CycleGANPaper_no_BreastMask_norm_0_255_resized [TENTATIVO 5].ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CycleGANPaper_no_BreastMask_norm_0_255_resized [TENTATIVO 5].ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm","mount_file_id":"1zgW4ffMAYCpvNb8zFojkv-V98S2T5G3x","authorship_tag":"ABX9TyMoTGAcEZW6bjICcuelWbd8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"wRhnDICZ2ejR"},"source":["#DA ESEGUIRE 1 SOLA VOLTA !!!!\n","import os\n","os.chdir(r\"/content/drive/My Drive/TesiUNINA/Colab Notebooks/tentativo5/\") \n","!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_K7enczgcUlE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607104666821,"user_tz":-60,"elapsed":1266,"user":{"displayName":"Paolo Maione","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj8WzXBalf8l5zWT1m3zDN4K8CMQVnssgsQR14FaI8=s64","userId":"02326155032486404430"}},"outputId":"b7bae6e4-8749-4b98-9ebf-392e6e54984c"},"source":["!nvidia-smi"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Fri Dec  4 17:57:46 2020       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 455.45.01    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 V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   33C    P0    26W / 300W |      0MiB / 16130MiB |      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                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"KNaMkzfVz-iI","executionInfo":{"status":"ok","timestamp":1609619733059,"user_tz":-60,"elapsed":1310,"user":{"displayName":"Paolo Maione","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj8WzXBalf8l5zWT1m3zDN4K8CMQVnssgsQR14FaI8=s64","userId":"02326155032486404430"}},"outputId":"6c8d12d8-816b-41c4-c513-0bf643349aa0","colab":{"base_uri":"https://localhost:8080/"}},"source":["!cat /proc/cpuinfo"],"execution_count":1,"outputs":[{"output_type":"stream","text":["processor\t: 0\n","vendor_id\t: GenuineIntel\n","cpu family\t: 6\n","model\t\t: 79\n","model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n","stepping\t: 0\n","microcode\t: 0x1\n","cpu MHz\t\t: 2200.000\n","cache size\t: 56320 KB\n","physical id\t: 0\n","siblings\t: 4\n","core id\t\t: 0\n","cpu cores\t: 2\n","apicid\t\t: 0\n","initial apicid\t: 0\n","fpu\t\t: yes\n","fpu_exception\t: yes\n","cpuid level\t: 13\n","wp\t\t: yes\n","flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n","bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n","bogomips\t: 4400.00\n","clflush size\t: 64\n","cache_alignment\t: 64\n","address sizes\t: 46 bits physical, 48 bits virtual\n","power management:\n","\n","processor\t: 1\n","vendor_id\t: GenuineIntel\n","cpu family\t: 6\n","model\t\t: 79\n","model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n","stepping\t: 0\n","microcode\t: 0x1\n","cpu MHz\t\t: 2200.000\n","cache size\t: 56320 KB\n","physical id\t: 0\n","siblings\t: 4\n","core id\t\t: 1\n","cpu cores\t: 2\n","apicid\t\t: 2\n","initial apicid\t: 2\n","fpu\t\t: yes\n","fpu_exception\t: yes\n","cpuid level\t: 13\n","wp\t\t: yes\n","flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n","bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n","bogomips\t: 4400.00\n","clflush size\t: 64\n","cache_alignment\t: 64\n","address sizes\t: 46 bits physical, 48 bits virtual\n","power management:\n","\n","processor\t: 2\n","vendor_id\t: GenuineIntel\n","cpu family\t: 6\n","model\t\t: 79\n","model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n","stepping\t: 0\n","microcode\t: 0x1\n","cpu MHz\t\t: 2200.000\n","cache size\t: 56320 KB\n","physical id\t: 0\n","siblings\t: 4\n","core id\t\t: 0\n","cpu cores\t: 2\n","apicid\t\t: 1\n","initial apicid\t: 1\n","fpu\t\t: yes\n","fpu_exception\t: yes\n","cpuid level\t: 13\n","wp\t\t: yes\n","flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n","bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n","bogomips\t: 4400.00\n","clflush size\t: 64\n","cache_alignment\t: 64\n","address sizes\t: 46 bits physical, 48 bits virtual\n","power management:\n","\n","processor\t: 3\n","vendor_id\t: GenuineIntel\n","cpu family\t: 6\n","model\t\t: 79\n","model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n","stepping\t: 0\n","microcode\t: 0x1\n","cpu MHz\t\t: 2200.000\n","cache size\t: 56320 KB\n","physical id\t: 0\n","siblings\t: 4\n","core id\t\t: 1\n","cpu cores\t: 2\n","apicid\t\t: 3\n","initial apicid\t: 3\n","fpu\t\t: yes\n","fpu_exception\t: yes\n","cpuid level\t: 13\n","wp\t\t: yes\n","flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n","bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n","bogomips\t: 4400.00\n","clflush size\t: 64\n","cache_alignment\t: 64\n","address sizes\t: 46 bits physical, 48 bits virtual\n","power