modeltc / dipoorlet Goto Github PK

torch.onnx.export(
        model, # torch model
        dummy_input, # random dummy input
        onnx_path, # save path of onnx format model
        export_params=True, # export all params
        verbose=True, # enable debug message
        training=torch.onnx.TrainingMode.EVAL, # export the model in inference mode
        input_names=input_names, # names to assign to input nodes of computation graph
        output_names=output_names, # names to assign to output nodes of computation graph
        opset_version=16, # version of opset
        # dynamic axes setting for dynamic input/output shapes
        dynamic_axes={
            "LR_bins":{0: "batch_size", 1:"temporal_dim"},
            "HR":{0: "batch_size", 1:"temporal_dim"}
        }

root@autodl-container-032d11993c-d711a821:~/autodl-tmp/Dipoorlet_Examples# sh verification_trial.sh
[2023-11-07 14:59:14 dipoorlet](__main__.py 118): INFO Do tensor calibration...
Minmax update: 0it [00:00, ?it/s]
Traceback (most recent call last):
  File "/root/miniconda3/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/root/miniconda3/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/root/autodl-tmp/Dipoorlet/dipoorlet/__main__.py", line 119, in <module>
    act_clip_val, weight_clip_val = tensor_calibration(onnx_graph, args)
  File "/root/autodl-tmp/Dipoorlet/dipoorlet/tensor_cali/tensor_cali_base.py", line 6, in tensor_calibration
    act_clip_val = tensor_cali_dispatcher(args.act_quant, onnx_graph, args)
  File "/root/autodl-tmp/Dipoorlet/dipoorlet/utils.py", line 297, in wrapper
    return dispatch(args[0])(*(args[1:]), **kw)
  File "/root/autodl-tmp/Dipoorlet/dipoorlet/tensor_cali/basic_algorithm.py", line 18, in find_clip_val_minmax
    stats_min_max = forward_get_minmax(onnx_graph, args)
  File "/root/autodl-tmp/Dipoorlet/dipoorlet/forward_net.py", line 215, in forward_get_minmax
    ort_inputs[name] = data[name][:].reshape(onnx_graph.get_tensor_shape(name))
ValueError: cannot reshape array of size 172800 into shape (0,0,3,180,320)

abs_x = np.abs(ort_inputs[i])
s_n = abs_x.sum() / abs_x[abs_x > 0].size
for _ in range(20):
    s_n_plus_1 = abs_x[abs_x > s_n].sum() / \
               (1 / (4 ** 8) / 3 / unsigned * abs_x[abs_x <= s_n].size + abs_x[abs_x > s_n].size)
    if np.abs(s_n_plus_1 - s_n) < 1e-6:
        break
    s_n = s_n_plus_1

 s_n_plus_1 = abs_x[abs_x > s_n].sum() / \
               (1 / (4 ** 8) / 3 / unsigned * abs_x[abs_x <= s_n].size + abs_x[abs_x > s_n].size)