CycleGANPaper_si_BreastMask_norm_0_255 [TENTATIVO 2].ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CycleGANPaper_si_BreastMask_norm_0_255 [TENTATIVO 2].ipynb","provenance":[],"machine_shape":"hm","mount_file_id":"1TEIel0DNPLBdQaUHwKYWDjaQwmBI_aSx","authorship_tag":"ABX9TyNrsqJuvujaR0c0cTnfOywN"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"v79-CiQAuzdB"},"source":["#DA ESEGUIRE 1 SOLA VOLTA !!!!\n","import os\n","os.chdir(r\"/content/drive/My Drive/TesiUNINA/Colab Notebooks/tentativo2/\") \n","!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"T-D2jdKIw08C","executionInfo":{"status":"ok","timestamp":1604847305570,"user_tz":-60,"elapsed":1025,"user":{"displayName":"Paolo Maione","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj8WzXBalf8l5zWT1m3zDN4K8CMQVnssgsQR14FaI8=s64","userId":"02326155032486404430"}},"outputId":"10b70f4b-ab54-4dd1-835a-f5272eaa2628","colab":{"base_uri":"https://localhost:8080/"}},"source":["!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Sun Nov  8 14:55:04 2020       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 455.32.00    Driver Version: 418.67       CUDA Version: 10.1     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   34C    P0    24W / 250W |      0MiB / 16280MiB |      0%      Default |\n","|                               |                      |                 ERR! |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 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requirements.txt (line 2)) (0.8.1+cu101)\n","Requirement already satisfied: dominate>=2.4.0 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (2.6.0)\n","Requirement already satisfied: visdom>=0.1.8.8 in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 4)) (0.1.8.9)\n","Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.0->-r requirements.txt (line 1)) (0.7)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.0->-r requirements.txt (line 1)) (3.7.4.3)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.0->-r requirements.txt (line 1)) (1.18.5)\n","Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.0->-r requirements.txt (line 1)) (0.16.0)\n","Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision>=0.5.0->-r requirements.txt (line 2)) (7.0.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.15.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (2.23.0)\n","Requirement already satisfied: pyzmq in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (19.0.2)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.4.1)\n","Requirement already satisfied: tornado in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (5.1.1)\n","Requirement already satisfied: jsonpatch in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.26)\n","Requirement already satisfied: websocket-client in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (0.57.0)\n","Requirement already satisfied: torchfile in /usr/local/lib/python3.6/dist-packages (from visdom>=0.1.8.8->-r requirements.txt (line 4)) (0.1.0)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (2020.6.20)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->visdom>=0.1.8.8->-r requirements.txt (line 4)) (1.24.3)\n","Requirement already satisfied: jsonpointer>=1.9 in /usr/local/lib/python3.6/dist-packages (from jsonpatch->visdom>=0.1.8.8->-r requirements.txt (line 4)) (2.0)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"i8yDT0xQxNRo","executionInfo":{"status":"ok","timestamp":1604859414784,"user_tz":-60,"elapsed":11722820,"user":{"displayName":"Paolo Maione","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj8WzXBalf8l5zWT1m3zDN4K8CMQVnssgsQR14FaI8=s64","userId":"02326155032486404430"}},"outputId":"eaf2c613-0560-4d2a-8b84-538e4ab4edc2","colab":{"base_uri":"https://localhost:8080/"}},"source":["\"\"\"\n","\n","\n","A (source domain), B (target domain).\n","        Generators: G_A: A -> B; G_B: B -> A.\n","        Discriminators: D_A: G_A(A) vs. B; D_B: G_B(B) vs. A.\n","        Forward cycle loss:  lambda_A * ||G_B(G_A(A)) - A|| (Eqn. (2) in the paper)\n","        Backward cycle loss: lambda_B * ||G_A(G_B(B)) - B|| (Eqn. (2) in the paper)\n","        Identity loss (optional): lambda_identity * (||G_A(B) - B|| * lambda_B + ||G_B(A) - A|| * lambda_A) (Sec 5.2 \"Photo generation from paintings\" in the paper)\n","        Dropout is not used in the original CycleGAN paper.\n","\n","\n","\n","\n","epoch: -- current epoch\n","\n","iters: -- current training iteration during this epoch (reset to 0 at the end of every epoch) \n","\n","time: -- current training iteration during this epoch (reset to 0 at the end of every epoch)\n","\n","data: -- data loading time per data point (normalized by batch_size) \n","\n","D_A: GAN loss for discriminator D_A\n","G_A: loss for generators G_A \n","cycle_A: cycle-consistency loss A \n","idt_A: identity loss B \n","\n","D_B: GAN loss for discriminator D_B \n","G_B: loss for generators G_B \n","cycle_B: cycle-consistency loss B \n","idt_B: identity loss B\n","\n","\n","\n","\n","\n","\n","# #epoche\n","# #epochs_decay\n","#dataroot\n","#input_nc\n","#output_nc\n","#norm\n","#no_dropout \n","#dataset_mode\n","#direction\n","#batch size (adesso è 1!)