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ncnn
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type conversion and MSVC _CRT_SECURE_NO_DEPRECATE warning fix (#1524) 

* fix type conversion warning

* fix MSVC _CRT_SECURE_NO_DEPRECATE warning

* type conversion: missing one

* fix  MSVC _CRT_SECURE_NO_DEPRECATE warning and type warning

* reformat

* convert google::protobuf::int64 to size_t to avoid type impl between msvc and gcc gap

* remove C++11 auto

* tab -> space
tags/20200226
kalcohol GitHub 6 years ago
parent
90f8359961
commit
01853010ea
No known key found for this signature in database GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 118 additions and 110 deletions
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  1. +112
    -108
      tools/caffe/caffe2ncnn.cpp
  2. +6
    -2
      tools/ncnnoptimize.cpp

+ 112
- 108
tools/caffe/caffe2ncnn.cpp View File

@@ -12,6 +12,10 @@
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#ifdef _MSC_VER
#define _CRT_SECURE_NO_DEPRECATE
#endif

#include <stdio.h>
#include <limits.h>
#include <math.h>
@@ -32,7 +36,7 @@

static inline size_t alignSize(size_t sz, int n)
{
return (sz + n-1) & -n;
return (sz + n - 1) & -n;
}

// convert float to half precision floating point
@@ -52,9 +56,9 @@ static unsigned short float2half(float value)
unsigned short exponent = (tmp.u & 0x7F800000) >> 23;
unsigned int significand = tmp.u & 0x7FFFFF;

// fprintf(stderr, "%d %d %d\n", sign, exponent, significand);
// fprintf(stderr, "%d %d %d\n", sign, exponent, significand);

// 1 : 5 : 10
// 1 : 5 : 10
unsigned short fp16;
if (exponent == 0)
{
@@ -69,7 +73,7 @@ static unsigned short float2half(float value)
else
{
// normalized
short newexp = exponent + (- 127 + 15);
short newexp = exponent + (-127 + 15);
if (newexp >= 31)
{
// overflow, return infinity
@@ -103,15 +107,15 @@ static unsigned short float2half(float value)
static signed char float2int8(float value)
{
float tmp;
if (value >= 0.f) tmp = value + 0.5;
else tmp = value - 0.5;
if (value >= 0.f) tmp = value + 0.5f;
else tmp = value - 0.5f;

if (tmp > 127)
return 127;
if (tmp < -127)
return -127;

return tmp;
return static_cast<signed char>(tmp);
}

static bool read_int8scale_table(const char* filepath, std::map<std::string, std::vector<float> >& blob_int8scale_table, std::map<std::string, std::vector<float> >& weight_int8scale_table)
@@ -154,11 +158,11 @@ static bool read_int8scale_table(const char* filepath, std::map<std::string, std
// XYZ_param_N pattern
if (strstr(keystr.c_str(), "_param_"))
{
weight_int8scale_table[ keystr ] = scales;
weight_int8scale_table[keystr] = scales;
}
else
{
blob_int8scale_table[ keystr ] = scales;
blob_int8scale_table[keystr] = scales;
}

keystr.clear();
@@ -181,11 +185,11 @@ static bool read_int8scale_table(const char* filepath, std::map<std::string, std
// XYZ_param_N pattern
if (strstr(keystr.c_str(), "_param_"))
{
weight_int8scale_table[ keystr ] = scales;
weight_int8scale_table[keystr] = scales;
}
else
{
blob_int8scale_table[ keystr ] = scales;
blob_int8scale_table[keystr] = scales;
}
}

@@ -215,13 +219,13 @@ static int quantize_weight(float *data, size_t data_length, std::vector<float> s
{
int8_weights.resize(data_length);

int length_per_group = data_length / scales.size();
const int length_per_group = static_cast<int>(data_length / scales.size());

for (size_t i = 0; i < data_length; i++)
{
float f = data[i];

signed char int8 = float2int8(f * scales[ i / length_per_group ]);
signed char int8 = float2int8(f * scales[i / length_per_group]);

int8_weights[i] = int8;
}
@@ -264,17 +268,17 @@ static bool quantize_weight(float *data, size_t data_length, int quantize_level,
// 3. Align data to the quantized value
for (size_t i = 0; i < data_length; ++i)
{
size_t table_index = int((data[i] - min_value) / strides);
table_index = std::min<float>(table_index, quantize_level - 1);
int table_index = int((data[i] - min_value) / strides);
table_index = std::min(table_index, quantize_level - 1);

float low_value = quantize_table[table_index];
float low_value = quantize_table[table_index];
float high_value = low_value + strides;

