1.2.1.1 简单一行代码,包裹model即可 model=DataParallel(model.cuda(),device_ids=[0,1,2,3])data=data.cuda() 1.2.1.2 模型保存与加载 1.2.1.2.1 模型保存 # torch.save 注意模型需要调用 model.module.state_dict()# 例子一torch.save(net.module.state_dict(),PATH)# 例子二net=Net()PATH="entire_...
map_location=device)model.eval()input_names=['input']output_names=['output']x=torch.randn(1,3,224,224,device=device)#与实际输入数据的shape一致即可,取值没有影响,所以用了随机数torch.onnx.export(model,x,'name
# a tuple of one or more example inputs are needed to trace the model example_inputs = next(iter(trainloader))[0] # prepare model_prepared = quantize_fx.prepare_fx(model_to_quantize, qconfig_mapping, example_inputs) # no calibration needed when we only have dynamic/weight_only quantiza...
with torch.inference_mode(): for _ in range(10): x = torch.rand(1, 2, 28, 28) model_prepared(x) # quantize model_quantized = quantize_fx.convert_fx(model_prepared) PS:直观对比EAGER模式和FX模式的代码量,可以看出FX模式真香! 感知量化[Quantization-aware Training (QAT)] PTQ方法适用于大型...
model=m, qconfig_spec={nn.LSTM, nn.Linear}, dtype=torch.qint8, inplace=False ) ## FX MODE from torch.quantization import quantize_fx qconfig_dict = {"": torch.quantization.default_dynamic_qconfig} # An empty key denotes the default applied to all modules ...
API Example: importtorch# define a floating point model where some layers could be statically quantizedclassM(torch.nn.Module):def__init__(self):super(M, self).__init__()# QuantStub converts tensors from floating point to quantizedself.quant = torch.quantization.QuantStub() ...
我们创建 UNet2DConditionModel 的下采样块的副本。 复制块通过零卷积层传递(权重初始化设置为零的层)。这样做是为了能更快地训练模型。如果没有通过零卷积层传递,复制块的添加可能会修改输入(包括文本潜变量、嘈杂图像的潜变量和输入人物轮廓的潜变量)到上采样块,导致上采样器之前没有见过的分布(例如,当复制块最...
Objective: My primary goal is to accelerate my model's performance using int8 + fp16 quantization. To achieve this, I first need to quantize the model and then calibrate it. As far as I understand, there are two quantization methods avai...
Example data Calibration dataset Prepare the quantization configuration: config_static=ipex.quantization.default_static_qconfig In this code sample, we are using the default quantization configuration, but you can also define your own. Prepare the model using the declared configuration: ...
The model is ready for graph-optimizations. I would think that this means that the quantized regions at the top and bottom of the modules grow until they "meet". In that event one could either have a re-quantization step (adjusting min/max and the quantized values) in the place offdequa...