Bug Summary

File:nnc/ccv_cnnp_model_core.c
Warning:line 356, column 1
Assigned value is garbage or undefined

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_cnnp_model_core.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/2024-10-29-232120-439362-1 -x c ccv_cnnp_model_core.c
1#include "ccv_nnc.h"
2#include "ccv_nnc_easy.h"
3#include "ccv_nnc_internal.h"
4#include "ccv_internal.h"
5#include "_ccv_cnnp_model.h"
6#include "3rdparty/khash/khash.h"
7
8// MARK - Baisc Layers
9
10static const ccv_cnnp_model_vtab_t ccv_cnnp_input_isa;
11
12#define CCV_CNNP_IS_MODEL_INPUT(x)((x)->isa == &ccv_cnnp_input_isa) ((x)->isa == &ccv_cnnp_input_isa)
13
14#define CCV_CNNP_IS_MODEL_PARAMETER(x)((x)->param_ref != 0 || (x)->param_sel != 0) ((x)->param_ref != 0 || (x)->param_sel != 0)
15
16typedef struct {
17 ccv_cnnp_model_t super;
18 int sequence_size;
19 ccv_cnnp_model_t* sequence[1];
20} ccv_cnnp_sequential_model_t;
21
22static void _ccv_cnnp_sequential_model_deinit(ccv_cnnp_model_t* const super)
23{
24 ccv_cnnp_sequential_model_t* const self = (ccv_cnnp_sequential_model_t*)super;
25 int i, j;
26 for (i = 0; i < self->sequence_size; i++)
27 {
28 ccv_cnnp_model_t* const model = self->sequence[i];
29 if (!model)
30 continue;
31 ccv_cnnp_model_free(model);
32 for (j = i + 1; j < self->sequence_size; j++)
33 if (self->sequence[j] == model)
34 self->sequence[j] = 0;
35 }
36}
37
38static void _ccv_cnnp_sequential_model_build(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
39{
40 ccv_cnnp_sequential_model_t* const self = (ccv_cnnp_sequential_model_t*)super;
41 PRINT(CCV_CLI_VERBOSE, "[cnnp_sequential_model_build] 1. %p, sequence_size: %d\n", self, self->sequence_size)do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_sequential_model_build] 1. %p, sequence_size: %d\n"
, self, self->sequence_size); fflush(stdout); } } while (0
)
;
42 ccv_cnnp_model_t* const sub_model = self->sequence[0];
43 // Go through each sub model to build the graph.
44 ccv_nnc_tensor_symbol_t input;
45 sub_model->data = self->super.data;
46 ccv_cnnp_model_build(sub_model, graph, inputs, input_size, &input, 1);
47 sub_model->data = 0;
48 int i;
49 for (i = 1; i < self->sequence_size; i++)
50 {
51 ccv_nnc_tensor_symbol_t output;
52 ccv_cnnp_model_t* const sub_model = self->sequence[i];
53 // Go through each sub model to build the graph.
54 sub_model->data = self->super.data;
55 ccv_cnnp_model_build(sub_model, graph, &input, 1, &output, 1);
56 sub_model->data = 0;
57 input = output;
58 }
59 outputs[0] = input;
60 PRINT(CCV_CLI_VERBOSE, "[cnnp_sequential_model_build] 2. %p\n", self)do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_sequential_model_build] 2. %p\n", self); fflush
(stdout); } } while (0)
;
61}
62
63static void _ccv_cnnp_sequential_model_init_states(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_cnnp_state_initializer_f initializer, void* const context)
64{
65 ccv_cnnp_sequential_model_t* const self = (ccv_cnnp_sequential_model_t*)super;
66 int i;
67 for (i = 0; i < self->sequence_size; i++)
68 ccv_cnnp_model_init_states(self->sequence[i], graph, initializer, context);
69}
70
71static void _ccv_cnnp_sequential_model_set_is_test(ccv_cnnp_model_t* const super, const int is_test, const ccv_cnnp_cmd_updater_f updater, void* const context)
72{
73 ccv_cnnp_sequential_model_t* const self = (ccv_cnnp_sequential_model_t*)super;
74 int i;
75 for (i = 0; i < self->sequence_size; i++)
76 ccv_cnnp_model_set_is_test(self->sequence[i], is_test, updater, context);
77}
78
79static ccv_cnnp_model_t* _ccv_cnnp_sequential_model_copy(const ccv_cnnp_model_t* const super, void* const context);
80
81static void _ccv_cnnp_sequential_model_add_to_parameter_indices(ccv_cnnp_model_t* const super, const int index, ccv_array_t* const parameter_indices)
82{
83 ccv_cnnp_sequential_model_t* const self = (ccv_cnnp_sequential_model_t*)super;
84 int i;
85 for (i = 0; i < self->sequence_size; i++)
86 ccv_cnnp_model_add_to_parameter_indices(self->sequence[i], index, parameter_indices);
87}
88
89static void _ccv_cnnp_sequential_model_notify(const ccv_cnnp_model_t* const super, const int tag, void* const payload)
90{
91 ccv_cnnp_sequential_model_t* const self = (ccv_cnnp_sequential_model_t*)super;
92 int i;
93 for (i = 0; i < self->sequence_size; i++)
94 ccv_cnnp_model_notify(self->sequence[i], tag, payload);
95}
96
97static const ccv_cnnp_model_vtab_t ccv_cnnp_sequential_model_isa = {
98 .deinit = _ccv_cnnp_sequential_model_deinit,
99 .build = _ccv_cnnp_sequential_model_build,
100 .init_states = _ccv_cnnp_sequential_model_init_states,
101 .copy = _ccv_cnnp_sequential_model_copy,
102 .set_is_test = _ccv_cnnp_sequential_model_set_is_test,
103 .add_to_parameter_indices = _ccv_cnnp_sequential_model_add_to_parameter_indices,
104 .notify = _ccv_cnnp_sequential_model_notify,
105};
106
107KHASH_MAP_INIT_INT64(model, ccv_cnnp_model_t*)typedef struct kh_model_s { khint_t n_buckets, size, n_occupied
, upper_bound; khint32_t *flags; khint64_t *keys; ccv_cnnp_model_t
* *vals; } kh_model_t; static inline __attribute__ ((__unused__
)) kh_model_t *kh_init_model(void) { return (kh_model_t*)calloc
(1,sizeof(kh_model_t)); } static inline __attribute__ ((__unused__
)) void kh_destroy_model(kh_model_t *h) { if (h) { free((void
*)h->keys); free(h->flags); free((void *)h->vals); free
(h); } } static inline __attribute__ ((__unused__)) void kh_clear_model
(kh_model_t *h) { if (h && h->flags) { memset(h->
flags, 0xaa, ((h->n_buckets) < 16? 1 : (h->n_buckets
)>>4) * sizeof(khint32_t)); h->size = h->n_occupied
= 0; } } static inline __attribute__ ((__unused__)) khint_t kh_get_model
(const kh_model_t *h, khint64_t key) { if (h->n_buckets) {
khint_t k, i, last, mask, step = 0; mask = h->n_buckets -
1; k = (khint32_t)((key)>>33^(key)^(key)<<11); i
= k & mask; last = i; while (!((h->flags[i>>4]>>
((i&0xfU)<<1))&2) && (((h->flags[i>>
4]>>((i&0xfU)<<1))&1) || !((h->keys[i]
) == (key)))) { i = (i + (++step)) & mask; if (i == last)
return h->n_buckets; } return ((h->flags[i>>4]>>
((i&0xfU)<<1))&3)? h->n_buckets : i; } else return
0; } static inline __attribute__ ((__unused__)) int kh_resize_model
(kh_model_t *h, khint_t new_n_buckets) { khint32_t *new_flags
= 0; khint_t j = 1; { (--(new_n_buckets), (new_n_buckets)|=(
new_n_buckets)>>1, (new_n_buckets)|=(new_n_buckets)>>
2, (new_n_buckets)|=(new_n_buckets)>>4, (new_n_buckets)
|=(new_n_buckets)>>8, (new_n_buckets)|=(new_n_buckets)>>
16, ++(new_n_buckets)); if (new_n_buckets < 4) new_n_buckets
= 4; if (h->size >= (khint_t)(new_n_buckets * __ac_HASH_UPPER
+ 0.5)) j = 0; else { new_flags = (khint32_t*)malloc(((new_n_buckets
) < 16? 1 : (new_n_buckets)>>4) * sizeof(khint32_t))
; if (!new_flags) return -1; memset(new_flags, 0xaa, ((new_n_buckets
) < 16? 1 : (new_n_buckets)>>4) * sizeof(khint32_t))
; if (h->n_buckets < new_n_buckets) { khint64_t *new_keys
= (khint64_t*)realloc((void *)h->keys,new_n_buckets * sizeof
(khint64_t)); if (!new_keys) { free(new_flags); return -1; } h
->keys = new_keys; if (1) { ccv_cnnp_model_t* *new_vals = (
ccv_cnnp_model_t**)realloc((void *)h->vals,new_n_buckets *
sizeof(ccv_cnnp_model_t*)); if (!new_vals) { free(new_flags)
; return -1; } h->vals = new_vals; } } } } if (j) { for (j
= 0; j != h->n_buckets; ++j) { if (((h->flags[j>>
4]>>((j&0xfU)<<1))&3) == 0) { khint64_t key
= h->keys[j]; ccv_cnnp_model_t* val; khint_t new_mask; new_mask
= new_n_buckets - 1; if (1) val = h->vals[j]; (h->flags
[j>>4]|=1ul<<((j&0xfU)<<1)); while (1) {
khint_t k, i, step = 0; k = (khint32_t)((key)>>33^(key
)^(key)<<11); i = k & new_mask; while (!((new_flags
[i>>4]>>((i&0xfU)<<1))&2)) i = (i +
(++step)) & new_mask; (new_flags[i>>4]&=~(2ul<<
((i&0xfU)<<1))); if (i < h->n_buckets &&
((h->flags[i>>4]>>((i&0xfU)<<1))&
3) == 0) { { khint64_t tmp = h->keys[i]; h->keys[i] = key
; key = tmp; } if (1) { ccv_cnnp_model_t* tmp = h->vals[i]
; h->vals[i] = val; val = tmp; } (h->flags[i>>4]|=
1ul<<((i&0xfU)<<1)); } else { h->keys[i] =
key; if (1) h->vals[i] = val; break; } } } } if (h->n_buckets
> new_n_buckets) { h->keys = (khint64_t*)realloc((void
*)h->keys,new_n_buckets * sizeof(khint64_t)); if (1) h->
vals = (ccv_cnnp_model_t**)realloc((void *)h->vals,new_n_buckets
* sizeof(ccv_cnnp_model_t*)); } free(h->flags); h->flags
= new_flags; h->n_buckets = new_n_buckets; h->n_occupied
= h->size; h->upper_bound = (khint_t)(h->n_buckets *
__ac_HASH_UPPER + 0.5); } return 0; } static inline __attribute__
((__unused__)) khint_t kh_put_model(kh_model_t *h, khint64_t
key, int *ret) { khint_t x; if (h->n_occupied >= h->
upper_bound) { if (h->n_buckets > (h->size<<1)
) { if (kh_resize_model(h, h->n_buckets - 1) < 0) { *ret
= -1; return h->n_buckets; } } else if (kh_resize_model(h
, h->n_buckets + 1) < 0) { *ret = -1; return h->n_buckets
; } } { khint_t k, i, site, last, mask = h->n_buckets - 1,
step = 0; x = site = h->n_buckets; k = (khint32_t)((key)>>
33^(key)^(key)<<11); i = k & mask; if (((h->flags
[i>>4]>>((i&0xfU)<<1))&2)) x = i; else
{ last = i; while (!((h->flags[i>>4]>>((i&
0xfU)<<1))&2) && (((h->flags[i>>4]
>>((i&0xfU)<<1))&1) || !((h->keys[i]) ==
(key)))) { if (((h->flags[i>>4]>>((i&0xfU
)<<1))&1)) site = i; i = (i + (++step)) & mask;
if (i == last) { x = site; break; } } if (x == h->n_buckets
) { if (((h->flags[i>>4]>>((i&0xfU)<<
1))&2) && site != h->n_buckets) x = site; else
x = i; } } } if (((h->flags[x>>4]>>((x&0xfU
)<<1))&2)) { h->keys[x] = key; (h->flags[x>>
4]&=~(3ul<<((x&0xfU)<<1))); ++h->size;
++h->n_occupied; *ret = 1; } else if (((h->flags[x>>
4]>>((x&0xfU)<<1))&1)) { h->keys[x] = key
; (h->flags[x>>4]&=~(3ul<<((x&0xfU)<<
1))); ++h->size; *ret = 2; } else *ret = 0; return x; } static
inline __attribute__ ((__unused__)) void kh_del_model(kh_model_t
*h, khint_t x) { if (x != h->n_buckets && !((h->
flags[x>>4]>>((x&0xfU)<<1))&3)) { (
h->flags[x>>4]|=1ul<<((x&0xfU)<<1));
--h->size; } }
108
109static ccv_cnnp_model_t* _ccv_cnnp_sequential_model_copy(const ccv_cnnp_model_t* const super, void* const context)
110{
111 const ccv_cnnp_sequential_model_t* const self = (const ccv_cnnp_sequential_model_t*)super;
112 ccv_cnnp_sequential_model_t* const sequential_model = (ccv_cnnp_sequential_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_sequential_model_t) + sizeof(ccv_cnnp_model_t*) * (self->sequence_size - 1) + sizeof(ccv_nnc_tensor_symbol_t));
113 sequential_model->super.isa = &ccv_cnnp_sequential_model_isa;
114 sequential_model->super.input_size = 1;
