Revert "Optimization: [EN] changed translator"

This reverts commit 68fbe6801e.
This commit is contained in:
kyO The NinjA 2020-06-03 13:51:54 -03:00
parent 68fbe6801e
commit 2c3919c2e1
38 changed files with 23 additions and 16025 deletions

Binary file not shown.

Before

Width:  |  Height:  |  Size: 301 KiB

View File

@ -1,7 +1,2 @@
@echo off @echo off
adb connect 127.0.0.1:5555
python -m uiautomator2 init
Py alas_en.pyw Py alas_en.pyw

Binary file not shown.

Before

Width:  |  Height:  |  Size: 10 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 9.2 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 18 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 18 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 13 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 26 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 9.2 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 14 KiB

After

Width:  |  Height:  |  Size: 10 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 10 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 9.2 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 18 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 18 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 13 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 26 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 9.2 KiB

View File

@ -6,7 +6,6 @@ from module.logger import logger
MAP = CampaignMap('10-2') MAP = CampaignMap('10-2')
MAP.shape = 'H6' MAP.shape = 'H6'
MAP.camera_data = ['E2', 'E4']
MAP.map_data = ''' MAP.map_data = '''
MB -- ME ME -- ++ ME MB MB -- ME ME -- ++ ME MB
-- ++ ++ ME ME ++ ME ME -- ++ ++ ME ME ++ ME ME
@ -23,6 +22,15 @@ MAP.map_data = '''
# 10 10 10 10 10 10 10 10 # 10 10 10 10 10 10 10 10
# 10 10 10 10 10 10 10 10 # 10 10 10 10 10 10 10 10
# ''' # '''
# MAP.camera_data = ['D3']
# MAP.spawn_data = [
# {'battle': 0, 'enemy': 3},
# {'battle': 1, 'enemy': 2, 'mystery': 1},
# {'battle': 2, 'enemy': 2, 'mystery': 1},
# {'battle': 3, 'enemy': 1, 'mystery': 2},
# {'battle': 4, 'enemy': 1},
# {'battle': 5, 'boss': 1},
# ]
A1, B1, C1, D1, E1, F1, G1, H1, \ A1, B1, C1, D1, E1, F1, G1, H1, \
A2, B2, C2, D2, E2, F2, G2, H2, \ A2, B2, C2, D2, E2, F2, G2, H2, \
@ -34,22 +42,7 @@ A6, B6, C6, D6, E6, F6, G6, H6, \
class Config: class Config:
INTERNAL_LINES_HOUGHLINES_THRESHOLD = 50 pass
EDGE_LINES_HOUGHLINES_THRESHOLD = 10
COINCIDENT_POINT_ENCOURAGE_DISTANCE = 1.5
INTERNAL_LINES_FIND_PEAKS_PARAMETERS = {
'height': (150, 255 - 24),
'width': (0.9, 10),
'prominence': 10,
'distance': 35,
}
EDGE_LINES_FIND_PEAKS_PARAMETERS = {
'height': (255 - 24, 255),
'prominence': 10,
'distance': 50,
'width': (0, 10),
'wlen': 1000,
}
class Campaign(CampaignBase): class Campaign(CampaignBase):

View File

@ -1,56 +0,0 @@
from module.campaign.campaign_base import CampaignBase
from module.map.map_base import CampaignMap
from module.map.map_grids import SelectedGrids, RoadGrids
from module.logger import logger
MAP = CampaignMap('a1')
MAP.shape = 'I5'
MAP.map_data = '''
SP -- ++ ME -- ME ++ -- --
-- ME -- -- ME -- ME ++ ME
-- -- MS -- -- MS __ -- --
-- ME -- -- ++ ME -- ME --
SP -- -- ME ++ -- ME -- MB
'''
MAP.camera_data = ['D1', 'D3', 'F1', 'F3']
class Config:
POOR_MAP_DATA = True
MAP_HAS_AMBUSH = False
MAP_HAS_FLEET_STEP = True
MAP_HAS_MOVABLE_ENEMY = True
MAP_HAS_SIREN = True
MAP_HAS_DYNAMIC_RED_BORDER = True
MAP_SIREN_COUNT = 1
TRUST_EDGE_LINES = True
INTERNAL_LINES_HOUGHLINES_THRESHOLD = 40
EDGE_LINES_HOUGHLINES_THRESHOLD = 40
COINCIDENT_POINT_ENCOURAGE_DISTANCE = 1.5
MID_DIFF_RANGE_H = (140 - 3, 140 + 3)
MID_DIFF_RANGE_V = (143 - 3, 143 + 3)
INTERNAL_LINES_FIND_PEAKS_PARAMETERS = {
'height': (80, 255 - 40),
'width': (0.9, 10),
'prominence': 10,
'distance': 35,
}
EDGE_LINES_FIND_PEAKS_PARAMETERS = {
'height': (255 - 40, 255),
'prominence': 10,
'distance': 50,
'wlen': 1000
}
class Campaign(CampaignBase):
MAP = MAP
def handle_boss_appear_refocus(self):
if self.battle_count == 4:
self.map_swipe((-3, -2))
return super().handle_boss_appear_refocus()

View File

@ -62,17 +62,17 @@ class Campaign(CampaignBase):
MAP = MAP MAP = MAP
def battle_0(self): def battle_0(self):
if self.fleet_2.clear_siren(): if self.clear_siren():
return True return True
if self.fleet_1.clear_enemy(scale=(2, 3)): if self.clear_enemy(scale=(2, 3)):
return True return True
return self.battle_default() return self.battle_default()
def battle_5(self): def battle_5(self):
if self.fleet_1.clear_enemy(scale=(1,)): if self.clear_enemy(scale=(1,)):
return True return True
if self.fleet_1.clear_enemy(scale=(2,)): if self.clear_enemy(scale=(2,)):
return True return True
return self.battle_default() return self.battle_default()

