diff --git a/.gitignore b/.gitignore index 186b2fce1..951a68614 100644 --- a/.gitignore +++ b/.gitignore @@ -28,6 +28,15 @@ share/python-wheels/ MANIFEST .idea/ +#Train data for surrogate models +data_from_comsol/generated_data +data_from_comsol/test_gen_data +data_from_comsol/gen_data_extend +#Weights +weights +*.npz +events.out* + # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. diff --git a/cases/microfluidic/config_generate.py b/cases/microfluidic/config_generate.py new file mode 100644 index 000000000..15c2ddec2 --- /dev/null +++ b/cases/microfluidic/config_generate.py @@ -0,0 +1,118 @@ +from pathlib import Path + +from gefest.core.configs.optimization_params import OptimizationParams +from gefest.core.configs.tuner_params import TunerParams +from gefest.core.geometry.datastructs.structure import Structure +from gefest.core.geometry.domain import Domain +from gefest.core.opt.objective.objective import Objective +from gefest.tools.estimators.simulators.comsol.comsol_interface import Comsol +from gefest.tools.estimators.estimator import Estimator + +class sosi_hiu(Estimator): + def __init__(self, domain, estimator): + super().__init__(domain, estimator) + def _evaluate(self, ind: Structure): + return 'SOLID соси хуй (С)Олег' + +# # # Metrics # # # +class ComsolMetric(Objective): + """Comsol metric.""" + def __init__(self, domain, estimator): + super().__init__(domain, estimator) + + def _evaluate(self, ind: Structure): + return self.estimator(ind) + +bottom_aa = [(-130 -49), +(-130, 21), +(21, 21), +(-7, -69), +(-31, -170), +(-130, -170), +(-130 ,-49)] +aasd = [(-130,-49),(-130,21),(21,21),(-7,-69),(-31, -170), +(-130, -170), +(-130 ,-49)] +standart_aa = [ + (-125, 100), + (-75, 170), + (15, 170), + (30, 90), + (-20, -130), + (-20, -170), + (-125, -170), + (-125, 100), + ] +# # # Precompute domain arguments # # # +pass +# # # + +domain_cfg = Domain( + allowed_area=aasd, + name='main', + min_poly_num=1, + max_poly_num=2, + min_points_num=3, + max_points_num=10, + polygon_side=0.0001, + min_dist_from_boundary=0.01, + geometry_is_convex=False, + geometry_is_closed=True, + geometry='2D', +) + +tuner_cfg = TunerParams( + tuner_type='optuna', + n_steps_tune=10, + hyperopt_dist='uniform', + verbose=True, + timeout_minutes=60, +) + +opt_params = OptimizationParams( + optimizer='gefest_ga', + domain=domain_cfg, + tuner_cfg=tuner_cfg, + n_steps=50, + pop_size=50, + postprocess_attempts=3, + mutation_prob=0.6, + crossover_prob=0.6, + mutations=[ + 'rotate_poly', + 'resize_poly', + 'add_point', + 'drop_point', + 'add_poly', + 'drop_poly', + 'pos_change_point', + ], + selector='tournament_selection', + mutation_each_prob=[0.125, 0.125, 0.15, 0.35, 0.00, 0.00, 0.25], + crossovers=[ + 'polygon_level', + 'structure_level', + ], + crossover_each_prob=[0.0, 1.0], + postprocess_rules=[ + 'not_out_of_bounds', + 'valid_polygon_geom', + 'not_self_intersects', + 'not_too_close_polygons', + # 'not_overlaps_prohibited', + 'not_too_close_points', + ], + extra=5, + n_jobs=-1, + log_dir='logs', + run_name='run_name', + golem_keep_histoy=False, + golem_genetic_scheme_type='steady_state', + golem_surrogate_each_n_gen=5, + objectives=[ + ComsolMetric( + domain_cfg, + sosi_hiu, + ), + ], +) diff --git a/cases/sound_waves/configuration/config_parallel.py b/cases/sound_waves/configuration/config_parallel.py index 0f112af1e..96e7285e1 100644 --- a/cases/sound_waves/configuration/config_parallel.py +++ b/cases/sound_waves/configuration/config_parallel.py @@ -82,16 +82,16 @@ def _evaluate(self, ind: Structure): hyperopt_dist='uniform', verbose=True, variacne_generator=partial(percent_edge_variance, percent=0.5), - timeout_minutes=30, + timeout_minutes=130, ) opt_params = OptimizationParams( - optimizer='gefest_ga', + optimizer='gefest_ga_h', domain=domain_cfg, tuner_cfg=tuner_cfg, - n_steps=100, - pop_size=100, + n_steps=10, + pop_size=10, postprocess_attempts=3, mutation_prob=0.9, crossover_prob=0.6, @@ -120,8 +120,8 @@ def _evaluate(self, ind: Structure): 'not_too_close_points', ], extra=5, - estimation_n_jobs=-1, - n_jobs=-1, + estimation_n_jobs=1, + n_jobs=1, log_dir='logs/tuners_exp', run_name='roulette_1_obj', golem_keep_histoy=True, diff --git a/data_from_comsol/coords.txt b/data_from_comsol/coords.txt new file mode 100644 index 000000000..0843f016b --- /dev/null +++ b/data_from_comsol/coords.txt @@ -0,0 +1,7 @@ +-130 -49.02983247582813 +-130 20.52414239317181 +20.604039813602455 20.524142393171942 +-6.907702544715789 -69.02983247582813 +-31.56130231388761 -170 +-130 -170 +-130 -49.02983247582813 diff --git a/data_from_comsol/create_matrix.py b/data_from_comsol/create_matrix.py new file mode 100644 index 000000000..284f83dd1 --- /dev/null +++ b/data_from_comsol/create_matrix.py @@ -0,0 +1,58 @@ +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +from skimage.morphology import reconstruction +from scipy.ndimage.morphology import grey_dilation, generate_binary_structure, iterate_structure + +def create_mask(path,dilation:bool=True): + df=pd.read_csv(path,names=['x','y','color'],sep='\t') + array = np.zeros((400,400)) + x = (np.array(df['x'])+200) + y = (np.array(df['y'])+200) + value = np.array(df['color']) + array[np.intc(x),np.intc(y)]=value + if dilation: + array = grey_dilation(array,size=(2,2)) + return array + +def create_flow(path,dilation:bool=True): + df=pd.read_csv(path,names=['x','y','color'],sep='\t') + array = np.ones((400,400))*-1 + x = (np.array(df['x'])+200) + y = (np.array(df['y'])+200) + value = np.array(df['color']) + array[np.intc(x),np.intc(y)]=value + if dilation: + array = grey_dilation(array,size=(2,2)) + return array + + + +if __name__=="__main__": + df=pd.read_csv('data_from_comsol//velocity.txt',names=['x','y','color'],sep='\t') + #df=pd.read_csv('data_from_comsol\mask.txt',names=['x','y','color'],sep='\t') + + x = (np.array(df['x'])+200) + y = (np.array(df['y'])+200) + value = np.array(df['color']) + + array_2 = np.ones((400,400))*-256 + array_2[np.intc(x),np.intc(y)]=value + + + array_111 = grey_dilation(array_2,size=(2,2)) + plt.imshow(array_111,interpolation='nearest') + plt.show() + + + array = np.ones((400,400))*-256 + array[np.intc(x),np.intc(y)]=value + # array[np.intc(x+0.25),np.intc(y+0.25)]=value + # array[np.intc(x-0.25),np.intc(y-0.25)]=value + + # array[np.intc(x+0.25),np.intc(y-0.25)]=value + # array[np.intc(x-0.25),np.intc(y+0.25)]=value + plt.imshow(array,interpolation='nearest') + plt.show() + + print() diff --git a/data_from_comsol/gen_setup.mph b/data_from_comsol/gen_setup.mph new file mode 100644 index 000000000..12d4f9379 Binary files /dev/null and b/data_from_comsol/gen_setup.mph differ diff --git a/data_from_comsol/gen_setup_bott.mph b/data_from_comsol/gen_setup_bott.mph new file mode 100644 index 000000000..43e844291 Binary files /dev/null and b/data_from_comsol/gen_setup_bott.mph differ diff --git a/data_from_comsol/rbc-trap-setup.mph b/data_from_comsol/rbc-trap-setup.mph new file mode 100644 index 000000000..0a1e7f310 Binary files /dev/null and b/data_from_comsol/rbc-trap-setup.mph differ diff --git a/data_from_comsol/read.py b/data_from_comsol/read.py new file mode 100644 index 000000000..b7b60774c --- /dev/null +++ b/data_from_comsol/read.py @@ -0,0 +1,11 @@ +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt +from skimage.morphology import reconstruction +from scipy.ndimage.morphology import grey_dilation, generate_binary_structure, iterate_structure + +data = np.load('data_from_comsol\generated_data\data_1.npz',allow_pickle=True) + +for i in data['arr_0'].item(0)['flow']: + plt.imshow(i,interpolation='nearest') + plt.show() \ No newline at end of file diff --git a/data_from_comsol/read_vtu.py b/data_from_comsol/read_vtu.py new file mode 100644 index 000000000..e603ecd47 --- /dev/null +++ b/data_from_comsol/read_vtu.py @@ -0,0 +1,12 @@ +import vtk + +# The source file +file_name = "GEFEST\data_from_comsol\plot_data.vtu" + +# Read the source file. +reader = vtk.vtkXMLUnstructuredGridReader() +reader.SetFileName(file_name) +reader.Update() # Needed because of GetScalarRange +output = reader.GetOutput() +potential = output.GetPointData().GetArray("potential") +print() \ No newline at end of file diff --git a/data_from_comsol/rename.py b/data_from_comsol/rename.py new file mode 100644 index 000000000..521c8c4e9 --- /dev/null +++ b/data_from_comsol/rename.py @@ -0,0 +1,6 @@ +import os +path = 'data_from_comsol\generated_data' +print(os.listdir(path)) + +for i , file in enumerate(os.listdir(path)): + os.rename(path+f'/{file}',path+f'/data_gen_{i}.npz') \ No newline at end of file diff --git a/data_from_comsol/sampler_test.py b/data_from_comsol/sampler_test.py new file mode 100644 index 000000000..f81c21afc --- /dev/null +++ b/data_from_comsol/sampler_test.py @@ -0,0 +1,41 @@ +import os +import sys +import numpy as np +sys.path.append(os.getcwd()) +sys.path.append(str(os.getcwd())+'/GEFEST/') +import matplotlib.pyplot as plt +from gefest.core.geometry.datastructs.structure import Structure,Polygon,Point +from gefest.core.geometry.domain import Domain +from gefest.tools.estimators.simulators.comsol.comsol_interface import Comsol_generate +from cases.microfluidic.config_generate import opt_params +from gefest.tools.samplers.standard.standard import StandardSampler +from data_from_comsol.create_matrix import create_mask,create_flow +ITERATIONS = range(10) +SAMPLES = 10 + +sampler = StandardSampler(opt_params=opt_params) + +def plot_polygons(struct:Structure): + + for points in struct: + x,y = [p.coords[0] for p in points],[p.coords[1] for p in points] + plt.plot(x,y) + + +domain_points = opt_params.domain.allowed_area.points +x,y = [p.coords[0] for p in domain_points],[p.coords[1] for p in domain_points] +plt.plot(x,y) +for i in ITERATIONS: + structs = sampler.sample(n_samples=SAMPLES) + for s in structs: + plot_polygons(s) + print() + plt.show() + + + + +print() + + + diff --git a/data_from_comsol/test_to_generate.py b/data_from_comsol/test_to_generate.py new file mode 100644 index 000000000..7bf01805f --- /dev/null +++ b/data_from_comsol/test_to_generate.py @@ -0,0 +1,34 @@ +import os +import sys +import numpy as np +sys.path.append(os.getcwd()) +print(os.getcwd()) +sys.path.append(str(os.getcwd())+'/GEFEST/') +from gefest.core.geometry.datastructs.structure import Structure,Polygon,Point +from gefest.core.geometry.domain import Domain +from gefest.tools.estimators.simulators.comsol.comsol_interface import Comsol_generate +from cases.microfluidic.config_generate import opt_params +from gefest.tools.samplers.standard.standard import StandardSampler +from data_from_comsol.create_matrix import create_mask,create_flow +ITERATIONS = range(100) +SAMPLES = 10 + +comsol = Comsol_generate(path_to_mph='data_from_comsol//gen_setup.mph') +sampler = StandardSampler(opt_params=opt_params) + +for i in ITERATIONS: + data = {'mask':[],'flow':[],'struct':[]} + structs = sampler.sample(n_samples=SAMPLES) + for s in structs: + results = comsol.estimate(structure = s) + flow = create_flow(path='data_from_comsol//velocity.txt') + mask = create_mask(path='data_from_comsol\mask.txt') + data['flow'].append(flow) + data['mask'].append(mask) + data['struct'].append(s) + np.savez(f'data_from_comsol/gen_data_extend/data__{i}',data,allow_pickle=False) + print(f'Saved {(i+1)*SAMPLES} samples of simulation') +print() + + + diff --git a/gefest/core/geometry/utils.py b/gefest/core/geometry/utils.py index 42717bf78..66f47e42d 100644 --- a/gefest/core/geometry/utils.py +++ b/gefest/core/geometry/utils.py @@ -166,7 +166,7 @@ def _create_area(domain: Domain, structure: Structure, geometry: Geometry2D) -> geom._poly_to_shapely_poly(poly).convex_hull.buffer(domain.dist_between_polygons, 1), ).intersection(area) - sigma_max = 0.95 * _get_sigma_max(area, (min(domain.max_x, domain.max_y) / 2) * 1.01) + sigma_max = 0.95 * _get_sigma_max(area, (min(domain.max_x, domain.max_y) / 2) * 6.01) sigma_min = max(domain.max_x - domain.min_x, domain.max_y - domain.min_y) * 0.05 sigma = np.random.uniform(sigma_min, sigma_max) diff --git a/gefest/surrogate_models/config.py b/gefest/surrogate_models/config.py new file mode 100644 index 000000000..9e7b78a8a --- /dev/null +++ b/gefest/surrogate_models/config.py @@ -0,0 +1,82 @@ +import argparse + +def crete_parser(batch_size = 16, + epochs:int = 100, + lr:float = 0.01): + parser = argparse.ArgumentParser( + description="Training U-Net model for segmentation of brain MRI" + ) + parser.add_argument( + "--batch_size", + type=int, + default=batch_size, + help="input batch size for training (default: 16)", + ) + parser.add_argument( + "--epochs", + type=int, + default=epochs, + help="number of epochs to train (default: 100)", + ) + parser.add_argument( + "--lr", + type=float, + default=lr, + help="initial learning rate (default: 0.001)", + ) + parser.add_argument( + "--device", + type=str, + default="cuda:0", + help="device for training (default: cuda:0)", + ) + parser.add_argument( + "--workers", + type=int, + default=4, + help="number of workers for data loading (default: 4)", + ) + parser.add_argument( + "--vis-images", + type=int, + default=200, + help="number of visualization images to save in log file (default: 200)", + ) + parser.add_argument( + "--vis-freq", + type=int, + default=10, + help="frequency of saving images to log file (default: 10)", + ) + parser.add_argument( + "--weights", type=str, default="./weights", help="folder to save weights" + ) + parser.add_argument( + "--path_to_data", type=str, default="data_from_comsol/generated_data", help="path to data with generated data" + ) + parser.add_argument( + "--logs", type=str, default="./logs", help="folder to save logs" + ) + parser.add_argument( + "--images", type=str, default="./kaggle_3m", help="root folder with images" + ) + parser.add_argument( + "--image-size", + type=int, + default=256, + help="target input image size (default: 256)", + ) + parser.add_argument( + "--aug-scale", + type=int, + default=0.05, + help="scale factor range for augmentation (default: 0.05)", + ) + parser.add_argument( + "--aug-angle", + type=int, + default=15, + help="rotation angle range in degrees for augmentation (default: 15)", + ) + args = parser.parse_args() + return args \ No newline at end of file diff --git a/gefest/surrogate_models/inference.py b/gefest/surrogate_models/inference.py new file mode 100644 index 000000000..c9a6b56dd --- /dev/null +++ b/gefest/surrogate_models/inference.py @@ -0,0 +1,41 @@ +import json +import os + +import numpy as np +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +from tqdm import tqdm +from utils.dataloader import create_single_dataloader +#from utils import log_images, dsc +from models import UNet +import pandas as pd + + +device = torch.device("cpu" if not torch.cuda.is_available() else 'cuda') +dataloader = create_single_dataloader(path_to_dir='data_from_comsol//test_gen_data',batch_size=10,shuffle=False) +model = UNet(in_channels=1,out_channels=1).to(device) +model.load_state_dict(torch.load(f'weights//unet_58_mask_ssim_100.pt',map_location=torch.device(device))) +CASE = 'ssim_plus_58' +model.eval() +#predicts = [] +truth = [] +with torch.no_grad(): + print('start inference') + for i, data in enumerate(dataloader): + x, y_true = data + x = x.to(device) + + y_pred = model(x,x).squeeze() + if i==0: + predicts = np.copy(y_pred.cpu().numpy()) + truth = np.copy(y_true) + masks = np.copy(x.cpu().numpy()) + else: + predicts = np.concatenate((predicts,y_pred.cpu().numpy())) + truth = np.concatenate((truth,y_true)) + masks = np.concatenate((masks,x.cpu().numpy())) + #predicts+=y_pred.cpu().numpy() + data_to_save = {'flow_predict':predicts,"flow":truth,'mask':masks} + np.savez(f'gefest/surrogate_models/gendata/{CASE}/data',data_to_save,allow_pickle=False) + print() diff --git a/gefest/surrogate_models/models.py b/gefest/surrogate_models/models.py new file mode 100644 index 000000000..4756f8c69 --- /dev/null +++ b/gefest/surrogate_models/models.py @@ -0,0 +1,614 @@ +import torch +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.sampler import SubsetRandomSampler + +from collections import OrderedDict +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init + +class UNet(nn.Module): + + def __init__(self, in_channels=3, out_channels=1, init_features=32): + super(UNet, self).