115 sequential_model->super.outputs = (ccv_nnc_tensor_symbol_t*)(sequential_model->sequence + self->sequence_size);
116 sequential_model->super.output_size = 1;
117 ccv_cnnp_model_copy_name(&sequential_model->super, self->super.name);
118 sequential_model->sequence_size = self->sequence_size;
119 int i;
120 khash_t(model)kh_model_t* model_map = context ? (khash_t(model)kh_model_t*)context : kh_init(model)kh_init_model();
121 for (i = 0; i < self->sequence_size; i++)
122 {
123 ccv_cnnp_model_t* const sub_model = self->sequence[i];
124 int ret;
125 khiter_t k = kh_put(model, model_map, (uint64_t)(uintptr_t)sub_model, &ret)kh_put_model(model_map, (uint64_t)(uintptr_t)sub_model, &
ret)
;
126 ccv_cnnp_model_t* model_copy;
127 if (ret != 0)
128 model_copy = kh_val(model_map, k)((model_map)->vals[k]) = _ccv_cnnp_model_copy(sub_model, model_map);
129 else
130 model_copy = kh_val(model_map, k)((model_map)->vals[k]);
131 sequential_model->sequence[i] = model_copy;
132 }
133 if (!context)
134 kh_destroy(model, model_map)kh_destroy_model(model_map);
135 return (ccv_cnnp_model_t*)sequential_model;
136}
137
138ccv_cnnp_model_t* ccv_cnnp_sequential_new(ccv_cnnp_model_t* const* const models, const int model_size, const int is_trainable, const char* const name)
139{
140 assert(model_size > 0)((void) sizeof ((model_size > 0) ? 1 : 0), __extension__ (
{ if (model_size > 0) ; else __assert_fail ("model_size > 0"
, "ccv_cnnp_model_core.c", 140, __extension__ __PRETTY_FUNCTION__
); }))
;
141 ccv_cnnp_sequential_model_t* const sequential_model = (ccv_cnnp_sequential_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_sequential_model_t) + sizeof(ccv_cnnp_model_t*) * (model_size - 1) + sizeof(ccv_nnc_tensor_symbol_t));
142 sequential_model->super.isa = &ccv_cnnp_sequential_model_isa;
143 sequential_model->super.input_size = models[0]->input_size;
144 sequential_model->super.outputs = (ccv_nnc_tensor_symbol_t*)(sequential_model->sequence + model_size);
145 sequential_model->super.output_size = 1;
146 sequential_model->super.is_trainable = is_trainable;
147 ccv_cnnp_model_copy_name(&sequential_model->super, name);
148 sequential_model->sequence_size = model_size;
149 memcpy(sequential_model->sequence, models, sizeof(ccv_cnnp_model_t*) * model_size);
150 return (ccv_cnnp_model_t*)sequential_model;
151}
152
153typedef struct {
154 ccv_cnnp_model_t super;
155 // The model's outputs, it is different from super.output_size, as latter is for actual tensor symbols.
156 int model_output_size;
157 // The name is similar to sequential model, but it is just topological sorted models.
158 int sequence_size;
159 int* model_outputs; // Which model, as in sequences, have some outputs.
160 ccv_cnnp_model_io_t sequence[1];
161} ccv_cnnp_functional_model_t;
162
163static void _ccv_cnnp_functional_model_deinit(ccv_cnnp_model_t* const super)
164{
165 ccv_cnnp_functional_model_t* const self = (ccv_cnnp_functional_model_t*)super;
166 int i, j = 0, k;
167 for (i = 0; i < self->sequence_size; i++)
168 {
169 ccv_cnnp_model_t* const model = self->sequence[i]->model;
170 if (!model)
171 continue;
172 self->sequence[j++] = (ccv_cnnp_model_io_t)model;
173 // Go through all their IO to remove itself as model.
174 assert(model->io)((void) sizeof ((model->io) ? 1 : 0), __extension__ ({ if (
model->io) ; else __assert_fail ("model->io", "ccv_cnnp_model_core.c"
, 174, __extension__ __PRETTY_FUNCTION__); }))
;
175 for (k = 0; k < model->io->rnum; k++)
176 {
177 ccv_cnnp_model_io_t model_io = *(ccv_cnnp_model_io_t*)ccv_array_get(model->io, k)((void*)(((char*)((model->io)->data)) + (size_t)(model->
io)->rsize * (size_t)(k)))
;
178 model_io->model = 0;
179 }
180 }
181 for (i = 0; i < j; i++)
182 ccv_cnnp_model_free((ccv_cnnp_model_t*)self->sequence[i]);
183}
184
185KHASH_MAP_INIT_INT64(io_node, ccv_array_t*)typedef struct kh_io_node_s { khint_t n_buckets, size, n_occupied
, upper_bound; khint32_t *flags; khint64_t *keys; ccv_array_t
* *vals; } kh_io_node_t; static inline __attribute__ ((__unused__
)) kh_io_node_t *kh_init_io_node(void) { return (kh_io_node_t
*)calloc(1,sizeof(kh_io_node_t)); } static inline __attribute__
((__unused__)) void kh_destroy_io_node(kh_io_node_t *h) { if
(h) { free((void *)h->keys); free(h->flags); free((void
*)h->vals); free(h); } } static inline __attribute__ ((__unused__
)) void kh_clear_io_node(kh_io_node_t *h) { if (h && h
->flags) { memset(h->flags, 0xaa, ((h->n_buckets) <
16? 1 : (h->n_buckets)>>4) * sizeof(khint32_t)); h->
size = h->n_occupied = 0; } } static inline __attribute__ (
(__unused__)) khint_t kh_get_io_node(const kh_io_node_t *h, khint64_t
key) { if (h->n_buckets) { khint_t k, i, last, mask, step
= 0; mask = h->n_buckets - 1; k = (khint32_t)((key)>>
33^(key)^(key)<<11); i = k & mask; last = i; while (
!((h->flags[i>>4]>>((i&0xfU)<<1))&
2) && (((h->flags[i>>4]>>((i&0xfU)
<<1))&1) || !((h->keys[i]) == (key)))) { i = (i +
(++step)) & mask; if (i == last) return h->n_buckets;
} return ((h->flags[i>>4]>>((i&0xfU)<<
1))&3)? h->n_buckets : i; } else return 0; } static inline
__attribute__ ((__unused__)) int kh_resize_io_node(kh_io_node_t
*h, khint_t new_n_buckets) { khint32_t *new_flags = 0; khint_t
j = 1; { (--(new_n_buckets), (new_n_buckets)|=(new_n_buckets
)>>1, (new_n_buckets)|=(new_n_buckets)>>2, (new_n_buckets
)|=(new_n_buckets)>>4, (new_n_buckets)|=(new_n_buckets)
>>8, (new_n_buckets)|=(new_n_buckets)>>16, ++(new_n_buckets
)); if (new_n_buckets < 4) new_n_buckets = 4; if (h->size
>= (khint_t)(new_n_buckets * __ac_HASH_UPPER + 0.5)) j = 0
; else { new_flags = (khint32_t*)malloc(((new_n_buckets) <
16? 1 : (new_n_buckets)>>4) * sizeof(khint32_t)); if (
!new_flags) return -1; memset(new_flags, 0xaa, ((new_n_buckets
) < 16? 1 : (new_n_buckets)>>4) * sizeof(khint32_t))
; if (h->n_buckets < new_n_buckets) { khint64_t *new_keys
= (khint64_t*)realloc((void *)h->keys,new_n_buckets * sizeof
(khint64_t)); if (!new_keys) { free(new_flags); return -1; } h
->keys = new_keys; if (1) { ccv_array_t* *new_vals = (ccv_array_t
**)realloc((void *)h->vals,new_n_buckets * sizeof(ccv_array_t
*)); if (!new_vals) { free(new_flags); return -1; } h->vals
= new_vals; } } } } if (j) { for (j = 0; j != h->n_buckets
; ++j) { if (((h->flags[j>>4]>>((j&0xfU)<<
1))&3) == 0) { khint64_t key = h->keys[j]; ccv_array_t
* val; khint_t new_mask; new_mask = new_n_buckets - 1; if (1)
val = h->vals[j]; (h->flags[j>>4]|=1ul<<((
j&0xfU)<<1)); while (1) { khint_t k, i, step = 0; k
= (khint32_t)((key)>>33^(key)^(key)<<11); i = k &
new_mask; while (!((new_flags[i>>4]>>((i&0xfU
)<<1))&2)) i = (i + (++step)) & new_mask; (new_flags
[i>>4]&=~(2ul<<((i&0xfU)<<1))); if (
i < h->n_buckets && ((h->flags[i>>4]>>
((i&0xfU)<<1))&3) == 0) { { khint64_t tmp = h->
keys[i]; h->keys[i] = key; key = tmp; } if (1) { ccv_array_t
* tmp = h->vals[i]; h->vals[i] = val; val = tmp; } (h->
flags[i>>4]|=1ul<<((i&0xfU)<<1)); } else
{ h->keys[i] = key; if (1) h->vals[i] = val; break; } }
} } if (h->n_buckets > new_n_buckets) { h->keys = (
khint64_t*)realloc((void *)h->keys,new_n_buckets * sizeof(
khint64_t)); if (1) h->vals = (ccv_array_t**)realloc((void
*)h->vals,new_n_buckets * sizeof(ccv_array_t*)); } free(h
->flags); h->flags = new_flags; h->n_buckets = new_n_buckets
; h->n_occupied = h->size; h->upper_bound = (khint_t
)(h->n_buckets * __ac_HASH_UPPER + 0.5); } return 0; } static
inline __attribute__ ((__unused__)) khint_t kh_put_io_node(kh_io_node_t
*h, khint64_t key, int *ret) { khint_t x; if (h->n_occupied
>= h->upper_bound) { if (h->n_buckets > (h->size
<<1)) { if (kh_resize_io_node(h, h->n_buckets - 1) <
0) { *ret = -1; return h->n_buckets; } } else if (kh_resize_io_node
(h, h->n_buckets + 1) < 0) { *ret = -1; return h->n_buckets
; } } { khint_t k, i, site, last, mask = h->n_buckets - 1,
step = 0; x = site = h->n_buckets; k = (khint32_t)((key)>>
33^(key)^(key)<<11); i = k & mask; if (((h->flags
[i>>4]>>((i&0xfU)<<1))&2)) x = i; else
{ last = i; while (!((h->flags[i>>4]>>((i&
0xfU)<<1))&2) && (((h->flags[i>>4]
>>((i&0xfU)<<1))&1) || !((h->keys[i]) ==
(key)))) { if (((h->flags[i>>4]>>((i&0xfU
)<<1))&1)) site = i; i = (i + (++step)) & mask;
if (i == last) { x = site; break; } } if (x == h->n_buckets
) { if (((h->flags[i>>4]>>((i&0xfU)<<
1))&2) && site != h->n_buckets) x = site; else
x = i; } } } if (((h->flags[x>>4]>>((x&0xfU
)<<1))&2)) { h->keys[x] = key; (h->flags[x>>
4]&=~(3ul<<((x&0xfU)<<1))); ++h->size;
++h->n_occupied; *ret = 1; } else if (((h->flags[x>>
4]>>((x&0xfU)<<1))&1)) { h->keys[x] = key
; (h->flags[x>>4]&=~(3ul<<((x&0xfU)<<
1))); ++h->size; *ret = 2; } else *ret = 0; return x; } static
inline __attribute__ ((__unused__)) void kh_del_io_node(kh_io_node_t
*h, khint_t x) { if (x != h->n_buckets && !((h->
flags[x>>4]>>((x&0xfU)<<1))&3)) { (
h->flags[x>>4]|=1ul<<((x&0xfU)<<1));
--h->size; } }
186
187typedef struct {
188 ccv_array_t* nodes;
189 ccv_nnc_graph_exec_symbol_new_hook_f previous_func;
190 void* previous_context;
191} ccv_functional_model_build_node_hook_t;
192
193static void _ccv_cnnp_functional_model_build_node_new(void* context, const ccv_nnc_graph_exec_symbol_t symbol, const ccv_nnc_cmd_t cmd, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, const ccv_nnc_tensor_symbol_t* const outputs, const int output_size, const char* const name)
194{
195 ccv_functional_model_build_node_hook_t* const hook = (ccv_functional_model_build_node_hook_t*)context;
196 ccv_array_push(hook->nodes, &symbol);
197 if (hook->previous_func)
198 hook->previous_func(hook->previous_context, symbol, cmd, inputs, input_size, outputs, output_size, name);
199}
200
201static void _ccv_cnnp_functional_model_build(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
202{
203 ccv_cnnp_functional_model_t* const self = (ccv_cnnp_functional_model_t*)super;
204 PRINT(CCV_CLI_VERBOSE, "[cnnp_functional_model_build] 1. %p, input_size: %d, output_size: %d\n", self, input_size, output_size)do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_functional_model_build] 1. %p, input_size: %d, output_size: %d\n"
, self, input_size, output_size); fflush(stdout); } } while (
0)
;
205 assert(self->super.input_size == input_size)((void) sizeof ((self->super.input_size == input_size) ? 1
: 0), __extension__ ({ if (self->super.input_size == input_size
) ; else __assert_fail ("self->super.input_size == input_size"
, "ccv_cnnp_model_core.c", 205, __extension__ __PRETTY_FUNCTION__
); }))
;
206 assert(self->super.output_size == output_size)((void) sizeof ((self->super.output_size == output_size) ?