View File

@ -1,976 +0,0 @@
{
"nodes": [
{
"op": "null",
"name": "data",
"inputs": []
},
{
"op": "null",
"name": "conv-0_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-0_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-0",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": [[0, 0, 0], [1, 0, 0], [2, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-0_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-0",
"inputs": [[3, 0, 0], [4, 0, 0], [5, 0, 0], [6, 0, 1], [7, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-0",
"inputs": [[8, 0, 0]]
},
{
"op": "null",
"name": "conv-1_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-1_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-1",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": [[9, 0, 0], [10, 0, 0], [11, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-1_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-1",
"inputs": [[12, 0, 0], [13, 0, 0], [14, 0, 0], [15, 0, 1], [16, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-1",
"inputs": [[17, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-0",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 2)"
},
"inputs": [[18, 0, 0]]
},
{
"op": "null",
"name": "conv-2_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-2_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-2",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[19, 0, 0], [20, 0, 0], [21, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-2_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-2",
"inputs": [[22, 0, 0], [23, 0, 0], [24, 0, 0], [25, 0, 1], [26, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-2",
"inputs": [[27, 0, 0]]
},
{
"op": "null",
"name": "conv-3_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-3_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-3",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": [[28, 0, 0], [29, 0, 0], [30, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-3_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-3",
"inputs": [[31, 0, 0], [32, 0, 0], [33, 0, 0], [34, 0, 1], [35, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-3",
"inputs": [[36, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-1",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 2)"
},
"inputs": [[37, 0, 0]]
},
{
"op": "null",
"name": "conv-4-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[38, 0, 0], [39, 0, 0], [40, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4-1",
"inputs": [[41, 0, 0]]
},
{
"op": "null",
"name": "conv-4_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[42, 0, 0], [43, 0, 0], [44, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4-2",
"inputs": [[45, 0, 0]]
},
{
"op": "null",
"name": "conv-4-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[46, 0, 0], [47, 0, 0], [48, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-4_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-4",
"inputs": [[49, 0, 0], [50, 0, 0], [51, 0, 0], [52, 0, 1], [53, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4",
"inputs": [[54, 0, 0]]
},
{
"op": "null",
"name": "conv-5-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[55, 0, 0], [56, 0, 0], [57, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5-1",
"inputs": [[58, 0, 0]]
},
{
"op": "null",
"name": "conv-5_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[59, 0, 0], [60, 0, 0], [61, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5-2",
"inputs": [[62, 0, 0]]
},
{
"op": "null",
"name": "conv-5-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[63, 0, 0], [64, 0, 0], [65, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-5_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-5",
"inputs": [[66, 0, 0], [67, 0, 0], [68, 0, 0], [69, 0, 1], [70, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5",
"inputs": [[71, 0, 0]]
},
{
"op": "elemwise_add",
"name": "_plus0",
"inputs": [[72, 0, 0], [38, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-2",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 1)"
},
"inputs": [[73, 0, 0]]
},
{
"op": "null",
"name": "conv-6-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[74, 0, 0], [75, 0, 0], [76, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6-1",
"inputs": [[77, 0, 0]]
},
{
"op": "null",
"name": "conv-6_weight",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6_bias",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[78, 0, 0], [79, 0, 0], [80, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6-2",
"inputs": [[81, 0, 0]]
},
{
"op": "null",
"name": "conv-6-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[82, 0, 0], [83, 0, 0], [84, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-6_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-6",
"inputs": [[85, 0, 0], [86, 0, 0], [87, 0, 0], [88, 0, 1], [89, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6",
"inputs": [[90, 0, 0]]
},
{
"op": "Dropout",
"name": "dropout0",
"attrs": {"p": "0.5"},
"inputs": [[91, 0, 0]]
},
{
"op": "squeeze",
"name": "squeeze0",
"attrs": {"axis": "2"},
"inputs": [[92, 0, 0]]
},
{
"op": "transpose",
"name": "transpose0",
"attrs": {"axes": "(2, 0, 1)"},
"inputs": [[93, 0, 0]]
},
{
"op": "null",
"name": "seq-fc_weight",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": []
},
{
"op": "null",
"name": "seq-fc_bias",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": []
},
{
"op": "FullyConnected",
"name": "seq-fc",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": [[94, 0, 0], [95, 0, 0], [96, 0, 0]]
},
{
"op": "Activation",
"name": "seq-relu",
"attrs": {"act_type": "relu"},
"inputs": [[97, 0, 0]]
},
{
"op": "Reshape",
"name": "reshape0",
"attrs": {"shape": "(-3, -2)"},
"inputs": [[98, 0, 0]]
},
{
"op": "null",
"name": "pred_fc_weight",
"attrs": {"num_hidden": "6426"},
"inputs": []
},
{
"op": "null",
"name": "pred_fc_bias",
"attrs": {"num_hidden": "6426"},
"inputs": []
},
{
"op": "FullyConnected",
"name": "pred_fc",
"attrs": {"num_hidden": "6426"},
"inputs": [[99, 0, 0], [100, 0, 0], [101, 0, 0]]
},
{
"op": "SoftmaxActivation",
"name": "softmaxactivation0",
"inputs": [[102, 0, 0]]
},
{
"op": "MakeLoss",
"name": "makeloss1",
"inputs": [[103, 0, 0]]
},
{
"op": "BlockGrad",
"name": "blockgrad0",
"inputs": [[104, 0, 0]]
},
{
"op": "Reshape",
"name": "reshape1",
"attrs": {"shape": "(-4, 69, -1, 0)"},
"inputs": [[102, 0, 0]]
},
{
"op": "null",
"name": "label",
"inputs": []
},
{
"op": "CTCLoss",
"name": "ctc_loss0",
"inputs": [[106, 0, 0], [107, 0, 0]]
},
{
"op": "MakeLoss",
"name": "makeloss0",
"inputs": [[108, 0, 0]]
}
],
"arg_nodes": [
0,
1,
2,
4,
5,
6,
7,
10,
11,
13,
14,
15,
16,
20,
21,
23,
24,
25,
26,
29,
30,
32,
33,
34,
35,
39,
40,
43,
44,
47,
48,
50,
51,
52,
53,
56,
57,
60,
61,
64,
65,
67,
68,
69,
70,
75,
76,
79,
80,
83,
84,
86,
87,
88,
89,
95,
96,
100,
101,
107
],
"node_row_ptr": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
11,
12,
13,
14,
15,
16,
17,
18,
19,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
34,
35,
36,
37,
38,
39,
40,
41,
42,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93,
94,
95,
96,
97,
98,
99,
100,
101,
102,
105,
106,
108,
109,
110,
111,
112,
113,
114,
115,
116,
117,
118,
119,
120,
121,
122,
123,
125,
126
],
"heads": [[105, 0, 0], [109, 0, 0]],
"attrs": {"mxnet_version": ["int", 10600]}
}

