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我从输出 2D Multiarray（分段）的 DeepLabV3+ mlmodel 开始。成功添加了一个将其作为输入并输出 GRAYSCALE 图像的层。

现在，我想将此灰度图像作为输入和输出 ARGB，我想让其中一种颜色透明。

如何设置这样的层？

我的python代码：

import coremltools
import coremltools.proto.FeatureTypes_pb2 as ft

coreml_model = coremltools.models.MLModel('DeepLabKP.mlmodel')
spec = coreml_model.get_spec()
spec_layers = getattr(spec,spec.WhichOneof("Type")).layers


# find the current output layer and save it for later reference
last_layer = spec_layers[-1]
 
# add the post-processing layer
new_layer = spec_layers.add()
new_layer.name = 'image_gray_to_RGB'
 
# Configure it as an activation layer
new_layer.activation.linear.alpha = 255
new_layer.activation.linear.beta = 0
 
# Use the original model's output as input to this layer
new_layer.input.append(last_layer.output[0])
 
# Name the output for later reference when saving the model
new_layer.output.append('image_gray_to_RGB')
 
# Find the original model's output description
output_description = next(x for x in spec.description.output if x.name==last_layer.output[0])
 
# Update it to use the new layer as output
output_description.name = new_layer.name


# Function to mark the layer as output
# https://forums.developer.apple.com/thread/81571#241998
def convert_grayscale_image_to_RGB(spec, feature_name, is_bgr=False): 
    """ 
    Convert an output multiarray to be represented as an image 
    This will modify the Model_pb spec passed in. 
    Example: 
        model = coremltools.models.MLModel('MyNeuralNetwork.mlmodel') 
        spec = model.get_spec() 
        convert_multiarray_output_to_image(spec,'imageOutput',is_bgr=False) 
        newModel = coremltools.models.MLModel(spec) 
        newModel.save('MyNeuralNetworkWithImageOutput.mlmodel') 
    Parameters 
    ---------- 
    spec: Model_pb 
        The specification containing the output feature to convert 
    feature_name: str 
        The name of the multiarray output feature you want to convert 
    is_bgr: boolean 
        If multiarray has 3 channels, set to True for RGB pixel order or false for BGR 
    """
    for output in spec.description.output: 
        if output.name != feature_name: 
            continue
        if output.type.WhichOneof('Type') != 'imageType': 
            raise ValueError("%s is not a image type" % output.name)
        output.type.imageType.colorSpace = ft.ImageFeatureType.ColorSpace.Value('RGB')
 
# Mark the new layer as image
convert_grayscale_image_to_RGB(spec, output_description.name, is_bgr=False)

updated_model = coremltools.models.MLModel(spec)
 
updated_model.author = 'Saran'
updated_model.license = 'MIT'
updated_model.short_description = 'Inherits DeepLab V3+ and adds a layer to turn scores into an image'
updated_model.input_description['image'] = 'Input Image'
updated_model.output_description[output_description.name] = 'RGB Image'
 
model_file_name = 'DeepLabKP-G2R.mlmodel'
updated_model.save(model_file_name)

虽然模型成功保存没有任何错误，但预测错误如下

result = model.predict({'image': img})
  File "/Users/saran/Library/Python/2.7/lib/python/site-packages/coremltools/models/model.py", line 336, in predict
    return self.__proxy__.predict(data, useCPUOnly)
RuntimeError: {
    NSLocalizedDescription = "Failed to convert output image_gray_to_RGB to image";
    NSUnderlyingError = "Error Domain=com.apple.CoreML Code=0 \"Invalid array shape (\n    1,\n    513,\n    513\n) for converting to gray image\" UserInfo={NSLocalizedDescription=Invalid array shape (\n    1,\n    513,\n    513\n) for converting to gray image}";
}

我觉得这与在这一层中如何设置激活有关。但是找不到任何可以尝试不同的方法。

很感谢任何形式的帮助。

我添加的图层产生的灰度图像

在此处输入图像描述

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1 回答 1

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看起来您的输出具有形状 (1, 513, 513)。第一个数字 1 是通道数。由于这是 1，Core ML 只能将输出变成灰度图像。一张彩色图像需要 3 个通道，或者是 (3, 513, 513) 的形状。

由于这是 DeepLab，我假设您的灰度图像中并没有真正的“颜色”，而是类的索引（换句话说，您已将 ARGMAX 置于预测之上）。在我看来，将这个灰度“图像”（实际上是分割蒙版）转换为彩色图像的最简单方法是在 Swift 或 Metal 中执行此操作。

这是一个源代码示例：https ://github.com/hollance/SemanticSegmentationMetalDemo

于 2020-09-10T11:15:48.460 回答