I modified the quantized weights a net post-quantization as follows:
# instantiate the quantized net (not shown here).
# get one of the conv layers
tmp = model_int8.state_dict()['features.0.weight']
scales = tmp.q_per_channel_scales()
zero_pts = tmp.q_per_channel_zero_points()
axis = tmp.q_per_channel_axis()
# get int repr
tmp_int8 = tmp.int_repr()
# change value (value change is dependent on the int8 value)
tmp_int8 = new_value
# how to convert tmp_int8 to torch.qint8 type?
new_tmp = torch._make_per_channel_quantized_tensor(tmp_int8, scales, zero_pts, axis)
# based on the above step:
model_int8.features.weight = new_tmp
model_int8.features.weight shows updated values, but
model_int8.state_dict()['features.0.weight'] shows old vales.
I also tried saving the modified model and reloading, but the problem persists.
Question is which weight values are being used for inference? I do not see change in the classification results.