File diff suppressed because it is too large Load Diff

View File

@ -1,976 +0,0 @@
{
"nodes": [
{
"op": "null",
"name": "data",
"inputs": []
},
{
"op": "null",
"name": "conv-0_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-0_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-0",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": [[0, 0, 0], [1, 0, 0], [2, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-0_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-0",
"inputs": [[3, 0, 0], [4, 0, 0], [5, 0, 0], [6, 0, 1], [7, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-0",
"inputs": [[8, 0, 0]]
},
{
"op": "null",
"name": "conv-1_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-1_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-1",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": [[9, 0, 0], [10, 0, 0], [11, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-1_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-1",
"inputs": [[12, 0, 0], [13, 0, 0], [14, 0, 0], [15, 0, 1], [16, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-1",
"inputs": [[17, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-0",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 2)"
},
"inputs": [[18, 0, 0]]
},
{
"op": "null",
"name": "conv-2_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-2_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-2",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[19, 0, 0], [20, 0, 0], [21, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-2_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-2",
"inputs": [[22, 0, 0], [23, 0, 0], [24, 0, 0], [25, 0, 1], [26, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-2",
"inputs": [[27, 0, 0]]
},
{
"op": "null",
"name": "conv-3_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-3_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-3",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": [[28, 0, 0], [29, 0, 0], [30, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-3_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-3",
"inputs": [[31, 0, 0], [32, 0, 0], [33, 0, 0], [34, 0, 1], [35, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-3",
"inputs": [[36, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-1",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 2)"
},
"inputs": [[37, 0, 0]]
},
{
"op": "null",
"name": "conv-4-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[38, 0, 0], [39, 0, 0], [40, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4-1",
"inputs": [[41, 0, 0]]
},
{
"op": "null",
"name": "conv-4_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[42, 0, 0], [43, 0, 0], [44, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4-2",
"inputs": [[45, 0, 0]]
},
{
"op": "null",
"name": "conv-4-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[46, 0, 0], [47, 0, 0], [48, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-4_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-4",
"inputs": [[49, 0, 0], [50, 0, 0], [51, 0, 0], [52, 0, 1], [53, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4",
"inputs": [[54, 0, 0]]
},
{
"op": "null",
"name": "conv-5-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[55, 0, 0], [56, 0, 0], [57, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5-1",
"inputs": [[58, 0, 0]]
},
{
"op": "null",
"name": "conv-5_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[59, 0, 0], [60, 0, 0], [61, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5-2",
"inputs": [[62, 0, 0]]
},
{
"op": "null",
"name": "conv-5-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[63, 0, 0], [64, 0, 0], [65, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-5_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-5",
"inputs": [[66, 0, 0], [67, 0, 0], [68, 0, 0], [69, 0, 1], [70, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5",
"inputs": [[71, 0, 0]]
},
{
"op": "elemwise_add",
"name": "_plus0",
"inputs": [[72, 0, 0], [38, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-2",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 1)"
},
"inputs": [[73, 0, 0]]
},
{
"op": "null",
"name": "conv-6-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[74, 0, 0], [75, 0, 0], [76, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6-1",
"inputs": [[77, 0, 0]]
},
{
"op": "null",
"name": "conv-6_weight",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6_bias",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[78, 0, 0], [79, 0, 0], [80, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6-2",
"inputs": [[81, 0, 0]]
},
{
"op": "null",
"name": "conv-6-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[82, 0, 0], [83, 0, 0], [84, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-6_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-6",
"inputs": [[85, 0, 0], [86, 0, 0], [87, 0, 0], [88, 0, 1], [89, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6",
"inputs": [[90, 0, 0]]
},
{
"op": "Dropout",
"name": "dropout0",
"attrs": {"p": "0.5"},
"inputs": [[91, 0, 0]]
},
{
"op": "squeeze",
"name": "squeeze0",
"attrs": {"axis": "2"},
"inputs": [[92, 0, 0]]
},
{
"op": "transpose",
"name": "transpose0",
"attrs": {"axes": "(2, 0, 1)"},
"inputs": [[93, 0, 0]]
},
{
"op": "null",
"name": "seq-fc_weight",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": []
},
{
"op": "null",
"name": "seq-fc_bias",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": []
},
{
"op": "FullyConnected",
"name": "seq-fc",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": [[94, 0, 0], [95, 0, 0], [96, 0, 0]]
},
{
"op": "Activation",
"name": "seq-relu",
"attrs": {"act_type": "relu"},
"inputs": [[97, 0, 0]]
},
{
"op": "Reshape",
"name": "reshape0",
"attrs": {"shape": "(-3, -2)"},
"inputs": [[98, 0, 0]]
},
{
"op": "null",
"name": "pred_fc_weight",
"attrs": {"num_hidden": "6426"},
"inputs": []
},
{
"op": "null",
"name": "pred_fc_bias",
"attrs": {"num_hidden": "6426"},
"inputs": []
},
{
"op": "FullyConnected",
"name": "pred_fc",
"attrs": {"num_hidden": "6426"},
"inputs": [[99, 0, 0], [100, 0, 0], [101, 0, 0]]
},
{
"op": "SoftmaxActivation",
"name": "softmaxactivation0",
"inputs": [[102, 0, 0]]
},
{
"op": "MakeLoss",
"name": "makeloss1",
"inputs": [[103, 0, 0]]
},
{
"op": "BlockGrad",
"name": "blockgrad0",
"inputs": [[104, 0, 0]]
},
{
"op": "Reshape",
"name": "reshape1",
"attrs": {"shape": "(-4, 69, -1, 0)"},
"inputs": [[102, 0, 0]]
},
{
"op": "null",
"name": "label",
"inputs": []
},
{
"op": "CTCLoss",
"name": "ctc_loss0",
"inputs": [[106, 0, 0], [107, 0, 0]]
},
{
"op": "MakeLoss",
"name": "makeloss0",
"inputs": [[108, 0, 0]]
}
],
"arg_nodes": [
0,
1,
2,
4,
5,
6,
7,
10,
11,
13,
14,
15,
16,
20,
21,
23,
24,
25,
26,
29,
30,
32,
33,
34,
35,
39,
40,
43,
44,
47,
48,
50,
51,
52,
53,
56,
57,
60,
61,
64,
65,
67,
68,
69,
70,
75,
76,
79,
80,
83,
84,
86,
87,
88,
89,
95,
96,
100,
101,
107
],
"node_row_ptr": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
11,
12,
13,
14,
15,
16,
17,
18,
19,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
34,
35,
36,
37,
38,
39,
40,
41,
42,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93,
94,
95,
96,
97,
98,
99,
100,
101,
102,
105,
106,
108,
109,
110,
111,
112,
113,
114,
115,
116,
117,
118,
119,
120,
121,
122,
123,
125,
126
],
"heads": [[105, 0, 0], [109, 0, 0]],
"attrs": {"mxnet_version": ["int", 10600]}
}

