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
import copy

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









    



/media/c_drive/Users/Fabian/Documents/ETH/BachelorThesis/eth_bsc/caffe_gt/python/caffe/pycaffe.py:13: RuntimeWarning: to-Python converter for boost::shared_ptr<caffe::Net<float> > already registered; second conversion method ignored.
  from ._caffe import \
/media/c_drive/Users/Fabian/Documents/ETH/BachelorThesis/eth_bsc/caffe_gt/python/caffe/pycaffe.py:13: RuntimeWarning: to-Python converter for boost::shared_ptr<caffe::Blob<float> > already registered; second conversion method ignored.
  from ._caffe import \
/media/c_drive/Users/Fabian/Documents/ETH/BachelorThesis/eth_bsc/caffe_gt/python/caffe/pycaffe.py:13: RuntimeWarning: to-Python converter for boost::shared_ptr<caffe::Solver<float> > already registered; second conversion method ignored.
  from ._caffe import \

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]]

# Create a network with no context loss (at least for training)
netconf.u_netconfs[0].use_deconvolution_uppath = True
# Create a W-Net (two U-Nets concatenated)
netconf.u_netconfs += [copy.deepcopy(netconf.u_netconfs[0])]

# Run a shortcut (deep residual, additive) over the first U-Net
netconf.u_netconfs[0].bridge = True

# Run a shortcut (deep residual, additive) over the second U-Net
netconf.u_netconfs[1].bridge = 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]
12 in [8, 16]
14 in [13, 16]
13 in [13, 13]
12 in [13, 12]
Current shape: 0, [100, 100, 100], 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]
9 in [10, 9]
Current shape: 1, [108, 108, 108], 9
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: 2, [116, 116, 116], 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: 3, [124, 124, 124], 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: 4, [132, 132, 132], 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: 5, [140, 140, 140], 4
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 6, [148, 148, 148], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
4 in [4, 4]
3 in [4, 3]
Current shape: 7, [156, 156, 156], 3
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 8, [164, 164, 164], 2
2 in [1, 1]
4 in [1, 1]
3 in [2, 4]
2 in [2, 2]
Current shape: 9, [172, 172, 172], 2
2 in [1, 1]
1 in [1, 2]
2 in [2, 2]
1 in [2, 1]
Current shape: 10, [180, 180, 180], 1
2 in [1, 1]
1 in [1, 2]
2 in [2, 2]
1 in [2, 1]
Current shape: 11, [188, 188, 188], 1
2 in [1, 1]
1 in [1, 2]
2 in [2, 2]
1 in [2, 1]
Current shape: 12, [196, 196, 196], 1

