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add sample summaries

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Starbeamrainbowlabs 2023-02-16 20:34:59 +00:00
parent 8778b6577b
commit 91ed19df50
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
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451
samples/deeplabv3+.txt Normal file
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Model: "DeepLabV3+"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 128, 128, 8 0 []
)]
conv1_pad (ZeroPadding2D) (None, 134, 134, 8) 0 ['input_1[0][0]']
conv1_conv (Conv2D) (None, 64, 64, 64) 25152 ['conv1_pad[0][0]']
conv1_bn (BatchNormalization) (None, 64, 64, 64) 256 ['conv1_conv[0][0]']
conv1_relu (Activation) (None, 64, 64, 64) 0 ['conv1_bn[0][0]']
pool1_pad (ZeroPadding2D) (None, 66, 66, 64) 0 ['conv1_relu[0][0]']
pool1_pool (MaxPooling2D) (None, 32, 32, 64) 0 ['pool1_pad[0][0]']
conv2_block1_1_conv (Conv2D) (None, 32, 32, 64) 4160 ['pool1_pool[0][0]']
conv2_block1_1_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block1_1_conv[0][0]']
ization)
conv2_block1_1_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block1_1_bn[0][0]']
n)
conv2_block1_2_conv (Conv2D) (None, 32, 32, 64) 36928 ['conv2_block1_1_relu[0][0]']
conv2_block1_2_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block1_2_conv[0][0]']
ization)
conv2_block1_2_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block1_2_bn[0][0]']
n)
conv2_block1_0_conv (Conv2D) (None, 32, 32, 256) 16640 ['pool1_pool[0][0]']
conv2_block1_3_conv (Conv2D) (None, 32, 32, 256) 16640 ['conv2_block1_2_relu[0][0]']
conv2_block1_0_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block1_0_conv[0][0]']
ization)
conv2_block1_3_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block1_3_conv[0][0]']
ization)
conv2_block1_add (Add) (None, 32, 32, 256) 0 ['conv2_block1_0_bn[0][0]',
'conv2_block1_3_bn[0][0]']
conv2_block1_out (Activation) (None, 32, 32, 256) 0 ['conv2_block1_add[0][0]']
conv2_block2_1_conv (Conv2D) (None, 32, 32, 64) 16448 ['conv2_block1_out[0][0]']
conv2_block2_1_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block2_1_conv[0][0]']
ization)
conv2_block2_1_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block2_1_bn[0][0]']
n)
conv2_block2_2_conv (Conv2D) (None, 32, 32, 64) 36928 ['conv2_block2_1_relu[0][0]']
conv2_block2_2_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block2_2_conv[0][0]']
ization)
conv2_block2_2_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block2_2_bn[0][0]']
n)
conv2_block2_3_conv (Conv2D) (None, 32, 32, 256) 16640 ['conv2_block2_2_relu[0][0]']
conv2_block2_3_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block2_3_conv[0][0]']
ization)
conv2_block2_add (Add) (None, 32, 32, 256) 0 ['conv2_block1_out[0][0]',
'conv2_block2_3_bn[0][0]']
conv2_block2_out (Activation) (None, 32, 32, 256) 0 ['conv2_block2_add[0][0]']
conv2_block3_1_conv (Conv2D) (None, 32, 32, 64) 16448 ['conv2_block2_out[0][0]']
conv2_block3_1_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block3_1_conv[0][0]']
ization)
conv2_block3_1_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block3_1_bn[0][0]']
n)
conv2_block3_2_conv (Conv2D) (None, 32, 32, 64) 36928 ['conv2_block3_1_relu[0][0]']
conv2_block3_2_bn (BatchNormal (None, 32, 32, 64) 256 ['conv2_block3_2_conv[0][0]']
ization)
conv2_block3_2_relu (Activatio (None, 32, 32, 64) 0 ['conv2_block3_2_bn[0][0]']
n)
conv2_block3_3_conv (Conv2D) (None, 32, 32, 256) 16640 ['conv2_block3_2_relu[0][0]']
conv2_block3_3_bn (BatchNormal (None, 32, 32, 256) 1024 ['conv2_block3_3_conv[0][0]']
ization)
conv2_block3_add (Add) (None, 32, 32, 256) 0 ['conv2_block2_out[0][0]',
'conv2_block3_3_bn[0][0]']
conv2_block3_out (Activation) (None, 32, 32, 256) 0 ['conv2_block3_add[0][0]']
