Support face-parsing model

[tucan9389]

Source Model Link

https://github.com/zllrunning/face-parsing.PyTorch

Core ML Model Download Link

https://github.com/tucan9389/SemanticSegmentation-CoreML/releases/download/support-face-parsing/FaceParsing.mlmodel

Model Spec

• Input: 512x512 image
• Output: 512x512 (Int32)
  • Catetory index of each pixel
  • Defined 19 categories: ['background', 'skin', 'l_brow', 'r_brow', 'l_eye', 'r_eye', 'eye_g', 'l_ear', 'r_ear', 'ear_r', 'nose', 'mouth', 'u_lip', 'l_lip', 'neck', 'neck_l', 'cloth', 'hair', 'hat']
• Size: 52.7 MB
• Inference time: 30-50 ms in iPhone 11 Pro

Conversion Script

import torch

import os.path as osp
import json
from PIL import Image
import torchvision.transforms as transforms
from model import BiSeNet

import coremltools as ct

dspth = 'res/test-img'
cp = '79999_iter.pth'
device = torch.device('cpu')

output_mlmodel_path = "FaceParsing.mlmodel"

labels = ['background', 'skin', 'l_brow', 'r_brow', 'l_eye', 'r_eye', 'eye_g', 'l_ear', 'r_ear', 'ear_r',
            'nose', 'mouth', 'u_lip', 'l_lip', 'neck', 'neck_l', 'cloth', 'hair', 'hat']
n_classes = len(labels)
print("n_classes:", n_classes)

class MyBiSeNet(torch.nn.Module):
    def __init__(self, n_classes, pretrained_model_path):
        super(MyBiSeNet, self).__init__()
        self.model = BiSeNet(n_classes=n_classes)
        self.model.load_state_dict(torch.load(pretrained_model_path, map_location=device))
        self.model.eval()

    def forward(self, x):
        x = self.model(x)
        x = x[0]
        x = torch.argmax(x, dim=1)
        x = torch.squeeze(x)
        return x

pretrained_model_path = osp.join('res/cp', cp)
model = MyBiSeNet(n_classes=n_classes, pretrained_model_path=pretrained_model_path)
model.eval()

example_input = torch.rand(1, 3, 512, 512)  # after test, will get 'size mismatch' error message with size 256x256
preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(
        mean=[0.485, 0.456, 0.406],
        std=[0.229, 0.224, 0.225],
    ),
])

traced_model = torch.jit.trace(model, example_input)


# Convert to Core ML using the Unified Conversion API
print(example_input.shape)

scale = 1.0 / (0.226 * 255.0)
red_bias   = -0.485 / 0.226
green_bias = -0.456 / 0.226
blue_bias  = -0.406 / 0.226

mlmodel = ct.convert(
    traced_model,
    inputs=[ct.ImageType(name="input",
                         shape=example_input.shape,
                         scale=scale,
                         color_layout="BGR",
                         bias=[blue_bias, green_bias, red_bias])], #name "input_1" is used in 'quickstart'
)



labels_json = {"labels": labels}

mlmodel.user_defined_metadata["com.apple.coreml.model.preview.type"] = "imageSegmenter"
mlmodel.user_defined_metadata['com.apple.coreml.model.preview.params'] = json.dumps(labels_json)

mlmodel.save(output_mlmodel_path)

import coremltools.proto.FeatureTypes_pb2 as ft

spec = ct.utils.load_spec(output_mlmodel_path)

for feature in spec.description.output:
    if feature.type.HasField("multiArrayType"):
        feature.type.multiArrayType.dataType = ft.ArrayFeatureType.INT32

ct.utils.save_spec(spec, output_mlmodel_path)

[lekhanhtoan37]

Thanks you very much. It saves me a lot of time.

[vjdaemp]

@tucan9389 I am interested in using coremltools to convert pytorch models to coreml models. Is the coremltools sample the best guide to follow for learning purposes? https://coremltools.readme.io/docs/pytorch-conversion-examples

I am able to run the face-parsing pytorch model, and successfully run the test script (after modifications to script to remove hard-coded values). However when I run your conversion script I am getting the failures below:

(face_parsing) vtron@vtron-Blade-15:~/Desktop/Pytorch/face-parsing.PyTorch$ python coreml_convert.py 
n_classes: 19
torch.Size([1, 3, 512, 512])
Traceback (most recent call last):
  File "/home/vtron/Pytorch/face-parsing.PyTorch/coreml_convert.py", line 60, in <module>
    mlmodel = ct.convert(
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/_converters_entry.py", line 176, in convert
    mlmodel = mil_convert(
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 128, in mil_convert
    proto = mil_convert_to_proto(model, convert_from, convert_to,
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 171, in mil_convert_to_proto
    prog = frontend_converter(model, **kwargs)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 85, in __call__
    return load(*args, **kwargs)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 85, in load
    raise e
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 75, in load
    prog = converter.convert()
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 220, in convert
    const = mb.const(val=val, mode=mode, name=name)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/ops/registry.py", line 62, in add_op
    return cls._add_op(op_cls, **kwargs)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/builder.py", line 193, in _add_op
    new_op.type_value_inference()
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/operation.py", line 189, in type_value_inference
    output_types = self.type_inference()
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/ops/defs/control_flow.py", line 135, in type_inference
    builtin_type, _ = self._get_type_val(self.val.val)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/ops/defs/control_flow.py", line 175, in _get_type_val
    _, builtin_type = numpy_val_to_builtin_val(value)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/types/type_mapping.py", line 263, in numpy_val_to_builtin_val
    builtintype = numpy_type_to_builtin_type(npval.dtype)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/mil/types/type_mapping.py", line 233, in numpy_type_to_builtin_type
    raise TypeError("Unsupported numpy type: %s" % (nptype))
TypeError: Unsupported numpy type: float32

Any ideas or recommendations to resolve above issues would be greatly appreciated!

[vjdaemp]

@tucan9389 Numpy 1.20.0 is not compatible with coremltools, according to apple/coremltools#1077

I was able to resolve the exception by updating the environment to install version 1.19.5 of numpy.

After changing this I encountered new error:

n_classes: 19
torch.Size([1, 3, 512, 512])
Converting Frontend ==> MIL Ops:   5%|▎    | 15/295 [00:00<00:00, 3844.22 ops/s]
Traceback (most recent call last):
  File "/home/vtron/Development/Pytorch/face-parsing.PyTorch/coreml_convert.py", line 60, in <module>
    mlmodel = ct.convert(
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/_converters_entry.py", line 176, in convert
    mlmodel = mil_convert(
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 128, in mil_convert
    proto = mil_convert_to_proto(model, convert_from, convert_to,
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 171, in mil_convert_to_proto
    prog = frontend_converter(model, **kwargs)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/converter.py", line 85, in __call__
    return load(*args, **kwargs)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 85, in load
    raise e
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/load.py", line 75, in load
    prog = converter.convert()
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/converter.py", line 224, in convert
    convert_nodes(self.context, self.graph)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 56, in convert_nodes
    _add_op(context, node)
  File "/home/vtron/anaconda3/envs/face_parsing/lib/python3.9/site-packages/coremltools/converters/mil/frontend/torch/ops.py", line 417, in _convolution
    raise ValueError(
ValueError: unexpected number of inputs for node input.1 (_convolution): 13

According to apple/coremltools#1012 coremltools version 4.1 must be used with torch 1.8. My environment was using 4.0.

I resolved this exception by installing coremltools 4.1 (instead of 4.0). The CoreML model has now been created :)

I will try using your conversion script as reference to create new conversion scripts for some other pytorch models I've been working with... Any advice or tips would be appreciated.