请问如何把Apple's MobileCLIP 导出为CoreML文件 #45
Comments
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My export code is very messy and disorganized. I haven't had the chance to clean it up yet. If you want to learn about model exporting, looking at these two .ipynb code snippet should be sufficient. I don't intend to add new ipynb files that would make the existing code more bloated. Additionally, it will be helpful for future readers if your post is in English. |
Thank you for the reply. Thanks |
In my current local Macbook, the env is: |
follow your env: pip install coremltools==8.0b2 torch==1.13.1 numpy==1.25.2i got an new error:
(python310) ➜ Queryable git:(cloud) ✗ pip show torch stack is : AttributeError Traceback (most recent call last)
Cell In[3], line 3
1 import torch
2 import clip
----> 3 import coremltools as ct
4 import numpy as np
5 from PIL import Image
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/__init__.py:97
93 _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_MILPROGRAM = _SPECIFICATION_VERSION_IOS_15
96 # expose sub packages as directories
---> 97 from . import converters, models, optimize, proto
98 # expose unified converter in coremltools package level
99 from .converters import ClassifierConfig
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/__init__.py:11
8 from . import coreml
10 if _HAS_TORCH:
---> 11 from . import torch
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/__init__.py:6
1 # Copyright (c) 2024, Apple Inc. All rights reserved.
2 #
3 # Use of this source code is governed by a BSD-3-clause license that can be
4 # found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
----> 6 from coremltools.optimize.torch import (
7 base_model_optimizer,
8 layerwise_compression,
9 optimization_config,
10 palettization,
11 pruning,
12 quantization,
13 )
15 from ._logging import init_root_logger as _init_root_logger
17 _logger = _init_root_logger()
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/palettization/__init__.py:59
1 # Copyright (c) 2024, Apple Inc. All rights reserved.
2 #
3 # Use of this source code is governed by a BSD-3-clause license that can be
4 # found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
6 """
7 .. _coremltools_optimize_torch_palettization:
8
(...)
56 :members: compress
57 """
---> 59 from .fake_palettize import FakePalettize
60 from .palettization_config import DKMPalettizerConfig, ModuleDKMPalettizerConfig
61 from .palettizer import DKMPalettizer
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/palettization/fake_palettize.py:27
25 from ._utils import get_shard_list as _get_shard_list
26 from ._utils import vectorize as _vectorize
---> 27 from .palettization_config import DEFAULT_PALETTIZATION_ADVANCED_OPTIONS
29 # This is the maximum torch version currently supported for supporting the
30 # FakePalettizerTensorHook as the backward graph tracing that the pack/unpack method
31 # does accepts certain names for functions which have been changed after this
32 # torch version
33 MAX_TORCH_VERSION_FOR_PALETT_MAX_MEM = "2.0.1"
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/palettization/palettization_config.py:397
392 else:
393 assert value is None, "group_size can't be specified along with per_tensor granularity."
396 _default_module_type_configs = _OrderedDict(
--> 397 {
398 key: ModuleDKMPalettizerConfig.from_dict(val)
399 for key, val in DEFAULT_PALETTIZATION_SCHEME.items()
400 }
401 )
404 _GlobalConfigType = _NewType(
405 "GlobalConfigType",
406 _Union[
(...)
409 ],
410 )
411 _ModuleTypeConfigType = _NewType(
412 "ModuleTypeConfigType", _Dict[_Union[_Callable, str], _GlobalConfigType]
413 )
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/palettization/palettization_config.py:398, in <dictcomp>(.0)
392 else:
393 assert value is None, "group_size can't be specified along with per_tensor granularity."
396 _default_module_type_configs = _OrderedDict(
397 {
--> 398 key: ModuleDKMPalettizerConfig.from_dict(val)
399 for key, val in DEFAULT_PALETTIZATION_SCHEME.items()
400 }
401 )
404 _GlobalConfigType = _NewType(
405 "GlobalConfigType",
406 _Union[
(...)
409 ],
410 )
411 _ModuleTypeConfigType = _NewType(
412 "ModuleTypeConfigType", _Dict[_Union[_Callable, str], _GlobalConfigType]
413 )
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/_utils/python_utils.py:87, in DictableDataClass.from_dict(cls, data_dict)
85 converter = _cattrs.Converter(forbid_extra_keys=True)
86 converter.register_structure_hook(_torch.Tensor, lambda obj, type: obj)
---> 87 return converter.structure_attrs_fromdict(data_dict, cls)
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/cattrs/converters.py:742, in BaseConverter.structure_attrs_fromdict(self, obj, cl)
739 # try .alias and .name because this code also supports dataclasses!