conv3_block1_1_conv (Conv2D) (None, 16, 16, 128) 32896 ['conv2_block3_out[0][0]']
conv3_block1_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block1_1_conv[0][0]']
ization)
conv3_block1_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block1_1_bn[0][0]']
n)
conv3_block1_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block1_1_relu[0][0]']
conv3_block1_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block1_2_conv[0][0]']
ization)
conv3_block1_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block1_2_bn[0][0]']
n)
conv3_block1_0_conv (Conv2D) (None, 16, 16, 512) 131584 ['conv2_block3_out[0][0]']
conv3_block1_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block1_2_relu[0][0]']
conv3_block1_0_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block1_0_conv[0][0]']
ization)
conv3_block1_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block1_3_conv[0][0]']
ization)
conv3_block1_add (Add) (None, 16, 16, 512) 0 ['conv3_block1_0_bn[0][0]',
'conv3_block1_3_bn[0][0]']
conv3_block1_out (Activation) (None, 16, 16, 512) 0 ['conv3_block1_add[0][0]']
conv3_block2_1_conv (Conv2D) (None, 16, 16, 128) 65664 ['conv3_block1_out[0][0]']
conv3_block2_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block2_1_conv[0][0]']
ization)
conv3_block2_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block2_1_bn[0][0]']
n)
conv3_block2_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block2_1_relu[0][0]']
conv3_block2_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block2_2_conv[0][0]']
ization)
conv3_block2_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block2_2_bn[0][0]']
n)
conv3_block2_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block2_2_relu[0][0]']
conv3_block2_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block2_3_conv[0][0]']
ization)
conv3_block2_add (Add) (None, 16, 16, 512) 0 ['conv3_block1_out[0][0]',
'conv3_block2_3_bn[0][0]']
conv3_block2_out (Activation) (None, 16, 16, 512) 0 ['conv3_block2_add[0][0]']
conv3_block3_1_conv (Conv2D) (None, 16, 16, 128) 65664 ['conv3_block2_out[0][0]']
conv3_block3_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block3_1_conv[0][0]']
ization)
conv3_block3_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block3_1_bn[0][0]']
n)
conv3_block3_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block3_1_relu[0][0]']
conv3_block3_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block3_2_conv[0][0]']
ization)
conv3_block3_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block3_2_bn[0][0]']
n)
conv3_block3_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block3_2_relu[0][0]']
conv3_block3_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block3_3_conv[0][0]']
ization)
conv3_block3_add (Add) (None, 16, 16, 512) 0 ['conv3_block2_out[0][0]',
'conv3_block3_3_bn[0][0]']
conv3_block3_out (Activation) (None, 16, 16, 512) 0 ['conv3_block3_add[0][0]']
conv3_block4_1_conv (Conv2D) (None, 16, 16, 128) 65664 ['conv3_block3_out[0][0]']
conv3_block4_1_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block4_1_conv[0][0]']
ization)
conv3_block4_1_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block4_1_bn[0][0]']
n)
conv3_block4_2_conv (Conv2D) (None, 16, 16, 128) 147584 ['conv3_block4_1_relu[0][0]']
conv3_block4_2_bn (BatchNormal (None, 16, 16, 128) 512 ['conv3_block4_2_conv[0][0]']
ization)
conv3_block4_2_relu (Activatio (None, 16, 16, 128) 0 ['conv3_block4_2_bn[0][0]']
n)
conv3_block4_3_conv (Conv2D) (None, 16, 16, 512) 66048 ['conv3_block4_2_relu[0][0]']
conv3_block4_3_bn (BatchNormal (None, 16, 16, 512) 2048 ['conv3_block4_3_conv[0][0]']
ization)
conv3_block4_add (Add) (None, 16, 16, 512) 0 ['conv3_block3_out[0][0]',
'conv3_block4_3_bn[0][0]']
