None Grad when training the network

Hi all, sorry if my question is basic, I am inept at PyTorch. I am trying to train a network for sparse feature maps (It works like generative models), where I have two different parameters. One is the Conv layer weights and another one is the feature map (z). when I print the grad for Conv layer weights, I get None. I think I am not wrapping or detaching it anywhere. Could you please take a look? I appreciate it.
Here is the code:

import torch 
import torch.nn.functional as F

from torchvision import datasets

from torchvision import transforms

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('device is:', device)

# dataset definition
mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor()]))
mnist_testset = datasets.MNIST(root='./data', train=False, download=True, transform=transforms.Compose([transforms.ToTensor()])) =[:10000]

from import DataLoader
train_dl = DataLoader(mnist_trainset, batch_size=16, shuffle=False)

from torch.optim import SGD
import torch.nn as nn
from torch.nn import Module
from torch.nn import Conv2d
from tqdm import tqdm
import matplotlib.pyplot as plt

from torch.autograd import Variable

class MNIST_ISTA(Module):
    # define model elements
    def __init__(self):
        self.lambda_ = 0
        super(MNIST_ISTA, self).__init__()
        self.scale1 = Conv2d(in_channels = 1, out_channels = 1, kernel_size=20, bias = False)
        self.alpha = 1e0
        self.z = None

    def ista_(self, img_batch):
        output_test = self.scale1(img_batch)
        self.z = nn.Parameter(torch.normal(0, 1, size = (output_test.shape[0], output_test.shape[1], output_test.shape[2], output_test.shape[3]), requires_grad=True))
        optim = SGD([{'params': self.z, "lr": 1e-5 }])
        converged = False
        while not converged:
            z1_old = self.z.clone().detach()
            output_image = (F.conv2d((self.z),(self.scale1.weight), padding=self.scale1.kernel_size[0]-1))
            loss  = ((img_batch-output_image)**2).sum() + self.alpha*torch.norm(self.z,p=1)
            converged  = torch.norm(self.z - z1_old)/torch.norm(z1_old)<1e-2
    def soft_thresholding_(self, x, alpha):        
        with torch.no_grad():
            rtn = F.relu(x-alpha)- F.relu(-x-alpha)
    def forward(self, img_batch):
        return F.conv2d((self.z),(self.scale1.weight), padding = self.scale1.kernel_size[0]-1)
    def zero_grad(self):

ista_model = MNIST_ISTA()

optim2 = SGD([{'params': ista_model.scale1.weight, "lr": 1e-3}])

for epoch in range(5):
    running_loss = 0

    for data in tqdm(train_dl, desc='training', total=len(train_dl)):
        img_batch = data[0]
        pred = ista_model(img_batch) #the original image size is returned
        criterion = nn.MSELoss()
        loss2 = criterion(pred, img_batch)
        running_loss += loss2.item()

You are setting the .requires_grad attribute of the conv layer’s weight parameter to False while you are using it in the forward pass, so no gradients will be calculated for it.
Resetting this attribute to True after its usage won’t change anything.

Thank you for your answer, actually I am doing something like generative algorithms. first I want to freeze the weights and train another parameter (z here) and vice e versa for the next time. Is there any way to freeze/unfreeze weights to do that?