__init__() + + features = init_features + self.encoder1 = UNet._block(in_channels, features, name="enc1") + self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.encoder2 = UNet._block(features, features * 2, name="enc2") + self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.encoder3 = UNet._block(features * 2, features * 4, name="enc3") + self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) + self.encoder4 = UNet._block(features * 4, features * 8, name="enc4") + self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) + + self.bottleneck = UNet._block(features * 8, features * 16, name="bottleneck") + + self.upconv4 = nn.ConvTranspose2d( + features * 16, features * 8, kernel_size=2, stride=2 + ) + self.decoder4 = UNet._block((features * 8) * 2, features * 8, name="dec4") + self.upconv3 = nn.ConvTranspose2d( + features * 8, features * 4, kernel_size=2, stride=2 + ) + self.decoder3 = UNet._block((features * 4) * 2, features * 4, name="dec3") + self.upconv2 = nn.ConvTranspose2d( + features * 4, features * 2, kernel_size=2, stride=2 + ) + self.decoder2 = UNet._block((features * 2) * 2, features * 2, name="dec2") + self.upconv1 = nn.ConvTranspose2d( + features * 2, features, kernel_size=2, stride=2 + ) + self.decoder1 = UNet._block(features * 2, features, name="dec1") + + self.conv = nn.Conv2d( + in_channels=features, out_channels=out_channels, kernel_size=1 + ) + + def forward(self, x,mask): + x = torch.unsqueeze(x,dim=1).float() + enc1 = self.encoder1(x) + enc2 = self.encoder2(self.pool1(enc1)) + enc3 = self.encoder3(self.pool2(enc2)) + enc4 = self.encoder4(self.pool3(enc3)) + + bottleneck = self.bottleneck(self.pool4(enc4)) + + dec4 = self.upconv4(bottleneck) + dec4 = torch.cat((dec4, enc4), dim=1) + dec4 = self.decoder4(dec4) + dec3 = self.upconv3(dec4) + dec3 = torch.cat((dec3, enc3), dim=1) + dec3 = self.decoder3(dec3) + dec2 = self.upconv2(dec3) + dec2 = torch.cat((dec2, enc2), dim=1) + dec2 = self.decoder2(dec2) + dec1 = self.upconv1(dec2) + dec1 = torch.cat((dec1, enc1), dim=1) + dec1 = self.decoder1(dec1) + return self.conv(dec1)#*mask.unsqueeze(1) + + @staticmethod + def _block(in_channels, features, name): + return nn.Sequential( + OrderedDict( + [ + ( + name + "conv1", + nn.Conv2d( + in_channels=in_channels, + out_channels=features, + kernel_size=3, + padding=1, + bias=False, + ), + ), + (name + "norm1", nn.BatchNorm2d(num_features=features)), + (name + "relu1", nn.ReLU(inplace=True)), + ( + name + "conv2", + nn.Conv2d( + in_channels=features, + out_channels=features, + kernel_size=3, + padding=1, + bias=False, + ), + ), + (name + "norm2", nn.BatchNorm2d(num_features=features)), + (name + "relu2", nn.ReLU(inplace=True)), + ] + ) + ) + + +class UNet_bi(nn.Module): + + def __init__(self, in_channels=3, out_channels=1, init_features=32): + super(UNet_bi, self).__init__() + + features = init_features + self.encoder1 = UNet._block(in_channels, features, name="enc1") + self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.encoder2 = UNet._block(features, features * 2, name="enc2") + self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.encoder3 = UNet._block(features * 2, features * 4, name="enc3") + self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) + self.encoder4 = UNet._block(features * 4, features * 8, name="enc4") + self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) + + self.bottleneck = UNet._block(features * 8, features * 16, name="bottleneck") + + self.upconv4 = nn.UpsamplingBilinear2d(scale_factor=2) + self.decoder4 = UNet._block((features * 8) * 3, features * 8, name="dec4") + self.upconv3 = nn.UpsamplingBilinear2d(scale_factor=2) + self.decoder3 = UNet._block((features * 4) * 3, features * 4, name="dec3") + self.upconv2 =nn.UpsamplingBilinear2d(scale_factor=2) + self.decoder2 = UNet._block((features * 2) * 3, features * 2, name="dec2") + self.upconv1 =nn.UpsamplingBilinear2d(scale_factor=2) + self.decoder1 = UNet._block(features * 3, features, name="dec1") + + self.conv = nn.Conv2d( + in_channels=features, out_channels=out_channels, kernel_size=1 + ) + + def forward(self, x,mask): + x = torch.unsqueeze(x,dim=1).float() + enc1 = self.encoder1(x) + enc2 = self.encoder2(self.pool1(enc1)) + enc3 = self.encoder3(self.pool2(enc2)) + enc4 = self.encoder4(self.pool3(enc3)) + + bottleneck = self.bottleneck(self.pool4(enc4)) + + dec4 = self.upconv4(bottleneck) + dec4 = torch.cat((dec4, enc4), dim=1) + dec4 = self.decoder4(dec4) + dec3 = self.upconv3(dec4) + dec3 = torch.cat((dec3, enc3), dim=1) + dec3 = self.decoder3(dec3) + dec2 = self.upconv2(dec3) + dec2 = torch.cat((dec2, enc2), dim=1) + dec2 = self.decoder2(dec2) + dec1 = self.upconv1(dec2) + dec1 = torch.cat((dec1, enc1), dim=1) + dec1 = self.decoder1(dec1) + return self.conv(dec1)#*mask.unsqueeze(1) + + @staticmethod + def _block(in_channels, features, name): + return nn.Sequential( + OrderedDict( + [ + ( + name + "conv1", + nn.Conv2d( + in_channels=in_channels, + out_channels=features, + kernel_size=3, + padding=1, + bias=False, + ), + ), + (name + "norm1", nn.BatchNorm2d(num_features=features)), + (name + "relu1", nn.ReLU(inplace=True)), + ( + name + "conv2", + nn.Conv2d( + in_channels=features, + out_channels=features, + kernel_size=3, + padding=1, + bias=False, + ), + ), + (name + "norm2", nn.BatchNorm2d(num_features=features)), + (name + "relu2", nn.ReLU(inplace=True)), + ] + ) + ) + + + + +def init_weights(net, init_type='normal', gain=0.02): + def init_func(m): + classname = m.__class__.__name__ + if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): + if init_type == 'normal': + init.normal_(m.weight.data, 0.0, gain) + elif init_type == 'xavier': + init.xavier_normal_(m.weight.data, gain=gain) + elif init_type == 'kaiming': + init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') + elif init_type == 'orthogonal': + init.orthogonal_(m.weight.data, gain=gain) + else: + raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + if hasattr(m, 'bias') and m.bias is not None: + init.constant_(m.bias.data, 0.0) + elif classname.find('BatchNorm2d') != -1: + init.normal_(m.weight.data, 1.0, gain) + init.constant_(m.bias.data, 0.0) + + print('initialize network with %s' % init_type) + net.apply(init_func) + +class conv_block(nn.Module): + def __init__(self,ch_in,ch_out): + super(conv_block,self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True), + nn.BatchNorm2d(ch_out), + nn.ReLU(inplace=True), + nn.Conv2d(ch_out, ch_out, kernel_size=3,stride=1,padding=1,bias=True), + nn.BatchNorm2d(ch_out), + nn.ReLU(inplace=True) + ) + + + def forward(self,x): + x = self.conv(x) + return x + +class up_conv(nn.Module): + def __init__(self,ch_in,ch_out): + super(up_conv,self).__init__() + self.up = nn.Sequential( + nn.Upsample(scale_factor=2), + nn.Conv2d(ch_in,ch_out,kernel_size=3,stride=1,padding=1,bias=True), + nn.BatchNorm2d(ch_out), + nn.ReLU(inplace=True) + ) + + def forward(self,x): + x = self.up(x) + return x + +class Recurrent_block(nn.Module): + def __init__(self,ch_out,t=2): + super(Recurrent_block,self).