PyTorch Lightning (Trainable Params - Wrong)

Question

I am employing MULTI-GPU training using pytorch lightning. The below output displays the model:

GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]
┏━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃    ┃ Name       ┃ Type              ┃ Params ┃
┡━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ 0  │ encoder    │ Encoder           │  2.0 M │
│ 1  │ classifier │ Sequential        │  8.8 K │
│ 2  │ criterion  │ BCEWithLogitsLoss │      0 │
│ 3  │ train_acc  │ Accuracy          │      0 │
│ 4  │ val_acc    │ Accuracy          │      0 │
│ 5  │ train_auc  │ AUROC             │      0 │
│ 6  │ val_auc    │ AUROC             │      0 │
│ 7  │ train_f1   │ F1Score           │      0 │
│ 8  │ val_f1     │ F1Score           │      0 │
│ 9  │ train_mcc  │ MatthewsCorrCoef  │      0 │
│ 10 │ val_mcc    │ MatthewsCorrCoef  │      0 │
│ 11 │ train_sens │ Recall            │      0 │
│ 12 │ val_sens   │ Recall            │      0 │
│ 13 │ train_spec │ Specificity       │      0 │
│ 14 │ val_spec   │ Specificity       │      0 │
└────┴────────────┴───────────────────┴────────┘
Trainable params: 2.0 M
Non-trainable params: 0

I have set Encoder to be untrainable using the below code:

ckpt = torch.load(chk_path)
self.encoder.load_state_dict(ckpt['state_dict'])
self.encoder.requires_grad = False

Shouldn't trainable params be 8.8 K rather than 2.0 M ?

My optimizer is the following:

optimizer =  torch.optim.RMSprop(filter(lambda p: p.requires_grad, self.parameters()), lr =self.lr, weight_decay = self.weight_decay)