch(layer->type)
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  {
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    case CCV_CONVNET_CONVOLUTIONAL:
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      assert(layer->net.convolutional.rows % 2); // as of now, don't support even number of kernel size
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      assert(layer->net.convolutional.cols % 2);
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      assert((input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows) % layer->net.convolutional.strides == 0);
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      assert((input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols) % layer->net.convolutional.strides == 0);
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      *rows = (input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *cols = (input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_FULL_CONNECT:
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      *rows = layer->net.full_connect.count;
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      *cols = 1;
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      *partition = 1;
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      break;
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    case CCV_CONVNET_LOCAL_RESPONSE_NORM:
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      *rows = input_rows;
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      *cols = input_cols;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_MAX_POOL:
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    case CCV_CONVNET_AVERAGE_POOL:
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      assert((input_rows + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      assert((input_cols + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      *rows = (input_rows + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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0
      *cols = (input_cols + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    default:
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      assert(0);
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      break;
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  }
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}
symbolic.graph.vgg.d.tests.c:ccv_convnet_make_output
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{
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  assert(rows != 0 && cols != 0);
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  switch(layer->type)
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  {
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    case CCV_CONVNET_CONVOLUTIONAL:
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      assert(layer->net.convolutional.rows % 2); // as of now, don't support even number of kernel size
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      assert(layer->net.convolutional.cols % 2);
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      assert((input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows) % layer->net.convolutional.strides == 0);
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      assert((input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols) % layer->net.convolutional.strides == 0);
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      *rows = (input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *cols = (input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_FULL_CONNECT:
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      *rows = layer->net.full_connect.count;
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      *cols = 1;
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      *partition = 1;
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      break;
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    case CCV_CONVNET_LOCAL_RESPONSE_NORM:
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      *rows = input_rows;
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      *cols = input_cols;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_MAX_POOL:
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    case CCV_CONVNET_AVERAGE_POOL:
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      assert((input_rows + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      assert((input_cols + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      *rows = (input_rows + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *cols = (input_cols + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    default:
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      assert(0);
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      break;
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  }
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}
graph.vgg.d.tests.c:ccv_convnet_make_output
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Source
5
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{
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  assert(rows != 0 && cols != 0);
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  switch(layer->type)
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  {
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    case CCV_CONVNET_CONVOLUTIONAL:
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      assert(layer->net.convolutional.rows % 2); // as of now, don't support even number of kernel size
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      assert(layer->net.convolutional.cols % 2);
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      assert((input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows) % layer->net.convolutional.strides == 0);
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      assert((input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols) % layer->net.convolutional.strides == 0);
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      *rows = (input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *cols = (input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_FULL_CONNECT:
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      *rows = layer->net.full_connect.count;
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      *cols = 1;
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      *partition = 1;
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      break;
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    case CCV_CONVNET_LOCAL_RESPONSE_NORM:
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      *rows = input_rows;
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      *cols = input_cols;
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0
      *partition = layer->input.matrix.partition;
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0
      break;
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    case CCV_CONVNET_MAX_POOL:
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    case CCV_CONVNET_AVERAGE_POOL:
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      assert((input_rows + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      assert((input_cols + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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      *rows = (input_rows + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *cols = (input_cols + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    default:
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      assert(0);
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      break;
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  }
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}
Unexecuted instantiation: dense.net.tests.c:ccv_convnet_make_output
ccv_convnet.c:ccv_convnet_make_output
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{
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  assert(rows != 0 && cols != 0);
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  switch(layer->type)
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  {
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    case CCV_CONVNET_CONVOLUTIONAL:
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      assert(layer->net.convolutional.rows % 2); // as of now, don't support even number of kernel size
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      assert(layer->net.convolutional.cols % 2);
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      assert((input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows) % layer->net.convolutional.strides == 0);
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      assert((input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols) % layer->net.convolutional.strides == 0);
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      *rows = (input_rows + layer->net.convolutional.border * 2 - layer->net.convolutional.rows + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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2.37k
      *cols = (input_cols + layer->net.convolutional.border * 2 - layer->net.convolutional.cols + layer->net.convolutional.strides - 1) / layer->net.convolutional.strides + 1;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_FULL_CONNECT:
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0
      *rows = layer->net.full_connect.count;
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0
      *cols = 1;
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0
      *partition = 1;
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0
      break;
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    case CCV_CONVNET_LOCAL_RESPONSE_NORM:
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      *rows = input_rows;
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      *cols = input_cols;
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      *partition = layer->input.matrix.partition;
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      break;
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    case CCV_CONVNET_MAX_POOL:
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17
    case CCV_CONVNET_AVERAGE_POOL:
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17
      assert((input_rows + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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17
      assert((input_cols + layer->net.pool.border * 2 - layer->net.pool.size) % layer->net.pool.strides == 0);
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17
      *rows = (input_rows + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
33
17
      *cols = (input_cols + layer->net.pool.border * 2 - layer->net.pool.size + layer->net.pool.strides - 1) / layer->net.pool.strides + 1;
34
17
      *partition = layer->input.matrix.partition;
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17
      break;
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17
    default:
37
0
      assert(0);
38
0
      break;
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3.21k
  }
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}
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#endif