Bug Summary

File:nnc/ccv_nnc_tensor.c
Warning:line 221, column 4
Null pointer passed to 1st parameter expecting 'nonnull'

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_nnc_tensor.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -fmath-errno -ffp-contract=on -fno-rounding-math -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib/nnc -resource-dir /usr/local/lib/clang/19 -I ../ -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -I /usr/local/include -internal-isystem /usr/local/lib/clang/19/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2025-05-31-231101-139028-1 -x c ccv_nnc_tensor.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#ifdef HAVE_CUDA1
5#include "gpu/ccv_nnc_compat.h"
6#elif defined(HAVE_MPS)
7#include "mps/ccv_nnc_mps.h"
8#endif
9#include <fcntl.h>
10#include <sys/mman.h>
11
12// MARK - Level-1 API
13
14const int ccv_nnc_no_ofs[CCV_NNC_MAX_DIM_ALLOC(12)] = {0};
15
16ccv_nnc_tensor_t* ccv_nnc_tensor_new(const void* const ptr, const ccv_nnc_tensor_param_t params, const int flags)
17{
18 ccv_nnc_tensor_t* tensor;
19 // this specific form can be toll-free bridging to ccv_dense_matrix_t (On CPU, and 3 dims (channels, rows, cols), and channels is smaller than max channels of ccv_dense_matrix_t).
20 const int tfb = (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && params.format == CCV_TENSOR_FORMAT_NHWC && params.dim[2] > 0 && params.dim[2] <= CCV_MAX_CHANNEL(0xFFF) && params.dim[0] > 0 && params.dim[1] > 0 && params.dim[3] == 0);
3
←
Assuming the condition is true
→
4
←
Assuming field 'format' is not equal to CCV_TENSOR_FORMAT_NHWC
→
21 if (ptr
4.1
'ptr' is null
 || (flags & CCV_NO_DATA_ALLOC))
5
←
Assuming the condition is true
→
6
←
Taking true branch
→
22 {
23 tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
24 tensor->dataof = 0;
25 tensor->alias_ref = 0;
26 tensor->sig = 0;
27 tensor->refcount = 1;
28 tensor->info = params;
29 if (tfb
6.1
'tfb' is 0
)
7
←
Taking false branch
→
30 {
31 tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
32 // This corresponding to mat->step
33 tensor->info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
0xFFF) + 3) & -4);
34 } else // This won't be recognized by ccv_dense_matrix_t
35 tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
36 tensor->data.u8 = (uint8_t*)ptr;
8
←
Null pointer value stored to field 'u8'
→
37 return tensor;
38 }
39 if (flags & CCV_TENSOR_CPU_MEMORY)
40 {
41 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 41, __extension__ __PRETTY_FUNCTION__);
}));
42 } else if (flags & CCV_TENSOR_GPU_MEMORY) {
43 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_GPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_GPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_GPU_MEMORY"
, "ccv_nnc_tensor.c", 43, __extension__ __PRETTY_FUNCTION__);
}));
44 }
45 const size_t tensor_hdr_size = (sizeof(ccv_nnc_tensor_t) + 63) & -64;
46 const size_t size = ccv_nnc_tensor_data_size(params);
47#ifdef HAVE_CUDA1
48 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
49 {
50 tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
51 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 51, __extension__ __PRETTY_FUNCTION__);
}));
52 if (size > 0)
53 tensor->data.u8 = (uint8_t*)cumalloc(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
54 else
55 tensor->data.u8 = 0;
56 } else {
57 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 57, __extension__ __PRETTY_FUNCTION__);
}));
58 ccmemalignposix_memalign((void **)&tensor, 64, tensor_hdr_size + size);
59 if (size > 0)
60 tensor->data.u8 = (uint8_t*)tensor + tensor_hdr_size;
61 else
62 tensor->data.u8 = 0;
63 }
64#elif defined(HAVE_MPS)
65 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
66 {
67 tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
68 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 68, __extension__ __PRETTY_FUNCTION__);
}));
69 if (size > 0)
70 tensor->data.u8 = (uint8_t*)mpobjmalloc(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
71 else
72 tensor->data.u8 = 0;
73 } else {
74 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 74, __extension__ __PRETTY_FUNCTION__);
}));
75 ccmemalignposix_memalign((void **)&tensor, 64, tensor_hdr_size + size);
76 if (size > 0)
77 tensor->data.u8 = (uint8_t*)tensor + tensor_hdr_size;
78 else
79 tensor->data.u8 = 0;
80 }
81#else
82 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 82, __extension__ __PRETTY_FUNCTION__);
}));
83 ccmemalignposix_memalign((void **)&tensor, 64, tensor_hdr_size + size);
84 if (size > 0)
85 tensor->data.u8 = (uint8_t*)tensor + tensor_hdr_size;
86 else
87 tensor->data.u8 = 0;
88#endif
89 tensor->dataof = 0;
90 tensor->alias_ref = 0;
91 tensor->data_size = size;
92 tensor->sig = 0;
93 tensor->refcount = 1;
94 tensor->info = params;
95 if (tfb)
96 {
97 tensor->type = CCV_UNMANAGED | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
98 // This corresponding to mat->step
99 tensor->info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
0xFFF) + 3) & -4);
100 } else
101 tensor->type = CCV_UNMANAGED | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
102 return tensor;
103}
104
105ccv_nnc_tensor_t* ccv_nnc_tensor_new_from_file(const ccv_nnc_tensor_param_t params, const char* const filename, const off_t offset, const int flags)
106{
107 ccv_nnc_tensor_t* tensor;
108 // this specific form can be toll-free bridging to ccv_dense_matrix_t (On CPU, and 3 dims (channels, rows, cols), and channels is smaller than max channels of ccv_dense_matrix_t).
109 const int tfb = (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && params.format == CCV_TENSOR_FORMAT_NHWC && params.dim[2] > 0 && params.dim[2] <= CCV_MAX_CHANNEL(0xFFF) && params.dim[0] > 0 && params.dim[1] > 0 && params.dim[3] == 0);
110 tensor = (ccv_nnc_tensor_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_t));
111 tensor->dataof = 0;
112 tensor->alias_ref = 0;
113 tensor->sig = 0;
114 tensor->refcount = 1;
115 tensor->info = params;
116 if (tfb)
117 {
118 tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
119 // This corresponding to mat->step
120 tensor->info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
0xFFF) + 3) & -4);
121 } else // This won't be recognized by ccv_dense_matrix_t
122 tensor->type = CCV_NO_DATA_ALLOC | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
123 const size_t size = ccv_nnc_tensor_data_size(params);
124#ifdef HAVE_CUDA1
125 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
126 {
127 // Remove this flag so it can be deallocated as usual.
128 tensor->type &= ~CCV_NO_DATA_ALLOC;
129 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 129, __extension__ __PRETTY_FUNCTION__)
; }));
130 if (size > 0)
131 {
132 void* ptr = 0;
133 // This is not supported yet on CUDA.
