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UNet_V18.py
UNet_V18.py 10.59 KiB
"""UNet_V6.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1yvtk3lFo_x0ZiqtFdnR8jgcjPKy3nZA4
"""
import torch
import torch.nn as nn
import numpy as np
import random
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import TensorDataset
import torch.nn.functional as F
from torch.utils.data import random_split
from torch.nn.modules.activation import ReLU
class depthwise_separable_conv(nn.Module):
def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
super(depthwise_separable_conv, self).__init__()
self.depthwise_1 = nn.Conv3d(in_c, in_c, kernel_size= kernel_size, padding=padding[0], groups=in_c, bias=True)
self.pointwise_1 = nn.Conv3d(in_c, out_1_c, kernel_size=1, bias=True)
self.batch_norm_1 = nn.BatchNorm3d(out_1_c)
self.relu = nn.ReLU()
self.droptout = nn.Dropout3d(p=0.5)
self.depthwise_2 = nn.Conv3d(out_1_c, out_1_c, kernel_size= kernel_size, padding=padding[1], groups=out_1_c, bias=True)
self.pointwise_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size=1, bias=True)
self.batch_norm_2 = nn.BatchNorm3d(out_2_c)
def forward(self, x):
x = self.batch_norm_1(self.relu(self.droptout(self.pointwise_1(self.depthwise_1(x)))))
return self.batch_norm_2(self.relu(self.droptout(self.pointwise_2(self.depthwise_2(x)))))
class convolution_Layer(nn.Module):
def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
super(convolution_Layer, self).__init__()
self.conv_1 = nn.Conv3d(in_c, out_1_c, kernel_size= kernel_size, padding=padding[0], bias=True)
self.batch_norm_1 = nn.BatchNorm3d(out_1_c)
self.relu = nn.ReLU()
self.conv_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size= kernel_size, padding=padding[1], bias=True)
self.batch_norm_2 = nn.BatchNorm3d(out_2_c)
def forward(self, x):
x = self.batch_norm_1(self.relu(self.conv_1(x)))
return self.batch_norm_2(self.relu(self.relu(self.conv_2(x))))
class head_layer(nn.Module):
def __init__(self, in_c, out_c = 1, padding = "same"):
super(head_layer, self).__init__()
self.conv = nn.Conv3d(in_c, out_c, kernel_size=1, bias=True)
self.sig = nn.Sigmoid()
def forward(self, x):
return self.sig(self.conv(x)) #convolution
#return self.sig(self.pointwise(self.depthwise(x))) #convolution
class Encoder(nn.Module):
def __init__(self,kernel_size, chs, padding=(("same","same"),("same","same"),("same","same"))):
super().__init__()
self.channels = chs
self.enc_blocks = nn.ModuleList([depthwise_separable_conv(chs[i][0], chs[i][1], chs[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs))])
self.pool = nn.MaxPool3d(kernel_size=2, stride=2)
#self.batch_norm = nn.ModuleList([nn.BatchNorm3d( chs[i][2]) for i in range(len(chs))])
def forward(self, x):
ftrs = []
for i in range(len(self.channels)):
ftrs.append(x)
x =self.enc_blocks[i](x)
#print(f'size of ftrs: {ftrs[i].size()}')
x = self.pool(x)
#print(f'size of x after pooling{x.size()}')
ftrs.append(x)
#print(f'size of ftrs: {ftrs[3].size()}')
#print(f'length of ftrs: {len(ftrs)}')
return ftrs
class Decoder(nn.Module):
def __init__(self,kernel_size, chs_upsampling, chs_conv, padding=(("same","same"),("same","same"),("same","same"))):
super().__init__()
assert len(chs_conv) == len(chs_upsampling)
self.chs = chs_upsampling
self.upconvs = nn.ModuleList([nn.ConvTranspose3d(chs_upsampling[i], chs_upsampling[i], 2, 2) for i in range(len(chs_upsampling))])
self.dec_blocks = nn.ModuleList([depthwise_separable_conv(chs_conv[i][0], chs_conv[i][1], chs_conv[i][2], kernel_size=kernel_size, padding=padding[i]) for i in range(len(chs_conv))])
self.head = head_layer(chs_conv[-1][2])
def forward(self, x, encoder_features):
for i in range(len(self.chs)):
x = self.upconvs[i](x)
#print(f'size after upsampling: {x.size()}')
enc_ftrs = self.crop(encoder_features[i], x)
x = torch.cat([x, enc_ftrs], dim=1)
#print(f'size after cropping&cat: {x.size()}')
x = self.dec_blocks[i](x)
#print(f'size after convolution: {x.size()}')
x = self.head(x)
return x
def crop(self, tensor, target_tensor):
target_size = target_tensor.size()[2]
tensor_size = tensor.size()[2]
delta = tensor_size - target_size
delta = delta // 2
return tensor[:,:,delta:tensor_size-delta,delta:tensor_size-delta,delta:tensor_size-delta]
class UNetBase(nn.Module):
def training_step(self, batch):
input, labels = batch
out = self(input) # Generate predictions
loss = F.l1_loss(out, labels) # Calculate loss
return loss
def validation_step(self, batch):
input, labels = batch
out = self(input) # Generate predictions
loss = F.l1_loss(out, labels) # Calculate loss
acc = accuracy(out.detach(), labels.detach(),normalization=self.normalization) # Calculate accuracy
return {'val_loss': loss.detach(), 'val_acc': acc}
def validation_epoch_end(self, outputs):
batch_losses = [x['val_loss'] for x in outputs]
epoch_loss = torch.stack(batch_losses).mean() # Combine losses
batch_accs = [x['val_acc'] for x in outputs]
epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies
return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}
def epoch_end(self, epoch, result):
print("Epoch [{}], train_loss: {:.6f}, val_loss: {:.6f}, val_acc: {:.6f}".format(
epoch, result['train_loss'], result['val_loss'], result['val_acc']))
def accuracy(outputs, labels,normalization, threshold = 0.05):
error = (abs((outputs) - (labels)))/(outputs+normalization[0]/normalization[1])
right_predic = torch.sum(error < threshold)
percentage = ((right_predic/torch.numel(error))*100.)
