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Merged
merged 1 commit into from
Oct 14, 2021
+17,023 −4,250
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IDF master #5761

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8 changes: 4 additions & 4 deletions platform.txt
View file

Large diffs are not rendered by default.

2 changes: 1 addition & 1 deletion tools/platformio-build-esp32.py
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Original file line number Diff line number Diff line change
@@ -300,7 +300,7 @@
"UNITY_INCLUDE_CONFIG_H",
"WITH_POSIX",
"_GNU_SOURCE",
("IDF_VER", '\\"v4.4-dev-3235-g3e370c4296\\"'),
("IDF_VER", '\\"v4.4-dev-3401-gb86fe0c66c\\"'),
"ESP_PLATFORM",
"ARDUINO_ARCH_ESP32",
"ESP32",
13 changes: 11 additions & 2 deletions tools/platformio-build-esp32c3.py
View file
Original file line number Diff line number Diff line change
@@ -263,6 +263,15 @@
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "wifi_provisioning", "include"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp_littlefs", "src"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp_littlefs", "include"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "tool"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "typedef"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "image"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "math"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "nn"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "layer"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "detect"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "esp-face", "include", "model_zoo"),
join(FRAMEWORK_DIR, "tools", "sdk", "esp32c3", "include", "fb_gfx", "include"),
join(FRAMEWORK_DIR, "cores", env.BoardConfig().get("build.core"))
],
@@ -273,7 +282,7 @@
],

LIBS=[
"-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lasio", "-lbt", "-lcbor", "-lunity", "-lcmock", "-lcoap", "-lconsole", "-lnghttp", "-lesp-tls", "-lesp_adc_cal", "-lesp_hid", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lesp_https_server", "-lesp_lcd", "-lprotobuf-c", "-lprotocomm", "-lmdns", "-lesp_local_ctrl", "-lsdmmc", "-lesp_serial_slave_link", "-lesp_websocket_client", "-lexpat", "-lwear_levelling", "-lfatfs", "-lfreemodbus", "-ljsmn", "-ljson", "-llibsodium", "-lmqtt", "-lopenssl", "-lspiffs", "-lwifi_provisioning", "-lesp_littlefs", "-lfb_gfx", "-lasio", "-lcbor", "-lcmock", "-lunity", "-lcoap", "-lesp_lcd", "-lesp_local_ctrl", "-lesp_https_server", "-lesp_websocket_client", "-lexpat", "-lfreemodbus", "-ljsmn", "-llibsodium", "-lmqtt", "-lesp_adc_cal", "-lesp_hid", "-lfatfs", "-lwear_levelling", "-lopenssl", "-lspiffs", "-lwifi_provisioning", "-lprotocomm", "-lbt", "-lbtdm_app", "-lprotobuf-c", "-lmdns", "-lconsole", "-ljson", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lphy", "-lbtbb", "-lesp_phy", "-lphy", "-lbtbb", "-lesp_phy", "-lphy", "-lbtbb", "-lm", "-lnewlib", "-lstdc++", "-lpthread", "-lgcc", "-lcxx", "-lapp_trace", "-lgcov", "-lapp_trace", "-lgcov", "-lc"
"-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lasio", "-lbt", "-lcbor", "-lunity", "-lcmock", "-lcoap", "-lconsole", "-lnghttp", "-lesp-tls", "-lesp_adc_cal", "-lesp_hid", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lesp_https_server", "-lesp_lcd", "-lprotobuf-c", "-lprotocomm", "-lmdns", "-lesp_local_ctrl", "-lsdmmc", "-lesp_serial_slave_link", "-lesp_websocket_client", "-lexpat", "-lwear_levelling", "-lfatfs", "-lfreemodbus", "-ljsmn", "-ljson", "-llibsodium", "-lmqtt", "-lopenssl", "-lspiffs", "-lwifi_provisioning", "-lesp_littlefs", "-lfb_gfx", "-lasio", "-lcbor", "-lcmock", "-lunity", "-lcoap", "-lesp_lcd", "-lesp_local_ctrl", "-lesp_https_server", "-lesp_websocket_client", "-lexpat", "-lfreemodbus", "-ljsmn", "-llibsodium", "-lmqtt", "-lesp_adc_cal", "-lesp_hid", "-lfatfs", "-lwear_levelling", "-lopenssl", "-lspiffs", "-lwifi_provisioning", "-lprotocomm", "-lbt", "-lbtdm_app", "-lprotobuf-c", "-lmdns", "-lconsole", "-ljson", "-lcat_face_detect", "-lhuman_face_detect", "-ldl", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lesp_ringbuf", "-lefuse", "-lesp_ipc", "-ldriver", "-lesp_pm", "-lmbedtls", "-lapp_update", "-lbootloader_support", "-lspi_flash", "-lnvs_flash", "-lpthread", "-lesp_gdbstub", "-lespcoredump", "-lesp_phy", "-lesp_system", "-lesp_rom", "-lhal", "-lvfs", "-lesp_eth", "-ltcpip_adapter", "-lesp_netif", "-lesp_event", "-lwpa_supplicant", "-lesp_wifi", "-llwip", "-llog", "-lheap", "-lsoc", "-lesp_hw_support", "-lriscv", "-lesp_common", "-lesp_timer", "-lfreertos", "-lnewlib", "-lcxx", "-lapp_trace", "-lnghttp", "-lesp-tls", "-ltcp_transport", "-lesp_http_client", "-lesp_http_server", "-lesp_https_ota", "-lsdmmc", "-lesp_serial_slave_link", "-lmbedtls", "-lmbedcrypto", "-lmbedx509", "-lcoexist", "-lcore", "-lespnow", "-lmesh", "-lnet80211", "-lpp", "-lsmartconfig", "-lwapi", "-lphy", "-lbtbb", "-lesp_phy", "-lphy", "-lbtbb", "-lesp_phy", "-lphy", "-lbtbb", "-lm", "-lnewlib", "-lstdc++", "-lpthread", "-lgcc", "-lcxx", "-lapp_trace", "-lgcov", "-lapp_trace", "-lgcov", "-lc"
],

CPPDEFINES=[
@@ -282,7 +291,7 @@
"UNITY_INCLUDE_CONFIG_H",
"WITH_POSIX",
"_GNU_SOURCE",
("IDF_VER", '\\"v4.4-dev-3235-g3e370c4296\\"'),
("IDF_VER", '\\"v4.4-dev-3401-gb86fe0c66c\\"'),
"ESP_PLATFORM",
"ARDUINO_ARCH_ESP32",
"ESP32",
2 changes: 1 addition & 1 deletion tools/platformio-build-esp32s2.py
View file
Original file line number Diff line number Diff line change
@@ -286,7 +286,7 @@
"UNITY_INCLUDE_CONFIG_H",
"WITH_POSIX",
"_GNU_SOURCE",
("IDF_VER", '\\"v4.4-dev-3235-g3e370c4296\\"'),
("IDF_VER", '\\"v4.4-dev-3401-gb86fe0c66c\\"'),
"ESP_PLATFORM",
"ARDUINO_ARCH_ESP32",
"ESP32",
5 changes: 5 additions & 0 deletions tools/sdk/esp32/include/asio/port/include/esp_asio_config.h
View file
Original file line number Diff line number Diff line change
@@ -18,6 +18,11 @@
# define ASIO_NO_TYPEID
# endif // CONFIG_COMPILER_RTTI

//
// Supress OpenSSL deprecation warning, when building ASIO
//
#define ESP_OPENSSL_SUPPRESS_LEGACY_WARNING

//
// LWIP compatibility inet and address macros/functions
//
3 changes: 3 additions & 0 deletions tools/sdk/esp32/include/coap/libcoap/include/coap3/coap_block_internal.h
View file
Original file line number Diff line number Diff line change
@@ -191,6 +191,9 @@ int coap_handle_response_get_block(coap_context_t *context,
void coap_block_delete_lg_xmit(coap_session_t *session,
coap_lg_xmit_t *lg_xmit);

coap_tick_t coap_block_check_lg_xmit_timeouts(coap_session_t *session,
coap_tick_t now);