// find a nearest value between low and high value.
float targetValue = data[i] - low_value < high_value - data[i] ? low_value : high_value;
const float targetValue = data[i] - low_value < high_value - data[i] ? low_value : high_value;

table_index = int((targetValue - min_value) / strides);
table_index = std::min<float>(table_index, quantize_level - 1);
table_index = std::min(table_index, quantize_level - 1);
quantize_index.push_back(table_index);
}

@@ -386,11 +390,11 @@ int main(int argc, char** argv)
// [layer count] [blob count]
int layer_count = proto.layer_size();
std::set<std::string> blob_names;
for (int i=0; i<layer_count; i++)
for (int i = 0; i < layer_count; i++)
{
const caffe::LayerParameter& layer = proto.layer(i);

for (int j=0; j<layer.bottom_size(); j++)
for (int j = 0; j < layer.bottom_size(); j++)
{
std::string blob_name = layer.bottom(j);
if (blob_name_decorated.find(blob_name) != blob_name_decorated.end())
@@ -418,7 +422,7 @@ int main(int argc, char** argv)
}
else
{
for (int j=0; j<layer.top_size(); j++)
for (int j = 0; j < layer.top_size(); j++)
{
std::string blob_name = layer.top(j);
blob_names.insert(blob_name);
@@ -437,16 +441,16 @@ int main(int argc, char** argv)
else
{
splitncnn_blob_count += it->second;
// fprintf(stderr, "%s %d\n", it->first.c_str(), it->second);
// fprintf(stderr, "%s %d\n", it->first.c_str(), it->second);
++it;
}
}
fprintf(pp, "%lu %lu\n", layer_count + bottom_reference.size(), blob_names.size() + splitncnn_blob_count);
fprintf(pp, "%d %d\n", int(layer_count + bottom_reference.size()), int(blob_names.size() + splitncnn_blob_count));

// populate
blob_name_decorated.clear();
int internal_split = 0;
for (int i=0; i<layer_count; i++)
for (int i = 0; i < layer_count; i++)
{
const caffe::LayerParameter& layer = proto.layer(i);

@@ -503,7 +507,7 @@ int main(int argc, char** argv)
}
fprintf(pp, " %-16s %d %d", layer.name().c_str(), layer.bottom_size(), layer.top_size());

for (int j=0; j<layer.bottom_size(); j++)
for (int j = 0; j < layer.bottom_size(); j++)
{
std::string blob_name = layer.bottom(j);
if (blob_name_decorated.find(layer.bottom(j)) != blob_name_decorated.end())
@@ -534,7 +538,7 @@ int main(int argc, char** argv)
}
else
{
for (int j=0; j<layer.top_size(); j++)
for (int j = 0; j < layer.top_size(); j++)
{
std::string blob_name = layer.top(j);
fprintf(pp, " %s", blob_name.c_str());
@@ -543,7 +547,7 @@ int main(int argc, char** argv)

// find blob binary by layer name
int netidx;
for (netidx=0; netidx<net.layer_size(); netidx++)
for (netidx = 0; netidx < net.layer_size(); netidx++)
{
if (net.layer(netidx).name() == layer.name())
{
@@ -570,7 +574,7 @@ int main(int argc, char** argv)
{
fwrite(mean_blob.data().data(), sizeof(float), mean_blob.data_size(), bp);
float tmp;
for (int j=0; j<var_blob.data_size(); j++)
for (int j = 0; j < var_blob.data_size(); j++)
{
tmp = var_blob.data().data()[j] + eps;
fwrite(&tmp, sizeof(float), 1, bp);
@@ -581,12 +585,12 @@ int main(int argc, char** argv)
float scale_factor = binlayer.blobs(2).data().data()[0] == 0 ? 0 : 1 / binlayer.blobs(2).data().data()[0];
// premultiply scale_factor to mean and variance
float tmp;
for (int j=0; j<mean_blob.data_size(); j++)
for (int j = 0; j < mean_blob.data_size(); j++)
{
tmp = mean_blob.data().data()[j] * scale_factor;
fwrite(&tmp, sizeof(float), 1, bp);
}
for (int j=0; j<var_blob.data_size(); j++)
for (int j = 0; j < var_blob.data_size(); j++)
{
tmp = var_blob.data().data()[j] * scale_factor + eps;
fwrite(&tmp, sizeof(float), 1, bp);
@@ -765,8 +769,8 @@ int main(int argc, char** argv)

