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In [1]:



    


from __future__ import print_function
import sys, os, math
import h5py
import numpy as np
from numpy import float32, int32, uint8, dtype

# Load PyGreentea
# Relative path to where PyGreentea resides
pygt_path = '../../PyGreentea'
sys.path.append(pygt_path)
import PyGreentea as pygt

# Create the network we want
netconf = pygt.netgen.NetConf()
netconf.ignore_conv_buffer = True
netconf.use_batchnorm = False
netconf.dropout = 0.0
netconf.fmap_start = 20
# netconf.unet_fmap_inc_rule = lambda self,fmaps: int(math.ceil(fmaps * 1))
# netconf.unet_fmap_dec_rule = lambda self,fmaps: int(math.ceil(fmaps / 1))

netconf.input_shape = [132,132,132]
netconf.output_shape = [44, 44, 44]

print ('Input shape: %s' % netconf.input_shape)
print ('Output shape: %s' % netconf.output_shape)
print ('Feature maps: %s' % netconf.fmap_start)

netconf.loss_function = "euclid"
train_net_conf_euclid, test_net_conf = pygt.netgen.create_nets(netconf)
netconf.loss_function = "malis"
train_net_conf_malis, test_net_conf = pygt.netgen.create_nets(netconf)

with open('net_train_euclid.prototxt', 'w') as f:
    print(train_net_conf_euclid, file=f)
with open('net_train_malis.prototxt', 'w') as f:
    print(train_net_conf_malis, file=f)
with open('net_test.prototxt', 'w') as f:
    print(test_net_conf, file=f)



    


















    






