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Copy pathFNORUNet_4layer_model.py
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FNORUNet_4layer_model.py
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import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import operator
from functools import reduce
from functools import partial
from timeit import default_timer
import scipy.io
import math
import gc
import h5py
class SpectralConv3d(nn.Module):
def __init__(self, in_channels, out_channels, modes1, modes2, modes3):
super(SpectralConv3d, self).__init__()
"""
3D Fourier layer. It does FFT, linear transform, and Inverse FFT.
"""
self.in_channels = in_channels
self.out_channels = out_channels
self.modes1 = modes1 #Number of Fourier modes to multiply, at most floor(N/2) + 1
self.modes2 = modes2
self.modes3 = modes3
self.scale = (1 / (in_channels * out_channels))
self.weights1 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, self.modes3, dtype=torch.cfloat))
self.weights2 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, self.modes3, dtype=torch.cfloat))
self.weights3 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, self.modes3, dtype=torch.cfloat))
self.weights4 = nn.Parameter(self.scale * torch.rand(in_channels, out_channels, self.modes1, self.modes2, self.modes3, dtype=torch.cfloat))
# Complex multiplication
def compl_mul3d(self, input, weights):
# (batch, in_channel, x,y,t ), (in_channel, out_channel, x,y,t) -> (batch, out_channel, x,y,t)
return torch.einsum("bixyz,ioxyz->boxyz", input, weights)
def forward(self, x):
batchsize = x.shape[0]
#Compute Fourier coeffcients up to factor of e^(- something constant)
x_ft = torch.fft.rfftn(x, dim=[-3,-2,-1])
# Multiply relevant Fourier modes
out_ft = torch.zeros(batchsize, self.out_channels, x.size(-3), x.size(-2), x.size(-1)//2 + 1, dtype=torch.cfloat, device=x.device)
out_ft[:, :, :self.modes1, :self.modes2, :self.modes3] = \
self.compl_mul3d(x_ft[:, :, :self.modes1, :self.modes2, :self.modes3], self.weights1)
out_ft[:, :, -self.modes1:, :self.modes2, :self.modes3] = \
self.compl_mul3d(x_ft[:, :, -self.modes1:, :self.modes2, :self.modes3], self.weights2)
out_ft[:, :, :self.modes1, -self.modes2:, :self.modes3] = \
self.compl_mul3d(x_ft[:, :, :self.modes1, -self.modes2:, :self.modes3], self.weights3)
out_ft[:, :, -self.modes1:, -self.modes2:, :self.modes3] = \
self.compl_mul3d(x_ft[:, :, -self.modes1:, -self.modes2:, :self.modes3], self.weights4)
#Return to physical space
x = torch.fft.irfftn(out_ft, s=(x.size(-3), x.size(-2), x.size(-1)))
return x
class U_net(nn.Module):
def __init__(self, input_channels, output_channels, kernel_size, dropout_rate):
super(U_net, self).__init__()
self.input_channels = input_channels
self.conv1 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=2, dropout_rate = dropout_rate)
self.conv2 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=2, dropout_rate = dropout_rate)
self.conv2_1 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
self.conv3 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=2, dropout_rate = dropout_rate)
self.conv3_1 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
self.resconv_1 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
self.resconv_2 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
self.resconv_3 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
self.resconv_4 = self.conv(input_channels, output_channels, kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
self.deconv2 = self.deconv(input_channels, output_channels)
self.deconv1 = self.deconv(input_channels*2, output_channels)
self.deconv0 = self.deconv(input_channels*2, output_channels)
self.output_layer0 = self.output_layer(input_channels*2, output_channels,
kernel_size=kernel_size, stride=1, dropout_rate = dropout_rate)
def forward(self, x):
out_conv1 = self.conv1(x)
out_conv2 = self.conv2_1(self.conv2(out_conv1))
out_conv3 = self.conv3_1(self.conv3(out_conv2))
# resconv 1
identity0 = out_conv3
resconv1 = self.resconv_1(out_conv3)
resconv1 = resconv1 + identity0
resconv1 = F.relu(resconv1)
# resconv 2
identity1 = resconv1
resconv2 = self.resconv_2(resconv1)
resconv2 = resconv2 + identity1
resconv2 = F.relu(resconv2)
# resconv 3
identity2 = resconv2
resconv3 = self.resconv_3(resconv2)
resconv3 = resconv3 + identity2
resconv3 = F.relu(resconv3)
# resconv 4
identity3 = resconv3
resconv4 = self.resconv_4(resconv3)
resconv4 = resconv4 + identity3
resconv4 = F.relu(resconv4)
out_deconv2 = self.deconv2(resconv4)
concat2 = torch.cat((out_conv2, out_deconv2), 1)
out_deconv1 = self.deconv1(concat2)
concat1 = torch.cat((out_conv1, out_deconv1), 1)
out_deconv0 = self.deconv0(concat1)
concat0 = torch.cat((x, out_deconv0), 1)
out = self.output_layer0(concat0)
return out
def conv(self, in_planes, output_channels, kernel_size, stride, dropout_rate):
return nn.Sequential(
nn.Conv3d(in_planes, output_channels, kernel_size=kernel_size,
stride=stride, padding=(kernel_size - 1) // 2, bias = False),
nn.BatchNorm3d(output_channels),
nn.LeakyReLU(0.1, inplace=True),
nn.Dropout(dropout_rate)
)
def deconv(self, input_channels, output_channels):
return nn.Sequential(
nn.ConvTranspose3d(input_channels, output_channels, kernel_size=4,
stride=2, padding=1),
nn.LeakyReLU(0.1, inplace=True)
)
def output_layer(self, input_channels, output_channels, kernel_size, stride, dropout_rate):
return nn.Conv3d(input_channels, output_channels, kernel_size=kernel_size,
stride=stride, padding=(kernel_size - 1) // 2)
class SimpleBlock3d(nn.Module):
def __init__(self, modes1, modes2, modes3, width):
super(SimpleBlock3d, self).__init__()
"""
The overall network. It contains 4 layers of the Fourier layer.
