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models.py
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import tensorflow as tf
import modules as md
# def GhostConv():
# return 1
def la(x, xi): # layers addition
return x.append(xi)
def build_custom_model(input_layers, num_classes=13):
x = []
la(x, md.GhostConv(input_layers, 32, 1))
la(x, md.GhostConv(x[-1], 32, 1))
la(x, md.Flatten(x[-1]))
la(x, md.Dense(x[-1], num_classes))
return x
def build_yolov7_model(input_layers, num_classes=13):
x = []
# Backbone
la(x, md.Conv(input_layers, 32, 3, 1)) # 0
la(x, md.Conv(x[-1], 32, 3, 1)) # 1
la(x, md.Conv(x[-1], 64, 3, 1))
la(x, md.Conv(x[-1], 128, 3, 2)) # 3-P2/4
la(x, md.Conv(x[-1], 64, 1, 1))
la(x, md.Conv(x[-2], 64, 1, 1))
la(x, md.Conv(x[-1], 64, 3, 1))
la(x, md.Conv(x[-1], 64, 3, 1))
la(x, md.Conv(x[-1], 64, 3, 1))
la(x, md.Conv(x[-1], 64, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.Conv(x[-1], 256, 1, 1)) # 11
la(x, md.MP(x[-1]))
la(x, md.Conv(x[-1], 128, 1, 1))
la(x, md.Conv(x[-3], 128, 1, 1))
la(x, md.Conv(x[-1], 128, 3, 2))
la(x, md.Concat([x[-1], x[-3]])) # 16-P3/8
la(x, md.Conv(x[-1], 128, 1, 1))
la(x, md.Conv(x[-2], 128, 1, 1))
la(x, md.Conv(x[-1], 128, 3, 1))
la(x, md.Conv(x[-1], 128, 3, 1))
la(x, md.Conv(x[-1], 128, 3, 1))
la(x, md.Conv(x[-1], 128, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.Conv(x[-1], 512, 1, 1)) # 24
la(x, md.MP(x[-1]))
la(x, md.Conv(x[-1], 256, 1, 1))
la(x, md.Conv(x[-3], 256, 1, 1))
la(x, md.Conv(x[-1], 256, 3, 2))
la(x, md.Concat([x[-1], x[-3]])) # 29-P4/16
la(x, md.Conv(x[-1], 256, 1, 1))
la(x, md.Conv(x[-2], 256, 1, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.Conv(x[-1], 1024, 1, 1)) # 37
la(x, md.MP(x[-1])) # [-1, 1, MP, []],
la(x, md.Conv(x[-1], 512, 1, 1))
la(x, md.Conv(x[-3], 512, 1, 1))
la(x, md.Conv(x[-1], 512, 3, 2))
la(x, md.Concat([x[-1], x[-3]])) # 42-P5/32
la(x, md.Conv(x[-1], 256, 1, 1))
la(x, md.Conv(x[-2], 256, 1, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Conv(x[-1], 256, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.Conv(x[-1], 1024, 1, 1)) # 50
# Head
la(x, md.SPPCSPC(x[50], 512)) # [[-1, 1, SPPCSPC, [512]], # 51
la(x, md.Conv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
la(x, md.Upsample(x[-1], 2)) # [-1, 1, nn.Upsample, [None, 2, 'nearest']],
la(x, md.Conv(x[37], 256, 1, 1)) # [37, 1, Conv, [256, 1, 1]], # route backbone P4
la(x, md.Concat([x[-1], x[-2]])) # [[-1, -2], 1, Concat, [1]],
la(x, md.Conv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
la(x, md.Conv(x[-2], 256, 1, 1)) # [-2, 1, Conv, [256, 1, 1]],
la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]], -1)) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
la(x, md.Conv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]], # 63
la(x, md.Conv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]],
la(x, md.Upsample(x[-1], 2)) # [-1, 1, nn.Upsample, [None, 2, 'nearest']],
la(x, md.Conv(x[24], 128, 1, 1)) # [24, 1, Conv, [128, 1, 1]], # route backbone P3
la(x, md.Concat([x[-1], x[-2]])) # [[-1, -2], 1, Concat, [1]],
la(x, md.Conv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]],
la(x, md.Conv(x[-2], 128, 1, 1)) # [-2, 1, Conv, [128, 1, 1]],
la(x, md.Conv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.Conv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.Conv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.Conv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]])) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
