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PyGreentea Network Generator

Load the dependencies





In [1]:



    


%matplotlib inline

from __future__ import print_function
import h5py
import numpy as np
from numpy import float32, int32, uint8, dtype
import sys
import matplotlib.pyplot as plt


pygt_path = '../PyGreentea'
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.getcwd()), pygt_path))

import math

import PyGreentea as pygt

Load the default network template





In [2]:



    


netconf = pygt.netgen.NetConf()

Set the memory limits for the GPU





In [3]:



    


# We use cuDNN, so:
netconf.ignore_conv_buffer = True
# 4 GB total, ignore convolution buffer. Let's keep 0.5 GB for implementation dependent buffers.
netconf.mem_global_limit = 3.5 * 1024 * 1024 * 1024
# 4 GB convolution buffer limit
netconf.mem_buf_limit = 3.5 * 1024 * 1024 * 1024

Explore possible network input/output shapes for the chosen settings





In [4]:



    


# We test memory usage for training
mode = pygt.netgen.caffe_pb2.TRAIN
# The minimum we're interested in
shape_min = [100,100,100]
# And maximum
shape_max = [200,200,200]
# We want Z == X == Y constrained
constraints = [None, lambda x: x[0], lambda x: x[1]]

netconf.u_netconfs[0].use_deconvolution_uppath = True

# Compute (can be quite intensive)
inshape, outshape, fmaps = pygt.netgen.compute_valid_io_shapes(netconf,mode,shape_min,shape_max,constraints=constraints)



    


















    






++++ Valid: [100] => [100]
-- Invalid: [101] => []
-- Invalid: [102] => []
-- Invalid: [103] => []
-- Invalid: [104] => []
-- Invalid: [105] => []
-- Invalid: [106] => []
-- Invalid: [107] => []
++++ Valid: [108] => [108]
-- Invalid: [109] => []
-- Invalid: [110] => []
-- Invalid: [111] => []
-- Invalid: [112] => []
-- Invalid: [113] => []
-- Invalid: [114] => []
-- Invalid: [115] => []
++++ Valid: [116] => [116]
-- Invalid: [117] => []
-- Invalid: [118] => []
-- Invalid: [119] => []
-- Invalid: [120] => []
-- Invalid: [121] => []
-- Invalid: [122] => []
-- Invalid: [123] => []
++++ Valid: [124] => [124]
-- Invalid: [125] => []
-- Invalid: [126] => []
-- Invalid: [127] => []
-- Invalid: [128] => []
-- Invalid: [129] => []
-- Invalid: [130] => []
-- Invalid: [131] => []
++++ Valid: [132] => [132]
-- Invalid: [133] => []
-- Invalid: [134] => []
-- Invalid: [135] => []
-- Invalid: [136] => []
-- Invalid: [137] => []
-- Invalid: [138] => []
-- Invalid: [139] => []
++++ Valid: [140] => [140]
-- Invalid: [141] => []
-- Invalid: [142] => []
-- Invalid: [143] => []
-- Invalid: [144] => []
-- Invalid: [145] => []
-- Invalid: [146] => []
-- Invalid: [147] => []
++++ Valid: [148] => [148]
-- Invalid: [149] => []
-- Invalid: [150] => []
-- Invalid: [151] => []
-- Invalid: [152] => []
-- Invalid: [153] => []
-- Invalid: [154] => []
-- Invalid: [155] => []
++++ Valid: [156] => [156]
-- Invalid: [157] => []
-- Invalid: [158] => []
-- Invalid: [159] => []
-- Invalid: [160] => []
-- Invalid: [161] => []
-- Invalid: [162] => []
-- Invalid: [163] => []
++++ Valid: [164] => [164]
-- Invalid: [165] => []
-- Invalid: [166] => []
-- Invalid: [167] => []
-- Invalid: [168] => []
-- Invalid: [169] => []
-- Invalid: [170] => []
-- Invalid: [171] => []
++++ Valid: [172] => [172]
-- Invalid: [173] => []
-- Invalid: [174] => []
-- Invalid: [175] => []
-- Invalid: [176] => []
-- Invalid: [177] => []
-- Invalid: [178] => []
-- Invalid: [179] => []
++++ Valid: [180] => [180]
-- Invalid: [181] => []
-- Invalid: [182] => []
-- Invalid: [183] => []
-- Invalid: [184] => []
-- Invalid: [185] => []
-- Invalid: [186] => []
-- Invalid: [187] => []
++++ Valid: [188] => [188]
-- Invalid: [189] => []
-- Invalid: [190] => []
-- Invalid: [191] => []
-- Invalid: [192] => []
-- Invalid: [193] => []
-- Invalid: [194] => []
-- Invalid: [195] => []
++++ Valid: [196] => [196]
-- Invalid: [197] => []
-- Invalid: [198] => []
-- Invalid: [199] => []
-- Invalid: [200] => []
++++ Valid: [100, 100] => [100, 100]
++++ Valid: [108, 108] => [108, 108]
++++ Valid: [116, 116] => [116, 116]
++++ Valid: [124, 124] => [124, 124]
++++ Valid: [132, 132] => [132, 132]
++++ Valid: [140, 140] => [140, 140]
++++ Valid: [148, 148] => [148, 148]
++++ Valid: [156, 156] => [156, 156]
++++ Valid: [164, 164] => [164, 164]
++++ Valid: [172, 172] => [172, 172]
++++ Valid: [180, 180] => [180, 180]
++++ Valid: [188, 188] => [188, 188]
++++ Valid: [196, 196] => [196, 196]
++++ Valid: [100, 100, 100] => [100, 100, 100]
++++ Valid: [108, 108, 108] => [108, 108, 108]
++++ Valid: [116, 116, 116] => [116, 116, 116]
++++ Valid: [124, 124, 124] => [124, 124, 124]
++++ Valid: [132, 132, 132] => [132, 132, 132]
++++ Valid: [140, 140, 140] => [140, 140, 140]
++++ Valid: [148, 148, 148] => [148, 148, 148]
++++ Valid: [156, 156, 156] => [156, 156, 156]
++++ Valid: [164, 164, 164] => [164, 164, 164]
++++ Valid: [172, 172, 172] => [172, 172, 172]
++++ Valid: [180, 180, 180] => [180, 180, 180]
++++ Valid: [188, 188, 188] => [188, 188, 188]
++++ Valid: [196, 196, 196] => [196, 196, 196]
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]
Current shape: 0, [100, 100, 100], 25
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]
Current shape: 1, [108, 108, 108], 20
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]
16 in [16, 16]
Current shape: 2, [116, 116, 116], 16
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: 3, [124, 124, 124], 13
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: 4, [132, 132, 132], 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]
8 in [8, 8]
Current shape: 5, [140, 140, 140], 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: 6, [148, 148, 148], 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: 7, [156, 156, 156], 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: 8, [164, 164, 164], 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: 9, [172, 172, 172], 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: 10, [180, 180, 180], 4
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 11, [188, 188, 188], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 12, [196, 196, 196], 3

