Loss and accuracy doesn't change when trying to run the GG experiment on a single custom dataset

[Nazia Tasnim]

From a pickle file, I load my custom dataloaders for a particular dataset like below:

train_loader = dataloader_dict[TASK_NAME]["train"]
val_loader = dataloader_dict[TASK_NAME]["val"]
test_loader = dataloader_dict[TASK_NAME]["test"]
print(f"Train size: {len(train_loader.dataset)}, Val size: {len(val_loader.dataset)}, Test size: {len(test_loader.dataset)}")
best_acc1 = [0.0 for _ in range(num_tasks+1)]
curr_acc1 = [0.0 for _ in range(num_tasks+1)]
adapt_acc1 = [0.0 for _ in range(num_tasks+1)]

I have slightly modified the resnet.py to accept a num_class property during initialization. So my training setup looks like this:

num_tasks = 1 
task_idx = 1
criterion = nn.CrossEntropyLoss()
model = utils.get_model("ResNet34", NUM_CLASSES)
CONFIG.device = device
CONFIG.output_size = NUM_CLASSES
model = model.to(device)
print(device)
model.apply(lambda x: setattr(x, "task", task_idx))
params = []
param_count = 0
for name, param in model.named_parameters():
    if not param.requires_grad: continue
    param_count += param.numel()
    split = name.split(".")
    if split[-1] in ["scores", "s", "t"]:
        params.append(param)
lr = 0.1
optimizer = torch.optim.Adam(params, lr=lr, weight_decay=0.0001)
train_epochs = 250
scheduler = CosineAnnealingLR(optimizer, train_epochs)

These are my training and eval code:

import tqdm
def train(model, writer, train_loader, optimizer, criterion, epoch, task_idx, data_loader=None):
    model.zero_grad()
    model.train()

    num_correct = 0
    total_seen = 0
    for batch_idx, (data, target) in tqdm.tqdm(enumerate(train_loader), desc = "TRAIN"):
        optimizer.zero_grad()
        data = data.to(device)
        target = target.to(device)
        output = model(data)
        loss = criterion(output, target)
        predictions = output.data.max(1, keepdim=True)[1]
        num_correct += predictions.eq(target.data.view_as(predictions)).sum()
        total_seen += target.size(0)
        loss.backward()
        optimizer.step()

        if batch_idx % 10 == 0:
            print(
                f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} "
                f"({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}"
            )

@torch.no_grad()
def evaluate(model, val_loader, epoch):
    model.eval()
    num_correct = 0
    total_seen = 0
    for id, (batch, labels) in tqdm.tqdm(enumerate(val_loader), desc = "EVAL"):
        batch = batch.to(device)
        labels = labels.to(device)
        logits = model(batch)
        predictions = logits.argmax(dim=-1)
        num_correct += (predictions == labels).float().sum()
        total_seen += logits.size(0) 
    

    print(f"Val Perf after {epoch + 1} epochs Acc@1 {(num_correct / total_seen):0.4f}")
    return num_correct / total_seen

I am using the config values from the rn50-supsup-adam.yaml file. The args are also set up accordingly.
However, no matter what even after 40-50 epochs, there's no stable change in loss or accuracy. What am I doing wrong here?
Additionally, for this single dataset I am using the following module types:

    conv_type="MaskConv",
    bn_type="NonAffineBN",
    conv_init="signed_constant",