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static char op_fname_inp[256] = "\0"; #if MULTI_LAYER_FRAMEWORK static char op_fname_out[MAX_LAYER][256] = { "\0", "\0"}; #else static char op_fname_out[256] = "\0"; #endif static int op_max_frm_num = 0; static int op_use_pic_signature = 0; static int op_clip_org_size = 0; static int op_bit_depth_output_cfg = 0; static int op_bit_depth_output = 0; #if CU_LEVEL_PRIVACY static int op_user_permission = 0; #endif

时间: 2025-07-21 07:57:10 浏览: 4
<think>我们有一组全局变量,其中一些使用了条件编译(取决于是否定义了MULTI_LAYER_FRAMEWORK和CU_LEVEL_PRIVACY)。这些变量用于存储配置信息,如输入文件名、输出文件名(多层框架下为每个层都有一个输出文件名)、最大帧数、是否使用图片签名、是否裁剪原始尺寸、输出位深度配置等。 接下来,我将解释这些变量,并展示一个可能的初始化函数或使用示例。由于这些变量是静态的(文件作用域),它们通常会在程序的配置解析部分(如main函数或配置读取函数)被设置。 注意:由于条件编译的存在,代码会根据预处理器定义的不同而编译不同的版本。 下面是一个示例,展示如何根据条件编译来初始化和使用这些变量。</think>## 全局配置变量设计详解 ### 代码分析与解释 ```c // 输入文件名缓冲区 static char op_fname_inp[256] = "\0"; // 输出文件名配置(条件编译) #if MULTI_LAYER_FRAMEWORK // 多层框架:为每个层分配文件名缓冲区 static char op_fname_out[MAX_LAYER][256] = { "\0", "\0"}; #else // 单层框架:单个输出文件名 static char op_fname_out[256] = "\0"; #endif // 通用配置参数 static int op_max_frm_num = 0; // 最大帧数限制 static int op_use_pic_signature = 0; // 是否使用图片签名 static int op_clip_org_size = 0; // 是否裁剪原始尺寸 static int op_bit_depth_output_cfg = 0; // 输出位深度配置 static int op_bit_depth_output = 0; // 实际输出位深度 // 权限控制(条件编译) #if CU_LEVEL_PRIVACY static int op_user_permission = 0; // 用户权限级别 #endif ``` ### 配置系统设计解析 #### 1. 文件名存储设计 ```mermaid graph TD A[输入文件] --> B[op_fname_inp] subgraph 输出文件 C{是否多层框架?} C -->|是| D[op_fname_out[MAX_LAYER]] C -->|否| E[op_fname_out] end ``` - **输入文件名**:固定256字节缓冲区,初始化为空字符串 - **输出文件名**: - 多层框架:二维数组 `[MAX_LAYER][256]`,每层独立文件名 - 单层框架:一维数组 `[256]`,单个输出文件 #### 2. 配置参数说明 | 变量名 | 类型 | 默认值 | 说明 | |--------|------|--------|------| | `op_max_frm_num` | int | 0 | 最大处理帧数(0表示无限制) | | `op_use_pic_signature` | int | 0 | 是否启用图片数字签名(0/1) | | `op_clip_org_size` | int | 0 | 是否裁剪为原始尺寸(0/1) | | `op_bit_depth_output_cfg` | int | 0 | 位深度配置(0=自动,1=8bit,2=10bit等) | | `op_bit_depth_output` | int | 0 | 实际输出位深度(运行时计算) | | `op_user_permission` | int | 0 | 用户权限级别(仅在`CU_LEVEL_PRIVACY`定义时存在) | ### 完整配置系统实现 ```c #include <stdio.h> #include <string.h> #include <stdlib.h> // 模拟配置宏 #define MULTI_LAYER_FRAMEWORK #define MAX_LAYER 3 #define CU_LEVEL_PRIVACY // 配置变量声明(通常放在头文件) static char op_fname_inp[256] = "\0"; #if MULTI_LAYER_FRAMEWORK static char op_fname_out[MAX_LAYER][256] = { "\0", "\0", "\0"}; #else static char op_fname_out[256] = "\0"; #endif static int op_max_frm_num = 0; static int op_use_pic_signature = 0; static int op_clip_org_size = 0; static int op_bit_depth_output_cfg = 0; static int op_bit_depth_output = 0; #if CU_LEVEL_PRIVACY static int op_user_permission = 0; #endif // 配置解析函数 void parse_config(const char* config_file) { FILE* fp = fopen(config_file, "r"); if (!fp) { perror("Failed to open config file"); return; } char line[256]; while (fgets(line, sizeof(line), fp)) { // 跳过注释和空行 if (line[0] == '#' || line[0] == '\n') continue; char key[64], value[192]; if (sscanf(line, "%63[^=]=%191s", key, value) == 2) { if (strcmp(key, "input") == 0) { strncpy(op_fname_inp, value, sizeof(op_fname_inp)-1); } #if MULTI_LAYER_FRAMEWORK else if (strncmp(key, "output_layer", 12) == 0) { int layer; if (sscanf(key, "output_layer%d", &layer) == 1 && layer >= 0 && layer < MAX_LAYER) { strncpy(op_fname_out[layer], value, sizeof(op_fname_out[0])-1); } } #else else if (strcmp(key, "output") == 0) { strncpy(op_fname_out, value, sizeof(op_fname_out)-1); } #endif else if (strcmp(key, "max_frames") == 0) { op_max_frm_num = atoi(value); } else if (strcmp(key, "use_signature") == 0) { op_use_pic_signature = (strcmp(value, "true") == 0) ? 1 : 0; } else if (strcmp(key, "clip_original") == 0) { op_clip_org_size = (strcmp(value, "true") == 0) ? 1 : 0; } else if (strcmp(key, "bit_depth") == 0) { op_bit_depth_output_cfg = atoi(value); } #if CU_LEVEL_PRIVACY else if (strcmp(key, "permission") == 0) { op_user_permission = atoi(value); } #endif } } fclose(fp); } // 打印当前配置 void print_config() { printf("===== Current Configuration =====\n"); printf("Input File: %s\n", op_fname_inp); #if MULTI_LAYER_FRAMEWORK printf("Output Files:\n"); for (int i = 0; i < MAX_LAYER; i++) { printf(" Layer %d: %s\n", i, op_fname_out[i]); } #else printf("Output File: %s\n", op_fname_out); #endif printf("Max Frames: %d\n", op_max_frm_num); printf("Use Signature: %s\n", op_use_pic_signature ? "Yes" : "No"); printf("Clip to Original Size: %s\n", op_clip_org_size ? "Yes" : "No"); printf("Bit Depth Config: %d\n", op_bit_depth_output_cfg); #if CU_LEVEL_PRIVACY printf("User Permission Level: %d\n", op_user_permission); #endif printf("=================================\n"); } // 应用配置到处理流程 void apply_config() { // 根据位深度配置计算实际输出位深度 if (op_bit_depth_output_cfg == 0) { // 自动模式:根据输入确定 op_bit_depth_output = 8; // 简化示例 } else { op_bit_depth_output = op_bit_depth_output_cfg; } // 权限检查 #if CU_LEVEL_PRIVACY if (op_user_permission < 1) { fprintf(stderr, "Error: Insufficient permissions\n"); exit(EXIT_FAILURE); } #endif printf("Configuration applied successfully\n"); } int main() { // 示例配置文件内容 const char* config = "input=video.hevc\n" "# Output configuration\n" "output_layer0=layer0.yuv\n" "output_layer1=layer1.yuv\n" "output_layer2=layer2.yuv\n" "max_frames=100\n" "use_signature=true\n" "clip_original=false\n" "bit_depth=10\n" "permission=2\n"; // 写入临时配置文件 FILE* fp = fopen("temp_config.cfg", "w"); fputs(config, fp); fclose(fp); // 解析并应用配置 parse_config("temp_config.cfg"); print_config(); apply_config(); // 清理 remove("temp_config.cfg"); return 0; } ``` ### 配置系统架构设计 ```mermaid graph TB subgraph 配置系统 A[配置文件] --> B[解析器] B --> C[全局配置变量] C --> D[应用初始化] D --> E[处理流程] end subgraph 运行时环境 E --> F{条件编译检查} F -->|MULTI_LAYER| G[多层处理] F -->|SINGLE_LAYER| H[单层处理] F -->|CU_LEVEL_PRIVACY| I[权限验证] end G --> J[输出文件生成] H --> J I --> K[安全操作] ``` ### 高级配置管理技巧 #### 1. 