// padding to 32bit align
int nwrite = ftell(bp) - p0;
int nalign = alignSize(nwrite, 4);
unsigned char padding[4] = {0x00, 0x00, 0x00, 0x00};
int nalign = int(alignSize(nwrite, 4));
unsigned char padding[4] = { 0x00, 0x00, 0x00, 0x00 };
fwrite(padding, sizeof(unsigned char), nalign - nwrite, bp);
}
else
@@ -886,22 +890,22 @@ int main(int argc, char** argv)
{
maxk = convolution_param.kernel_size(0) * convolution_param.kernel_size(0);
}
for (int g=0; g<group; g++)
{
// reorder weight from inch-outch to outch-inch
int num_output = convolution_param.num_output() / group;
int num_input = weight_blob.data_size() / maxk / num_output / group;
const float* weight_data_ptr = weight_blob.data().data() + g * maxk * num_output * num_input;
for (int k=0; k<num_output; k++)
for (int g = 0; g < group; g++)
{
for (int j=0; j<num_input; j++)
// reorder weight from inch-outch to outch-inch
int num_output = convolution_param.num_output() / group;
int num_input = weight_blob.data_size() / maxk / num_output / group;
const float* weight_data_ptr = weight_blob.data().data() + g * maxk * num_output * num_input;
for (int k = 0; k < num_output; k++)
{
fwrite(weight_data_ptr + (j*num_output + k) * maxk, sizeof(float), maxk, bp);
for (int j = 0; j < num_input; j++)
{
fwrite(weight_data_ptr + (j*num_output + k) * maxk, sizeof(float), maxk, bp);
}
}
}
}

for (int j=1; j<binlayer.blobs_size(); j++)
for (int j = 1; j < binlayer.blobs_size(); j++)
{
const caffe::BlobProto& blob = binlayer.blobs(j);
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
@@ -932,7 +936,7 @@ int main(int argc, char** argv)
int coeff_size = eltwise_param.coeff_size();
fprintf(pp, " 0=%d", (int)eltwise_param.operation());
fprintf(pp, " -23301=%d", coeff_size);
for (int j=0; j<coeff_size; j++)
for (int j = 0; j < coeff_size; j++)
{
fprintf(pp, ",%e", eltwise_param.coeff(j));
}
@@ -953,7 +957,7 @@ int main(int argc, char** argv)
fprintf(pp, " 2=%d", embed_param.bias_term());
fprintf(pp, " 3=%d", weight_blob.data_size());

for (int j=0; j<binlayer.blobs_size(); j++)
for (int j = 0; j < binlayer.blobs_size(); j++)
{
int quantize_tag = 0;
const caffe::BlobProto& blob = binlayer.blobs(j);
@@ -995,8 +999,8 @@ int main(int argc, char** argv)
}
// padding to 32bit align
int nwrite = ftell(bp) - p0;
int nalign = alignSize(nwrite, 4);
unsigned char padding[4] = {0x00, 0x00, 0x00, 0x00};
int nalign = int(alignSize(nwrite, 4));
unsigned char padding[4] = { 0x00, 0x00, 0x00, 0x00 };
fwrite(padding, sizeof(unsigned char), nalign - nwrite, bp);
}
else
@@ -1042,7 +1046,7 @@ int main(int argc, char** argv)
}
}

for (int j=0; j<binlayer.blobs_size(); j++)
for (int j = 0; j < binlayer.blobs_size(); j++)
{
int quantize_tag = 0;
const caffe::BlobProto& blob = binlayer.blobs(j);
@@ -1107,8 +1111,8 @@ int main(int argc, char** argv)