Input shape: [132, 132, 132]
Output shape: [44, 44, 44]
Feature maps: 20
f: 1 w: [132, 132, 132] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [130, 130, 130] d: [1, 1, 1]
WM: 2160
CM: 248396544
AM: 0
f: 20 w: [128, 128, 128] d: [1, 1, 1]
WM: 43200
CM: 4745520000
AM: 0
f: 20 w: [64, 64, 64] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [62, 62, 62] d: [1, 1, 1]
WM: 129600
CM: 566231040
AM: 0
f: 60 w: [60, 60, 60] d: [1, 1, 1]
WM: 388800
CM: 1544365440
AM: 0
f: 60 w: [30, 30, 30] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 1166400
CM: 174960000
AM: 0
f: 180 w: [26, 26, 26] d: [1, 1, 1]
WM: 3499200
CM: 426746880
AM: 0
f: 180 w: [13, 13, 13] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 540 w: [11, 11, 11] d: [1, 1, 1]
WM: 10497600
CM: 42709680
AM: 0
f: 540 w: [9, 9, 9] d: [1, 1, 1]
WM: 31492800
CM: 77623920
AM: 0
f: 540 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [18, 18, 18] d: [1, 1, 1]
WM: 388800
CM: 100776960
AM: 0
f: 360 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [16, 16, 16] d: [1, 1, 1]
WM: 6998400
CM: 226748160
AM: 0
f: 180 w: [14, 14, 14] d: [1, 1, 1]
WM: 3499200
CM: 79626240
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [28, 28, 28] d: [1, 1, 1]
WM: 43200
CM: 126443520
AM: 0
f: 120 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [26, 26, 26] d: [1, 1, 1]
WM: 777600
CM: 284497920
AM: 0
f: 60 w: [24, 24, 24] d: [1, 1, 1]
WM: 388800
CM: 113892480
AM: 0
f: 60 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [48, 48, 48] d: [1, 1, 1]
WM: 4800
CM: 212336640
AM: 0
f: 40 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [46, 46, 46] d: [1, 1, 1]
WM: 86400
CM: 477757440
AM: 0
f: 20 w: [44, 44, 44] d: [1, 1, 1]
WM: 43200
CM: 210245760
AM: 0
f: 3 w: [44, 44, 44] d: [1, 1, 1]
WM: 240
CM: 6814720
AM: 0
Max. memory requirements: 6128345920 B
Weight memory: 59450400 B
Max. conv buffer: 4745520000 B
f: 1 w: [132, 132, 132] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [130, 130, 130] d: [1, 1, 1]
WM: 2160
CM: 248396544
AM: 0
f: 20 w: [128, 128, 128] d: [1, 1, 1]
WM: 43200
CM: 4745520000
AM: 0
f: 20 w: [64, 64, 64] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [62, 62, 62] d: [1, 1, 1]
WM: 129600
CM: 566231040
AM: 0
f: 60 w: [60, 60, 60] d: [1, 1, 1]
WM: 388800
CM: 1544365440
AM: 0
f: 60 w: [30, 30, 30] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 1166400
CM: 174960000
AM: 0
f: 180 w: [26, 26, 26] d: [1, 1, 1]
WM: 3499200
CM: 426746880
AM: 0
f: 180 w: [13, 13, 13] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 540 w: [11, 11, 11] d: [1, 1, 1]
WM: 10497600
CM: 42709680
AM: 0
f: 540 w: [9, 9, 9] d: [1, 1, 1]
WM: 31492800
CM: 77623920
AM: 0
f: 540 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [18, 18, 18] d: [1, 1, 1]
WM: 388800
CM: 100776960
AM: 0
f: 360 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [16, 16, 16] d: [1, 1, 1]
WM: 6998400
CM: 226748160
AM: 0
f: 180 w: [14, 14, 14] d: [1, 1, 1]
WM: 3499200
CM: 79626240
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [28, 28, 28] d: [1, 1, 1]
WM: 43200
CM: 126443520
AM: 0
f: 120 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [26, 26, 26] d: [1, 1, 1]
WM: 777600
CM: 284497920
AM: 0
f: 60 w: [24, 24, 24] d: [1, 1, 1]
WM: 388800
CM: 113892480
AM: 0
f: 60 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [48, 48, 48] d: [1, 1, 1]
WM: 4800
CM: 212336640
AM: 0
f: 40 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [46, 46, 46] d: [1, 1, 1]
WM: 86400
CM: 477757440
AM: 0
f: 20 w: [44, 44, 44] d: [1, 1, 1]
WM: 43200
CM: 210245760
AM: 0
f: 3 w: [44, 44, 44] d: [1, 1, 1]
WM: 240
CM: 6814720
AM: 0
Max. memory requirements: 5466658160 B
Weight memory: 59450400 B
Max. conv buffer: 4745520000 B
f: 1 w: [132, 132, 132] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [130, 130, 130] d: [1, 1, 1]
WM: 2160
CM: 248396544
AM: 0
f: 20 w: [128, 128, 128] d: [1, 1, 1]
WM: 43200
CM: 4745520000
AM: 0
f: 20 w: [64, 64, 64] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [62, 62, 62] d: [1, 1, 1]
WM: 129600
CM: 566231040
AM: 0
f: 60 w: [60, 60, 60] d: [1, 1, 1]
WM: 388800
CM: 1544365440
AM: 0
f: 60 w: [30, 30, 30] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 1166400
CM: 174960000
AM: 0
f: 180 w: [26, 26, 26] d: [1, 1, 1]
WM: 3499200
CM: 426746880
AM: 0
f: 180 w: [13, 13, 13] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 540 w: [11, 11, 11] d: [1, 1, 1]
WM: 10497600
CM: 42709680
AM: 0
f: 540 w: [9, 9, 9] d: [1, 1, 1]
WM: 31492800
CM: 77623920
AM: 0
f: 540 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [18, 18, 18] d: [1, 1, 1]
WM: 388800
CM: 100776960
AM: 0
f: 360 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [16, 16, 16] d: [1, 1, 1]
WM: 6998400
CM: 226748160
AM: 0
f: 180 w: [14, 14, 14] d: [1, 1, 1]
WM: 3499200
CM: 79626240
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [28, 28, 28] d: [1, 1, 1]
WM: 43200
CM: 126443520
AM: 0
f: 120 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [26, 26, 26] d: [1, 1, 1]
WM: 777600
CM: 284497920
AM: 0
f: 60 w: [24, 24, 24] d: [1, 1, 1]
WM: 388800
CM: 113892480
AM: 0
f: 60 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [48, 48, 48] d: [1, 1, 1]
WM: 4800
CM: 212336640
AM: 0
f: 40 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [46, 46, 46] d: [1, 1, 1]
WM: 86400
CM: 477757440
AM: 0
f: 20 w: [44, 44, 44] d: [1, 1, 1]
WM: 43200
CM: 210245760
AM: 0
f: 3 w: [44, 44, 44] d: [1, 1, 1]
WM: 240
CM: 6814720
AM: 0
Max. memory requirements: 6128345920 B
Weight memory: 59450400 B
Max. conv buffer: 4745520000 B
f: 1 w: [132, 132, 132] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [130, 130, 130] d: [1, 1, 1]
WM: 2160
CM: 248396544
AM: 0
f: 20 w: [128, 128, 128] d: [1, 1, 1]
WM: 43200
CM: 4745520000
AM: 0
f: 20 w: [64, 64, 64] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [62, 62, 62] d: [1, 1, 1]
WM: 129600
CM: 566231040
AM: 0
f: 60 w: [60, 60, 60] d: [1, 1, 1]
WM: 388800
CM: 1544365440
AM: 0
f: 60 w: [30, 30, 30] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 1166400
CM: 174960000
AM: 0
f: 180 w: [26, 26, 26] d: [1, 1, 1]
WM: 3499200
CM: 426746880
AM: 0
f: 180 w: [13, 13, 13] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 540 w: [11, 11, 11] d: [1, 1, 1]
WM: 10497600
CM: 42709680
AM: 0
f: 540 w: [9, 9, 9] d: [1, 1, 1]
WM: 31492800
CM: 77623920
AM: 0
f: 540 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [18, 18, 18] d: [1, 1, 1]
WM: 388800
CM: 100776960
AM: 0
f: 360 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [16, 16, 16] d: [1, 1, 1]
WM: 6998400
CM: 226748160
AM: 0
f: 180 w: [14, 14, 14] d: [1, 1, 1]
WM: 3499200
CM: 79626240
AM: 0
f: 180 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [28, 28, 28] d: [1, 1, 1]
WM: 43200
CM: 126443520
AM: 0
f: 120 w: [28, 28, 28] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [26, 26, 26] d: [1, 1, 1]
WM: 777600
CM: 284497920
AM: 0
f: 60 w: [24, 24, 24] d: [1, 1, 1]
WM: 388800
CM: 113892480
AM: 0
f: 60 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [48, 48, 48] d: [1, 1, 1]
WM: 4800
CM: 212336640
AM: 0
f: 40 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [46, 46, 46] d: [1, 1, 1]
WM: 86400
CM: 477757440
AM: 0
f: 20 w: [44, 44, 44] d: [1, 1, 1]
WM: 43200
CM: 210245760
AM: 0
f: 3 w: [44, 44, 44] d: [1, 1, 1]
WM: 240
CM: 6814720
AM: 0
Max. memory requirements: 5466658160 B
Weight memory: 59450400 B
Max. conv buffer: 4745520000 B

















In [4]:



    