134 if (flags & CCV_NNC_TENSOR_MEMORY_MAP_ON_DEMAND)
135 ptr = cumallocmanaged(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
136 if (ptr) // If allocated successfully. Otherwise we go through the fallback path.
137 {
138 tensor->data.u8 = (uint8_t*)ptr;
139 int fd = open(filename, O_RDONLY00, 0);
140 cufileread(fd, offset, tensor->data.u8, size);
141 close(fd);
142 cumemadvisereadmostly(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), tensor->data.u8, size);
143 tensor->type |= CCV_MAPPED_MEM; // This denotes the tensor is mapped to CPU, and would prefer a explicit prefetch call.
144 } else {
145 tensor->data.u8 = (uint8_t*)cumalloc(CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8), size);
146 int fd = open(filename, O_RDONLY00, 0);
147 cufileread(fd, offset, tensor->data.u8, size);
148 close(fd);
149 }
150 } else
151 tensor->data.u8 = 0;
152 } else {
153 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 153, __extension__ __PRETTY_FUNCTION__)
; }));
154 if (size > 0)
155 {
156 int fd = open(filename, O_RDONLY00, 0);
157 void* bufptr = mmap(0, size, PROT_READ0x1, MAP_PRIVATE0x02, fd, offset);
158 close(fd);
159 madvise(bufptr, size, MADV_SEQUENTIAL2 | MADV_WILLNEED3);
160 tensor->data.u8 = bufptr;
161 tensor->type |= CCV_MAPPED_MEM;
162 } else
163 tensor->data.u8 = 0;
164 }
165#elif defined(HAVE_MPS)
166 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
167 {
168 // Remove this flag so it can be deallocated as usual.
169 tensor->type &= ~CCV_NO_DATA_ALLOC;
170 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 170, __extension__ __PRETTY_FUNCTION__)
; }));
171 if (size > 0)
172 tensor->data.u8 = (uint8_t*)mpmemmap(filename, size, offset, flags);
173 else
174 tensor->data.u8 = 0;
175 } else {
176 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 176, __extension__ __PRETTY_FUNCTION__)
; }));
177 if (size > 0)
178 {
179 int fd = open(filename, O_RDONLY00, 0);
180 void* bufptr = mmap(0, size, PROT_READ0x1, MAP_PRIVATE0x02, fd, offset);
181 close(fd);
182 madvise(bufptr, size, MADV_SEQUENTIAL2 | MADV_WILLNEED3);
183 tensor->data.u8 = bufptr;
184 tensor->type |= CCV_MAPPED_MEM;
185 } else
186 tensor->data.u8 = 0;
187 }
188#else
189 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 189, __extension__ __PRETTY_FUNCTION__)
; }));
190 if (size > 0)
191 {
192 int fd = open(filename, O_RDONLY00, 0);
193 void* bufptr = mmap(0, size, PROT_READ0x1, MAP_PRIVATE0x02, fd, offset);
194 close(fd);
195 madvise(bufptr, size, MADV_SEQUENTIAL2 | MADV_WILLNEED3);
196 tensor->data.u8 = bufptr;
197 tensor->type |= CCV_MAPPED_MEM;
198 } else
199 tensor->data.u8 = 0;
200#endif
201 return tensor;
202}
203
204ccv_nnc_tensor_t* ccv_nnc_tensor_new_from_raw(const ccv_nnc_tensor_param_t params, const void* const bufptr, const size_t buf_size, const int flags)
205{
206 ccv_nnc_tensor_t* tensor = ccv_nnc_tensor_new(0, params, flags);
1
Passing null pointer value via 1st parameter 'ptr'
→
2
←
Calling 'ccv_nnc_tensor_new'
→
9
←
Returning from 'ccv_nnc_tensor_new'
→
207 const size_t size = ccv_min(ccv_nnc_tensor_data_size_without_padding(params), buf_size)({ typeof (ccv_nnc_tensor_data_size_without_padding(params)) _a
= (ccv_nnc_tensor_data_size_without_padding(params)); typeof
(buf_size) _b = (buf_size); (_a < _b) ? _a : _b; });
10
←
Assuming '_a' is >= '_b'
→
11
←
'?' condition is false
→
208#ifdef HAVE_CUDA1
209 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
12
←
Taking false branch
→
210 {
211 // Remove this flag so it can be deallocated as usual.
212 tensor->type &= ~CCV_NO_DATA_ALLOC;
213 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 213, __extension__ __PRETTY_FUNCTION__)
; }));
214 if (size > 0)
215 cumemcpy(tensor->data.u8, tensor->info.type, bufptr, CCV_TENSOR_CPU_MEMORY, size);
216 else
217 tensor->data.u8 = 0;
218 } else {
219 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 219, __extension__ __PRETTY_FUNCTION__)
; }));
13
←
Taking true branch
→
220 if (size > 0)
14
←
Assuming 'size' is > 0
→
15
←
Taking true branch
→
221 memcpy(tensor->data.u8, bufptr, size);
16
←
Null pointer passed to 1st parameter expecting 'nonnull'
222 else
223 tensor->data.u8 = 0;
224 }
225#elif defined(HAVE_MPS)
226 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
227 {
228 // Remove this flag so it can be deallocated as usual.
229 tensor->type &= ~CCV_NO_DATA_ALLOC;
230 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 230, __extension__ __PRETTY_FUNCTION__)
; }));
231 if (size > 0)
232 mpmemcpy(tensor->data.u8, tensor->dataof, tensor->info.type, bufptr, 0, CCV_TENSOR_CPU_MEMORY, size);
233 else
234 tensor->data.u8 = 0;
235 } else {
236 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 236, __extension__ __PRETTY_FUNCTION__)
; }));
237 if (size > 0)
238 memcpy(tensor->data.u8, bufptr, size);
239 else
240 tensor->data.u8 = 0;
241 }
242#else
243 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 243, __extension__ __PRETTY_FUNCTION__)
; }));
244 if (size > 0)
245 memcpy(tensor->data.u8, bufptr, size);
246 else
247 tensor->data.u8 = 0;
248#endif
249 return tensor;
250}
251
252ccv_nnc_tensor_t* ccv_nnc_tensor_resize(ccv_nnc_tensor_t* const tensor, const ccv_nnc_tensor_param_t params)
253{
254 assert(!CCV_IS_TENSOR_VIEW(tensor))((void) sizeof ((!((*(int*)(tensor)) & CCV_TENSOR_VIEW)) ?