\n","#load_size\n","#crop_size\n","#preprocess\n","# save_latest_freq default 5000\n","# save_epoch_freq default 5\n","\"\"\"\n","import os\n","os.chdir(\"/content/drive/My Drive/TesiUNINA/Colab Notebooks/tentativo2/pytorch-CycleGAN-and-pix2pix\")\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer --batch_size 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --model cycle_gan --dataset_mode dceMri --input_nc 1 --output_nc 1 --n_epochs 2 --n_epochs_decay 13 --lr 0.002\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer --batch_size 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --model cycle_gan --dataset_mode dceMri --input_nc 1 --output_nc 1 --n_epochs 1 --n_epochs_decay 14 --direction BtoA --lr 0.002\n","\n","\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_normV1 --batch_size 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --model cycle_gan --dataset_mode dceMri --input_nc 1 --output_nc 1 --n_epochs 2 --n_epochs_decay 8 --lr 0.002 --checkpoints_dir './checkpoints_normV1'\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_normV1 --batch_size 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --model cycle_gan --dataset_mode dceMri --input_nc 1 --output_nc 1 --n_epochs 1 --n_epochs_decay 14 --direction BtoA --lr 0.002 --checkpoints_dir './checkpoints_normV1'\n","\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_normV2 --batch_size 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --model cycle_gan --input_nc 1 --output_nc 1 --n_epochs 2 --n_epochs_decay 1 --lr 0.002 --checkpoints_dir './checkpoints_normV2'\n","#CYCLEGAN 30 X 30\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_normV2 --batch_size 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --preprocess 'none' --no_flip --display_id 0 --display_winsize 384 --model cycle_gan --input_nc 1 --output_nc 1 --n_epochs 4 --n_epochs_decay 11 --lr 0.002 --checkpoints_dir './checkpoints_normV2'\n","#CYCLEGAN 70 X 70\n","!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_normV2 --batch_size 5 --name DCEMRI_cyclegan --load_size 256 --netD n_layers --n_layers_D 2 --no_flip --display_id 0 --display_winsize 384 --model cycle_gan --netG unet_256 --input_nc 1 --output_nc 1 --n_epochs 10 --n_epochs_decay 30 --lr 0.0002 --checkpoints_dir './checkpoints_normV2_lr_0_0002'"],"execution_count":3,"outputs":[{"output_type":"stream","text":["----------------- Options ---------------\n","               batch_size: 5                             \t[default: 1]\n","                    beta1: 0.5                           \n","          checkpoints_dir: ./checkpoints_normV2_lr_0_0002\t[default: ./checkpoints]\n","           continue_train: False                         \n","                crop_size: 256                           \n","                 dataroot: ./datasets/DCE_MRI_breastCancer_normV2\t[default: None]\n","             dataset_mode: unaligned                     \n","                direction: AtoB                          \n","              display_env: main                          \n","             display_freq: 400                           \n","               display_id: 0                             \t[default: 1]\n","            display_ncols: 4                             \n","             display_port: 8097                          \n","           display_server: http://localhost              \n","          display_winsize: 384                           \t[default: 256]\n","                    epoch: latest                        \n","              epoch_count: 1                             \n","                 gan_mode: lsgan                         \n","                  gpu_ids: 0                             \n","                init_gain: 0.02                          \n","                init_type: normal                        \n","                 input_nc: 1                             \t[default: 3]\n","                  isTrain: True                          \t[default: None]\n","                 lambda_A: 10.0                          \n","                 lambda_B: 10.0                          \n","          lambda_identity: 0.5                           \n","                load_iter: 0                             \t[default: 0]\n","                load_size: 256                           \t[default: 286]\n","                       lr: 0.0002                        \n","           lr_decay_iters: 50                            \n","                lr_policy: linear                        \n","         max_dataset_size: inf                           \n","                    model: cycle_gan                     \n","                 n_epochs: 10                            \t[default: 100]\n","           n_epochs_decay: 30                            \t[default: 100]\n","               n_layers_D: 2                             \t[default: 3]\n","                     name: DCEMRI_cyclegan               \t[default: experiment_name]\n","                      ndf: 64                            \n","                     netD: n_layers                      \t[default: basic]\n","                     netG: unet_256                      \t[default: resnet_9blocks]\n","                      ngf: 64                            \n","               no_dropout: True                          \n","                  no_flip: True                          \t[default: False]\n","                  no_html: False                         \n","                     norm: instance                      \n","              num_threads: 4                             \n","                output_nc: 1                             \t[default: 3]\n","                    phase: train                         \n","                pool_size: 50                            \n","               preprocess: resize_and_crop               \n","               print_freq: 100                           \n","             save_by_iter: False                         \n","          save_epoch_freq: 1                             \n","         save_latest_freq: 115                           \n","           serial_batches: False                         \n","                   suffix:                               \n","         update_html_freq: 1000                          \n","                  verbose: False                         \n","----------------- End -------------------\n","dataset [UnalignedDataset] was created\n","The number of training images = 2725\n","initialize network with normal\n","initialize network with normal\n","initialize network with normal\n","initialize network with normal\n","model [CycleGANModel] was created\n","---------- Networks initialized -------------\n","[Network G_A] Total number of parameters : 54.403 M\n","[Network G_B] Total number of parameters : 54.403 M\n","[Network D_A] Total number of parameters : 0.661 M\n","[Network D_B] Total number of parameters : 0.661 M\n","-----------------------------------------------\n","create web directory ./checkpoints_normV2_lr_0_0002/DCEMRI_cyclegan/web...\n","/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n","  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n","learning rate 0.0002000 -> 0.0002000\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 115)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 230)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 345)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 460)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 575)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 690)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 805)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 920)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1035)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1150)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1265)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1380)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1495)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1610)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1725)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1840)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 1955)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 2070)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 2185)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 2300)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 2415)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 2530)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 1, total_iters 2645)\n","[epoch : 1 ] ---> learning rate= 0.0002\n","\n","saving the model at the end of epoch 1, iters 2725\n","End of epoch 1 / 40 \t Time Taken: 1126 sec\n","(epoch: 1, iters: 2725, time: 0.081, data: 0.997) D_A: 0.189 G_A: 0.351 cycle_A: 0.130 idt_A: 0.096 D_B: 0.205 G_B: 0.399 cycle_B: 0.273 idt_B: 0.071 \n","learning rate 0.0002000 -> 0.0002000\n","saving the latest model (epoch 2, total_iters 2760)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 2875)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 2990)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 3105)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 3220)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 3335)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 3450)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 3565)\n","[epoch : 2 ] ---> 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2, total_iters 4600)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 4715)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 4830)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 4945)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 5060)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 5175)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 5290)\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","[epoch : 2 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 2, total_iters 5405)\n","saving the model at the end of epoch 2, iters 5450\n","End of epoch 2 / 40 \t Time Taken: 275 sec\n","(epoch: 2, iters: 2725, time: 0.099, data: 0.001) D_A: 0.258 G_A: 0.313 cycle_A: 0.130 idt_A: 0.061 D_B: 0.174 G_B: 0.302 cycle_B: 0.215 idt_B: 0.059 \n","learning rate 0.0002000 -> 0.0002000\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 5520)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 5635)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 5750)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 5865)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 5980)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 6095)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 6210)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 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7360)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 7475)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 7590)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 7705)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 7820)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 7935)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 8050)\n","[epoch : 3 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 3, total_iters 8165)\n","saving the model at the end of epoch 3, iters 8175\n","End of epoch 3 / 40 \t Time Taken: 271 sec\n","(epoch: 3, iters: 2725, time: 0.099, data: 0.001) D_A: 0.240 G_A: 0.256 cycle_A: 0.138 idt_A: 0.047 D_B: 0.217 G_B: 0.367 cycle_B: 0.203 idt_B: 0.060 \n","learning rate 0.0002000 -> 