__init__() + self.t = t + self.ch_out = ch_out + self.conv = nn.Sequential( + nn.Conv2d(ch_out,ch_out,kernel_size=3,stride=1,padding=1,bias=True), + nn.BatchNorm2d(ch_out), + nn.ReLU(inplace=True) + ) + + def forward(self,x): + for i in range(self.t): + + if i==0: + x1 = self.conv(x) + + x1 = self.conv(x+x1) + return x1 + +class RRCNN_block(nn.Module): + def __init__(self,ch_in,ch_out,t=2): + super(RRCNN_block,self).__init__() + self.RCNN = nn.Sequential( + Recurrent_block(ch_out,t=t), + Recurrent_block(ch_out,t=t) + ) + self.Conv_1x1 = nn.Conv2d(ch_in,ch_out,kernel_size=1,stride=1,padding=0) + + def forward(self,x): + x = self.Conv_1x1(x) + x1 = self.RCNN(x) + return x+x1 + + +class single_conv(nn.Module): + def __init__(self,ch_in,ch_out): + super(single_conv,self).__init__() + self.conv = nn.Sequential( + nn.Conv2d(ch_in, ch_out, kernel_size=3,stride=1,padding=1,bias=True), + nn.BatchNorm2d(ch_out), + nn.ReLU(inplace=True) + ) + + def forward(self,x): + x = self.conv(x) + return x + +class Attention_block(nn.Module): + def __init__(self,F_g,F_l,F_int): + super(Attention_block,self).__init__() + self.W_g = nn.Sequential( + nn.Conv2d(F_g, F_int, kernel_size=1,stride=1,padding=0,bias=True), + nn.BatchNorm2d(F_int) + ) + + self.W_x = nn.Sequential( + nn.Conv2d(F_l, F_int, kernel_size=1,stride=1,padding=0,bias=True), + nn.BatchNorm2d(F_int) + ) + + self.psi = nn.Sequential( + nn.Conv2d(F_int, 1, kernel_size=1,stride=1,padding=0,bias=True), + nn.BatchNorm2d(1), + nn.Sigmoid() + ) + + self.relu = nn.ReLU(inplace=True) + + def forward(self,g,x): + g1 = self.W_g(g) + x1 = self.W_x(x) + psi = self.relu(g1+x1) + psi = self.psi(psi) + + return x*psi + + +class U_Net(nn.Module): + def __init__(self,img_ch=3,output_ch=1): + super(U_Net,self).__init__() + + self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2) + + self.Conv1 = conv_block(ch_in=img_ch,ch_out=64) + self.Conv2 = conv_block(ch_in=64,ch_out=128) + self.Conv3 = conv_block(ch_in=128,ch_out=256) + self.Conv4 = conv_block(ch_in=256,ch_out=512) + self.Conv5 = conv_block(ch_in=512,ch_out=1024) + + self.Up5 = up_conv(ch_in=1024,ch_out=512) + self.Up_conv5 = conv_block(ch_in=1024, ch_out=512) + + self.Up4 = up_conv(ch_in=512,ch_out=256) + self.Up_conv4 = conv_block(ch_in=512, ch_out=256) + + self.Up3 = up_conv(ch_in=256,ch_out=128) + self.Up_conv3 = conv_block(ch_in=256, ch_out=128) + + self.Up2 = up_conv(ch_in=128,ch_out=64) + self.Up_conv2 = conv_block(ch_in=128, ch_out=64) + + self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0) + + + def forward(self,x): + # encoding path + x1 = self.Conv1(x) + + x2 = self.Maxpool(x1) + x2 = self.Conv2(x2) + + x3 = self.Maxpool(x2) + x3 = self.Conv3(x3) + + x4 = self.Maxpool(x3) + x4 = self.Conv4(x4) + + x5 = self.Maxpool(x4) + x5 = self.Conv5(x5) + + # decoding + concat path + d5 = self.Up5(x5) + d5 = torch.cat((x4,d5),dim=1) + + d5 = self.Up_conv5(d5) + + d4 = self.Up4(d5) + d4 = torch.cat((x3,d4),dim=1) + d4 = self.Up_conv4(d4) + + d3 = self.Up3(d4) + d3 = torch.cat((x2,d3),dim=1) + d3 = self.Up_conv3(d3) + + d2 = self.Up2(d3) + d2 = torch.cat((x1,d2),dim=1) + d2 = self.Up_conv2(d2) + + d1 = self.Conv_1x1(d2) + + return d1 + + +class R2U_Net(nn.Module): + def __init__(self,img_ch=3,output_ch=1,t=2): + super(R2U_Net,self).__init__() + + self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2) + self.Upsample = nn.Upsample(scale_factor=2) + + self.RRCNN1 = RRCNN_block(ch_in=img_ch,ch_out=64,t=t) + + self.RRCNN2 = RRCNN_block(ch_in=64,ch_out=128,t=t) + + self.RRCNN3 = RRCNN_block(ch_in=128,ch_out=256,t=t) + + self.RRCNN4 = RRCNN_block(ch_in=256,ch_out=512,t=t) + + self.RRCNN5 = RRCNN_block(ch_in=512,ch_out=1024,t=t) + + + self.Up5 = up_conv(ch_in=1024,ch_out=512) + self.Up_RRCNN5 = RRCNN_block(ch_in=1024, ch_out=512,t=t) + + self.Up4 = up_conv(ch_in=512,ch_out=256) + self.Up_RRCNN4 = RRCNN_block(ch_in=512, ch_out=256,t=t) + + self.Up3 = up_conv(ch_in=256,ch_out=128) + self.Up_RRCNN3 = RRCNN_block(ch_in=256, ch_out=128,t=t) + + self.Up2 = up_conv(ch_in=128,ch_out=64) + self.Up_RRCNN2 = RRCNN_block(ch_in=128, ch_out=64,t=t) + + self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0) + + + def forward(self,x): + # encoding path + x1 = self.RRCNN1(x) + + x2 = self.Maxpool(x1) + x2 = self.RRCNN2(x2) + + x3 = self.Maxpool(x2) + x3 = self.RRCNN3(x3) + + x4 = self.Maxpool(x3) + x4 = self.RRCNN4(x4) + + x5 = self.Maxpool(x4) + x5 = self.RRCNN5(x5) + + # decoding + concat path + d5 = self.Up5(x5) + d5 = torch.cat((x4,d5),dim=1) + d5 = self.Up_RRCNN5(d5) + + d4 = self.Up4(d5) + d4 = torch.cat((x3,d4),dim=1) + d4 = self.Up_RRCNN4(d4) + + d3 = self.Up3(d4) + d3 = torch.cat((x2,d3),dim=1) + d3 = self.Up_RRCNN3(d3) + + d2 = self.Up2(d3) + d2 = torch.cat((x1,d2),dim=1) + d2 = self.Up_RRCNN2(d2) + + d1 = self.Conv_1x1(d2) + + return d1 + + + +class AttU_Net(nn.Module): + def __init__(self,img_ch=3,output_ch=1): + super(AttU_Net,self).__init__() + + self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2) + + self.Conv1 = conv_block(ch_in=img_ch,ch_out=64) + self.Conv2 = conv_block(ch_in=64,ch_out=128) + self.Conv3 = conv_block(ch_in=128,ch_out=256) + self.Conv4 = conv_block(ch_in=256,ch_out=512) + self.Conv5 = conv_block(ch_in=512,ch_out=1024) + + self.Up5 = up_conv(ch_in=1024,ch_out=512) + self.Att5 = Attention_block(F_g=512,F_l=512,F_int=256) + self.Up_conv5 = conv_block(ch_in=1024, ch_out=512) + + self.Up4 = up_conv(ch_in=512,ch_out=256) + self.Att4 = Attention_block(F_g=256,F_l=256,F_int=128) + self.Up_conv4 = conv_block(ch_in=512, ch_out=256) + + self.Up3 = up_conv(ch_in=256,ch_out=128) + self.Att3 = Attention_block(F_g=128,F_l=128,F_int=64) + self.Up_conv3 = conv_block(ch_in=256, ch_out=128) + + self.Up2 = up_conv(ch_in=128,ch_out=64) + self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32) + self.Up_conv2 = conv_block(ch_in=128, ch_out=64) + + self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0) + + + def forward(self,x): + # encoding path + x1 = self.Conv1(x) + + x2 = self.Maxpool(x1) + x2 = self.Conv2(x2) + + x3 = self.Maxpool(x2) + x3 = self.Conv3(x3) + + x4 = self.Maxpool(x3) + x4 = self.Conv4(x4) + + x5 = self.Maxpool(x4) + x5 = self.Conv5(x5) + + # decoding + concat path + d5 = self.Up5(x5) + x4 = self.Att5(g=d5,x=x4) + d5 = torch.cat((x4,d5),dim=1) + d5 = self.Up_conv5(d5) + + d4 = self.Up4(d5) + x3 = self.Att4(g=d4,x=x3) + d4 = torch.cat((x3,d4),dim=1) + d4 = self.Up_conv4(d4) + + d3 = self.Up3(d4) + x2 = self.Att3(g=d3,x=x2) + d3 = torch.cat((x2,d3),dim=1) + d3 = self.Up_conv3(d3) + + d2 = self.Up2(d3) + x1 = self.Att2(g=d2,x=x1) + d2 = torch.cat((x1,d2),dim=1) + d2 = self.Up_conv2(d2) + + d1 = self.Conv_1x1(d2) + + return d1 + + +class R2AttU_Net(nn.Module): + def __init__(self,img_ch=3,output_ch=1,t=2): + super(R2AttU_Net,self).