device_mesh, layer_parallelize_plan) # Parallelize the output submodule. output_parallelize_plan = RowwiseParallel(input_layouts=Replicate()) parallelize_module(model.lm_head, device_mesh, output_
(IntermediateLayerGetter, self).__init__(layers) self.return_layers = orig_return_layers def forward(self, x): out = OrderedDict() for name, module in self.named_children(): x = module(x) if name in self.return_layers: out_name = self.return_layers[name] out[out_name] = x ...
首先通过IntermediateLayerGetter得到resnet不同分辨率的features: IntermediateLayerGetter( (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (bn1): FrozenBatchNorm2d(64) (relu): ReLU(inplace=True) (maxpool): MaxPool2d(kernel_size=3, stride=2, ...
print_model_profile(profile_step=1, module_depth=-1, top_modules=3, detailed=True, output_file=None)-打印模型profile。 stop_profile()-停止性能分析。这将停止模型中的浮点运算计数。 end_profile()-进行清理。这将清理在性能分析过程中添加到模型的性能分析属性。这应该在性能分析结束并且在调用get_total...
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers) # 输出层 self.output = nn.Linear(hidden_dim, num_class) def forward(self, inputs, lengths): #注意输入的input,每一列表示一个样本,总共有batch_size列 inputs = torch.transpose(inputs, 0, 1) ...
outputcontains the output of the traced function in itsargs[0]attribute. This corresponds to the “return” statement in the Graph printout. 相比torchscript的IR,FX的可就简单多了,我们理解使用起来也很简单。 symbolic tracer 回到一开头示例的那段代码,其中有一行是symbolic_traced : torch.fx.GraphModule...
Display the last two layers of the updated networknetUpdated. netUpdated.Layers(end-1:end) ans = 2×1 Layer array with layers: 1 'new_fc' Fully Connected 5 fully connected layer 2 'activation_1' Softmax softmax The output shows the new layer followed by the softmax l...
""" Get a vector of weights for every channel in the target layer.Methods that return weights channels,will typically need to only implement this function. """@staticmethoddef get_cam_weights(grads):return np.mean(grads, axis=(2, 3), keepdims=True)@staticmethoddef get_loss(output, target...
() # sum up batch loss pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability ddp_loss[1] += pred.eq(target.view_as(pred)).sum().item() ddp_loss[2] += len(data) dist.all_reduce(ddp_loss, op=dist.ReduceOp.SUM) if rank == 0: test_loss...
(model,return_layers=return_layers,keep_output=True)mid_outputs,model_output=mid_getter(torch.randn(1,2))print(model_output)>>tensor([[0.3219]],grad_fn=<AddmmBackward>)print(mid_outputs)>>OrderedDict([('fc2',tensor([[-1.5125,0.9334]],grad_fn=<AddmmBackward>)), ('interaction',tensor([...