0.0002000\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8280)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8395)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8510)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8625)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8740)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8855)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 8970)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9085)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9200)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9315)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9430)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9545)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9660)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9775)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 9890)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10005)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10120)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10235)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10350)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10465)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10580)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10695)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 4, total_iters 10810)\n","[epoch : 4 ] ---> learning rate= 0.0002\n","\n","saving the model at the end of epoch 4, iters 10900\n","End of epoch 4 / 40 \t Time Taken: 264 sec\n","(epoch: 4, iters: 2725, time: 0.099, data: 0.002) D_A: 0.246 G_A: 0.311 cycle_A: 0.076 idt_A: 0.032 D_B: 0.143 G_B: 0.328 cycle_B: 0.143 idt_B: 0.030 \n","learning rate 0.0002000 -> 0.0002000\n","saving the latest model (epoch 5, total_iters 10925)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11040)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11155)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11270)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11385)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11500)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11615)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11730)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11845)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 11960)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12075)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12190)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12305)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12420)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12535)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12650)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12765)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12880)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 12995)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 13110)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 13225)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 13340)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 13455)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 5, total_iters 13570)\n","[epoch : 5 ] ---> learning rate= 0.0002\n","\n","saving the model at the end of epoch 5, iters 13625\n","End of epoch 5 / 40 \t Time Taken: 267 sec\n","(epoch: 5, iters: 2725, time: 0.099, data: 0.002) D_A: 0.240 G_A: 0.332 cycle_A: 0.119 idt_A: 0.026 D_B: 0.193 G_B: 0.433 cycle_B: 0.114 idt_B: 0.047 \n","learning rate 0.0002000 -> 0.0002000\n","saving the latest model (epoch 6, total_iters 13685)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 13800)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 13915)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14030)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14145)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14260)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14375)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14490)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14605)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14720)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14835)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 14950)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15065)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15180)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15295)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15410)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15525)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15640)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15755)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15870)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 15985)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 16100)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 16215)\n","[epoch : 6 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 6, total_iters 16330)\n","saving the model at the end of epoch 6, iters 16350\n","End of epoch 6 / 40 \t Time Taken: 272 sec\n","(epoch: 6, iters: 2725, time: 0.099, data: 0.002) D_A: 0.246 G_A: 0.316 cycle_A: 0.090 idt_A: 0.025 D_B: 0.229 G_B: 0.616 cycle_B: 0.127 idt_B: 0.034 \n","learning rate 0.0002000 -> 0.0002000\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 16445)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 16560)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 16675)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 16790)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 