__init__() + + self.Maxpool = nn.MaxPool2d(kernel_size=2,stride=2) + self.Upsample = nn.Upsample(scale_factor=2) + + self.RRCNN1 = RRCNN_block(ch_in=img_ch,ch_out=64,t=t) + + self.RRCNN2 = RRCNN_block(ch_in=64,ch_out=128,t=t) + + self.RRCNN3 = RRCNN_block(ch_in=128,ch_out=256,t=t) + + self.RRCNN4 = RRCNN_block(ch_in=256,ch_out=512,t=t) + + self.RRCNN5 = RRCNN_block(ch_in=512,ch_out=1024,t=t) + + + self.Up5 = up_conv(ch_in=1024,ch_out=512) + self.Att5 = Attention_block(F_g=512,F_l=512,F_int=256) + self.Up_RRCNN5 = RRCNN_block(ch_in=1024, ch_out=512,t=t) + + self.Up4 = up_conv(ch_in=512,ch_out=256) + self.Att4 = Attention_block(F_g=256,F_l=256,F_int=128) + self.Up_RRCNN4 = RRCNN_block(ch_in=512, ch_out=256,t=t) + + self.Up3 = up_conv(ch_in=256,ch_out=128) + self.Att3 = Attention_block(F_g=128,F_l=128,F_int=64) + self.Up_RRCNN3 = RRCNN_block(ch_in=256, ch_out=128,t=t) + + self.Up2 = up_conv(ch_in=128,ch_out=64) + self.Att2 = Attention_block(F_g=64,F_l=64,F_int=32) + self.Up_RRCNN2 = RRCNN_block(ch_in=128, ch_out=64,t=t) + + self.Conv_1x1 = nn.Conv2d(64,output_ch,kernel_size=1,stride=1,padding=0) + + + def forward(self,x): + # encoding path + x1 = self.RRCNN1(x) + + x2 = self.Maxpool(x1) + x2 = self.RRCNN2(x2) + + x3 = self.Maxpool(x2) + x3 = self.RRCNN3(x3) + + x4 = self.Maxpool(x3) + x4 = self.RRCNN4(x4) + + x5 = self.Maxpool(x4) + x5 = self.RRCNN5(x5) + + # decoding + concat path + d5 = self.Up5(x5) + x4 = self.Att5(g=d5,x=x4) + d5 = torch.cat((x4,d5),dim=1) + d5 = self.Up_RRCNN5(d5) + + d4 = self.Up4(d5) + x3 = self.Att4(g=d4,x=x3) + d4 = torch.cat((x3,d4),dim=1) + d4 = self.Up_RRCNN4(d4) + + d3 = self.Up3(d4) + x2 = self.Att3(g=d3,x=x2) + d3 = torch.cat((x2,d3),dim=1) + d3 = self.Up_RRCNN3(d3) + + d2 = self.Up2(d3) + x1 = self.Att2(g=d2,x=x1) + d2 = torch.cat((x1,d2),dim=1) + d2 = self.Up_RRCNN2(d2) + + d1 = self.Conv_1x1(d2) + + return d1 \ No newline at end of file diff --git a/gefest/surrogate_models/read_history.py b/gefest/surrogate_models/read_history.py new file mode 100644 index 000000000..f22d3b006 --- /dev/null +++ b/gefest/surrogate_models/read_history.py @@ -0,0 +1,21 @@ +import pandas as pd +import numpy as np +from matplotlib import pyplot as plt + +step = 100 +df = pd.read_csv('result_train_mask_ssim.csv') +df_test = pd.read_csv('result_test_mask_ssim.csv') +bins = [df['train_loss'][i:i+step].mean() for i in range(0,len((df['train_loss'])),step)] +#mean = df['train_loss'].groupby(pd.cut(df['train_loss'],bins)) +plt.plot(df_test['test_loss'],label='base_test') +plt.plot(bins,label='base_train') +#plt.plot([0.013666182630779233,0.00986096077757238,0.006160223454961358,0.00486703837721719,0.005280730882590731,0.0028021071640542045]) + +df = pd.read_csv('result_train_bi.csv') +df_test = pd.read_csv('result_test_bi.csv') +bins = [df['train_loss'][i:i+step].mean() for i in range(0,len((df['train_loss'])),step)] +plt.plot(df_test['test_loss'],label='ssim_test') +plt.plot(bins,label='ssim_test') +plt.grid() +plt.legend() +plt.show() \ No newline at end of file diff --git a/gefest/surrogate_models/train.py b/gefest/surrogate_models/train.py new file mode 100644 index 000000000..5f4cab9a7 --- /dev/null +++ b/gefest/surrogate_models/train.py @@ -0,0 +1,138 @@ +import json +import os + +import numpy as np +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +from tqdm import tqdm +from utils.dataloader import create_dataloaders +#from utils import log_images, dsc +from models import UNet,UNet_bi +import pandas as pd +from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM + +def main(args): + makedirs(args) + #snapshotargs(args) + device = torch.device("cpu" if not torch.cuda.is_available() else args.device) + + loader_train, loader_valid = create_dataloaders(args.path_to_data, + batch_size = args.batch_size, + validation_split = 0.2, + shuffle_dataset = True, + random_seed = 42, + end=1) + loaders = {"train": loader_train, "valid": loader_valid} + + unet = UNet_bi(in_channels=1, out_channels=1) + unet.to(device) + ssim_loss = SSIM(data_range=1, size_average=True, channel=1) + dsc_loss = torch.nn.L1Loss()#torch.nn.MSELoss() + best_validation_dsc = 0.0 + + optimizer = optim.AdamW(unet.parameters(), lr=args.lr) + + #logger = Logger(args.logs) + loss_train = [] + loss_valid = [] + mean_loss_train = [] + mean_loss_test = [] + + step = 0 + + for epoch in tqdm(range(args.epochs), total=args.epochs): + for phase in ["train", "valid"]: + if phase == "train": + unet.train() + else: + unet.eval() + + validation_pred = [] + validation_true = [] + + for i, data in enumerate(loaders[phase]): + if phase == "train": + step += 1 + + x, y_true = data + x, y_true = x.to(device), y_true.to(device) + + optimizer.zero_grad() + + with torch.set_grad_enabled(phase == "train"): + y_pred = unet(x,x) + + loss = (1-ssim_loss( y_pred, y_true.unsqueeze(1).float())) + dsc_loss(y_pred.squeeze(), y_true) + #ssim( y_pred, y_true.unsqueeze(1).float(), data_range=255, size_average=False) + if phase == "valid": + loss_valid.append(loss.item()) + + # y_pred_np = y_pred.detach().cpu().numpy() + # validation_pred.extend( + # [y_pred_np[s] for s in range(y_pred_np.shape[0])] + # ) + # y_true_np = y_true.detach().cpu().numpy() + # validation_true.extend( + # [y_true_np[s] for s in range(y_true_np.shape[0])] + # ) + # if (epoch % args.vis_freq == 0) or (epoch == args.epochs - 1): + # if i * args.batch_size < args.vis_images: + # tag = "image/{}".format(i) + # num_images = args.vis_images - i * args.batch_size + # logger.image_list_summary( + # tag, + # log_images(x, y_true, y_pred)[:num_images], + # step, + # ) + + if phase == "train": + loss_train.append(loss.item()) + loss.backward() + optimizer.step() + + if phase == "train": + mean_loss_train.append(np.mean(loss_train)) + #mean_loss_test.append('') + print('train_loss',mean_loss_train[-1]) + loss_train = [] + + if phase == "valid": + mean_loss_test.append(np.mean(loss_valid)) + print('test_loss',mean_loss_test[-1]) + torch.save(unet.state_dict(), os.path.join(args.weights, f"unet_{epoch}_mask_ssim.pt")) + loss_valid = [] + result_train = pd.DataFrame(data = {'train_loss_ssim':mean_loss_train}) + result_test = pd.DataFrame(data = {'test_loss_ssim':mean_loss_test}) + result_test.to_csv('result_test_mask_ssim.csv') + result_train.to_csv('result_train_mask_ssim.csv') + #print("Best validation mean DSC: {:4f}".format(best_validation_dsc)) + +def makedirs(args): + os.makedirs(args.weights, exist_ok=True) + os.makedirs(args.logs, exist_ok=True) + +# def snapshotargs(args): +# args_file = os.path.join(args.logs, "args.json") +# with open(args_file, "w") as fp: +# json.dump(vars(args), fp) + +# def log_loss_summary(logger, loss, step, prefix=""): +# logger.scalar_summary(prefix + "loss", np.mean(loss), step) + +# def dsc_per_volume(validation_pred, validation_true, patient_slice_index): +# dsc_list = [] +# num_slices = np.bincount([p[0] for p in patient_slice_index]) +# index = 0 +# for p in range(len(num_slices)): +# y_pred = np.array(validation_pred[index : index + num_slices[p]]) +# y_true = np.array(validation_true[index : index + num_slices[p]]) +# dsc_list.append(dsc(y_pred, y_true)) +# index += num_slices[p] +# return dsc_list + + +if __name__ == "__main__": + from config import crete_parser + args = crete_parser(batch_size=16,epochs=25,lr=0.05) + main(args=args) diff --git a/gefest/surrogate_models/train_fno.py b/gefest/surrogate_models/train_fno.py new file mode 100644 index 000000000..c5620f7e7 --- /dev/null +++ b/gefest/surrogate_models/train_fno.py @@ -0,0 +1,139 @@ +import json +import os + +import numpy as np +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +from tqdm import tqdm +from utils.dataloader import create_dataloaders +#from utils import log_images, dsc +from models import UNet +import pandas as pd +from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM +from neuralop.models import TFNO + +def main(args): + makedirs(args) + #snapshotargs(args) + device = torch.device("cpu" if not torch.cuda.is_available() else args.device) + + loader_train, loader_valid = create_dataloaders(args.path_to_data, + batch_size = args.batch_size, + validation_split = 0.2, + shuffle_dataset = True, + random_seed = 42, + end=1) + loaders = {"train": loader_train, "valid": loader_valid} + + model = TFNO(n_modes=(16, 16), hidden_channels=32, projection_channels=64, factorization='tucker', rank=0.42) + model.to(device) + ssim_loss = SSIM(data_range=1, size_average=True, channel=1) + dsc_loss = torch.nn.L1Loss()#torch.nn.MSELoss() + best_validation_dsc = 0.0 + + optimizer = optim.AdamW(model.parameters(), lr=args.lr) + + #logger = Logger(args.logs) + loss_train = [] + loss_valid = [] + mean_loss_train = [] + mean_loss_test = [] + + step = 0 + + for epoch in tqdm(range(args.epochs), total=args.epochs): + for phase in ["train", "valid"]: + if phase == "train": + model.train() + else: + model.eval() + + validation_pred = [] + validation_true = [] + + for i, data in enumerate(loaders[phase]): + if phase == "train": + step += 1 + + x, y_true = data + x, y_true = x.to(device), y_true.to(device) + + optimizer.zero_grad() + + with torch.set_grad_enabled(phase == "train"): + y_pred = model(x,x) + + loss = (1-ssim_loss( y_pred, y_true.unsqueeze(1).float())) + dsc_loss(y_pred.squeeze(), y_true) + #ssim( y_pred, y_true.unsqueeze(1).float(), data_range=255, size_average=False) + if phase == "valid": + loss_valid.append(loss.item()) + + # y_pred_np = y_pred.detach().cpu().numpy() + # validation_pred.extend( + # [y_pred_np[s] for s in range(y_pred_np.shape[0])] + # ) + # y_true_np = y_true.detach().cpu().numpy() + # validation_true.extend( + # [y_true_np[s] for s in range(y_true_np.shape[0])] + # ) + # if (epoch % args.vis_freq == 0) or (epoch == args.epochs - 1): + # if i * args.batch_size < args.vis_images: + # tag = "image/{}".format(i) + # num_images = args.vis_images - i * args.batch_size + # logger.image_list_summary( + # tag, + # log_images(x, y_true, y_pred)[:num_images], + # step, + # ) + + if phase == "train": + loss_train.append(loss.item()) + loss.backward() + optimizer.step() + + if phase == "train": + mean_loss_train.append(np.mean(loss_train)) + #mean_loss_test.append('') + print('train_loss',mean_loss_train[-1]) + loss_train = [] + + if phase == "valid": + mean_loss_test.append(np.mean(loss_valid)) + print('test_loss',mean_loss_test[-1]) + torch.save(model.state_dict(), os.path.join(args.weights, f"fno_{epoch}_mask_ssim.pt")) + loss_valid = [] + result_train = pd.DataFrame(data = {'train_loss_ssim':mean_loss_train}) + result_test = pd.DataFrame(data = {'test_loss_ssim':mean_loss_test}) + result_test.to_csv('result_test_mask_ssim.csv') + result_train.to_csv('result_train_mask_ssim.csv') + #print("Best validation mean DSC: {:4f}".format(best_validation_dsc)) + +def makedirs(args): + os.makedirs(args.weights, exist_ok=True) + os.makedirs(args.logs, exist_ok=True) + +# def snapshotargs(args): +# args_file = os.path.join(args.logs, "args.json") +# with open(args_file, "w") as fp: +# json.dump(vars(args), fp) + +# def log_loss_summary(logger, loss, step, prefix=""): +# logger.scalar_summary(prefix + "loss", np.mean(loss), step) + +# def dsc_per_volume(validation_pred, validation_true, patient_slice_index): +# dsc_list = [] +# num_slices = np.bincount([p[0] for p in patient_slice_index]) +# index = 0 +# for p in range(len(num_slices)): +# y_pred = np.array(validation_pred[index : index + num_slices[p]]) +# y_true = np.array(validation_true[index : index + num_slices[p]]) +# dsc_list.append(dsc(y_pred, y_true)) +# index += num_slices[p] +# return dsc_list + + +if __name__ == "__main__": + from config import crete_parser + args = crete_parser(batch_size=16,epochs=25,lr=0.05) + main(args=args) diff --git a/gefest/surrogate_models/train_sched.py b/gefest/surrogate_models/train_sched.py new file mode 100644 index 000000000..b456ca9a8 --- /dev/null +++ b/gefest/surrogate_models/train_sched.py @@ -0,0 +1,156 @@ +import json +import os +import sys +sys.path.append(os.getcwd()) +import numpy as np +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +from tqdm import tqdm +from utils.dataloader import create_dataloaders +#from utils import log_images, dsc +from models import UNet,UNet_bi +import pandas as pd +from timm.scheduler.cosine_lr import CosineLRScheduler +from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM +from torch.utils.tensorboard import SummaryWriter + +def main(args): + makedirs(args) + + writer = SummaryWriter('writer/Loss') + #snapshotargs(args) + device = torch.device("cpu" if not torch.cuda.is_available() else args.device) + + loader_train, loader_valid = create_dataloaders(args.path_to_data, + batch_size = args.batch_size, + validation_split = 0.2, + shuffle_dataset = True, + random_seed = 42, + end=None) + loaders = {"train": loader_train, "valid": loader_valid} + + unet = UNet_bi(in_channels=1, out_channels=1) + unet.to(device) + + ssim_loss = SSIM(data_range=2, size_average=True, channel=1) + mae_loss = torch.nn.L1Loss()#torch.nn.MSELoss() + best_validation_dsc = 0.0 + + optimizer = optim.AdamW(unet.parameters(), lr=args.lr) + sched =CosineLRScheduler(optimizer, t_initial=args.epochs-35, lr_min=0.0001, + cycle_mul=1.0, cycle_decay=1.0, cycle_limit=1, + warmup_t=3, warmup_lr_init=0.00005, warmup_prefix=False, t_in_epochs=True, + noise_range_t=None, noise_pct=0.67, noise_std=1.0, + noise_seed=42, k_decay=1.0, initialize=True) + #logger = Logger(args.logs) + loss_train = [] + loss_valid = [] + mean_loss_train = [] + mean_loss_test = [] + + step = 0 + ep = 0 + for epoch in tqdm(range(args.epochs), total=args.epochs): + writer.add_scalar('Lr',optimizer.param_groups[0]['lr'],ep) + sched.step(ep) + ep+=1 + #print(optimizer.param_groups[0]['lr']) + # for param_group in optimizer.param_groups: + # current_lr = param_group['lr'] + + for phase in ["train", "valid"]: + if phase == "train": + unet.train() + else: + unet.eval() + + validation_pred = [] + validation_true = [] + + for i, data in enumerate(loaders[phase]): + if phase == "train": + step += 1 + + x, y_true = data + x, y_true = x.to(device), y_true.to(device) + + optimizer.zero_grad() + + with torch.set_grad_enabled(phase == "train"): + y_pred = unet(x,x) + + loss = 0.5*(1-ssim_loss( y_pred, y_true.unsqueeze(1).float())) + mae_loss(y_pred.squeeze(), y_true)#loss = dsc_loss(y_pred.squeeze(), y_true) + + if phase == "valid": + loss_valid.append(loss.item()) + writer.add_scalar('Loss/test',loss_valid[-1],step) + # y_pred_np = y_pred.detach().cpu().numpy() + # validation_pred.extend( + # [y_pred_np[s] for s in range(y_pred_np.shape[0])] + # ) + # y_true_np = y_true.detach().cpu().numpy() + # validation_true.extend( + # [y_true_np[s] for s in range(y_true_np.shape[0])] + # ) + # if (epoch % args.vis_freq == 0) or (epoch == args.epochs - 1): + # if i * args.batch_size < args.vis_images: + # tag = "image/{}".format(i) + # num_images = args.vis_images - i * args.batch_size + # logger.image_list_summary( + # tag, + # log_images(x, y_true, y_pred)[:num_images], + # step, + # ) + + if phase == "train": + loss_train.append(loss.item()) + writer.add_scalar('Loss/train',loss_train[-1],step) + loss.backward() + optimizer.step() + + if phase == "train": + mean_loss_train.append(np.mean(loss_train)) + #mean_loss_test.append('') + print('train_loss',mean_loss_train[-1]) + loss_train = [] + + if phase == "valid": + mean_loss_test.append(np.mean(loss_valid)) + print('test_loss',mean_loss_test[-1]) + torch.save(unet.state_dict(), os.path.join(args.weights, f"unet_{epoch}_bi.pt")) + loss_valid = [] + result_train = pd.DataFrame(data = {'train_loss':mean_loss_train}) + result_test = pd.DataFrame(data = {'test_loss':mean_loss_test}) + result_test.to_csv('result_test_bi.csv') + result_train.to_csv('result_train_bi.csv') + #print("Best validation mean DSC: {:4f}".format(best_validation_dsc)) + +def makedirs(args): + os.makedirs(args.weights, exist_ok=True) + os.makedirs(args.logs, exist_ok=True) + +# def snapshotargs(args): +# args_file = os.path.join(args.logs, "args.json") +# with open(args_file, "w") as fp: +# json.dump(vars(args), fp) + +# def log_loss_summary(logger, loss, step, prefix=""): +# logger.scalar_summary(prefix + "loss", np.mean(loss), step) + +# def dsc_per_volume(validation_pred, validation_true, patient_slice_index): +# dsc_list = [] +# num_slices = np.bincount([p[0] for p in patient_slice_index]) +# index = 0 +# for p in