1 : 0), __extension__ ({ if (!((*(int*)(tensor)) & CCV_TENSOR_VIEW
)) ; else __assert_fail ("!CCV_IS_TENSOR_VIEW(tensor)", "ccv_nnc_tensor.c"
, 254, __extension__ __PRETTY_FUNCTION__); }));
255 assert(tensor->type & CCV_UNMANAGED)((void) sizeof ((tensor->type & CCV_UNMANAGED) ? 1 : 0
), __extension__ ({ if (tensor->type & CCV_UNMANAGED) ;
else __assert_fail ("tensor->type & CCV_UNMANAGED", "ccv_nnc_tensor.c"
, 255, __extension__ __PRETTY_FUNCTION__); }));
256 assert(tensor->data_size > 0)((void) sizeof ((tensor->data_size > 0) ? 1 : 0), __extension__
({ if (tensor->data_size > 0) ; else __assert_fail ("tensor->data_size > 0"
, "ccv_nnc_tensor.c", 256, __extension__ __PRETTY_FUNCTION__)
; }));
257 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_GET_MEMORY(tensor->info.type))((void) sizeof ((((params.type) & 0x3) == ((tensor->info
.type) & 0x3)) ? 1 : 0), __extension__ ({ if (((params.type
) & 0x3) == ((tensor->info.type) & 0x3)) ; else __assert_fail
("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_GET_MEMORY(tensor->info.type)"
, "ccv_nnc_tensor.c", 257, __extension__ __PRETTY_FUNCTION__)
; }));
258 assert(CCV_TENSOR_GET_DEVICE(params.type) == CCV_TENSOR_GET_DEVICE(tensor->info.type))((void) sizeof ((((params.type) & 0xfff00) == ((tensor->
info.type) & 0xfff00)) ? 1 : 0), __extension__ ({ if (((params
.type) & 0xfff00) == ((tensor->info.type) & 0xfff00
)) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) == CCV_TENSOR_GET_DEVICE(tensor->info.type)"
, "ccv_nnc_tensor.c", 258, __extension__ __PRETTY_FUNCTION__)
; }));
259 const size_t size = ccv_nnc_tensor_data_size(params);
260 const int tfb = (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && params.format == CCV_TENSOR_FORMAT_NHWC && params.dim[2] > 0 && params.dim[2] <= CCV_MAX_CHANNEL(0xFFF) && params.dim[0] > 0 && params.dim[1] > 0 && params.dim[3] == 0);
261 tensor->info = params;
262#ifdef HAVE_CUDA1
263 const int pinned_mem = (tensor->type & CCV_PINNED_MEM);
264#endif
265 if (tfb)
266 {
267 tensor->type = CCV_UNMANAGED | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
268 // This corresponding to mat->step
269 tensor->info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
0xFFF) + 3) & -4);
270 } else
271 tensor->type = CCV_UNMANAGED | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
272 if (size <= tensor->data_size) // Nothing.
273 {
274#ifdef HAVE_CUDA1
275 if (pinned_mem)
276 tensor->type |= CCV_PINNED_MEM;
277#endif
278 return tensor;
279 }
280 ccv_nnc_tensor_t* new_tensor = tensor;
281 const size_t tensor_hdr_size = (sizeof(ccv_nnc_tensor_t) + 63) & -64;
282#ifdef HAVE_CUDA1
283 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
284 {
285 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 285, __extension__ __PRETTY_FUNCTION__)
; }));
286 const int device_id = CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8);
287 assert(device_id == CCV_TENSOR_GET_DEVICE_ID(tensor->info.type))((void) sizeof ((device_id == (((tensor->info.type) & 0xfff00
) >> 8)) ? 1 : 0), __extension__ ({ if (device_id == ((
(tensor->info.type) & 0xfff00) >> 8)) ; else __assert_fail
("device_id == CCV_TENSOR_GET_DEVICE_ID(tensor->info.type)"
, "ccv_nnc_tensor.c", 287, __extension__ __PRETTY_FUNCTION__)
; }));
288 cufree(device_id, tensor->data.u8);
289 new_tensor->data.u8 = (uint8_t*)cumalloc(device_id, size);
290 } else {
291 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 291, __extension__ __PRETTY_FUNCTION__)
; }));
292 assert(CCV_TENSOR_GET_MEMORY(tensor->info.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((tensor->info.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((tensor->info.type) &
0x3) == CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(tensor->info.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 292, __extension__ __PRETTY_FUNCTION__)
; }));
293 // pin memory again.
294 if (pinned_mem)
295 cuunregister(new_tensor->data.u8);
296 new_tensor = ccreallocrealloc(new_tensor, tensor_hdr_size + size);
297 new_tensor->data.u8 = (uint8_t*)new_tensor + tensor_hdr_size;
298 }
299#elif defined(HAVE_MPS)
300 if (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY)
301 {
302 assert(CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY)((void) sizeof ((((params.type) & 0xfff00) != CCV_COMPUTE_DEVICE_ANY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0xfff00
) != CCV_COMPUTE_DEVICE_ANY) ; else __assert_fail ("CCV_TENSOR_GET_DEVICE(params.type) != CCV_COMPUTE_DEVICE_ANY"
, "ccv_nnc_tensor.c", 302, __extension__ __PRETTY_FUNCTION__)
; }));
303 const int device_id = CCV_TENSOR_GET_DEVICE_ID(params.type)(((params.type) & 0xfff00) >> 8);
304 assert(device_id == CCV_TENSOR_GET_DEVICE_ID(tensor->info.type))((void) sizeof ((device_id == (((tensor->info.type) & 0xfff00
) >> 8)) ? 1 : 0), __extension__ ({ if (device_id == ((
(tensor->info.type) & 0xfff00) >> 8)) ; else __assert_fail
("device_id == CCV_TENSOR_GET_DEVICE_ID(tensor->info.type)"
, "ccv_nnc_tensor.c", 304, __extension__ __PRETTY_FUNCTION__)
; }));
305 mpobjfree(device_id, tensor->data.u8);
306 new_tensor->data.u8 = (uint8_t*)mpobjmalloc(device_id, size);
307 } else {
308 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 308, __extension__ __PRETTY_FUNCTION__)
; }));
309 assert(CCV_TENSOR_GET_MEMORY(tensor->info.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((tensor->info.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((tensor->info.type) &
0x3) == CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(tensor->info.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 309, __extension__ __PRETTY_FUNCTION__)
