a long nmr sequence wavelet coefficients are input as cnn-resnet, and the corresponding nmr sequence wavelet coefficients are used as cnn-resnet18 by pytorch output for denoising in python

First, we need to load the dataset of the NMR sequences along with their wavelet coefficients. Then we need to split the dataset into training and testing sets.

Next, we will define and train a CNN-ResNet18 model using PyTorch. We will define the model using the PyTorch nn module, and we will also use the wavelet_transform module to perform the wavelet transform on the input data.

Once the model has been trained, we can use it to perform denoising. To do this, we can pass a noisy NMR sequence with its wavelet coefficients to the model, and it will output a denoised NMR sequence with corresponding wavelet coefficients.

Here's some sample code to get you started:


main.py
import torch
import torch.nn as nn
from pytorch_wavelets import DWTForward, DWTInverse

# Load dataset of NMR sequences and corresponding wavelet coefficients
# Split dataset into training and testing sets

# Define CNN-ResNet18 model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.dwt = DWTForward(J=3, mode='zero', wave='db1')

        self.conv1 = nn.Conv2d(4, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        self.res1 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64)
        )

        self.res2 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64)
        )

        self.res3 = nn.Sequential(
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64)
        )

        self.conv2 = nn.Conv2d(64, 4, kernel_size=3, stride=1, padding=1, bias=False)
        self.iwt = DWTInverse(mode='zero', wave='db1')

    def forward(self, x):
        x = self.dwt(x)

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.res1(out) + out
        out = self.res2(out) + out
        out = self.res3(out) + out

        out = self.conv2(out)
        out = self.iwt(out)

        return out

model = Net()

# Define loss function and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Train model on training set
for epoch in range(num_epochs):
    for data in train_loader:
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

# Test model on testing set
for data in test_loader:
    inputs, labels = data
    outputs = model(inputs)
    # Perform denoising using output and wavelet coefficients

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Note that the above code assumes that the input NMR sequences have 4 channels, corresponding to the 4 wavelet coefficients (low-low, low-high, high-low, high-high). You may need to modify this depending on your input data.