conv3_block4_out (Activation) (None, 16, 16, 512) 0 ['conv3_block4_add[0][0]']
conv4_block1_1_conv (Conv2D) (None, 8, 8, 256) 131328 ['conv3_block4_out[0][0]']
conv4_block1_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block1_1_conv[0][0]']
ization)
conv4_block1_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block1_1_bn[0][0]']
n)
conv4_block1_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block1_1_relu[0][0]']
conv4_block1_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block1_2_conv[0][0]']
ization)
conv4_block1_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block1_2_bn[0][0]']
n)
conv4_block1_0_conv (Conv2D) (None, 8, 8, 1024) 525312 ['conv3_block4_out[0][0]']
conv4_block1_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block1_2_relu[0][0]']
conv4_block1_0_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block1_0_conv[0][0]']
ization)
conv4_block1_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block1_3_conv[0][0]']
ization)
conv4_block1_add (Add) (None, 8, 8, 1024) 0 ['conv4_block1_0_bn[0][0]',
'conv4_block1_3_bn[0][0]']
conv4_block1_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block1_add[0][0]']
conv4_block2_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block1_out[0][0]']
conv4_block2_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block2_1_conv[0][0]']
ization)
conv4_block2_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block2_1_bn[0][0]']
n)
conv4_block2_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block2_1_relu[0][0]']
conv4_block2_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block2_2_conv[0][0]']
ization)
conv4_block2_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block2_2_bn[0][0]']
n)
conv4_block2_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block2_2_relu[0][0]']
conv4_block2_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block2_3_conv[0][0]']
ization)
conv4_block2_add (Add) (None, 8, 8, 1024) 0 ['conv4_block1_out[0][0]',
'conv4_block2_3_bn[0][0]']
conv4_block2_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block2_add[0][0]']
conv4_block3_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block2_out[0][0]']
conv4_block3_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block3_1_conv[0][0]']
ization)
conv4_block3_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block3_1_bn[0][0]']
n)
conv4_block3_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block3_1_relu[0][0]']
conv4_block3_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block3_2_conv[0][0]']
ization)
conv4_block3_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block3_2_bn[0][0]']
n)
conv4_block3_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block3_2_relu[0][0]']
conv4_block3_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block3_3_conv[0][0]']
ization)
conv4_block3_add (Add) (None, 8, 8, 1024) 0 ['conv4_block2_out[0][0]',
'conv4_block3_3_bn[0][0]']
conv4_block3_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block3_add[0][0]']
conv4_block4_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block3_out[0][0]']
conv4_block4_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block4_1_conv[0][0]']
ization)
conv4_block4_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block4_1_bn[0][0]']
n)
conv4_block4_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block4_1_relu[0][0]']
conv4_block4_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block4_2_conv[0][0]']
ization)
conv4_block4_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block4_2_bn[0][0]']
n)
conv4_block4_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block4_2_relu[0][0]']
conv4_block4_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block4_3_conv[0][0]']
ization)
conv4_block4_add (Add) (None, 8, 8, 1024) 0 ['conv4_block3_out[0][0]',