File diff suppressed because it is too large Load Diff

View File

@ -1,976 +0,0 @@
{
"nodes": [
{
"op": "null",
"name": "data",
"inputs": []
},
{
"op": "null",
"name": "conv-0_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-0_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-0",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "64",
"pad": "(1, 1)"
},
"inputs": [[0, 0, 0], [1, 0, 0], [2, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-0_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-0_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-0",
"inputs": [[3, 0, 0], [4, 0, 0], [5, 0, 0], [6, 0, 1], [7, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-0",
"inputs": [[8, 0, 0]]
},
{
"op": "null",
"name": "conv-1_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-1_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-1",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "128",
"pad": "(1, 1)"
},
"inputs": [[9, 0, 0], [10, 0, 0], [11, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-1_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-1_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-1",
"inputs": [[12, 0, 0], [13, 0, 0], [14, 0, 0], [15, 0, 1], [16, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-1",
"inputs": [[17, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-0",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 2)"
},
"inputs": [[18, 0, 0]]
},
{
"op": "null",
"name": "conv-2_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-2_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-2",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[19, 0, 0], [20, 0, 0], [21, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-2_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-2_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-2",
"inputs": [[22, 0, 0], [23, 0, 0], [24, 0, 0], [25, 0, 1], [26, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-2",
"inputs": [[27, 0, 0]]
},
{
"op": "null",
"name": "conv-3_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-3_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-3",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "512",
"pad": "(1, 1)"
},
"inputs": [[28, 0, 0], [29, 0, 0], [30, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-3_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-3_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-3",
"inputs": [[31, 0, 0], [32, 0, 0], [33, 0, 0], [34, 0, 1], [35, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-3",
"inputs": [[36, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-1",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 2)"
},
"inputs": [[37, 0, 0]]
},
{
"op": "null",
"name": "conv-4-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[38, 0, 0], [39, 0, 0], [40, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4-1",
"inputs": [[41, 0, 0]]
},
{
"op": "null",
"name": "conv-4_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[42, 0, 0], [43, 0, 0], [44, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4-2",
"inputs": [[45, 0, 0]]
},
{
"op": "null",
"name": "conv-4-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-4-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-4-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[46, 0, 0], [47, 0, 0], [48, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-4_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-4_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-4",
"inputs": [[49, 0, 0], [50, 0, 0], [51, 0, 0], [52, 0, 1], [53, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-4",
"inputs": [[54, 0, 0]]
},
{
"op": "null",
"name": "conv-5-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[55, 0, 0], [56, 0, 0], [57, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5-1",
"inputs": [[58, 0, 0]]
},
{
"op": "null",
"name": "conv-5_weight",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5_bias",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5",
"attrs": {
"kernel": "(3, 3)",
"num_filter": "256",
"pad": "(1, 1)"
},
"inputs": [[59, 0, 0], [60, 0, 0], [61, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5-2",
"inputs": [[62, 0, 0]]
},
{
"op": "null",
"name": "conv-5-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-5-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-5-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[63, 0, 0], [64, 0, 0], [65, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-5_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-5_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-5",
"inputs": [[66, 0, 0], [67, 0, 0], [68, 0, 0], [69, 0, 1], [70, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-5",
"inputs": [[71, 0, 0]]
},
{
"op": "elemwise_add",
"name": "_plus0",
"inputs": [[72, 0, 0], [38, 0, 0]]
},
{
"op": "Pooling",
"name": "pool-2",
"attrs": {
"kernel": "(2, 2)",
"pool_type": "max",
"stride": "(2, 1)"
},
"inputs": [[73, 0, 0]]
},
{
"op": "null",
"name": "conv-6-1-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6-1-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6-1-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[74, 0, 0], [75, 0, 0], [76, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6-1",
"inputs": [[77, 0, 0]]
},
{
"op": "null",
"name": "conv-6_weight",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6_bias",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6",
"attrs": {
"kernel": "(4, 1)",
"num_filter": "256",
"pad": "(0, 0)"
},
"inputs": [[78, 0, 0], [79, 0, 0], [80, 0, 0]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6-2",
"inputs": [[81, 0, 0]]
},
{
"op": "null",
"name": "conv-6-2-1x1_weight",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "null",
"name": "conv-6-2-1x1_bias",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": []
},
{
"op": "Convolution",
"name": "conv-6-2-1x1",
"attrs": {
"kernel": "(1, 1)",
"num_filter": "512",
"pad": "(0, 0)"
},
"inputs": [[82, 0, 0], [83, 0, 0], [84, 0, 0]]
},
{
"op": "null",
"name": "batchnorm-6_gamma",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_beta",
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_moving_mean",
"attrs": {"__init__": "[\"zero\", {}]"},
"inputs": []
},
{
"op": "null",
"name": "batchnorm-6_moving_var",
"attrs": {"__init__": "[\"one\", {}]"},
"inputs": []
},
{
"op": "BatchNorm",
"name": "batchnorm-6",
"inputs": [[85, 0, 0], [86, 0, 0], [87, 0, 0], [88, 0, 1], [89, 0, 1]]
},
{
"op": "LeakyReLU",
"name": "leakyrelu-6",
"inputs": [[90, 0, 0]]
},
{
"op": "Dropout",
"name": "dropout0",
"attrs": {"p": "0.5"},
"inputs": [[91, 0, 0]]
},
{
"op": "squeeze",
"name": "squeeze0",
"attrs": {"axis": "2"},
"inputs": [[92, 0, 0]]
},
{
"op": "transpose",
"name": "transpose0",
"attrs": {"axes": "(2, 0, 1)"},
"inputs": [[93, 0, 0]]
},
{
"op": "null",
"name": "seq-fc_weight",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": []
},
{
"op": "null",
"name": "seq-fc_bias",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": []
},
{
"op": "FullyConnected",
"name": "seq-fc",
"attrs": {
"flatten": "False",
"num_hidden": "200"
},
"inputs": [[94, 0, 0], [95, 0, 0], [96, 0, 0]]
},
{
"op": "Activation",
"name": "seq-relu",
"attrs": {"act_type": "relu"},
"inputs": [[97, 0, 0]]
},
{
"op": "Reshape",
"name": "reshape0",
"attrs": {"shape": "(-3, -2)"},
"inputs": [[98, 0, 0]]
},
{
"op": "null",
"name": "pred_fc_weight",
"attrs": {"num_hidden": "6426"},
"inputs": []
},
{
"op": "null",
"name": "pred_fc_bias",
"attrs": {"num_hidden": "6426"},
"inputs": []
},
{
"op": "FullyConnected",
"name": "pred_fc",
"attrs": {"num_hidden": "6426"},
"inputs": [[99, 0, 0], [100, 0, 0], [101, 0, 0]]
},
{
"op": "SoftmaxActivation",
"name": "softmaxactivation0",
"inputs": [[102, 0, 0]]
},
{
"op": "MakeLoss",
"name": "makeloss1",
"inputs": [[103, 0, 0]]
},
{
"op": "BlockGrad",
"name": "blockgrad0",
"inputs": [[104, 0, 0]]
},
{
"op": "Reshape",
"name": "reshape1",
"attrs": {"shape": "(-4, 69, -1, 0)"},
"inputs": [[102, 0, 0]]
},
{
"op": "null",
"name": "label",
"inputs": []
},
{
"op": "CTCLoss",
"name": "ctc_loss0",
"inputs": [[106, 0, 0], [107, 0, 0]]
},
{
"op": "MakeLoss",
"name": "makeloss0",
"inputs": [[108, 0, 0]]
}
],
"arg_nodes": [
0,
1,
2,
4,
5,
6,
7,
10,
11,
13,
14,
15,
16,
20,
21,
23,
24,
25,
26,
29,
30,
32,
33,
34,
35,
39,
40,
43,
44,
47,
48,
50,
51,
52,
53,
56,
57,
60,
61,
64,
65,
67,
68,
69,
70,
75,
76,
79,
80,
83,
84,
86,
87,
88,
89,
95,
96,
100,
101,
107
],
"node_row_ptr": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
11,
12,
13,
14,
15,
16,
17,
18,
19,
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
34,
35,
36,
37,
38,
39,
40,
41,
42,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93,
94,
95,
96,
97,
98,
99,
100,
101,
102,
105,
106,
108,
109,
110,
111,
112,
113,
114,
115,
116,
117,
118,
119,
120,
121,
122,
123,
125,
126
],
"heads": [[105, 0, 0], [109, 0, 0]],
"attrs": {"mxnet_version": ["int", 10600]}
}

Binary file not shown.

Before

Width:  |  Height:  |  Size: 301 KiB

View File

@ -1,5 +0,0 @@
@echo off
python -m dev_tools.button_extract

View File

@ -29,7 +29,7 @@ dic_true_eng_to_eng = {
'emulator': 'emulator', 'emulator': 'emulator',
'daily': 'daily', 'daily': 'daily',
'event_daily_bonus': 'event_daily_ab', 'event_daily_bonus': 'event_daily_ab',
'Main_campaign': 'main', 'main': 'main',
'event': 'event', 'event': 'event',
'semi_auto': 'semi_auto', 'semi_auto': 'semi_auto',
'c7-2_mystery_farming': 'c72_mystery_farming', 'c7-2_mystery_farming': 'c72_mystery_farming',

View File

@ -2,7 +2,7 @@ from module.campaign.run import CampaignRun
RECORD_SINCE = (0,) RECORD_SINCE = (0,)
CAMPAIGN_NAME = ['c1', 'c2', 'c3'] CAMPAIGN_NAME = ['a1', 'a2', 'a3', 'b1', 'b2', 'b3']
# CAMPAIGN_ALL = ['a1', 'a2', 'a3', 'b1', 'b2', 'b3', 'c1', 'c2', 'c3', 'd1', 'd2', 'd3'] # Trying implement all event maps # CAMPAIGN_ALL = ['a1', 'a2', 'a3', 'b1', 'b2', 'b3', 'c1', 'c2', 'c3', 'd1', 'd2', 'd3'] # Trying implement all event maps

