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module: models.quantizationIssues related to the quantizable/quantized modelsIssues related to the quantizable/quantized models
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Currently if the user wants to have the fused model while preserving the original, they have to do
import copy
import torchvision.models.quantization as models
model = models.resnet18(pretrained=True, progress=True, quantize=False)
model_fused = copy.deepcopy(model)
model_fused.fuse_model()
Given that the quantization API provides an inplace variant for the torch.quantization.fuse_modules
, we need to have an option for inplace in the model methods as well.
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module: models.quantizationIssues related to the quantizable/quantized modelsIssues related to the quantizable/quantized models