; }));
310 new_tensor = ccreallocrealloc(new_tensor, tensor_hdr_size + size);
311 new_tensor->data.u8 = (uint8_t*)new_tensor + tensor_hdr_size;
312 }
313#else
314 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 314, __extension__ __PRETTY_FUNCTION__)
; }));
315 new_tensor = ccreallocrealloc(new_tensor, tensor_hdr_size + size);
316 new_tensor->data.u8 = (uint8_t*)new_tensor + tensor_hdr_size;
317#endif
318 new_tensor->data_size = size;
319#ifdef HAVE_CUDA1
320 if (pinned_mem)
321 ccv_nnc_tensor_pin_memory(new_tensor);
322#endif
323 return new_tensor;
324}
325
326ccv_nnc_tensor_t ccv_nnc_tensor(const void* const ptr, const ccv_nnc_tensor_param_t params, const int flags)
327{
328 // this specific form can be toll-free bridging to ccv_dense_matrix_t
329 const int tfb = (CCV_TENSOR_GET_MEMORY(params.type)((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && params.format == CCV_TENSOR_FORMAT_NHWC && params.dim[2] > 0 && params.dim[2] <= CCV_MAX_CHANNEL(0xFFF) && params.dim[0] > 0 && params.dim[1] > 0 && params.dim[3] == 0);
330 ccv_nnc_tensor_t tensor;
331 tensor.dataof = 0;
332 tensor.alias_ref = 0;
333 tensor.sig = 0;
334 tensor.refcount = 1;
335 tensor.info = params;
336 if (flags & CCV_TENSOR_CPU_MEMORY)
337 {
338 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 338, __extension__ __PRETTY_FUNCTION__)
; }));
339 } else if (flags & CCV_TENSOR_GPU_MEMORY) {
340 assert(CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_GPU_MEMORY)((void) sizeof ((((params.type) & 0x3) == CCV_TENSOR_GPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((params.type) & 0x3) ==
CCV_TENSOR_GPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(params.type) == CCV_TENSOR_GPU_MEMORY"
, "ccv_nnc_tensor.c", 340, __extension__ __PRETTY_FUNCTION__)
; }));
341 }
342 if (tfb)
343 {
344 tensor.type = CCV_NO_DATA_ALLOC | CCV_UNMANAGED | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000) | params.dim[2];
345 // This corresponding to mat->step
346 tensor.info.dim[4] = CCV_GET_STEP(params.dim[1], (CCV_GET_DATA_TYPE(params.datatype) | params.dim[2]))(((params.dim[1]) * _ccv_get_data_type_size[(((((params.datatype
) & 0xFF000) | params.dim[2])) & 0xFF000) >> 12
] * (((((params.datatype) & 0xFF000) | params.dim[2])) &
0xFFF) + 3) & -4);
347 } else // This won't be recognized by ccv_dense_matrix_t
348 tensor.type = CCV_NO_DATA_ALLOC | CCV_UNMANAGED | CCV_MATRIX_DENSE | CCV_GET_DATA_TYPE(params.datatype)((params.datatype) & 0xFF000);
349 if (params.dim[0] > 0)
350 tensor.data.u8 = (uint8_t*)ptr;
351 else
352 tensor.data.u8 = 0;
353 tensor.data_size = 0;
354 return tensor;
355}
356
357int ccv_nnc_tensor_pin_memory(ccv_nnc_tensor_t* const tensor)
358{
359#ifdef HAVE_CUDA1
360 assert(CCV_TENSOR_GET_MEMORY(tensor->info.type) == CCV_TENSOR_CPU_MEMORY)((void) sizeof ((((tensor->info.type) & 0x3) == CCV_TENSOR_CPU_MEMORY
) ? 1 : 0), __extension__ ({ if (((tensor->info.type) &
0x3) == CCV_TENSOR_CPU_MEMORY) ; else __assert_fail ("CCV_TENSOR_GET_MEMORY(tensor->info.type) == CCV_TENSOR_CPU_MEMORY"
, "ccv_nnc_tensor.c", 360, __extension__ __PRETTY_FUNCTION__)
; }));
361 if (!(tensor->type & CCV_PINNED_MEM) && tensor->data_size)
362 {
363 const int success = curegister(tensor->data.u8, tensor->data_size);
364 if (success)
365 tensor->type |= CCV_PINNED_MEM;
366 return success ? 0 : -1;
367 }
368#endif
369 return 0;
370}
371
372void ccv_nnc_tensor_free(ccv_nnc_tensor_t* const tensor)
373{
374 if (CCV_TENSOR_GET_MEMORY(tensor->info.type)((tensor->info.type) & 0x3) == CCV_TENSOR_CPU_MEMORY && (tensor->type & CCV_MAPPED_MEM))
375 {
376 // The size might be different than the ones when we allocated (for example, the tensor might rewrite its size to be smaller).
377 // This might cause issues in the future.
378 const size_t size = ccv_nnc_tensor_data_size(tensor->info);
379 munmap(tensor->data.u8, size);
380 }
381#ifdef HAVE_CUDA1
382 if (tensor->type & CCV_PINNED_MEM)
383 cuunregister(tensor->data.u8);
384 if (CCV_TENSOR_GET_MEMORY(tensor->info.type)((tensor->info.type) & 0x3) == CCV_TENSOR_GPU_MEMORY &&
385 !(tensor->type & CCV_NO_DATA_ALLOC)) // If this is GPU memory and it is allocated, free.
386 cufree(CCV_TENSOR_GET_DEVICE_ID(tensor->info.type)(((tensor->info.type) & 0xfff00) >> 8), tensor->data.u8);
387#elif defined(HAVE_MPS)
388 if (CCV_TENSOR_GET_MEMORY(tensor->info.type)((tensor->info.type) & 0x3) == CCV_TENSOR_GPU_MEMORY &&
389 !(tensor->type & CCV_NO_DATA_ALLOC)) // If this is GPU memory and it is allocated, free.
390 mpobjfree(CCV_TENSOR_GET_DEVICE_ID(tensor->info.type)(((tensor->info.type) & 0xfff00) >> 8), tensor->data.u8);
391#endif
392 ccfreefree(tensor);
393}
394
395static inline void _ccv_nnc_tensor_view_set(ccv_nnc_tensor_view_t* const tv, const ccv_nnc_tensor_t* const tensor, const int dim[CCV_NNC_MAX_DIM_ALLOC(12)], const int ofs[CCV_NNC_MAX_DIM_ALLOC(12)], const int stride[CCV_NNC_MAX_DIM_ALLOC(12)])
396{
397 memcpy(tv->stride, stride, sizeof(int) * CCV_NNC_MAX_DIM_ALLOC(12));
398 memcpy(tv->info.dim, dim, sizeof(int) * CCV_NNC_MAX_DIM_ALLOC(12));
399 uint8_t* const p = tensor->data.u8;
400 const off_t off = tv->off = ccv_nnc_tensor_view_offset(tv->info.datatype, stride, ofs);
401 tv->contiguous = ccv_nnc_tensor_view_is_contiguous(dim, stride);
402 assert(off + CCV_GET_DATA_TYPE_SIZE(tv->info.datatype) * ccv_nnc_dimension_upper_bound(tv->info.dim, tv->stride) <= CCV_GET_DATA_TYPE_SIZE(tensor->info.datatype) * ccv_nnc_tensor_count(tensor->info))((void) sizeof ((off + _ccv_get_data_type_size[((tv->info.