'conv4_block4_3_bn[0][0]']
conv4_block4_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block4_add[0][0]']
conv4_block5_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block4_out[0][0]']
conv4_block5_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block5_1_conv[0][0]']
ization)
conv4_block5_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block5_1_bn[0][0]']
n)
conv4_block5_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block5_1_relu[0][0]']
conv4_block5_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block5_2_conv[0][0]']
ization)
conv4_block5_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block5_2_bn[0][0]']
n)
conv4_block5_3_conv (Conv2D) (None, 8, 8, 1024) 263168 ['conv4_block5_2_relu[0][0]']
conv4_block5_3_bn (BatchNormal (None, 8, 8, 1024) 4096 ['conv4_block5_3_conv[0][0]']
ization)
conv4_block5_add (Add) (None, 8, 8, 1024) 0 ['conv4_block4_out[0][0]',
'conv4_block5_3_bn[0][0]']
conv4_block5_out (Activation) (None, 8, 8, 1024) 0 ['conv4_block5_add[0][0]']
conv4_block6_1_conv (Conv2D) (None, 8, 8, 256) 262400 ['conv4_block5_out[0][0]']
conv4_block6_1_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block6_1_conv[0][0]']
ization)
conv4_block6_1_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block6_1_bn[0][0]']
n)
conv4_block6_2_conv (Conv2D) (None, 8, 8, 256) 590080 ['conv4_block6_1_relu[0][0]']
conv4_block6_2_bn (BatchNormal (None, 8, 8, 256) 1024 ['conv4_block6_2_conv[0][0]']
ization)
conv4_block6_2_relu (Activatio (None, 8, 8, 256) 0 ['conv4_block6_2_bn[0][0]']
n)
average_pooling2d (AveragePool (None, 1, 1, 256) 0 ['conv4_block6_2_relu[0][0]']
ing2D)
conv2d (Conv2D) (None, 1, 1, 256) 65792 ['average_pooling2d[0][0]']
batch_normalization (BatchNorm (None, 1, 1, 256) 1024 ['conv2d[0][0]']
alization)
conv2d_1 (Conv2D) (None, 8, 8, 256) 65536 ['conv4_block6_2_relu[0][0]']
conv2d_2 (Conv2D) (None, 8, 8, 256) 589824 ['conv4_block6_2_relu[0][0]']
conv2d_3 (Conv2D) (None, 8, 8, 256) 589824 ['conv4_block6_2_relu[0][0]']
conv2d_4 (Conv2D) (None, 8, 8, 256) 589824 ['conv4_block6_2_relu[0][0]']
tf.nn.relu (TFOpLambda) (None, 1, 1, 256) 0 ['batch_normalization[0][0]']
batch_normalization_1 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_1[0][0]']
rmalization)
batch_normalization_2 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_2[0][0]']
rmalization)
batch_normalization_3 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_3[0][0]']
rmalization)
batch_normalization_4 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_4[0][0]']
rmalization)
up_sampling2d (UpSampling2D) (None, 8, 8, 256) 0 ['tf.nn.relu[0][0]']
tf.nn.relu_1 (TFOpLambda) (None, 8, 8, 256) 0 ['batch_normalization_1[0][0]']
tf.nn.relu_2 (TFOpLambda) (None, 8, 8, 256) 0 ['batch_normalization_2[0][0]']
tf.nn.relu_3 (TFOpLambda) (None, 8, 8, 256) 0 ['batch_normalization_3[0][0]']
tf.nn.relu_4 (TFOpLambda) (None, 8, 8, 256) 0 ['batch_normalization_4[0][0]']
concatenate (Concatenate) (None, 8, 8, 1280) 0 ['up_sampling2d[0][0]',
'tf.nn.relu_1[0][0]',
'tf.nn.relu_2[0][0]',
'tf.nn.relu_3[0][0]',
'tf.nn.relu_4[0][0]']
conv2d_5 (Conv2D) (None, 8, 8, 256) 327680 ['concatenate[0][0]']
batch_normalization_5 (BatchNo (None, 8, 8, 256) 1024 ['conv2d_5[0][0]']
rmalization)
conv2d_6 (Conv2D) (None, 32, 32, 48) 3072 ['conv2_block3_2_relu[0][0]']
tf.nn.relu_5 (TFOpLambda) (None, 8, 8, 256) 0 ['batch_normalization_5[0][0]']
batch_normalization_6 (BatchNo (None, 32, 32, 48) 192 ['conv2d_6[0][0]']
rmalization)
up_sampling2d_1 (UpSampling2D) (None, 32, 32, 256) 0 ['tf.nn.relu_5[0][0]']
tf.nn.relu_6 (TFOpLambda) (None, 32, 32, 48) 0 ['batch_normalization_6[0][0]']
concatenate_1 (Concatenate) (None, 32, 32, 304) 0 ['up_sampling2d_1[0][0]',
'tf.nn.relu_6[0][0]']
conv2d_7 (Conv2D) (None, 32, 32, 256) 700416 ['concatenate_1[0][0]']