16905)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17020)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17135)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17250)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17365)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17480)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17595)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17710)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17825)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 17940)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18055)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18170)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18285)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18400)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18515)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18630)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18745)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18860)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 7, total_iters 18975)\n","[epoch : 7 ] ---> learning rate= 0.0002\n","\n","saving the model at the end of epoch 7, iters 19075\n","End of epoch 7 / 40 \t Time Taken: 266 sec\n","(epoch: 7, iters: 2725, time: 0.099, data: 0.002) D_A: 0.234 G_A: 0.248 cycle_A: 0.091 idt_A: 0.024 D_B: 0.233 G_B: 0.325 cycle_B: 0.156 idt_B: 0.032 \n","learning rate 0.0002000 -> 0.0002000\n","saving the latest model (epoch 8, total_iters 19090)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19205)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19320)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19435)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19550)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19665)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19780)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 19895)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20010)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20125)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20240)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20355)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20470)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20585)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20700)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20815)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 20930)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21045)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21160)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21275)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21390)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21505)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21620)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 8, total_iters 21735)\n","[epoch : 8 ] ---> learning rate= 0.0002\n","\n","saving the model at the end of epoch 8, iters 21800\n","End of epoch 8 / 40 \t Time Taken: 269 sec\n","(epoch: 8, iters: 2725, time: 0.099, data: 0.002) D_A: 0.215 G_A: 0.217 cycle_A: 0.114 idt_A: 0.020 D_B: 0.250 G_B: 0.301 cycle_B: 0.122 idt_B: 0.032 \n","learning rate 0.0002000 -> 0.0002000\n","saving the latest model (epoch 9, total_iters 21850)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 21965)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22080)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22195)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22310)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22425)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22540)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22655)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22770)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 22885)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23000)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23115)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23230)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23345)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23460)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23575)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23690)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23805)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 23920)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 24035)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 24150)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 24265)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 24380)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the latest model (epoch 9, total_iters 24495)\n","[epoch : 9 ] ---> learning rate= 0.0002\n","\n","saving the model at the end of epoch 9, iters 24525\n","End of epoch 9 / 40 \t Time Taken: 270 sec\n","(epoch: 9, iters: 2725, time: 0.081, data: 0.002) D_A: 0.237 G_A: 0.334 cycle_A: 0.076 idt_A: 0.016 D_B: 0.210 G_B: 0.271 cycle_B: 0.116 idt_B: 0.027 \n","learning rate 0.0002000 -> 0.0001935\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 24610)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 24725)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 24840)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 24955)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 25070)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 25185)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 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total_iters 27025)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the latest model (epoch 10, total_iters 27140)\n","[epoch : 10 ] ---> learning rate= 0.00019354838709677422\n","\n","saving the model at the end of epoch 10, iters 27250\n","End of epoch 10 / 40 \t Time Taken: 266 sec\n","(epoch: 10, iters: 2725, time: 0.099, data: 0.002) D_A: 0.232 