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train.py
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train.py
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import argparse
import os
import scipy.io
import torch.nn as nn
from utils.torch_utils import *
from utils.utils import *
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
ONNX_EXPORT = False
pathd = "data/"
pathr = "results/"
labels = ["train", "validate", "test"]
torch.manual_seed(1)
def train(H, model, str, lr=0.001):
"""Trains a given model on provided data with specified hyperparameters and saves training results."""
data = "wavedata25ns.mat"
cuda = torch.cuda.is_available()
os.makedirs(f"{pathr}models", exist_ok=True)
name = f"{data[:-4]}{H[:]}{lr:g}lr{str}".replace(", ", ".").replace("[", "_").replace("]", "_")
print(f"Running {name}")
device = select_device()
if not os.path.isfile(pathd + data):
os.system(f"wget -P data/ https://storage.googleapis.com/ultralytics/{data}")
mat = scipy.io.loadmat(pathd + data)
x = mat["inputs"][:] # inputs (nx512) [waveform1 waveform2]
y = mat["outputs"][:, 0:2] # outputs (nx4) [position(mm), time(ns), PE, E(MeV)]
nz, nx = x.shape
ny = y.shape[1]
x, _, _ = normalize(x, 1) # normalize each input row
y, ymu, ys = normalize(y, 0) # normalize each output column
x, y = torch.Tensor(x), torch.Tensor(y)
x, y, xv, yv, xt, yt = splitdata(x, y, train=0.70, validate=0.15, test=0.15, shuffle=False)
# torch.nn.init.constant_(model.out.weight.data, ys.item(0))
# torch.nn.init.constant_(model.out.bias.data, ymu.item(0))
# ys = 1
if cuda:
x, xv, xt = x.to(device), xv.to(device), xt.to(device)
y, yv, yt = y.to(device), yv.to(device), yt.to(device)
if torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
model = model.to(device)
# Loss criteria
MSE = nn.MSELoss()
# Optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=.9)
# Scheduler
stopper = patienceStopper(epochs=opt.epochs, patience=24, printerval=opt.printerval)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, patience=20, factor=0.1, min_lr=1e-5, verbose=True
)
lossv = 1e6
bs = opt.batch_size
nb = int(np.ceil(x.shape[0] / bs))
L = np.full((opt.epochs, 3), np.nan)
model_info(model)
for i in range(opt.epochs):
scheduler.step(lossv)
# Train
model.train()
for bi in range(nb):
j = range(bi * bs, min((bi + 1) * bs, x.shape[0]))
if ONNX_EXPORT:
_ = torch.onnx._export(model, x, "model.onnx", verbose=True)
return
loss = MSE(model(x[j]), y[j])
L[i, 0] = loss.item() # train
# Zero gradients, backward pass, update parameters
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Test
model.eval()
with torch.no_grad():
yv_ = model(xv)
lossv = MSE(yv_, yv)
L[i, 1] = lossv.item() # validate
if i % opt.printerval == 0:
std = (yv_ - yv).std(0).detach().cpu().numpy() * ys
if stopper.step(lossv, model=None, metrics=std):
break
# Print and save final results
# torch.save(stopper.bestmodel.state_dict(), pathr + 'models/' + name + '.pt')
stopper.bestmodel.eval()
loss, std = np.zeros(3), np.zeros((3, ny))
for i, (xi, yi) in enumerate(((x, y), (xv, yv), (xt, yt))):
with torch.no_grad():
r = stopper.bestmodel(xi) - yi # residuals, ().detach?
loss[i] = (r**2).mean().cpu().item()
std[i] = r.std(0).cpu().numpy() * ys
print(f"{loss[i]:.5f} {std[i, :]} {labels[i]}")
scipy.io.savemat(pathr + name + ".mat", dict(bestepoch=stopper.bestloss, loss=loss, std=std, L=L, name=name))
# files.download(pathr + name + '.mat')
return np.concatenate(([stopper.bestloss], np.array(loss), np.array(std.ravel())))
# 400 5.1498e-05 0.023752 12.484 0.15728 # var 0
class WAVE(torch.nn.Module):
"""Implements a neural network model for waveform data processing using a multi-layer perceptron architecture."""
def __init__(self, n=(512, 64, 8, 2)):
"""Initializes the WAVE model architecture with specified layer sizes."""
super().__init__()
self.fc0 = nn.Linear(n[0], n[1])
self.fc1 = nn.Linear(n[1], n[2])
self.fc2 = nn.Linear(n[2], n[3])
def forward(self, x): # x.shape = [bs, 512]
"""Performs a forward pass through the WAVE model transforming input x from shape [bs, 512] to [bs, 2]."""