# Biggest possible network for testing on 12 GB
netconf.ignore_conv_buffer = True
netconf.mem_global_limit = 10 * 1024 * 1024 * 1024
mode = pygt.netgen.caffe_pb2.TEST
shape_min = [100,100,100]
shape_max = [500,500,500]
constraints = [None, lambda x: x[0], lambda x: x[1]]

inshape,outshape,fmaps = pygt.netgen.compute_valid_io_shapes(netconf,mode,shape_min,shape_max,constraints=constraints)

# We choose the maximum that still gives us 20 fmaps:
index = [n for n, i in enumerate(fmaps) if i>=20][-1]
print("Index to use: %s" % index)

# Some patching to allow our new parameters
netconf.input_shape = inshape[index]
netconf.output_shape = outshape[index]
# Workaround to allow any size (train net unusably big)
netconf.mem_global_limit = 200 * 1024 * 1024 * 1024
netconf.mem_buf_limit = 200 * 1024 * 1024 * 1024
# Define some loss function (irrelevant for testing though)
netconf.loss_function = "euclid"

# Generate the nework, store it
train_net_big_conf, test_net_big_conf = pygt.netgen.create_nets(netconf)

with open('net_test_big.prototxt', 'w') as f:
    print(test_net_big_conf, file=f)



    


















    