/**
* The function that does all the work for the coap_add_data_large*()
* functions.
6 changes: 6 additions & 0 deletions tools/sdk/esp32/include/coap/libcoap/include/coap3/coap_dtls.h
View file
Original file line number Diff line number Diff line change
@@ -27,6 +27,12 @@ typedef struct coap_dtls_pki_t coap_dtls_pki_t;
#ifndef COAP_DTLS_HINT_LENGTH
#define COAP_DTLS_HINT_LENGTH 128
#endif
#ifndef COAP_DTLS_MAX_PSK_IDENTITY
#define COAP_DTLS_MAX_PSK_IDENTITY 64
#endif
#ifndef COAP_DTLS_MAX_PSK
#define COAP_DTLS_MAX_PSK 64
#endif

typedef enum coap_dtls_role_t {
COAP_DTLS_ROLE_CLIENT, /**< Internal function invoked for client */
28 changes: 14 additions & 14 deletions tools/sdk/esp32/include/coap/libcoap/include/coap3/coap_event.h
View file
Original file line number Diff line number Diff line change
@@ -24,34 +24,34 @@
* Scalar type to represent different events, e.g. DTLS events or
* retransmission timeouts.
*/
typedef unsigned int coap_event_t;

typedef enum coap_event_t {
/**
* (D)TLS events for COAP_PROTO_DTLS and COAP_PROTO_TLS
*/
#define COAP_EVENT_DTLS_CLOSED 0x0000
#define COAP_EVENT_DTLS_CONNECTED 0x01DE
#define COAP_EVENT_DTLS_RENEGOTIATE 0x01DF
#define COAP_EVENT_DTLS_ERROR 0x0200
COAP_EVENT_DTLS_CLOSED = 0x0000,
COAP_EVENT_DTLS_CONNECTED = 0x01DE,
COAP_EVENT_DTLS_RENEGOTIATE = 0x01DF,
COAP_EVENT_DTLS_ERROR = 0x0200,

/**
* TCP events for COAP_PROTO_TCP and COAP_PROTO_TLS
*/
#define COAP_EVENT_TCP_CONNECTED 0x1001
#define COAP_EVENT_TCP_CLOSED 0x1002
#define COAP_EVENT_TCP_FAILED 0x1003
COAP_EVENT_TCP_CONNECTED = 0x1001,
COAP_EVENT_TCP_CLOSED = 0x1002,
COAP_EVENT_TCP_FAILED = 0x1003,

/**
* CSM exchange events for reliable protocols only
*/
#define COAP_EVENT_SESSION_CONNECTED 0x2001
#define COAP_EVENT_SESSION_CLOSED 0x2002
#define COAP_EVENT_SESSION_FAILED 0x2003
COAP_EVENT_SESSION_CONNECTED = 0x2001,
COAP_EVENT_SESSION_CLOSED = 0x2002,
COAP_EVENT_SESSION_FAILED = 0x2003,

/**
* BLOCK2 receive errors
* (Q-)BLOCK receive errors
*/
#define COAP_EVENT_PARTIAL_BLOCK 0x3001
COAP_EVENT_PARTIAL_BLOCK = 0x3001
} coap_event_t;

/**
* Type for event handler functions that can be registered with a CoAP
4 changes: 4 additions & 0 deletions tools/sdk/esp32/include/coap/libcoap/include/coap3/coap_time.h
View file
Original file line number Diff line number Diff line change
@@ -88,7 +88,11 @@ COAP_STATIC_INLINE uint64_t coap_ticks_to_rt_us(coap_tick_t t) {
#elif defined(RIOT_VERSION)
#include <xtimer.h>

#ifdef XTIMER_HZ
#define COAP_TICKS_PER_SECOND (XTIMER_HZ)
#else /* XTIMER_HZ */
#define COAP_TICKS_PER_SECOND (XTIMER_HZ_BASE)
#endif /* XTIMER_HZ */

typedef uint64_t coap_tick_t;
typedef int64_t coap_tick_diff_t;
1 change: 1 addition & 0 deletions tools/sdk/esp32/include/coap/libcoap/include/coap3/net.h
View file
Original file line number Diff line number Diff line change
@@ -15,6 +15,7 @@
#include <stdlib.h>
#include <string.h>
#ifndef _WIN32
#include <sys/select.h>
#include <sys/time.h>
#endif
#include <time.h>
1 change: 0 additions & 1 deletion tools/sdk/esp32/include/coap/libcoap/include/coap3/pdu.h
View file
Original file line number Diff line number Diff line change
@@ -299,7 +299,6 @@ typedef enum coap_pdu_code_t {
COAP_REQUEST_CODE_PATCH = COAP_REQUEST_PATCH,
COAP_REQUEST_CODE_IPATCH = COAP_REQUEST_IPATCH,

COAP_RESPONSE_CODE_OK = COAP_RESPONSE_CODE(200),
COAP_RESPONSE_CODE_CREATED = COAP_RESPONSE_CODE(201),
COAP_RESPONSE_CODE_DELETED = COAP_RESPONSE_CODE(202),
COAP_RESPONSE_CODE_VALID = COAP_RESPONSE_CODE(203),
3 changes: 2 additions & 1 deletion tools/sdk/esp32/include/coap/libcoap/include/coap3/resource.h
View file
Original file line number Diff line number Diff line change
@@ -83,7 +83,8 @@ typedef void (*coap_method_handler_t)
* variable of coap_str_const_t has to point to constant text, or point to data
* within the allocated coap_str_const_t parameter.
*
* @param uri_path The string URI path of the new resource.
* @param uri_path The string URI path of the new resource. The leading '/' is
* not normally required - e.g. just "full/path/for/resource".
* @param flags Flags for memory management (in particular release of
* memory). Possible values:@n
*
5 changes: 4 additions & 1 deletion tools/sdk/esp32/include/config/sdkconfig.h
View file
Original file line number Diff line number Diff line change
@@ -26,6 +26,7 @@
#define CONFIG_BOOTLOADER_FLASH_XMC_SUPPORT 1
#define CONFIG_ESPTOOLPY_BAUD_OTHER_VAL 115200
#define CONFIG_ESPTOOLPY_FLASHMODE_DIO 1
#define CONFIG_ESPTOOLPY_FLASH_SAMPLE_MODE_STR 1
#define CONFIG_ESPTOOLPY_FLASHMODE "dio"
#define CONFIG_ESPTOOLPY_FLASHFREQ_40M 1
#define CONFIG_ESPTOOLPY_FLASHFREQ "40m"
@@ -369,6 +370,7 @@
#define CONFIG_LWIP_GARP_TMR_INTERVAL 60
#define CONFIG_LWIP_TCPIP_RECVMBOX_SIZE 32
#define CONFIG_LWIP_DHCP_RESTORE_LAST_IP 1
#define CONFIG_LWIP_DHCP_OPTIONS_LEN 68
#define CONFIG_LWIP_DHCPS 1
#define CONFIG_LWIP_DHCPS_LEASE_UNIT 60
#define CONFIG_LWIP_DHCPS_MAX_STATION_NUM 8
@@ -480,6 +482,7 @@
#define CONFIG_MDNS_TASK_AFFINITY 0x0
#define CONFIG_MDNS_SERVICE_ADD_TIMEOUT_MS 2000
#define CONFIG_MDNS_TIMER_PERIOD_MS 100
#define CONFIG_MDNS_MULTIPLE_INSTANCE 1
#define CONFIG_MQTT_PROTOCOL_311 1
#define CONFIG_MQTT_TRANSPORT_SSL 1
#define CONFIG_MQTT_TRANSPORT_WEBSOCKET 1
@@ -675,5 +678,5 @@
#define CONFIG_ULP_COPROC_ENABLED CONFIG_ESP32_ULP_COPROC_ENABLED
#define CONFIG_ULP_COPROC_RESERVE_MEM CONFIG_ESP32_ULP_COPROC_RESERVE_MEM
#define CONFIG_WARN_WRITE_STRINGS CONFIG_COMPILER_WARN_WRITE_STRINGS
#define CONFIG_ARDUINO_IDF_COMMIT "3e370c4296"
#define CONFIG_ARDUINO_IDF_COMMIT "b86fe0c66c"
#define CONFIG_ARDUINO_IDF_BRANCH "master"
25 changes: 22 additions & 3 deletions tools/sdk/esp32/include/driver/include/driver/rmt.h
View file
Original file line number Diff line number Diff line change
@@ -856,16 +856,35 @@ esp_err_t rmt_remove_channel_from_group(rmt_channel_t channel);

#if SOC_RMT_SUPPORT_TX_LOOP_COUNT
/**
* @brief Set loop count for RMT TX channel
* @brief Set loop count threshold value for RMT TX channel
*
* When tx loop count reaches this value, an ISR callback will notify user
*
* @param channel RMT channel
* @param count loop count
* @param count loop count, 1 ~ 1023
* @return
* - ESP_ERR_INVALID_ARG Parameter error
* - ESP_OK Success
*/
esp_err_t rmt_set_tx_loop_count(rmt_channel_t channel, uint32_t count);
#endif