1 : 0), __extension__ ({ if (self->super.output_size == output_size
) ; else __assert_fail ("self->super.output_size == output_size"
, "ccv_cnnp_model_core.c", 206, __extension__ __PRETTY_FUNCTION__
); }))
;
207 int i, j, k;
208 for (i = 0; i < self->super.input_size; i++)
209 self->sequence[i]->outputs[0] = self->sequence[i]->model->outputs[0] = inputs[i]; // Assigning the output symbol of input layer to be the input symbol.
210 ccv_array_t* input_symbols = ccv_array_new(sizeof(ccv_nnc_tensor_symbol_t), 1, 0);
211 ccv_array_t* parameter_indices = 0;
212 khash_t(io_node)kh_io_node_t* io_node_map = kh_init(io_node)kh_init_io_node();
213 for (i = self->super.input_size; i < self->sequence_size; i++)
214 {
215 ccv_cnnp_model_t* const sub_model = self->sequence[i]->model;
216 ccv_array_clear(input_symbols);
217 const ccv_array_t* const incomings = self->sequence[i]->incomings;
218 if (incomings)
219 for (j = 0; j < incomings->rnum; j++)
220 {
221 const ccv_cnnp_model_io_t input = *(ccv_cnnp_model_io_t*)ccv_array_get(incomings, j)((void*)(((char*)((incomings)->data)) + (size_t)(incomings
)->rsize * (size_t)(j)))
;
222 if (CCV_CNNP_IS_MODEL_PARAMETER(input)((input)->param_ref != 0 || (input)->param_sel != 0))
223 {
224 if (!parameter_indices)
225 parameter_indices = ccv_array_new(sizeof(int), 0, 0);
226 else
227 ccv_array_clear(parameter_indices);
228 const int param_sel = input->param_sel > 0 ? input->param_sel - 1 : input->param_sel;
229 assert(input->param_sel != 0)((void) sizeof ((input->param_sel != 0) ? 1 : 0), __extension__
({ if (input->param_sel != 0) ; else __assert_fail ("input->param_sel != 0"
, "ccv_cnnp_model_core.c", 229, __extension__ __PRETTY_FUNCTION__
); }))
;
230 ccv_cnnp_model_add_to_parameter_indices(input->model, param_sel, parameter_indices);
231 assert(parameter_indices->rnum > 0)((void) sizeof ((parameter_indices->rnum > 0) ? 1 : 0),
__extension__ ({ if (parameter_indices->rnum > 0) ; else
__assert_fail ("parameter_indices->rnum > 0", "ccv_cnnp_model_core.c"
, 231, __extension__ __PRETTY_FUNCTION__); }))
;
232 const int param_ref = input->param_ref > 0 ? input->param_ref - 1 : input->param_ref;
233 assert(input->param_ref != 0)((void) sizeof ((input->param_ref != 0) ? 1 : 0), __extension__
({ if (input->param_ref != 0) ; else __assert_fail ("input->param_ref != 0"
, "ccv_cnnp_model_core.c", 233, __extension__ __PRETTY_FUNCTION__
); }))
;
234 if (param_ref >= 0)
235 {
236 assert(param_ref < parameter_indices->rnum)((void) sizeof ((param_ref < parameter_indices->rnum) ?
1 : 0), __extension__ ({ if (param_ref < parameter_indices
->rnum) ; else __assert_fail ("param_ref < parameter_indices->rnum"
, "ccv_cnnp_model_core.c", 236, __extension__ __PRETTY_FUNCTION__
); }))
;
237 const ccv_nnc_tensor_symbol_t parameter = ccv_cnnp_parameter_from_indice(super, *(int*)ccv_array_get(parameter_indices, param_ref)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(param_ref)))
);
238 ccv_array_push(input_symbols, &parameter);
239 } else // Otherwise, all of them.
240 for (k = 0; k < parameter_indices->rnum; k++)
241 {
242 const ccv_nnc_tensor_symbol_t parameter = ccv_cnnp_parameter_from_indice(super, *(int*)ccv_array_get(parameter_indices, k)((void*)(((char*)((parameter_indices)->data)) + (size_t)(parameter_indices
)->rsize * (size_t)(k)))
);
243 ccv_array_push(input_symbols, &parameter);
244 }
245 } else {
246 for (k = 0; k < input->model->output_size; k++)
247 ccv_array_push(input_symbols, &input->outputs[k]);
248 }
249 }
250 // Go through each sub model to build the graph.
251 ccv_array_t* nodes;
252 ccv_functional_model_build_node_hook_t hook;
253 const ccv_array_t* const dependencies = self->sequence[i]->dependencies;
254 if ((dependencies && dependencies->rnum > 0) || self->sequence[i]->dependents > 0)
255 {
256 int ret;
257 khiter_t k = kh_put(io_node, io_node_map, (uint64_t)(uintptr_t)self->sequence[i], &ret)kh_put_io_node(io_node_map, (uint64_t)(uintptr_t)self->sequence
[i], &ret)
;
258 if (ret != 0)
259 nodes = kh_val(io_node_map, k)((io_node_map)->vals[k]) = ccv_array_new(sizeof(ccv_nnc_graph_exec_symbol_t), 1, 0);
260 else
261 nodes = kh_val(io_node_map, k)((io_node_map)->vals[k]);
262 hook.nodes = nodes;
263 hook.previous_context = ccv_nnc_graph_exec_symbol_new_hook(graph, _ccv_cnnp_functional_model_build_node_new, &hook, &hook.previous_func);
264 }
265 sub_model->data = self->super.data;
266 ccv_cnnp_model_build(sub_model, graph, (ccv_nnc_tensor_symbol_t*)ccv_array_get(input_symbols, 0)((void*)(((char*)((input_symbols)->data)) + (size_t)(input_symbols
)->rsize * (size_t)(0)))
, input_symbols->rnum, self->sequence[i]->outputs, sub_model->output_size);
267 if ((dependencies && dependencies->rnum > 0) || self->sequence[i]->dependents > 0)
268 {
269 ccv_nnc_graph_exec_symbol_new_hook(graph, hook.previous_func, hook.previous_context, 0);
270 if (dependencies)
271 for (j = 0; j < dependencies->rnum; j++)
272 {
273 const ccv_cnnp_model_io_t dependency = *(ccv_cnnp_model_io_t*)ccv_array_get(dependencies, j)((void*)(((char*)((dependencies)->data)) + (size_t)(dependencies
)->rsize * (size_t)(j)))
;
274 khiter_t k = kh_get(io_node, io_node_map, (uint64_t)(uintptr_t)dependency)kh_get_io_node(io_node_map, (uint64_t)(uintptr_t)dependency);
275 if (k == kh_end(io_node_map)((io_node_map)->n_buckets))
276 continue;
277 const ccv_array_t* const dependency_nodes = kh_val(io_node_map, k)((io_node_map)->vals[k]);
278 int x, y;
279 for (y = 0; y < dependency_nodes->rnum; y++)
280 for (x = 0; x < nodes->rnum; x++)
281 ccv_nnc_graph_exec_symbol_concat(graph, *(ccv_nnc_graph_exec_symbol_t*)ccv_array_get(dependency_nodes, y)((void*)(((char*)((dependency_nodes)->data)) + (size_t)(dependency_nodes
)->rsize * (size_t)(y)))
, *(ccv_nnc_graph_exec_symbol_t*)ccv_array_get(nodes, x)((void*)(((char*)((nodes)->data)) + (size_t)(nodes)->rsize
* (size_t)(x)))
);
282 }
283 }
284 sub_model->data = 0;
285 }
286 khiter_t it;
287 for (it = kh_begin(io_node_map)(khint_t)(0); it != kh_end(io_node_map)((io_node_map)->n_buckets); ++it)
288 {
289 if (!kh_exist(io_node_map, it)(!(((io_node_map)->flags[(it)>>4]>>(((it)&
0xfU)<<1))&3))
)
290 continue;
291 ccv_array_t* const nodes = kh_val(io_node_map, it)((io_node_map)->vals[it]);
292 ccv_array_free(nodes);
293 }
294 kh_destroy(io_node, io_node_map)kh_destroy_io_node(io_node_map);
295 ccv_array_free(input_symbols);
296 if (parameter_indices)
297 ccv_array_free(parameter_indices);
298 for (i = 0, k = 0; k < self->model_output_size; k++)
299 {
300 ccv_cnnp_model_t* const sub_model = self->sequence[self->model_outputs[k]]->model;
301 for (j = 0; j < sub_model->output_size; j++)
302 outputs[i + j] = self->sequence[self->model_outputs[k]]->outputs[j];
303 i += sub_model->output_size;
304 }
305 assert(i == output_size)((void) sizeof ((i == output_size) ? 1 : 0), __extension__ ({
if (i == output_size) ; else __assert_fail ("i == output_size"
, "ccv_cnnp_model_core.c", 305, __extension__ __PRETTY_FUNCTION__
); }))
;
306 PRINT(CCV_CLI_VERBOSE, "[cnnp_functional_model_build] 2. %p\n", self)do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_functional_model_build] 2. %p\n", self); fflush
(stdout); } } while (0)
;
307}
308
309static void _ccv_cnnp_functional_model_init_states(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_cnnp_state_initializer_f initializer, void* const context)
310{
311 ccv_cnnp_functional_model_t* const self = (ccv_cnnp_functional_model_t*)super;
312 int i;
313 for (i = self->super.input_size; i < self->sequence_size; i++)
314 ccv_cnnp_model_init_states(self->sequence[i]->model, graph, initializer, context);
315}
316
317static void _ccv_cnnp_functional_model_set_is_test(ccv_cnnp_model_t* const super, const int is_test, const ccv_cnnp_cmd_updater_f updater, void* const context)
318{
319 ccv_cnnp_functional_model_t* const self = (ccv_cnnp_functional_model_t*)super;
320 int i;
321 for (i = self->super.input_size; i < self->sequence_size; i++)
322 ccv_cnnp_model_set_is_test(self->sequence[i]->model, is_test, updater, context);
323}
324
325static void _ccv_cnnp_functional_model_add_to_parameter_indices(ccv_cnnp_model_t* const super, const int index, ccv_array_t* const parameter_indices)
326{
327 ccv_cnnp_functional_model_t* const self = (ccv_cnnp_functional_model_t*)super;
328 int i;
329 for (i = self->super.input_size; i < self->sequence_size; i++)
330 ccv_cnnp_model_add_to_parameter_indices(self->sequence[i]->model, index, parameter_indices);
331}
332
333static void _ccv_cnnp_functional_model_notify(const ccv_cnnp_model_t* const super, const int tag, void* const payload)
334{
335 ccv_cnnp_functional_model_t* const self = (ccv_cnnp_functional_model_t*)super;