management:\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ZHNFSdSocVEe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1607290569401,"user_tz":-60,"elapsed":15160,"user":{"displayName":"Paolo Maione","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj8WzXBalf8l5zWT1m3zDN4K8CMQVnssgsQR14FaI8=s64","userId":"02326155032486404430"}},"outputId":"95e55f09-1677-4f0f-9b7c-b63851695fb3"},"source":["import os\n","os.chdir(r\"/content/drive/My Drive/TesiUNINA/Colab Notebooks/tentativo5/pytorch-CycleGAN-and-pix2pix\") \n","!pip install -r requirements.txt"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Requirement already 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sha256=cd8cc88da739c94a80f758d4d7c074873434b85a7d8d9b90b71a85885332749c\n","  Stored in directory: /root/.cache/pip/wheels/70/19/a7/6d589ed967f4dfefd33bc166d081257bd4ed0cb618dccfd62a\n","  Building wheel for torchfile (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for torchfile: filename=torchfile-0.1.0-cp36-none-any.whl size=5711 sha256=702b93c30ff8b24b8797d86020debdc8c9e5eb6169505011b738bed9a042e6df\n","  Stored in directory: /root/.cache/pip/wheels/b1/c3/d6/9a1cc8f3a99a0fc1124cae20153f36af59a6e683daca0a0814\n","Successfully built visdom torchfile\n","Installing collected packages: dominate, jsonpointer, jsonpatch, torchfile, websocket-client, visdom\n","Successfully installed dominate-2.6.0 jsonpatch-1.28 jsonpointer-2.0 torchfile-0.1.0 visdom-0.1.8.9 websocket-client-0.57.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Xa8NUW4FcczP"},"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/tentativo5/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","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --save_epoch_freq 5 --batch_size 4 --name DCEMRI_cyclegan --load_size 256 --no_flip --display_id 0 --display_winsize 384 --model cycle_gan --input_nc 1 --output_nc 1 --n_epochs 10 --n_epochs_decay 0 --lr 0.0002 --checkpoints_dir './checkpoints_noBM_normV2_resized_27_11_2020'\n","#!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --batch_size 4 --name DCEMRI_cyclegan --netD n_layers --n_layers_D 2 --load_size 256 --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 40 --lr 0.0002 --checkpoints_dir './checkpoints_noBM_normV2_resized_lr_0_0002'\n","!python train.py --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --netG unet_256 --save_epoch_freq 1 --batch_size 1 --name DCEMRI_cyclegan --load_size 256 --no_flip --display_id 0 --display_winsize 384 --model cycle_gan --input_nc 1 --output_nc 1 --n_epochs 2 --n_epochs_decay 0 --lr 0.0002 --checkpoints_dir './checkpoints_noBM_normV2_resized_1_12_2020_unet256'\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UPUJ0P2xcyUx"},"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","\n","#!python test.py --phase valid --eval --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --checkpoints_dir './checkpoints_noBM_normV2_resized_27_11_2020' --no_flip --load_size 256 --display_winsize 384 --results_dir './results_noBM_normV2_resized__27_11_2020/'\n","#!python test.py --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --netD n_layers --n_layers_D 2 --checkpoints_dir './checkpoints_noBM_normV2_resized_lr_0_0002' --load_size 256 --no_flip --display_winsize 384 --netG unet_256 --results_dir './results_noBM_normV2_resized_lr_0_0002/'\n","#python test.py --phase valid --eval --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --checkpoints_dir './checkpoints_noBM_normV2_resized_28_11_2020_BtoA' --no_flip --load_size 256 --display_winsize 384 --results_dir './results_noBM_normV2_resized__28_11_2020_BtoA/'\n","\n","#FINAL TEST PHASE for unet\n","#!python test.py --eval --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --netG unet_256 --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --checkpoints_dir './checkpoints_noBM_normV2_resized_finalTrain_unet256_40epoche' --no_flip --load_size 256 --display_winsize 384 --results_dir './results_normV2_resized_finalTrain_unet256_40epoche/'\n","\n","#FINAL TEST PHASE for resnet\n","!python test.py --eval --dataroot ./datasets/DCE_MRI_breastCancer_noBM_normV2_resized --input_nc 1 --output_nc 1 --name DCEMRI_cyclegan --model cycle_gan --checkpoints_dir './checkpoints_noBM_normV2_resized_finalTrain_resnet_10epoche' --no_flip --load_size 256 --display_winsize 384 --results_dir './results_normV2_resized_finalTrain_resnet_10epoche/'\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"XXKH7w9fHKVQ"},"source":["import os\n","!sudo apt-get install bc\n","os.chdir(r\"/content/drive/My Drive/TesiUNINA/Colab Notebooks/tentativo5/pytorch-CycleGAN-and-pix2pix\") \n","\n","#!bash ./run.sh\n","!bash ./finalTrain_run.sh"],"execution_count":null,"outputs":[]}]}