/**
* @brief Enable or disable the feature that when loop count reaches the threshold, RMT will stop transmitting.
*
* - When the loop auto-stop feature is enabled will halt RMT transmission after the loop count reaches a certain threshold
* - When disabled, the RMT transmission continue indefinitely until halted by the users
*
* @note The auto-stop feature is implemented in hardware on particular targets (i.e. those with SOC_RMT_SUPPORT_TX_LOOP_AUTOSTOP defined).
* Otherwise, the auto-stop feature is implemented in software via the interrupt.
*
* @param channel RMT channel
* @param en enable bit
* @return
* - ESP_ERR_INVALID_ARG Parameter error
* - ESP_OK Success
*/
esp_err_t rmt_enable_tx_loop_autostop(rmt_channel_t channel, bool en);
#endif // SOC_RMT_SUPPORT_TX_LOOP_COUNT

/**
* @brief Reset RMT TX/RX memory index.
19 changes: 14 additions & 5 deletions tools/sdk/esp32/include/esp-face/include/dl_define.hpp
View file
Original file line number Diff line number Diff line change
@@ -10,7 +10,7 @@
#define DL_LOG_LAYER_LATENCY 0 /*<! - 1: print the latency of each parts of layer */
/*<! - 0: mute */

#if CONFIG_SPIRAM_SUPPORT || CONFIG_ESP32_SPIRAM_SUPPORT || CONFIG_ESP32S3_SPIRAM_SUPPORT
#if CONFIG_SPIRAM_SUPPORT || CONFIG_ESP32_SPIRAM_SUPPORT || CONFIG_ESP32S2_SPIRAM_SUPPORT || CONFIG_ESP32S3_SPIRAM_SUPPORT
#define DL_SPIRAM_SUPPORT 1
#else
#define DL_SPIRAM_SUPPORT 0
@@ -83,8 +83,17 @@ namespace dl

typedef enum
{
PADDING_VALID, /*<! no padding >*/
PADDING_SAME, /*<! SAME in TensorFlow style >*/
PADDING_SAME_MXNET /*<! SAME in MXNET style >*/
PADDING_NOT_SET,
PADDING_VALID, /*<! no padding >*/
PADDING_SAME_BEGIN, /*<! SAME in MXNET style >*/
PADDING_SAME_END, /*<! SAME in TensorFlow style >*/
} padding_type_t;
} // namespace dl

typedef enum
{
CONSTANT,
EDGE,
REFLECT,
SYMMETRIC,
} padding_mode_t;
} // namespace dl
61 changes: 60 additions & 1 deletion tools/sdk/esp32/include/esp-face/include/image/dl_image.hpp
View file
Original file line number Diff line number Diff line change
@@ -370,11 +370,70 @@ namespace dl
*/
uint32_t get_moving_point_number(uint8_t *f1, uint8_t *f2, const uint32_t height, const uint32_t width, const uint32_t stride, const uint32_t threshold = 5);


/**
* @brief Apply an affine transformation to an image.
*
* @tparam T
* @param input the input image.
* @param output the output image.
* @param M_inv the inverse transformation matrix.
*/
template <typename T>
void warp_affine(dl::Tensor<T> *input, dl::Tensor<T> *output, dl::math::Matrix<float> *M_inv);

/**
* @brief Apply an affine transformation to an image.
*
* @tparam T
* @param input the pointer of the input image.
* @param shape the shape of the input image.
* @param output the output image.
* @param M_inv the inverse transformation matrix.
*/
template <typename T>
void warp_affine(uint16_t *input, std::vector<int> shape, dl::Tensor<T> *output, dl::math::Matrix<float> *M_inv);

/**
* @brief Get the otsu thresh object.
*
* @param image the gray image.
* @return uint8_t the otsu thresh.
*/
uint8_t get_otsu_thresh(Tensor<uint8_t> &image);

/**
* @brief Convert RGB image to gray image
*
* @param image input image
* @param bgr true: the image is in BGR format
* false: the image is in RGB format
* @return Tensor<uint8_t>* output image in gray format
*/
Tensor<uint8_t> *rgb2gray(Tensor<uint8_t> &image, bool bgr = false);

/**
* @brief Convert RGB image to LAB image
*
* @param image input image
* @param bgr true: the image is in BGR format
* false: the image is in RGB format
* @param fast true: use the fast alogrithm， but the accuracy will be reduced
* false: do not use the fast alogrithm
* @return Tensor<uint8_t>* output image in LAB foramt
*/
Tensor<uint8_t> *rgb2lab(Tensor<uint8_t> &image, bool bgr = false, bool fast = true);

/**
* @brief Convert RGB image to HSV image
*
* @param image input image
* @param bgr true: the image is in BGR format
* false: the image is in RGB format
* @param fast true: use the fast alogrithm， but the accuracy will be reduced
* false: do not use the fast alogrithm
* @return Tensor<uint8_t>* output image in HSV format
*/
Tensor<uint8_t> *rgb2hsv(Tensor<uint8_t> &image, bool bgr = false, bool fast = true);

} // namespace image
} // namespace dl
36 changes: 27 additions & 9 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_add2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -25,7 +25,8 @@ namespace dl
const int output_exponent; /*<! exponent of output >*/
Tensor<feature_t> *output; /*<! output ptr of add2d >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of add2d >*/

public:
/**
@@ -35,19 +36,21 @@ namespace dl
* @param activation activation of add2d, if you don't specify anything, no activation is applied
* @param name name of add2d
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
Add2D(const int output_exponent, const Activation<feature_t> *activation = NULL, const char *name = NULL, bool inplace = false) : Layer(name), activation(activation), output_exponent(output_exponent), output(NULL)
{
this->inplace = inplace;
}
Add2D(const int output_exponent, const Activation<feature_t> *activation = NULL, const char *name = "Add2D", bool inplace = false) : Layer(name),
activation(activation),
output_exponent(output_exponent),
output(NULL),
inplace(inplace),
output_shape({}) {}

/**
* @brief Destroy the Add2D object
*/
~Add2D()
{
if((!this->inplace) && (this->output != NULL))
if ((!this->inplace) && (this->output != NULL))
{
delete this->output;
}
@@ -59,10 +62,12 @@ namespace dl
*
* @param input0 as one input
* @param input1 as another input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1)
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1, bool print_shape = false)
{
assert(input0.is_same_shape(input1));
this->output_shape = input0.shape;

if (!this->inplace)
{
@@ -78,6 +83,11 @@ namespace dl
{
this->output = &input0;
}
if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -105,7 +115,11 @@ namespace dl
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -116,6 +130,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::add2d(*this->output, input0, input1, this->activation, assign_core, this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "add2d");
}
68 changes: 46 additions & 22 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_avg_pool2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -24,40 +24,50 @@ namespace dl
std::vector<int> filter_shape; /*<! filter shape in [filter_height, filter_width] >*/
const int stride_y; /*<! stride in height >*/
const int stride_x; /*<! stride in width >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN >*/
std::vector<int> padding; /*<! padding size needed in [top, bottom, left, right] of this operation >*/
Tensor<feature_t> *output; /*<! output ptr of AvgPool2D >*/
Tensor<feature_t> *output; /*<! output ptr of AvgPool2D >*/
std::vector<int> output_shape; /*<! output shape of AvgPool2D >*/

public:

/**
* @brief Construct a new AvgPool2D object.
*
* @param output_exponent exponent of output
* @param filter_shape filter shape in [filter_height, filter_width]
* @param padding_type one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET,
* @param padding_type one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN or PADDING_NOT_SET,
* - PADDING_VALID means no padding
* PADDING_SAME and PADDING_SAME_MXNET results in padding with zeros evenly to the left/right or up/down of the input
* PADDING_SAME_END and PADDING_SAME_BEGIN results in padding with zeros evenly to the left/right or up/down of the input
* such that output has the same height/width dimension as the input,
* - PADDING_SAME results padding in TensorFlow style
* - PADDING_SAME_MXNET results padding in MXNET style
* - PADDING_SAME_END results padding in TensorFlow style
* - PADDING_SAME_BEGIN results padding in MXNET style
* - PADDING_NOT_SET means padding with the specific "padding" value below.
* @param padding if padding_type is PADDING_NOT_SET, this value will be used as padding size.
* the shape must be 4, the value of each position is: [padding top, padding bottom, padding left, padding right]
* @param stride_y stride in height
* @param stride_x stride in width
* @param name name of layer
*/
AvgPool2D(const int output_exponent,
const std::vector<int> filter_shape,
const padding_type_t padding_type = PADDING_VALID,
std::vector<int> padding = {},
const int stride_y = 1,
const int stride_x = 1,
const char *name = NULL) : Layer(name),
output_exponent(output_exponent),
filter_shape(filter_shape),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type)
const char *name = "AvgPool2D") : Layer(name),
output_exponent(output_exponent),
filter_shape(filter_shape),
padding_type(padding_type),
padding(padding),
stride_y(stride_y),
stride_x(stride_x),
output_shape({})
{
this->output = new Tensor<feature_t>;
if (this->padding_type == PADDING_NOT_SET)
{
assert(this->padding.size() == 4);
}
}