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: 12
Shape: [0]
f: 1 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [1]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 1296
CM: 108000000
AM: 96000000
Shape: [2]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [3]
f: 12 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [4]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 46656
CM: 143327232
AM: 31850496
Shape: [5]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [6]
f: 36 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [7]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 419904
CM: 41399424
AM: 9199872
Shape: [8]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [9]
f: 108 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [10]
f: 324 w: [7, 7, 7] d: [1, 1, 1]
WM: 3779136
CM: 8503056
AM: 1889568
Shape: [11]
f: 324 w: [9, 9, 9] d: [1, 1, 1]
WM: 11337408
CM: 12002256
AM: 889056
Shape: [12]
f: 324 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [13]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 139968
CM: 60466176
AM: 0
Shape: [14]
f: 216 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [15]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 2519424
CM: 136048896
AM: 5038848
Shape: [16]
f: 108 w: [22, 22, 22] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [17]
f: 108 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [18]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 15552
CM: 294395904
AM: 0
Shape: [19]
f: 72 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [20]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 279936
CM: 662390784
AM: 24532992
Shape: [21]
f: 36 w: [48, 48, 48] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [22]
f: 36 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [23]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 1728
CM: 1019215872
AM: 0
Shape: [24]
f: 24 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [25]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 31104
CM: 2293235712
AM: 84934656
Shape: [26]
f: 12 w: [100, 100, 100] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [27]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 15552
CM: 1296000000
AM: 96000000
Shape: [28]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [29]
f: 12 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [30]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 46656
CM: 143327232
AM: 31850496
Shape: [31]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [32]
f: 36 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [33]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 419904
CM: 41399424
AM: 9199872
Shape: [34]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [35]
f: 108 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [36]
f: 324 w: [7, 7, 7] d: [1, 1, 1]
WM: 3779136
CM: 8503056
AM: 1889568
Shape: [37]
f: 324 w: [9, 9, 9] d: [1, 1, 1]
WM: 11337408
CM: 12002256
AM: 889056
Shape: [38]
f: 324 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [39]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 139968
CM: 60466176
AM: 0
Shape: [40]
f: 216 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [41]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 2519424
CM: 136048896
AM: 5038848
Shape: [42]
f: 108 w: [22, 22, 22] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [43]
f: 108 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [44]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 15552
CM: 294395904
AM: 0
Shape: [45]
f: 72 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [46]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 279936
CM: 662390784
AM: 24532992
Shape: [47]
f: 36 w: [48, 48, 48] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [48]
f: 36 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [49]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 1728
CM: 1019215872
AM: 0
Shape: [50]
f: 24 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [51]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 31104
CM: 2293235712
AM: 84934656
Shape: [52]
f: 12 w: [100, 100, 100] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [53]
f: 12 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [54]
f: 3 w: [100, 100, 100] d: [1, 1, 1]
WM: 144
CM: 48000000
AM: 0
Max. memory requirements: 4876627616 B
Weight memory: 42819552 B
Max. conv buffer: 2293235712 B
Shape: [0]
f: 1 w: [100, 100, 100] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [1]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 1296
CM: 108000000
AM: 96000000
Shape: [2]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [3]
f: 12 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [4]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 46656
CM: 143327232
AM: 31850496
Shape: [5]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [6]
f: 36 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [7]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 419904
CM: 41399424
AM: 9199872
Shape: [8]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [9]
f: 108 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [10]
f: 324 w: [7, 7, 7] d: [1, 1, 1]
WM: 3779136
CM: 8503056
AM: 1889568
Shape: [11]
f: 324 w: [9, 9, 9] d: [1, 1, 1]
WM: 11337408
CM: 12002256
AM: 889056
Shape: [12]
f: 324 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [13]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 139968
CM: 60466176
AM: 0
Shape: [14]
f: 216 w: [18, 18, 18] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [15]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 2519424
CM: 136048896
AM: 5038848
Shape: [16]
f: 108 w: [22, 22, 22] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [17]
f: 108 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [18]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 15552
CM: 294395904
AM: 0
Shape: [19]
f: 72 w: [44, 44, 44] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [20]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 279936
CM: 662390784
AM: 24532992
Shape: [21]
f: 36 w: [48, 48, 48] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [22]
f: 36 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [23]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 1728
CM: 1019215872
AM: 0
Shape: [24]
f: 24 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [25]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 31104
CM: 2293235712
AM: 84934656
Shape: [26]
f: 12 w: [100, 100, 100] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [27]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 15552
CM: 1296000000
AM: 96000000
Shape: [28]
f: 12 w: [96, 96, 96] d: [1, 1, 1]
WM: 15552
CM: 1219784832
AM: 90354432
Shape: [29]
f: 12 w: [48, 48, 48] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [30]
f: 36 w: [46, 46, 46] d: [1, 1, 1]
WM: 46656
CM: 143327232
AM: 31850496
Shape: [31]
f: 36 w: [44, 44, 44] d: [1, 1, 1]
WM: 139968
CM: 378442368
AM: 28032768
Shape: [32]
f: 36 w: [22, 22, 22] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [33]
f: 108 w: [20, 20, 20] d: [1, 1, 1]
WM: 419904
CM: 41399424
AM: 9199872
Shape: [34]
f: 108 w: [18, 18, 18] d: [1, 1, 1]
WM: 1259712
CM: 93312000
AM: 6912000
Shape: [35]
f: 108 w: [9, 9, 9] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [36]
f: 324 w: [7, 7, 7] d: [1, 1, 1]
WM: 3779136
CM: 8503056
AM: 1889568
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CM: 12002256
AM: 889056
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CM: 136048896
AM: 5038848
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CM: 93312000
AM: 6912000
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AM: 24532992
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CM: 378442368
AM: 28032768
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AM: 0