datatype) & 0xFF000) >> 12] * ccv_nnc_dimension_upper_bound
(tv->info.dim, tv->stride) <= _ccv_get_data_type_size
[((tensor->info.datatype) & 0xFF000) >> 12] * ccv_nnc_tensor_count
(tensor->info)) ? 1 : 0), __extension__ ({ if (off + _ccv_get_data_type_size
[((tv->info.datatype) & 0xFF000) >> 12] * ccv_nnc_dimension_upper_bound
(tv->info.dim, tv->stride) <= _ccv_get_data_type_size
[((tensor->info.datatype) & 0xFF000) >> 12] * ccv_nnc_tensor_count
(tensor->info)) ; else __assert_fail ("off + CCV_GET_DATA_TYPE_SIZE(tv->info.datatype) * ccv_nnc_dimension_upper_bound(tv->info.dim, tv->stride) <= CCV_GET_DATA_TYPE_SIZE(tensor->info.datatype) * ccv_nnc_tensor_count(tensor->info)"
, "ccv_nnc_tensor.c", 402, __extension__ __PRETTY_FUNCTION__)
; }));
403 ccv_nnc_tensor_data(tv->info, p, off + tensor->dataof, &tv->data, &tv->dataof);
404}
405
406ccv_nnc_tensor_view_t* ccv_nnc_tensor_view_new(const ccv_nnc_tensor_t* const tensor, const ccv_nnc_tensor_param_t params, const int ofs[CCV_NNC_MAX_DIM_ALLOC(12)], const int stride[CCV_NNC_MAX_DIM_ALLOC(12)])
407{
408 ccv_nnc_tensor_view_t* tv = (ccv_nnc_tensor_view_t*)ccmallocmalloc(sizeof(ccv_nnc_tensor_view_t));
409 tv->type = (tensor->type & ~0xfff) | CCV_TENSOR_VIEW;
410 tv->dataof = 0;
411 tv->alias_ref = (uintptr_t)tensor;
412 tv->refcount = 1;
413 tv->sig = 0;
414 tv->data_size = 0;
415 assert(params.type == tensor->info.type)((void) sizeof ((params.type == tensor->info.type) ? 1 : 0
), __extension__ ({ if (params.type == tensor->info.type) ;
else __assert_fail ("params.type == tensor->info.type", "ccv_nnc_tensor.c"
, 415, __extension__ __PRETTY_FUNCTION__); }));
416 assert(params.datatype == tensor->info.datatype)((void) sizeof ((params.datatype == tensor->info.datatype)
? 1 : 0), __extension__ ({ if (params.datatype == tensor->
info.datatype) ; else __assert_fail ("params.datatype == tensor->info.datatype"
, "ccv_nnc_tensor.c", 416, __extension__ __PRETTY_FUNCTION__)
; }));
417 tv->info = params;
418 _ccv_nnc_tensor_view_set(tv, tensor, params.dim, ofs, stride);
419 return tv;
420}
421
422ccv_nnc_tensor_view_t ccv_nnc_tensor_view(const ccv_nnc_tensor_t* const tensor, const ccv_nnc_tensor_param_t params, const int ofs[CCV_NNC_MAX_DIM_ALLOC(12)], const int stride[CCV_NNC_MAX_DIM_ALLOC(12)])
423{
424 assert(!CCV_IS_TENSOR_VIEW(tensor))((void) sizeof ((!((*(int*)(tensor)) & CCV_TENSOR_VIEW)) ?
1 : 0), __extension__ ({ if (!((*(int*)(tensor)) & CCV_TENSOR_VIEW
)) ; else __assert_fail ("!CCV_IS_TENSOR_VIEW(tensor)", "ccv_nnc_tensor.c"
, 424, __extension__ __PRETTY_FUNCTION__); }));
425 assert(params.type == tensor->info.type)((void) sizeof ((params.type == tensor->info.type) ? 1 : 0
), __extension__ ({ if (params.type == tensor->info.type) ;
else __assert_fail ("params.type == tensor->info.type", "ccv_nnc_tensor.c"
, 425, __extension__ __PRETTY_FUNCTION__); }));
426 assert(params.datatype == tensor->info.datatype)((void) sizeof ((params.datatype == tensor->info.datatype)
? 1 : 0), __extension__ ({ if (params.datatype == tensor->
info.datatype) ; else __assert_fail ("params.datatype == tensor->info.datatype"
, "ccv_nnc_tensor.c", 426, __extension__ __PRETTY_FUNCTION__)
; }));
427 ccv_nnc_tensor_view_t tv = {
428 .dataof = 0,
429 .alias_ref = (uintptr_t)tensor,
430 .type = (tensor->type & ~0xfff) | CCV_TENSOR_VIEW, // clean up the channel bits, and then add CCV_TENSOR_VIEW identifier
431 .refcount = 1,
432 .sig = 0,
433 .info = params,
434 .data_size = 0,
435 };
436 _ccv_nnc_tensor_view_set(&tv, tensor, params.dim, ofs, stride);
437 return tv;
438}
439
440void ccv_nnc_tensor_view_free(ccv_nnc_tensor_view_t* const tensor_view)
441{
442 ccfreefree(tensor_view);
443}
444
445void _ccv_nnc_tensor_set_zero(unsigned char* u8, const int nd, const int* const dim, const int* const stride, const size_t data_size)
446{
447 if (nd == 1)
448 {
449 if (stride[0] == 1)
450 {
451 memset(u8, 0, data_size * dim[0]);
452 return;
453 }
454 int i;
455 for (i = 0; i < dim[0]; i++)
456 memset(u8 + i * stride[0] * data_size, 0, data_size);
457 } else if (nd == 2) {
458 if (stride[1] == 1 && stride[0] == dim[1])
459 {
460 memset(u8, 0, data_size * dim[1] * dim[0]);
461 return;
462 }
463 int x, y;
464 for (y = 0; y < dim[0]; y++)
465 {
466 unsigned char* const u8y = u8 + y * stride[0] * data_size;
467 for (x = 0; x < dim[1]; x++)
468 memset(u8y + x * stride[1] * data_size, 0, data_size);
469 }
470 } else if (nd == 3) {
471 if (stride[2] == 1 && stride[1] == dim[2] && stride[0] == dim[1] * dim[2])
472 {
473 memset(u8, 0, data_size * dim[2] * dim[1] * dim[0]);
474 return;
475 }
476 int x, y, z;
477 for (z = 0; z < dim[0]; z++)
478 {
479 unsigned char* const u8z = u8 + z * stride[0] * data_size;
480 for (y = 0; y < dim[1]; y++)
481 {
482 unsigned char* const u8y = u8z + y * stride[1] * data_size;
483 for (x = 0; x < dim[2]; x++)
484 memset(u8y + x * stride[2] * data_size, 0, data_size);
485 }
486 }
487 } else if (nd == 4) {
488 if (stride[3] == 1 && stride[2] == dim[3] && stride[1] == dim[2] * dim[3] && stride[0] == dim[1] * dim[2] * dim[3])
489 {
490 memset(u8, 0, data_size * dim[3] * dim[2] * dim[1] * dim[0]);
491 return;
492 }
493 int x, y, z, s;
494 for (s = 0; s < dim[0]; s++)
495 {
496 unsigned char* const u8s = u8 + s * stride[0] * data_size;
497 for (z = 0; z < dim[1]; z++)
498 {
499 unsigned char* const u8z = u8s + z * stride[1] * data_size;
500 for (y = 0; y < dim[2]; y++)
501 {
502 unsigned char* const u8y = u8z + y * stride[2] * data_size;
503 for (x = 0; x < dim[3]; x++)
504 memset(u8y + x * stride[3] * data_size, 0, data_size);
505 }
506 }
507 }
508 } else {
509 int i;
510 for (i = 0; i < dim[0]; i++)
511 _ccv_nnc_tensor_set_zero(u8 + i * stride[0] * data_size, nd - 1, dim + 1, stride + 1, data_size);
512 }
513}
514
515void ccv_nnc_tensor_zero(void* const tensor)
516{
517 ccv_nnc_tensor_view_t* tv = (ccv_nnc_tensor_view_t*)tensor;
518 const size_t data_size = CCV_GET_DATA_TYPE_SIZE(tv->info.datatype)_ccv_get_data_type_size[((tv->info.datatype) & 0xFF000
) >> 12];
519 if (CCV_IS_TENSOR_CONTIGUOUS(tv)(!((*(int*)(tv)) & CCV_TENSOR_VIEW) || (((ccv_nnc_tensor_view_t
*)tv)->contiguous == 1)))
520 {
521 memset(tv->data.u8, 0, data_size * ccv_nnc_tensor_count(tv->info));
522 return;
523 }
524 const int nd = ccv_nnc_tensor_nd(tv->info.dim);
525 assert(nd >= 1)((void) sizeof ((nd >= 1) ? 1 : 0), __extension__ ({ if (nd
>= 1) ; else __assert_fail ("nd >= 1", "ccv_nnc_tensor.c"
, 525, __extension__ __PRETTY_FUNCTION__); }));
526 const int* const tvstride = tv->stride;
527 // Go through this recursively.
