f.write('\nPrefix : '+prefix) f.write('\n3D Patches. Patch_size : '+str(patch_size_x)+', '+str(patch_size_y)+', '+str(patch_size_z)) f.write('\nHidden Layer Sizes : ['+', '.join(map(str,hidden_layers_sizes))+' ]') f.write('\nNoise Type : '+noise_dict[noise_type]) f.write('\nCorruption Levels : ['+', '.join(map(str,corruption_levels))+' ]') f.write('\nNo. of pre-training epochs : '+str(pretraining_epochs)) f.write('\nNo. of Fine-tuning epochs : '+str(training_epochs)) f.write('\nPretraining Learning rate : '+str(pretrain_lr)) f.write('\nFine-tuning learning rate : '+str(finetune_lr)) f.close() test_SdA(finetune_lr, pretraining_epochs, pretrain_lr, training_epochs, root+'BRATS_training_patches/b_trainpatch_3D_'+prefix+'_.npy', root+'BRATS_training_patches/b_trainlabel_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/b_validpatch_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/b_validlabel_3D_'+prefix+'_.npy', root+'BRATS_training_patches/u_trainpatch_3D_'+prefix+'_.npy', root+'BRATS_training_patches/u_trainlabel_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/u_validpatch_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/u_validlabel_3D_'+prefix+'_.npy',batch_size, n_ins, n_outs, hidden_layers_sizes, test_root + prefix, corruption_levels) print 'Network Trained and Saved!' test_network(test_root , prefix, test_path, patch_size_x, patch_size_y, patch_size_z, recon_flag) # convert_mha(root+'testing', prefix)
f.write("\nNo. of pre-training epochs : " + str(pretraining_epochs)) f.write("\nNo. of Fine-tuning epochs : " + str(training_epochs)) f.write("\nPretraining Learning rate : " + str(pretrain_lr)) f.write("\nFine-tuning learning rate : " + str(finetune_lr)) f.close() test_SdA( finetune_lr, pretraining_epochs, pretrain_lr, training_epochs, root + "BRATS_training_patches/b_10_trainpatch_2D_" + prefix + "_.npy", root + "BRATS_training_patches/b_10_trainlabel_2D_" + prefix + "_.npy", root + "BRATS_validation_patches/b_10_validpatch_2D_" + prefix + "_.npy", root + "BRATS_validation_patches/b_10_validlabel_2D_" + prefix + "_.npy", root + "BRATS_training_patches/u_10_trainpatch_2D_" + prefix + "_.npy", root + "BRATS_training_patches/u_10_trainlabel_2D_" + prefix + "_.npy", root + "BRATS_validation_patches/u_10_validpatch_2D_" + prefix + "_.npy", root + "BRATS_validation_patches/u_10_validlabel_2D_" + prefix + "_.npy", batch_size, n_ins, n_outs, hidden_layers_sizes, test_root + prefix, corruption_levels, ) print "Network Trained and Saved!" test_network(test_root, prefix, test_path, patch_size, patch_size, patch_size, recon_flag, 2) # convert_mha(root+'testing', prefix, 2)
def segment_script(patch_size_x, patch_size_y, patch_size_z, hidden_layers_sizes, corruption_levels, prefix): root = '../../varghese/10_1_brain_mean/' #patch_size_x = 9 #patch_size_y = 9 #patch_size_z = 9 recon_flag = False batch_size = 100 if recon_flag == True: n_ins = patch_size_x * patch_size_y * patch_size_z * 5 else: n_ins = patch_size_x * patch_size_y * patch_size_z * 4 n_outs = 5 noise_type = 1 noise_dict = {1:'Gaussian Noise',0:'Masking Noise'} #hidden_layers_sizes = [1000,1000,1000] #corruption_levels = [0.01,0.01,0.01] test_path = root + 'testing' #prefix = 'xxx' print 'Extracting Balanaced training patches...' B_Patch_Preprocess_recon_3D(patch_size_x,patch_size_y,patch_size_z,prefix,root+'training', root+'BRATS_training_patches/',False) print 'Training patches extracted!' print 'Extracting Balanced validation patches...' B_Patch_Preprocess_recon_3D(patch_size_x,patch_size_y,patch_size_z,prefix,root+'validation', root+'BRATS_validation_patches/',False) print 'Validation patches extracted!' print 'Extracting UnBalanced training patches...' U_Patch_Preprocess_recon_3D(patch_size_x,patch_size_y,patch_size_z,prefix,root+'training', root+'BRATS_training_patches/',False) print 'Training patches extracted!' print 'Extracting Unbalanced validation