Pytorch Arcface: mat1 and mat2 shapes cannot be multiplied (20x30 and 512x30)

i just wanna training arcface in pytorch batch size is 20, and image size is 112.

why F.linear() is not working?

this is error message:

RuntimeError: mat1 and mat2 shapes cannot be multiplied (20x30 and 512x30)

class ArcMarginProduct(nn.Module):
    def __init__(self, in_features, out_features, s=30.0, m=0.50, easy_margin=False):
        super(ArcMarginProduct, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.s = s
        self.m = m
        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
        nn.init.xavier_uniform_(self.weight)

        self.easy_margin = easy_margin
        self.cos_m = math.cos(m)
        self.sin_m = math.sin(m)
        self.th = math.cos(math.pi - m)
        self.mm = math.sin(math.pi - m) * m

    def forward(self, input, label):
        # --------------------------- cos(theta) & phi(theta) ---------------------------
        cosine = F.linear(F.normalize(input), F.normalize(self.weight))
        sine = torch.sqrt((1.0 - torch.pow(cosine, 2)).clamp(0, 1))
        phi = cosine * self.cos_m - sine * self.sin_m
        if self.easy_margin:
            phi = torch.where(cosine > 0, phi, cosine)
        else:
            phi = torch.where(cosine > self.th, phi, cosine - self.mm)
        # --------------------------- convert label to one-hot ---------------------------
        # one_hot = torch.zeros(cosine.size(), requires_grad=True, device='cuda')
        one_hot = torch.zeros(cosine.size(), device=device)
        one_hot.scatter_(1, label.view(-1, 1).long(), 1)
        # -------------torch.where(out_i = {x_i if condition_i else y_i) -------------
        output = (one_hot * phi) + ((1.0 - one_hot) * cosine)  # you can use torch.where if your torch.__version__ is 0.4
        output *= self.s
        # print(output)

        return output

this is my training code

model = resnet18
optimizer = optim.Adam(model.parameters(), lr=1e-3 ) # optimizer 
criterion = torch.nn.CrossEntropyLoss()   # loss func
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size = 5, gamma = 0.9) 


metric_fc = ArcMarginProduct(512, len(col_list), s=30, m=0.50, easy_margin=False).to(device)


start = time.time()
for epoch in range(num_epochs):
  e_time = time.time()
    for i, (images, label) in enumerate(trainloader):
    model.train()
    images = images.to(device)
    label = label.to(device)

    optimizer.zero_grad()
    feature = model(images)

    outputs = metric_fc(feature, label)
    loss = criterion(outputs, label)
    loss.backward()
    optimizer.step()  

    if (i+1) % num_epochs == 0:
      print(f'Epoch: {epoch} - Batch: {i*batch_size} - Loss: {loss:.6f} - Time:{time.time() - e_time}')

print("time :", time.time() - start)

and, Please let me know if you have a simple example to test arcface training with data.