G_A: 0.234 cycle_A: 0.099 idt_A: 0.018 D_B: 0.210 G_B: 0.233 cycle_B: 0.132 idt_B: 0.035 \n","learning rate 0.0001935 -> 0.0001871\n","saving the latest model (epoch 11, total_iters 27255)\n","[epoch : 11 ] ---> learning rate= 0.00018709677419354842\n","\n","saving the latest model (epoch 11, total_iters 27370)\n","[epoch : 11 ] ---> learning rate= 0.00018709677419354842\n","\n","saving the latest model (epoch 11, total_iters 27485)\n","[epoch : 11 ] ---> learning rate= 0.00018709677419354842\n","\n","[epoch : 11 ] ---> learning rate= 0.00018709677419354842\n","\n","saving the latest model (epoch 11, total_iters 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0.0001806\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30015)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30130)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30245)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30360)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30475)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30590)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 30705)\n","[epoch : 12 ] ---> learning rate= 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0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 32545)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the latest model (epoch 12, total_iters 32660)\n","[epoch : 12 ] ---> learning rate= 0.00018064516129032257\n","\n","saving the model at the end of epoch 12, iters 32700\n","End of epoch 12 / 40 \t Time Taken: 266 sec\n","(epoch: 12, iters: 2725, time: 0.098, data: 0.002) D_A: 0.254 G_A: 0.298 cycle_A: 0.064 idt_A: 0.013 D_B: 0.197 G_B: 0.260 cycle_B: 0.104 idt_B: 0.027 \n","learning rate 0.0001806 -> 0.0001742\n","saving the latest model (epoch 13, total_iters 32775)\n","[epoch : 13 ] ---> learning rate= 0.00017419354838709678\n","\n","saving the latest model (epoch 13, total_iters 32890)\n","[epoch : 13 ] ---> learning rate= 0.00017419354838709678\n","\n","[epoch : 13 ] ---> learning rate= 0.00017419354838709678\n","\n","saving the latest model (epoch 13, total_iters 33005)\n","[epoch : 13 ] ---> learning rate= 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0.353 cycle_B: 0.173 idt_B: 0.023 \n","learning rate 0.0001742 -> 0.0001677\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, total_iters 35535)\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, total_iters 35650)\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, total_iters 35765)\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, total_iters 35880)\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, total_iters 35995)\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, total_iters 36110)\n","[epoch : 14 ] ---> learning rate= 0.00016774193548387098\n","\n","saving the latest model (epoch 14, 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0.0001484 -> 0.0001419\n","saving the latest model (epoch 18, total_iters 46345)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, total_iters 46460)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, total_iters 46575)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, total_iters 46690)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, total_iters 46805)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, total_iters 46920)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, total_iters 47035)\n","[epoch : 18 ] ---> learning rate= 0.00014193548387096775\n","\n","saving the latest model (epoch 18, 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0.0001226 -> 0.0001161\n","saving the latest model (epoch 22, total_iters 57270)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 57385)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 57500)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 57615)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 57730)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 57845)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 57960)\n","[epoch : 22 ] ---> learning rate= 0.0001161290322580645\n","\n","saving the latest model (epoch 22, total_iters 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total_iters 65205)\n","[epoch : 24 ] ---> learning rate= 0.0001032258064516129\n","\n","saving the latest model (epoch 24, total_iters 65320)\n","[epoch : 24 ] ---> learning rate= 0.0001032258064516129\n","\n","saving the model at the end of epoch 24, iters 65400\n","End of epoch 24 / 40 \t Time Taken: 267 sec\n","(epoch: 24, iters: 2725, time: 0.098, data: 0.002) D_A: 0.244 G_A: 0.205 cycle_A: 0.067 idt_A: 0.009 D_B: 0.246 G_B: 0.282 cycle_B: 0.085 idt_B: 0.022 \n","learning rate 0.0001032 -> 0.0000968\n","saving the latest model (epoch 25, total_iters 65435)\n","[epoch : 25 ] ---> learning rate= 9.677419354838711e-05\n","\n","saving the latest model (epoch 25, total_iters 65550)\n","[epoch : 25 ] ---> learning rate= 9.677419354838711e-05\n","\n","saving the latest model (epoch 25, total_iters 65665)\n","[epoch : 25 ] ---> learning rate= 9.677419354838711e-05\n","\n","saving the latest model (epoch 25, total_iters 65780)\n","[epoch : 25 ] ---> learning rate= 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7.741935483870968e-05\n","\n","saving the latest model (epoch 28, total_iters 76245)\n","[epoch : 28 ] ---> learning rate= 7.741935483870968e-05\n","\n","saving the model at the end of epoch 28, iters 76300\n","End of epoch 28 / 40 \t Time Taken: 270 sec\n","(epoch: 28, iters: 2725, time: 0.098, data: 0.002) D_A: 0.229 G_A: 0.247 cycle_A: 0.070 idt_A: 0.009 D_B: 0.207 G_B: 0.282 cycle_B: 0.076 idt_B: 0.023 \n","learning rate 0.0000774 -> 0.0000710\n","saving the latest model (epoch 29, total_iters 76360)\n","[epoch : 29 ] ---> learning rate= 7.096774193548388e-05\n","\n","saving the latest model (epoch 29, total_iters 76475)\n","[epoch : 29 ] ---> learning rate= 7.096774193548388e-05\n","\n","saving the latest model (epoch 29, total_iters 76590)\n","[epoch : 29 ] ---> learning rate= 7.096774193548388e-05\n","\n","[epoch : 29 ] ---> learning rate= 7.096774193548388e-05\n","\n","saving the latest model (epoch 29, total_iters 76705)\n","[epoch : 29 ] ---> learning rate= 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6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79120)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79235)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79350)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79465)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79580)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79695)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, total_iters 79810)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the latest model (epoch 30, 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total_iters 81650)\n","[epoch : 30 ] ---> learning rate= 6.451612903225808e-05\n","\n","saving the model at the end of epoch 30, iters 81750\n","End of epoch 30 / 40 \t Time Taken: 267 sec\n","(epoch: 30, iters: 2725, time: 0.098, data: 0.002) D_A: 0.211 G_A: 0.382 cycle_A: 0.045 idt_A: 0.013 D_B: 0.260 G_B: 0.230 cycle_B: 0.092 idt_B: 0.013 \n","learning rate 0.0000645 -> 0.0000581\n","saving the latest model (epoch 31, total_iters 81765)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 81880)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 81995)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 82110)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 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total_iters 83950)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 84065)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 84180)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 84295)\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","[epoch : 31 ] ---> learning rate= 5.806451612903225e-05\n","\n","saving the latest model (epoch 31, total_iters 84410)\n","saving the model at the end of epoch 31, iters 84475\n","End of epoch 31 / 40 \t Time Taken: 269 sec\n","(epoch: 31, iters: 2725, time: 0.098, data: 0.002) D_A: 0.267 G_A: 0.267 cycle_A: 0.078 idt_A: 0.010 D_B: 0.246 G_B: 0.237 cycle_B: 0.083 idt_B: 0.021 \n","learning rate 0.0000581 -> 0.0000516\n","[epoch : 32 ] ---> learning rate= 5.161290322580645e-05\n","\n","saving the latest model (epoch 32, total_iters 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4.516129032258065e-05\n","\n","saving the latest model (epoch 33, total_iters 89470)\n","[epoch : 33 ] ---> learning rate= 4.516129032258065e-05\n","\n","saving the latest model (epoch 33, total_iters 89585)\n","[epoch : 33 ] ---> learning rate= 4.516129032258065e-05\n","\n","[epoch : 33 ] ---> learning rate= 4.516129032258065e-05\n","\n","saving the latest model (epoch 33, total_iters 89700)\n","[epoch : 33 ] ---> learning rate= 4.516129032258065e-05\n","\n","saving the latest model (epoch 33, total_iters 89815)\n","[epoch : 33 ] ---> learning rate= 4.516129032258065e-05\n","\n","saving the model at the end of epoch 33, iters 89925\n","End of epoch 33 / 40 \t Time Taken: 267 sec\n","(epoch: 33, iters: 2725, time: 0.098, data: 0.002) D_A: 0.262 G_A: 0.245 cycle_A: 0.076 idt_A: 0.013 D_B: 0.243 G_B: 0.244 cycle_B: 0.091 idt_B: 0.023 \n","learning rate 0.0000452 -> 0.0000387\n","saving the latest model (epoch 34, total_iters 89930)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90045)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90160)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90275)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90390)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90505)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90620)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, total_iters 90735)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the latest model (epoch 34, 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total_iters 92575)\n","[epoch : 34 ] ---> learning rate= 3.870967741935485e-05\n","\n","saving the model at the end of epoch 34, iters 92650\n","End of epoch 34 / 40 \t Time Taken: 272 sec\n","(epoch: 34, iters: 2725, time: 0.098, data: 0.002) D_A: 0.239 G_A: 0.283 cycle_A: 0.085 idt_A: 0.016 D_B: 0.231 G_B: 0.278 cycle_B: 0.102 idt_B: 0.024 \n","learning rate 0.0000387 -> 0.0000323\n","saving the latest model (epoch 35, total_iters 92690)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 92805)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 92920)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 93035)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 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total_iters 94875)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 94990)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 95105)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 95220)\n","[epoch : 35 ] ---> learning rate= 3.225806451612903e-05\n","\n","saving the latest model (epoch 35, total_iters 95335)\n","saving the model at the end of epoch 35, iters 95375\n","End of epoch 35 / 40 \t Time Taken: 273 sec\n","(epoch: 35, iters: 2725, time: 0.098, data: 0.002) D_A: 0.223 G_A: 0.314 cycle_A: 0.067 idt_A: 0.008 D_B: 0.230 G_B: 0.241 cycle_B: 0.056 idt_B: 0.019 \n","learning rate 0.0000323 -> 0.0000258\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 