配置版本控制 ```c #define CONFIG_VERSION 2 struct config_header { int version; int checksum; size_t size; }; void save_config(const char* filename) { FILE* fp = fopen(filename, "wb"); if (!fp) return; struct config_header header = { .version = CONFIG_VERSION, .checksum = 0, // 实际计算校验和 .size = sizeof(op_fname_inp) + #if MULTI_LAYER_FRAMEWORK sizeof(op_fname_out) + #else sizeof(op_fname_out) + #endif sizeof(op_max_frm_num) + sizeof(op_use_pic_signature) + sizeof(op_clip_org_size) + sizeof(op_bit_depth_output_cfg) + sizeof(op_bit_depth_output) + #if CU_LEVEL_PRIVACY sizeof(op_user_permission) #else 0 #endif }; fwrite(&header, sizeof(header), 1, fp); // 写入各配置变量... fclose(fp); } ``` #### 2. 环境变量覆盖 ```c void load_env_overrides() { const char* env; if ((env = getenv("INPUT_FILE")) != NULL) { strncpy(op_fname_inp, env, sizeof(op_fname_inp)-1); } #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { char env_name[32]; snprintf(env_name, sizeof(env_name), "OUTPUT_LAYER%d", i); if ((env = getenv(env_name)) != NULL) { strncpy(op_fname_out[i], env, sizeof(op_fname_out[0])-1); } } #else if ((env = getenv("OUTPUT_FILE")) != NULL) { strncpy(op_fname_out, env, sizeof(op_fname_out)-1); } #endif // 其他参数类似处理... } ``` #### 3. 配置验证函数 ```c int validate_config() { // 检查输入文件是否设置 if (op_fname_inp[0] == '\0') { fprintf(stderr, "Error: Input file not specified\n"); return 0; } #if MULTI_LAYER_FRAMEWORK // 检查至少一层输出文件 int has_output = 0; for (int i = 0; i < MAX_LAYER; i++) { if (op_fname_out[i][0] != '\0') { has_output = 1; break; } } if (!has_output) { fprintf(stderr, "Error: No output files specified\n"); return 0; } #else if (op_fname_out[0] == '\0') { fprintf(stderr, "Error: Output file not specified\n"); return 0; } #endif // 检查位深度配置 if (op_bit_depth_output_cfg < 0 || op_bit_depth_output_cfg > 16) { fprintf(stderr, "Error: Invalid bit depth configuration\n"); return 0; } #if CU_LEVEL_PRIVACY // 检查权限级别 if (op_user_permission < 0 || op_user_permission > 3) { fprintf(stderr, "Error: Invalid permission level\n"); return 0; } #endif return 1; } ``` ### 配置系统最佳实践 ```mermaid graph LR A[配置来源] --> B[默认值] A --> C[配置文件] A --> D[环境变量] A --> E[命令行参数] B & C & D & E --> F[配置解析器] F --> G[配置验证] G --> H[有效配置] G --> I[无效配置] H --> J[应用配置] I --> K[错误处理] ```
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unsigned char * bs_buf5 = NULL; #endif #if MULTI_LAYER_FRAMEWORK DEC id[MAX_LAYER] = { NULL, NULL }; DEC_CDSC cdsc[MAX_LAYER]; #else DEC id = NULL; DEC_CDSC cdsc; #endif COM_BITB bitb; COM_IMGB * imgb; DEC_STAT stat; int ret; COM_CLK clk_beg, clk_tot; int bs_cnt, pic_cnt; int bs_size = 0, bs_read_pos = 0; int width, height; FILE * fp_bs = NULL; #if LIB_PIC_ERR_TOL int libpic_num_patch = 0; int *libpic_bs_size = NULL; unsigned char * libpic_bs_buf = NULL; int num_nalu = 0; int collect_libpic_state = 0; unsigned int libpic_addr = 0; #endif #if SVAC_AI_SEG_EXT fp_seg = fopen("dec_aiseg.bin", "wb"); #endif #if ENC_DEC_TRACE fp_trace = NULL; #endif #if CU_LEVEL_PRIVACY unsigned int libpic_addr_privacy = 0; int *libpic_bs_size_privacy = NULL; unsigned char * libpic_bs_buf_privacy = NULL; int num_nalu_privacy = 0; #endif #if LINUX signal(SIGSEGV, handler); // install our handler #endif #if DECODING_TIME_TEST clk_beg = com_clk_get(); #endif /* parse options */ ret = com_args_parse_all(argc, argv, options); if(ret != 0) { if(ret > 0) v0print("-%c argument should be set\n", ret); print_usage(); return -1; } /* open input bitstream */ fp_bs = fopen(op_fname_inp, "rb"); if(fp_bs == NULL) { v0print("ERROR: cannot open bitstream file = %s\n", op_fname_inp); print_usage(); return -1; } if(op_flag[OP_FLAG_FNAME_OUT]) { /* remove decoded file contents if exists */ FILE * fp; #if MULTI_LAYER_FRAMEWORK fp = fopen(op_fname_out[0], "wb"); #else fp = fopen(op_fname_out, "wb"); #endif if(fp == NULL) { v0print("ERROR: cannot create a decoded file\n"); print_usage(); return -1; } fclose(fp); } #if MULTI_LAYER_FRAMEWORK if (op_flag[OP_FLAG_FNAME_OUT1]) { /* remove decoded file contents if exists */ FILE* fp; fp = fopen(op_fname_out[1], "wb"); if (fp == NULL) { v0print("ERROR: cannot create a EL decoded file\n"); print_usage(); return -1; } fclose(fp); } #endif bs_buf = malloc(MAX_BS_BUF); if(bs_buf == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } bs_buf2 = malloc(MAX_BS_BUF); if (bs_buf2 == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } #if SVAC_UD_MD5_STREAM bs_buf5 = malloc(MAX_BS_BUF); if (bs_buf5 == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } bitb.addr3 = bs_buf5; bitb.addr3_beg = bs_buf5; bs_buf4 = malloc(MAX_BS_BUF); if (bs_buf4 == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } unsigned char * buf4_p = bs_buf4; unsigned char * buf4_beg = bs_buf4; #endif #if CU_LEVEL_PRIVACY unsigned char *bs_buf3 = malloc(MAX_BS_BUF); if (bs_buf3 == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } #endif #if LIBVC_ON #if MULTI_LAYER_FRAMEWORK LibVCData libvc_data[MAX_LAYER]; for (int i = 0; i < MAX_LAYER; i++) { init_libvcdata(&libvc_data[i]); } #else LibVCData libvc_data; init_libvcdata(&libvc_data); #endif if (op_flag[OP_FLAG_FNAME_INP_LIBPICS]) { #if MULTI_LAYER_FRAMEWORK int err; for (int i = 0; i < MAX_LAYER; i++) { err = decode_libpics(&cdsc[i], &libvc_data[i]); if (err) { v0print("Error when decode lib pic!"); return -1; } libvc_data[i].is_libpic_prepared = 1; } #else int err = decode_libpics(&cdsc, &libvc_data); if (err) { v0print("Error when decode lib pic!"); return -1; } libvc_data.is_libpic_prepared = 1; #endif } #endif #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { id[i] = dec_create(&cdsc[i], NULL); dec_setlayer(id[i],i); if (id[i] == NULL) { v0print("ERROR: cannot create decoder\n"); return -1; } } #else id = dec_create(&cdsc, NULL); if(id == NULL) { v0print("ERROR: cannot create decoder\n"); return -1; } #endif #if LIBVC_ON #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { set_livcdata_dec(id[i], &libvc_data[i]); } #else set_livcdata_dec(id, &libvc_data); #endif #endif #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { if (set_extra_config(id[i])) { v0print("ERROR: cannot set extra configurations\n"); return -1; } } #else if (set_extra_config(id)) { v0print("ERROR: cannot set extra configurations\n"); return -1; } #endif pic_cnt = 0; clk_tot = 0; bs_cnt = 0; width = height = 0; #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { g_CountDOICyCleTime[i] = 0; // initialized the number . g_DOIPrev[i] = 0; } #else g_CountDOICyCleTime = 0; // initialized the number . g_DOIPrev = 0; #endif #if CU_LEVEL_PRIVACY int bs_buf3_size = 0; #endif while (1) { #if MULTI_LAYER_FRAMEWORK int nal_layer_id = 0; #endif if (state == STATE_DECODING) { bs_size = read_a_bs(fp_bs, &bs_read_pos, bs_buf, bs_buf2 #if CU_LEVEL_PRIVACY , bs_buf3, &bs_buf3_size #endif #if SVAC_UD_MD5_STREAM , &bitb #endif ); if (bs_size <= 0) { state = STATE_BUMPING; v1print("bumping process starting...