336 int i;
337 for (i = 0; i < self->sequence_size; i++)
338 {
339 const ccv_cnnp_model_t* const model = self->sequence[i]->model;
340 ccv_cnnp_model_notify(model, tag, payload);
341 }
342}
343
344static ccv_cnnp_model_t* _ccv_cnnp_functional_model_copy(const ccv_cnnp_model_t* const super, void* const context);
345
346static const ccv_cnnp_model_vtab_t ccv_cnnp_functional_model_isa = {
347 .deinit = _ccv_cnnp_functional_model_deinit,
348 .build = _ccv_cnnp_functional_model_build,
349 .init_states = _ccv_cnnp_functional_model_init_states,
350 .copy = _ccv_cnnp_functional_model_copy,
351 .set_is_test = _ccv_cnnp_functional_model_set_is_test,
352 .add_to_parameter_indices = _ccv_cnnp_functional_model_add_to_parameter_indices,
353 .notify = _ccv_cnnp_functional_model_notify,
354};
355
356KHASH_MAP_INIT_INT64(model_io, ccv_cnnp_model_io_t)typedef struct kh_model_io_s { khint_t n_buckets, size, n_occupied
, upper_bound; khint32_t *flags; khint64_t *keys; ccv_cnnp_model_io_t
*vals; } kh_model_io_t; static inline __attribute__ ((__unused__
)) kh_model_io_t *kh_init_model_io(void) { return (kh_model_io_t
*)calloc(1,sizeof(kh_model_io_t)); } static inline __attribute__
((__unused__)) void kh_destroy_model_io(kh_model_io_t *h) { if
(h) { free((void *)h->keys); free(h->flags); free((void
*)h->vals); free(h); } } static inline __attribute__ ((__unused__
)) void kh_clear_model_io(kh_model_io_t *h) { if (h &&
h->flags) { memset(h->flags, 0xaa, ((h->n_buckets) <
16? 1 : (h->n_buckets)>>4) * sizeof(khint32_t)); h->
size = h->n_occupied = 0; } } static inline __attribute__ (
(__unused__)) khint_t kh_get_model_io(const kh_model_io_t *h,
khint64_t key) { if (h->n_buckets) { khint_t k, i, last, mask
, step = 0; mask = h->n_buckets - 1; k = (khint32_t)((key)
>>33^(key)^(key)<<11); i = k & mask; last = i
; while (!((h->flags[i>>4]>>((i&0xfU)<<
1))&2) && (((h->flags[i>>4]>>((i&
0xfU)<<1))&1) || !((h->keys[i]) == (key)))) { i =
(i + (++step)) & mask; if (i == last) return h->n_buckets
; } return ((h->flags[i>>4]>>((i&0xfU)<<
1))&3)? h->n_buckets : i; } else return 0; } static inline
__attribute__ ((__unused__)) int kh_resize_model_io(kh_model_io_t
*h, khint_t new_n_buckets) { khint32_t *new_flags = 0; khint_t
j = 1; { (--(new_n_buckets), (new_n_buckets)|=(new_n_buckets
)>>1, (new_n_buckets)|=(new_n_buckets)>>2, (new_n_buckets
)|=(new_n_buckets)>>4, (new_n_buckets)|=(new_n_buckets)
>>8, (new_n_buckets)|=(new_n_buckets)>>16, ++(new_n_buckets
)); if (new_n_buckets < 4) new_n_buckets = 4; if (h->size
>= (khint_t)(new_n_buckets * __ac_HASH_UPPER + 0.5)) j = 0
; else { new_flags = (khint32_t*)malloc(((new_n_buckets) <
16? 1 : (new_n_buckets)>>4) * sizeof(khint32_t)); if (
!new_flags) return -1; memset(new_flags, 0xaa, ((new_n_buckets
) < 16? 1 : (new_n_buckets)>>4) * sizeof(khint32_t))
; if (h->n_buckets < new_n_buckets) { khint64_t *new_keys
= (khint64_t*)realloc((void *)h->keys,new_n_buckets * sizeof
(khint64_t)); if (!new_keys) { free(new_flags); return -1; } h
->keys = new_keys; if (1) { ccv_cnnp_model_io_t *new_vals =
(ccv_cnnp_model_io_t*)realloc((void *)h->vals,new_n_buckets
* sizeof(ccv_cnnp_model_io_t)); if (!new_vals) { free(new_flags
); return -1; } h->vals = new_vals; } } } } if (j) { for (
j = 0; j != h->n_buckets; ++j) { if (((h->flags[j>>
4]>>((j&0xfU)<<1))&3) == 0) { khint64_t key
= h->keys[j]; ccv_cnnp_model_io_t val; khint_t new_mask; new_mask
= new_n_buckets - 1; if (1) val = h->vals[j]; (h->flags
[j>>4]|=1ul<<((j&0xfU)<<1)); while (1) {
khint_t k, i, step = 0; k = (khint32_t)((key)>>33^(key
)^(key)<<11); i = k & new_mask; while (!((new_flags
[i>>4]>>((i&0xfU)<<1))&2)) i = (i +
(++step)) & new_mask; (new_flags[i>>4]&=~(2ul<<
((i&0xfU)<<1))); if (i < h->n_buckets &&
((h->flags[i>>4]>>((i&0xfU)<<1))&
3) == 0) { { khint64_t tmp = h->keys[i]; h->keys[i] = key
; key = tmp; } if (1) { ccv_cnnp_model_io_t tmp = h->vals[
i]; h->vals[i] = val; val = tmp; } (h->flags[i>>4
]|=1ul<<((i&0xfU)<<1)); } else { h->keys[i
] = key; if (1) h->vals[i] = val; break; } } } } if (h->
n_buckets > new_n_buckets) { h->keys = (khint64_t*)realloc
((void *)h->keys,new_n_buckets * sizeof(khint64_t)); if (1
) h->vals = (ccv_cnnp_model_io_t*)realloc((void *)h->vals
,new_n_buckets * sizeof(ccv_cnnp_model_io_t)); } free(h->flags
); h->flags = new_flags; h->n_buckets = new_n_buckets; h
->n_occupied = h->size; h->upper_bound = (khint_t)(h
->n_buckets * __ac_HASH_UPPER + 0.5); } return 0; } static
inline __attribute__ ((__unused__)) khint_t kh_put_model_io(
kh_model_io_t *h, khint64_t key, int *ret) { khint_t x; if (h
->n_occupied >= h->upper_bound) { if (h->n_buckets
> (h->size<<1)) { if (kh_resize_model_io(h, h->
n_buckets - 1) < 0) { *ret = -1; return h->n_buckets; }
} else if (kh_resize_model_io(h, h->n_buckets + 1) < 0
) { *ret = -1; return h->n_buckets; } } { khint_t k, i, site
, last, mask = h->n_buckets - 1, step = 0; x = site = h->
n_buckets; k = (khint32_t)((key)>>33^(key)^(key)<<
11); i = k & mask; if (((h->flags[i>>4]>>(
(i&0xfU)<<1))&2)) x = i; else { last = i; while
(!((h->flags[i>>4]>>((i&0xfU)<<1))&
2) && (((h->flags[i>>4]>>((i&0xfU)
<<1))&1) || !((h->keys[i]) == (key)))) { if (((h
->flags[i>>4]>>((i&0xfU)<<1))&1)
) site = i; i = (i + (++step)) & mask; if (i == last) { x
= site; break; } } if (x == h->n_buckets) { if (((h->flags
[i>>4]>>((i&0xfU)<<1))&2) &&
site != h->n_buckets) x = site; else x = i; } } } if (((h
->flags[x>>4]>>((x&0xfU)<<1))&2)
) { h->keys[x] = key; (h->flags[x>>4]&=~(3ul<<
((x&0xfU)<<1))); ++h->size; ++h->n_occupied; *
ret = 1; } else if (((h->flags[x>>4]>>((x&
0xfU)<<1))&1)) { h->keys[x] = key; (h->flags[
x>>4]&=~(3ul<<((x&0xfU)<<1))); ++h->
size; *ret = 2; } else *ret = 0; return x; } static inline __attribute__
((__unused__)) void kh_del_model_io(kh_model_io_t *h, khint_t
x) { if (x != h->n_buckets && !((h->flags[x>>
4]>>((x&0xfU)<<1))&3)) { (h->flags[x>>
4]|=1ul<<((x&0xfU)<<1)); --h->size; } }
14
Taking true branch
15
Taking false branch
16
Calling 'kh_resize_model_io'
17
Taking true branch
18
Assuming the condition is false
19
Taking false branch
20
'?' condition is true
21
Assuming 'new_flags' is non-null
22
Taking false branch
23
'?' condition is true
24
Taking true branch
25
Storing uninitialized value
26
Assuming 'new_keys' is non-null
27
Taking false branch
28
Taking true branch
29
Assuming 'new_vals' is non-null
30
Taking false branch
31
Taking true branch
32
Loop condition is false. Execution continues on line 356
33
Taking false branch
34
Returning from 'kh_resize_model_io'
35
Taking false branch
36
Assuming right operand of bit shift is less than 32
37
Assuming the condition is true
38
Taking true branch
39
Assuming the condition is false
40
Taking false branch
41
Assuming the condition is false
42
Taking false branch
48
Assuming field 'n_occupied' is >= field 'upper_bound'
49
Taking true branch
50
Taking true branch
51
Calling 'kh_resize_model_io'
52
Taking false branch
53
Assuming the condition is false
54
Taking false branch
55
'?' condition is true
56
Assuming 'new_flags' is non-null
57
Taking false branch
58
'?' condition is true
59
Taking false branch
60
Taking true branch
61
The value 0 is assigned to 'j'
62
Loop condition is true. Entering loop body
63
Assuming the condition is true
64
Taking true branch
65
Assigned value is garbage or undefined
357
358static ccv_cnnp_model_t* _ccv_cnnp_functional_model_copy(const ccv_cnnp_model_t* const super, void* const context)
359{
360 const ccv_cnnp_functional_model_t* const self = (const ccv_cnnp_functional_model_t*)super;
361 ccv_cnnp_functional_model_t* const functional_model = (ccv_cnnp_functional_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_functional_model_t) + sizeof(ccv_cnnp_model_t*) * (self->sequence_size - 1) + sizeof(ccv_nnc_tensor_symbol_t) * self->super.output_size + sizeof(int) * self->model_output_size);
362 functional_model->super.isa = &ccv_cnnp_functional_model_isa;
363 functional_model->super.outputs = (ccv_nnc_tensor_symbol_t*)(functional_model->sequence + self->sequence_size);
364 functional_model->super.output_size = self->super.output_size;
365 functional_model->super.input_size = self->super.input_size;
366 ccv_cnnp_model_copy_name(&functional_model->super, self->super.name);
367 functional_model->sequence_size = self->sequence_size;
368 functional_model->model_output_size = self->model_output_size;
369 functional_model->model_outputs = (int*)(functional_model->super.outputs + functional_model->super.output_size);
370 memcpy(functional_model->model_outputs, self->model_outputs, sizeof(int) * self->model_output_size);
371 // Now the difficult part, copy over the model_io.
