52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
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from typing import Any, List, Optional, Union
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import torch
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from torch import nn
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def _replace_relu(module: nn.Module) -> None:
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reassign = {}
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for name, mod in module.named_children():
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_replace_relu(mod)
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# Checking for explicit type instead of instance
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# as we only want to replace modules of the exact type
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# not inherited classes
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if type(mod) is nn.ReLU or type(mod) is nn.ReLU6:
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reassign[name] = nn.ReLU(inplace=False)
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for key, value in reassign.items():
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module._modules[key] = value
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def quantize_model(model: nn.Module, backend: str) -> None:
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_dummy_input_data = torch.rand(1, 3, 299, 299)
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if backend not in torch.backends.quantized.supported_engines:
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raise RuntimeError("Quantized backend not supported ")
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torch.backends.quantized.engine = backend
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model.eval()
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# Make sure that weight qconfig matches that of the serialized models
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if backend == "fbgemm":
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model.qconfig = torch.ao.quantization.QConfig( # type: ignore[assignment]
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activation=torch.ao.quantization.default_observer,
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weight=torch.ao.quantization.default_per_channel_weight_observer,
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)
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elif backend == "qnnpack":
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model.qconfig = torch.ao.quantization.QConfig( # type: ignore[assignment]
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activation=torch.ao.quantization.default_observer, weight=torch.ao.quantization.default_weight_observer
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)
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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model.fuse_model() # type: ignore[operator]
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torch.ao.quantization.prepare(model, inplace=True)
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model(_dummy_input_data)
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torch.ao.quantization.convert(model, inplace=True)
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def _fuse_modules(
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model: nn.Module, modules_to_fuse: Union[List[str], List[List[str]]], is_qat: Optional[bool], **kwargs: Any
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):
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if is_qat is None:
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is_qat = model.training
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method = torch.ao.quantization.fuse_modules_qat if is_qat else torch.ao.quantization.fuse_modules
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return method(model, modules_to_fuse, **kwargs)
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