/**
@@ -66,7 +76,7 @@ namespace dl
*/
~AvgPool2D()
{
if(this->output != NULL)
if (this->output != NULL)
{
delete this->output;
}
@@ -76,20 +86,31 @@ namespace dl
* @brief Update output shape and padding.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(input.shape[0] > 0);
assert(input.shape[1] > 0);
std::vector<int> output_shape = nn::get_output_shape(input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type);
this->output->set_shape(output_shape);
assert(input.shape.size() == 3);

this->output_shape = nn::get_output_shape(input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type, false, this->padding);
this->output->set_shape(this->output_shape);
this->output->set_exponent(this->output_exponent);

this->padding = nn::get_pad_size(output_shape, input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type);
input.set_padding_size(this->padding);
if (this->padding_type != PADDING_NOT_SET)
{
this->padding = nn::get_pad_size(this->output_shape, input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type);
}

this->output->free_element();
}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
* @brief Get the output
@@ -108,15 +129,18 @@ namespace dl
* @param autoload_enable one of true or false,
* - true: load input and output from PSRAM to CACHE automatically
* - false: do not
* @param assign_core not effective yet
* @return AvgPool2D result
*/
Tensor<feature_t> &call(Tensor<feature_t> &input, uint8_t autoload_enable = 0)
{
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

1 change: 1 addition & 0 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_base.hpp
View file
Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
#pragma once
#include "dl_tool.hpp"
#include "dl_tool_cache.hpp"
#include <iostream>

namespace dl
{
139 changes: 139 additions & 0 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_concat.hpp
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,139 @@
#pragma once

#include <assert.h>
#include <vector>

#include "dl_constant.hpp"
#include "dl_variable.hpp"
#include "dl_tool.hpp"
#include "dl_layer_base.hpp"
#include "dl_nn_concat.hpp"

namespace dl
{
namespace layer
{
/**
* @brief Concat(input1, input2, input3, ...).
*
* @tparam feature_t support all kinds of integer and float data type
*/
template <typename feature_t>
class Concat : Layer
{
private:
int output_exponent; /*<! exponent of output >*/
int axis; /*<! The axis along which the Tensor will be concatenated. >*/
Tensor<feature_t> *output; /*<! output ptr of Concat >*/
std::vector<int> output_shape; /*<! output shape of Concat >*/
public:
/**
* @brief Construct a new Concat object.
*
* @param name name of layer
* @param axis The axis along which the Tensor will be concatenated.
*/
Concat(int axis, const char *name = "Concat") : Layer(name), axis(axis), output_shape({})
{
this->output = new Tensor<feature_t>;
}

/**
* @brief Destroy the Concat object
*/
~Concat()
{
if (this->output != NULL)
{
delete this->output;
}
}

/**
* @brief Collect inputs' channel and memory offset, called in Model.build().
*
* @param args pointers of concatenated Tensor
* @param print_shape whether to print the output shape.
*/
void build(std::vector<Tensor<feature_t> *> args, bool print_shape = false)
{
assert(args.size() > 1);
int shape_size = args[0]->shape.size();

if (this->axis < 0)
{
this->axis = shape_size + this->axis;
}
assert((this->axis < shape_size) && (this->axis > -1));

int output_shape_axis = args[0]->shape[this->axis];

for (int i = 1; i < args.size(); i++)
{
assert(shape_size == args[i]->shape.size());
assert(args[i]->exponent == args[i - 1]->exponent);
output_shape_axis += args[i]->shape[this->axis];

for (int j = 0; j < shape_size; j++)
{
if (j != this->axis)
{
assert(args[i]->shape[j] == args[i - 1]->shape[j]);
}
}
}

this->output_exponent = args[0]->exponent;
this->output_shape = args[0]->shape;
this->output_shape[this->axis] = output_shape_axis;

this->output->set_shape(this->output_shape);
this->output->set_exponent(this->output_exponent);
this->output->free_element();

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
* @brief Call Concat operation
*
* @param inputs the pointers of inputs
* @param free_inputs true: free the inputs after call
* false: do not free inputs
* @return Tensor<feature_t>& concat result
*/
Tensor<feature_t> &call(std::vector<Tensor<feature_t> *> inputs, bool free_inputs = false)
{
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

DL_LOG_LAYER_LATENCY_START();
nn::concat(*this->output, inputs, this->axis, free_inputs);
DL_LOG_LAYER_LATENCY_END(this->name, "concat");
return *this->output;
}

/**
* @brief Get the output
*
* @return Tensor<feature_t>& Concat result
*/
Tensor<feature_t> &get_output()
{
return *this->output;
}
};
} // namespace layer
} // namespace dl
77 changes: 53 additions & 24 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_conv2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -13,57 +13,70 @@ namespace dl
* @tparam feature_t supports int16_t and int8_t,
* - int16_t: stands for operation in int16_t quantize
* - int8_t: stands for operation in int8_t quantize
* @tparam bias_t supports int16_t and int8_t, must specify when using int8 per-channel quantization
* - int16_t: for int16 quantization and int8 per-channel quantization
* - int8_t: for int8 per-tensor quantization
*/
template <typename feature_t>
template <typename feature_t, typename bias_t = feature_t>
class Conv2D : public Layer
{
private:
const int output_exponent; /*<! exponent of output >*/
const Filter<feature_t> *filter; /*<! filter of Conv2D >*/
const int stride_y; /*<! stride in height >*/
const int stride_x; /*<! stride in width >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET >*/
const Bias<feature_t> *bias; /*<! bias of Conv2D, if you don't specify anything, no bias is added >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN >*/
const Bias<bias_t> *bias; /*<! bias of Conv2D, if you don't specify anything, no bias is added >*/
const Activation<feature_t> *activation; /*<! activation of Conv2D, if you don't specify anything, no activation is applied >*/
std::vector<int> padding; /*<! padding size needed in [top, bottom, left, right] of this operation >*/
Tensor<feature_t> *output; /*<! output ptr of Conv2D >*/
Tensor<feature_t> *output; /*<! output ptr of Conv2D >*/
std::vector<int> output_shape; /*<! output shape of Conv2D >*/

public:

/**
* @brief Construct a new Conv2D object.
*
* @param output_exponent exponent of output
* @param filter filter of Conv2D
* @param bias bias of Conv2D, if you don't specify anything, no bias is added
* @param activation activation of Conv2D, if you don't specify anything, no activation is applied
* @param padding_type one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET,
* @param padding_type one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN or PADDING_NOT_SET,
* - PADDING_VALID means no padding
* PADDING_SAME and PADDING_SAME_MXNET results in padding with zeros evenly to the left/right or up/down of the input
* PADDING_SAME_END and PADDING_SAME_BEGIN results in padding with zeros evenly to the left/right or up/down of the input
* such that output has the same height/width dimension as the input,
* - PADDING_SAME results padding in TensorFlow style
* - PADDING_SAME_MXNET results padding in MXNET style
* - PADDING_SAME_END results padding in TensorFlow style
* - PADDING_SAME_BEGIN results padding in MXNET style
* - PADDING_NOT_SET means padding with the specific "padding" value below.
* @param padding if padding_type is PADDING_NOT_SET, this value will be used as padding size.
* the shape must be 4, the value of each position is: [padding top, padding bottom, padding left, padding right]
* @param stride_y stride in height
* @param stride_x stride in width
* @param name name of layer
*/
Conv2D(const int output_exponent,
const Filter<feature_t> *filter,
const Bias<feature_t> *bias = NULL,
const Bias<bias_t> *bias = NULL,
const Activation<feature_t> *activation = NULL,
const padding_type_t padding_type = PADDING_VALID,
std::vector<int> padding = {},
const int stride_y = 1,
const int stride_x = 1,
const char *name = NULL) : Layer(name),
output_exponent(output_exponent),
filter(filter),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type),
bias(bias),
activation(activation)
const char *name = "Conv2D") : Layer(name),
output_exponent(output_exponent),
filter(filter),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type),
bias(bias),
activation(activation),
padding(padding),
output_shape({})
{
this->output = new Tensor<feature_t>;
if (this->padding_type == PADDING_NOT_SET)
{
assert(this->padding.size() == 4);
}
}