372 khash_t(model_io)kh_model_io_t* model_io_map = kh_init(model_io)kh_init_model_io();
373 khash_t(model)kh_model_t* model_map = context ? (khash_t(model)kh_model_t*)context : kh_init(model)kh_init_model();
1
Assuming 'context' is null
2
'?' condition is false
374 int i, j;
375 for (i = 0; i < self->sequence_size; i++)
3
Assuming 'i' is >= field 'sequence_size'
4
Loop condition is false. Execution continues on line 401
376 {
377 const ccv_cnnp_model_t* const sub_model = self->sequence[i]->model;
378 int ret;
379 khiter_t k = kh_put(model, model_map, (uint64_t)(uintptr_t)sub_model, &ret)kh_put_model(model_map, (uint64_t)(uintptr_t)sub_model, &
ret)
;
380 ccv_cnnp_model_t* model_copy;
381 if (ret != 0)
382 model_copy = kh_val(model_map, k)((model_map)->vals[k]) = _ccv_cnnp_model_copy(sub_model, model_map);
383 else
384 model_copy = kh_val(model_map, k)((model_map)->vals[k]);
385 ccv_cnnp_model_io_t model_io = functional_model->sequence[i] = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t) * sub_model->output_size);
386 model_io->param_ref = 0;
387 model_io->param_sel = 0;
388 model_io->visit = 0;
389 model_io->model = model_copy;
390 model_io->dependencies = 0;
391 model_io->dependents = 0;
392 model_io->incomings = 0;
393 model_io->outgoings = 0;
394 model_io->outputs = (ccv_nnc_tensor_symbol_t*)(model_io + 1);
395 if (!model_copy->io)
396 model_copy->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
397 ccv_array_push(model_copy->io, &model_io);
398 k = kh_put(model_io, model_io_map, (uint64_t)(uintptr_t)self->sequence[i], &ret)kh_put_model_io(model_io_map, (uint64_t)(uintptr_t)self->sequence
[i], &ret)
;
399 kh_val(model_io_map, k)((model_io_map)->vals[k]) = functional_model->sequence[i];
400 }
401 for (i = self->super.input_size; i < self->sequence_size; i++)
5
Assuming 'i' is < field 'sequence_size'
6
Loop condition is true. Entering loop body
402 {
403 if (self->sequence[i]->incomings)
7
Assuming field 'incomings' is non-null
8
Taking true branch
404 for (j = 0; j < self->sequence[i]->incomings->rnum; j++)
9
Loop condition is true. Entering loop body
45
Loop condition is true. Entering loop body
405 {
406 const ccv_cnnp_model_io_t input = *(ccv_cnnp_model_io_t*)ccv_array_get(self->sequence[i]->incomings, j)((void*)(((char*)((self->sequence[i]->incomings)->data
)) + (size_t)(self->sequence[i]->incomings)->rsize *
(size_t)(j)))
;
407 if (CCV_CNNP_IS_MODEL_PARAMETER(input)((input)->param_ref != 0 || (input)->param_sel != 0)) // I am pretty sure this is not in the model_io_map.
10
Assuming field 'param_ref' is equal to 0
11
Assuming field 'param_sel' is not equal to 0
12
Taking true branch
46
Assuming field 'param_ref' is not equal to 0
408 {
409 int ret;
410 khiter_t k = kh_put(model_io, model_io_map, (uint64_t)(uintptr_t)input, &ret)kh_put_model_io(model_io_map, (uint64_t)(uintptr_t)input, &
ret)
;
13
Calling 'kh_put_model_io'
43
Returning from 'kh_put_model_io'
47
Calling 'kh_put_model_io'
411 if (ret
43.1
'ret' is equal to 0
!= 0)
44
Taking false branch
412 {
413 // The model may not exist on the map due to wrapping (it is inside another sequential or functional model).
414 khiter_t m = kh_get(model, model_map, (uint64_t)(uintptr_t)input->model)kh_get_model(model_map, (uint64_t)(uintptr_t)input->model);
415 assert(m != kh_end(model_map))((void) sizeof ((m != ((model_map)->n_buckets)) ? 1 : 0), __extension__
({ if (m != ((model_map)->n_buckets)) ; else __assert_fail
("m != kh_end(model_map)", "ccv_cnnp_model_core.c", 415, __extension__
__PRETTY_FUNCTION__); }))
;
416 ccv_cnnp_model_t* const model_copy = kh_val(model_map, m)((model_map)->vals[m]);
417 ccv_cnnp_model_io_t model_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s));
418 model_io->param_ref = input->param_ref;
419 model_io->param_sel = input->param_sel;
420 model_io->visit = 0;
421 model_io->model = model_copy;
422 model_io->incomings = 0;
423 model_io->dependencies = 0;
424 model_io->dependents = 0;
425 model_io->outgoings = 0;
426 model_io->outputs = 0;
427 if (!model_copy->io)
428 model_copy->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
429 ccv_array_push(model_copy->io, &model_io);
430 kh_val(model_io_map, k)((model_io_map)->vals[k]) = model_io;
431 if (input->outgoings)
432 {
433 model_io->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), input->outgoings->rnum, 0);
434 int x;
435 for (x = 0; x < input->outgoings->rnum; x++)
436 {
437 khiter_t k = kh_get(model_io, model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t*)ccv_array_get(input->outgoings, x)))kh_get_model_io(model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t
*)((void*)(((char*)((input->outgoings)->data)) + (size_t
)(input->outgoings)->rsize * (size_t)(x)))))
;
438 assert(k != kh_end(model_io_map))((void) sizeof ((k != ((model_io_map)->n_buckets)) ? 1 : 0
), __extension__ ({ if (k != ((model_io_map)->n_buckets)) ;
else __assert_fail ("k != kh_end(model_io_map)", "ccv_cnnp_model_core.c"
, 438, __extension__ __PRETTY_FUNCTION__); }))
;
439 ccv_cnnp_model_io_t outgoing_io = kh_val(model_io_map, k)((model_io_map)->vals[k]);
440 ccv_array_push(model_io->outgoings, &outgoing_io);
441 }
442 }
443 }
444 }
445 }
446 }
447 if (!context)
448 kh_destroy(model, model_map)kh_destroy_model(model_map);
449 for (i = 0; i < self->sequence_size; i++)
450 {
451 const ccv_cnnp_model_io_t model_io = self->sequence[i];
452 ccv_cnnp_model_io_t model_io_copy = functional_model->sequence[i];
453 model_io_copy->param_ref = model_io->param_ref;
454 model_io_copy->param_sel = model_io->param_sel;
455 if (model_io->incomings)
456 {
457 model_io_copy->incomings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), model_io->incomings->rnum, 0);
458 for (j = 0; j < model_io->incomings->rnum; j++)
459 {
460 khiter_t k = kh_get(model_io, model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t*)ccv_array_get(model_io->incomings, j)))kh_get_model_io(model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t
*)((void*)(((char*)((model_io->incomings)->data)) + (size_t
)(model_io->incomings)->rsize * (size_t)(j)))))
;
461 assert(k != kh_end(model_io_map))((void) sizeof ((k != ((model_io_map)->n_buckets)) ? 1 : 0
), __extension__ ({ if (k != ((model_io_map)->n_buckets)) ;
else __assert_fail ("k != kh_end(model_io_map)", "ccv_cnnp_model_core.c"
, 461, __extension__ __PRETTY_FUNCTION__); }))
;
462 ccv_cnnp_model_io_t input_io = kh_val(model_io_map, k)((model_io_map)->vals[k]);
463 ccv_array_push(model_io_copy->incomings, &input_io);
464 }
465 }
466 if (model_io->dependencies)
467 {
468 model_io_copy->dependencies = ccv_array_new(sizeof(ccv_cnnp_model_io_t), model_io->dependencies->rnum, 0);
469 for (j = 0; j < model_io->dependencies->rnum; j++)
470 {
471 khiter_t k = kh_get(model_io, model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t*)ccv_array_get(model_io->dependencies, j)))kh_get_model_io(model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t
*)((void*)(((char*)((model_io->dependencies)->data)) + (
size_t)(model_io->dependencies)->rsize * (size_t)(j))))
)
;
472 assert(k != kh_end(model_io_map))((void) sizeof ((k != ((model_io_map)->n_buckets)) ? 1 : 0
), __extension__ ({ if (k != ((model_io_map)->n_buckets)) ;
else __assert_fail ("k != kh_end(model_io_map)", "ccv_cnnp_model_core.c"
, 472, __extension__ __PRETTY_FUNCTION__); }))
;
473 ccv_cnnp_model_io_t input_io = kh_val(model_io_map, k)((model_io_map)->vals[k]);
474 ccv_array_push(model_io_copy->dependencies, &input_io);
475 }
476 }
477 model_io_copy->dependents = model_io->dependents;
478 if (model_io->outgoings)
479 {
480 model_io_copy->outgoings = ccv_array_new(sizeof(ccv_cnnp_model_io_t), model_io->outgoings->rnum, 0);
481 for (j = 0; j < model_io->outgoings->rnum; j++)
482 {
483 khiter_t k = kh_get(model_io, model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t*)ccv_array_get(model_io->outgoings, j)))kh_get_model_io(model_io_map, (uint64_t)(uintptr_t)(*(ccv_cnnp_model_io_t
*)((void*)(((char*)((model_io->outgoings)->data)) + (size_t
)(model_io->outgoings)->rsize * (size_t)(j)))))
;
484 assert(k != kh_end(model_io_map))((void) sizeof ((k != ((model_io_map)->n_buckets)) ? 1 : 0
), __extension__ ({ if (k != ((model_io_map)->n_buckets)) ;
else __assert_fail ("k != kh_end(model_io_map)", "ccv_cnnp_model_core.c"
, 484, __extension__ __PRETTY_FUNCTION__); }))
;
485 ccv_cnnp_model_io_t outgoing_io = kh_val(model_io_map, k)((model_io_map)->vals[k]);
486 ccv_array_push(model_io_copy->outgoings, &outgoing_io);
487 }
488 }
489 }
490 kh_destroy(model_io, model_io_map)kh_destroy_model_io(model_io_map);
491 return (ccv_cnnp_model_t*)functional_model;
492}
493
494ccv_cnnp_model_t* ccv_cnnp_model_new(const ccv_cnnp_model_io_t* const inputs, const int input_size, const ccv_cnnp_model_io_t* const outputs, const int output_size, const int is_trainable, const char* const name)
495{
496 assert(output_size > 0)((void) sizeof ((output_size > 0) ? 1 : 0), __extension__ (
{ if (output_size > 0) ; else __assert_fail ("output_size > 0"
, "ccv_cnnp_model_core.c", 496, __extension__ __PRETTY_FUNCTION__
); }))
;
497 // Do topological sort.
498 ccv_array_t* const reverse_top = ccv_array_new(sizeof(ccv_cnnp_model_io_t), output_size, 0);
499 int i, j, k;
500 // Go through output one by one, reverse traversal them, to detect potential overlap (overlap means, for example,
501 // outputs[1] is an incoming node for outputs[0]. Thus, if we reverse them, we may have outputs[0] build before outputs[1],
502 // hence, having issues.
503 for (i = 0; i < output_size; i++)
504 outputs[i]->visit = 2;
505 for (i = output_size - 1; i >= 0; i--)
506 {
507 if (outputs[i]->visit == 3) // If we need to remove it, no need to visit.