\n"); continue; } bs_read_pos += bs_size; bitb.addr = bs_buf; #if CU_LEVEL_PRIVACY bitb.addr2 = bs_buf3; bitb.ssize2 = bs_buf3_size; bitb.ssize = bs_size - bs_buf3_size; #else bitb.ssize = bs_size; #endif bitb.bsize = MAX_BS_BUF; #if MULTI_LAYER_FRAMEWORK nal_layer_id = (bs_buf[4] >> 1) & 0x07; if (nal_layer_id == 0) { v1print("BL---[%4d]-th BS (%07dbytes) --> ", bs_cnt++, bs_size); } else { v1print("EL---[%4d]-th BS (%07dbytes) --> ", bs_cnt++, bs_size); } #else v1print("[%4d]-th BS (%07dbytes) --> ", bs_cnt++, bs_size); #endif #if LIB_PIC_ERR_TOL #if SVAC_PROGRESSIVE // 7 6 5 4 3 & 2 // temporal_nesting library_picture_enable library_stream duplicate_sqh library_picture_mode_index int library_picture_enable = bs_buf[7] & 0x40; int library_stream_flag = bs_buf[7] & 0x20; int library_picture_mode_index = (bs_buf[7] >> 2) & 3; #else // 7 6 5 4 3 2 1 & 0 // progressive_sequence field_coded_sequence temporal_nesting library_picture_enable library_stream duplicate_sqh library_picture_mode_index int library_picture_enable = bs_buf[7] & 0x10; int library_picture_enable_flag = bs_buf[7] & 8; #if EMULATION_PREVENT_BUGFIX int library_picture_mode_index = bs_buf[7] & 3; #else int library_picture_mode_index = ((bs_buf[7] & 1) << 1) + (bs_buf[8] >> 7); #endif #endif if (bs_buf[3] == SVAC_SPS && library_stream_flag && library_picture_mode_index == 0) { if (libpic_bs_buf == NULL) { libpic_bs_buf = malloc(MAX_BS_BUF); if (libpic_bs_buf == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } } if (libpic_bs_size == NULL) { libpic_bs_size = malloc(sizeof(int) * 260); if (libpic_bs_size == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } } #if CU_LEVEL_PRIVACY if (libpic_bs_buf_privacy == NULL) { libpic_bs_buf_privacy = malloc(MAX_BS_BUF); if (libpic_bs_buf_privacy == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } } if (libpic_bs_size_privacy == NULL) { libpic_bs_size_privacy = malloc(sizeof(int) * 260); if (libpic_bs_size_privacy == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } } num_nalu_privacy = 0; #endif memcpy(libpic_bs_buf, bs_buf, bs_real_size); state = STATE_LIBPIC_COLLECTING; libpic_bs_size[0] = bs_real_size; collect_libpic_state = 1; num_nalu = 1; libpic_addr = bs_real_size; #if SVAC_UD_MD5_STREAM unsigned int tmp_bs_size = (unsigned int)((unsigned char *)bitb.addr3 - (unsigned char *)bitb.addr3_beg); memcpy(buf4_p, bitb.addr3_beg, tmp_bs_size); buf4_p = buf4_p + tmp_bs_size; bitb.addr3 = (unsigned char *)bitb.addr3_beg; #endif continue; } else if (bs_buf[3] == SVAC_SPS && library_picture_enable && library_stream_flag == 0 && library_picture_mode_index == 0) { libpic_addr = 0; #if CU_LEVEL_PRIVACY libpic_addr_privacy = 0; #endif #if SVAC_UD_MD5_STREAM bitb.addr3_beg = buf4_beg; buf4_beg = bs_buf5; unsigned char * tmp_p = buf4_p; buf4_p = bitb.addr3; bitb.addr3 = tmp_p; #endif for (int i = 0; i < num_nalu; i++) { #if !DECODING_TIME_TEST clk_beg = com_clk_get(); #endif /* main decoding block */ bitb.addr = libpic_bs_buf + libpic_addr; bitb.ssize = libpic_bs_size[i] - 4; bitb.bsize = MAX_BS_BUF; libpic_addr += libpic_bs_size[i]; #if CU_LEVEL_PRIVACY if (i >= num_nalu - num_nalu_privacy) { bitb.addr2 = libpic_bs_buf_privacy + libpic_addr_privacy; bitb.ssize2 = libpic_bs_size_privacy[i - num_nalu + num_nalu_privacy] + 4 ; libpic_addr_privacy += libpic_bs_size_privacy[i - num_nalu + num_nalu_privacy]; } #endif #if MULTI_LAYER_FRAMEWORK ret = dec_cnk((DEC_CTX*)id[0], &bitb, &stat); #else ret = dec_cnk((DEC_CTX*)id, &bitb, &stat); #endif if (stat.ctype == COM_CT_SEQ_END) { state = STATE_BUMPING; v1print("bumping process starting...\n"); continue; } if (COM_FAILED(ret)) { v0print("failed to decode bitstream\n"); return -1; } #if !DECODING_TIME_TEST clk_tot += com_clk_from(clk_beg); #endif print_stat(&stat, ret); } #if SVAC_UD_MD5_STREAM bitb.addr3_beg = buf4_beg; bitb.addr3 = buf4_p; buf4_beg = bs_buf4; buf4_p = bs_buf4; #endif num_nalu = 0; libpic_addr = 0; bitb.addr = bs_buf; bitb.ssize = bs_size; bitb.bsize = MAX_BS_BUF; } #endif #if !DECODING_TIME_TEST clk_beg = com_clk_get(); #endif /* main decoding block */ #if MULTI_LAYER_FRAMEWORK if (nal_layer_id == 0) { DEC_CTX* ctx_t = (DEC_CTX*)id[0]; ctx_t->ctx_e = id[1]; ret = dec_cnk((DEC_CTX*)id[0], &bitb, &stat); } else { DEC_CTX* ctx_e = (DEC_CTX*)id[nal_layer_id]; assert(ctx_e->layer_id == nal_layer_id); if (!ctx_e->init_sps_pps) { DEC_CTX* ctx_b = (DEC_CTX*)id[0]; ctx_e->ctx_b =id[ctx_b->info.sqh.ref_layer_id[nal_layer_id]]; COM_SQH* sqh_e = &ctx_e->info.sqh; COM_SQH* sqh_b = &ctx_b->info.sqh; memcpy(sqh_e, sqh_b, sizeof(COM_SQH)); COM_PIC_PARA_SET* pps_e = &ctx_e->info.pps[0]; memcpy(pps_e, ctx_b->info.pps, sizeof(COM_PIC_PARA_SET)); #if LIBVC_ON ctx_e->dpm.libvc_data->is_libpic_processing = sqh_b->library_stream_flag; ctx_e->dpm.libvc_data->library_picture_enable_flag = sqh_b->library_picture_enable_flag; #endif #if HIGH_LEVEL_PRIVACY COM_PRIVACY* pri_e = &ctx_e->ctx_privacy_data; COM_PRIVACY* pri_b = &ctx_b->ctx_privacy_data; memcpy(pri_e, pri_b, sizeof(COM_PRIVACY)); #endif } ret = dec_cnk((DEC_CTX*)id[nal_layer_id], &bitb, &stat); } #else ret = dec_cnk((DEC_CTX*)id, &bitb, &stat); #endif if (stat.ctype == COM_CT_SEQ_END) { state = STATE_BUMPING; v1print("bumping process starting...