x = torch.tanh(self.fc0(x)) # [bs, 64]
x = torch.tanh(self.fc1(x)) # [bs, 8]
return self.fc2(x) # [bs, 2]
# https://github.com/yunjey/pytorch-tutorial/tree/master/tutorials/02-intermediate
# 121 0.47059 0.0306 14.184 0.1608
class WAVE4(nn.Module):
"""Implements a convolutional neural network for waveform data processing with customizable output layers."""
def __init__(self, n_out=2):
"""Initializes the WAVE4 model with specified output layers and configurations for convolutional layers."""
super().__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=(1, 9), stride=(1, 2), padding=(0, 4), bias=False),
nn.BatchNorm2d(32),
nn.LeakyReLU(0.1),
)
# nn.MaxPool2d(kernel_size=(1, 2), stride=1))
self.layer2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=(1, 9), stride=(1, 2), padding=(0, 4), bias=False),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.1),
)
# nn.MaxPool2d(kernel_size=(1, 2), stride=1))
self.layer3 = nn.Conv2d(64, n_out, kernel_size=(2, 64), stride=(1, 1), padding=(0, 0))
def forward(self, x): # x.shape = [bs, 512]
"""Forward pass for processing input tensor through convolutional layers and reshaping output for
classification.
"""
x = x.view((-1, 2, 256)) # [bs, 2, 256]
x = x.unsqueeze(1) # [bs, 1, 2, 256] = = [N, C, H, W]
x = self.layer1(x) # [bs, 32, 1, 128]
x = self.layer2(x) # [bs, 64, 1, 64]
x = self.layer3(x)
return x.reshape(x.size(0), -1) # [bs, 64*64]
# 65 4.22e-05 0.021527 11.883 0.14406
class WAVE3(nn.Module):
"""WAVE3 implements a convolutional neural network for feature extraction and classification on waveform data."""
def __init__(self, n_out=2):
"""Initializes the WAVE3 class with neural network layers for feature extraction and classification in a
sequential manner.
"""
super().__init__()
n = 32
self.layer1 = nn.Sequential(
nn.Conv2d(in_channels=2, out_channels=n, kernel_size=(1, 33), stride=(1, 2), padding=(0, 16), bias=False),
nn.BatchNorm2d(n),
nn.LeakyReLU(0.1),
)
self.layer2 = nn.Sequential(
nn.Conv2d(
in_channels=n, out_channels=n * 2, kernel_size=(1, 17), stride=(1, 2), padding=(0, 8), bias=False
),
nn.BatchNorm2d(n * 2),
nn.LeakyReLU(0.1),
)
self.layer3 = nn.Sequential(
nn.Conv2d(
in_channels=n * 2, out_channels=n * 4, kernel_size=(1, 9), stride=(1, 2), padding=(0, 4), bias=False
),
nn.BatchNorm2d(n * 4),
nn.LeakyReLU(0.1),
)
self.layer4 = nn.Conv2d(n * 4, n_out, kernel_size=(1, 32), stride=1, padding=0)
def forward(self, x): # x.shape = [bs, 512]
"""Performs the forward pass for input tensor `x` through the defined neural network layers, reshaping as
necessary.
"""
x = x.view((-1, 2, 256)) # [bs, 2, 256]
x = x.unsqueeze(2) # [bs, 2, 1, 256] = [N, C, H, W]
x = self.layer1(x) # [bs, 32, 1, 128]
# print(x.shape)
x = self.layer2(x) # [bs, 64, 1, 64]
# print(x.shape)
x = self.layer3(x) # [bs, 128, 1, 32]
# print(x.shape)
x = self.layer4(x)
return x.reshape(x.size(0), -1) # [bs, 64*64]
# 121 2.6941e-05 0.021642 11.923 0.14201 # var 1
class WAVE2(nn.Module):
"""Implements a convolutional neural network for waveform data processing with configurable output dimensions."""
def __init__(self, n_out=2):
"""Initializes the WAVE2 model architecture components."""
