hi, why the decoder need to loop T times

[DeveloperZWK]

@carlesventura
Excuse me, could you tell me why the decoder need to loop T times?

for t in range(0, T):
#prev_hidden_temporal_list is a list with the hidden state for all instances from previous time instant
#If this is the first frame of the sequence, hidden_temporal is initialized to None. Otherwise, it is set with the value from previous time instant.
if prev_hidden_temporal_list is not None:
hidden_temporal = prev_hidden_temporal_list[t]
if args.only_temporal:
hidden_spatial = None
else:
hidden_temporal = None

    #The decoder receives two hidden state variables: hidden_spatial (a tuple, with hidden_state and cell_state) which refers to the
    #hidden state from the previous object instance from the same time instant, and hidden_temporal which refers to the hidden state from the same
    #object instance from the previous time instant.
    out_mask, hidden = decoder(feats, hidden_spatial, hidden_temporal)
    hidden_tmp = []
    for ss in range(len(hidden)):
        if mode == 'train':
            hidden_tmp.append(hidden[ss][0])
        else:
            hidden_tmp.append(hidden[ss][0].data)
    hidden_spatial = hidden
    hidden_temporal_list.append(hidden_tmp)

    upsample_match = nn.UpsamplingBilinear2d(size=(x.size()[-2], x.size()[-1]))
    out_mask = upsample_match(out_mask) # batch_size * 1 * height * width
    out_mask = out_mask.view(out_mask.size(0), -1) # batch_size * height x width
    
    # repeat predicted mask as many times as elements in ground truth.
    # to compute iou against all ground truth elements at once
    y_pred_i = out_mask.unsqueeze(0) # out_mask: batch_size * height x width -> 1 * batch_size * height x width
    y_pred_i = y_pred_i.permute(1,0,2) # 1 * batch_size * height * width -> batch_size * 1 * height x width
    y_pred_i = y_pred_i.repeat(1,y_mask.size(1),1) 
    y_pred_i = y_pred_i.view(y_mask.size(0)*y_mask.size(1),y_mask.size(2))# torch.Size([10, 10, 114688]) -> torch.Size([100, 114688])
    y_true_p = y_mask.view(y_mask.size(0)*y_mask.size(1),y_mask.size(2))# torch.Size([100, 114688])

    c = args.iou_weight * softIoU(y_true_p, y_pred_i)
    c = c.view(sw_mask.size(0),-1)
    scores[:,:,t] = c.cpu().data

    # get predictions in list to concat later
    out_masks.append(out_mask)

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Glad to waiting for your answer. Thanks!
Best ragards,
zwk