View File

@ -22,7 +22,7 @@ IN_MAP = Button(area={'cn': (749, 654, 921, 707), 'en': (748, 652, 922, 702)}, c
IN_STAGE = Button(area={'cn': (122, 16, 172, 39), 'en': (120, 18, 208, 40)}, color={'cn': (149, 167, 207), 'en': (104, 118, 157)}, button={'cn': (122, 16, 172, 39), 'en': (120, 18, 208, 40)}, file={'cn': './assets/cn/handler/IN_STAGE.png', 'en': './assets/en/handler/IN_STAGE.png'}) IN_STAGE = Button(area={'cn': (122, 16, 172, 39), 'en': (120, 18, 208, 40)}, color={'cn': (149, 167, 207), 'en': (104, 118, 157)}, button={'cn': (122, 16, 172, 39), 'en': (120, 18, 208, 40)}, file={'cn': './assets/cn/handler/IN_STAGE.png', 'en': './assets/en/handler/IN_STAGE.png'})
LOGIN_ANNOUNCE = Button(area={'cn': (1160, 45, 1227, 90), 'en': (1159, 44, 1228, 91)}, color={'cn': (174, 61, 56), 'en': (193, 79, 73)}, button={'cn': (1160, 45, 1227, 90), 'en': (1159, 44, 1228, 91)}, file={'cn': './assets/cn/handler/LOGIN_ANNOUNCE.png', 'en': './assets/en/handler/LOGIN_ANNOUNCE.png'}) LOGIN_ANNOUNCE = Button(area={'cn': (1160, 45, 1227, 90), 'en': (1159, 44, 1228, 91)}, color={'cn': (174, 61, 56), 'en': (193, 79, 73)}, button={'cn': (1160, 45, 1227, 90), 'en': (1159, 44, 1228, 91)}, file={'cn': './assets/cn/handler/LOGIN_ANNOUNCE.png', 'en': './assets/en/handler/LOGIN_ANNOUNCE.png'})
LOGIN_CHECK = Button(area={'cn': (77, 655, 154, 711), 'en': (77, 655, 154, 711)}, color={'cn': (33, 36, 33), 'en': (33, 36, 33)}, button={'cn': (416, 294, 534, 400), 'en': (416, 294, 534, 400)}, file={'cn': './assets/cn/handler/LOGIN_CHECK.png', 'en': './assets/en/handler/LOGIN_CHECK.png'}) LOGIN_CHECK = Button(area={'cn': (77, 655, 154, 711), 'en': (77, 655, 154, 711)}, color={'cn': (33, 36, 33), 'en': (33, 36, 33)}, button={'cn': (416, 294, 534, 400), 'en': (416, 294, 534, 400)}, file={'cn': './assets/cn/handler/LOGIN_CHECK.png', 'en': './assets/en/handler/LOGIN_CHECK.png'})
LOGIN_GAME_UPDATE = Button(area={'cn': (700, 471, 873, 529), 'en': (699, 470, 874, 529)}, color={'cn': (238, 170, 78), 'en': (236, 168, 76)}, button={'cn': (700, 471, 873, 529), 'en': (699, 470, 874, 529)}, file={'cn': './assets/cn/handler/LOGIN_GAME_UPDATE.png', 'en': './assets/en/handler/LOGIN_GAME_UPDATE.png'}) LOGIN_GAME_UPDATE = Button(area={'cn': (700, 471, 873, 529), 'en': (700, 471, 873, 529)}, color={'cn': (238, 170, 78), 'en': (238, 170, 78)}, button={'cn': (700, 471, 873, 529), 'en': (700, 471, 873, 529)}, file={'cn': './assets/cn/handler/LOGIN_GAME_UPDATE.png', 'en': './assets/en/handler/LOGIN_GAME_UPDATE.png'})
LOGIN_RETURN_SIGN = Button(area={'cn': (1, 7, 104, 47), 'en': (1195, 524, 1272, 547)}, color={'cn': (158, 214, 229), 'en': (202, 202, 203)}, button={'cn': (1, 7, 104, 47), 'en': (1195, 524, 1272, 547)}, file={'cn': './assets/cn/handler/LOGIN_RETURN_SIGN.png', 'en': './assets/en/handler/LOGIN_RETURN_SIGN.png'}) LOGIN_RETURN_SIGN = Button(area={'cn': (1, 7, 104, 47), 'en': (1195, 524, 1272, 547)}, color={'cn': (158, 214, 229), 'en': (202, 202, 203)}, button={'cn': (1, 7, 104, 47), 'en': (1195, 524, 1272, 547)}, file={'cn': './assets/cn/handler/LOGIN_RETURN_SIGN.png', 'en': './assets/en/handler/LOGIN_RETURN_SIGN.png'})
MAP_AIR_RAID = Button(area={'cn': (350, 447, 1280, 472), 'en': (350, 447, 1280, 472)}, color={'cn': (154, 43, 46), 'en': (154, 43, 46)}, button={'cn': (350, 447, 1280, 472), 'en': (350, 447, 1280, 472)}, file={'cn': './assets/cn/handler/MAP_AIR_RAID.png', 'en': './assets/en/handler/MAP_AIR_RAID.png'}) MAP_AIR_RAID = Button(area={'cn': (350, 447, 1280, 472), 'en': (350, 447, 1280, 472)}, color={'cn': (154, 43, 46), 'en': (154, 43, 46)}, button={'cn': (350, 447, 1280, 472), 'en': (350, 447, 1280, 472)}, file={'cn': './assets/cn/handler/MAP_AIR_RAID.png', 'en': './assets/en/handler/MAP_AIR_RAID.png'})
MAP_AMBUSH = Button(area={'cn': (261, 433, 1280, 449), 'en': (261, 433, 1280, 449)}, color={'cn': (161, 41, 43), 'en': (161, 41, 43)}, button={'cn': (261, 433, 1280, 449), 'en': (261, 433, 1280, 449)}, file={'cn': './assets/cn/handler/MAP_AMBUSH.png', 'en': './assets/en/handler/MAP_AMBUSH.png'}) MAP_AMBUSH = Button(area={'cn': (261, 433, 1280, 449), 'en': (261, 433, 1280, 449)}, color={'cn': (161, 41, 43), 'en': (161, 41, 43)}, button={'cn': (261, 433, 1280, 449), 'en': (261, 433, 1280, 449)}, file={'cn': './assets/cn/handler/MAP_AMBUSH.png', 'en': './assets/en/handler/MAP_AMBUSH.png'})