la(x, md.Conv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]], # 75
# la(x, md.MP(x[-1])) # [-1, 1, MP, []],
# la(x, md.Conv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]],
# la(x, md.Conv(x[-3], 128, 1, 1)) # [-3, 1, Conv, [128, 1, 1]],
# la(x, md.Conv(x[-1], 128, 3, 2)) # [-1, 1, Conv, [128, 3, 2]],
# la(x, md.Concat([x[-1], x[-3], x[63]])) # [[-1, -3, 63], 1, Concat, [1]],
# la(x, md.Conv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
# la(x, md.Conv(x[-2], 256, 1, 1)) # [-2, 1, Conv, [256, 1, 1]],
# la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.Conv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]])) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
# la(x, md.Conv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]], # 88
# la(x, md.MP(x[-1])) # [-1, 1, MP, []],
# la(x, md.Conv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
# la(x, md.Conv(x[-3], 256, 1, 1)) # [-3, 1, Conv, [256, 1, 1]],
# la(x, md.Conv(x[-1], 256, 3, 2)) # [-1, 1, Conv, [256, 3, 2]],
# la(x, md.Concat([x[-1], x[-3], x[51]])) # [[-1, -3, 51], 1, Concat, [1]],
# la(x, md.Conv(x[-1], 512, 1, 1)) # [-1, 1, Conv, [512, 1, 1]],
# la(x, md.Conv(x[-2], 512, 1, 1)) # [-2, 1, Conv, [512, 1, 1]],
# la(x, md.Conv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.Conv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.Conv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.Conv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]])) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
# la(x, md.Conv(x[-1], 512, 1, 1)) # [-1, 1, Conv, [512, 1, 1]], # 101
# la(x, md.RepConv(x[75], 256, 3, 1, name='repconv1')) # [75, 1, RepConv, [256, 3, 1]], # 102
# la(x, md.RepConv(x[88], 512, 3, 1, name='repconv2')) # [88, 1, RepConv, [512, 3, 1]], # 103
# la(x, md.RepConv(x[101], 1024, 3, 1, name='repconv3')) # [101, 1, RepConv, [1024, 3, 1]], # 104
# la(x, md.Last([x[102], x[103], x[104]], num_classes))
# [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
# regression part
# la(x, md.RegFC(input_layers))
# la(x, md.RegFC(x[50]))
la(x, md.RegFC(x[75]))
return x
def build_mondi_model(input_layers, mode='auto', filename='auto_mondi'):
x = []
# Backbone
la(x, md.GhostConv(input_layers, 32, 3, 1)) # 0
la(x, md.GhostConv(x[-1], 32, 3, 1)) # 1
la(x, md.GhostConv(x[-1], 64, 3, 1))
la(x, md.GhostConv(x[-1], 128, 3, 2)) # 3-P2/4
la(x, md.GhostConv(x[-1], 64, 1, 1))
la(x, md.GhostConv(x[-2], 64, 1, 1))
la(x, md.GhostConv(x[-1], 64, 3, 1))
la(x, md.GhostConv(x[-1], 64, 3, 1))
la(x, md.GhostConv(x[-1], 64, 3, 1))
la(x, md.GhostConv(x[-1], 64, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.GhostConv(x[-1], 256, 1, 1)) # 11
la(x, md.cbam_block(x[-1], 256))
la(x, md.MP(x[-1]))
la(x, md.GhostConv(x[-1], 128, 1, 1))
la(x, md.GhostConv(x[-3], 128, 1, 1))
la(x, md.GhostConv(x[-1], 128, 3, 2))
la(x, md.Concat([x[-1], x[-3]])) # 17-P3/8
la(x, md.GhostConv(x[-1], 128, 1, 1))
la(x, md.GhostConv(x[-2], 128, 1, 1))
la(x, md.GhostConv(x[-1], 128, 3, 1))
la(x, md.GhostConv(x[-1], 128, 3, 1))
la(x, md.GhostConv(x[-1], 128, 3, 1))
la(x, md.GhostConv(x[-1], 128, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.GhostConv(x[-1], 512, 1, 1)) # 25
la(x, md.cbam_block(x[-1], 512))
la(x, md.MP(x[-1]))
la(x, md.GhostConv(x[-1], 256, 1, 1))
la(x, md.GhostConv(x[-3], 256, 1, 1))
la(x, md.GhostConv(x[-1], 256, 3, 2))
la(x, md.Concat([x[-1], x[-3]])) # 31-P4/16
la(x, md.GhostConv(x[-1], 256, 1, 1))