508 continue;
509 assert(outputs[i]->visit == 2)((void) sizeof ((outputs[i]->visit == 2) ? 1 : 0), __extension__
({ if (outputs[i]->visit == 2) ; else __assert_fail ("outputs[i]->visit == 2"
, "ccv_cnnp_model_core.c", 509, __extension__ __PRETTY_FUNCTION__
); }))
;
510 ccv_array_clear(reverse_top);
511 ccv_array_push(reverse_top, &outputs[i]);
512 for (j = 0; j < reverse_top->rnum; j++)
513 {
514 const ccv_cnnp_model_io_t output = *(ccv_cnnp_model_io_t*)ccv_array_get(reverse_top, j)((void*)(((char*)((reverse_top)->data)) + (size_t)(reverse_top
)->rsize * (size_t)(j)))
;
515 assert(!CCV_CNNP_IS_MODEL_INPUT(output->model))((void) sizeof ((!((output->model)->isa == &ccv_cnnp_input_isa
)) ? 1 : 0), __extension__ ({ if (!((output->model)->isa
== &ccv_cnnp_input_isa)) ; else __assert_fail ("!CCV_CNNP_IS_MODEL_INPUT(output->model)"
, "ccv_cnnp_model_core.c", 515, __extension__ __PRETTY_FUNCTION__
); }))
;
516 // If it is input, push it here.
517 if (output->incomings && !CCV_CNNP_IS_MODEL_PARAMETER(output)((output)->param_ref != 0 || (output)->param_sel != 0))
518 for (k = 0; k < output->incomings->rnum; k++)
519 {
520 const ccv_cnnp_model_io_t input = *(ccv_cnnp_model_io_t*)ccv_array_get(output->incomings, k)((void*)(((char*)((output->incomings)->data)) + (size_t
)(output->incomings)->rsize * (size_t)(k)))
;
521 // If it is an input or parameter, skip.
522 if (CCV_CNNP_IS_MODEL_INPUT(input->model)((input->model)->isa == &ccv_cnnp_input_isa) || CCV_CNNP_IS_MODEL_PARAMETER(input)((input)->param_ref != 0 || (input)->param_sel != 0))
523 continue;
524 if (input->visit == 1 || input->visit == 3) // Visited, skip.
525 continue;
526 // If this is an output, we need to remove it from the output array. Otherwise mark it as visited.
527 input->visit = input->visit == 2 ? 3 : 1;
528 ccv_array_push(reverse_top, &input);
529 }
530 // Similar for dependencies.
531 if (output->dependencies && !CCV_CNNP_IS_MODEL_PARAMETER(output)((output)->param_ref != 0 || (output)->param_sel != 0))
532 for (k = 0; k < output->dependencies->rnum; k++)
533 {
534 const ccv_cnnp_model_io_t dependency = *(ccv_cnnp_model_io_t*)ccv_array_get(output->dependencies, k)((void*)(((char*)((output->dependencies)->data)) + (size_t
)(output->dependencies)->rsize * (size_t)(k)))
;
535 // If it is an input or parameter, skip.
536 if (CCV_CNNP_IS_MODEL_INPUT(dependency->model)((dependency->model)->isa == &ccv_cnnp_input_isa) || CCV_CNNP_IS_MODEL_PARAMETER(dependency)((dependency)->param_ref != 0 || (dependency)->param_sel
!= 0)
)
537 continue;
538 if (dependency->visit == 1 || dependency->visit == 3) // Visited, skip.
539 continue;
540 // If this is an output, we need to remove it from the output array. Otherwise mark it as visited.
541 dependency->visit = dependency->visit == 2 ? 3 : 1;
542 ccv_array_push(reverse_top, &dependency);
543 }
544 }
545 for (j = 1; j < reverse_top->rnum; j++)
546 {
547 const ccv_cnnp_model_io_t output = *(ccv_cnnp_model_io_t*)ccv_array_get(reverse_top, j)((void*)(((char*)((reverse_top)->data)) + (size_t)(reverse_top
)->rsize * (size_t)(j)))
;
548 if (output->visit == 1) // Clean the visit back.
549 output->visit = 0;
550 }
551 }
552 ccv_array_clear(reverse_top);
553 for (i = 0; i < output_size; i++) // We will assign sequence in reverse order, thus, reverse the reverse top when copying the outputs.
554 {
555 if (outputs[output_size - 1 - i]->visit == 2)
556 ccv_array_push(reverse_top, &outputs[output_size - 1 - i]);
557 assert(outputs[output_size - 1 - i]->visit == 2 || outputs[output_size - 1 - i]->visit == 3)((void) sizeof ((outputs[output_size - 1 - i]->visit == 2 ||
outputs[output_size - 1 - i]->visit == 3) ? 1 : 0), __extension__
({ if (outputs[output_size - 1 - i]->visit == 2 || outputs
[output_size - 1 - i]->visit == 3) ; else __assert_fail ("outputs[output_size - 1 - i]->visit == 2 || outputs[output_size - 1 - i]->visit == 3"
, "ccv_cnnp_model_core.c", 557, __extension__ __PRETTY_FUNCTION__
); }))
;
558 outputs[output_size - 1 - i]->visit = 0; // Clean up all visits.
559 }
560 // Go from the output, until we meet inputs.
561 uint64_t input_bitmask[((input_size - 1) >> 6) + 1];
562 memset(input_bitmask, 0, sizeof(uint64_t) * (((input_size - 1) >> 6) + 1));
563 int tensor_output_size = 0; // io can be mapped to multiple tensor outputs, therefore, need to compute the exact tensor output size.
564 for (i = 0; i < output_size; i++)
565 tensor_output_size += outputs[i]->model->output_size;
566 for (i = 0; i < reverse_top->rnum; i++)
567 {
568 const ccv_cnnp_model_io_t output = *(ccv_cnnp_model_io_t*)ccv_array_get(reverse_top, i)((void*)(((char*)((reverse_top)->data)) + (size_t)(reverse_top
)->rsize * (size_t)(i)))
;
569 assert(!CCV_CNNP_IS_MODEL_INPUT(output->model))((void) sizeof ((!((output->model)->isa == &ccv_cnnp_input_isa
)) ? 1 : 0), __extension__ ({ if (!((output->model)->isa
== &ccv_cnnp_input_isa)) ; else __assert_fail ("!CCV_CNNP_IS_MODEL_INPUT(output->model)"
, "ccv_cnnp_model_core.c", 569, __extension__ __PRETTY_FUNCTION__
); }))
;
570 // If it is input, push it here.
571 if (output->incomings && !CCV_CNNP_IS_MODEL_PARAMETER(output)((output)->param_ref != 0 || (output)->param_sel != 0))
572 for (j = 0; j < output->incomings->rnum; j++)
573 {
574 const ccv_cnnp_model_io_t input = *(ccv_cnnp_model_io_t*)ccv_array_get(output->incomings, j)((void*)(((char*)((output->incomings)->data)) + (size_t
)(output->incomings)->rsize * (size_t)(j)))
;
575 ++input->visit; // Mark it as visited.
576 if (input->visit != input->outgoings->rnum + input->dependents) // Not all dependencies visited.
577 continue;
578 if (!CCV_CNNP_IS_MODEL_INPUT(input->model)((input->model)->isa == &ccv_cnnp_input_isa) && !CCV_CNNP_IS_MODEL_PARAMETER(input)((input)->param_ref != 0 || (input)->param_sel != 0))
579 ccv_array_push(reverse_top, &input);
580 else if (CCV_CNNP_IS_MODEL_INPUT(input->model)((input->model)->isa == &ccv_cnnp_input_isa)) {
581 for (k = 0; k < input_size; k++)
582 if (input == inputs[k])
583 break;
584 assert(k < input_size)((void) sizeof ((k < input_size) ? 1 : 0), __extension__ (
{ if (k < input_size) ; else __assert_fail ("k < input_size"
, "ccv_cnnp_model_core.c", 584, __extension__ __PRETTY_FUNCTION__
); }))
;
585 input_bitmask[k >> 6] |= ((uint64_t)1 << (k & 63));
586 }
587 }
588 if (output->dependencies && !CCV_CNNP_IS_MODEL_PARAMETER(output)((output)->param_ref != 0 || (output)->param_sel != 0))
589 for (j = 0; j < output->dependencies->rnum; j++)
590 {
591 const ccv_cnnp_model_io_t dependency = *(ccv_cnnp_model_io_t*)ccv_array_get(output->dependencies, j)((void*)(((char*)((output->dependencies)->data)) + (size_t
)(output->dependencies)->rsize * (size_t)(j)))
;
592 ++dependency->visit; // Mark it as visited.
593 if (dependency->visit != (dependency->outgoings ? dependency->outgoings->rnum : 0) + dependency->dependents) // Not all dependencies visited.
594 continue;
595 if (!CCV_CNNP_IS_MODEL_INPUT(dependency->model)((dependency->model)->isa == &ccv_cnnp_input_isa) && !CCV_CNNP_IS_MODEL_PARAMETER(dependency)((dependency)->param_ref != 0 || (dependency)->param_sel
!= 0)
)
596 ccv_array_push(reverse_top, &dependency);
597 else if (CCV_CNNP_IS_MODEL_INPUT(dependency->model)((dependency->model)->isa == &ccv_cnnp_input_isa)) {
598 for (k = 0; k < input_size; k++)
599 if (dependency == inputs[k])
600 break;
601 assert(k < input_size)((void) sizeof ((k < input_size) ? 1 : 0), __extension__ (
{ if (k < input_size) ; else __assert_fail ("k < input_size"
, "ccv_cnnp_model_core.c", 601, __extension__ __PRETTY_FUNCTION__
); }))
;
602 input_bitmask[k >> 6] |= ((uint64_t)1 << (k & 63));
603 }
604 }
605 }
606 for (i = 0; i < reverse_top->rnum; i++)
607 {
608 const ccv_cnnp_model_io_t output = *(ccv_cnnp_model_io_t*)ccv_array_get(reverse_top, i)((void*)(((char*)((reverse_top)->data)) + (size_t)(reverse_top
)->rsize * (size_t)(i)))
;
609 output->visit = 0; // Clean the visit back.
610 }
611 for (i = 0; i < input_size; i++)
612 inputs[i]->visit = 0; // Clean the visit back.
613 for (i = 0; i < input_size; i++)
614 { assert((input_bitmask[i >> 6] & ((uint64_t)1 << (i & 63))))((void) sizeof (((input_bitmask[i >> 6] & ((uint64_t
)1 << (i & 63)))) ? 1 : 0), __extension__ ({ if ((input_bitmask
[i >> 6] & ((uint64_t)1 << (i & 63)))) ; else
__assert_fail ("(input_bitmask[i >> 6] & ((uint64_t)1 << (i & 63)))"
, "ccv_cnnp_model_core.c", 614, __extension__ __PRETTY_FUNCTION__
); }))
; } // Assuming they all match.