View File

@ -34,17 +34,17 @@ dictionary_cn = {
dictionary_en = { dictionary_en = {
'major_comm': ['SelfTraining', 'DefenseExercise', 'ResearchMission', 'Prep', 'Class', 'CargoTransport'], 'major_comm': ['SelfTraining', 'DefenseExercise', 'ResearchMission', 'Prep', 'Class', 'CargoTransport'],
'daily_comm': ['Daily', 'Awakening'], 'daily_comm': ['Daily', 'Awakening'],
'extra_drill': ['Sailing', 'DefensePatrol', 'oy', 'saingTraining'], 'extra_drill': ['Sailing', 'DefensePatrol', 'Buoy', 'saingTraining'],
'extra_part': ['Protection', 'protecton'], 'extra_part': ['Protection'],
'extra_cube': ['FleetExercise', 'EscortExercise', 'FleetCargo', 'CombatExercise'], 'extra_cube': ['FleetExercise', 'EscortExercise', 'FleetCargo', 'CombatExercise'],
'extra_oil': ['oi'], 'extra_oil': ['oil'],
'extra_book': ['MerchantEscort', 'SmalIMerchantEscor'], 'extra_book': ['MerchantEscort'],
'urgent_drill': ['CargoDefense', 'Destroy'], 'urgent_drill': ['CargoDefense', 'Destroy'],
'urgent_part': ['Lavella', 'Maui', 'Rendova', 'banna', 'Mannelsland'], 'urgent_part': ['Lavella', 'Maui', 'Rendova', 'banna', 'Mannelsland'],
'urgent_book': ['Tyrant', 'Poro', 'Makira', 'Kapolo', 'Mary', 'Isle', 'Kotlin'], 'urgent_book': ['Tyrant', 'Poro', 'Makira', 'Kapolo', 'Mary', 'Isle', 'Kotlin'],
'urgent_box': ['Gear', 'Handover'], 'urgent_box': ['Gear', 'Handover'],
'urgent_cube': ['MerchantRescue', 'Attack'], 'urgent_cube': ['MerchantRescue', 'Attack'],
'urgent_gem': ['VIP ', 'Holiday', 'PatrolEscort', 'PEscort'], 'urgent_gem': ['VIP ', 'Holiday', 'PatrolEscort'],
'urgent_ship': ['Launch'] 'urgent_ship': ['Launch']
} }
@ -200,7 +200,7 @@ class Commission:
Returns: Returns:
str: Commission genre, such as 'urgent_gem'. str: Commission genre, such as 'urgent_gem'.
""" """
string = string.replace('-', '') string = string.replace(' ', '').replace('-', '')
for key, value in dictionary_en.items(): for key, value in dictionary_en.items():
for keyword in value: for keyword in value:
if keyword in string: if keyword in string:

View File

@ -4,8 +4,6 @@ from module.base.template import Template
# This file is generated by module.dev_tools.asset_extract. # This file is generated by module.dev_tools.asset_extract.
# Don't modified it manually. # Don't modified it manually.
ARCHIVE_CHECK = Button(area={'cn': (898, 37, 1001, 77), 'en': (898, 37, 1001, 77)}, color={'cn': (70, 88, 145), 'en': (70, 88, 145)}, button={'cn': (898, 37, 1001, 77), 'en': (898, 37, 1001, 77)}, file={'cn': './assets/cn/ui/ARCHIVE_CHECK.png', 'en': './assets/en/ui/ARCHIVE_CHECK.png'})
ARCHIVE_GOTO_CAMPAIGN = Button(area={'cn': (589, 246, 692, 253), 'en': (589, 246, 692, 253)}, color={'cn': (82, 94, 105), 'en': (82, 94, 105)}, button={'cn': (589, 246, 692, 253), 'en': (589, 246, 692, 253)}, file={'cn': './assets/cn/ui/ARCHIVE_GOTO_CAMPAIGN.png', 'en': './assets/en/ui/ARCHIVE_GOTO_CAMPAIGN.png'})
BACK_ARROW = Button(area={'cn': (36, 53, 82, 55), 'en': (36, 53, 82, 55)}, color={'cn': (251, 251, 255), 'en': (251, 251, 255)}, button={'cn': (33, 31, 81, 78), 'en': (33, 31, 81, 78)}, file={'cn': './assets/cn/ui/BACK_ARROW.png', 'en': './assets/en/ui/BACK_ARROW.png'}) BACK_ARROW = Button(area={'cn': (36, 53, 82, 55), 'en': (36, 53, 82, 55)}, color={'cn': (251, 251, 255), 'en': (251, 251, 255)}, button={'cn': (33, 31, 81, 78), 'en': (33, 31, 81, 78)}, file={'cn': './assets/cn/ui/BACK_ARROW.png', 'en': './assets/en/ui/BACK_ARROW.png'})
CAMPAIGN_CHECK = Button(area={'cn': (173, 78, 194, 98), 'en': (129, 79, 186, 91)}, color={'cn': (165, 182, 221), 'en': (112, 130, 175)}, button={'cn': (173, 78, 194, 98), 'en': (129, 79, 186, 91)}, file={'cn': './assets/cn/ui/CAMPAIGN_CHECK.png', 'en': './assets/en/ui/CAMPAIGN_CHECK.png'}) CAMPAIGN_CHECK = Button(area={'cn': (173, 78, 194, 98), 'en': (129, 79, 186, 91)}, color={'cn': (165, 182, 221), 'en': (112, 130, 175)}, button={'cn': (173, 78, 194, 98), 'en': (129, 79, 186, 91)}, file={'cn': './assets/cn/ui/CAMPAIGN_CHECK.png', 'en': './assets/en/ui/CAMPAIGN_CHECK.png'})
CAMPAIGN_GOTO_DAILY = Button(area={'cn': (804, 648, 892, 703), 'en': (807, 648, 891, 703)}, color={'cn': (189, 145, 78), 'en': (165, 123, 54)}, button={'cn': (804, 648, 892, 703), 'en': (807, 648, 891, 703)}, file={'cn': './assets/cn/ui/CAMPAIGN_GOTO_DAILY.png', 'en': './assets/en/ui/CAMPAIGN_GOTO_DAILY.png'}) CAMPAIGN_GOTO_DAILY = Button(area={'cn': (804, 648, 892, 703), 'en': (807, 648, 891, 703)}, color={'cn': (189, 145, 78), 'en': (165, 123, 54)}, button={'cn': (804, 648, 892, 703), 'en': (807, 648, 891, 703)}, file={'cn': './assets/cn/ui/CAMPAIGN_GOTO_DAILY.png', 'en': './assets/en/ui/CAMPAIGN_GOTO_DAILY.png'})
@ -16,7 +14,6 @@ DAILY_CHECK = Button(area={'cn': (23, 656, 67, 698), 'en': (23, 656, 67, 698)},
EVENT_CHECK = Button(area={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, color={'cn': (88, 104, 138), 'en': (88, 104, 138)}, button={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, file={'cn': './assets/cn/ui/EVENT_CHECK.png', 'en': './assets/en/ui/EVENT_CHECK.png'}) EVENT_CHECK = Button(area={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, color={'cn': (88, 104, 138), 'en': (88, 104, 138)}, button={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, file={'cn': './assets/cn/ui/EVENT_CHECK.png', 'en': './assets/en/ui/EVENT_CHECK.png'})
EVENT_LIST_CHECK = Button(area={'cn': (123, 15, 232, 39), 'en': (123, 15, 223, 39)}, color={'cn': (151, 166, 206), 'en': (109, 122, 163)}, button={'cn': (123, 15, 232, 39), 'en': (123, 15, 223, 39)}, file={'cn': './assets/cn/ui/EVENT_LIST_CHECK.png', 'en': './assets/en/ui/EVENT_LIST_CHECK.png'}) EVENT_LIST_CHECK = Button(area={'cn': (123, 15, 232, 39), 'en': (123, 15, 223, 39)}, color={'cn': (151, 166, 206), 'en': (109, 122, 163)}, button={'cn': (123, 15, 232, 39), 'en': (123, 15, 223, 39)}, file={'cn': './assets/cn/ui/EVENT_LIST_CHECK.png', 'en': './assets/en/ui/EVENT_LIST_CHECK.png'})
EXERCISE_CHECK = Button(area={'cn': (1065, 340, 1204, 382), 'en': (1078, 343, 1190, 382)}, color={'cn': (129, 166, 220), 'en': (138, 174, 225)}, button={'cn': (1065, 340, 1204, 382), 'en': (1078, 343, 1190, 382)}, file={'cn': './assets/cn/ui/EXERCISE_CHECK.png', 'en': './assets/en/ui/EXERCISE_CHECK.png'}) EXERCISE_CHECK = Button(area={'cn': (1065, 340, 1204, 382), 'en': (1078, 343, 1190, 382)}, color={'cn': (129, 166, 220), 'en': (138, 174, 225)}, button={'cn': (1065, 340, 1204, 382), 'en': (1078, 343, 1190, 382)}, file={'cn': './assets/cn/ui/EXERCISE_CHECK.png', 'en': './assets/en/ui/EXERCISE_CHECK.png'})
FALLEN_WINGS = Button(area={'cn': (412, 485, 483, 546), 'en': (412, 485, 483, 546)}, color={'cn': (214, 199, 192), 'en': (214, 199, 192)}, button={'cn': (412, 485, 483, 546), 'en': (412, 485, 483, 546)}, file={'cn': './assets/cn/ui/FALLEN_WINGS.png', 'en': './assets/en/ui/FALLEN_WINGS.png'})
FLEET_CHECK = Button(area={'cn': (1044, 641, 1243, 702), 'en': (1042, 640, 1244, 703)}, color={'cn': (237, 186, 112), 'en': (236, 187, 113)}, button={'cn': (1044, 641, 1243, 702), 'en': (1042, 640, 1244, 703)}, file={'cn': './assets/cn/ui/FLEET_CHECK.png', 'en': './assets/en/ui/FLEET_CHECK.png'}) FLEET_CHECK = Button(area={'cn': (1044, 641, 1243, 702), 'en': (1042, 640, 1244, 703)}, color={'cn': (237, 186, 112), 'en': (236, 187, 113)}, button={'cn': (1044, 641, 1243, 702), 'en': (1042, 640, 1244, 703)}, file={'cn': './assets/cn/ui/FLEET_CHECK.png', 'en': './assets/en/ui/FLEET_CHECK.png'})
GOTO_MAIN = Button(area={'cn': (1230, 17, 1253, 45), 'en': (1230, 17, 1253, 45)}, color={'cn': (112, 132, 159), 'en': (112, 132, 159)}, button={'cn': (1228, 18, 1255, 49), 'en': (1228, 18, 1255, 49)}, file={'cn': './assets/cn/ui/GOTO_MAIN.png', 'en': './assets/en/ui/GOTO_MAIN.png'}) GOTO_MAIN = Button(area={'cn': (1230, 17, 1253, 45), 'en': (1230, 17, 1253, 45)}, color={'cn': (112, 132, 159), 'en': (112, 132, 159)}, button={'cn': (1228, 18, 1255, 49), 'en': (1228, 18, 1255, 49)}, file={'cn': './assets/cn/ui/GOTO_MAIN.png', 'en': './assets/en/ui/GOTO_MAIN.png'})
MAIN_GOTO_CAMPAIGN = Button(area={'cn': (1008, 289, 1154, 435), 'en': (1013, 298, 1148, 363)}, color={'cn': (216, 171, 99), 'en': (234, 198, 121)}, button={'cn': (1008, 289, 1154, 435), 'en': (1013, 298, 1148, 363)}, file={'cn': './assets/cn/ui/MAIN_GOTO_CAMPAIGN.png', 'en': './assets/en/ui/MAIN_GOTO_CAMPAIGN.png'}) MAIN_GOTO_CAMPAIGN = Button(area={'cn': (1008, 289, 1154, 435), 'en': (1013, 298, 1148, 363)}, color={'cn': (216, 171, 99), 'en': (234, 198, 121)}, button={'cn': (1008, 289, 1154, 435), 'en': (1013, 298, 1148, 363)}, file={'cn': './assets/cn/ui/MAIN_GOTO_CAMPAIGN.png', 'en': './assets/en/ui/MAIN_GOTO_CAMPAIGN.png'})
@ -31,6 +28,3 @@ REWARD_GOTO_MAIN = Button(area={'cn': (1037, 611, 1107, 656), 'en': (1037, 612,
REWARD_GOTO_TACTICAL = Button(area={'cn': (383, 404, 503, 444), 'en': (385, 404, 502, 444)}, color={'cn': (89, 140, 198), 'en': (84, 137, 196)}, button={'cn': (383, 404, 503, 444), 'en': (385, 404, 502, 444)}, file={'cn': './assets/cn/ui/REWARD_GOTO_TACTICAL.png', 'en': './assets/en/ui/REWARD_GOTO_TACTICAL.png'}) REWARD_GOTO_TACTICAL = Button(area={'cn': (383, 404, 503, 444), 'en': (385, 404, 502, 444)}, color={'cn': (89, 140, 198), 'en': (84, 137, 196)}, button={'cn': (383, 404, 503, 444), 'en': (385, 404, 502, 444)}, file={'cn': './assets/cn/ui/REWARD_GOTO_TACTICAL.png', 'en': './assets/en/ui/REWARD_GOTO_TACTICAL.png'})
SP_CHECK = Button(area={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, color={'cn': (95, 110, 145), 'en': (95, 110, 145)}, button={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, file={'cn': './assets/cn/ui/SP_CHECK.png', 'en': './assets/en/ui/SP_CHECK.png'}) SP_CHECK = Button(area={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, color={'cn': (95, 110, 145), 'en': (95, 110, 145)}, button={'cn': (123, 63, 206, 109), 'en': (123, 63, 206, 109)}, file={'cn': './assets/cn/ui/SP_CHECK.png', 'en': './assets/en/ui/SP_CHECK.png'})
TACTICAL_CHECK = Button(area={'cn': (122, 14, 231, 38), 'en': (120, 16, 292, 40)}, color={'cn': (145, 161, 200), 'en': (94, 107, 144)}, button={'cn': (122, 14, 231, 38), 'en': (120, 16, 292, 40)}, file={'cn': './assets/cn/ui/TACTICAL_CHECK.png', 'en': './assets/en/ui/TACTICAL_CHECK.png'}) TACTICAL_CHECK = Button(area={'cn': (122, 14, 231, 38), 'en': (120, 16, 292, 40)}, color={'cn': (145, 161, 200), 'en': (94, 107, 144)}, button={'cn': (122, 14, 231, 38), 'en': (120, 16, 292, 40)}, file={'cn': './assets/cn/ui/TACTICAL_CHECK.png', 'en': './assets/en/ui/TACTICAL_CHECK.png'})
VISITORS_RED = Button(area={'cn': (411, 168, 486, 237), 'en': (411, 168, 486, 237)}, color={'cn': (189, 161, 151), 'en': (189, 161, 151)}, button={'cn': (411, 168, 486, 237), 'en': (411, 168, 486, 237)}, file={'cn': './assets/cn/ui/VISITORS_RED.png', 'en': './assets/en/ui/VISITORS_RED.png'})
WAR_ARCHIVES = Button(area={'cn': (281, 659, 374, 696), 'en': (281, 659, 374, 696)}, color={'cn': (90, 109, 150), 'en': (90, 109, 150)}, button={'cn': (281, 659, 374, 696), 'en': (281, 659, 374, 696)}, file={'cn': './assets/cn/ui/WAR_ARCHIVES.png', 'en': './assets/en/ui/WAR_ARCHIVES.png'})
WINTERS_CROWN = Button(area={'cn': (1049, 179, 1159, 264), 'en': (1049, 179, 1159, 264)}, color={'cn': (199, 158, 155), 'en': (199, 158, 155)}, button={'cn': (1049, 179, 1159, 264), 'en': (1049, 179, 1159, 264)}, file={'cn': './assets/cn/ui/WINTERS_CROWN.png', 'en': './assets/en/ui/WINTERS_CROWN.png'})

