Function variable_analyse

ppq/quantization/analyse/layerwise.py:137–176

(
    graph: BaseGraph,
    dataloader: Iterable,
    interested_outputs: Union[str, List[str]],
    collate_fn: Callable = None,
    running_device = 'cuda',
    samples_per_step: int = 65536,
    steps: int = 8,
    dequantize: bool = False)

def variable_analyse(
    graph: BaseGraph,
    dataloader: Iterable,
    interested_outputs: Union[str, List[str]],
    collate_fn: Callable = None,
    running_device = 'cuda',
    samples_per_step: int = 65536,
    steps: int = 8,
    dequantize: bool = False):

    quant_graph = QuantableGraph(graph)

    executor = TorchExecutor(graph=graph, device=running_device)
    if dequantize: quant_graph.dequantize_graph()

    data_collector = defaultdict(list)
    for idx, batch in enumerate(dataloader):
        if collate_fn is not None: batch = collate_fn(batch)
        fp_outputs = executor.forward(inputs=batch, output_names=interested_outputs)
        for output, output_name in zip(fp_outputs, interested_outputs):
            data_collector[output_name].append(
                tensor_random_fetch(tensor=output, num_of_fetches=samples_per_step).unsqueeze(0)
            )
        if idx >= steps: break

    for name in interested_outputs:
        tensor = torch.cat(data_collector[name]).flatten()
        tensor = convert_any_to_numpy(tensor)

        try:
            from matplotlib import pyplot as plt
        except ImportError as e:
            raise Exception('Install matplotlib before using this function.')

        plt.figure(figsize=[12, 8])
        plt.title(f'Histogram Result of Variable {name}:')
        plt.hist(tensor, bins=64)
        plt.show()

    if dequantize: quant_graph.restore_quantize_state()


def parameter_analyse(graph: BaseGraph):