\n"); continue; } if (COM_FAILED(ret)) { v0print("failed to decode bitstream\n"); return -1; } #if !DECODING_TIME_TEST clk_tot += com_clk_from(clk_beg); #endif print_stat(&stat, ret); } #if LIB_PIC_ERR_TOL if (state == STATE_LIBPIC_COLLECTING) { bs_size = read_a_bs(fp_bs, &bs_read_pos, bs_buf, bs_buf2 #if CU_LEVEL_PRIVACY , bs_buf3, &bs_buf3_size #endif #if SVAC_UD_MD5_STREAM , &bitb #endif ); if (bs_size <= 0) { state = STATE_BUMPING; v1print("bumping process starting...\n"); continue; } bs_read_pos += bs_size; bitb.addr = bs_buf; bitb.ssize = bs_size; bitb.bsize = MAX_BS_BUF; #if CU_LEVEL_PRIVACY bitb.addr2 = bs_buf3; bitb.ssize2 = bs_buf3_size; #endif v1print("[%4d]-th BS (%07dbytes) --> ", bs_cnt++, bs_size); if (bs_buf[3] == SVAC_PPS && collect_libpic_state == 1) { memcpy(libpic_bs_buf + libpic_addr, bs_buf, bs_real_size); libpic_bs_size[num_nalu++] = bs_real_size; collect_libpic_state = 2; libpic_addr += bs_real_size; #if SVAC_UD_MD5_STREAM unsigned int tmp_bs_size = (unsigned int)((unsigned char *)bitb.addr3 - (unsigned char *)bitb.addr3_beg); memcpy(buf4_p, bitb.addr3_beg, tmp_bs_size); buf4_p = buf4_p + tmp_bs_size; bitb.addr3 = (unsigned char *)bitb.addr3_beg; #endif continue; } else if (bs_buf[3] == SVAC_PH && collect_libpic_state == 2) { memcpy(libpic_bs_buf + libpic_addr, bs_buf, bs_real_size); libpic_bs_size[num_nalu++] = bs_real_size; collect_libpic_state = 3; libpic_addr += bs_real_size; #if SVAC_UD_MD5_STREAM unsigned int tmp_bs_size = (unsigned int)((unsigned char *)bitb.addr3 - (unsigned char *)bitb.addr3_beg); memcpy(buf4_p, bitb.addr3_beg, tmp_bs_size); buf4_p = buf4_p + tmp_bs_size; bitb.addr3 = (unsigned char *)bitb.addr3_beg; #endif continue; } else if (bs_buf[3] == SVAC_CRR_DL) { memcpy(libpic_bs_buf + libpic_addr, bs_buf, bs_real_size); libpic_bs_size[num_nalu++] = bs_real_size; assert(collect_libpic_state == 3 || collect_libpic_state == 4); libpic_addr += bs_real_size; #if SVAC_UD_MD5_STREAM unsigned int tmp_bs_size = (unsigned int)((unsigned char *)bitb.addr3 - (unsigned char *)bitb.addr3_beg); memcpy(buf4_p, bitb.addr3_beg, tmp_bs_size); buf4_p = buf4_p + tmp_bs_size; bitb.addr3 = (unsigned char *)bitb.addr3_beg; #endif #if CU_LEVEL_PRIVACY memcpy(libpic_bs_buf_privacy + libpic_addr_privacy, bs_buf3, bs_buf3_size); libpic_bs_size_privacy[num_nalu_privacy++] = bs_buf3_size; libpic_addr_privacy += bs_buf3_size; #endif if (collect_libpic_state == 3) collect_libpic_state = 4; else if (collect_libpic_state == 4) { state = STATE_DECODING; } continue; } else if (bs_buf[3] == SVAC_CRR_L) { memcpy(libpic_bs_buf + libpic_addr, bs_buf, bs_real_size); libpic_bs_size[num_nalu++] = bs_real_size; assert(collect_libpic_state == 3 || collect_libpic_state == 4); libpic_addr += bs_real_size; #if CU_LEVEL_PRIVACY memcpy(libpic_bs_buf_privacy + libpic_addr_privacy, bs_buf3, bs_buf3_size); libpic_bs_size_privacy[num_nalu_privacy++] = bs_buf3_size; libpic_addr_privacy += bs_buf3_size; #endif #if SVAC_UD_MD5_STREAM unsigned int tmp_bs_size = (unsigned int)((unsigned char *)bitb.addr3 - (unsigned char *)bitb.addr3_beg); memcpy(buf4_p, bitb.addr3_beg, tmp_bs_size); buf4_p = buf4_p + tmp_bs_size; bitb.addr3 = (unsigned char *)bitb.addr3_beg; #endif if (collect_libpic_state == 3) { state = STATE_DECODING; } continue; } else if (bs_buf[3] == SVAC_SPS && (bs_buf[7] & 16) && (bs_buf[7] & 8) == 0) { v0print("ERROR: not recieve enough libpic NALU\n"); return -1; } #if !DECODING_TIME_TEST clk_beg = com_clk_get(); #endif /* main decoding block */ #if MULTI_LAYER_FRAMEWORK ret = dec_cnk((DEC_CTX*)id[0], &bitb, &stat); #else ret = dec_cnk((DEC_CTX*)id, &bitb, &stat); #endif if (stat.ctype == COM_CT_SEQ_END) { state = STATE_BUMPING; v1print("bumping process starting...\n"); continue; } if (COM_FAILED(ret)) { v0print("failed to decode bitstream\n"); return -1; } #if !DECODING_TIME_TEST clk_tot += com_clk_from(clk_beg); #endif print_stat(&stat, ret); } #endif if (stat.fnum >= 0 || state == STATE_BUMPING) { #if MULTI_LAYER_FRAMEWORK ret = dec_pull_frm((DEC_CTX*)id[nal_layer_id], &imgb, state); #else ret = dec_pull_frm((DEC_CTX*)id, &imgb, state); #endif if (ret == COM_ERR_UNEXPECTED) { v1print("bumping process completed\n"); if (bs_size <= 0) { goto END; } else { state = STATE_DECODING; } } else if (COM_FAILED(ret)) { v0print("failed to pull the decoded image\n"); return -1; } } else { imgb = NULL; } if (imgb) { width = imgb->width[0]; height = imgb->height[0]; #if LIB_PIC_MIXBIN #if MULTI_LAYER_FRAMEWORK if (!((DEC_CTX*)id[0])->info.sqh.library_stream_flag #else if (!((DEC_CTX*)id)->info.sqh.library_stream_flag #endif #if LIBPIC_DISPLAY #if MULTI_LAYER_FRAMEWORK || (((DEC_CTX*)id[0])->info.sqh.library_stream_flag && ((DEC_CTX*)id[0])->info.sqh.library_picture_mode_index == 1) #else || (((DEC_CTX*)id)->info.sqh.library_stream_flag && ((DEC_CTX*)id)->info.sqh.library_picture_mode_index == 1) #endif #endif ) { #endif #if MULTI_LAYER_FRAMEWORK if (op_flag[OP_FLAG_FNAME_OUT] && nal_layer_id == 0) { write_dec_img(id[0], op_fname_out[0], imgb, ((DEC_CTX*)id[0])->info.bit_depth_internal); } if (op_flag[OP_FLAG_FNAME_OUT1] && nal_layer_id == 1) { write_dec_img(id[1], op_fname_out[1], imgb, ((DEC_CTX*)id[1])->info.bit_depth_internal); } #else if (op_flag[OP_FLAG_FNAME_OUT]) { write_dec_img(id, op_fname_out, imgb, ((DEC_CTX*)id)->info.bit_depth_internal); } #endif imgb->release(imgb); pic_cnt++; #if LIB_PIC_MIXBIN } else if (op_flag[OP_FLAG_FNAME_OUT_LIBPICS]) { #if MULTI_LAYER_FRAMEWORK write_dec_img(id[0], op_fname_out_libpics, imgb, ((DEC_CTX*)id[0])->info.bit_depth_internal); #else write_dec_img(id, op_fname_out_libpics, imgb, ((DEC_CTX*)id)->info.bit_depth_internal); #endif } #endif } } END: #if DECODING_TIME_TEST clk_tot += com_clk_from(clk_beg); #endif v1print("===========================================================\n"); v1print("Resolution (decoding) = %d x %d\n", width, height); #if MULTI_LAYER_FRAMEWORK v1print("BL Resolution (output) = %d x %d\n", ((DEC_CTX*)id[0])->info.sqh.horizontal_size[0], ((DEC_CTX*)id[0])->info.sqh.vertical_size[0]); v1print("EL1 Resolution (output) = %d x %d\n", ((DEC_CTX*)id[0])->info.sqh.horizontal_size[1], ((DEC_CTX*)id[0])->info.sqh.vertical_size[1]); #else v1print("Resolution (output) = %d x %d\n", ((DEC_CTX *)id)->info.sqh.horizontal_size, ((DEC_CTX *)id)->info.sqh.vertical_size); #endif v1print("Processed BS count = %d\n", bs_cnt); v1print("Decoded frame count = %d\n", pic_cnt); if(pic_cnt > 0) { v1print("total decoding time = %d msec,", (int)com_clk_msec(clk_tot)); v1print(" %.3f sec\n", (float)(com_clk_msec(clk_tot) /1000.0)); v1print("Average decoding time for a frame = %d msec\n", (int)com_clk_msec(clk_tot)/pic_cnt); v1print("Average decoding speed = %.3f frames/sec\n", ((float)pic_cnt*1000)/((float)com_clk_msec(clk_tot))); } v1print("===========================================================\n"); if (op_flag[OP_FLAG_USE_PIC_SIGN] && pic_cnt > 0) { v1print("Decode Match: 1 (HASH)\n"); #if SVAC_UD_MD5_STREAM #if MULTI_LAYER_FRAMEWORK if (((DEC_CTX *)id[0])->stream_sign_check_flag) #else if (((DEC_CTX *)id)->stream_sign_check_flag) #endif v1print("Stream Decode Match: 1 (HASH)\n"); #endif v1print("===========================================================\n"); } #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { if (id[i]) dec_delete(id[i]); } #else if(id) dec_delete(id); #endif if(fp_bs) fclose(fp_bs); if(bs_buf) free(bs_buf); if (bs_buf2) { free(bs_buf2); bs_buf2 = NULL; } #if CU_LEVEL_PRIVACY if (bs_buf3) { free(bs_buf3); bs_buf3 = NULL; } #endif #if SVAC_UD_MD5_STREAM if (bs_buf5) { free(bs_buf5); bs_buf5 = NULL; } if (bs_buf4) { free(bs_buf4); bs_buf4 = NULL; } #endif #if LIBVC_ON #if MULTI_LAYER_FRAMEWORK for (int i = 0; i < MAX_LAYER; i++) { delete_libvcdata(&libvc_data[i]); } #else delete_libvcdata(&libvc_data); #endif #endif #if ENC_DEC_TRACE if( fp_trace ) { fclose( fp_trace ); fp_trace = NULL; } #endif #if SVAC_AI_SEG_EXT if (fp_seg) { fclose(fp_seg); fp_seg = NULL; } #endif return 0; }对这段代码做注释

int decode_libpics(DEC_CDSC * cdsc, LibVCData* libvc_data) { STATES state_lib = STATE_DECODING; unsigned char * bs_buf_lib = NULL; unsigned char * bs_buf_lib2 = NULL; DEC id_lib = NULL; COM_BITB bitb_lib; COM_IMGB * imgb_lib; DEC_STAT stat_lib; int ret; COM_CLK clk_beg_lib, clk_tot_lib; int bs_cnt_lib, pic_cnt_lib; int bs_size_lib, bs_read_pos_lib = 0; int width_lib, height_lib; FILE * fp_bs_lib = NULL; #if LINUX signal(SIGSEGV, handler); // install our handler #endif #if DECODING_TIME_TEST clk_beg_lib = com_clk_get(); #endif /* open input bitstream */ fp_bs_lib = fopen(op_fname_inp_libpics, "rb"); if (fp_bs_lib == NULL) { v0print("ERROR: cannot open libpics bitstream file = %s\n", op_fname_inp_libpics); print_usage(); return -1; } if (op_flag[OP_FLAG_FNAME_OUT_LIBPICS]) { /* remove decoded file contents if exists */ FILE * fp; fp = fopen(op_fname_out_libpics, "wb"); if (fp == NULL) { v0print("ERROR: cannot create a decoded libpics file\n"); print_usage(); return -1; } fclose(fp); } bs_buf_lib = malloc(MAX_BS_BUF); if (bs_buf_lib == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } bs_buf_lib2 = malloc(MAX_BS_BUF); if (bs_buf_lib2 == NULL) { v0print("ERROR: cannot allocate bit buffer, size2=%d\n", MAX_BS_BUF); return -1; } #if CU_LEVEL_PRIVACY unsigned char *bs_buf_lib3 = malloc(MAX_BS_BUF); if (bs_buf_lib3 == NULL) { v0print("ERROR: cannot allocate bit buffer, size2=%d\n", MAX_BS_BUF); return -1; } #endif #if SVAC_UD_MD5_STREAM unsigned char * bs_buf4 = NULL; unsigned char * bs_buf5 = NULL; bs_buf5 = malloc(MAX_BS_BUF); if (bs_buf5 == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } bitb_lib.addr3 = bs_buf5; bitb_lib.addr3_beg = bs_buf5; bs_buf4 = malloc(MAX_BS_BUF); if (bs_buf4 == NULL) { v0print("ERROR: cannot allocate bit buffer, size=%d\n", MAX_BS_BUF); return -1; } unsigned char * buf4_p = bs_buf4; unsigned char * buf4_beg = bs_buf4; #endif id_lib = dec_create(cdsc, NULL); if (id_lib == NULL) { v0print("ERROR: cannot create decoder\n"); return -1; } set_livcdata_dec(id_lib, libvc_data); if (set_extra_config(id_lib)) { v0print("ERROR: cannot set extra configurations\n"); return -1; } pic_cnt_lib = 0; clk_tot_lib = 0; bs_cnt_lib = 0; width_lib = height_lib = 0; #if CU_LEVEL_PRIVACY int bs_buf3_size = 0; #endif while (1) { if (state_lib == STATE_DECODING) { bs_size_lib = read_a_bs(fp_bs_lib, &bs_read_pos_lib, bs_buf_lib, bs_buf_lib2 #if CU_LEVEL_PRIVACY , bs_buf_lib3, &bs_buf3_size #endif #if SVAC_UD_MD5_STREAM , &bitb_lib #endif ); if (bs_size_lib <= 0) { state_lib = STATE_BUMPING; v1print("bumping process starting...\n"); continue; } bs_read_pos_lib += (bs_size_lib); bitb_lib.addr = bs_buf_lib; bitb_lib.ssize = bs_size_lib; bitb_lib.bsize = MAX_BS_BUF; #if CU_LEVEL_PRIVACY bitb_lib.addr2 = bs_buf_lib3; bitb_lib.ssize2 = bs_buf3_size; #endif #if !DECODING_TIME_TEST clk_beg_lib = com_clk_get(); #endif /* main decoding block */ ret = dec_cnk((DEC_CTX *)id_lib, &bitb_lib, &stat_lib); if (COM_FAILED(ret)) { v0print("failed to decode bitstream\n"); return -1; } #if !DECODING_TIME_TEST clk_tot_lib += com_clk_from(clk_beg_lib); #endif } if (stat_lib.fnum >= 0 || state_lib == STATE_BUMPING) { ret = dec_pull_frm((DEC_CTX *)id_lib, &imgb_lib, state_lib); if (ret == COM_ERR_UNEXPECTED) { v1print("bumping process completed\n"); goto END; } else if (COM_FAILED(ret)) { v0print("failed to pull the decoded image\n"); return -1; } } else { imgb_lib = NULL; } if (imgb_lib) { width_lib = imgb_lib->width[0]; height_lib = imgb_lib->height[0]; if (op_flag[OP_FLAG_FNAME_OUT_LIBPICS]) { write_dec_img(id_lib, op_fname_out_libpics, imgb_lib, ((DEC_CTX *)id_lib)->info.bit_depth_internal); } imgb_lib->release(imgb_lib); pic_cnt_lib++; } } END: #if DECODING_TIME_TEST clk_tot_lib += com_clk_from(clk_beg_lib); #endif libvc_data->num_lib_pic = pic_cnt_lib; #if CU_LEVEL_PRIVACY if (bs_buf_lib3) { free(bs_buf_lib3); bs_buf_lib3 = NULL; } #endif if (id_lib) dec_delete(id_lib); if (fp_bs_lib) fclose(fp_bs_lib); if (bs_buf_lib) free(bs_buf_lib); return 0; } #endif