++++ Valid: [100] => [12]
-- Invalid: [101] => []
-- Invalid: [102] => []
-- Invalid: [103] => []
-- Invalid: [104] => []
-- Invalid: [105] => []
-- Invalid: [106] => []
-- Invalid: [107] => []
++++ Valid: [108] => [20]
-- Invalid: [109] => []
-- Invalid: [110] => []
-- Invalid: [111] => []
-- Invalid: [112] => []
-- Invalid: [113] => []
-- Invalid: [114] => []
-- Invalid: [115] => []
++++ Valid: [116] => [28]
-- Invalid: [117] => []
-- Invalid: [118] => []
-- Invalid: [119] => []
-- Invalid: [120] => []
-- Invalid: [121] => []
-- Invalid: [122] => []
-- Invalid: [123] => []
++++ Valid: [124] => [36]
-- Invalid: [125] => []
-- Invalid: [126] => []
-- Invalid: [127] => []
-- Invalid: [128] => []
-- Invalid: [129] => []
-- Invalid: [130] => []
-- Invalid: [131] => []
++++ Valid: [132] => [44]
-- Invalid: [133] => []
-- Invalid: [134] => []
-- Invalid: [135] => []
-- Invalid: [136] => []
-- Invalid: [137] => []
-- Invalid: [138] => []
-- Invalid: [139] => []
++++ Valid: [140] => [52]
-- Invalid: [141] => []
-- Invalid: [142] => []
-- Invalid: [143] => []
-- Invalid: [144] => []
-- Invalid: [145] => []
-- Invalid: [146] => []
-- Invalid: [147] => []
++++ Valid: [148] => [60]
-- Invalid: [149] => []
-- Invalid: [150] => []
-- Invalid: [151] => []
-- Invalid: [152] => []
-- Invalid: [153] => []
-- Invalid: [154] => []
-- Invalid: [155] => []
++++ Valid: [156] => [68]
-- Invalid: [157] => []
-- Invalid: [158] => []
-- Invalid: [159] => []
-- Invalid: [160] => []
-- Invalid: [161] => []
-- Invalid: [162] => []
-- Invalid: [163] => []
++++ Valid: [164] => [76]
-- Invalid: [165] => []
-- Invalid: [166] => []
-- Invalid: [167] => []
-- Invalid: [168] => []
-- Invalid: [169] => []
-- Invalid: [170] => []
-- Invalid: [171] => []
++++ Valid: [172] => [84]
-- Invalid: [173] => []
-- Invalid: [174] => []
-- Invalid: [175] => []
-- Invalid: [176] => []
-- Invalid: [177] => []
-- Invalid: [178] => []
-- Invalid: [179] => []
++++ Valid: [180] => [92]
-- Invalid: [181] => []
-- Invalid: [182] => []
-- Invalid: [183] => []
-- Invalid: [184] => []
-- Invalid: [185] => []
-- Invalid: [186] => []
-- Invalid: [187] => []
++++ Valid: [188] => [100]
-- Invalid: [189] => []
-- Invalid: [190] => []
-- Invalid: [191] => []
-- Invalid: [192] => []
-- Invalid: [193] => []
-- Invalid: [194] => []
-- Invalid: [195] => []
++++ Valid: [196] => [108]
-- Invalid: [197] => []
-- Invalid: [198] => []
-- Invalid: [199] => []
-- Invalid: [200] => []
-- Invalid: [201] => []
-- Invalid: [202] => []
-- Invalid: [203] => []
++++ Valid: [204] => [116]
-- Invalid: [205] => []
-- Invalid: [206] => []
-- Invalid: [207] => []
-- Invalid: [208] => []
-- Invalid: [209] => []
-- Invalid: [210] => []
-- Invalid: [211] => []
++++ Valid: [212] => [124]
-- Invalid: [213] => []
-- Invalid: [214] => []
-- Invalid: [215] => []
-- Invalid: [216] => []
-- Invalid: [217] => []
-- Invalid: [218] => []
-- Invalid: [219] => []
++++ Valid: [220] => [132]
-- Invalid: [221] => []
-- Invalid: [222] => []
-- Invalid: [223] => []
-- Invalid: [224] => []
-- Invalid: [225] => []
-- Invalid: [226] => []
-- Invalid: [227] => []
++++ Valid: [228] => [140]
-- Invalid: [229] => []
-- Invalid: [230] => []
-- Invalid: [231] => []
-- Invalid: [232] => []
-- Invalid: [233] => []
-- Invalid: [234] => []
-- Invalid: [235] => []
++++ Valid: [236] => [148]
-- Invalid: [237] => []
-- Invalid: [238] => []
-- Invalid: [239] => []
-- Invalid: [240] => []
-- Invalid: [241] => []
-- Invalid: [242] => []
-- Invalid: [243] => []
++++ Valid: [244] => [156]
-- Invalid: [245] => []
-- Invalid: [246] => []
-- Invalid: [247] => []
-- Invalid: [248] => []
-- Invalid: [249] => []
-- Invalid: [250] => []
-- Invalid: [251] => []
++++ Valid: [252] => [164]
-- Invalid: [253] => []
-- Invalid: [254] => []
-- Invalid: [255] => []
-- Invalid: [256] => []
-- Invalid: [257] => []
-- Invalid: [258] => []
-- Invalid: [259] => []
++++ Valid: [260] => [172]
-- Invalid: [261] => []
-- Invalid: [262] => []
-- Invalid: [263] => []
-- Invalid: [264] => []
-- Invalid: [265] => []
-- Invalid: [266] => []
-- Invalid: [267] => []
++++ Valid: [268] => [180]
-- Invalid: [269] => []
-- Invalid: [270] => []
-- Invalid: [271] => []
-- Invalid: [272] => []
-- Invalid: [273] => []
-- Invalid: [274] => []
-- Invalid: [275] => []
++++ Valid: [276] => [188]
-- Invalid: [277] => []
-- Invalid: [278] => []
-- Invalid: [279] => []
-- Invalid: [280] => []
-- Invalid: [281] => []
-- Invalid: [282] => []
-- Invalid: [283] => []
++++ Valid: [284] => [196]
-- Invalid: [285] => []
-- Invalid: [286] => []
-- Invalid: [287] => []
-- Invalid: [288] => []
-- Invalid: [289] => []
-- Invalid: [290] => []
-- Invalid: [291] => []
++++ Valid: [292] => [204]
-- Invalid: [293] => []
-- Invalid: [294] => []
-- Invalid: [295] => []
-- Invalid: [296] => []
-- Invalid: [297] => []
-- Invalid: [298] => []
-- Invalid: [299] => []
++++ Valid: [300] => [212]
-- Invalid: [301] => []
-- Invalid: [302] => []
-- Invalid: [303] => []
-- Invalid: [304] => []
-- Invalid: [305] => []
-- Invalid: [306] => []
-- Invalid: [307] => []
++++ Valid: [308] => [220]
-- Invalid: [309] => []