batch_normalization_7 (BatchNo (None, 32, 32, 256) 1024 ['conv2d_7[0][0]']
rmalization)
tf.nn.relu_7 (TFOpLambda) (None, 32, 32, 256) 0 ['batch_normalization_7[0][0]']
conv2d_8 (Conv2D) (None, 32, 32, 256) 589824 ['tf.nn.relu_7[0][0]']
batch_normalization_8 (BatchNo (None, 32, 32, 256) 1024 ['conv2d_8[0][0]']
rmalization)
tf.nn.relu_8 (TFOpLambda) (None, 32, 32, 256) 0 ['batch_normalization_8[0][0]']
up_sampling2d_2 (UpSampling2D) (None, 128, 128, 25 0 ['tf.nn.relu_8[0][0]']
6)
conv2d_9 (Conv2D) (None, 128, 128, 2) 514 ['up_sampling2d_2[0][0]']
==================================================================================================
Total params: 11,868,290
Trainable params: 11,835,554
Non-trainable params: 32,736
__________________________________________________________________________________________________

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Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
bidirectional (Bidirectiona (None, 100, 256) 336896
l)
bidirectional_1 (Bidirectio (None, 256) 394240
nal)
dense (Dense) (None, 2) 514
=================================================================
Total params: 731,650
Trainable params: 731,650
Non-trainable params: 0
_________________________________________________________________

409
samples/mono-failure.txt Normal file
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Model: "mono-failure"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 105, 174, 8 0 []
)]
convnext0 (Functional) (None, 512) 11787584 ['input_1[0][0]']
cns.stage.bottleneck.dense2 (D (None, 512) 262656 ['convnext0[0][0]']
ense)
cns.stage.bottleneck.gelu2 (Ac (None, 512) 0 ['cns.stage.bottleneck.dense2[0][
tivation) 0]']
cns.stage.bottleneck.norm2 (La (None, 512) 1024 ['cns.stage.bottleneck.gelu2[0][0
yerNormalization) ]']
cns.stage.bottleneck.dropout ( (None, 512) 0 ['cns.stage.bottleneck.norm2[0][0
Dropout) ]']
layer_stack2_image (LayerStack (None, 4, 4, 512) 0 ['cns.stage.bottleneck.dropout[0]
2Image) [0]']
cns.stage.begin.dense2 (Dense) (None, 4, 4, 512) 262656 ['layer_stack2_image[0][0]']
cns.stage_begin.relu2 (Activat (None, 4, 4, 512) 0 ['cns.stage.begin.dense2[0][0]']
ion)
cns.stage_begin.norm2 (LayerNo (None, 4, 4, 512) 1024 ['cns.stage_begin.relu2[0][0]']
rmalization)
cns.stage0.end.convtp (Conv2DT (None, 8, 8, 528) 1081872 ['cns.stage_begin.norm2[0][0]']
ranspose)
cns.stage0.end.norm (LayerNorm (None, 8, 8, 528) 1056 ['cns.stage0.end.convtp[0][0]']
alization)
cns.stage0.block.0.dwconv (Dep (None, 8, 8, 528) 26400 ['cns.stage0.end.norm[0][0]']
thwiseConv2D)
cns.stage0.block.0.norm (Layer (None, 8, 8, 528) 1056 ['cns.stage0.block.0.dwconv[0][0]
Normalization) ']
cns.stage0.block.0.pwconv1 (De (None, 8, 8, 2112) 1117248 ['cns.stage0.block.0.norm[0][0]']
nse)
cns.stage0.block.0.act (Activa (None, 8, 8, 2112) 0 ['cns.stage0.block.0.pwconv1[0][0
tion) ]']
cns.stage0.block.0.pwconv2 (De (None, 8, 8, 528) 1115664 ['cns.stage0.block.0.act[0][0]']
nse)
cns.stage0.block.0.gamma (Laye (None, 8, 8, 528) 0 ['cns.stage0.block.0.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage0.block.0.drop_path ( (None, 8, 8, 528) 0 ['cns.stage0.end.norm[0][0]',
StochasticDepth) 'cns.stage0.block.0.gamma[0][0]'
]
cns.stage0.block.1.dwconv (Dep (None, 8, 8, 528) 26400 ['cns.stage0.block.0.drop_path[0]
thwiseConv2D) [0]']
cns.stage0.block.1.norm (Layer (None, 8, 8, 528) 1056 ['cns.stage0.block.1.dwconv[0][0]
Normalization) ']
cns.stage0.block.1.pwconv1 (De (None, 8, 8, 2112) 1117248 ['cns.stage0.block.1.norm[0][0]']
nse)
cns.stage0.block.1.act (Activa (None, 8, 8, 2112) 0 ['cns.stage0.block.1.pwconv1[0][0
tion) ]']
cns.stage0.block.1.pwconv2 (De (None, 8, 8, 528) 1115664 ['cns.stage0.block.1.act[0][0]']
nse)