// padding to 32bit align
int nwrite = ftell(bp) - p0;
int nalign = alignSize(nwrite, 4);
unsigned char padding[4] = {0x00, 0x00, 0x00, 0x00};
int nalign = int(alignSize(nwrite, 4));
unsigned char padding[4] = { 0x00, 0x00, 0x00, 0x00 };
fwrite(padding, sizeof(unsigned char), nalign - nwrite, bp);
}
else
@@ -1137,19 +1141,19 @@ int main(int argc, char** argv)
const caffe::BlobShape& bs = input_param.shape(0);
if (bs.dim_size() == 4)
{
fprintf(pp, " 0=%ld", bs.dim(3));
fprintf(pp, " 1=%ld", bs.dim(2));
fprintf(pp, " 2=%ld", bs.dim(1));
fprintf(pp, " 0=%zd", size_t(bs.dim(3)));
fprintf(pp, " 1=%zd", size_t(bs.dim(2)));
fprintf(pp, " 2=%zd", size_t(bs.dim(1)));
}
else if (bs.dim_size() == 3)
{
fprintf(pp, " 0=%ld", bs.dim(2));
fprintf(pp, " 1=%ld", bs.dim(1));
fprintf(pp, " 0=%zd", size_t(bs.dim(2)));
fprintf(pp, " 1=%zd", size_t(bs.dim(1)));
fprintf(pp, " 2=-233");
}
else if (bs.dim_size() == 2)
{
fprintf(pp, " 0=%ld", bs.dim(1));
fprintf(pp, " 0=%zd", size_t(bs.dim(1)));
fprintf(pp, " 1=-233");
fprintf(pp, " 2=-233");
}
@@ -1180,7 +1184,7 @@ int main(int argc, char** argv)
fprintf(pp, " 0=%d", recurrent_param.num_output());
fprintf(pp, " 1=%d", weight_blob.data_size());

for (int j=0; j<binlayer.blobs_size(); j++)
for (int j = 0; j < binlayer.blobs_size(); j++)
{
int quantize_tag = 0;
const caffe::BlobProto& blob = binlayer.blobs(j);
@@ -1220,8 +1224,8 @@ int main(int argc, char** argv)
}
// padding to 32bit align
int nwrite = ftell(bp) - p0;
int nalign = alignSize(nwrite, 4);
unsigned char padding[4] = {0x00, 0x00, 0x00, 0x00};
int nalign = int(alignSize(nwrite, 4));
unsigned char padding[4] = { 0x00, 0x00, 0x00, 0x00 };
fwrite(padding, sizeof(unsigned char), nalign - nwrite, bp);
}
else
@@ -1371,7 +1375,7 @@ int main(int argc, char** argv)
const caffe::PriorBoxParameter& prior_box_param = layer.prior_box_param();

int num_aspect_ratio = prior_box_param.aspect_ratio_size();
for (int j=0; j<prior_box_param.aspect_ratio_size(); j++)
for (int j = 0; j < prior_box_param.aspect_ratio_size(); j++)
{
float ar = prior_box_param.aspect_ratio(j);
if (fabs(ar - 1.) < 1e-6) {
@@ -1379,7 +1383,7 @@ int main(int argc, char** argv)
}
}

float variances[4] = {0.1f, 0.1f, 0.1f, 0.1f};
float variances[4] = { 0.1f, 0.1f, 0.1f, 0.1f };
if (prior_box_param.variance_size() == 4)
{
variances[0] = prior_box_param.variance(0);
@@ -1424,17 +1428,17 @@ int main(int argc, char** argv)
}

fprintf(pp, " -23300=%d", prior_box_param.min_size_size());
for (int j=0; j<prior_box_param.min_size_size(); j++)
for (int j = 0; j < prior_box_param.min_size_size(); j++)
{
fprintf(pp, ",%e", prior_box_param.min_size(j));
}
fprintf(pp, " -23301=%d", prior_box_param.max_size_size());
for (int j=0; j<prior_box_param.max_size_size(); j++)
for (int j = 0; j < prior_box_param.max_size_size(); j++)
{
fprintf(pp, ",%e", prior_box_param.max_size(j));
}
fprintf(pp, " -23302=%d", num_aspect_ratio);
for (int j=0; j<prior_box_param.aspect_ratio_size(); j++)
for (int j = 0; j < prior_box_param.aspect_ratio_size(); j++)
{
float ar = prior_box_param.aspect_ratio(j);
if (fabs(ar - 1.) < 1e-6) {
@@ -1472,11 +1476,11 @@ int main(int argc, char** argv)
sscanf(python_param.param_str().c_str(), "'feat_stride': %d", &feat_stride);