static COM_ARGS_OPTION options[] = \ { { 'i', "input", ARGS_TYPE_STRING | ARGS_TYPE_MANDATORY, &op_flag[OP_FLAG_FNAME_INP], op_fname_inp, "file name of input bitstream"}, #if MULTI_LAYER_FRAMEWORK { 'o', "output", ARGS_TYPE_STRING, &op_flag[OP_FLAG_FNAME_OUT], op_fname_out[0],"file name of decoded output" }, { 'e', "output1", ARGS_TYPE_STRING, &op_flag[OP_FLAG_FNAME_OUT1], op_fname_out[1],"file name of decoded output" }, #else { 'o', "output", ARGS_TYPE_STRING, &op_flag[OP_FLAG_FNAME_OUT], op_fname_out,"file name of decoded output" }, #endif { 'f', "frames", ARGS_TYPE_INTEGER, &op_flag[OP_FLAG_MAX_FRM_NUM], &op_max_frm_num,"maximum number of frames to be decoded" }, { 's', "signature", COM_ARGS_VAL_TYPE_NONE, &op_flag[OP_FLAG_USE_PIC_SIGN], &op_use_pic_signature,"conformance check using picture signature (HASH)" }, { 'c', "clip_org_size", COM_ARGS_VAL_TYPE_NONE, &op_flag[OP_FLAG_CLIP_ORG_SIZE], &op_clip_org_size,"clip the output size to the original size (the input size at the encoder side)" }, { COM_ARGS_NO_KEY, "output_bit_depth", ARGS_TYPE_INTEGER, &op_flag[OP_FLAG_OUT_BIT_DEPTH], &op_bit_depth_output_cfg, "output bitdepth : 8 / 10 (default: internal bitdepth)" }, #if CU_LEVEL_PRIVACY { 'u', "user_permission", ARGS_TYPE_INTEGER , &op_flag[OP_USER_PERMISSION], &op_user_permission, "decoder user permission: 0 / 1 (default: 0)" }, #endif { 'v', "verbose", ARGS_TYPE_INTEGER, &op_flag[OP_FLAG_VERBOSE], &op_verbose, "verbose level\n" "\t 0: no message\n" "\t 1: frame-level messages (default)\n" "\t 2: all messages\n" }, #if LIBVC_ON { COM_ARGS_NO_KEY, "input_libpics", ARGS_TYPE_STRING, &op_flag[OP_FLAG_FNAME_INP_LIBPICS], op_fname_inp_libpics, "file name of input libpics bitstream" }, { COM_ARGS_NO_KEY, "output_libpics", ARGS_TYPE_STRING, &op_flag[OP_FLAG_FNAME_OUT_LIBPICS], op_fname_out_libpics, "file name of decoded libpics output" }, #endif { 0, "", COM_ARGS_VAL_TYPE_NONE, NULL, NULL, "" } /* termination */ };

这个代码报错 File "D:\pycharm project\jiyi\5133.py", line 31, in chinese_seg ltp = LTP() File "D:\python 3.10.10\lib\site-packages\ltp\interface.py", line 117, in LTP raise FileNotFoundError(f"{CONFIG_NAME} not found in {model_id}") FileNotFoundError: config.json not found in LTP/small # -*- coding: utf-8 -*- import numpy as np import torch from ltp import LTP from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from torch.utils.data import Dataset, DataLoader # ==================== # 1. 数据预处理 # ==================== # 假设数据已加载为dataframe格式 import pandas as pd import pandas as pd data = pd.read_csv("D:\pycharm project\jiyi\douban2.csv",encoding='iso-8859-1') # 包含text和label两列 # 分词函数(只能用LTP) def chinese_seg(text, tool="ltp"): if tool == "ltp": ltp = LTP() seg, _ = ltp.seg([text]) return ' '.join(seg[0]) # 全量数据分词处理 data['seg_text'] = data['text'].apply(lambda x: chinese_seg(x, tool="ltp")) # ==================== # 2. TF-IDF向量化 # ==================== vectorizer = TfidfVectorizer(max_features=3000) # 控制特征维度[^3] tfidf_matrix = vectorizer.fit_transform(data['seg_text']) # 转换为PyTorch张量 X = torch.from_numpy(tfidf_matrix.toarray()).float() y = torch.from_numpy(data['label'].values).long() # 划分训练集/测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # ==================== # 3. 构建数据集管道 # ==================== class CommentDataset(Dataset): def __init__(self, features, labels): self.features = features self.labels = labels def __len__(self): return len(self.labels) def __getitem__(self, idx): # RNN需要序列输入,将特征向量reshape为(seq_len, input_size) return self.features[idx].view(1, -1), self.labels[idx] # seq_len=1 train_loader = DataLoader(CommentDataset(X_train, y_train), batch_size=32, shuffle=True) test_loader = DataLoader(CommentDataset(X_test, y_test), batch_size=32) # ==================== # 4. 定义RNN模型 # ==================== class RNNClassifier(torch.nn.Module): def __init__(self, input_size, hidden_size, num_classes): super().__init__() self.rnn = torch.nn.RNN(input_size, hidden_size, batch_first=True) self.fc = torch.nn.Linear(hidden_size, num_classes) def forward(self, x): # x形状: (batch_size, seq_len=1, input_size) out, _ = self.rnn(x) # 输出形状: (batch_size, seq_len, hidden_size) return self.fc(out[:, -1, :]) # 初始化模型 model = RNNClassifier( input_size=3000, # 对应TF-IDF特征维度 hidden_size=128, # 根据引用[2]建议设置 num_classes=2 ) # ==================== # 5. 训练与评估 # ==================== device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model.to(device) criterion = torch.nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # 训练循环 for epoch in range(10): model.train() for inputs, labels in train_loader: inputs, labels = inputs.to(device), labels.to(device) outputs = model(inputs) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() # 评估函数(含准确率和F1值[^4]) from sklearn.metrics import accuracy_score, f1_score def evaluate(model, loader): model.eval() all_preds, all_labels = [], [] with torch.no_grad(): for inputs, labels in loader: inputs = inputs.to(device) outputs = model(inputs) preds = torch.argmax(outputs, dim=1).cpu().numpy() all_preds.extend(preds) all_labels.extend(labels.numpy()) return { "accuracy": accuracy_score(all_labels, all_preds), "f1": f1_score(all_labels, all_preds, average='macro') } print("测试集性能:", evaluate(model, test_loader))

import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import hsv_to_rgb import discretisedfield as df from pathlib import Path import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from pprint import pprint import warnings warnings.filterwarnings('ignore') path = Path("/home/tlliu/matlab/") fnames = [] # for i in list(path.glob("str_-0.013*phi_0.0002*V/*R12_2*/STO*")):10000000_polar for i in list(path.glob("meron/meron/PTO_tip_0-0.1")): try: fnames.append(list(i.glob("polar_tip_-0.1.mat"))[-1]) except IndexError: pass pprint(fnames) # 物理参数配置 Nx, Ny, Nz = 128, 128, 25 dx = 1.0 # nm (x方向间隔) dy = 1.0 # nm (y方向间隔) dz = 0.2 # nm (z方向间隔) for fname in fnames: field = df.Field.fromfile(fname) # 提取数据数组(根据之前确认的正确方式) p_x = field.p_x.value p_y = field.p_y.value p_z = field.p_z.value # 计算颜色编码(与之前相同) abs_px = np.abs(p_x) abs_py = np.abs(p_y) abs_pz = np.abs(p_z) max_abs = np.maximum(np.maximum(abs_px, abs_py), abs_pz) color_hue = np.zeros_like(p_x) mask_px = (abs_px == max_abs) color_hue[(mask_px) & (p_x > 0)] = 60 color_hue[(mask_px) & (p_x <= 0)] = 120 mask_py = (abs_py == max_abs) color_hue[(mask_py) & (p_y > 0)] = 30 color_hue[(mask_py) & (p_y <= 0)] = 180 mask_pz = (abs_pz == max_abs) color_hue[(mask_pz) & (p_z < 0)] = 240 color_hue[(mask_pz) & (p_z >= 0)] = 0 # 物理坐标生成 x_phys = np.arange(0, Nx*dx, dx) # 0到127 nm y_phys = np.arange(0, Ny*dy, dy) # 0到127 nm # 降采样参数 step = 1 x_sub = x_phys[::step] y_sub = y_phys[::step] # 提取颜色数据子集 colors = color_hue[::step, ::step, 13].flatten() # 只取z=0层的颜色数据 # 转换为RGB h = colors / 360.0 rgb = hsv_to_rgb(np.stack([h, np.ones_like(h), np.ones_like(h)], axis=1)) hue_2d = color_hue[::step, ::step, 13].T # 关键转置操作 # 创建x-y平面图 plt.figure(figsize=(10, 8)) extent = [0, Nx*dx, 0,

# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== # pylint: disable=invalid-name """Inception-ResNet V2 model for Keras. Reference: - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) (AAAI 2017) """ from tensorflow.python.keras import backend from tensorflow.python.keras.applications import imagenet_utils from tensorflow.python.keras.engine import training from tensorflow.python.keras.layers import VersionAwareLayers from tensorflow.python.keras.utils import data_utils from tensorflow.python.keras.utils import layer_utils from tensorflow.python.lib.io import file_io from tensorflow.python.util.tf_export import keras_export BASE_WEIGHT_URL = ('https://storage.googleapis.com/tensorflow/' 'keras-applications/inception_resnet_v2/') layers = None @keras_export('keras.applications.inception_resnet_v2.InceptionResNetV2', 'keras.applications.InceptionResNetV2') def InceptionResNetV2(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation='softmax', **kwargs): """Instantiates the Inception-ResNet v2 architecture. Reference: - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) (AAAI 2017) This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet. For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For InceptionResNetV2, call tf.keras.applications.inception_resnet_v2.preprocess_input on your inputs before passing them to the model. inception_resnet_v2.preprocess_input will scale input pixels between -1 and 1. Args: include_top: whether to include the fully-connected layer at the top of the network. weights: one of None (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model. input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (299, 299, 3) (with 'channels_last' data format) or (3, 299, 299) (with 'channels_first' data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. (150, 150, 3) would be one valid value. pooling: Optional pooling mode for feature extraction when include_top is False. - None means that the output of the model will be the 4D tensor output of the last convolutional block. - 'avg' means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - 'max' means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified. classifier_activation: A str or callable. The activation function to use on the "top" layer. Ignored unless include_top=True. Set classifier_activation=None to return the logits of the "top" layer. When loading pretrained weights, classifier_activation can only be None or "softmax". **kwargs: For backwards compatibility only. Returns: A keras.Model instance. """ global layers if 'layers' in kwargs: layers = kwargs.pop('layers') else: layers = VersionAwareLayers() if kwargs: raise ValueError('Unknown argument(s): %s' % (kwargs,)) if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)): raise ValueError('The weights argument should be either ' 'None (random initialization), imagenet ' '(pre-training on ImageNet), ' 'or the path to the weights file to be loaded.') if weights == 'imagenet' and include_top and classes != 1000: raise ValueError('If using weights as "imagenet" with include_top' ' as true, classes should be 1000') # Determine proper input shape input_shape = imagenet_utils.obtain_input_shape( input_shape, default_size=299, min_size=75, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # Stem block: 35 x 35 x 192 x = conv2d_bn(img_input, 32, 3, strides=2, padding='valid') x = conv2d_bn(x, 32, 3, padding='valid') x = conv2d_bn(x, 64, 3) x = layers.MaxPooling2D(3, strides=2)(x) x = conv2d_bn(x, 80, 1, padding='valid') x = conv2d_bn(x, 192, 3, padding='valid') x = layers.MaxPooling2D(3, strides=2)(x) # Mixed 5b (Inception-A block): 35 x 35 x 320 branch_0 = conv2d_bn(x, 96, 1) branch_1 = conv2d_bn(x, 48, 1) branch_1 = conv2d_bn(branch_1, 64, 5) branch_2 = conv2d_bn(x, 64, 1) branch_2 = conv2d_bn(branch_2, 96, 3) branch_2 = conv2d_bn(branch_2, 96, 3) branch_pool = layers.AveragePooling2D(3, strides=1, padding='same')(x) branch_pool = conv2d_bn(branch_pool, 64, 1) branches = [branch_0, branch_1, branch_2, branch_pool] channel_axis = 1 if backend.image_data_format() == 'channels_first' else 3 x = layers.Concatenate(axis=channel_axis, name='mixed_5b')(branches) # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320 for block_idx in range(1, 11): x = inception_resnet_block( x, scale=0.17, block_type='block35', block_idx=block_idx) # Mixed 6a (Reduction-A block): 17 x 17 x 1088 branch_0 = conv2d_bn(x, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 256, 3) branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding='valid') branch_pool = layers.MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_pool] x = layers.Concatenate(axis=channel_axis, name='mixed_6a')(branches) # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088 for block_idx in range(1, 21): x = inception_resnet_block( x, scale=0.1, block_type='block17', block_idx=block_idx) # Mixed 7a (Reduction-B block): 8 x 8 x 2080 branch_0 = conv2d_bn(x, 256, 1) branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding='valid') branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding='valid') branch_2 = conv2d_bn(x, 256, 1) branch_2 = conv2d_bn(branch_2, 288, 3) branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding='valid') branch_pool = layers.MaxPooling2D(3, strides=2, padding='valid')(x) branches = [branch_0, branch_1, branch_2, branch_pool] x = layers.Concatenate(axis=channel_axis, name='mixed_7a')(branches) # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080 for block_idx in range(1, 10): x = inception_resnet_block( x, scale=0.2, block_type='block8', block_idx=block_idx) x = inception_resnet_block( x, scale=1., activation=None, block_type='block8', block_idx=10) # Final convolution block: 8 x 8 x 1536 x = conv2d_bn(x, 1536, 1, name='conv_7b') if include_top: # Classification block x = layers.GlobalAveragePooling2D(name='avg_pool')(x) imagenet_utils.validate_activation(classifier_activation, weights) x = layers.Dense(classes, activation=classifier_activation, name='predictions')(x) else: if pooling == 'avg': x = layers.GlobalAveragePooling2D()(x) elif pooling == 'max': x = layers.GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of input_tensor. if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = training.Model(inputs, x, name='inception_resnet_v2') # Load weights. if weights == 'imagenet': if include_top: fname = 'inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5' weights_path = data_utils.get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir='models', file_hash='e693bd0210a403b3192acc6073ad2e96') else: fname = ('inception_resnet_v2_weights_' 'tf_dim_ordering_tf_kernels_notop.h5') weights_path = data_utils.get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir='models', file_hash='d19885ff4a710c122648d3b5c3b684e4') model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model def conv2d_bn(x, filters, kernel_size, strides=1, padding='same', activation='relu', use_bias=False, name=None): """Utility function to apply conv + BN. Args: x: input tensor. filters: filters in Conv2D. kernel_size: kernel size as in Conv2D. strides: strides in Conv2D. padding: padding mode in Conv2D. activation: activation in Conv2D. use_bias: whether to use a bias in Conv2D. name: name of the ops; will become name + '_ac' for the activation and name + '_bn' for the batch norm layer. Returns: Output tensor after applying Conv2D and BatchNormalization. """ x = layers.Conv2D( filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias, name=name)( x) if not use_bias: bn_axis = 1 if backend.image_data_format() == 'channels_first' else 3 bn_name = None if name is None else name + '_bn' x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x) if activation is not None: ac_name = None if name is None else name + '_ac' x = layers.Activation(activation, name=ac_name)(x) return x def inception_resnet_block(x, scale, block_type, block_idx, activation='relu'): """Adds an Inception-ResNet block. This function builds 3 types of Inception-ResNet blocks mentioned in the paper, controlled by the block_type argument (which is the block name used in the official TF-slim implementation): - Inception-ResNet-A: block_type='block35' - Inception-ResNet-B: block_type='block17' - Inception-ResNet-C: block_type='block8' Args: x: input tensor. scale: scaling factor to scale the residuals (i.e., the output of passing x through an inception module) before adding them to the shortcut branch. Let r be the output from the residual branch, the output of this block will be x + scale * r. block_type: 'block35', 'block17' or 'block8', determines the network structure in the residual branch. block_idx: an int used for generating layer names. The Inception-ResNet blocks are repeated many times in this network. We use block_idx to identify each of the repetitions. For example, the first Inception-ResNet-A block will have block_type='block35', block_idx=0, and the layer names will have a common prefix 'block35_0'. activation: activation function to use at the end of the block (see [activations](../activations.md)). When activation=None, no activation is applied (i.e., "linear" activation: a(x) = x). Returns: Output tensor for the block. Raises: ValueError: if block_type is not one of 'block35', 'block17' or 'block8'. """ if block_type == 'block35': branch_0 = conv2d_bn(x, 32, 1) branch_1 = conv2d_bn(x, 32, 1) branch_1 = conv2d_bn(branch_1, 32, 3) branch_2 = conv2d_bn(x, 32, 1) branch_2 = conv2d_bn(branch_2, 48, 3) branch_2 = conv2d_bn(branch_2, 64, 3) branches = [branch_0, branch_1, branch_2] elif block_type == 'block17': branch_0 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(x, 128, 1) branch_1 = conv2d_bn(branch_1, 160, [1, 7]) branch_1 = conv2d_bn(branch_1, 192, [7, 1]) branches = [branch_0, branch_1] elif block_type == 'block8': branch_0 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(branch_1, 224, [1, 3]) branch_1 = conv2d_bn(branch_1, 256, [3, 1]) branches = [branch_0, branch_1] else: raise ValueError('Unknown Inception-ResNet block type. ' 'Expects "block35", "block17" or "block8", ' 'but got: ' + str(block_type)) block_name = block_type + '_' + str(block_idx) channel_axis = 1 if backend.image_data_format() == 'channels_first' else 3 mixed = layers.Concatenate( axis=channel_axis, name=block_name + '_mixed')( branches) up = conv2d_bn( mixed, backend.int_shape(x)[channel_axis], 1, activation=None, use_bias=True, name=block_name + '_conv') x = layers.Lambda( lambda inputs, scale: inputs[0] + inputs[1] * scale, output_shape=backend.int_shape(x)[1:], arguments={'scale': scale}, name=block_name)([x, up]) if activation is not None: x = layers.Activation(activation, name=block_name + '_ac')(x) return x @keras_export('keras.applications.inception_resnet_v2.preprocess_input') def preprocess_input(x, data_format=None): return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf') @keras_export('keras.applications.inception_resnet_v2.decode_predictions') def decode_predictions(preds, top=5): return imagenet_utils.decode_predictions(preds, top=top) preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( mode='', ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF, error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC) decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ 根据代码来看。我应该把手动下载的https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5放在哪里来跳过下载

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