Hi, all:

I am trying to quantize a siam-style tracking model, and unfortunately I found that with int8 qat, the resulted model didn’t work at all. I am not sure whether is my qat setup goes wrong or int8 is not enough for such task.

Since I only want a quantized backbone, the qat setup is like:

```
1.replace skip-connection "+" with nn.quantized.FloatFunctional()
2.coding fuse model method for structure like conv-bn-relu, then:
if cfg.TRAIN.QAT:
model.eval()
model.backbone.fuse_model()
model.backbone.qconfig = quantization.QConfig(weight=wt_fake_quant_per_channel_8bit, activation=act_fake_quant_8bit,)
model.backbone = quantization.prepare_qat(model.backbone, inplace=True)
if cfg.ADJUST.ADJUST:
model.neck.apply(quantization.disable_fake_quant)
if cfg.BAN.BAN:
model.ban_head.apply(quantization.disable_fake_quant)
model.backbone.features[-1].skip_add.activation_post_process.apply(quantization.disable_fake_quant)
model.train()
# start training
train(dist_model, optimizer, lr_scheduler, tb_writer)
```

**Is the code above has something wrong? I disable the last activation quantization because the followed layers are working on float32, is this step necessary?**

* If such code looks good, I guess it is just int8 is not enough, So, is it possible to do int 16 qat, I try something like*:

```
act_fake_quant_16bit = quantization.FakeQuantize.with_args(
observer=MinMaxObserver_Clip6.with_args(
quant_min=-32767,
quant_max=32767,
dtype=torch.qint32,
qscheme=torch.per_tensor_symmetric,
reduce_range=False
),
quant_min=-32767,
quant_max=32767,
dtype=torch.qint32,
qscheme=torch.per_tensor_symmetric,
reduce_range=False
)
wt_fake_quant_per_channel_16bit = quantization.FakeQuantize.with_args(
observer=quantization.PerChannelMinMaxObserver.with_args(
quant_min=-32767,
quant_max=32767,
dtype=torch.qint32,
qscheme=torch.per_channel_symmetric,
reduce_range=False
),
quant_min=-32767,
quant_max=32767,
dtype=torch.qint32,
qscheme=torch.per_channel_symmetric,
reduce_range=False
)
```

but get errors from torch says that only int8/uint8 dtype is acceptable.