615 const int sequence_size = reverse_top->rnum + input_size;
616 ccv_cnnp_functional_model_t* const functional_model = (ccv_cnnp_functional_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_functional_model_t) + sizeof(ccv_cnnp_model_t*) * (sequence_size - 1) + sizeof(ccv_nnc_tensor_symbol_t) * tensor_output_size + sizeof(int) * output_size);
617 functional_model->super.isa = &ccv_cnnp_functional_model_isa;
618 functional_model->super.outputs = (ccv_nnc_tensor_symbol_t*)(functional_model->sequence + sequence_size);
619 functional_model->super.output_size = tensor_output_size;
620 functional_model->super.input_size = input_size;
621 functional_model->super.is_trainable = is_trainable;
622 functional_model->model_output_size = output_size;
623 functional_model->model_outputs = (int*)(functional_model->super.outputs + tensor_output_size);
624 ccv_cnnp_model_copy_name(&functional_model->super, name);
625 functional_model->sequence_size = sequence_size;
626 memcpy(functional_model->sequence, inputs, sizeof(ccv_cnnp_model_io_t) * input_size);
627 for (i = 0; i < reverse_top->rnum; i++)
628 functional_model->sequence[input_size + i] = *(ccv_cnnp_model_io_t*)ccv_array_get(reverse_top, reverse_top->rnum - 1 - i)((void*)(((char*)((reverse_top)->data)) + (size_t)(reverse_top
)->rsize * (size_t)(reverse_top->rnum - 1 - i)))
;
629 for (i = 0; i < output_size; i++)
630 {
631 for (j = sequence_size - 1; j >= input_size; j--)
632 if (functional_model->sequence[j] == outputs[i])
633 {
634 functional_model->model_outputs[i] = j;
635 break;
636 }
637 }
638 ccv_array_free(reverse_top);
639 return (ccv_cnnp_model_t*)functional_model;
640}
641
642static ccv_cnnp_model_t* _ccv_cnnp_input_copy(const ccv_cnnp_model_t* const self, void* const context)
643{
644 ccv_cnnp_model_t* const input = (ccv_cnnp_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_model_t) + sizeof(ccv_nnc_tensor_symbol_t));
645 input->isa = &ccv_cnnp_input_isa;
646 input->outputs = (ccv_nnc_tensor_symbol_t*)(input + 1);
647 input->output_size = 1;
648 return input;
649}
650
651static const ccv_cnnp_model_vtab_t ccv_cnnp_input_isa = {
652 .copy = _ccv_cnnp_input_copy,
653};
654
655ccv_cnnp_model_io_t ccv_cnnp_input(void)
656{
657 ccv_cnnp_model_t* const input = (ccv_cnnp_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_model_t) + sizeof(ccv_nnc_tensor_symbol_t));
658 input->isa = &ccv_cnnp_input_isa;
659 input->io = ccv_array_new(sizeof(ccv_cnnp_model_io_t), 1, 0);
660 ccv_cnnp_model_io_t input_io = ccmallocmalloc(sizeof(struct ccv_cnnp_model_io_s) + sizeof(ccv_nnc_tensor_symbol_t));
661 input_io->param_ref = 0;
662 input_io->param_sel = 0;
663 input_io->visit = 0;
664 input_io->incomings = 0;
665 input_io->dependencies = 0;
666 input_io->dependents = 0;
667 input_io->outgoings = 0;
668 input_io->model = input;
669 input_io->outputs = (ccv_nnc_tensor_symbol_t*)(input_io + 1);
670 ccv_array_push(input->io, &input_io);
671 input->outputs = (ccv_nnc_tensor_symbol_t*)(input + 1);
672 input->output_size = 1;
673 return input_io;
674}
675
676// MARK - Dynamic Layer
677
678typedef struct {
679 ccv_cnnp_model_t super;
680 ccv_cnnp_model_dynamic_f func;
681 void* context;
682 ccv_cnnp_model_t* model;
683} ccv_cnnp_dynamic_model_t;
684
685static void _ccv_cnnp_dynamic_model_deinit(ccv_cnnp_model_t* const super)
686{
687 ccv_cnnp_dynamic_model_t* const self = (ccv_cnnp_dynamic_model_t*)super;
688 if (self->model)
689 ccv_cnnp_model_free(self->model);
690}
691
692static void _ccv_cnnp_dynamic_model_build(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
693{
694 ccv_cnnp_dynamic_model_t* const self = (ccv_cnnp_dynamic_model_t*)super;
695 PRINT(CCV_CLI_VERBOSE, "[cnnp_dynamic_model_build] 1. %p, func: %p\n", self, self->func)do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_dynamic_model_build] 1. %p, func: %p\n", self
, self->func); fflush(stdout); } } while (0)
;
696 if (!self->model)
697 {
698 ccv_nnc_tensor_param_t input_params[input_size];
699 int i;
700 for (i = 0; i < input_size; i++)
701 input_params[i] = ccv_nnc_tensor_symbol_params(graph, inputs[i]);
702 self->model = self->func(input_params, input_size, self->context);
703 // Update to use the settings of the compiled model.
704 self->super.input_size = self->model->input_size;
705 self->super.outputs = self->model->outputs;
706 self->super.output_size = self->model->output_size;
707 }
708 self->model->data = self->super.data;
709 ccv_cnnp_model_build(self->model, graph, inputs, input_size, outputs, output_size);
710 self->model->data = 0;
711 PRINT(CCV_CLI_VERBOSE, "[cnnp_dynamic_model_build] 2. %p\n", self)do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_dynamic_model_build] 2. %p\n", self); fflush
(stdout); } } while (0)
;
712}
713
714static void _ccv_cnnp_dynamic_model_init_states(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_cnnp_state_initializer_f initializer, void* const context)
715{
716 ccv_cnnp_dynamic_model_t* const self = (ccv_cnnp_dynamic_model_t*)super;
717 assert(self->model)((void) sizeof ((self->model) ? 1 : 0), __extension__ ({ if
(self->model) ; else __assert_fail ("self->model", "ccv_cnnp_model_core.c"
, 717, __extension__ __PRETTY_FUNCTION__); }))
;
718 ccv_cnnp_model_init_states(self->model, graph, initializer, context);
719}
720
721static void _ccv_cnnp_dynamic_model_set_is_test(ccv_cnnp_model_t* const super, const int is_test, const ccv_cnnp_cmd_updater_f updater, void* const context)
722{
723 ccv_cnnp_dynamic_model_t* const self = (ccv_cnnp_dynamic_model_t*)super;
724 assert(self->model)((void) sizeof ((self->model) ? 1 : 0), __extension__ ({ if
(self->model) ; else __assert_fail ("self->model", "ccv_cnnp_model_core.c"
, 724, __extension__ __PRETTY_FUNCTION__); }))
;
725 ccv_cnnp_model_set_is_test(self->model, is_test, updater, context);
726}
727
728static ccv_cnnp_model_t* _ccv_cnnp_dynamic_model_copy(const ccv_cnnp_model_t* const super, void* const context);
729
730static void _ccv_cnnp_dynamic_model_add_to_parameter_indices(ccv_cnnp_model_t* const super, const int index, ccv_array_t* const parameter_indices)
731{
732 ccv_cnnp_dynamic_model_t* const self = (ccv_cnnp_dynamic_model_t*)super;
733 assert(self->model)((void) sizeof ((self->model) ? 1 : 0), __extension__ ({ if
(self->model) ; else __assert_fail ("self->model", "ccv_cnnp_model_core.c"
, 733, __extension__ __PRETTY_FUNCTION__); }))
;
734 ccv_cnnp_model_add_to_parameter_indices(self->model, index, parameter_indices);
735}
736
737static void _ccv_cnnp_dynamic_model_notify(const ccv_cnnp_model_t* const super, const int tag, void* const payload)
738{
739 ccv_cnnp_dynamic_model_t* const self = (ccv_cnnp_dynamic_model_t*)super;
740 if (self->model)
741 ccv_cnnp_model_notify(self->model, tag, payload);
742}
743
744static const ccv_cnnp_model_vtab_t ccv_cnnp_dynamic_model_isa = {
745 .deinit = _ccv_cnnp_dynamic_model_deinit,
746 .build = _ccv_cnnp_dynamic_model_build,
747 .init_states = _ccv_cnnp_dynamic_model_init_states,
748 .copy = _ccv_cnnp_dynamic_model_copy,
749 .set_is_test = _ccv_cnnp_dynamic_model_set_is_test,
750 .add_to_parameter_indices = _ccv_cnnp_dynamic_model_add_to_parameter_indices,
751 .notify = _ccv_cnnp_dynamic_model_notify,
752};
753
754ccv_cnnp_model_t* ccv_cnnp_dynamic_new(ccv_cnnp_model_dynamic_f func, void* const context, const char* const name)
755{
756 ccv_cnnp_dynamic_model_t* const dynamic_model = (ccv_cnnp_dynamic_model_t*)cccalloccalloc(1, sizeof(ccv_cnnp_dynamic_model_t));
757 dynamic_model->super.isa = &ccv_cnnp_dynamic_model_isa;
758 dynamic_model->super.is_trainable = -1;
759 dynamic_model->func = func;
760 dynamic_model->context = context;
761 ccv_cnnp_model_copy_name(&dynamic_model->super, name);
762 return (ccv_cnnp_model_t*)dynamic_model;
763}
764
765static ccv_cnnp_model_t* _ccv_cnnp_dynamic_model_copy(const ccv_cnnp_model_t* const super, void* const context)
766{
767 const ccv_cnnp_dynamic_model_t* const self = (const ccv_cnnp_dynamic_model_t*)super;
768 return ccv_cnnp_dynamic_new(self->func, self->context, self->super.name);
769}
770
771// MARK - Command Layer
772
773typedef struct {
774 ccv_cnnp_model_t super;
775 ccv_nnc_cmd_t cmd;
776 ccv_nnc_hint_t hint;
777 ccv_nnc_tensor_symbol_t* input_symbols; // This is only valid for INIT_SHARED_TENSOR / INIT_SHARED_TENSOR_AS_TRAINABLE
778 ccv_nnc_tensor_symbol_t* output_symbols; // This is just for the output symbol (in case we need to have no tensor symbol).
779 ccv_cnnp_cmd_exec_io_t* inputs;
780 int flags;
781 int input_size;
782 int* outputs;
783 int output_size;
784} ccv_cnnp_model_cmd_exec_t;
785
786static void _ccv_cnnp_cmd_exec_build(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_nnc_tensor_symbol_t* const inputs, const int input_size, ccv_nnc_tensor_symbol_t* const outputs, const int output_size)
787{
788 ccv_cnnp_model_cmd_exec_t* const self = (ccv_cnnp_model_cmd_exec_t*)super;
789 PRINT(CCV_CLI_VERBOSE, "[cnnp_cmd_exec_build] -\n")do { if ((CCV_CLI_VERBOSE & ccv_cli_get_output_levels()))
{ printf("[cnnp_cmd_exec_build] -\n"); fflush(stdout); } } while
(0)
;
790 ccv_nnc_tensor_param_t input_params[ccv_max(1, self->input_size)({ typeof (1) _a = (1); typeof (self->input_size) _b = (self
->input_size); (_a > _b) ? _a : _b; })
];
791 int i, j;
792 for (i = 0, j = 0; i < self->input_size; i++)
793 if (self->inputs[i].type == CCV_CNNP_IO)
794 {
795 self->input_symbols[i] = inputs[j++];
796 input_params[i] = ccv_nnc_tensor_symbol_params(graph, self->input_symbols[i]);
797 } else if (self->inputs[i].type == CCV_CNNP_NO_TENSOR) {
798 self->input_symbols[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
}
;
799 } else if (!self->input_symbols[i].graph) {
800 // Otherwise, we only create this symbol if it doesn't exist.
801 const ccv_nnc_tensor_param_t params = self->inputs[i].init_state.info;
802 input_params[i] = params;
803 self->input_symbols[i] = ccv_nnc_tensor_symbol_new(graph, params, 0);
804 }
805 // We cannot simply mark the outputs as auto, because the subsequent build call may require this output to have params setup.
806 // Infer the parameters here.
807 ccv_nnc_tensor_param_t output_params[ccv_max(1, self->output_size)({ typeof (1) _a = (1); typeof (self->output_size) _b = (self
->output_size); (_a > _b) ? _a : _b; })
];
808 ccv_nnc_hint_tensor_auto(self->cmd, input_params, self->input_size, self->hint, output_params, self->output_size);
809 for (i = 0, j = 0; i < self->output_size; i++)
810 if (self->outputs[i] == CCV_CNNP_IO)
811 self->output_symbols[i] = outputs[j++] = ccv_nnc_tensor_symbol_new(graph, output_params[i], 0);
812 else if (self->outputs[i] == CCV_CNNP_TENSOR_NOT_OUTPUT)
813 self->output_symbols[i] = ccv_nnc_tensor_symbol_new(graph, output_params[i], 0);
814 else
815 self->output_symbols[i] = NO_TENSOR_SYMBOL(const ccv_nnc_tensor_symbol_t){.d = CCV_NNC_NO_TENSOR_SYMBOL
}
;
816 ccv_nnc_graph_exec_symbol_new(graph, self->cmd, self->input_symbols, self->input_size, self->output_symbols, self->output_size, 0);
817}
818
819static void _ccv_cnnp_cmd_exec_init_states(ccv_cnnp_model_t* const super, ccv_nnc_symbolic_graph_t* const graph, const ccv_cnnp_state_initializer_f initializer, void* const context)
820{
821 ccv_cnnp_model_cmd_exec_t* const self = (ccv_cnnp_model_cmd_exec_t*)super;
822 int i;
823 for (i = 0; i < self->input_size; i++)
824 if (self->inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR || self->inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE)
825 self->inputs[i].init_state.init(self->input_symbols[i], initializer, context, self->inputs[i].init_state.context);
826}
827
828static void _ccv_cnnp_cmd_exec_add_to_output(ccv_cnnp_model_t* const super, const ccv_cnnp_add_to_array_f add_to_array, void* const outputs)
829{
830 ccv_cnnp_model_cmd_exec_t* const self = (ccv_cnnp_model_cmd_exec_t*)super;
831 int i;
832 for (i = 0; i < self->input_size; i++)
833 if (self->inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR)
834 add_to_array(outputs, self->input_symbols[i], 0); // Push this as retainable because it need to be init.