-- Invalid: [310] => []
-- Invalid: [311] => []
-- Invalid: [312] => []
-- Invalid: [313] => []
-- Invalid: [314] => []
-- Invalid: [315] => []
++++ Valid: [316] => [228]
-- Invalid: [317] => []
-- Invalid: [318] => []
-- Invalid: [319] => []
-- Invalid: [320] => []
-- Invalid: [321] => []
-- Invalid: [322] => []
-- Invalid: [323] => []
++++ Valid: [324] => [236]
-- Invalid: [325] => []
-- Invalid: [326] => []
-- Invalid: [327] => []
-- Invalid: [328] => []
-- Invalid: [329] => []
-- Invalid: [330] => []
-- Invalid: [331] => []
++++ Valid: [332] => [244]
-- Invalid: [333] => []
-- Invalid: [334] => []
-- Invalid: [335] => []
-- Invalid: [336] => []
-- Invalid: [337] => []
-- Invalid: [338] => []
-- Invalid: [339] => []
++++ Valid: [340] => [252]
-- Invalid: [341] => []
-- Invalid: [342] => []
-- Invalid: [343] => []
-- Invalid: [344] => []
-- Invalid: [345] => []
-- Invalid: [346] => []
-- Invalid: [347] => []
++++ Valid: [348] => [260]
-- Invalid: [349] => []
-- Invalid: [350] => []
-- Invalid: [351] => []
-- Invalid: [352] => []
-- Invalid: [353] => []
-- Invalid: [354] => []
-- Invalid: [355] => []
++++ Valid: [356] => [268]
-- Invalid: [357] => []
-- Invalid: [358] => []
-- Invalid: [359] => []
-- Invalid: [360] => []
-- Invalid: [361] => []
-- Invalid: [362] => []
-- Invalid: [363] => []
++++ Valid: [364] => [276]
-- Invalid: [365] => []
-- Invalid: [366] => []
-- Invalid: [367] => []
-- Invalid: [368] => []
-- Invalid: [369] => []
-- Invalid: [370] => []
-- Invalid: [371] => []
++++ Valid: [372] => [284]
-- Invalid: [373] => []
-- Invalid: [374] => []
-- Invalid: [375] => []
-- Invalid: [376] => []
-- Invalid: [377] => []
-- Invalid: [378] => []
-- Invalid: [379] => []
++++ Valid: [380] => [292]
-- Invalid: [381] => []
-- Invalid: [382] => []
-- Invalid: [383] => []
-- Invalid: [384] => []
-- Invalid: [385] => []
-- Invalid: [386] => []
-- Invalid: [387] => []
++++ Valid: [388] => [300]
-- Invalid: [389] => []
-- Invalid: [390] => []
-- Invalid: [391] => []
-- Invalid: [392] => []
-- Invalid: [393] => []
-- Invalid: [394] => []
-- Invalid: [395] => []
++++ Valid: [396] => [308]
-- Invalid: [397] => []
-- Invalid: [398] => []
-- Invalid: [399] => []
-- Invalid: [400] => []
-- Invalid: [401] => []
-- Invalid: [402] => []
-- Invalid: [403] => []
++++ Valid: [404] => [316]
-- Invalid: [405] => []
-- Invalid: [406] => []
-- Invalid: [407] => []
-- Invalid: [408] => []
-- Invalid: [409] => []
-- Invalid: [410] => []
-- Invalid: [411] => []
++++ Valid: [412] => [324]
-- Invalid: [413] => []
-- Invalid: [414] => []
-- Invalid: [415] => []
-- Invalid: [416] => []
-- Invalid: [417] => []
-- Invalid: [418] => []
-- Invalid: [419] => []
++++ Valid: [420] => [332]
-- Invalid: [421] => []
-- Invalid: [422] => []
-- Invalid: [423] => []
-- Invalid: [424] => []
-- Invalid: [425] => []
-- Invalid: [426] => []
-- Invalid: [427] => []
++++ Valid: [428] => [340]
-- Invalid: [429] => []
-- Invalid: [430] => []
-- Invalid: [431] => []
-- Invalid: [432] => []
-- Invalid: [433] => []
-- Invalid: [434] => []
-- Invalid: [435] => []
++++ Valid: [436] => [348]
-- Invalid: [437] => []
-- Invalid: [438] => []
-- Invalid: [439] => []
-- Invalid: [440] => []
-- Invalid: [441] => []
-- Invalid: [442] => []
-- Invalid: [443] => []
++++ Valid: [444] => [356]
-- Invalid: [445] => []
-- Invalid: [446] => []
-- Invalid: [447] => []
-- Invalid: [448] => []
-- Invalid: [449] => []
-- Invalid: [450] => []
-- Invalid: [451] => []
++++ Valid: [452] => [364]
-- Invalid: [453] => []
-- Invalid: [454] => []
-- Invalid: [455] => []
-- Invalid: [456] => []
-- Invalid: [457] => []
-- Invalid: [458] => []
-- Invalid: [459] => []
++++ Valid: [460] => [372]
-- Invalid: [461] => []
-- Invalid: [462] => []
-- Invalid: [463] => []
-- Invalid: [464] => []
-- Invalid: [465] => []
-- Invalid: [466] => []
-- Invalid: [467] => []
++++ Valid: [468] => [380]
-- Invalid: [469] => []
-- Invalid: [470] => []
-- Invalid: [471] => []
-- Invalid: [472] => []
-- Invalid: [473] => []
-- Invalid: [474] => []
-- Invalid: [475] => []
++++ Valid: [476] => [388]
-- Invalid: [477] => []
-- Invalid: [478] => []
-- Invalid: [479] => []
-- Invalid: [480] => []
-- Invalid: [481] => []
-- Invalid: [482] => []
-- Invalid: [483] => []
++++ Valid: [484] => [396]
-- Invalid: [485] => []
-- Invalid: [486] => []
-- Invalid: [487] => []
-- Invalid: [488] => []
-- Invalid: [489] => []
-- Invalid: [490] => []
-- Invalid: [491] => []
++++ Valid: [492] => [404]
-- Invalid: [493] => []
-- Invalid: [494] => []
-- Invalid: [495] => []
-- Invalid: [496] => []
-- Invalid: [497] => []
-- Invalid: [498] => []
-- Invalid: [499] => []
++++ Valid: [500] => [412]
++++ Valid: [100, 100] => [12, 12]
++++ Valid: [108, 108] => [20, 20]
++++ Valid: [116, 116] => [28, 28]
++++ Valid: [124, 124] => [36, 36]
++++ Valid: [132, 132] => [44, 44]
++++ Valid: [140, 140] => [52, 52]
++++ Valid: [148, 148] => [60, 60]
++++ Valid: [156, 156] => [68, 68]
++++ Valid: [164, 164] => [76, 76]
++++ Valid: [172, 172] => [84, 84]
++++ Valid: [180, 180] => [92, 92]
++++ Valid: [188, 188] => [100, 100]
++++ Valid: [196, 196] => [108, 108]