cns.stage0.block.1.gamma (Laye (None, 8, 8, 528) 0 ['cns.stage0.block.1.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage0.block.1.drop_path ( (None, 8, 8, 528) 0 ['cns.stage0.block.0.drop_path[0]
StochasticDepth) [0]',
'cns.stage0.block.1.gamma[0][0]'
]
cns.stage0.block.2.dwconv (Dep (None, 8, 8, 528) 26400 ['cns.stage0.block.1.drop_path[0]
thwiseConv2D) [0]']
cns.stage0.block.2.norm (Layer (None, 8, 8, 528) 1056 ['cns.stage0.block.2.dwconv[0][0]
Normalization) ']
cns.stage0.block.2.pwconv1 (De (None, 8, 8, 2112) 1117248 ['cns.stage0.block.2.norm[0][0]']
nse)
cns.stage0.block.2.act (Activa (None, 8, 8, 2112) 0 ['cns.stage0.block.2.pwconv1[0][0
tion) ]']
cns.stage0.block.2.pwconv2 (De (None, 8, 8, 528) 1115664 ['cns.stage0.block.2.act[0][0]']
nse)
cns.stage0.block.2.gamma (Laye (None, 8, 8, 528) 0 ['cns.stage0.block.2.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage0.block.2.drop_path ( (None, 8, 8, 528) 0 ['cns.stage0.block.1.drop_path[0]
StochasticDepth) [0]',
'cns.stage0.block.2.gamma[0][0]'
]
cns.stage1.end.convtp (Conv2DT (None, 16, 16, 264) 557832 ['cns.stage0.block.2.drop_path[0]
ranspose) [0]']
cns.stage1.end.norm (LayerNorm (None, 16, 16, 264) 528 ['cns.stage1.end.convtp[0][0]']
alization)
cns.stage1.block.0.dwconv (Dep (None, 16, 16, 264) 13200 ['cns.stage1.end.norm[0][0]']
thwiseConv2D)
cns.stage1.block.0.norm (Layer (None, 16, 16, 264) 528 ['cns.stage1.block.0.dwconv[0][0]
Normalization) ']
cns.stage1.block.0.pwconv1 (De (None, 16, 16, 1056 279840 ['cns.stage1.block.0.norm[0][0]']
nse) )
cns.stage1.block.0.act (Activa (None, 16, 16, 1056 0 ['cns.stage1.block.0.pwconv1[0][0
tion) ) ]']
cns.stage1.block.0.pwconv2 (De (None, 16, 16, 264) 279048 ['cns.stage1.block.0.act[0][0]']
nse)
cns.stage1.block.0.gamma (Laye (None, 16, 16, 264) 0 ['cns.stage1.block.0.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage1.block.0.drop_path ( (None, 16, 16, 264) 0 ['cns.stage1.end.norm[0][0]',
StochasticDepth) 'cns.stage1.block.0.gamma[0][0]'
]
cns.stage1.block.1.dwconv (Dep (None, 16, 16, 264) 13200 ['cns.stage1.block.0.drop_path[0]
thwiseConv2D) [0]']
cns.stage1.block.1.norm (Layer (None, 16, 16, 264) 528 ['cns.stage1.block.1.dwconv[0][0]
Normalization) ']
cns.stage1.block.1.pwconv1 (De (None, 16, 16, 1056 279840 ['cns.stage1.block.1.norm[0][0]']
nse) )
cns.stage1.block.1.act (Activa (None, 16, 16, 1056 0 ['cns.stage1.block.1.pwconv1[0][0
tion) ) ]']
cns.stage1.block.1.pwconv2 (De (None, 16, 16, 264) 279048 ['cns.stage1.block.1.act[0][0]']
nse)
cns.stage1.block.1.gamma (Laye (None, 16, 16, 264) 0 ['cns.stage1.block.1.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage1.block.1.drop_path ( (None, 16, 16, 264) 0 ['cns.stage1.block.0.drop_path[0]
StochasticDepth) [0]',
'cns.stage1.block.1.gamma[0][0]'
]
cns.stage1.block.2.dwconv (Dep (None, 16, 16, 264) 13200 ['cns.stage1.block.1.drop_path[0]
thwiseConv2D) [0]']
cns.stage1.block.2.norm (Layer (None, 16, 16, 264) 528 ['cns.stage1.block.2.dwconv[0][0]
Normalization) ']
cns.stage1.block.2.pwconv1 (De (None, 16, 16, 1056 279840 ['cns.stage1.block.2.norm[0][0]']
nse) )
cns.stage1.block.2.act (Activa (None, 16, 16, 1056 0 ['cns.stage1.block.2.pwconv1[0][0
tion) ) ]']
cns.stage1.block.2.pwconv2 (De (None, 16, 16, 264) 279048 ['cns.stage1.block.2.act[0][0]']
nse)
cns.stage1.block.2.gamma (Laye (None, 16, 16, 264) 0 ['cns.stage1.block.2.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage1.block.2.drop_path ( (None, 16, 16, 264) 0 ['cns.stage1.block.1.drop_path[0]
StochasticDepth) [0]',
'cns.stage1.block.2.gamma[0][0]'
]
cns.stage1.block.3.dwconv (Dep (None, 16, 16, 264) 13200 ['cns.stage1.block.2.drop_path[0]
thwiseConv2D) [0]']
cns.stage1.block.3.norm (Layer (None, 16, 16, 264) 528 ['cns.stage1.block.3.dwconv[0][0]
Normalization) ']