range(len(num_slices)): +# y_pred = np.array(validation_pred[index : index + num_slices[p]]) +# y_true = np.array(validation_true[index : index + num_slices[p]]) +# dsc_list.append(dsc(y_pred, y_true)) +# index += num_slices[p] +# return dsc_list + + +if __name__ == "__main__": + from config import crete_parser + args = crete_parser(batch_size=16,epochs=70,lr=0.1) + main(args=args) diff --git a/gefest/surrogate_models/utils/animation.py b/gefest/surrogate_models/utils/animation.py new file mode 100644 index 000000000..9af30c814 --- /dev/null +++ b/gefest/surrogate_models/utils/animation.py @@ -0,0 +1,94 @@ +import numpy as np +import os +import sys +from matplotlib import pyplot as plt +import time +sys.path.append(os.getcwd()) +print(os.getcwd()) +sys.path.append(str(os.getcwd())+'/GEFEST/') +#path_to_dir = 'data_from_comsol//generated_data' + +def animation_data_npz(path_to_dir): + l_dir = os.listdir(path_to_dir) + cnt = 0 + plt.ion() + fig, axs = plt.subplots(1,2, figsize=(15, 10)) + summ_mae = 0 + for file in l_dir: + mask = np.load(path_to_dir+f'/{file}',allow_pickle = True)['arr_0'].item(0)['mask']#mask + flow = np.load(path_to_dir+f'/{file}',allow_pickle = True)['arr_0'].item(0)['flow'] + for m,f in zip(mask,flow): + # cnt+=1 + # mse = np.square(f-np.where(m>-0.1,m,-1)).mean() + # mae = np.sum(np.absolute(f-np.where(m>-0.1,m,-1)))/400/400 + # summ_mae+=np.sum(np.absolute(np.where(f>0,f,0)-np.where(m>-0.1,m,0))) + m,f = np.where(m>-0.1,m,np.inf),np.where(f>0,f,np.inf) + a1 = axs[0].imshow(m) + + # axs[1].set_title(f'summMAE: {summ_mae}') + # axs[2].set_title(f'MAE: {mae},MSE: {mse}') + a2 =axs[1].imshow(f) + + #a3 = axs[2].imshow(np.absolute(f-m),vmin = 0, vmax = 0.007) + + # fig.colorbar(a1,ax=axs[0]) + # fig.colorbar(a2,ax=axs[1]) + # fig.colorbar(a3,ax=axs[2]) + + fig.canvas.draw() + fig.canvas.flush_events() + axs[0].clear() + axs[1].clear() + #axs[2].clear() + + + #time.sleep(1.2) + a1.remove(),a2.remove() + print(summ_mae) + print(cnt) + +def animation_npz(path_to_dir): + l_dir = os.listdir(path_to_dir) + cnt = 0 + plt.ion() + fig, axs = plt.subplots(1,3, figsize=(15, 10)) + summ_mae = 0 + for file in l_dir: + mask = np.load(path_to_dir+f'/{file}',allow_pickle = True)['arr_0'].item(0)['flow_predict']#mask + flow = np.load(path_to_dir+f'/{file}',allow_pickle = True)['arr_0'].item(0)['flow'] + for m,f in zip(mask,flow): + cnt+=1 + mse = np.square(f-np.where(m>-0.01,m,-1)).mean() + mae = np.sum(np.absolute(f-np.where(m>-0.01,m,-1)))/400/400 + summ_mae+=np.sum(np.absolute(np.where(f>0,f,0)-np.where(m>-0.1,m,0))) + m,f = np.where(m>-0.1,m,np.inf),np.where(f>0,f,np.inf) + a1 = axs[0].imshow(m, vmin = 0, vmax = 0.05) + + axs[1].set_title(f'summMAE: {summ_mae}') + axs[2].set_title(f'MAE: {mae},MSE: {mse}') + a2 =axs[1].imshow(f, vmin = 0, vmax = 0.05) + + a3 = axs[2].imshow(np.absolute(f-m),vmin = 0, vmax = 0.007) + + # fig.colorbar(a1,ax=axs[0]) + # fig.colorbar(a2,ax=axs[1]) + # fig.colorbar(a3,ax=axs[2]) + + fig.canvas.draw() + fig.canvas.flush_events() + axs[0].clear() + axs[1].clear() + axs[2].clear() + + + time.sleep(2.2) + a1.remove(),a2.remove(),a3.remove() + print(summ_mae) + print(cnt) + + +#animation_npz(path_to_dir='gefest\surrogate_models\gendata/ssim') +#animation_npz(path_to_dir='gefest\surrogate_models\gendata/ssim_23') +#animation_npz(path_to_dir='gefest\surrogate_models\gendata/ssim_plus_57') +#animation_data_npz(path_to_dir='data_from_comsol/gen_data_extend') +animation_npz(path_to_dir='gefest\surrogate_models\gendata/ssim_plus_58') \ No newline at end of file diff --git a/gefest/surrogate_models/utils/dataloader.py b/gefest/surrogate_models/utils/dataloader.py new file mode 100644 index 000000000..5a3b53b35 --- /dev/null +++ b/gefest/surrogate_models/utils/dataloader.py @@ -0,0 +1,78 @@ +import numpy as np +import os +import sys +from matplotlib import pyplot as plt +import torch +from torch.utils.data import Dataset, DataLoader +from torch.utils.data.sampler import SubsetRandomSampler + +#path_to_dir = 'data_from_comsol//generated_data' + +class SurrDataset(Dataset): + def __init__(self, path_to_dir,end=None): + self.path_to_dir = path_to_dir + self.end = end + self.flow = [] + self.mask = [] + self.__load_npz__() + + + def __len__(self): + return len(self.flow) + + def __getitem__(self, idx): + mask = self.mask[idx] + flow = self.flow[idx] + return mask,flow + + def __load_npz__(self): + l_dir = os.listdir(self.path_to_dir) + if self.end is not None: + l_dir = l_dir[:self.end] + for file in l_dir: + mask = np.load(self.path_to_dir+f'/{file}',allow_pickle = True)['arr_0'].item(0)['mask'] + flow = np.load(self.path_to_dir+f'/{file}',allow_pickle = True)['arr_0'].item(0)['flow'] + for m,f in zip(mask,flow): + self.flow.append(f) + self.mask.append(m) + +def create_single_dataloader(path_to_dir, + batch_size:int = 16, + shuffle=False): + dataset = SurrDataset(path_to_dir=path_to_dir) + train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,shuffle=shuffle) + return train_loader + +def create_dataloaders(path_to_dir, + batch_size:int = 16, + validation_split:float = 0.2, + shuffle_dataset:bool = True, + random_seed:int = 42, + end=None): + """ + This function is return two dataloaders (train and test). + it work for dataset, that consist of train and test data.(in my case i create only one folder with generated data + and i whant to split Dataset but not folder with data) + """ + + dataset = SurrDataset(path_to_dir=path_to_dir,end=end) + dataset_size = len(dataset) + indices = list(range(dataset_size)) + split = int(np.floor(validation_split * dataset_size)) + if shuffle_dataset : + np.random.seed(random_seed) + np.random.shuffle(indices) + train_indices, val_indices = indices[split:], indices[:split] + + # Creating PT data samplers and loaders: + train_sampler = SubsetRandomSampler(train_indices) + valid_sampler = SubsetRandomSampler(val_indices) + + train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, + sampler=train_sampler) + test_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, + sampler=valid_sampler) + return train_loader,test_loader + + +#train_loader,test_loader = create_dataloaders(path_to_dir,batch_size=32) diff --git a/gefest/tools/estimators/simulators/comsol/comsol_interface.py b/gefest/tools/estimators/simulators/comsol/comsol_interface.py index 02a8c982b..757e6eac9 100644 --- a/gefest/tools/estimators/simulators/comsol/comsol_interface.py +++ b/gefest/tools/estimators/simulators/comsol/comsol_interface.py @@ -8,7 +8,7 @@ import pickledb from gefest.core.geometry import Structure -from gefest.tools import Estimator +from gefest.tools.estimators.estimator import Estimator os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE' USE_AVG_CONST = False @@ -187,3 +187,138 @@ def _load_fitness(self, configuration): fitness = float(result) return float(fitness), model_uid + + +class Comsol_generate(Estimator): + """Comsol wrapper class for microfluidic problem estimation.""" + + def __init__(self, path_to_mph: str) -> None: + + super().