528 _ccv_nnc_tensor_set_zero(tv->data.u8, nd, tv->info.dim, tvstride, data_size);
529}
530
531int ccv_nnc_tensor_eq(const ccv_nnc_tensor_t* const a, const ccv_nnc_tensor_t* const b)
532{
533 assert(!CCV_IS_TENSOR_VIEW(a))((void) sizeof ((!((*(int*)(a)) & CCV_TENSOR_VIEW)) ? 1 :
0), __extension__ ({ if (!((*(int*)(a)) & CCV_TENSOR_VIEW
)) ; else __assert_fail ("!CCV_IS_TENSOR_VIEW(a)", "ccv_nnc_tensor.c"
, 533, __extension__ __PRETTY_FUNCTION__); }));
534 assert(!CCV_IS_TENSOR_VIEW(b))((void) sizeof ((!((*(int*)(b)) & CCV_TENSOR_VIEW)) ? 1 :
0), __extension__ ({ if (!((*(int*)(b)) & CCV_TENSOR_VIEW
)) ; else __assert_fail ("!CCV_IS_TENSOR_VIEW(b)", "ccv_nnc_tensor.c"
, 534, __extension__ __PRETTY_FUNCTION__); }));
535 // If a is a dense matrix, just use ccv_matrix_eq
536 if (CCV_TENSOR_IS_DENSE_MATRIX(a->type)(((a->type) & 0xFFF) > 0))
537 return ccv_matrix_eq((ccv_matrix_t*)a, (ccv_matrix_t*)b);
538 // Otherwise, do our own thing.
539 if (CCV_GET_DATA_TYPE(a->type)((a->type) & 0xFF000) != CCV_GET_DATA_TYPE(b->type)((b->type) & 0xFF000))
540 return -1;
541 int i, c = 1;
542 for (i = 0; i < CCV_NNC_MAX_DIM_ALLOC(12); i++)
543 {
544 if (!a->info.dim[i] && !b->info.dim[i])
545 break;
546 if (a->info.dim[i] != b->info.dim[i])
547 return -1;
548 c *= a->info.dim[i];
549 }
550 if (CCV_GET_DATA_TYPE(a->type)((a->type) & 0xFF000) == CCV_32S)
551 return memcmp(a->data.i32, b->data.i32, sizeof(int) * c) == 0 ? 0 : -1;
552 // Only support 32F at this point.
553 assert(CCV_GET_DATA_TYPE(a->type) == CCV_32F || CCV_GET_DATA_TYPE(a->type) == CCV_64F)((void) sizeof ((((a->type) & 0xFF000) == CCV_32F || (
(a->type) & 0xFF000) == CCV_64F) ? 1 : 0), __extension__
({ if (((a->type) & 0xFF000) == CCV_32F || ((a->type
) & 0xFF000) == CCV_64F) ; else __assert_fail ("CCV_GET_DATA_TYPE(a->type) == CCV_32F || CCV_GET_DATA_TYPE(a->type) == CCV_64F"
, "ccv_nnc_tensor.c", 553, __extension__ __PRETTY_FUNCTION__)
; }));
554 // Read: http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm
555 // http://floating-point-gui.de/errors/comparison/
556 if (CCV_GET_DATA_TYPE(a->type)((a->type) & 0xFF000) == CCV_32F)
557 {
558 static const float epsi = FLT_EPSILON1.19209290e-7F;
559 static const int32_t ulps = 128; // so that for 1 and 1.000015 will be treated as the same.
560 for (i = 0; i < c; i++)
561 {
562 // Although this is float point, I use integer as a way to compare.
563 int32_t i32a = a->data.i32[i];
564 if (i32a < 0)
565 i32a = 0x80000000 - i32a;
566 int32_t i32b = b->data.i32[i];
567 if (i32b < 0)
568 i32b = 0x80000000 - i32b;
569 if (abs(i32a - i32b) > ulps && fabsf(a->data.f32[i] - b->data.f32[i]) > epsi)
570 return -1;
571 }
572 } else if (CCV_GET_DATA_TYPE(a->type)((a->type) & 0xFF000) == CCV_64F) {
573 typedef union {
574 double f64;
575 int64_t i64;
576 } Float64;
577 static const double epsi = DBL_EPSILON2.2204460492503131e-16;
578 static const int64_t ulps = 128; // so that for 1 and 1.000015 will be treated as the same.
579 for (i = 0; i < c; i++)
580 {
581 // Although this is float point, I use integer as a way to compare.