int base_size = 16;
// float ratio;
// float scale;
// float ratio;
// float scale;
int pre_nms_topN = 6000;
int after_nms_topN = 300;
float nms_thresh = 0.7;
float nms_thresh = 0.7f;
int min_size = 16;
fprintf(pp, " 0=%d", feat_stride);
fprintf(pp, " 1=%d", base_size);
@@ -1512,19 +1516,19 @@ int main(int argc, char** argv)
const caffe::BlobShape& bs = reshape_param.shape();
if (bs.dim_size() == 1)
{
fprintf(pp, " 0=%ld 1=-233 2=-233", bs.dim(0));
fprintf(pp, " 0=%zd 1=-233 2=-233", size_t(bs.dim(0)));
}
else if (bs.dim_size() == 2)
{
fprintf(pp, " 0=%ld 1=-233 2=-233", bs.dim(1));
fprintf(pp, " 0=%zd 1=-233 2=-233", size_t(bs.dim(1)));
}
else if (bs.dim_size() == 3)
{
fprintf(pp, " 0=%ld 1=%ld 2=-233", bs.dim(2), bs.dim(1));
fprintf(pp, " 0=%zd 1=%zd 2=-233", size_t(bs.dim(2)), bs.dim(1));
}
else // bs.dim_size() == 4
{
fprintf(pp, " 0=%ld 1=%ld 2=%ld", bs.dim(3), bs.dim(2), bs.dim(1));
fprintf(pp, " 0=%zd 1=%zd 2=%zd", size_t(bs.dim(3)), size_t(bs.dim(2)), size_t(bs.dim(1)));
}
fprintf(pp, " 3=0");// permute
}
@@ -1551,7 +1555,7 @@ int main(int argc, char** argv)
if (scale_weight)
{
const caffe::BlobProto& weight_blob = binlayer.blobs(0);
fprintf(pp, " 0=%d", (int)weight_blob.data_size());
fprintf(pp, " 0=%d", int(weight_blob.data_size()));
}
else
{
@@ -1560,7 +1564,7 @@ int main(int argc, char** argv)

fprintf(pp, " 1=%d", scale_param.bias_term());

for (int j=0; j<binlayer.blobs_size(); j++)
for (int j = 0; j < binlayer.blobs_size(); j++)
{
const caffe::BlobProto& blob = binlayer.blobs(j);
fwrite(blob.data().data(), sizeof(float), blob.data_size(), bp);
@@ -1578,7 +1582,7 @@ int main(int argc, char** argv)
{
int num_slice = layer.top_size();
fprintf(pp, " -23300=%d", num_slice);
for (int j=0; j<num_slice; j++)
for (int j = 0; j < num_slice; j++)
{
fprintf(pp, ",-233");
}
@@ -1588,7 +1592,7 @@ int main(int argc, char** argv)
int num_slice = slice_param.slice_point_size() + 1;
fprintf(pp, " -23300=%d", num_slice);
int prev_offset = 0;
for (int j=0; j<slice_param.slice_point_size(); j++)
for (int j = 0; j < slice_param.slice_point_size(); j++)
{
int offset = slice_param.slice_point(j);
fprintf(pp, ",%d", offset - prev_offset);
@@ -1630,40 +1634,40 @@ int main(int argc, char** argv)