835}
836
837static void _ccv_cnnp_cmd_exec_add_to_parameter(ccv_cnnp_model_t* const super, const ccv_cnnp_add_to_array_f add_to_array, void* const parameters, const int is_trainable)
838{
839 ccv_cnnp_model_cmd_exec_t* const self = (ccv_cnnp_model_cmd_exec_t*)super;
840 int i;
841 for (i = 0; i < self->input_size; i++)
842 if (self->inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE)
843 add_to_array(parameters, self->input_symbols[i], is_trainable); // Push this as parameter.
844}
845
846static void _ccv_cnnp_cmd_exec_deinit(ccv_cnnp_model_t* const super)
847{
848 ccv_cnnp_model_cmd_exec_t* const self = (ccv_cnnp_model_cmd_exec_t*)super;
849 int i, j;
850 for (i = 0; i < self->input_size; i++)
851 if ((self->inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR || self->inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE) &&
852 self->inputs[i].init_state.context)
853 {
854 void* const context = self->inputs[i].init_state.context;
855 if (self->inputs[i].init_state.deinit)
856 self->inputs[i].init_state.deinit(context);
857 self->inputs[i].init_state.init = 0;
858 self->inputs[i].init_state.deinit = 0;
859 self->inputs[i].init_state.context = 0;
860 for (j = i + 1; j < self->input_size; j++)
861 if (self->inputs[j].init_state.context == context)
862 {
863 self->inputs[j].init_state.init = 0;
864 self->inputs[j].init_state.deinit = 0;
865 self->inputs[j].init_state.context = 0;
866 }
867 }
868}
869
870static ccv_cnnp_model_t* _ccv_cnnp_cmd_exec_copy(const ccv_cnnp_model_t* const super, void* const context);
871
872static const ccv_cnnp_model_vtab_t ccv_cnnp_cmd_exec_isa = {
873 .build = _ccv_cnnp_cmd_exec_build,
874 .init_states = _ccv_cnnp_cmd_exec_init_states,
875 .add_to_parameter = _ccv_cnnp_cmd_exec_add_to_parameter,
876 .add_to_output = _ccv_cnnp_cmd_exec_add_to_output,
877 .deinit = _ccv_cnnp_cmd_exec_deinit,
878 .copy = _ccv_cnnp_cmd_exec_copy,
879};
880
881static ccv_cnnp_model_t* _ccv_cnnp_cmd_exec(const ccv_nnc_cmd_t cmd, int copy_io, const ccv_nnc_hint_t hint, const int flags, const ccv_cnnp_cmd_exec_io_t* const inputs, const int input_size, const int* const outputs, const int output_size, const int is_trainable, const char* const name)
882{
883 assert(input_size >= 0)((void) sizeof ((input_size >= 0) ? 1 : 0), __extension__ (
{ if (input_size >= 0) ; else __assert_fail ("input_size >= 0"
, "ccv_cnnp_model_core.c", 883, __extension__ __PRETTY_FUNCTION__
); }))
;
884 assert(output_size > 0)((void) sizeof ((output_size > 0) ? 1 : 0), __extension__ (
{ if (output_size > 0) ; else __assert_fail ("output_size > 0"
, "ccv_cnnp_model_core.c", 884, __extension__ __PRETTY_FUNCTION__
); }))
;
885 int i;
886 int io_input_size = 0;
887 for (i = 0; i < input_size; i++)
888 if (inputs[i].type == CCV_CNNP_IO)
889 ++io_input_size;
890 else {
891 assert(inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR || inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE)((void) sizeof ((inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR
|| inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE
) ? 1 : 0), __extension__ ({ if (inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR
|| inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE
) ; else __assert_fail ("inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR || inputs[i].type == CCV_CNNP_INIT_SHARED_TENSOR_AS_TRAINABLE"
, "ccv_cnnp_model_core.c", 891, __extension__ __PRETTY_FUNCTION__
); }))
;
892 assert(inputs[i].init_state.init)((void) sizeof ((inputs[i].init_state.init) ? 1 : 0), __extension__
({ if (inputs[i].init_state.init) ; else __assert_fail ("inputs[i].init_state.init"
, "ccv_cnnp_model_core.c", 892, __extension__ __PRETTY_FUNCTION__
); }))
;
893 }
894 int io_output_size = 0;
895 for (i = 0; i < output_size; i++)
896 if (outputs[i] == CCV_CNNP_IO)
897 ++io_output_size;
898 else {
899 assert(outputs[i] == CCV_CNNP_TENSOR_NOT_OUTPUT || outputs[i] == CCV_CNNP_NO_TENSOR)((void) sizeof ((outputs[i] == CCV_CNNP_TENSOR_NOT_OUTPUT || outputs
[i] == CCV_CNNP_NO_TENSOR) ? 1 : 0), __extension__ ({ if (outputs
[i] == CCV_CNNP_TENSOR_NOT_OUTPUT || outputs[i] == CCV_CNNP_NO_TENSOR
) ; else __assert_fail ("outputs[i] == CCV_CNNP_TENSOR_NOT_OUTPUT || outputs[i] == CCV_CNNP_NO_TENSOR"
, "ccv_cnnp_model_core.c", 899, __extension__ __PRETTY_FUNCTION__
); }))
;
900 }
901 assert(io_output_size > 0)((void) sizeof ((io_output_size > 0) ? 1 : 0), __extension__
({ if (io_output_size > 0) ; else __assert_fail ("io_output_size > 0"
, "ccv_cnnp_model_core.c", 901, __extension__ __PRETTY_FUNCTION__
); }))
;
902 ccv_cnnp_model_cmd_exec_t* const model_cmd_exec = (ccv_cnnp_model_cmd_exec_t*)cccalloccalloc(1, sizeof(ccv_cnnp_model_cmd_exec_t) + sizeof(ccv_nnc_tensor_symbol_t) * (io_output_size + input_size + output_size) + sizeof(ccv_cnnp_cmd_exec_io_t) * input_size + sizeof(int) * output_size);
903 model_cmd_exec->super.isa = &ccv_cnnp_cmd_exec_isa;
904 model_cmd_exec->super.input_size = io_input_size;
905 model_cmd_exec->super.outputs = (ccv_nnc_tensor_symbol_t*)(model_cmd_exec + 1);
906 model_cmd_exec->super.output_size = io_output_size;
907 model_cmd_exec->super.is_trainable = is_trainable;
908 ccv_cnnp_model_copy_name(&model_cmd_exec->super, name);
909 model_cmd_exec->cmd = cmd;
910 model_cmd_exec->hint = hint;
911 model_cmd_exec->flags = flags;
912 model_cmd_exec->input_size = input_size;
913 model_cmd_exec->input_symbols = model_cmd_exec->super.outputs + io_output_size;
914 model_cmd_exec->output_symbols = model_cmd_exec->input_symbols + input_size;
915 model_cmd_exec->inputs = (ccv_cnnp_cmd_exec_io_t*)(model_cmd_exec->output_symbols + output_size);
916 if (input_size > 0)
917 {
918 memcpy(model_cmd_exec->inputs, inputs, sizeof(ccv_cnnp_cmd_exec_io_t) * input_size);
919 if (copy_io)
920 for (i = 0; i < input_size; i++)
921 if (inputs[i].type != CCV_CNNP_IO && inputs[i].init_state.copy)
922 model_cmd_exec->inputs[i].init_state.context = inputs[i].init_state.copy(inputs[i].init_state.context);
923 }
924 model_cmd_exec->output_size = output_size;
925 model_cmd_exec->outputs = (int*)(model_cmd_exec->inputs + input_size);
926 if (output_size > 0)
927 memcpy(model_cmd_exec->outputs, outputs, sizeof(int) * output_size);
928 return (ccv_cnnp_model_t*)model_cmd_exec;
929}
930
931ccv_cnnp_model_t* ccv_cnnp_cmd_exec(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, const ccv_cnnp_cmd_exec_io_t* const inputs, const int input_size, const int* const outputs, const int output_size, const int is_trainable, const char* const name)
932{
933 return _ccv_cnnp_cmd_exec(cmd, 0, hint, flags, inputs, input_size, outputs, output_size, is_trainable, name);
934}
935
936static ccv_cnnp_model_t* _ccv_cnnp_cmd_exec_copy(const ccv_cnnp_model_t* const super, void* const context)
937{
938 const ccv_cnnp_model_cmd_exec_t* const self = (const ccv_cnnp_model_cmd_exec_t*)super;
939 return _ccv_cnnp_cmd_exec(self->cmd, 1, self->hint, self->flags, self->inputs, self->input_size, self->outputs, self->output_size, self->super.is_trainable, self->super.name);
940}
941
942static void _ccv_cnnp_cmd_exec_io_copy(const ccv_nnc_tensor_symbol_t tensor_symbol, const ccv_cnnp_state_initializer_f initializer, void* const initializer_context, void* const context)
943{
944 initializer(initializer_context, CMD_DATA_TRANSFER_FORWARD()ccv_nnc_cmd(CCV_NNC_DATA_TRANSFER_FORWARD, 0, ccv_nnc_cmd_auto
, 0)
, ccv_nnc_no_hint, 0, (ccv_nnc_tensor_t*)context, tensor_symbol);
945}
946
947ccv_cnnp_cmd_exec_io_init_state_t ccv_cnnp_cmd_exec_io_copy(const ccv_nnc_tensor_t* const tensor)
948{
949 return (ccv_cnnp_cmd_exec_io_init_state_t){
950 .info = tensor->info,
951 .context = (void *)tensor,
952 .init = _ccv_cnnp_cmd_exec_io_copy,
953 };
954}
955
956typedef struct {
957 ccv_nnc_cmd_t cmd;
958 ccv_nnc_hint_t hint;
959 int flags;
960} ccv_cnnp_cmd_exec_io_set_by_t;
961
962static void _ccv_cnnp_cmd_exec_io_set_by(const ccv_nnc_tensor_symbol_t tensor_symbol, const ccv_cnnp_state_initializer_f initializer, void* const initializer_context, void* const context)
963{
964 const ccv_cnnp_cmd_exec_io_set_by_t* const set_by = (ccv_cnnp_cmd_exec_io_set_by_t*)context;
965 initializer(initializer_context, set_by->cmd, set_by->hint, set_by->flags, 0, tensor_symbol);
966}
967
968static void* _ccv_cnnp_cmd_exec_io_set_by_copy(void* const context)
969{
970 ccv_cnnp_cmd_exec_io_set_by_t* const set_by = (ccv_cnnp_cmd_exec_io_set_by_t*)ccmallocmalloc(sizeof(ccv_cnnp_cmd_exec_io_set_by_t));
971 memcpy(set_by, context, sizeof(ccv_cnnp_cmd_exec_io_set_by_t));
972 return set_by;
973}
974
975ccv_cnnp_cmd_exec_io_init_state_t ccv_cnnp_cmd_exec_io_set_by(const ccv_nnc_cmd_t cmd, const ccv_nnc_hint_t hint, const int flags, const ccv_nnc_tensor_param_t params)
976{
977 ccv_cnnp_cmd_exec_io_set_by_t* const set_by = (ccv_cnnp_cmd_exec_io_set_by_t*)ccmallocmalloc(sizeof(ccv_cnnp_cmd_exec_io_set_by_t));
978 set_by->cmd = cmd;
979 set_by->hint = hint;
980 set_by->flags = flags;
981 return (ccv_cnnp_cmd_exec_io_init_state_t){
982 .info = params,
983 .context = set_by,
984 .init = _ccv_cnnp_cmd_exec_io_set_by,
985 .copy = _ccv_cnnp_cmd_exec_io_set_by_copy,
986 .deinit = ccfreefree,
987 };
988}