740 conv_obj[getattr(a, "alias", a.name)] = self._structure_attribute(a, val)
--> 742 return cl(**conv_obj)
File <attrs generated init coremltools.optimize.torch.palettization.palettization_config.ModuleDKMPalettizerConfig>:13, in __init__(self, n_bits, weight_threshold, granularity, group_size, channel_axis, enable_per_channel_scale, milestone, cluster_dim, quant_min, quant_max, dtype, lut_dtype, quantize_activations, cluster_permute, palett_max_mem, kmeans_max_iter, prune_threshold, kmeans_init, kmeans_opt1d_threshold, enforce_zero, palett_mode, palett_tau, palett_epsilon, palett_lambda, add_extra_centroid, palett_cluster_tol, palett_min_tsize, palett_unique, palett_shard, palett_batch_mode, palett_dist, per_channel_scaling_factor_scheme, percentage_palett_enable, kmeans_batch_threshold, kmeans_n_init, zero_threshold, kmeans_error_bnd, partition_size, cluster_dtype)
11 _setattr('quant_min', quant_min)
12 _setattr('quant_max', quant_max)
---> 13 _setattr('dtype', __attr_converter_dtype(dtype))
14 _setattr('lut_dtype', lut_dtype)
15 _setattr('quantize_activations', quantize_activations)
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/optimize/torch/_utils/torch_utils.py:101, in maybe_convert_str_to_dtype(dtype)
86 def maybe_convert_str_to_dtype(dtype: _Union[str, _torch.dtype]) -> _torch.dtype:
87 _str_to_dtype_map = {
88 "quint8": _torch.quint8,
89 "qint8": _torch.qint8,
90 "float32": _torch.float32,
91 "int8": _torch.int8,
92 "uint8": _torch.uint8,
93 # Torch doesn't support int4 or int3
94 # but we can represent it as int8
95 "int4": _torch.int8,
96 "uint4": _torch.uint8,
97 "qint4": _torch.qint8,
98 "quint4": _torch.quint8,
99 "uint3": _torch.uint8,
100 "int3": _torch.int8,
--> 101 "fp8_e4m3": _torch.float8_e4m3fn,
102 "fp8_e5m2": _torch.float8_e5m2,
103 }
104 if isinstance(dtype, str):
105 dtype = dtype.lower() |
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when i downgrade coremltools to 6.3 , but new error:
TypeError Traceback (most recent call last)
Cell In[19], line 3
1 max_seq_length = 77
----> 3 text_encoder_model = ct.convert(
4 traced_model,
5 convert_to="mlprogram",
6 minimum_deployment_target=ct.target.iOS16,
7 inputs=[ct.TensorType(name="prompt",
8 shape=[1,max_seq_length],
9 dtype=np.int32)],
10 outputs=[ct.TensorType(name="embOutput", dtype=np.float32)],
11 # compute_units=ct.ComputeUnit[args.compute_unit],
12 # skip_model_load=True,
13 )
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/converters/_converters_entry.py:635, in convert(model, source, inputs, outputs, classifier_config, minimum_deployment_target, convert_to, compute_precision, skip_model_load, compute_units, package_dir, debug, pass_pipeline, states)
633 return True
634 else:
--> 635 raise ValueError(f"Invalid value of the argument 'compute_precision': {compute_precision}")
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:188, in mil_convert(model, convert_from, convert_to, compute_units, **kwargs)
149 @_profile
150 def mil_convert(
151 model,
(...)
155 **kwargs
156 ):
157 """
158 Convert model from a specified frontend `convert_from` to a specified
159 converter backend `convert_to`.
(...)
186 See `coremltools.converters.convert`
187 """
--> 188 return _mil_convert(model, convert_from, convert_to, ConverterRegistry, MLModel, compute_units, **kwargs)
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/converters/mil/converter.py:231, in _mil_convert(model, convert_from, convert_to, registry, modelClass, compute_units, **kwargs)
227 elif convert_to == "mlprogram":
228 package_path = _create_mlpackage(
229 proto, kwargs.get("weights_dir"), kwargs.get("package_dir")
230 )
--> 231 return modelClass(
232 package_path,
233 is_temp_package=not kwargs.get("package_dir"),
234 mil_program=mil_program,
235 skip_model_load=kwargs.get("skip_model_load", False),
236 compute_units=compute_units,
237 )
239 return modelClass(
240 proto,
241 mil_program=mil_program,
242 skip_model_load=kwargs.get("skip_model_load", False),
243 compute_units=compute_units,
244 )
File ~/anaconda3/envs/python310/lib/python3.10/site-packages/coremltools/models/model.py:337, in __init__(self, model, is_temp_package, mil_program, skip_model_load, compute_units, weights_dir, function_name)
333 if not isinstance(compute_units, _ComputeUnit):
334 raise TypeError('"compute_units" parameter must be of type: coremltools.ComputeUnit')
335 elif (compute_units == _ComputeUnit.CPU_AND_NE
336 and _is_macos()
--> 337 and _macos_version() < (13, 0)
338 ):
339 raise ValueError(
340 'coremltools.ComputeUnit.CPU_AND_NE is only available on macOS >= 13.0'
341 )
342 self.compute_unit = compute_units |
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Okay, I finally found someone has already made the .ipynb notebook for converting MobileCLIP: And the full project: https://github.com/fguzman82/CLIP-Finder2 |
你好，感谢开源如此优秀的应用，
我是一个CoreML新手，看您的应用最近支持了Apple's MobileCLIP，
我想学习一下如何在iOS开发中使用，也就是想学习如何把MobileCLIP导出成 .mlmodelc 格式的。
请问能添加一个新的 .ipynb 文件，用于描述如何导出
ImageEncoder_mobileCLIP_s2.mlmodelc
merges.txt
TextEncoder_mobileCLIP_s2.mlmodelc
vocab.json
这四个文件的么？
感谢～
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