1. Lift the input to the desire channel dimension by self.fc0 .
2. 4 layers of the integral operators u' = (W + K)(u).
W defined by self.w; K defined by self.conv .
3. Project from the channel space to the output space by self.fc1 and self.fc2 .
input: the solution of the first 10 timesteps + 3 locations (u(1, x, y), ..., u(10, x, y), x, y, t). It's a constant function in time, except for the last index.
input shape: (batchsize, x=64, y=64, t=40, c=13)
output: the solution of the next 40 timesteps
output shape: (batchsize, x=64, y=64, t=40, c=1)
"""
self.modes1 = modes1
self.modes2 = modes2
self.modes3 = modes3
self.width = width
self.fc0 = nn.Linear(12, self.width)
# input channel is 12: the solution of the first 10 timesteps + 3 locations (u(1, x, y), ..., u(10, x, y), x, y, t)
self.conv0 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)
self.conv1 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)
self.conv2 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)
self.conv3 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)
self.conv4 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)
self.conv5 = SpectralConv3d(self.width, self.width, self.modes1, self.modes2, self.modes3)
self.w0 = nn.Conv1d(self.width, self.width, 1)
self.w1 = nn.Conv1d(self.width, self.width, 1)
self.w2 = nn.Conv1d(self.width, self.width, 1)
self.w3 = nn.Conv1d(self.width, self.width, 1)
self.w4 = nn.Conv1d(self.width, self.width, 1)
self.w5 = nn.Conv1d(self.width, self.width, 1)
self.unet3 = U_net(self.width, self.width, 3, 0)
self.unet4 = U_net(self.width, self.width, 3, 0)
self.unet5 = U_net(self.width, self.width, 3, 0)
self.fc1 = nn.Linear(self.width, 128)
self.fc2 = nn.Linear(128, 1)
def forward(self, x):
batchsize = x.shape[0]
size_x, size_y, size_z = x.shape[1], x.shape[2], x.shape[3]
x = self.fc0(x)
x = x.permute(0, 4, 1, 2, 3)
x1 = self.conv0(x)
x2 = self.w0(x.view(batchsize, self.width, -1)).view(batchsize, self.width, size_x, size_y, size_z)
x = x1 + x2
x = F.relu(x)
x1 = self.conv1(x)
x2 = self.w1(x.view(batchsize, self.width, -1)).view(batchsize, self.width, size_x, size_y, size_z)
x = x1 + x2
x = F.relu(x)
x1 = self.conv2(x)
x2 = self.w2(x.view(batchsize, self.width, -1)).view(batchsize, self.width, size_x, size_y, size_z)
x = x1 + x2
x = F.relu(x)
x1 = self.conv3(x)
x2 = self.w3(x.view(batchsize, self.width, -1)).view(batchsize, self.width, size_x, size_y, size_z)
# print(x.shape)
x3 = self.unet3(x)
# print(x3.shape)
x = x1 + x2 + x3
x = F.relu(x)
x1 = self.conv4(x)
x2 = self.w4(x.view(batchsize, self.width, -1)).view(batchsize, self.width, size_x, size_y, size_z)
x3 = self.unet4(x)
x = x1 + x2 + x3
x = F.relu(x)
x1 = self.conv5(x)
x2 = self.w5(x.view(batchsize, self.width, -1)).view(batchsize, self.width, size_x, size_y, size_z)
x3 = self.unet5(x)
x = x1 + x2 + x3
x = F.relu(x)
x = x.permute(0, 2, 3, 4, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
return x
class Net3d(nn.Module):
def __init__(self, modes1, modes2, modes3, width):
super(Net3d, self).__init__()
"""
A wrapper function
"""
self.conv1 = SimpleBlock3d(modes1, modes2, modes3, width)
def forward(self, x):
batchsize = x.shape[0]
size_x, size_y, size_z = x.shape[1], x.shape[2], x.shape[3]
x = F.pad(F.pad(x, (0,0,0,8,0,8), "replicate"), (0,0,0,0,0,0,0,8), 'constant', 0)
x = self.conv1(x)
# print(x.shape)
x = x.view(batchsize, size_x+8, size_y+8, size_z+8, 1)[..., :-8,:-8,:-8, :]
return x.squeeze()
def count_params(self):
c = 0
for p in self.parameters():
c += reduce(operator.mul, list(p.size()))
return c