++++ Valid: [204, 204] => [116, 116]
++++ Valid: [212, 212] => [124, 124]
++++ Valid: [220, 220] => [132, 132]
++++ Valid: [228, 228] => [140, 140]
++++ Valid: [236, 236] => [148, 148]
++++ Valid: [244, 244] => [156, 156]
++++ Valid: [252, 252] => [164, 164]
++++ Valid: [260, 260] => [172, 172]
++++ Valid: [268, 268] => [180, 180]
++++ Valid: [276, 276] => [188, 188]
++++ Valid: [284, 284] => [196, 196]
++++ Valid: [292, 292] => [204, 204]
++++ Valid: [300, 300] => [212, 212]
++++ Valid: [308, 308] => [220, 220]
++++ Valid: [316, 316] => [228, 228]
++++ Valid: [324, 324] => [236, 236]
++++ Valid: [332, 332] => [244, 244]
++++ Valid: [340, 340] => [252, 252]
++++ Valid: [348, 348] => [260, 260]
++++ Valid: [356, 356] => [268, 268]
++++ Valid: [364, 364] => [276, 276]
++++ Valid: [372, 372] => [284, 284]
++++ Valid: [380, 380] => [292, 292]
++++ Valid: [388, 388] => [300, 300]
++++ Valid: [396, 396] => [308, 308]
++++ Valid: [404, 404] => [316, 316]
++++ Valid: [412, 412] => [324, 324]
++++ Valid: [420, 420] => [332, 332]
++++ Valid: [428, 428] => [340, 340]
++++ Valid: [436, 436] => [348, 348]
++++ Valid: [444, 444] => [356, 356]
++++ Valid: [452, 452] => [364, 364]
++++ Valid: [460, 460] => [372, 372]
++++ Valid: [468, 468] => [380, 380]
++++ Valid: [476, 476] => [388, 388]
++++ Valid: [484, 484] => [396, 396]
++++ Valid: [492, 492] => [404, 404]
++++ Valid: [500, 500] => [412, 412]
++++ Valid: [100, 100, 100] => [12, 12, 12]
++++ Valid: [108, 108, 108] => [20, 20, 20]
++++ Valid: [116, 116, 116] => [28, 28, 28]
++++ Valid: [124, 124, 124] => [36, 36, 36]
++++ Valid: [132, 132, 132] => [44, 44, 44]
++++ Valid: [140, 140, 140] => [52, 52, 52]
++++ Valid: [148, 148, 148] => [60, 60, 60]
++++ Valid: [156, 156, 156] => [68, 68, 68]
++++ Valid: [164, 164, 164] => [76, 76, 76]
++++ Valid: [172, 172, 172] => [84, 84, 84]
++++ Valid: [180, 180, 180] => [92, 92, 92]
++++ Valid: [188, 188, 188] => [100, 100, 100]
++++ Valid: [196, 196, 196] => [108, 108, 108]
++++ Valid: [204, 204, 204] => [116, 116, 116]
++++ Valid: [212, 212, 212] => [124, 124, 124]
++++ Valid: [220, 220, 220] => [132, 132, 132]
++++ Valid: [228, 228, 228] => [140, 140, 140]
++++ Valid: [236, 236, 236] => [148, 148, 148]
++++ Valid: [244, 244, 244] => [156, 156, 156]
++++ Valid: [252, 252, 252] => [164, 164, 164]
++++ Valid: [260, 260, 260] => [172, 172, 172]
++++ Valid: [268, 268, 268] => [180, 180, 180]
++++ Valid: [276, 276, 276] => [188, 188, 188]
++++ Valid: [284, 284, 284] => [196, 196, 196]
++++ Valid: [292, 292, 292] => [204, 204, 204]
++++ Valid: [300, 300, 300] => [212, 212, 212]
++++ Valid: [308, 308, 308] => [220, 220, 220]
++++ Valid: [316, 316, 316] => [228, 228, 228]
++++ Valid: [324, 324, 324] => [236, 236, 236]
++++ Valid: [332, 332, 332] => [244, 244, 244]
++++ Valid: [340, 340, 340] => [252, 252, 252]
++++ Valid: [348, 348, 348] => [260, 260, 260]
++++ Valid: [356, 356, 356] => [268, 268, 268]
++++ Valid: [364, 364, 364] => [276, 276, 276]
++++ Valid: [372, 372, 372] => [284, 284, 284]
++++ Valid: [380, 380, 380] => [292, 292, 292]
++++ Valid: [388, 388, 388] => [300, 300, 300]
++++ Valid: [396, 396, 396] => [308, 308, 308]
++++ Valid: [404, 404, 404] => [316, 316, 316]
++++ Valid: [412, 412, 412] => [324, 324, 324]
++++ Valid: [420, 420, 420] => [332, 332, 332]
++++ Valid: [428, 428, 428] => [340, 340, 340]
++++ Valid: [436, 436, 436] => [348, 348, 348]
++++ Valid: [444, 444, 444] => [356, 356, 356]
++++ Valid: [452, 452, 452] => [364, 364, 364]
++++ Valid: [460, 460, 460] => [372, 372, 372]
++++ Valid: [468, 468, 468] => [380, 380, 380]
++++ Valid: [476, 476, 476] => [388, 388, 388]
++++ Valid: [484, 484, 484] => [396, 396, 396]
++++ Valid: [492, 492, 492] => [404, 404, 404]
++++ Valid: [500, 500, 500] => [412, 412, 412]
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
224 in [193, 256]
240 in [225, 256]
232 in [225, 239]
236 in [233, 239]
234 in [233, 235]
233 in [233, 233]
Current shape: 0, [100, 100, 100], 233
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
224 in [193, 256]
208 in [193, 223]
216 in [209, 223]
220 in [217, 223]
222 in [221, 223]
223 in [223, 223]
Current shape: 1, [108, 108, 108], 223
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
224 in [193, 256]
208 in [193, 223]
216 in [209, 223]
212 in [209, 215]
210 in [209, 211]
211 in [211, 211]
Current shape: 2, [116, 116, 116], 211
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
224 in [193, 256]
208 in [193, 223]
200 in [193, 207]
196 in [193, 199]
198 in [197, 199]
197 in [197, 197]
196 in [197, 196]
Current shape: 3, [124, 124, 124], 196
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
159 in [128, 191]
175 in [160, 191]
183 in [176, 191]
179 in [176, 182]
181 in [180, 182]
180 in [180, 180]
Current shape: 4, [132, 132, 132], 180
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
159 in [128, 191]
175 in [160, 191]
167 in [160, 174]
163 in [160, 166]
165 in [164, 166]
164 in [164, 164]
163 in [164, 163]
Current shape: 5, [140, 140, 140], 163
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
159 in [128, 191]
143 in [128, 158]
151 in [144, 158]
147 in [144, 150]
