From 37b20cfaefd1a46f4580b81abf62ab62aa18032d Mon Sep 17 00:00:00 2001 From: digger yu Date: Fri, 14 Jul 2023 15:45:18 +0800 Subject: [PATCH] fix some typo en/docs/basics/ --- en/docs/basics/01_quickstart.md | 2 +- en/docs/basics/04_build_network.md | 2 +- en/docs/basics/05_autograd.md | 2 +- en/docs/basics/08_nn_graph.md | 8 ++++---- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/en/docs/basics/01_quickstart.md b/en/docs/basics/01_quickstart.md index 1a9c80c4..a56aa73e 100644 --- a/en/docs/basics/01_quickstart.md +++ b/en/docs/basics/01_quickstart.md @@ -12,7 +12,7 @@ from flowvision import datasets ``` [FlowVision](https://github.com/Oneflow-Inc/vision) is a tool library matching with OneFlow, specific to computer vision tasks. It contains a number of models, data augmentation methods, data transformation operations and datasets. Here we import and use the data transformation module `transforms` and datasets module `datasets` provided by FlowVision. -Settting batch size and device： +Setting batch size and device： ```python BATCH_SIZE=64 diff --git a/en/docs/basics/04_build_network.md b/en/docs/basics/04_build_network.md index d16f113d..37428696 100644 --- a/en/docs/basics/04_build_network.md +++ b/en/docs/basics/04_build_network.md @@ -131,7 +131,7 @@ Comparing the similarities and differences between the `NeuralNetwork` and `Func `nn.Sequential` is a special container. Any class inherited from `nn.Module` can be placed in it. -Its specialty is that when Sequential propagates forward, Sequential automatically "concatenates" the layers contained in the container. Specifically, the output of the previous layer will be automatically transferred as the input of the next layer according to the sequence of Sequential added to each layer until the output of the last layer of the whole Moudle is obtained. +Its specialty is that when Sequential propagates forward, Sequential automatically "concatenates" the layers contained in the container. Specifically, the output of the previous layer will be automatically transferred as the input of the next layer according to the sequence of Sequential added to each layer until the output of the last layer of the whole Module is obtained. The following is an example of building a network without Sequential (not recommended): diff --git a/en/docs/basics/05_autograd.md b/en/docs/basics/05_autograd.md index 8c346b56..b11ac489 100644 --- a/en/docs/basics/05_autograd.md +++ b/en/docs/basics/05_autograd.md @@ -139,7 +139,7 @@ tensor(20., dtype=oneflow.float32) ### Disabled Gradient Calculation -By default, OneFlow will trace and calculate gradients of Tensors with `requires_grad = Ture`. +By default, OneFlow will trace and calculate gradients of Tensors with `requires_grad = True`. However, in some cases, we don't need OneFlow to keep tracing gradients such as just wanting the forward pass for inference. Then we can use [oneflow.no_grad](https://oneflow.readthedocs.io/en/v0.8.1/generated/oneflow.no_grad.html) or [oneflow.Tensor.detach](https://oneflow.readthedocs.io/en/master/generated/oneflow.Tensor.detach.html#oneflow.Tensor.detach) to set. ```python diff --git a/en/docs/basics/08_nn_graph.md b/en/docs/basics/08_nn_graph.md index 0a47b8ae..053a0249 100644 --- a/en/docs/basics/08_nn_graph.md +++ b/en/docs/basics/08_nn_graph.md @@ -109,7 +109,7 @@ class GraphMyLinear(nn.Graph): The simple example above contains the important steps needed to customize a Graph: - Inherits `nn.Graph`. -- Call `super().__init__()` at the begining of `__init__` method to get OneFlow to do the necessary initialization for the Graph. +- Call `super().__init__()` at the beginning of `__init__` method to get OneFlow to do the necessary initialization for the Graph. - In `__init__`, reuse the `nn.Module` object in Eager mode (`self.model = model`) - Describes the computational process in `build` method. @@ -323,7 +323,7 @@ If you use `print` **after** the Graph object is called, in addition to the stru **The second** way is that by calling the [debug](https://oneflow.readthedocs.io/en/v0.8.1/generated/oneflow.nn.Graph.debug.html) method of Graph objects, Graph’s debug mode is turned on. ```python -graph_mobile_net_v2.debug(v_level=1) # The defalut of v_level is 0. +graph_mobile_net_v2.debug(v_level=1) # The default of v_level is 0. ``` which can also be written in a simplified way: @@ -337,7 +337,7 @@ OneFlow prints debug information when it compiles the computation graph. If the ```text (GRAPH:GraphMobileNetV2_0:GraphMobileNetV2) end building graph. (GRAPH:GraphMobileNetV2_0:GraphMobileNetV2) start compiling plan and init graph runtime. -(GRAPH:GraphMobileNetV2_0:GraphMobileNetV2) end compiling plan and init graph rumtime. +(GRAPH:GraphMobileNetV2_0:GraphMobileNetV2) end compiling plan and init graph runtime. ``` The advantage of using `debug` is that the debug information is composed and printed at the same time, which makes it easy to find the problem if there is any error in the graph building process. @@ -358,7 +358,7 @@ In addition, in order for developers to have a clearer understanding of the type | MODULE | Corresponding to `nn.Module` , MODULE can be under the Graph tag, and there is also a hierarchical relationship between multiple modules. | `(MODULE:model:MobileNetV2())`, and `MobileNetV2` reuses the Module class name in Eager mode for users. | | PARAMETER | Shows the clearer information of weight and bias. In addition, when building the graph, the data content of the tensor is less important, so it is more important for building network to only display the meta information of the tensor. | `(PARAMETER:model.features.0.1.weight:tensor(..., device='cuda:0', size=(32,), dtype=oneflow.float32, requires_grad=True))` | | BUFFER | Statistical characteristics and other content generated during training, such as running_mean and running_var. | `(BUFFER:model.features.0.1.running_mean:tensor(..., device='cuda:0', size=(32,), dtype=oneflow.float32))` | -| INPUT & OUPTUT | Tensor information representing input and output. | `(INPUT:_model_input.0.0_2:tensor(..., device='cuda:0', is_lazy='True', size=(16, 3, 32, 32), dtype=oneflow.float32))` | +| INPUT & OUTPUT | Tensor information representing input and output. | `(INPUT:_model_input.0.0_2:tensor(..., device='cuda:0', is_lazy='True', size=(16, 3, 32, 32), dtype=oneflow.float32))` | In addition to the methods described above, getting the parameters of the gradient during the training process, accessing to the learning rate and other functions are also under development and will come up soon.