/**
@@ -82,19 +95,30 @@ namespace dl
* @brief Update output padding and input padding.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(input.shape[0] > 0);
assert(input.shape[1] > 0);
assert(input.shape.size() == 3);
assert(this->filter->shape.size() == 4);
assert(input.shape[2] == this->filter->shape[2]);

std::vector<int> output_shape = nn::get_output_shape(input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type, true);
this->output->set_shape(output_shape);
this->output_shape = nn::get_output_shape(input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type, true, this->padding);
this->output->set_shape(this->output_shape);
this->output->set_exponent(this->output_exponent);
this->output->free_element();
if (this->padding_type != PADDING_NOT_SET)
{
this->padding = nn::get_pad_size(this->output_shape, input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type);
}

this->padding = nn::get_pad_size(output_shape, input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type);
input.set_padding_size(this->padding);
if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -122,7 +146,11 @@ namespace dl
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -153,5 +181,6 @@ namespace dl
dl::tool::cache::preload_func((uint32_t)(this->filter->element), size);
}
};

} // namespace layer
} // namespace dl
80 changes: 55 additions & 25 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_depthwise_conv2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -13,64 +13,77 @@ namespace dl
* @tparam feature_t supports int16_t and int8_t,
* - int16_t: stands for operation in int16_t quantize
* - int8_t: stands for operation in int8_t quantize
* @tparam bias_t supports int16_t and int8_t, must specify when using int8 per-channel quantization
* - int16_t: for int16 quantization and int8 per-channel quantization
* - int8_t: for int8 per-tensor quantization
*/
template <typename feature_t>
template <typename feature_t, typename bias_t = feature_t>
class DepthwiseConv2D : public Layer
{
private:
const int output_exponent; /*<! exponent of output >*/
const Filter<feature_t> *filter; /*<! filter of DepthwiseConv2D >*/
const int stride_y; /*<! stride in height >*/
const int stride_x; /*<! stride in width >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET >*/
const Bias<feature_t> *bias; /*<! bias of DepthwiseConv2D, if you don't specify anything, no bias is added >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN >*/
const Bias<bias_t> *bias; /*<! bias of DepthwiseConv2D, if you don't specify anything, no bias is added >*/
const Activation<feature_t> *activation; /*<! activation of DepthwiseConv2D, if you don't specify anything, no activation is applied >*/
std::vector<int> padding; /*<! padding size needed in [top, bottom, left, right] of this operation >*/
Tensor<feature_t> *output; /*<! output ptr of DepthwiseConv2D >*/
std::vector<int> output_shape; /*<! output shape of DepthwiseConv2D >*/

public:

/**
* @brief Construct a new DepthwiseConv2D object.
*
* @param output_exponent exponent of output
* @param filter filter of DepthwiseConv2D
* @param bias bias of DepthwiseConv2D, if you don't specify anything, no bias is added
* @param activation activation of DepthwiseConv2D, if you don't specify anything, no activation is applied
* @param padding_type one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET,
* @param padding_type one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN or PADDING_NOT_SET,
* - PADDING_VALID means no padding
* PADDING_SAME and PADDING_SAME_MXNET results in padding with zeros evenly to the left/right or up/down of the input
* such that output has the same height/width dimension as the input
* - PADDING_SAME results padding in TensorFlow style
* - PADDING_SAME_MXNET results padding in MXNET style
* PADDING_SAME_END and PADDING_SAME_BEGIN results in padding with zeros evenly to the left/right or up/down of the input
* such that output has the same height/width dimension as the input,
* - PADDING_SAME_END results padding in TensorFlow style
* - PADDING_SAME_BEGIN results padding in MXNET style
* - PADDING_NOT_SET means padding with the specific "padding" value below.
* @param padding if padding_type is PADDING_NOT_SET, this value will be used as padding size.
* the shape must be 4, the value of each position is: [padding top, padding bottom, padding left, padding right]
* @param stride_y - stride in height
* @param stride_x - stride in width
* @param name name of layer
*/
DepthwiseConv2D(const int output_exponent,
const Filter<feature_t> *filter,
const Bias<feature_t> *bias = NULL,
const Bias<bias_t> *bias = NULL,
const Activation<feature_t> *activation = NULL,
const padding_type_t padding_type = PADDING_VALID,
std::vector<int> padding = {},
const int stride_y = 1,
const int stride_x = 1,
const char *name = NULL) : Layer(name),
output_exponent(output_exponent),
filter(filter),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type),
bias(bias),
activation(activation)
const char *name = "DepthwiseConv2D") : Layer(name),
output_exponent(output_exponent),
filter(filter),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type),
bias(bias),
activation(activation),
padding(padding),
output_shape({})
{
this->output = new Tensor<feature_t>;
if (this->padding_type == PADDING_NOT_SET)
{
assert(this->padding.size() == 4);
}
}

/**
* @brief Destroy the DepthwiseConv2D object.
*
*/
~DepthwiseConv2D()
~DepthwiseConv2D()
{
if (this->output != NULL)
{
@@ -82,19 +95,31 @@ namespace dl
* @brief Update output shape and padding.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(input.shape[0] > 0);
assert(input.shape[1] > 0);
assert(input.shape.size() == 3);
assert(this->filter->shape.size() == 4);
assert(input.shape[2] == this->filter->shape[2]);

std::vector<int> output_shape = nn::get_output_shape(input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type);
this->output->set_shape(output_shape);
this->output_shape = nn::get_output_shape(input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type, false, this->padding);
this->output->set_shape(this->output_shape);
this->output->set_exponent(this->output_exponent);

this->padding = nn::get_pad_size(output_shape, input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type);
input.set_padding_size(this->padding);
if (this->padding_type != PADDING_NOT_SET)
{
this->padding = nn::get_pad_size(this->output_shape, input.shape, this->filter->shape_with_dilation, this->stride_y, this->stride_x, this->padding_type);
}
this->output->free_element();

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -122,7 +147,12 @@ namespace dl
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}

this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

128 changes: 128 additions & 0 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_expand_dims.hpp
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,128 @@
#pragma once

#include "dl_constant.hpp"
#include "dl_variable.hpp"
#include "dl_tool.hpp"
#include "dl_layer_base.hpp"

namespace dl
{
namespace layer
{
/**
* @brief
*
* @tparam feature_t
*/
template <typename feature_t>
class ExpandDims : public Layer
{
private:
std::vector<int> output_shape; /*<! output shape of ExpandDims >*/
std::vector<int> axis; /*<! position where the new axis is placed. >*/
Tensor<feature_t> *output; /*<! output ptr of ExpandDims >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/

public:
int output_exponent;

/**
* @brief Construct a new ExpandDims object
*
* @param axis position where the new axis is placed.
* @param name name of layer
* @param inplace true: the output will store to input
* false: the output will store to a separate memory
*/
ExpandDims(std::vector<int> axis, const char *name = "ExpandDims", bool inplace = false) : Layer(name),
axis(axis), inplace(inplace), output_shape({})
{
}

/**
* @brief Destroy the ExpandDims object
*
*/
~ExpandDims()
{
if ((!this->inplace) && (this->output != NULL))
{
delete this->output;
}
}

/**
* @brief Update output shape.
*
* @param input as an input.
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input, bool print_shape = false)
{
this->output_exponent = input.exponent;
if (!this->inplace)
{
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_exponent(this->output_exponent);
this->output->set_shape(this->output_shape);
this->output->expand_dims(this->axis);
this->output->free_element();
}
else
{
this->output = &input;
this->output->set_shape(this->output_shape);
this->output->expand_dims(this->axis);
}
this->output_shape = this->output->shape;

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
* @brief Get the output
*
* @return Tensor<feature_t>& ExpandDims result
*/
Tensor<feature_t> &get_output()
{
return *this->output;
}

/**
* @brief call ExpandDims opeartion
*
* @param input
* @return Tensor<feature_t>& ExpandDims result
*/
Tensor<feature_t> &call(Tensor<feature_t> &input)
{
DL_LOG_LAYER_LATENCY_INIT();

if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->set_exponent(input.exponent);
this->output->set_shape(this->output_shape);
this->output->copy_element(input, true);
DL_LOG_LAYER_LATENCY_END(this->name, "ExpandDims");
}
else
{
DL_LOG_LAYER_LATENCY_START();
this->output->set_shape(this->output_shape);
DL_LOG_LAYER_LATENCY_END(this->name, "ExpandDims");
}
return *this->output;
}
};
} // namespace layer
} // namespace dl
120 changes: 120 additions & 0 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_flatten.hpp
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,120 @@
#pragma once