patches...' U_Patch_Preprocess_recon_3D(patch_size_x,patch_size_y,patch_size_z,prefix,root+'validation', root+'BRATS_validation_patches/',False) path = '../results/' for subdir, dirs, files in os.walk(path): test_num = len(dirs)+1 break os.mkdir('../results/test_'+str(test_num)+'_'+prefix) test_root = '../results/test_'+str(test_num)+'_'+prefix+'/' print 'Calling test_SdA...' finetune_lr = 0.1 pretraining_epochs = 50 pretrain_lr = 0.001 training_epochs = 100 f = open(test_root+prefix+'_params_info.txt', 'w') f.write( "Current date & time " + time.strftime("%c")) f.write('\nPrefix : '+prefix) f.write('\n3D Patches. Patch_size : '+str(patch_size_x)+', '+str(patch_size_y)+', '+str(patch_size_z)) f.write('\nHidden Layer Sizes : ['+', '.join(map(str,hidden_layers_sizes))+' ]') f.write('\nNoise Type : '+noise_dict[noise_type]) f.write('\nCorruption Levels : ['+', '.join(map(str,corruption_levels))+' ]') f.write('\nNo. of pre-training epochs : '+str(pretraining_epochs)) f.write('\nNo. of Fine-tuning epochs : '+str(training_epochs)) f.write('\nPretraining Learning rate : '+str(pretrain_lr)) f.write('\nFine-tuning learning rate : '+str(finetune_lr)) f.close() test_SdA(finetune_lr, pretraining_epochs, pretrain_lr, training_epochs, root+'BRATS_training_patches/b_trainpatch_3D_'+prefix+'_.npy', root+'BRATS_training_patches/b_trainlabel_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/b_validpatch_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/b_validlabel_3D_'+prefix+'_.npy', root+'BRATS_training_patches/u_trainpatch_3D_'+prefix+'_.npy', root+'BRATS_training_patches/u_trainlabel_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/u_validpatch_3D_'+prefix+'_.npy', root+'BRATS_validation_patches/u_validlabel_3D_'+prefix+'_.npy',batch_size, n_ins, n_outs, hidden_layers_sizes, test_root + prefix, corruption_levels) print 'Network Trained and Saved!' test_network(test_root , prefix, test_path, patch_size_x, patch_size_y, patch_size_z, recon_flag) convert_mha(root+'testing', prefix) print '####################################################################' print print 'Completed one network with prefix : ', prefix print print '####################################################################'
def segment_2D_script(patch_size, hidden_layers_sizes, corruption_levels, prefix): root = '../../varghese/10_1_brain_mean/' # patch_size = 11 recon_flag = False batch_size = 100 if recon_flag == True: n_ins = patch_size * patch_size * 5 else: n_ins = patch_size * patch_size * 4 n_outs = 5 # hidden_layers_sizes = [1000,1000,1000] # corruption_levels = [0.01,0.01,0.01] # noise_type = 1 #1- Gaussian, 0 - masking test_path = root + 'testing' # prefix = 'yyy' print 'Extracting training patches...' Patch_Preprocess_recon_2D(patch_size,patch_size, prefix,root+'training',root+'BRATS_training_patches/',False) print 'Training patches extracted!' print 'Extracting validation patches...' Patch_Preprocess_recon_2D(patch_size,patch_size,prefix,root+'validation', root+'BRATS_validation_patches/',False) print 'Validation patches extracted!' path = '../results/' for subdir, dirs, files in os.walk(path): test_num = len(dirs)+1 break # print test_num ##########---------SET PREFIX--------########## os.mkdir('../results/test2D'+str(test_num)+'_'+prefix) # test_root = '../results/test2D'+str(test_num)+'_'+prefix+'/' print 'Calling test_SdA...' finetune_lr = 0.1 pretraining_epochs = 1 pretrain_lr = 0.001 training_epochs = 1 test_SdA(finetune_lr, pretraining_epochs, pretrain_lr, training_epochs, root+'BRATS_training_patches/trainpatch_2D_'+prefix+'_.npy', root+'BRATS_training_patches/trainlabel_2D_'+prefix+'_.npy', root+'BRATS_validation_patches/validpatch_2D_'+prefix+'_.npy', root+'BRATS_validation_patches/validlabel_2D_'+prefix+'_.npy',batch_size, n_ins, n_outs, hidden_layers_sizes, test_root + prefix, corruption_levels) print 'Network Trained and Saved!' test_network(test_root , prefix, test_path, patch_size, patch_size, patch_size, recon_flag, 2) convert_mha(root+'testing', prefix, 2) # #