View File

@ -60,16 +60,6 @@ page_sp.link(button=GOTO_MAIN, destination=page_main)
page_sp.link(button=BACK_ARROW, destination=page_campaign) page_sp.link(button=BACK_ARROW, destination=page_campaign)
page_campaign.link(button=CAMPAIGN_GOTO_EVENT, destination=page_sp) page_campaign.link(button=CAMPAIGN_GOTO_EVENT, destination=page_sp)
# War Archives
page_archive = Page(ARCHIVE_CHECK)
page_campaign.link(button=WAR_ARCHIVES, destination=page_archive)
# page_archive.link(button=GOTO_MAIN, destination=page_main)
page_archive.link(button=ARCHIVE_GOTO_CAMPAIGN, destination=page_campaign)
page_archive.link(button=VISITORS_RED, destination=page_event)
page_archive.link(button=FALLEN_WINGS, destination=page_event)
page_archive.link(button=WINTERS_CROWN, destination=page_event)
# Reward # Reward
page_reward = Page(REWARD_CHECK) page_reward = Page(REWARD_CHECK)
page_reward.link(button=REWARD_GOTO_MAIN, destination=page_main) page_reward.link(button=REWARD_GOTO_MAIN, destination=page_main)

View File

@ -1,7 +0,0 @@
from module.base.button import Button
from module.base.template import Template
# This file is generated by module.dev_tools.asset_extract.
# Don't modified it manually.
OCR_ARCHIVES_REMAIN = Button(area={'cn': (158, 47, 183, 68), 'en': (158, 47, 183, 68)}, color={'cn': (158, 157, 164), 'en': (158, 157, 164)}, button={'cn': (158, 47, 183, 68), 'en': (158, 47, 183, 68)}, file={'cn': './assets/cn/war_archives/OCR_ARCHIVES_REMAIN.png', 'en': './assets/en/war_archives/OCR_ARCHIVES_REMAIN.png'})

View File

@ -1,128 +0,0 @@
import numpy as np
from module.base.ocr import Digit
from module.base.utils import get_color
from module.combat.combat import Combat
from module.war_archives.assets import *
from module.equipment.fleet_equipment import DailyEquipment
from module.logger import logger
from module.ui.ui import page_daily, page_campaign, BACK_ARROW, DAILY_CHECK
DAILY_MISSION_LIST = [DAILY_MISSION_1, DAILY_MISSION_2, DAILY_MISSION_3]
OCR_ARCHIVES_REMAIN = Digit(OCR_ARCHIVES_REMAIN, letter=(173, 247, 74), back=(66, 89, 140), limit=30)
# OCR_DAILY_FLEET_INDEX = Digit(OCR_DAILY_FLEET_INDEX, letter=(90, 154, 255), back=(24, 32, 49), length=1,
# white_list='123456')
class Daily(Combat, DailyEquipment):
daily_current: int
daily_checked: list
def is_active(self):
color = get_color(image=self.device.image, area=DAILY_ACTIVE.area)
color = np.array(color).astype(float)
color = (np.max(color) + np.min(color)) / 2
active = color > 30
if active:
logger.attr(f'Daily_{self.daily_current}', 'active')
else:
logger.attr(f'Daily_{self.daily_current}', 'inactive')
return active
def _wait_daily_switch(self):
self.device.sleep((1, 1.2))
def next(self):
self.daily_current += 1
logger.info('Switch to %s' % str(self.daily_current))
self.device.click(DAILY_NEXT)
self._wait_daily_switch()
self.device.screenshot()
def prev(self):
self.daily_current -= 1
logger.info('Switch to %s' % str(self.daily_current))
self.device.click(DAILY_PREV)
self._wait_daily_switch()
self.device.screenshot()
def daily_execute(self, remain):
logger.hr(f'Daily {self.daily_current}')
self.ui_click(click_button=DAILY_ENTER, check_button=DAILY_ENTER_CHECK, appear_button=DAILY_CHECK)
def daily_end():
return self.appear(DAILY_ENTER_CHECK) or self.appear(BACK_ARROW)
button = DAILY_MISSION_LIST[self.config.DAILY_CHOOSE[self.daily_current] - 1]
for n in range(remain):
logger.hr(f'Count {n + 1}')
self.ui_click(click_button=button, check_button=self.combat_appear, appear_button=DAILY_ENTER_CHECK)
self.ui_ensure_index(self.config.FLEET_DAILY, letter=OCR_DAILY_FLEET_INDEX, prev_button=DAILY_FLEET_PREV,
next_button=DAILY_FLEET_NEXT, fast=False, skip_first_screenshot=True)
self.combat(emotion_reduce=False, save_get_items=False, expected_end=daily_end, balance_hp=False)
self.ui_click(click_button=BACK_ARROW, check_button=DAILY_CHECK)
self.device.sleep((1, 1.2))
def daily_check(self, n=None):
if not n:
n = self.daily_current
self.daily_checked.append(n)
logger.info(f'Checked daily {n}')
logger.info(f'Checked_list: {self.daily_checked}')
def daily_run_one(self):
self.ui_ensure(page_daily)
self.device.sleep(0.2)
self.device.screenshot()
self.daily_current = 1
logger.info(f'Checked_list: {self.daily_checked}')
for _ in range(max(self.daily_checked)):
self.next()
while 1:
if self.daily_current > 5:
break
if self.daily_current == 3:
logger.info('Skip submarine daily.')
self.daily_check()
self.next()
continue
if not self.is_active():
self.daily_check()
self.next()
continue
remain = OCR_REMAIN.ocr(self.device.image)
if remain == 0:
self.daily_check()
self.next()
continue
else:
self.daily_execute(remain=remain)
self.daily_check()
# The order of daily tasks will be disordered after execute a daily, exit and re-enter to reset.
# 打完一次之后每日任务的顺序会乱掉, 退出再进入来重置顺序.
self.ui_ensure(page_campaign)
break
def daily_run(self):
self.daily_checked = [0]
while 1:
self.daily_run_one()
if max(self.daily_checked) >= 5:
logger.info('Daily clear complete.')
break
def run(self):
self.equipment_take_on()
self.daily_run()
self.equipment_take_off()
self.ui_goto_main()
def record_executed_since(self):
return self.config.record_executed_since(option=RECORD_OPTION, since=RECORD_SINCE)
def record_save(self):
return self.config.record_save(option=RECORD_OPTION)