return percentage
class UNet(UNetBase):
def __init__(self,kernel_size = 7, enc_chs=((6,32,32), (32,64,64), (64,128,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(70,32,32)),normalization=np.array([0,1])):
super().__init__()
self.encoder = Encoder(kernel_size = kernel_size, chs = enc_chs)
self.decoder = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv)
#self.head = depthwise_separable_conv(1, 1, padding = "same", kernel_size=1)
self.normalization = normalization
def forward(self, x):
enc_ftrs = self.encoder(x)
out = self.decoder(enc_ftrs[::-1][0], enc_ftrs[::-1][1:])
#out = self.head(out)
return out
@torch.no_grad()
def evaluate(model, val_loader):
model.eval()
outputs = [model.validation_step(batch) for batch in val_loader]
return model.validation_epoch_end(outputs)
def fit(epochs, lr, model, train_loader, val_loader, path, opt_func=torch.optim.Adam):
history = []
optimizer = opt_func(model.parameters(), lr, eps=1e-07)
for epoch in range(epochs):
# Training Phase
model.train()
train_losses = []
for batch in train_loader:
loss = model.training_step(batch)
train_losses.append(loss)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Validation phase
result = evaluate(model, val_loader)
result['train_loss'] = torch.stack(train_losses).mean().item()
model.epoch_end(epoch, result)
history.append(result)
torch.save(model.state_dict(),f'{path}/Unet_dict_V18.pth')
torch.save(history,f'{path}/history_V18.pt')
return history
def get_default_device():
"""Pick GPU if available, else CPU"""
if torch.cuda.is_available():
return torch.device('cuda')
else:
print('no GPU found')
return torch.device('cpu')
def to_device(data, device):
"""Move tensor(s) to chosen device"""
if isinstance(data, (list,tuple)):
return [to_device(x, device) for x in data]
return data.to(device, non_blocking=True)
class DeviceDataLoader():
"""Wrap a dataloader to move data to a device"""
def __init__(self, dl, device):
self.dl = dl
self.device = device
def __iter__(self):
"""Yield a batch of data after moving it to device"""
for b in self.dl:
yield to_device(b, self.device)
def __len__(self):
"""Number of batches"""
return len(self.dl)
def Create_Dataloader(path, batch_size = 100, percent_val = 0.2):
dataset = torch.load(path) # create the pytorch dataset
#size_data = 500 #shrink dataset for colab
#rest = len(dataset) -size_data
#dataset,_ = torch.utils.data.random_split(dataset, [size_data, rest])
val_size = int(len(dataset) * percent_val)
train_size = len(dataset) - val_size
train_ds, val_ds = random_split(dataset, [train_size, val_size])
# Create DataLoader
train_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=1, pin_memory=True)
valid_dl = DataLoader(val_ds, batch_size, num_workers=1, pin_memory=True)
return train_dl, valid_dl
if __name__ == '__main__':
#os.chdir('F:/RWTH/HiWi_IEHK/DAMASK3/UNet/Trainingsdata')
path_to_rep = '/home/yk138599/Hiwi/damask3'
use_seeds = True
seed = 2199910834
num_epochs = 200
b_size = 32
opt_func = torch.optim.Adam
lr = 0.00003
kernel = 7
print(f'number auf epochs: {num_epochs}')
print(f'batchsize: {b_size}')
print(f'learning rate: {lr}')
print(f'kernel size is: {kernel}')
if not use_seeds:
seed = random.randrange(2**32 - 1)
print(f' seed is: {seed}')
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
device = get_default_device()
normalization = np.load(f'{path_to_rep}/UNet/Trainingsdata/Norm_min_max_32_angles.npy', allow_pickle = True)
train_dl, valid_dl = Create_Dataloader(f'{path_to_rep}/UNet/Trainingsdata/TD_norm_32_angles.pt', batch_size= b_size )
train_dl = DeviceDataLoader(train_dl, device)
valid_dl = DeviceDataLoader(valid_dl, device)
model = to_device(UNet(kernel_size=kernel,normalization=normalization).double(), device)
history = fit(num_epochs, lr, model, train_dl, valid_dl,f'{path_to_rep}/UNet/output', opt_func)