def measure_layer()

in pycls/utils/metrics.py [0:0]

• 45 lines of code
• 8 McCabe index (conditional complexity)

def measure_layer(layer, x):
    global count_ops, count_params
    delta_ops = 0
    delta_params = 0
    multi_add = 1
    type_name = get_layer_info(layer)

    ### ops_conv
    if type_name in ['Conv2d']:
        out_h = int((x.size()[2] + 2 * layer.padding[0] - layer.kernel_size[0]) /
                    layer.stride[0] + 1)
        out_w = int((x.size()[3] + 2 * layer.padding[1] - layer.kernel_size[1]) /
                    layer.stride[1] + 1)
        delta_ops = layer.in_channels * layer.out_channels * layer.kernel_size[0] *  \
                layer.kernel_size[1] * out_h * out_w / layer.groups * multi_add
        print(layer)
        print('out_h: ', out_h,  'out_w:', out_w)
        delta_params = get_layer_param(layer)

    ### ops_nonlinearity
    elif type_name in ['ReLU']:
        delta_ops = x.numel()
        delta_params = get_layer_param(layer)

    ### ops_pooling
    elif type_name in ['AvgPool2d', 'MaxPool2d']:
        in_w = x.size()[2]
        kernel_ops = layer.kernel_size * layer.kernel_size
        out_w = int((in_w + 2 * layer.padding - layer.kernel_size) / layer.stride + 1)
        out_h = int((in_w + 2 * layer.padding - layer.kernel_size) / layer.stride + 1)
        delta_ops = x.size()[0] * x.size()[1] * out_w * out_h * kernel_ops
        delta_params = get_layer_param(layer)

    elif type_name in ['AdaptiveAvgPool2d']:
        delta_ops = x.size()[0] * x.size()[1] * x.size()[2] * x.size()[3]
        delta_params = get_layer_param(layer)

    ### ops_linear
    elif type_name in ['Linear']:
        weight_ops = layer.weight.numel() * multi_add
        bias_ops = layer.bias.numel()
        delta_ops = x.size()[0] * (weight_ops + bias_ops)
        delta_params = get_layer_param(layer)

    elif type_name in ['WeightedSumTransform']:
        weight_ops = layer.weight.numel() * multi_add
        delta_ops = x.size()[0] * (weight_ops)
        delta_params = get_layer_param(layer)

    ### ops_nothing
    elif type_name in ['BatchNorm2d', 'Dropout2d', 'DropChannel', 'Dropout', 'Sigmoid', 'DirichletWeightedSumTransform', 'Softmax', 'Identity', 'Sequential']:
        delta_params = get_layer_param(layer)

    ### unknown layer type
    else:
        raise TypeError('unknown layer type: %s' % type_name)

    count_ops += delta_ops
    count_params += delta_params
    return