QuantConv1d、QuantConv2d、Quant Conv3d、QuantConvTranspose1d、quantConvTranspose 2d、QuantiConvTransbose 3d、QuantiLinear、QuantAvgPool1d、QuantAvg Pool2d、Quant AvgPool 3d、QuantMaxPool1d、QuantMax Pool2D、Quant MaxPool3d 4、训练后量化 训练后量化(Post-Training Quantization)是一种在训练完成后对深度神...
self.convtrans = nn.Sequential(nn.ConvTranspose2d(in_channels=32, out_channels=64, kernel_size=2, stride=4), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(kernel_size=2)) #(64, 23, 23) -> (128, 5, 5) self.conv2 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128,...
nn.Conv2dis used to define the convolutional layers. We define the channels they receive and how much should they return along with the kernel size. We start from 3 channels, as we are using RGB images nn.MaxPool2dis a max-pooling layer that just requires the kernel size and the stride...
"conv2d", "conv3d", "conv_tbc", "conv_transpose1d", "conv_transpose2d", "conv_transpose3d", "cosine_similarity", "dropout_with_byte_mask", "elu_", "gelu", "handle_torch_function", "hardshrink", "hardtanh_", "has_torch_function", "has_torch_funct...
include abiasparameter, even withbias=Falsespecified. The regression is now fixed in PyTorch 1.9, making thebiasflag correctly apply to both the input and output projection layers. This fix is BC-breaking for thebias=Falsecase as it will now result in nobiasparameter for the output projection ...
Layer (type) Output Shape Param # === Conv2d-1 [-1, 64, 112, 112] 9,408 BatchNorm2d-2 [-1, 64, 112, 112] 128 ReLU-3 [-1, 64, 112, 112] 0 MaxPool2d-4 [-1, 64, 56, 56] 0 Conv2d-5 [-1, 64, 56, 56] 36,864 BatchNorm2d...
( model_name=args.model_name, num_classes=args.output_dim ) model = model.cuda().eval() if args.use_asp: from apex.contrib.sparsity import ASP ASP.init_model_for_pruning(model, mask_calculator="m4n2_1d", verbosity=2, whitelist=[torch.nn.Linear, torch.nn.Conv2d], allow_recompute_...
"conv2d", "conv3d", "conv_tbc", "conv_transpose1d", "conv_transpose2d", "conv_transpose3d", "cosine_similarity", "dropout_with_byte_mask", "elu_", "gelu", "handle_torch_function", "hardshrink", "hardtanh_", "has_torch_function", "has_torch_funct...
"conv2d", "conv3d", "conv_tbc", "conv_transpose1d", "conv_transpose2d", "conv_transpose3d", "cosine_similarity", "dropout_with_byte_mask", "elu_", "gelu", "handle_torch_function", "hardshrink", "hardtanh_", "has_torch_function", "has_torch_funct...
"conv_transpose2d", "conv_transpose3d", "cosine_similarity", "dropout_with_byte_mask", "elu_", "gelu", "handle_torch_function", "hardshrink", "hardtanh_", "has_torch_function", "has_torch_function_unary", "has_torch_function_variadic", "leaky_relu_", ...