Visualization





In [5]:



    


plt.figure()
# Combined output size versus feature map count
plt.scatter([x[0]*x[1]*x[2] for x in outshape], fmaps, alpha = 0.5)
plt.ylabel('Feature maps')
plt.xlabel('Combined output size')
plt.show()

Pick parameters, actually generate and store the network





In [6]:



    


netconf.input_shape = inshape[0]
netconf.output_shape = outshape[0]
netconf.fmap_start = fmaps[0]

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: [100, 100, 100]
Output shape: [100, 100, 100]
Feature maps: 25
Shape: [0]
f: 1 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [1]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 2700
CM: 108000000
AM: 200000000
Shape: [2]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [3]
f: 25 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [4]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 202500
CM: 298598400
AM: 66355200
Shape: [5]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [6]
f: 75 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [7]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 1822500
CM: 86248800
AM: 19166400
Shape: [8]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [9]
f: 225 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [10]
f: 675 w: [7, 7, 7] d: [1, 1, 1]
WM: 16402500
CM: 17714700
AM: 3936600
Shape: [11]
f: 675 w: [9, 9, 9] d: [1, 1, 1]
WM: 49207500
CM: 25004700
AM: 1852200
Shape: [12]
f: 675 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [13]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 607500
CM: 125971200
AM: 0
Shape: [14]
f: 450 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [15]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 10935000
CM: 283435200
AM: 10497600
Shape: [16]
f: 225 w: [22, 22, 22] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [17]
f: 225 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [18]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 67500
CM: 613324800
AM: 0
Shape: [19]
f: 150 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [20]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 1215000
CM: 1379980800
AM: 51110400
Shape: [21]
f: 75 w: [48, 48, 48] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [22]
f: 75 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [23]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 7500
CM: 2123366400
AM: 0
Shape: [24]
f: 50 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [25]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 135000
CM: 4777574400
AM: 176947200
Shape: [26]
f: 25 w: [100, 100, 100] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [27]
f: 3 w: [100, 100, 100] d: [1, 1, 1]
WM: 300
CM: 100000000
AM: 0
Max. memory requirements: 7415561100 B
Weight memory: 92890500 B
Max. conv buffer: 4777574400 B
Shape: [0]
f: 1 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [1]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 2700
CM: 108000000
AM: 200000000
Shape: [2]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [3]
f: 25 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [4]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 202500
CM: 298598400
AM: 66355200
Shape: [5]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [6]
f: 75 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [7]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 1822500
CM: 86248800
AM: 19166400
Shape: [8]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [9]
f: 225 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [10]
f: 675 w: [7, 7, 7] d: [1, 1, 1]
WM: 16402500
CM: 17714700
AM: 3936600
Shape: [11]
f: 675 w: [9, 9, 9] d: [1, 1, 1]
WM: 49207500
CM: 25004700
AM: 1852200
Shape: [12]
f: 675 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [13]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 607500
CM: 125971200
AM: 0
Shape: [14]
f: 450 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [15]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 10935000
CM: 283435200
AM: 10497600
Shape: [16]
f: 225 w: [22, 22, 22] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [17]
f: 225 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [18]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 67500
CM: 613324800
AM: 0
Shape: [19]
f: 150 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [20]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 1215000
CM: 1379980800
AM: 51110400
Shape: [21]
f: 75 w: [48, 48, 48] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [22]
f: 75 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [23]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 7500
CM: 2123366400
AM: 0
Shape: [24]
f: 50 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [25]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 135000
CM: 4777574400
AM: 176947200
Shape: [26]