la(x, md.GhostConv(x[-2], 256, 1, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.GhostConv(x[-1], 1024, 1, 1)) # 39
la(x, md.cbam_block(x[-1], 1024))
la(x, md.MP(x[-1])) # [-1, 1, MP, []],
la(x, md.GhostConv(x[-1], 512, 1, 1))
la(x, md.GhostConv(x[-3], 512, 1, 1))
la(x, md.GhostConv(x[-1], 512, 3, 2))
la(x, md.Concat([x[-1], x[-3]])) # 45-P5/32
la(x, md.GhostConv(x[-1], 256, 1, 1))
la(x, md.GhostConv(x[-2], 256, 1, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.GhostConv(x[-1], 256, 3, 1))
la(x, md.Concat([x[-1], x[-3], x[-5], x[-6]]))
la(x, md.GhostConv(x[-1], 1024, 1, 1)) # 53
# Head
la(x, md.SPPCSPC(x[53], 512)) # [[-1, 1, SPPCSPC, [512]], # 54
la(x, md.GhostConv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
la(x, md.Upsample(x[-1], 2)) # [-1, 1, nn.Upsample, [None, 2, 'nearest']],
la(x, md.GhostConv(x[39], 256, 1, 1)) # [37, 1, Conv, [256, 1, 1]], # route backbone P4
la(x, md.Concat([x[-1], x[-2]])) # [[-1, -2], 1, Concat, [1]],
la(x, md.GhostConv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
la(x, md.GhostConv(x[-2], 256, 1, 1)) # [-2, 1, Conv, [256, 1, 1]],
la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]], -1)) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
la(x, md.GhostConv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]], # 63
la(x, md.GhostConv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]],
la(x, md.Upsample(x[-1], 2)) # [-1, 1, nn.Upsample, [None, 2, 'nearest']],
la(x, md.GhostConv(x[25], 128, 1, 1)) # [24, 1, Conv, [128, 1, 1]], # route backbone P3
la(x, md.Concat([x[-1], x[-2]])) # [[-1, -2], 1, Concat, [1]],
la(x, md.GhostConv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]],
la(x, md.GhostConv(x[-2], 128, 1, 1)) # [-2, 1, Conv, [128, 1, 1]],
la(x, md.GhostConv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.GhostConv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.GhostConv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.GhostConv(x[-1], 64, 3, 1)) # [-1, 1, Conv, [64, 3, 1]],
la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]])) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
la(x, md.GhostConv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]], # 78
# la(x, md.MP(x[-1])) # [-1, 1, MP, []],
# la(x, md.GhostConv(x[-1], 128, 1, 1)) # [-1, 1, Conv, [128, 1, 1]],
# la(x, md.GhostConv(x[-3], 128, 1, 1)) # [-3, 1, Conv, [128, 1, 1]],
# la(x, md.GhostConv(x[-1], 128, 3, 2)) # [-1, 1, Conv, [128, 3, 2]],
# la(x, md.Concat([x[-1], x[-3], x[63]])) # [[-1, -3, 63], 1, Concat, [1]],
# la(x, md.GhostConv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
# la(x, md.GhostConv(x[-2], 256, 1, 1)) # [-2, 1, Conv, [256, 1, 1]],
# la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.GhostConv(x[-1], 128, 3, 1)) # [-1, 1, Conv, [128, 3, 1]],
# la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]])) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
# la(x, md.GhostConv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]], # 88
# la(x, md.MP(x[-1])) # [-1, 1, MP, []],
# la(x, md.GhostConv(x[-1], 256, 1, 1)) # [-1, 1, Conv, [256, 1, 1]],
# la(x, md.GhostConv(x[-3], 256, 1, 1)) # [-3, 1, Conv, [256, 1, 1]],
# la(x, md.GhostConv(x[-1], 256, 3, 2)) # [-1, 1, Conv, [256, 3, 2]],
# la(x, md.Concat([x[-1], x[-3], x[51]])) # [[-1, -3, 51], 1, Concat, [1]],
# la(x, md.GhostConv(x[-1], 512, 1, 1)) # [-1, 1, Conv, [512, 1, 1]],
# la(x, md.GhostConv(x[-2], 512, 1, 1)) # [-2, 1, Conv, [512, 1, 1]],
