与此问题相关，其中包含源代码。
我正在尝试将 TensorFlow 的量化感知训练与每通道量化一起使用。如上述问题所述，使用 AllValuesQuantizer 似乎不起作用。因此，我尝试使用 LastValue 和 MovingAverage 量化器，它们都产生类似的张量形状相关错误。
显然，量化过程需要一些形状 [] 的张量，我不明白为什么。这是 TF 中的错误，还是我的模型不兼容？
同样，这是在我的函数调用中使用 per_axis=True 和 quantizer=QAT_LAST_VALUE 或 QAT_MA 时。
最后值量化器输出：
File "c:\Users\Lucas\Universitet\Examensarbete\cnn-optimization-vt2021\TEMP_QAT.py", line 161, in <module>
w_bits=8, a_bits=8, symmetric=False, narrow_range=False, per_axis=True, quantizer=QAT_LAST_VALUE, batch_size=64, epochs=1)
File "c:\Users\Lucas\Universitet\Examensarbete\cnn-optimization-vt2021\TEMP_QAT.py", line 120, in quantization_aware_training
q_aware_model = quantize_apply(annotated_model)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize.py", line 421, in quantize_apply
transformed_model, input_tensors=None, clone_function=_quantize)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\models.py", line 426, in clone_model
model, input_tensors=input_tensors, layer_fn=clone_function)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\models.py", line 335, in _clone_sequential_model
cloned_model = Sequential(layers=layers, name=model.name)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\sequential.py", line 142, in __init__
self.add(layer)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\sequential.py", line 221, in add
output_tensor = layer(self.outputs[0])
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 926, in __call__
input_list)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1117, in _functional_construction_call
outputs = call_fn(cast_inputs, *args, **kwargs)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\autograph\impl\api.py", line 258, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize_wrapper.py:144 call *
quantized_weight = utils.smart_cond(
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\keras\utils.py:54 smart_cond *
pred, true_fn=true_fn, false_fn=false_fn, name=name)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantizers.py:190 __call__ *
weights['max_var'],
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quant_ops.py:200 LastValueQuantize *
assign_min = tf_compat.assign(min_var, range_min, name='AssignMinLast')
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\keras\compat.py:28 assign *
return ref.assign(value, name=name)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:858 assign **
self._shape.assert_is_compatible_with(value_tensor.shape)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes () and (32,) are incompatible
MovingAverageQuantizer 输出：
File "c:\Users\Lucas\Universitet\Examensarbete\cnn-optimization-vt2021\TEMP_QAT.py", line 161, in <module>
w_bits=8, a_bits=8, symmetric=False, narrow_range=False, per_axis=True, quantizer=QAT_MA, batch_size=64, epochs=1)
File "c:\Users\Lucas\Universitet\Examensarbete\cnn-optimization-vt2021\TEMP_QAT.py", line 120, in quantization_aware_training
q_aware_model = quantize_apply(annotated_model)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize.py", line 421, in quantize_apply
transformed_model, input_tensors=None, clone_function=_quantize)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\models.py", line 426, in clone_model
model, input_tensors=input_tensors, layer_fn=clone_function)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\models.py", line 335, in _clone_sequential_model
cloned_model = Sequential(layers=layers, name=model.name)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\sequential.py", line 142, in __init__
self.add(layer)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\tracking\base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\sequential.py", line 221, in add
output_tensor = layer(self.outputs[0])
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 926, in __call__
input_list)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1117, in _functional_construction_call
outputs = call_fn(cast_inputs, *args, **kwargs)
File "C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\autograph\impl\api.py", line 258, in wrapper
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantize_wrapper.py:144 call *
quantized_weight = utils.smart_cond(
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\keras\utils.py:54 smart_cond *
pred, true_fn=true_fn, false_fn=false_fn, name=name)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quantizers.py:261 __call__ *
weights['max_var'],
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow_model_optimization\python\core\quantization\keras\quant_ops.py:297 MovingAvgQuantize *
assign_min = moving_averages.assign_moving_average(
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\moving_averages.py:111 assign_moving_average **
return replica_context.merge_call(merge_fn, args=(variable, value))
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2715 merge_call
return self._merge_call(merge_fn, args, kwargs)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2722 _merge_call
return merge_fn(self._strategy, *args, **kwargs)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\moving_averages.py:109 merge_fn **
return update(strategy, v, value)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\moving_averages.py:100 update
return _update(strategy, v, update_fn, args=(value,))
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\moving_averages.py:192 _update
return strategy.extended.update(var, update_fn, args)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2300 update
return self._update(var, fn, args, kwargs, group)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2955 _update
return self._update_non_slot(var, fn, (var,) + tuple(args), kwargs, group)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2961 _update_non_slot
result = fn(*args, **kwargs)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\training\moving_averages.py:94 update_fn **
return state_ops.assign_sub(v, (v - value) * decay, name=scope)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\ops\state_ops.py:164 assign_sub
return ref.assign_sub(value)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:801 assign_sub
name=name)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\ops\gen_resource_variable_ops.py:107 assign_sub_variable_op
"AssignSubVariableOp", resource=resource, value=value, name=name)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\framework\op_def_library.py:744 _apply_op_helper
attrs=attr_protos, op_def=op_def)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\framework\func_graph.py:593 _create_op_internal
compute_device)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\framework\ops.py:3485 _create_op_internal
op_def=op_def)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\framework\ops.py:1975 __init__
control_input_ops, op_def)
C:\Users\Lucas\.conda\envs\env\lib\site-packages\tensorflow\python\framework\ops.py:1815 _create_c_op
raise ValueError(str(e))
ValueError: Shapes must be equal rank, but are 0 and 1 for '{{node quant_conv2d/cond/MovingAvgQuantize/AssignMinEma/AssignSubVariableOp}} = AssignSubVariableOp[dtype=DT_FLOAT](quant_conv2d/cond/MovingAvgQuantize/AssignMinEma/ReadVariableOp/resource, quant_conv2d/cond/MovingAvgQuantize/AssignMinEma/mul)' with input shapes: [], [32].