582 Float64 f64a, f64b;
583 f64a.f64 = a->data.f64[i];
584 f64b.f64 = b->data.f64[i];
585 if (f64a.i64 < 0)
586 f64a.i64 = 0x8000000000000000 - f64a.i64;
587 if (f64b.i64 < 0)
588 f64b.i64 = 0x8000000000000000 - f64b.i64;
589 if (llabs(f64a.i64 - f64b.i64) > ulps && fabs(a->data.f64[i] - b->data.f64[i]) > epsi)
590 return -1;
591 }
592 }
593 return 0;
594}
595
596static void _strcat(char** str, int* written, size_t* len, char* from, int from_size)
597{
598 if (*len - *written < from_size)
599 {
600 *len += from_size * 2;
601 *str = (char*)ccreallocrealloc(*str, *len);
602 }
603 memcpy(*str + *written, from, from_size);
604 *written += from_size;
605}
606
607#define _STRPRINTF(str, written, len, format, ...)do { const int newly_written = snprintf((str) + (written), (len
) - (written), format, ...); if ((len) - (written) < newly_written
) { (len) += newly_written * 2; (str) = (char*)realloc((str),
(len)); (written) += snprintf((str) + (written), (len) - (written
), format, ...); } else (written) += newly_written; } while (
0) \
608do { \
609 const int newly_written = snprintf((str) + (written), (len) - (written), format, ## __VA_ARGS__); \
610 if ((len) - (written) < newly_written) \
611 { \
612 (len) += newly_written * 2; \
613 (str) = (char*)ccreallocrealloc((str), (len)); \
614 (written) += snprintf((str) + (written), (len) - (written), format, ## __VA_ARGS__); \
615 } else \
616 (written) += newly_written; \
617} while (0)
618
619static void _strv(char** str, int* written, size_t* len, const ccv_nnc_tensor_t* const a, int i)
620{
621 if (a->info.datatype == CCV_32F)
622 _STRPRINTF(*str, *written, *len, "%10.5g", a->data.f32[i])do { const int newly_written = snprintf((*str) + (*written), (
*len) - (*written), "%10.5g", a->data.f32[i]); if ((*len) -
(*written) < newly_written) { (*len) += newly_written * 2
; (*str) = (char*)realloc((*str), (*len)); (*written) += snprintf
((*str) + (*written), (*len) - (*written), "%10.5g", a->data
.f32[i]); } else (*written) += newly_written; } while (0);
623 else if (a->info.datatype == CCV_64F)
624 _STRPRINTF(*str, *written, *len, "%10.5g", a->data.f64[i])do { const int newly_written = snprintf((*str) + (*written), (
*len) - (*written), "%10.5g", a->data.f64[i]); if ((*len) -
(*written) < newly_written) { (*len) += newly_written * 2
; (*str) = (char*)realloc((*str), (*len)); (*written) += snprintf
((*str) + (*written), (*len) - (*written), "%10.5g", a->data
.f64[i]); } else (*written) += newly_written; } while (0);
625 else if (a->info.datatype == CCV_16F) {
626 float v;
627 ccv_half_precision_to_float((uint16_t*)(a->data.f16 + i), &v, 1);
628 _STRPRINTF(*str, *written, *len, "%10.5g", v)do { const int newly_written = snprintf((*str) + (*written), (
*len) - (*written), "%10.5g", v); if ((*len) - (*written) <
newly_written) { (*len) += newly_written * 2; (*str) = (char
*)realloc((*str), (*len)); (*written) += snprintf((*str) + (*
written), (*len) - (*written), "%10.5g", v); } else (*written
) += newly_written; } while (0);
629 } else if (a->info.datatype == CCV_32S)
630 _STRPRINTF(*str, *written, *len, "%10d", a->data.i32[i])do { const int newly_written = snprintf((*str) + (*written), (
*len) - (*written), "%10d", a->data.i32[i]); if ((*len) - (
*written) < newly_written) { (*len) += newly_written * 2; (
*str) = (char*)realloc((*str), (*len)); (*written) += snprintf
((*str) + (*written), (*len) - (*written), "%10d", a->data
.i32[i]); } else (*written) += newly_written; } while (0);
631 else if (a->info.datatype == CCV_64S)
632 _STRPRINTF(*str, *written, *len, "%12lld", (long long int)a->data.i64[i])do { const int newly_written = snprintf((*str) + (*written), (
*len) - (*written), "%12lld", (long long int)a->data.i64[i
]); if ((*len) - (*written) < newly_written) { (*len) += newly_written
* 2; (*str) = (char*)realloc((*str), (*len)); (*written) += snprintf
((*str) + (*written), (*len) - (*written), "%12lld", (long long
int)a->data.i64[i]); } else (*written) += newly_written; }
while (0);
633 else if (a->info.datatype == CCV_8U)
634 _STRPRINTF(*str, *written, *len, "%3d", (int)a->data.u8[i])do { const int newly_written = snprintf((*str) + (*written), (
*len) - (*written), "%3d", (int)a->data.u8[i]); if ((*len)
- (*written) < newly_written) { (*len) += newly_written *
2; (*str) = (char*)realloc((*str), (*len)); (*written) += snprintf
((*str) + (*written), (*len) - (*written), "%3d", (int)a->
data.u8[i]); } else (*written) += newly_written; } while (0);
635}
636
637static void _strt(char** str, int* written, size_t* len, const ccv_nnc_tensor_t* const a, int nd, int spacer, const int* const dim, const int* const stride, int idx)
638{
639 assert(nd != 1)((void) sizeof ((nd != 1) ? 1 : 0), __extension__ ({ if (nd !=
1) ; else __assert_fail ("nd != 1", "ccv_nnc_tensor.c", 639,
__extension__ __PRETTY_FUNCTION__); }));
640 if (nd == 2)
641 {
642 // Print columns and the rows.