f: 25 w: [100, 100, 100] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [27]
f: 3 w: [100, 100, 100] d: [1, 1, 1]
WM: 300
CM: 100000000
AM: 0
Max. memory requirements: 6143013000 B
Weight memory: 92890500 B
Max. conv buffer: 4777574400 B
Shape: [0]
f: 1 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [1]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 2700
CM: 108000000
AM: 200000000
Shape: [2]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [3]
f: 25 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [4]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 202500
CM: 298598400
AM: 66355200
Shape: [5]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [6]
f: 75 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [7]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 1822500
CM: 86248800
AM: 19166400
Shape: [8]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [9]
f: 225 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [10]
f: 675 w: [7, 7, 7] d: [1, 1, 1]
WM: 16402500
CM: 17714700
AM: 3936600
Shape: [11]
f: 675 w: [9, 9, 9] d: [1, 1, 1]
WM: 49207500
CM: 25004700
AM: 1852200
Shape: [12]
f: 675 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [13]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 607500
CM: 125971200
AM: 0
Shape: [14]
f: 450 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [15]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 10935000
CM: 283435200
AM: 10497600
Shape: [16]
f: 225 w: [22, 22, 22] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [17]
f: 225 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [18]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 67500
CM: 613324800
AM: 0
Shape: [19]
f: 150 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [20]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 1215000
CM: 1379980800
AM: 51110400
Shape: [21]
f: 75 w: [48, 48, 48] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [22]
f: 75 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [23]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 7500
CM: 2123366400
AM: 0
Shape: [24]
f: 50 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [25]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 135000
CM: 4777574400
AM: 176947200
Shape: [26]
f: 25 w: [100, 100, 100] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [27]
f: 3 w: [100, 100, 100] d: [1, 1, 1]
WM: 300
CM: 100000000
AM: 0
Max. memory requirements: 7415561100 B
Weight memory: 92890500 B
Max. conv buffer: 4777574400 B
Shape: [0]
f: 1 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [1]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 2700
CM: 108000000
AM: 200000000
Shape: [2]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [3]
f: 25 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [4]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 202500
CM: 298598400
AM: 66355200
Shape: [5]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [6]
f: 75 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [7]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 1822500
CM: 86248800
AM: 19166400
Shape: [8]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [9]
f: 225 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [10]
f: 675 w: [7, 7, 7] d: [1, 1, 1]
WM: 16402500
CM: 17714700
AM: 3936600
Shape: [11]
f: 675 w: [9, 9, 9] d: [1, 1, 1]
WM: 49207500
CM: 25004700
AM: 1852200
Shape: [12]
f: 675 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [13]
f: 225 w: [18, 18, 18] d: [1, 1, 1]
WM: 607500
CM: 125971200
AM: 0
Shape: [14]
f: 450 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [15]
f: 225 w: [20, 20, 20] d: [1, 1, 1]
WM: 10935000
CM: 283435200
AM: 10497600
Shape: [16]
f: 225 w: [22, 22, 22] d: [1, 1, 1]
WM: 5467500
CM: 194400000
AM: 14400000
Shape: [17]
f: 225 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [18]
f: 75 w: [44, 44, 44] d: [1, 1, 1]
WM: 67500
CM: 613324800
AM: 0
Shape: [19]
f: 150 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [20]
f: 75 w: [46, 46, 46] d: [1, 1, 1]
WM: 1215000
CM: 1379980800
AM: 51110400
Shape: [21]
f: 75 w: [48, 48, 48] d: [1, 1, 1]
WM: 607500
CM: 788421600
AM: 58401600
Shape: [22]
f: 75 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [23]
f: 25 w: [96, 96, 96] d: [1, 1, 1]
WM: 7500
CM: 2123366400
AM: 0
Shape: [24]
f: 50 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [25]
f: 25 w: [98, 98, 98] d: [1, 1, 1]
WM: 135000
CM: 4777574400
AM: 176947200
Shape: [26]
f: 25 w: [100, 100, 100] d: [1, 1, 1]
WM: 67500
CM: 2541218400
AM: 188238400
Shape: [27]
f: 3 w: [100, 100, 100] d: [1, 1, 1]
WM: 300
CM: 100000000
AM: 0
Max. memory requirements: 6143013000 B
Weight memory: 92890500 B
Max. conv buffer: 4777574400 B

















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Content source: naibaf7/caffe_neural_models

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