super().__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 32, kernel_size=(2, 30), stride=(1, 2), padding=(1, 15), bias=False),
nn.BatchNorm2d(32),
nn.LeakyReLU(0.1),
nn.MaxPool2d(kernel_size=(1, 2), stride=1),
)
self.layer2 = nn.Sequential(
nn.Conv2d(32, 64, kernel_size=(2, 30), stride=(1, 2), padding=(0, 15), bias=False),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.1),
nn.MaxPool2d(kernel_size=(1, 2), stride=1),
)
self.layer3 = nn.Sequential(nn.Conv2d(64, n_out, kernel_size=(2, 64), stride=(1, 1), padding=(0, 0)))
def forward(self, x): # x.shape = [bs, 512]
"""Forward pass for processing input tensor x through sequential layers, reshaping as needed for the model."""
x = x.view((-1, 2, 256)) # [bs, 2, 256]
x = x.unsqueeze(1) # [bs, 1, 2, 256]
x = self.layer1(x) # [bs, 32, 1, 128]
x = self.layer2(x) # [bs, 64, 1, 64]
x = self.layer3(x)
return x.reshape(x.size(0), -1) # [bs, 64*64]
H = [512, 64, 8, 2]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--epochs", type=int, default=5000, help="number of epochs")
parser.add_argument("--batch-size", type=int, default=2000, help="size of each image batch")
parser.add_argument("--printerval", type=int, default=1, help="print results interval")
parser.add_argument("--var", nargs="+", default=[3], help="debug list")
opt = parser.parse_args()
opt.var = [float(x) for x in opt.var]
print(opt, end="\n\n")
init_seeds()
if opt.var[0] == 0:
_ = train(H, model=WAVE(), str=".Tanh")
elif opt.var[0] == 2:
_ = train(H, model=WAVE2(), str=".Tanh")
elif opt.var[0] == 3:
_ = train(H, model=WAVE3(), str=".Tanh")
elif opt.var[0] == 4:
_ = train(H, model=WAVE4(), str=".Tanh")
# 100K SET ---------------------------------------------------------------------
# Model Summary: 8 layers, 33376 parameters, 33376 gradients
# epoch time loss metric(s)
# 0 0.23533 0.72525 57.944 4.6891
# 1000 6.1377 0.027707 13.409 0.23723
# 2000 6.1811 0.025165 12.82 0.19568
# 3000 6.1135 0.024321 12.614 0.18148
# 4000 6.1703 0.023974 12.528 0.17578
# 5000 6.0297 0.023792 12.48 0.17282
# 6000 6.044 0.023641 12.443 0.17017
# 7000 6.022 0.025316 12.86 0.16977
# 8000 6.0789 0.023559 12.424 0.16832
# 9000 6.0554 0.023912 12.464 0.16599
# 10000 6.0805 0.02347 12.403 0.16509
# 11000 6.1321 0.024346 12.579 0.16366
# 12000 6.0378 0.025261 12.618 0.16218
# 13000 6.003 0.023413 12.391 0.16071
# 14000 6.0259 0.023771 12.46 0.15963
# 15000 6.0809 0.023371 12.382 0.158
# 16000 6.0842 0.02339 12.389 0.15699
# 3000 Patience exceeded at epoch 16857.
# Finished 50000 epochs in 102.663s (487.032 epochs/s). Best results:
# 13856 5.1492 0.023221 12.391 0.16071
# 0.01641 [ 10.358 0.15294] train
# 0.02322 [ 12.34 0.15902] validate
# 0.02316 [ 12.328 0.15611] test
# BS 2K
# 100 Patience exceeded at epoch 510.
# Finished 1000 epochs in 27.223s (36.733 epochs/s). Best results:
# 409 5.7936e-05 0.02456 12.69 0.15899
# 0.01756 [ 10.706 0.15338] train
# 0.02456 [ 12.69 0.15899] validate
# 0.02457 [ 12.687 0.15632] test
# 400 5.1498e-05 0.023752 12.484 0.15728 # var 0
# 121 2.6941e-05 0.021642 11.923 0.14201 # var 1
# 10K TEST SET
# 3000 Patience exceeded at epoch 4162.
# Finished 50000 epochs in 8.108s (6166.670 epochs/s). Best results:
# 1161 0.0035503 0.035007 15.125 0.25265
# 0.01647 [ 10.276 0.2399] train
# 0.03501 [ 15.104 0.25241] validate
# 0.04057 [ 16.274 0.26408] test
# BASELINE TRAIN ON FIRST 10K
# 100 Patience exceeded at epoch 301.
# Finished 1000 epochs in 279.341s (3.580 epochs/s). Best results:
# 200 2.5511e-05 0.027798 13.435 0.21111
# 0.01846 [ 10.901 0.17024] train
# 0.02752 [ 13.41 0.18784] validate
# 0.03360 [ 14.818 0.19295] test