Binet with aug and normalization

Binet_aug_norm/architecture.txt

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+Torch summary
+Running torch model on: cuda
+==========================================================================================
+Layer (type:depth-idx)                   Output Shape              Param #
+==========================================================================================
+Autoencoder                              [32, 1, 256, 256]         --
+├─Conv2d: 1-1                            [32, 64, 128, 128]        1,088
+├─Conv2d: 1-2                            [32, 128, 64, 64]         131,200
+├─BatchNorm2d: 1-3                       [32, 128, 64, 64]         256
+├─Conv2d: 1-4                            [32, 256, 32, 32]         524,544
+├─BatchNorm2d: 1-5                       [32, 256, 32, 32]         512
+├─Conv2d: 1-6                            [32, 512, 16, 16]         2,097,664
+├─BatchNorm2d: 1-7                       [32, 512, 16, 16]         1,024
+├─Conv2d: 1-8                            [32, 512, 8, 8]           4,194,816
+├─BatchNorm2d: 1-9                       [32, 512, 8, 8]           1,024
+├─Conv2d: 1-10                           [32, 512, 4, 4]           4,194,816
+├─BatchNorm2d: 1-11                      [32, 512, 4, 4]           1,024
+├─Conv2d: 1-12                           [32, 512, 2, 2]           4,194,816
+├─BatchNorm2d: 1-13                      [32, 512, 2, 2]           1,024
+├─Conv2d: 1-14                           [32, 512, 1, 1]           4,194,816
+├─BatchNorm2d: 1-15                      [32, 512, 1, 1]           1,024
+├─ConvTranspose2d: 1-16                  [32, 512, 2, 2]           4,194,816
+├─BatchNorm2d: 1-17                      [32, 512, 2, 2]           1,024
+├─ConvTranspose2d: 1-18                  [32, 512, 4, 4]           8,389,120
+├─BatchNorm2d: 1-19                      [32, 512, 4, 4]           1,024
+├─ConvTranspose2d: 1-20                  [32, 512, 8, 8]           8,389,120
+├─BatchNorm2d: 1-21                      [32, 512, 8, 8]           1,024
+├─ConvTranspose2d: 1-22                  [32, 512, 16, 16]         8,389,120
+├─BatchNorm2d: 1-23                      [32, 512, 16, 16]         1,024
+├─ConvTranspose2d: 1-24                  [32, 256, 32, 32]         4,194,560
+├─BatchNorm2d: 1-25                      [32, 256, 32, 32]         512
+├─ConvTranspose2d: 1-26                  [32, 128, 64, 64]         1,048,704
+├─BatchNorm2d: 1-27                      [32, 128, 64, 64]         256
+├─ConvTranspose2d: 1-28                  [32, 64, 128, 128]        262,208
+├─BatchNorm2d: 1-29                      [32, 64, 128, 128]        128
+├─ConvTranspose2d: 1-30                  [32, 1, 256, 256]         2,049
+==========================================================================================
+Total params: 54,414,337
+Trainable params: 54,414,337
+Non-trainable params: 0
+Total mult-adds (G): 570.96
+==========================================================================================
+Input size (MB): 8.39
+Forward/backward pass size (MB): 1805.91
+Params size (MB): 217.66
+Estimated Total Size (MB): 2031.96
+==========================================================================================
+---- Direct Print ----
+Autoencoder(
+  (e1): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e2_batch): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (e3): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e3_batch): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (e4): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e4_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (e5): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e5_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (e6): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e6_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (e7): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e7_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (e8): Conv2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (e8_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d1): ConvTranspose2d(512, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d1_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d2): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d2_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d3): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d3_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d4): ConvTranspose2d(1024, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d4_batch): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d5): ConvTranspose2d(1024, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d5_batch): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d6): ConvTranspose2d(512, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d6_batch): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d7): ConvTranspose2d(256, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (d7_batch): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+  (d8): ConvTranspose2d(128, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))
+  (criterion): L1Loss()
+)

Binet_aug_norm/epoch_log_split_0_Mar5-11-56-12.csv

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+    "patience": 50,
+    "model_name": "autoencoder",
+    "img_input_size": 256,
+    "learning_rate": 0.0002,
+    "batch_size": 32,
+    "transform": "Compose(\n    ToTensor()\n    Normalize(mean=[0.6144142615182454], std=[0.2606306621135704])\n)",
+    "dataset": "dibco",
+    "seed": 43
+}