cns.stage1.block.3.pwconv1 (De (None, 16, 16, 1056 279840 ['cns.stage1.block.3.norm[0][0]']
nse) )
cns.stage1.block.3.act (Activa (None, 16, 16, 1056 0 ['cns.stage1.block.3.pwconv1[0][0
tion) ) ]']
cns.stage1.block.3.pwconv2 (De (None, 16, 16, 264) 279048 ['cns.stage1.block.3.act[0][0]']
nse)
cns.stage1.block.3.gamma (Laye (None, 16, 16, 264) 0 ['cns.stage1.block.3.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage1.block.3.drop_path ( (None, 16, 16, 264) 0 ['cns.stage1.block.2.drop_path[0]
StochasticDepth) [0]',
'cns.stage1.block.3.gamma[0][0]'
]
cns.stage1.block.4.dwconv (Dep (None, 16, 16, 264) 13200 ['cns.stage1.block.3.drop_path[0]
thwiseConv2D) [0]']
cns.stage1.block.4.norm (Layer (None, 16, 16, 264) 528 ['cns.stage1.block.4.dwconv[0][0]
Normalization) ']
cns.stage1.block.4.pwconv1 (De (None, 16, 16, 1056 279840 ['cns.stage1.block.4.norm[0][0]']
nse) )
cns.stage1.block.4.act (Activa (None, 16, 16, 1056 0 ['cns.stage1.block.4.pwconv1[0][0
tion) ) ]']
cns.stage1.block.4.pwconv2 (De (None, 16, 16, 264) 279048 ['cns.stage1.block.4.act[0][0]']
nse)
cns.stage1.block.4.gamma (Laye (None, 16, 16, 264) 0 ['cns.stage1.block.4.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage1.block.4.drop_path ( (None, 16, 16, 264) 0 ['cns.stage1.block.3.drop_path[0]
StochasticDepth) [0]',
'cns.stage1.block.4.gamma[0][0]'
]
cns.stage1.block.5.dwconv (Dep (None, 16, 16, 264) 13200 ['cns.stage1.block.4.drop_path[0]
thwiseConv2D) [0]']
cns.stage1.block.5.norm (Layer (None, 16, 16, 264) 528 ['cns.stage1.block.5.dwconv[0][0]
Normalization) ']
cns.stage1.block.5.pwconv1 (De (None, 16, 16, 1056 279840 ['cns.stage1.block.5.norm[0][0]']
nse) )
cns.stage1.block.5.act (Activa (None, 16, 16, 1056 0 ['cns.stage1.block.5.pwconv1[0][0
tion) ) ]']
cns.stage1.block.5.pwconv2 (De (None, 16, 16, 264) 279048 ['cns.stage1.block.5.act[0][0]']
nse)
cns.stage1.block.5.gamma (Laye (None, 16, 16, 264) 0 ['cns.stage1.block.5.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage1.block.5.drop_path ( (None, 16, 16, 264) 0 ['cns.stage1.block.4.drop_path[0]
StochasticDepth) [0]',
'cns.stage1.block.5.gamma[0][0]'
]
cns.stage2.end.convtp (Conv2DT (None, 32, 32, 132) 139524 ['cns.stage1.block.5.drop_path[0]
ranspose) [0]']
cns.stage2.end.norm (LayerNorm (None, 32, 32, 132) 264 ['cns.stage2.end.convtp[0][0]']
alization)
cns.stage2.block.0.dwconv (Dep (None, 32, 32, 132) 6600 ['cns.stage2.end.norm[0][0]']
thwiseConv2D)
cns.stage2.block.0.norm (Layer (None, 32, 32, 132) 264 ['cns.stage2.block.0.dwconv[0][0]
Normalization) ']
cns.stage2.block.0.pwconv1 (De (None, 32, 32, 528) 70224 ['cns.stage2.block.0.norm[0][0]']
nse)
cns.stage2.block.0.act (Activa (None, 32, 32, 528) 0 ['cns.stage2.block.0.pwconv1[0][0
tion) ]']
cns.stage2.block.0.pwconv2 (De (None, 32, 32, 132) 69828 ['cns.stage2.block.0.act[0][0]']
nse)
cns.stage2.block.0.gamma (Laye (None, 32, 32, 132) 0 ['cns.stage2.block.0.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage2.block.0.drop_path ( (None, 32, 32, 132) 0 ['cns.stage2.end.norm[0][0]',
StochasticDepth) 'cns.stage2.block.0.gamma[0][0]'
]
cns.stage2.block.1.dwconv (Dep (None, 32, 32, 132) 6600 ['cns.stage2.block.0.drop_path[0]
thwiseConv2D) [0]']
cns.stage2.block.1.norm (Layer (None, 32, 32, 132) 264 ['cns.stage2.block.1.dwconv[0][0]
Normalization) ']
cns.stage2.block.1.pwconv1 (De (None, 32, 32, 528) 70224 ['cns.stage2.block.1.norm[0][0]']
nse)
cns.stage2.block.1.act (Activa (None, 32, 32, 528) 0 ['cns.stage2.block.1.pwconv1[0][0
tion) ]']
cns.stage2.block.1.pwconv2 (De (None, 32, 32, 132) 69828 ['cns.stage2.block.1.act[0][0]']
nse)
cns.stage2.block.1.gamma (Laye (None, 32, 32, 132) 0 ['cns.stage2.block.1.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage2.block.1.drop_path ( (None, 32, 32, 132) 0 ['cns.stage2.block.0.drop_path[0]