145 in [144, 146]
146 in [146, 146]
145 in [146, 145]
Current shape: 6, [148, 148, 148], 145
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
256 in [1, 1]
192 in [128, 256]
159 in [128, 191]
143 in [128, 158]
135 in [128, 142]
131 in [128, 134]
129 in [128, 130]
128 in [128, 128]
Current shape: 7, [156, 156, 156], 128
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
96 in [64, 128]
112 in [97, 128]
104 in [97, 111]
108 in [105, 111]
110 in [109, 111]
111 in [111, 111]
Current shape: 8, [164, 164, 164], 111
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
96 in [64, 128]
112 in [97, 128]
104 in [97, 111]
100 in [97, 103]
98 in [97, 99]
97 in [97, 97]
96 in [97, 96]
Current shape: 9, [172, 172, 172], 96
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
96 in [64, 128]
79 in [64, 95]
87 in [80, 95]
83 in [80, 86]
85 in [84, 86]
84 in [84, 84]
83 in [84, 83]
Current shape: 10, [180, 180, 180], 83
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
128 in [1, 1]
96 in [64, 128]
79 in [64, 95]
71 in [64, 78]
75 in [72, 78]
73 in [72, 74]
72 in [72, 72]
71 in [72, 71]
Current shape: 11, [188, 188, 188], 71
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
48 in [32, 64]
56 in [49, 64]
60 in [57, 64]
62 in [61, 64]
61 in [61, 61]
Current shape: 12, [196, 196, 196], 61
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
48 in [32, 64]
56 in [49, 64]
52 in [49, 55]
54 in [53, 55]
53 in [53, 53]
Current shape: 13, [204, 204, 204], 53
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
48 in [32, 64]
39 in [32, 47]
43 in [40, 47]
45 in [44, 47]
46 in [46, 47]
47 in [47, 47]
46 in [47, 46]
Current shape: 14, [212, 212, 212], 46
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
48 in [32, 64]
39 in [32, 47]
43 in [40, 47]
41 in [40, 42]
40 in [40, 40]
Current shape: 15, [220, 220, 220], 40
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
64 in [1, 1]
48 in [32, 64]
39 in [32, 47]
35 in [32, 38]
37 in [36, 38]
36 in [36, 36]
35 in [36, 35]
Current shape: 16, [228, 228, 228], 35
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
24 in [16, 32]
28 in [25, 32]
30 in [29, 32]
31 in [31, 32]
30 in [31, 30]
Current shape: 17, [236, 236, 236], 30
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
24 in [16, 32]
28 in [25, 32]
26 in [25, 27]
27 in [27, 27]
Current shape: 18, [244, 244, 244], 27
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
24 in [16, 32]
28 in [25, 32]
26 in [25, 27]
25 in [25, 25]
24 in [25, 24]
Current shape: 19, [252, 252, 252], 24
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
24 in [16, 32]
19 in [16, 23]
21 in [20, 23]
22 in [22, 23]
21 in [22, 21]
Current shape: 20, [260, 260, 260], 21
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
24 in [16, 32]
19 in [16, 23]
21 in [20, 23]
20 in [20, 20]
19 in [20, 19]
Current shape: 21, [268, 268, 268], 19
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
32 in [1, 1]
24 in [16, 32]
19 in [16, 23]
17 in [16, 18]
18 in [18, 18]
17 in [18, 17]
Current shape: 22, [276, 276, 276], 17
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
14 in [13, 16]
15 in [15, 16]
16 in [16, 16]
15 in [16, 15]
Current shape: 23, [284, 284, 284], 15
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
14 in [13, 16]
13 in [13, 13]
Current shape: 24, [292, 292, 292], 13
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
14 in [13, 16]
13 in [13, 13]
12 in [13, 12]
Current shape: 25, [300, 300, 300], 12
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
9 in [8, 11]
10 in [10, 11]
11 in [11, 11]
Current shape: 26, [308, 308, 308], 11
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
9 in [8, 11]
10 in [10, 11]
11 in [11, 11]
10 in [11, 10]
Current shape: 27, [316, 316, 316], 10
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
9 in [8, 11]
10 in [10, 11]
9 in [10, 9]
Current shape: 28, [324, 324, 324], 9
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
16 in [1, 1]
12 in [8, 16]
9 in [8, 11]
8 in [8, 8]
Current shape: 29, [332, 332, 332], 8
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
7 in [7, 8]
8 in [8, 8]
7 in [8, 7]
Current shape: 30, [340, 340, 340], 7
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
7 in [7, 8]
8 in [8, 8]
7 in [8, 7]
Current shape: 31, [348, 348, 348], 7
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
7 in [7, 8]
6 in [7, 6]
Current shape: 32, [356, 356, 356], 6
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
7 in [7, 8]
6 in [7, 6]
Current shape: 33, [364, 364, 364], 6
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
4 in [4, 5]
5 in [5, 5]
Current shape: 34, [372, 372, 372], 5
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
4 in [4, 5]
5 in [5, 5]
Current shape: 35, [380, 380, 380], 5
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
4 in [4, 5]
5 in [5, 5]
4 in [5, 4]
Current shape: 36, [388, 388, 388], 4
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
4 in [4, 5]
5 in [5, 5]
4 in [5, 4]
Current shape: 37, [396, 396, 396], 4
2 in [1, 1]
4 in [1, 1]
8 in [1, 1]
6 in [4, 8]
4 in [4, 5]
5 in [5, 5]
4 in [5, 4]
Current shape: 38, [404, 404, 404], 4
