mirror of
https://github.com/zebrajr/pytorch.git
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b2f489dc57
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/40212 Test Plan: Imported from OSS Reviewed By: z-a-f Differential Revision: D22144745 fbshipit-source-id: 38a19b5afdddbbce262eea8ddf5b68458e6017b3
38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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from .quantize import *
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from .observer import *
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from .qconfig import *
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from .fake_quantize import *
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from .fuse_modules import fuse_modules
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from .stubs import *
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from .quantize_jit import *
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def default_eval_fn(model, calib_data):
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r"""
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Default evaluation function takes a torch.utils.data.Dataset or a list of
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input Tensors and run the model on the dataset
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"""
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for data, target in calib_data:
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model(data)
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_all__ = [
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'QuantWrapper', 'QuantStub', 'DeQuantStub',
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# Top level API for eager mode quantization
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'quantize', 'quantize_dynamic', 'quantize_qat',
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'prepare', 'convert', 'prepare_qat',
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# Top level API for graph mode quantization
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'quantize_jit', 'quantize_dynamic_jit',
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# Sub functions for `prepare` and `swap_module`
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'propagate_qconfig_', 'add_quant_dequant', 'add_observer_', 'swap_module',
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'default_eval_fn', 'get_observer_dict',
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# Observers
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'ObserverBase', 'WeightObserver', 'observer', 'default_observer',
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'default_weight_observer',
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# QConfig
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'QConfig', 'default_qconfig', 'default_dynamic_qconfig', 'float16_dynamic_qconfig',
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# QAT utilities
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'default_qat_qconfig', 'prepare_qat', 'quantize_qat',
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# module transformations
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'fuse_modules',
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]
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