#include "dl_constant.hpp"
#include "dl_variable.hpp"
#include "dl_tool.hpp"
#include "dl_layer_base.hpp"

namespace dl
{
namespace layer
{
/**
* @brief
*
* @tparam feature_t
*/
template <typename feature_t>
class Flatten : public Layer
{
private:
int output_exponent; /*<! exponent of output >*/
Tensor<feature_t> *output; /*<! output ptr of Flatten >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of Flatten >*/

public:
/**
* @brief Construct a new Flatten object
*
* @param name name of layer
* @param inplace true: the output will store to input0
* false: the output will store to a separate memory
*/
Flatten(const char *name = "Flatten", bool inplace = false) : Layer(name), inplace(inplace), output_shape({})
{}

/**
* @brief Destroy the Flatten object
*
*/
~Flatten()
{
if ((!this->inplace) && (this->output != NULL))
{
delete this->output;
}
}

/**
* @brief Update output shape.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input, bool print_shape = false)
{
this->output_exponent = input.exponent;
this->output_shape = {input.get_size()};
if (!this->inplace)
{
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_exponent(this->output_exponent);
this->output->set_shape(this->output_shape);
this->output->free_element();
}
else
{
this->output = &input;
this->output->set_shape(this->output_shape);
}
if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
* @brief Get the output
*
* @return Tensor<feature_t>& Flatten result
*/
Tensor<feature_t> &get_output()
{
return *this->output;
}

/**
* @brief Call Flatten operation.
*
* @param input as an input
* @return Tensor<feature_t>& Flatten result
*/
Tensor<feature_t> &call(Tensor<feature_t> &input)
{
DL_LOG_LAYER_LATENCY_INIT();

if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->set_exponent(input.exponent);
this->output->flatten();
this->output->copy_element(input, true);
DL_LOG_LAYER_LATENCY_END(this->name, "flatten");
}
else
{
DL_LOG_LAYER_LATENCY_START();
this->output->flatten();
DL_LOG_LAYER_LATENCY_END(this->name, "flatten");
}
return *this->output;
}
};
} // namespace layer
} // namespace dl
167 changes: 167 additions & 0 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_fullyconnected.hpp
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,167 @@
#pragma once

#include "dl_nn_fully_connected.hpp"
#include "dl_layer_base.hpp"

namespace dl
{
namespace layer
{
/**
* @brief Activation(FullyConnected(input, filter) + bias).
*
* @tparam feature_t supports int16_t and int8_t,
* - int16_t: stands for operation in int16_t quantize
* - int8_t: stands for operation in int8_t quantize
* @tparam bias_t supports int16_t and int8_t, must specify when using int8 per-channel quantization
* - int16_t: for int16 quantization and int8 per-channel quantization
* - int8_t: for int8 per-tensor quantization
*/
template <typename feature_t, typename bias_t = feature_t>
class FullyConnected : public Layer
{
private:
const int output_exponent; /*<! exponent of output >*/
const bool flatten; /*<! true: input shape is [x1, x2, ..., xn], filter shape is [1, 1, x1 * x2 * ... * xn, output_dim], output shape is [output_dim]
false: input shape is [x1, x2, ..., xn, input_dim], filter shape is [1, 1, input_dim, output_dim], output shape is [x1, x2, ...., xn, output_dim] >*/
const Filter<feature_t> *filter; /*<! filter of FullyConnected >*/
const Bias<bias_t> *bias; /*<! bias of FullyConnected, if you don't specify anything, no bias is added >*/
const Activation<feature_t> *activation; /*<! activation of FullyConnected, if you don't specify anything, no activation is applied >*/
Tensor<feature_t> *output; /*<! output ptr of FullyConnected >*/
std::vector<int> output_shape; /*<! output shape of FullyConnected >*/

public:
/**
* @brief Construct a new FullyConnected object.
*
* @param output_exponent exponent of output
* @param filter filter of FullyConnected
* @param bias bias of FullyConnected, if you don't specify anything, no bias is added
* @param activation activation of FullyConnected, if you don't specify anything, no activation is applied
* @param flatten true: input shape is [x1, x2, ..., xn], filter shape is [1, 1, x1 * x2 * ... * xn, output_dim], output shape is [output_dim]
false: input shape is [x1, x2, ..., xn, input_dim], filter shape is [1, 1, input_dim, output_dim], output shape is [x1, x2, ...., xn, output_dim]
* @param name name of layer
*/
FullyConnected(const int output_exponent,
const Filter<feature_t> *filter,
const Bias<bias_t> *bias = NULL,
const Activation<feature_t> *activation = NULL,
const bool flatten = true,
const char *name = "FullyConnected") : Layer(name),
output_exponent(output_exponent),
flatten(flatten),
filter(filter),
bias(bias),
activation(activation),
output_shape({})
{
this->output = new Tensor<feature_t>;
}

/**
* @brief Destroy the FullyConnected object.
*
*/
~FullyConnected()
{
if (this->output != NULL)
{
delete this->output;
}
}

/**
* @brief Update output padding and input padding.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(this->filter->shape.size() == 4);
assert(this->filter->shape[0] == 1);
assert(this->filter->shape[1] == 1);
if (this->flatten)
{
assert(input.get_size() == this->filter->shape[2]);
this->output_shape = {this->filter->shape[3]};
}
else
{
assert(input.shape.back() == this->filter->shape[2]);
this->output_shape = input.shape;
this->output_shape[this->output_shape.size() - 1] = this->filter->shape[3];
}
this->output->set_shape(this->output_shape);
this->output->set_exponent(this->output_exponent);
this->output->free_element();

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
* @brief Get the output
*
* @return Tensor<feature_t>& FullyConnected result
*/
Tensor<feature_t> &get_output()
{
return *this->output;
}

/**
* @brief Call FullyConnected operation
*
* @param input as an input.
* @param autoload_enable one of true or false,
* - true: load input and output from PSRAM to CACHE automatically
* - false: do not
* @param assign_core not effective yet
* @return FullyConnected result
*/
Tensor<feature_t> &call(Tensor<feature_t> &input, bool autoload_enable = false, const std::vector<int> &assign_core = CONFIG_DEFAULT_ASSIGN_CORE)
{
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

if (autoload_enable)
{
dl::tool::cache::autoload_func((uint32_t)(this->output->element), this->output->get_size() * sizeof(feature_t),
(uint32_t)(input.element), input.get_size() * sizeof(feature_t));
}

DL_LOG_LAYER_LATENCY_START();
nn::fully_connected(*this->output, input, *(this->filter), this->bias, this->activation, this->flatten, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "fully_connected");
return *this->output;
}

/**
* @brief Preload the filter to Cache.
* NOTE: Call this layer's preload() before previous layer's call() such that filter could be loaded while previous layer is doing calculation.
*/
void preload()
{
size_t size = sizeof(feature_t);
int shape_size = this->filter->shape.size();
for (int i = 0; i < shape_size; ++i)
{
size *= filter->shape[i];
}
dl::tool::cache::preload_func((uint32_t)(this->filter->element), size);
}
};
} // namespace layer
} // namespace dl
29 changes: 22 additions & 7 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_global_avg_pool2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -20,17 +20,19 @@ namespace dl
class GlobalAveragePool2D : public Layer
{
private:
const int output_exponent; /*<! exponent of output >*/
Tensor<feature_t> *output; /*<! output ptr of GlobalAveragePool2D >*/
const int output_exponent; /*<! exponent of output >*/
std::vector<int> output_shape; /*<! output shape of GlobalAveragePool2D >*/
Tensor<feature_t> *output; /*<! output ptr of GlobalAveragePool2D >*/
public:
/**
* @brief Construct a new GlobalAveragePool2D object.
*
* @param output_exponent exponent of output
* @param name name of layer
*/
GlobalAveragePool2D(const int output_exponent, const char *name = NULL) : Layer(name),
output_exponent(output_exponent)
GlobalAveragePool2D(const int output_exponent, const char *name = "GlobalAveragePool2D") : Layer(name),
output_exponent(output_exponent),
output_shape({})

{
this->output = new Tensor<feature_t>;
@@ -52,17 +54,26 @@ namespace dl
* @brief Update output shape.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(input.shape[0] > 0);
assert(input.shape[1] > 0);
assert(input.shape.size() == 3);

std::vector<int> output_shape(input.shape.size(), 1);
output_shape[2] = input.shape[2];
this->output->set_shape(output_shape);
this->output_shape = output_shape;
this->output->set_shape(this->output_shape);
this->output->set_exponent(this->output_exponent);
this->output->free_element();

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -90,7 +101,11 @@ namespace dl
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

25 changes: 19 additions & 6 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_global_max_pool2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -20,15 +20,15 @@ namespace dl
class GlobalMaxPool2D : public Layer
{
private:
Tensor<feature_t> *output; /*<! output ptr of GlobalMaxPool2D >*/
Tensor<feature_t> *output; /*<! output ptr of GlobalMaxPool2D >*/
std::vector<int> output_shape; /*<! output shape of GlobalMaxPool2D >*/
public:

/**
* @brief Construct a new GlobalMaxPool2D object.
*
* @param name name of layer
*/
GlobalMaxPool2D(const char *name = NULL) : Layer(name)
GlobalMaxPool2D(const char *name = "GlobalMaxPool2D") : Layer(name), output_shape({})
{
this->output = new Tensor<feature_t>;
}
@@ -49,17 +49,26 @@ namespace dl
* @brief Update output shape and exponent.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(input.shape[0] > 0);
assert(input.shape[1] > 0);
assert(input.shape.size() == 3);
this->output->set_exponent(input.exponent);

std::vector<int> output_shape(input.shape.size(), 1);
output_shape[2] = input.shape[2];
this->output->set_shape(output_shape);
this->output_shape = output_shape;
this->output->set_shape(this->output_shape);
this->output->free_element();

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -87,7 +96,11 @@ namespace dl
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(input.exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

52 changes: 34 additions & 18 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_leakyrelu.hpp
View file
Original file line number Diff line number Diff line change
@@ -2,7 +2,7 @@

#include "dl_constant.hpp"
#include "dl_variable.hpp"
#include "dl_nn_LeakyReLU.hpp"
#include "dl_nn_leakyrelu.hpp"
#include "dl_layer_base.hpp"

namespace dl
@@ -20,23 +20,23 @@ namespace dl
class LeakyReLU : public Layer
{
private:
feature_t activation_alpha; /*<! quantized alpha >*/
int activation_exponent; /*<! exponent of quantized alpha >*/
Tensor<feature_t> *output; /*<! output ptr of leakyrelu>*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
feature_t activation_alpha; /*<! quantized alpha >*/
int activation_exponent; /*<! exponent of quantized alpha >*/
Tensor<feature_t> *output; /*<! output ptr of leakyrelu>*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of leakyrelu >*/
public:

/**
* @brief Construct a new LeakyReLU object
*
* @param activation_alpha quantized alpha
* @param activation_exponent exponent of quantized alpha
* @param name name of leakyrelu
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
LeakyReLU(const int activation_alpha, const int activation_exponent, const char *name = NULL, bool inplace = false) : Layer(name), output(NULL)
LeakyReLU(const int activation_alpha, const int activation_exponent, const char *name = "LeakyReLU", bool inplace = false) : Layer(name), output(NULL), output_shape({})
{
this->activation_alpha = activation_alpha;
this->activation_exponent = activation_exponent;
@@ -47,7 +47,7 @@ namespace dl
* @brief Destroy the LeakyReLU object
*
*/
~LeakyReLU()
~LeakyReLU()
{
if ((!this->inplace) && (this->output != NULL))
{
@@ -59,24 +59,32 @@ namespace dl
* @brief Update output shape and exponent
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
if(!this->inplace)
this->output_shape = input.shape;
if (!this->inplace)
{
if(this->output != NULL)
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_shape(input.shape);
}
this->output->set_shape(this->output_shape);
this->output->set_exponent(input.exponent);
this->output->free_element();
}
else
{
this->output = &input;
this->output->set_shape(this->output_shape);
}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}

}

/**
@@ -100,10 +108,14 @@ namespace dl
{
DL_LOG_LAYER_LATENCY_INIT();

if(!this->inplace)
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(input.exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -114,6 +126,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::leakyrelu<true>(*this->output, input, this->activation_alpha, this->activation_exponent, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "leakyrelu");
}
46 changes: 32 additions & 14 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_max2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -22,28 +22,28 @@ namespace dl
class Max2D : public Layer
{
private:
Tensor<feature_t> *output; /*<! output ptr of max2d >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
Tensor<feature_t> *output; /*<! output ptr of max2d >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of max2d >*/
public:

/**
* @brief Construct a new Max2D object.
*
* @param name name of max2d
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
Max2D(const char *name = NULL, bool inplace = false) : Layer(name), output(NULL)
Max2D(const char *name = "Max2D", bool inplace = false) : Layer(name),
output(NULL), inplace(inplace), output_shape({})
{
this->inplace = inplace;
}

/**
* @brief Destroy the Max2D object
*
*/
~Max2D()
~Max2D()
{
if ((!this->inplace) && (this->output != NULL))
{
@@ -58,24 +58,34 @@ namespace dl
*
* @param input0 as one input
* @param input1 as another input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1)
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1, bool print_shape = false)
{
assert(input0.is_same_shape(input1));
assert(input0.exponent == input1.exponent);
this->output_shape = input0.shape;

if(!this->inplace)
if (!this->inplace)
{
if(this->output != NULL)
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_exponent(this->output_exponent);
this->output->set_shape(input0.shape);
this->output->set_shape(this->output_shape);
this->output->free_element();
}
else
{
this->output = &input0;
}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -100,10 +110,14 @@ namespace dl
{
DL_LOG_LAYER_LATENCY_INIT();

if(!this->inplace)
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(input0.exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -114,6 +128,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::max2d<true>(*this->output, input0, input1, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "max2d");
}
61 changes: 43 additions & 18 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_max_pool2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -23,44 +23,54 @@ namespace dl
std::vector<int> filter_shape; /*<! filter shape in [filter_height, filter_width] >*/
const int stride_y; /*<! stride in height >*/
const int stride_x; /*<! stride in width >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET >*/
const padding_type_t padding_type; /*<! one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN >*/
std::vector<int> padding; /*<! padding size needed in [top, bottom, left, right] of this operation >*/
Tensor<feature_t> *output; /*<! output ptr of MaxPool2D >*/
std::vector<int> output_shape; /*<! output shape of MaxPool2D >*/

public:

/**
* @brief Construct a new MaxPool2D object.
*
* @param filter_shape filter shape in [filter_height, filter_width]
* @param padding_type one of PADDING_VALID or PADDING_SAME or PADDING_SAME_MXNET,
* @param padding_type one of PADDING_VALID or PADDING_SAME_END or PADDING_SAME_BEGIN or PADDING_NOT_SET,
* - PADDING_VALID means no padding
* PADDING_SAME and PADDING_SAME_MXNET results in padding with zeros evenly to the left/right or up/down of the input
* PADDING_SAME_END and PADDING_SAME_BEGIN results in padding with zeros evenly to the left/right or up/down of the input
* such that output has the same height/width dimension as the input,
* - PADDING_SAME results padding in TensorFlow style
* - PADDING_SAME_MXNET results padding in MXNET style
* - PADDING_SAME_END results padding in TensorFlow style
* - PADDING_SAME_BEGIN results padding in MXNET style
* - PADDING_NOT_SET means padding with the specific "padding" value below.
* @param padding if padding_type is PADDING_NOT_SET, this value will be used as padding size.
* the shape must be 4, the value of each position is: [padding top, padding bottom, padding left, padding right]
* @param stride_y stride in height
* @param stride_x stride in width
* @param name name of layer
*/
MaxPool2D(const std::vector<int> filter_shape,
const padding_type_t padding_type = PADDING_VALID,
std::vector<int> padding = {},
const int stride_y = 1,
const int stride_x = 1,
const char *name = NULL) : Layer(name),
filter_shape(filter_shape),
stride_y(stride_y),
stride_x(stride_x),
padding_type(padding_type)
const char *name = "MaxPool2D") : Layer(name),
filter_shape(filter_shape),
padding_type(padding_type),
padding(padding),
stride_y(stride_y),
stride_x(stride_x),
output_shape({})
{
this->output = new Tensor<feature_t>;
if (this->padding_type == PADDING_NOT_SET)
{
assert(this->padding.size() == 4);
}
}

/**
* @brief Destroy the MaxPool2D object.
*
*/
~MaxPool2D()
~MaxPool2D()
{
if (this->output != NULL)
{
@@ -72,18 +82,29 @@ namespace dl
* @brief Update output shape and padding.
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
assert(input.shape[0] > 0);
assert(input.shape[1] > 0);
assert(input.shape.size() == 3);

this->output->set_exponent(input.exponent);
std::vector<int> output_shape = nn::get_output_shape(input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type);
this->output->set_shape(output_shape);
this->output_shape = nn::get_output_shape(input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type, false, this->padding);
this->output->set_shape(this->output_shape);

this->padding = nn::get_pad_size(output_shape, input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type);
input.set_padding_size(this->padding);
if (this->padding_type != PADDING_NOT_SET)
{
this->padding = nn::get_pad_size(this->output_shape, input.shape, filter_shape, this->stride_y, this->stride_x, this->padding_type);
}
this->output->free_element();