__init__() + + self.client = mph.Client() + self.path_to_mph = path_to_mph + + def estimate(self, structure: Structure) -> int: + """Estimates given structure using comsol multiphysics. + + Args: + structure (Structure): GEFEST structure. + + Returns: + int: Performance. + """ + gc.collect() + target, idx = self._load_fitness(structure) + if target is None: + model, idx = self._load_simulation_result(self.client, structure) + if model is None: + poly_box = [] + print('Start COMSOL') + for _, pol in enumerate(structure.polygons): + poly_repr = [] + poly_repr.append(' '.join([str(pt.x) for pt in pol.points])) + poly_repr.append(' '.join([str(pt.y) for pt in pol.points])) + poly_box.append(poly_repr) + + model = self.client.load(self.path_to_mph) + + try: + model = self._poly_add(model, poly_box) + model.build() + model.mesh() + model.solve() + except Exception as ex: + print(ex) + self.client.clear() + return 0.0 + + try: + veloc = model.export('velocity') + mask = model.export('mask') + self.client.clear() + return veloc,mask + outs = [ + model.evaluate('vlct_1'), + model.evaluate('vlct_2'), + model.evaluate('vlct_3'), + model.evaluate('vlct_4'), + model.evaluate('vlct_5'), + model.evaluate('vlct_side'), + model.evaluate('vlct_main'), + ] + except Exception as ex: + print(ex) + self.client.clear() + return 0.0 + + def _poly_add(self, model, polygons): + for n, poly in enumerate(polygons): + try: + model.java.component('comp1').geom('geom1').create('pol' + str(n + 1), 'Polygon') + except Exception: + pass + + model.java.component('comp1').geom('geom1').feature('pol' + str(n + 1)).set( + 'x', + poly[0], + ) + model.java.component('comp1').geom('geom1').feature('pol' + str(n + 1)).set( + 'y', + poly[1], + ) + + return model + + def _save_simulation_result(self, configuration, model): + if not os.path.exists('./models'): + os.mkdir('./models') + + model_uid = str(uuid4()) + model.save(f'./models/{model_uid}.mph') + db = pickledb.load('comsol_db.saved', False) + db.set(str(configuration), model_uid) + db.dump() + + if not os.path.exists('./structures'): + os.mkdir('./structures') + + with open(f'./structures/{model_uid}.str', 'wb') as f: + pickle.dump(configuration, f) + + return model_uid + + def _load_simulation_result(self, client, configuration): + db = pickledb.load('comsol_db.saved', False) + + model_uid = db.get(str(configuration)) + + if model_uid is False: + return None, None + + model = client.load(f'./models/{model_uid}.mph') + + return model, model_uid + + def _save_fitness(self, configuration, fitness): + array_structure = [[[p.x, p.y] for p in poly.points] for poly in configuration.polygons] + db = pickledb.load('fitness_db.saved', False) + db.set(str(array_structure), str(round(fitness, 4))) + db.dump() + + def _load_fitness(self, configuration): + array_structure = [[[p.x, p.y] for p in poly.points] for poly in configuration.polygons] + db = pickledb.load('fitness_db.saved', False) + + db_models = pickledb.load('comsol_db.saved', False) + + model_uid = db_models.get(str(configuration)) + + result = db.get(str(array_structure)) + + if result is False: + return None, None + else: + fitness = float(result) + + return float(fitness), model_uid \ No newline at end of file diff --git a/gefest/tools/optimizers/GA/GA_history.py b/gefest/tools/optimizers/GA/GA_history.py new file mode 100644 index 000000000..69364ae6c --- /dev/null +++ b/gefest/tools/optimizers/GA/GA_history.py @@ -0,0 +1,86 @@ +from typing import Callable +from venv import logger + +from tqdm import tqdm + +from gefest.core.geometry import Structure +from gefest.core.opt import strategies +from gefest.core.opt.objective.objective_eval import ObjectivesEvaluator +from gefest.tools.optimizers.optimizer import Optimizer + + +class BaseGA(Optimizer): + """Implemets default genetic optimization algorithm steps. + + Can be used as base class for others GA based optimizers. + Can be configured using modules with different realization of operations, + e.g. crossover, mutation, selection operations or crossover, mutation strategies. + + """ + + def __init__( + self, + opt_params, + initial_population=None, + **kwargs, + ): + super().__init__(opt_params.log_dispatcher) + self.opt_params = opt_params + self.crossover = getattr(strategies, opt_params.crossover_strategy)(opt_params=opt_params) + self.mutation = getattr(strategies, opt_params.mutation_strategy)(opt_params=opt_params) + self.sampler: Callable = opt_params.sampler + self.objectives_evaluator: ObjectivesEvaluator = ObjectivesEvaluator( + opt_params.objectives, + opt_params.estimation_n_jobs, + ) + self.pop_size = opt_params.pop_size + self.n_steps = opt_params.n_steps + self.domain = self.opt_params.domain + if initial_population: + self._pop = initial_population + else: + self._pop: list[Structure] = self.sampler(self.opt_params.pop_size) + + self._pop = self.objectives_evaluator(self._pop) + + self.selector: Callable = opt_params.selector.value + if len(self.opt_params.objectives) > 1: + if self.opt_params.multiobjective_selector.__name__ == 'MOEAD': + self.opt_params.extra = 0 + logger.warning('For moead extra not available.') + + self.selector = self.opt_params.multiobjective_selector( + single_demention_selection=self.selector, + init_pop=self._pop, + moead_n_neighbors=self.opt_params.moead_multi_objective_selector_neighbors, + steps=self.n_steps, + ) + + self.log_dispatcher.log_pop(self._pop, '00000_init') + + def optimize(self) -> list[Structure]: + """Optimizes population. + + Returns: + list[Structure]: Optimized population. + """ + pbar = tqdm(range(self.n_steps)) + for step in pbar: + pbar.set_description(f'Best fitness: {self._pop[0].fitness}') + print('##################') + print([id.id_ for id in self._pop]) + print([id.fitness for id in self._pop]) + self._pop = self.selector(self._pop, self.pop_size) + print('##################') + print([id.id_ for id in self._pop]) + print([id.fitness for id in self._pop]) + + child = self.crossover(self._pop) + mutated_child = self.mutation(child) + self._pop.extend(mutated_child) + self._pop.extend(self.sampler(self.opt_params.extra)) + self._pop = self.objectives_evaluator(self._pop) + self.log_dispatcher.log_pop(self._pop, str(step + 1)) + + pbar.set_description(f'Best fitness: {self._pop[0].fitness}') + return self._pop diff --git a/gefest/tools/optimizers/__init__.py b/gefest/tools/optimizers/__init__.py index b41605c18..4224a2947 100644 --- a/gefest/tools/optimizers/__init__.py +++ b/gefest/tools/optimizers/__init__.py @@ -1,6 +1,7 @@ from enum import Enum from .GA.GA import BaseGA +from .GA.GA_history import BaseGA as BaseGA_h from .golem_optimizer.standard import StandardOptimizer from .golem_optimizer.surrogate import SurrogateOptimizer @@ -9,5 +10,6 @@ class OptimizerTypes(Enum): """Enumeration of all optimizers.""" gefest_ga = BaseGA + gefest_ga_h = BaseGA_h golem_optimizer = StandardOptimizer golem_surrogate = SurrogateOptimizer diff --git a/gefest/tools/run_experiment.py b/gefest/tools/run_experiment.py index b6509b5b6..9504aa313 100644 --- a/gefest/tools/run_experiment.py +++ b/gefest/tools/run_experiment.py @@ -1,6 +1,8 @@ from loguru import logger from tqdm import tqdm - +import sys +import os +sys.path.append(os.getcwd()) from gefest.core.configs.utils import load_config from gefest.core.viz.struct_vizualizer import GIFMaker from gefest.tools.tuners.tuner import GolemTuner @@ -32,3 +34,5 @@ def run_experiment(config_path: str): gm.create_frame(st, {'Tuned': st.fitness}) gm.make_gif('Tuned individuals', 500) + +run_experiment(config_path = 'D:\Projects\GEFEST\GEFEST_surr\GEFEST\cases\sound_waves\configuration\config_parallel.py') diff --git a/result_bi.csv b/result_bi.csv new file mode 100644 index 000000000..be4c98187 --- /dev/null +++ b/result_bi.csv @@ -0,0 +1,437 @@ +,train_loss +0,1.0918841002983952 +1,0.787273882343386 +2,0.8500652062401539 +3,0.3102615010775808 +4,0.6854640140196713 +5,0.6154757691441921 +6,0.5259282425678803 +7,0.49876089952429015 +8,0.5205908627695105 +9,0.5250625196033967 +10,0.49680799336057224 +11,0.4813457187386455 +12,0.4612163092195469 +13,0.47114936319873046 +14,0.4704038714052562 +15,0.45680881708626603 +16,0.4383483420739325 +17,0.44304590249224296 +18,0.45366697857865146 +19,0.4417163933450568 +20,0.4426709937191331 +21,0.4474188338187795 +22,0.43449356852773585 +23,0.4289568187849175 +24,0.4371277921757455 +25,0.43433468111263934 +26,0.4257453266143123 +27,0.4253462903790667 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