int num_bias = yolo_detection_output_param.biases_size();
fprintf(pp, " -23304=%d", num_bias);
for (int j=0; j<num_bias; j++)
for (int j = 0; j < num_bias; j++)
{
fprintf(pp, ",%e", yolo_detection_output_param.biases(j));
}
}
else if (layer.type() == "Yolov3DetectionOutput")
{
const caffe::Yolov3DetectionOutputParameter& yolov3_detection_output_param = layer.yolov3_detection_output_param();
fprintf(pp, " 0=%d", yolov3_detection_output_param.num_classes());
fprintf(pp, " 1=%d", yolov3_detection_output_param.num_box());
fprintf(pp, " 2=%e", yolov3_detection_output_param.confidence_threshold());
fprintf(pp, " 3=%e", yolov3_detection_output_param.nms_threshold());
int num_bias = yolov3_detection_output_param.biases_size();
fprintf(pp, " -23304=%d", num_bias);
for (int j = 0; j<num_bias; j++)
{
fprintf(pp, ",%e", yolov3_detection_output_param.biases(j));
}
int num_mask = yolov3_detection_output_param.mask_size();
fprintf(pp, " -23305=%d", num_mask);
for (int j = 0; j<num_mask; j++)
{
fprintf(pp, ",%e", (float)yolov3_detection_output_param.mask(j));
}
int num_anchors = yolov3_detection_output_param.anchors_scale_size();
fprintf(pp, " -23306=%d", num_anchors);
for (int j = 0; j<num_anchors; j++)
{
fprintf(pp, ",%e", (float)yolov3_detection_output_param.anchors_scale(j));
}
fprintf(pp, " 7=%d", yolov3_detection_output_param.mask_group_num());
}
else if (layer.type() == "Yolov3DetectionOutput")
{
const caffe::Yolov3DetectionOutputParameter& yolov3_detection_output_param = layer.yolov3_detection_output_param();
fprintf(pp, " 0=%d", yolov3_detection_output_param.num_classes());
fprintf(pp, " 1=%d", yolov3_detection_output_param.num_box());
fprintf(pp, " 2=%e", yolov3_detection_output_param.confidence_threshold());
fprintf(pp, " 3=%e", yolov3_detection_output_param.nms_threshold());
int num_bias = yolov3_detection_output_param.biases_size();
fprintf(pp, " -23304=%d", num_bias);
for (int j = 0; j < num_bias; j++)
{
fprintf(pp, ",%e", yolov3_detection_output_param.biases(j));
}
int num_mask = yolov3_detection_output_param.mask_size();
fprintf(pp, " -23305=%d", num_mask);
for (int j = 0; j < num_mask; j++)
{
fprintf(pp, ",%e", (float)yolov3_detection_output_param.mask(j));
}
int num_anchors = yolov3_detection_output_param.anchors_scale_size();
fprintf(pp, " -23306=%d", num_anchors);
for (int j = 0; j < num_anchors; j++)
{
fprintf(pp, ",%e", (float)yolov3_detection_output_param.anchors_scale(j));
}
fprintf(pp, " 7=%d", yolov3_detection_output_param.mask_group_num());
}
fprintf(pp, "\n");

// add split layer if top reference larger than one
@@ -1680,7 +1684,7 @@ int main(int argc, char** argv)
fprintf(pp, "%-16s %-16s %d %d", "Split", splitname, 1, refcount);
fprintf(pp, " %s", blob_name.c_str());

for (int j=0; j<refcount; j++)
for (int j = 0; j < refcount; j++)
{
fprintf(pp, " %s_splitncnn_%d", blob_name.c_str(), j);
}
@@ -1692,7 +1696,7 @@ int main(int argc, char** argv)
}
else
{
for (int j=0; j<layer.top_size(); j++)
for (int j = 0; j < layer.top_size(); j++)
{
std::string blob_name = layer.top(j);
if (bottom_reference.find(blob_name) != bottom_reference.end())
@@ -1705,7 +1709,7 @@ int main(int argc, char** argv)
fprintf(pp, "%-16s %-16s %d %d", "Split", splitname, 1, refcount);
fprintf(pp, " %s", blob_name.c_str());

for (int j=0; j<refcount; j++)
for (int j = 0; j < refcount; j++)
{
fprintf(pp, " %s_splitncnn_%d", blob_name.c_str(), j);
}


+ 6
- 2
tools/ncnnoptimize.cpp View File

@@ -12,6 +12,10 @@
// CONDITIONS OF ANY KIND, either express or implied. See the License for the
// specific language governing permissions and limitations under the License.

#ifdef _MSC_VER
#define _CRT_SECURE_NO_DEPRECATE
#endif

#include <algorithm>
#include <set>
#include <vector>
@@ -1794,7 +1798,7 @@ int NetOptimize::shape_inference()
if (dims == 2) m.create(w, h);
if (dims == 3) m.create(w, h, c);

ex.input(i, m);
ex.input(int(i), m);
}

fprintf(stderr, "shape_inference\n");
@@ -1998,7 +2002,7 @@ int NetOptimize::save(const char* parampath, const char* binpath)
}
if (shape_ready)
{
fprintf(pp, " -23330=%d", top_count*4);
fprintf(pp, " -23330=%zd", top_count * 4);
for (int j=0; j<top_count; j++)
{
int top_blob_index = layer->tops[j];


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