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WM: 0
CM: 0
AM: 0
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CM: 2293235712
AM: 84934656
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AM: 90354432
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WM: 0
CM: 0
AM: 0
Shape: [54]
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WM: 144
CM: 48000000
AM: 0
Max. memory requirements: 3606341440 B
Weight memory: 42819552 B
Max. conv buffer: 2293235712 B
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CM: 0
AM: 0
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AM: 96000000
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CM: 1219784832
AM: 90354432
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CM: 0
AM: 0
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AM: 31850496
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CM: 378442368
AM: 28032768
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AM: 9199872
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AM: 6912000
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AM: 0
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AM: 1889568
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CM: 12002256
AM: 889056
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AM: 0
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AM: 5038848
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AM: 6912000
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CM: 0
AM: 0
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AM: 0
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CM: 662390784
AM: 24532992
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CM: 378442368
AM: 28032768
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CM: 0
AM: 0
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CM: 2293235712
AM: 84934656
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CM: 1219784832
AM: 90354432
Shape: [27]
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WM: 15552
CM: 1296000000
AM: 96000000
Shape: [28]
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WM: 15552
CM: 1219784832
AM: 90354432
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CM: 0
AM: 0
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AM: 31850496
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CM: 378442368
AM: 28032768
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CM: 0
AM: 0
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CM: 41399424
AM: 9199872
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WM: 1259712
CM: 93312000
AM: 6912000
Shape: [35]
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WM: 0
CM: 0
AM: 0
Shape: [36]
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WM: 3779136
CM: 8503056
AM: 1889568
Shape: [37]
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WM: 11337408
CM: 12002256
AM: 889056
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WM: 0
CM: 0
AM: 0
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CM: 60466176
AM: 0
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CM: 0
AM: 0
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CM: 136048896
AM: 5038848
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WM: 1259712
CM: 93312000
AM: 6912000
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WM: 0
CM: 0
AM: 0
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CM: 294395904
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AM: 0
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CM: 662390784
AM: 24532992
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WM: 139968
CM: 378442368
AM: 28032768
Shape: [48]
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WM: 0
CM: 0
AM: 0
Shape: [49]
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WM: 1728
CM: 1019215872
AM: 0
Shape: [50]
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WM: 0
CM: 0
AM: 0
Shape: [51]
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WM: 31104
CM: 2293235712
AM: 84934656
Shape: [52]
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CM: 1219784832
AM: 90354432
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WM: 0
CM: 0
AM: 0
Shape: [54]
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WM: 144
CM: 48000000
AM: 0
Max. memory requirements: 4876627616 B
Weight memory: 42819552 B
Max. conv buffer: 2293235712 B
Shape: [0]
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WM: 0
CM: 0
AM: 0
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AM: 96000000
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CM: 1219784832
AM: 90354432
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CM: 0
AM: 0
Shape: [4]
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CM: 143327232
AM: 31850496
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CM: 378442368
AM: 28032768
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AM: 9199872
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AM: 0
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AM: 1889568
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CM: 12002256
AM: 889056
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AM: 0
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AM: 6912000
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CM: 0
AM: 0
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AM: 0
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CM: 0
AM: 0
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CM: 662390784
AM: 24532992
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CM: 378442368
AM: 28032768
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CM: 2293235712
AM: 84934656
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CM: 1219784832
AM: 90354432
Shape: [27]
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CM: 1296000000
AM: 96000000
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CM: 1219784832
AM: 90354432
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AM: 0
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AM: 9199872
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CM: 93312000
AM: 6912000
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WM: 0
CM: 0
AM: 0
Shape: [36]
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WM: 3779136
CM: 8503056
AM: 1889568
Shape: [37]
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WM: 11337408
CM: 12002256
AM: 889056
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CM: 0
AM: 0
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CM: 60466176
AM: 0
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CM: 0
AM: 0
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CM: 136048896
AM: 5038848
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WM: 1259712
CM: 93312000
AM: 6912000
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CM: 0
AM: 0
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CM: 294395904
AM: 0
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AM: 0
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CM: 662390784
AM: 24532992
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WM: 139968
CM: 378442368
AM: 28032768
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WM: 0
CM: 0
AM: 0
Shape: [49]
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WM: 1728
CM: 1019215872
AM: 0
Shape: [50]
f: 24 w: [96, 96, 96] d: [1, 1, 1]
WM: 0
CM: 0
AM: 0
Shape: [51]
f: 12 w: [98, 98, 98] d: [1, 1, 1]
WM: 31104
CM: 2293235712
AM: 84934656
Shape: [52]
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WM: 15552
CM: 1219784832
AM: 90354432
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WM: 0
CM: 0
AM: 0
Shape: [54]
f: 3 w: [100, 100, 100] d: [1, 1, 1]
WM: 144
CM: 48000000
AM: 0
Max. memory requirements: 3606341440 B
Weight memory: 42819552 B
Max. conv buffer: 2293235712 B



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