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -111,7 +132,11 @@ namespace dl
DL_LOG_LAYER_LATENCY_INIT();

DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(input.exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

53 changes: 35 additions & 18 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_min2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -22,28 +22,28 @@ namespace dl
class Min2D : public Layer
{
private:
Tensor<feature_t> *output; /*<! output of ptr min2d>*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
public:

Tensor<feature_t> *output; /*<! output of ptr min2d>*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of min2d >*/
public:
/**
* @brief Construct a new Min2D object
*
* @param name name of min2d
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
Min2D(const char *name = NULL, bool inplace = false) : Layer(name), output(NULL)
{
this->inplace = inplace;
}
Min2D(const char *name = "Min2D", bool inplace = false) : Layer(name),
output(NULL),
inplace(inplace),
output_shape({}) {}

/**
* @brief Destroy the Min2D object
*
*/
~Min2D()
~Min2D()
{
if ((!this->inplace) && (this->output != NULL))
{
@@ -58,25 +58,34 @@ namespace dl
*
* @param input0 as one input
* @param input1 as another input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1)
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1, bool print_shape = false)
{
assert(input0.is_same_shape(input1));
assert(input0.exponent == input1.exponent);
this->output_shape = input0.shape;

if(!this->inplace)
if (!this->inplace)
{
if(this->output != NULL)
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_shape(input0.shape);
this->output->set_shape(this->output_shape);
this->output->set_exponent(input0.exponent);
this->output->free_element();
}
else
{
this->output = &input0;

}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -101,10 +110,14 @@ namespace dl
{
DL_LOG_LAYER_LATENCY_INIT();

if(!this->inplace)
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(input0.exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -115,6 +128,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::min2d<true>(*this->output, input0, input1, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "min2d");
}
55 changes: 39 additions & 16 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_mul2d.hpp
View file
Original file line number Diff line number Diff line change
@@ -21,33 +21,38 @@ namespace dl
class Mul2D : public Layer
{
private:
const int output_exponent; /*<! exponent of output >*/
const int output_exponent; /*<! exponent of output >*/
const Activation<feature_t> *activation; /*<! activation of Mul2D, if you don't specify anything, no activation is applied >*/
Tensor<feature_t> *output; /*<! output ptr of Mul2D >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
Tensor<feature_t> *output; /*<! output ptr of Mul2D >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of Mul2D >*/
public:
const int output_exponent; /*<! exponent of output >*/

/**
* @brief Construct a new Mul2D object.
*
* @param output_exponent exponent of output
* @param activation activation of Mul2D, if you don't specify anything, no activation is applied
* @param name name of layer
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
Mul2D(const int output_exponent, const Activation<feature_t> *activation = NULL, const char *name = NULL, bool inplace = false) : Layer(name),
output_exponent(output_exponent),activation(activation), output(NULL)
Mul2D(const int output_exponent,
const Activation<feature_t> *activation = NULL,
const char *name = "Mul2D",
bool inplace = false) : Layer(name),
output_exponent(output_exponent),
activation(activation),
output(NULL),
inplace(inplace),
output_shape({})
{
this->inplace = inplace;
}

/**
* @brief Destroy the Multiply2D object.
*/
~Mul2D()
~Mul2D()
{
if ((!this->inplace) && (this->output != NULL))
{
@@ -61,24 +66,34 @@ namespace dl
*
* @param input0 as one input
* @param input1 as another input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1)
void build(Tensor<feature_t> &input0, Tensor<feature_t> &input1, bool print_shape = false)
{
assert(input0.is_same_shape(input1));
this->output_shape = input0.shape;

if (!this->inplace)
{
if(this->output != NULL)
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_exponent(this->output_exponent);
this->output->set_shape(input0.shape);
this->output->set_shape(this->output_shape);
this->output->free_element();
}

else
{
this->output = &input0;
}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -106,7 +121,11 @@ namespace dl
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(this->output_exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -117,6 +136,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::mul2d<true>(*this->output, input0, input1, this->activation, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "mul2d");
}
49 changes: 35 additions & 14 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_prelu.hpp
View file
Original file line number Diff line number Diff line change
@@ -24,30 +24,35 @@ namespace dl
int activation_exponent; /*<! exponent of quantized alpha elements >*/
Tensor<feature_t> *output; /*<! output ptr of prelu >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of prelu >*/
public:

/**
* @brief Construct a new PReLU object
*
* @param activation_element quantized alpha elements along channel axis
* @param activation_exponent exponent of quantized alpha elements
* @param name name of prelu
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
PReLU(const feature_t *activation_element, const int activation_exponent = 0, const char *name = NULL, bool inplace = false) : Layer(name), output(NULL)
PReLU(const feature_t *activation_element,
const int activation_exponent = 0,
const char *name = NULL,
bool inplace = "PReLU") : Layer(name),
activation_element(activation_element),
activation_exponent(activation_exponent),
output(NULL),
inplace(inplace),
output_shape({})
{
this->activation_element = activation_element;
this->activation_exponent = activation_exponent;
this->inplace = inplace;
}

/**
* @brief Destroy the PReLU object
*
*/
~PReLU()
~PReLU()
{
if ((!this->inplace) && (this->output != NULL))
{
@@ -59,23 +64,31 @@ namespace dl
* @brief Update output shape and exponent
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
if(!this->inplace)
this->output_shape = input.shape;
if (!this->inplace)
{
if(this->output != NULL)
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_exponent(input.exponent);
this->output->set_shape(input.shape);
this->output->set_shape(this->output_shape);
this->output->free_element();
}
else
{
this->output = &input;
}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -99,11 +112,15 @@ namespace dl
{
DL_LOG_LAYER_LATENCY_INIT();

if(!this->inplace)
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->set_exponent(input.exponent);
this->output->apply_element();
this->output->malloc_element();
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

DL_LOG_LAYER_LATENCY_START();
@@ -113,6 +130,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::prelu(*this->output, input, this->activation_element, this->activation_exponent, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "leakyrelu");
}
45 changes: 30 additions & 15 deletions tools/sdk/esp32/include/esp-face/include/layer/dl_layer_relu.hpp
View file
Original file line number Diff line number Diff line change
@@ -21,29 +21,28 @@ namespace dl
class ReLU : public Layer
{
private:
Tensor<feature_t> *output; /*<! output ptr of relu >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a seperate memeory >*/
Tensor<feature_t> *output; /*<! output ptr of relu >*/
bool inplace; /*<! true: the output will store to input0
false: the output will store to a separate memory >*/
std::vector<int> output_shape; /*<! output shape of relu >*/
public:


/**
* @brief Construct a new ReLU object
*
* @param name name of relu
* @param inplace true: the output will store to input0
* false: the output will store to a seperate memeory
* false: the output will store to a separate memory
*/
ReLU(const char *name = NULL, bool inplace = false) : Layer(name), output(NULL)
ReLU(const char *name = "ReLU", bool inplace = false) : Layer(name),
output(NULL), inplace(inplace), output_shape({})
{
this->inplace = inplace;
}

/**
* @brief Destroy the ReLU object
*
*/
~ReLU()
~ReLU()
{
if ((!this->inplace) && (this->output != NULL))
{
@@ -55,23 +54,31 @@ namespace dl
* @brief Update output shape and exponent
*
* @param input as an input
* @param print_shape whether to print the output shape.
*/
void build(Tensor<feature_t> &input)
void build(Tensor<feature_t> &input, bool print_shape = false)
{
if(!this->inplace)
this->output_shape = input.shape;
if (!this->inplace)
{
if(this->output != NULL)
if (this->output != NULL)
{
this->output = new Tensor<feature_t>;
}
this->output->set_exponent(input.exponent);
this->output->set_shape(input.shape);
this->output->set_shape(this->output_shape);
this->output->free_element();
}
else
{
this->output = &input;
}

if (print_shape)
{
std::cout << this->name << " | ";
this->output->print_shape();
}
}

/**
@@ -95,10 +102,14 @@ namespace dl
{
DL_LOG_LAYER_LATENCY_INIT();

if(!this->inplace)
if (!this->inplace)
{
DL_LOG_LAYER_LATENCY_START();
this->output->apply_element();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
this->output->malloc_element();
this->output->set_exponent(input.exponent);
DL_LOG_LAYER_LATENCY_END(this->name, "apply");

@@ -109,6 +120,10 @@ namespace dl
else
{
DL_LOG_LAYER_LATENCY_START();
if (this->output->shape != this->output_shape)
{
this->output->set_shape(this->output_shape);
}
nn::relu(*this->output, input, assign_core);
DL_LOG_LAYER_LATENCY_END(this->name, "relu");
}
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