假设我在Keras中创建了最简单的模型:
from keras.layers import *
from keras import Input, Model
import coremltools
def MyModel(inputs_shape=(None,None,3), channels=64):
inpt = Input(shape=inputs_shape)
# channels
skip = Conv2D(channels, (3, 3), strides=1, activation=None, padding='same', name='conv_in')(inpt)
out = Conv2D(3, (3, 3), strides=1, padding='same', activation='tanh',name='out')(skip)
return Model(inputs=inpt, outputs=out)
model = MyModel()
coreml_model = coremltools.converters.keras.convert(model,
input_names=["inp1"],
output_names=["out1"],
image_scale=1.0,
model_precision='float32',
use_float_arraytype=True,
input_name_shape_dict={'inp1': [None, 384, 384, 3]}
)
spec = coreml_model._spec
print(spec.description.input[0])
print(spec.description.input[0].type.multiArrayType.shape)
print(spec.description.output[0])
coremltools.utils.save_spec(spec, "test.mlmodel")
输出为:
2 : out, <keras.layers.convolutional.Conv2D object at 0x7f08ca491470>
3 : out__activation__, <keras.layers.core.Activation object at 0x7f08ca4b0b70>
name: "inp1"
type {
multiArrayType {
shape: 3
shape: 384
shape: 384
dataType: FLOAT32
}
}
[3, 384, 384]
name: "out1"
type {
multiArrayType {
shape: 3
dataType: FLOAT32
}
}
因此,输出形状为3,这是不正确的。当我尝试摆脱input_name_shape_dict
时,我得到:
Please provide a finite height (H), width (W) & channel value (C) using input_name_shape_dict arg with key = 'inp1' and value = [None, H, W, C]
Converted .mlmodel can be modified to have flexible input shape using coremltools.models.neural_network.flexible_shape_utils
所以它想要NHWC。
尝试得出推断:
Layer 'conv_in' of type 'Convolution' has input rank 3 but expects rank at least 4
当我尝试为输入添加额外的尺寸时:
spec.description.input[0].type.multiArrayType.shape.extend([1, 3, 384, 384])
del spec.description.input[0].type.multiArrayType.shape[0]
del spec.description.input[0].type.multiArrayType.shape[0]
del spec.description.input[0].type.multiArrayType.shape[0]
[name: "inp1"
type {
multiArrayType {
shape: 1
shape: 3
shape: 384
shape: 384
dataType: FLOAT32
}
}
]
我可以推断出
Shape (1 x 384 x 384 x 3) was not in enumerated set of allowed shapes
跟随this advice并使其变为输入形状(1,1,384,384,3)
并没有帮助。
如何使其正常工作并产生正确的输出?
推断:
From PIL import Image
model_cml = coremltools.models.MLModel('my.mlmodel')
# load image
img = np.array(Image.open('patch4.png').convert('RGB'))[np.newaxis,...]/127.5 - 1
# Make predictions
predictions = model_cml.predict({'inp1':img})
# save result
res = predictions['out1']
res = np.clip((res[0]+1)*127.5,0,255).astype(np.uint8)
Image.fromarray(res).save('out32.png')
更新:
我能够使用输入(3,1,384,384)
来运行该模型,结果产生的是(1,3,3,384,384)
,这对我来说没有任何意义。
更新2:
在Keras中设置固定形状
def MyModel(inputs_shape=(384,384,3), channels=64):
inpt = Input(shape=inputs_shape)
解决了输出形状问题,但是我仍然无法运行模型(Layer 'conv_in' of type 'Convolution' has input rank 3 but expects rank at least 4
)
更新:
以下工作可消除输入和conv_in形状不匹配的情况。
1)。降级为coremltools==3.0
。版本3.3(模型版本4)似乎已损坏。
2。)在keras模型中使用固定形状,在coreml模型中不使用input_shape_dist,而使用可变形状
from keras.layers import *
from keras import Input, Model
import coremltools
def MyModel(inputs_shape=(384,384,3), channels=64):
inpt = Input(shape=inputs_shape)
# channels
skip = Conv2D(channels, (3, 3), strides=1, activation=None, padding='same', name='conv_in')(inpt)
out = Conv2D(3, (3, 3), strides=1, padding='same', activation='tanh',name='out')(skip)
return Model(inputs=inpt, outputs=out)
model = MyModel()
model.save('test.model')
print(model.summary())
'''
# v.3.3
coreml_model = coremltools.converters.keras.convert(model,
input_names=["image"],
output_names="out1",
image_scale=1.0,
model_precision='float32',
use_float_arraytype=True,
input_name_shape_dict={'inp1': [None, 384, 384, 3]}
)
'''
coreml_model = coremltools.converters.keras.convert(model,
input_names=["image"],
output_names="out1",
image_scale=1.0,
model_precision='float32',
)
spec = coreml_model._spec
from coremltools.models.neural_network import flexible_shape_utils
shape_range = flexible_shape_utils.NeuralNetworkMultiArrayShapeRange()
shape_range.add_channel_range((3,3))
shape_range.add_height_range((64, 384))
shape_range.add_width_range((64, 384))
flexible_shape_utils.update_multiarray_shape_range(spec, feature_name='image', shape_range=shape_range)
print(spec.description.input)
print(spec.description.input[0].type.multiArrayType.shape)
print(spec.description.output)
coremltools.utils.save_spec(spec, "my.mlmodel")
在推理脚本中,馈送形状为(1,1,3,384,384)
的数组:
img = np.zeros((1,1,3,384,384))
# Make predictions
predictions = model_cml.predict({'inp1':img})
res = predictions['out1'] # (3, 384,384)
答案 0 :(得分:1)
如果不正确,可以忽略mlmodel文件在输出形状中的内容。这更多的是元数据问题,即该模型仍然可以正常工作并做正确的事情。转换器并不总是能够找出正确的输出形状(不确定原因)。