643 int i, j, k;
644 if (dim[0] <= 8)
645 {
646 for (i = 0; i < dim[0]; i++)
647 {
648 if (i != 0)
649 {
650 _strcat(str, written, len, " ", 2);
651 for (k = 0; k < spacer; k++)
652 _strcat(str, written, len, " ", 1);
653 }
654 _strcat(str, written, len, "[", 1);
655 if (dim[1] <= 8)
656 {
657 for (j = 0; j < dim[1]; j++)
658 {
659 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
660 if (j < dim[1] - 1)
661 _strcat(str, written, len, ", ", 2);
662 }
663 if (i < dim[0] - 1)
664 _strcat(str, written, len, "],\n", 3);
665 } else {
666 for (j = 0; j < 3; j++)
667 {
668 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
669 _strcat(str, written, len, ", ", 2);
670 }
671 _strcat(str, written, len, " ..., ", 6);
672 for (j = dim[1] - 3; j < dim[1]; j++)
673 {
674 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
675 if (j < dim[1] - 1)
676 _strcat(str, written, len, ", ", 2);
677 }
678 if (i < dim[0] - 1)
679 _strcat(str, written, len, "],\n", 3);
680 }
681 }
682 _strcat(str, written, len, "]", 1);
683 } else {
684 for (i = 0; i < 3; i++)
685 {
686 if (i != 0)
687 {
688 _strcat(str, written, len, " ", 2);
689 for (k = 0; k < spacer; k++)
690 _strcat(str, written, len, " ", 1);
691 }
692 _strcat(str, written, len, "[", 1);
693 if (dim[1] <= 8)
694 {
695 for (j = 0; j < dim[1]; j++)
696 {
697 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
698 if (j < dim[1] - 1)
699 _strcat(str, written, len, ", ", 2);
700 }
701 _strcat(str, written, len, "],\n", 3);
702 } else {
703 for (j = 0; j < 3; j++)
704 {
705 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
706 _strcat(str, written, len, ", ", 2);
707 }
708 _strcat(str, written, len, " ..., ", 6);
709 for (j = dim[1] - 3; j < dim[1]; j++)
710 {
711 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
712 if (j < dim[1] - 1)
713 _strcat(str, written, len, ", ", 2);
714 }
715 _strcat(str, written, len, "],\n", 3);
716 }
717 }
718 _strcat(str, written, len, " ", 2);
719 for (k = 0; k < spacer; k++)
720 _strcat(str, written, len, " ", 1);
721 _strcat(str, written, len, "...,\n", 5);
722 for (i = dim[0] - 3; i < dim[0]; i++)
723 {
724 _strcat(str, written, len, " ", 2);
725 for (k = 0; k < spacer; k++)
726 _strcat(str, written, len, " ", 1);
727 _strcat(str, written, len, "[", 1);
728 if (dim[1] < 8)
729 {
730 for (j = 0; j < dim[1]; j++)
731 {
732 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
733 if (j < dim[1] - 1)
734 _strcat(str, written, len, ", ", 2);
735 }
736 if (i < dim[0] - 1)
737 _strcat(str, written, len, "],\n", 3);
738 } else {
739 for (j = 0; j < 3; j++)
740 {
741 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
742 _strcat(str, written, len, ", ", 2);
743 }
744 _strcat(str, written, len, " ..., ", 6);
745 for (j = dim[1] - 3; j < dim[1]; j++)
746 {
747 _strv(str, written, len, a, idx + i * stride[0] + j * stride[1]);
748 if (j < dim[1] - 1)
749 _strcat(str, written, len, ", ", 2);
750 }
751 if (i < dim[0] - 1)
752 _strcat(str, written, len, "],\n", 3);
753 }
754 }
755 _strcat(str, written, len, "]", 1);
756 }
757 return;
758 }
759 int i, j;
760 if (dim[0] > 4)
761 {
762 for (i = 0; i < 2; i++)
763 {
764 _strcat(str, written, len, "[", 1);
765 _strt(str, written, len, a, nd - 1, spacer + 1, dim + 1, stride + 1, idx + stride[0] * i);
766 _strcat(str, written, len, "],\n ", 5);
767 for (j = 0; j < spacer; j++)
768 _strcat(str, written, len, " ", 1);
769 }
770 _strcat(str, written, len, "...,\n", 5);
771 _strcat(str, written, len, " ", 2);
772 for (j = 0; j < spacer; j++)
773 _strcat(str, written, len, " ", 1);
774 for (i = dim[0] - 2; i < dim[0]; i++)
775 {
776 _strcat(str, written, len, "[", 1);
777 _strt(str, written, len, a, nd - 1, spacer + 1, dim + 1, stride + 1, idx + stride[0] * i);
778 if (i < dim[0] - 1)
779 {
780 _strcat(str, written, len, "],\n ", 5);
781 for (j = 0; j < spacer; j++)
782 _strcat(str, written, len, " ", 1);
783 }
784 }
785 _strcat(str, written, len, "]", 1);
786 } else {
787 for (i = 0; i < dim[0]; i++)
788 {
789 _strcat(str, written, len, "[", 1);
790 _strt(str, written, len, a, nd - 1, spacer + 1, dim + 1, stride + 1, idx + stride[0] * i);
791 if (i < dim[0] - 1)
792 {
793 _strcat(str, written, len, "],\n", 3);
794 _strcat(str, written, len, " ", 2);
795 for (j = 0; j < spacer; j++)
796 _strcat(str, written, len, " ", 1);
797 }
798 }
799 _strcat(str, written, len, "]", 1);
800 }
801}
802
803char* ccv_nnc_tensor_format_new(const ccv_nnc_tensor_t* const a)
804{
805 const int nd = ccv_nnc_tensor_nd(a->info.dim);
806 int i;
807 int rows = 8; // 8 rows for the first one, and then just first and last.
808 for (i = 2; i < nd; i++)
809 rows *= 5; // Maximum 3 rows beyond the first two.
810 int columns = nd * 2 + 16 * 8;
811 size_t len = sizeof(char) * columns * rows;
812 // Allocate return string buffer.
813 char* str = (char*)ccmallocmalloc(len);
814 int written = 0;
815 int stride[CCV_NNC_MAX_DIM_ALLOC(12)];
816 if (CCV_IS_TENSOR_VIEW(a)((*(int*)(a)) & CCV_TENSOR_VIEW))
817 memcpy(stride, ((ccv_nnc_tensor_view_t*)a)->stride, sizeof(int) * CCV_NNC_MAX_DIM_ALLOC(12));
818 else
819 ccv_nnc_tensor_get_stride(a->info.dim, stride);
820 _strcat(&str, &written, &len, "[\n ", 4);
821 if (nd == 1)
822 {
823 // Special casing for vector.
824 if (a->info.dim[0] <= 64)
825 for (i = 0; i < a->info.dim[0]; i++)
826 {
827 _strv(&str, &written, &len, a, i * stride[0]);
828 if (i < a->info.dim[0] - 1)
829 {
830 if ((i + 1) % 8 == 0)
831 _strcat(&str, &written, &len, ",\n ", 4);
832 else
833 _strcat(&str, &written, &len, ", ", 2);
834 }
835 }
836 else {
837 // First 3 rows.
838 for (i = 0; i < 24; i++)
839 {
840 _strv(&str, &written, &len, a, i * stride[0]);
841 if ((i + 1) % 8 == 0)
842 _strcat(&str, &written, &len, ",\n ", 4);
843 else
844 _strcat(&str, &written, &len, ", ", 2);
845 }
846 _strcat(&str, &written, &len, "...,\n ", 7);
847 // Last 3 rows (aligned to 8 items per row).
848 int start = ((a->info.dim[0] + 7) / 8 - 3) * 8;
849 for (i = start; i < a->info.dim[0]; i++)
850 {
851 _strv(&str, &written, &len, a, i * stride[0]);
852 if (i < a->info.dim[0] - 1)
853 {
854 if ((i + 1) % 8 == 0)
855 _strcat(&str, &written, &len, ",\n ", 4);
856 else
857 _strcat(&str, &written, &len, ", ", 2);
858 }
859 }
860 }
861 } else {
862 _strt(&str, &written, &len, a, nd, 0, a->info.dim, stride, 0);
863 }
864 _strcat(&str, &written, &len, "\n]", 3); // Including the terminal \0.
865 str = (char*)ccreallocrealloc(str, written); // Don't need the extra spaces.
866 return str;
867}