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 39, [412, 412, 412], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 40, [420, 420, 420], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 41, [428, 428, 428], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 42, [436, 436, 436], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 43, [444, 444, 444], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 44, [452, 452, 452], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 45, [460, 460, 460], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 46, [468, 468, 468], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 47, [476, 476, 476], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 48, [484, 484, 484], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 49, [492, 492, 492], 2
2 in [1, 1]
1 in [1, 2]
2 in [2, 2]
1 in [2, 1]
Current shape: 50, [500, 500, 500], 1
Index to use: 20
f: 1 w: [260, 260, 260] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [258, 258, 258] d: [1, 1, 1]
WM: 2160
CM: 1898208000
AM: 0
f: 20 w: [256, 256, 256] d: [1, 1, 1]
WM: 43200
CM: 37094785920
AM: 0
f: 20 w: [128, 128, 128] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [126, 126, 126] d: [1, 1, 1]
WM: 129600
CM: 4529848320
AM: 0
f: 60 w: [124, 124, 124] d: [1, 1, 1]
WM: 388800
CM: 12962436480
AM: 0
f: 60 w: [62, 62, 62] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [60, 60, 60] d: [1, 1, 1]
WM: 1166400
CM: 1544365440
AM: 0
f: 180 w: [58, 58, 58] d: [1, 1, 1]
WM: 3499200
CM: 4199040000
AM: 0
f: 180 w: [29, 29, 29] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 540 w: [27, 27, 27] d: [1, 1, 1]
WM: 10497600
CM: 474122160
AM: 0
f: 540 w: [25, 25, 25] d: [1, 1, 1]
WM: 31492800
CM: 1147912560
AM: 0
f: 540 w: [50, 50, 50] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [50, 50, 50] d: [1, 1, 1]
WM: 388800
CM: 2160000000
AM: 0
f: 360 w: [50, 50, 50] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [48, 48, 48] d: [1, 1, 1]
WM: 6998400
CM: 4860000000
AM: 0
f: 180 w: [46, 46, 46] d: [1, 1, 1]
WM: 3499200
CM: 2149908480
AM: 0
f: 180 w: [92, 92, 92] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [92, 92, 92] d: [1, 1, 1]
WM: 43200
CM: 4485242880
AM: 0
f: 120 w: [92, 92, 92] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [90, 90, 90] d: [1, 1, 1]
WM: 777600
CM: 10091796480
AM: 0
f: 60 w: [88, 88, 88] d: [1, 1, 1]
WM: 388800
CM: 4723920000
AM: 0
f: 60 w: [176, 176, 176] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [176, 176, 176] d: [1, 1, 1]
WM: 4800
CM: 10467409920
AM: 0
f: 40 w: [176, 176, 176] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [174, 174, 174] d: [1, 1, 1]
WM: 86400
CM: 23551672320
AM: 0
f: 20 w: [172, 172, 172] d: [1, 1, 1]
WM: 43200
CM: 11378931840
AM: 0
f: 3 w: [172, 172, 172] d: [1, 1, 1]
WM: 240
CM: 407075840
AM: 0
Max. memory requirements: 57143842112 B
Weight memory: 59450400 B
Max. conv buffer: 37094785920 B
f: 1 w: [260, 260, 260] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [258, 258, 258] d: [1, 1, 1]
WM: 2160
CM: 1898208000
AM: 0
f: 20 w: [256, 256, 256] d: [1, 1, 1]
WM: 43200
CM: 37094785920
AM: 0
f: 20 w: [128, 128, 128] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [126, 126, 126] d: [1, 1, 1]
WM: 129600
CM: 4529848320
AM: 0
f: 60 w: [124, 124, 124] d: [1, 1, 1]
WM: 388800
CM: 12962436480
AM: 0
f: 60 w: [62, 62, 62] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [60, 60, 60] d: [1, 1, 1]
WM: 1166400
CM: 1544365440
AM: 0
f: 180 w: [58, 58, 58] d: [1, 1, 1]
WM: 3499200
CM: 4199040000
AM: 0
f: 180 w: [29, 29, 29] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 540 w: [27, 27, 27] d: [1, 1, 1]
WM: 10497600
CM: 474122160
AM: 0
f: 540 w: [25, 25, 25] d: [1, 1, 1]
WM: 31492800
CM: 1147912560
AM: 0
f: 540 w: [50, 50, 50] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [50, 50, 50] d: [1, 1, 1]
WM: 388800
CM: 2160000000
AM: 0
f: 360 w: [50, 50, 50] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 180 w: [48, 48, 48] d: [1, 1, 1]
WM: 6998400
CM: 4860000000
AM: 0
f: 180 w: [46, 46, 46] d: [1, 1, 1]
WM: 3499200
CM: 2149908480
AM: 0
f: 180 w: [92, 92, 92] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [92, 92, 92] d: [1, 1, 1]
WM: 43200
CM: 4485242880
AM: 0
f: 120 w: [92, 92, 92] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 60 w: [90, 90, 90] d: [1, 1, 1]
WM: 777600
CM: 10091796480
AM: 0
f: 60 w: [88, 88, 88] d: [1, 1, 1]
WM: 388800
CM: 4723920000
AM: 0
f: 60 w: [176, 176, 176] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [176, 176, 176] d: [1, 1, 1]
WM: 4800
CM: 10467409920
AM: 0
f: 40 w: [176, 176, 176] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
f: 20 w: [174, 174, 174] d: [1, 1, 1]
WM: 86400
CM: 23551672320
AM: 0
f: 20 w: [172, 172, 172] d: [1, 1, 1]
WM: 43200
CM: 11378931840
AM: 0
f: 3 w: [172, 172, 172] d: [1, 1, 1]
WM: 240
CM: 407075840
AM: 0
Max. memory requirements: 47149039216 B
Weight memory: 59450400 B
Max. conv buffer: 37094785920 B

















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Content source: naibaf7/caffe_neural_models

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