95450)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 95565)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 95680)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 95795)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 95910)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 96025)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 96140)\n","[epoch : 36 ] ---> learning rate= 2.5806451612903226e-05\n","\n","saving the latest model (epoch 36, total_iters 96255)\n","[epoch : 36 ] ---> learning rate= 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\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"NMSyW07F6sDN","executionInfo":{"status":"ok","timestamp":1604867376165,"user_tz":-60,"elapsed":119628,"user":{"displayName":"Paolo Maione","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj8WzXBalf8l5zWT1m3zDN4K8CMQVnssgsQR14FaI8=s64","userId":"02326155032486404430"}},"outputId":"1ebc8b32-e807-475e-fc17-1c7e3fc4ca7f","colab":{"base_uri":"https://localhost:8080/"}},"source":["#!python test.py --dataroot ./datasets/DCE_MRI_breastCancer/ --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --netD n_layers --n_layers_D 2 --dataset_mode dceMri --num_test size_testA\n","\n","#!python test.py --dataroot ./datasets/DCE_MRI_breastCancer_normV1 --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --netD n_layers --n_layers_D 2 --dataset_mode dceMri --checkpoints_dir './checkpoints_normV1' --results_dir './results_normV1/' --num_test 388\n","#CYCLE GAN 30X30\n","#!python test.py --dataroot ./datasets/DCE_MRI_breastCancer_normV2 --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --netD n_layers --n_layers_D 2 --checkpoints_dir './checkpoints_normV2' --preprocess 'none' --no_flip --display_winsize 384 --results_dir './results_normV2/' #--num_test 523\n","#CYCLE GAN 30X30\n","!python test.py --dataroot ./datasets/DCE_MRI_breastCancer_normV2 --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --model cycle_gan --checkpoints_dir './checkpoints_normV2_lr_0_0002' --load_size 256 --no_flip --netG unet_256 --display_winsize 384 --results_dir './results_normV2_lr_0_0002/' #--num_test 523"],"execution_count":5,"outputs":[{"output_type":"stream","text":["----------------- Options ---------------\n","             aspect_ratio: 1.0                           \n","               batch_size: 1                             \n","          checkpoints_dir: ./checkpoints_normV2_lr_0_0002\t[default: ./checkpoints]\n","                crop_size: 256                           \n","                 dataroot: ./datasets/DCE_MRI_breastCancer_normV2\t[default: None]\n","             dataset_mode: unaligned                     \n","                direction: AtoB                          \n","          display_winsize: 384                           \t[default: 256]\n","                    epoch: latest                        \n","                     eval: False                         \n","                  gpu_ids: 0                             \n","                init_gain: 0.02                          \n","                init_type: normal                        \n","                 input_nc: 1                             \t[default: 3]\n","                  isTrain: False                         \t[default: None]\n","                load_iter: 0                             \t[default: 0]\n","                load_size: 256                           \n","         max_dataset_size: inf                           \n","                    model: cycle_gan                     \t[default: test]\n","               n_layers_D: 2                             \t[default: 3]\n","                     name: DCEMRI_cyclegan               \t[default: experiment_name]\n","                      ndf: 64                            \n","                     netD: n_layers                      \t[default: basic]\n","                     netG: unet_256                      \t[default: resnet_9blocks]\n","                      ngf: 64                            \n","               no_dropout: True                          \n","                  no_flip: True                          \t[default: False]\n","                     norm: instance                      \n","                 num_test: 50                            \n","              num_threads: 4                             \n","                output_nc: 1                             \t[default: 3]\n","                    phase: test                          \n","               preprocess: resize_and_crop               \n","              results_dir: ./results_normV2_lr_0_0002/   \t[default: ./results/]\n","           serial_batches: False                         \n","                   suffix:                               \n","                  verbose: False                         \n","----------------- End -------------------\n","dataset [UnalignedDataset] was created\n","initialize network with normal\n","initialize network with normal\n","model [CycleGANModel] was created\n","loading the model from ./checkpoints_normV2_lr_0_0002/DCEMRI_cyclegan/latest_net_G_A.pth\n","loading the model from ./checkpoints_normV2_lr_0_0002/DCEMRI_cyclegan/latest_net_G_B.pth\n","---------- Networks initialized -------------\n","[Network G_A] Total number of parameters : 54.403 M\n","[Network G_B] Total number of parameters : 54.403 M\n","-----------------------------------------------\n","creating web directory ./results_normV2_lr_0_0002/DCEMRI_cyclegan/test_latest\n","processing (0000)-th image... 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