# la(x, md.GhostConv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.GhostConv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.GhostConv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.GhostConv(x[-1], 256, 3, 1)) # [-1, 1, Conv, [256, 3, 1]],
# la(x, md.Concat([x[-1], x[-2], x[-3], x[-4], x[-5], x[-6]])) # [[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
# la(x, md.GhostConv(x[-1], 512, 1, 1)) # [-1, 1, Conv, [512, 1, 1]], # 101
# la(x, md.RepConv(x[75], 256, 3, 1, name='repconv1')) # [75, 1, RepConv, [256, 3, 1]], # 102
# la(x, md.RepConv(x[88], 512, 3, 1, name='repconv2')) # [88, 1, RepConv, [512, 3, 1]], # 103
# la(x, md.RepConv(x[101], 1024, 3, 1, name='repconv3')) # [101, 1, RepConv, [1024, 3, 1]], # 104
# la(x, md.Last([x[102], x[103], x[104]], num_classes))
# [[102,103,104], 1, IDetect, [nc, anchors]], # Detect(P3, P4, P5)
# regression part
if mode == 'fc':
la(x, md.RegFCMondi(input_layers, fc=True))
if mode == 'cnn':
la(x, md.RegFCMondi(x[53]))
if mode == 'auto':
la(x, md.RegFCMondi(x[78]))
return x, filename
def yolov7_regression(input_layers, mode='auto', filename='auto_yolo'):
x = []
# Backbone
la(x, md.Conv(input_layers, 32, 3, 1)) # 0
la(x, md.Conv(x[-1], 32, 3, 1)) # 1
la(x, md.Conv(x[-1], 64, 3, 1))
la(x, md.Conv(x[-1], 128, 3, 2)) # 3-P2/4
if mode=='fc':
la(x, md.Flatten(x[-1]))
la(x, md.Dense(x[-1], 16))
if mode=='cnn':
la(x, tf.keras.layers.Conv2D(128, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(256, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(512, 3, activation='relu')(x[-1]))
la(x, md.Flatten(x[-1]))
if mode=='auto':
la(x, tf.keras.layers.Conv2D(128, 3, activation='relu')(x[-1])) # encoder
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(256, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(512, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.Conv2D(512, 3, activation='relu')(x[-1])) # decoder
la(x, tf.keras.layers.UpSampling2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(256, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.UpSampling2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(128, 3, activation='relu')(x[-1]))
la(x, md.Flatten(x[-1]))
la(x, md.Dense(x[-1], 4))
la(x, md.Dense(x[-1], 1))
return x, filename
def mondi_regression(input_layers, mode='auto', filename='auto_yolo'):
x = []
# Backbone
la(x, md.GhostConv(input_layers, 32, 3, 1)) # 0
la(x, md.GhostConv(x[-1], 32, 3, 1)) # 1
la(x, md.GhostConv(x[-1], 64, 3, 1))
la(x, md.GhostConv(x[-1], 128, 3, 2)) # 3-P2/4
if mode=='fc':
la(x, md.Flatten(x[-1]))
la(x, md.Dense(x[-1], 16))
if mode=='cnn':
la(x, tf.keras.layers.Conv2D(128, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(256, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(512, 3, activation='relu')(x[-1]))
la(x, md.Flatten(x[-1]))
if mode=='auto':
la(x, tf.keras.layers.Conv2D(128, 3, activation='relu')(x[-1])) # encoder
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(256, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.MaxPool2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(512, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.Conv2D(512, 3, activation='relu')(x[-1])) # decoder
la(x, tf.keras.layers.UpSampling2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(256, 3, activation='relu')(x[-1]))
la(x, tf.keras.layers.UpSampling2D()(x[-1]))
la(x, tf.keras.layers.Conv2D(128, 3, activation='relu')(x[-1]))
la(x, md.Flatten(x[-1]))
la(x, md.Dense(x[-1], 4))
la(x, md.Dense(x[-1], 1))
return x, filename