StochasticDepth) [0]',
'cns.stage2.block.1.gamma[0][0]'
]
cns.stage2.block.2.dwconv (Dep (None, 32, 32, 132) 6600 ['cns.stage2.block.1.drop_path[0]
thwiseConv2D) [0]']
cns.stage2.block.2.norm (Layer (None, 32, 32, 132) 264 ['cns.stage2.block.2.dwconv[0][0]
Normalization) ']
cns.stage2.block.2.pwconv1 (De (None, 32, 32, 528) 70224 ['cns.stage2.block.2.norm[0][0]']
nse)
cns.stage2.block.2.act (Activa (None, 32, 32, 528) 0 ['cns.stage2.block.2.pwconv1[0][0
tion) ]']
cns.stage2.block.2.pwconv2 (De (None, 32, 32, 132) 69828 ['cns.stage2.block.2.act[0][0]']
nse)
cns.stage2.block.2.gamma (Laye (None, 32, 32, 132) 0 ['cns.stage2.block.2.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage2.block.2.drop_path ( (None, 32, 32, 132) 0 ['cns.stage2.block.1.drop_path[0]
StochasticDepth) [0]',
'cns.stage2.block.2.gamma[0][0]'
]
cns.stage3.end.convtp (Conv2DT (None, 64, 64, 66) 34914 ['cns.stage2.block.2.drop_path[0]
ranspose) [0]']
cns.stage3.end.norm (LayerNorm (None, 64, 64, 66) 132 ['cns.stage3.end.convtp[0][0]']
alization)
cns.stage3.block.0.dwconv (Dep (None, 64, 64, 66) 3300 ['cns.stage3.end.norm[0][0]']
thwiseConv2D)
cns.stage3.block.0.norm (Layer (None, 64, 64, 66) 132 ['cns.stage3.block.0.dwconv[0][0]
Normalization) ']
cns.stage3.block.0.pwconv1 (De (None, 64, 64, 264) 17688 ['cns.stage3.block.0.norm[0][0]']
nse)
cns.stage3.block.0.act (Activa (None, 64, 64, 264) 0 ['cns.stage3.block.0.pwconv1[0][0
tion) ]']
cns.stage3.block.0.pwconv2 (De (None, 64, 64, 66) 17490 ['cns.stage3.block.0.act[0][0]']
nse)
cns.stage3.block.0.gamma (Laye (None, 64, 64, 66) 0 ['cns.stage3.block.0.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage3.block.0.drop_path ( (None, 64, 64, 66) 0 ['cns.stage3.end.norm[0][0]',
StochasticDepth) 'cns.stage3.block.0.gamma[0][0]'
]
cns.stage3.block.1.dwconv (Dep (None, 64, 64, 66) 3300 ['cns.stage3.block.0.drop_path[0]
thwiseConv2D) [0]']
cns.stage3.block.1.norm (Layer (None, 64, 64, 66) 132 ['cns.stage3.block.1.dwconv[0][0]
Normalization) ']
cns.stage3.block.1.pwconv1 (De (None, 64, 64, 264) 17688 ['cns.stage3.block.1.norm[0][0]']
nse)
cns.stage3.block.1.act (Activa (None, 64, 64, 264) 0 ['cns.stage3.block.1.pwconv1[0][0
tion) ]']
cns.stage3.block.1.pwconv2 (De (None, 64, 64, 66) 17490 ['cns.stage3.block.1.act[0][0]']
nse)
cns.stage3.block.1.gamma (Laye (None, 64, 64, 66) 0 ['cns.stage3.block.1.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage3.block.1.drop_path ( (None, 64, 64, 66) 0 ['cns.stage3.block.0.drop_path[0]
StochasticDepth) [0]',
'cns.stage3.block.1.gamma[0][0]'
]
cns.stage3.block.2.dwconv (Dep (None, 64, 64, 66) 3300 ['cns.stage3.block.1.drop_path[0]
thwiseConv2D) [0]']
cns.stage3.block.2.norm (Layer (None, 64, 64, 66) 132 ['cns.stage3.block.2.dwconv[0][0]
Normalization) ']
cns.stage3.block.2.pwconv1 (De (None, 64, 64, 264) 17688 ['cns.stage3.block.2.norm[0][0]']
nse)
cns.stage3.block.2.act (Activa (None, 64, 64, 264) 0 ['cns.stage3.block.2.pwconv1[0][0
tion) ]']
cns.stage3.block.2.pwconv2 (De (None, 64, 64, 66) 17490 ['cns.stage3.block.2.act[0][0]']
nse)
cns.stage3.block.2.gamma (Laye (None, 64, 64, 66) 0 ['cns.stage3.block.2.pwconv2[0][0
rConvNeXtGamma) ]']
cns.stage3.block.2.drop_path ( (None, 64, 64, 66) 0 ['cns.stage3.block.1.drop_path[0]
StochasticDepth) [0]',
'cns.stage3.block.2.gamma[0][0]'
]
dense (Dense) (None, 64, 64, 32) 2144 ['cns.stage3.block.2.drop_path[0]
[0]']
conv2d (Conv2D) (None, 64, 64, 1) 33 ['dense[0][0]']
reshape (Reshape) (None, 64, 64) 0 ['conv2d[0][0]']
==================================================================================================
Total params: 24,906,621
Trainable params: 24,906,621
Non-trainable params: 0
__________________________________________________________________________________________________