Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_dist_transpiler_develop
typhoonzero
7 years ago
commit
67d6f3a8ce
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# Build using Docker
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## What Developers Need
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To contribute to PaddlePaddle, you need
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1. A computer -- Linux, BSD, Windows, MacOS, and
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1. Docker.
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Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image. We run all the tools by running this image.
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## General Process
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1. Retrieve source code.
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```bash
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git clone https://github.com/paddlepaddle/paddle
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```
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2. Install build tools into a Docker image.
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```bash
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cd paddle; docker build -t paddle:dev .
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```
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Please be aware of the `.` at the end of the command, which refers to the [`./Dockerfile` file](https://github.com/PaddlePaddle/Paddle/blob/develop/Dockerfile). `docker build` follows instructions in this file to create a Docker image named `paddle:dev`, and installs building tools into it.
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3. Build from source.
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This following command starts a Docker container that executes the Docker image `paddle:dev`, mapping the current directory to `/paddle/` in the container, and runs the default entry-point [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh) as specified in the Dockefile. `build.sh` invokes `cmake` and `make` to build PaddlePaddle source code, which had been mapped to `/paddle`, and writes outputs to `/paddle/build`, which maps to `build` in the current source directory on the computer.
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```bash
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docker run -v $PWD:/paddle paddle:dev
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```
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Above command builds a CUDA-enabled version. If we want to build a CPU-only version, we can type
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```bash
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docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev
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```
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4. Run unit tests.
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To run all unit tests using the first GPU of a node:
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```bash
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NV_GPU=0 nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest"
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```
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If we used `WITH_GPU=OFF` at build time, it generates only CPU-based unit tests, and we don't need nvidia-docker to run them. We can just run
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```bash
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docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest"
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```
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Sometimes we want to run a specific unit test, say `memory_test`, we can run
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```bash
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nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test"
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```
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5. Clean Build.
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Sometimes, we might want to clean all thirt-party dependents and built binaries. To do so, just
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```bash
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rm -rf build
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```
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## Docker, Or Not?
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- What is Docker?
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If you haven't heard of it, consider it something like Python's virtualenv.
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- Docker or virtual machine?
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Some people compare Docker with VMs, but Docker doesn't virtualize any hardware nor running a guest OS, which means there is no compromise on the performance.
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- Why Docker?
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Using a Docker image of build tools standardizes the building environment, which makes it easier for others to reproduce your problems and to help.
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Also, some build tools don't run on Windows or Mac or BSD, but Docker runs almost everywhere, so developers can use whatever computer they want.
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- Can I choose not to use Docker?
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Sure, you don't have to install build tools into a Docker image; instead, you can install them in your local computer. This document exists because Docker would make the development way easier.
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- How difficult is it to learn Docker?
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It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have.
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- Can I use my favorite IDE?
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Yes, of course. The source code resides on your local computer, and you can edit it using whatever editor you like.
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Many PaddlePaddle developers are using Emacs. They add the following few lines into their `~/.emacs` configure file:
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```emacs
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(global-set-key "\C-cc" 'compile)
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(setq compile-command
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"docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev")
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```
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so they could type `Ctrl-C` and `c` to build PaddlePaddle from source.
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- Does Docker do parallel building?
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Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your CPU cores.
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## Some Gotchas
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- Docker requires sudo
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An owner of a computer has the administrative privilege, a.k.a., sudo, and Docker requires this privilege to work properly. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn't require sudo.
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- Docker on Windows/MacOS builds slowly
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On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details.
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- Not enough disk space
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Examples in this article uses option `--rm` with the `docker run` command. This option ensures that stopped containers do not exist on hard disks. We can use `docker ps -a` to list all containers, including stopped. Sometimes `docker build` generates some intermediate dangling images, which also take disk space. To clean them, please refer to [this article](https://zaiste.net/posts/removing_docker_containers/).
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@ -0,0 +1,116 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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All layers just related to the detection neural network.
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"""
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from ..layer_helper import LayerHelper
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__all__ = ['detection_output', ]
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def detection_output(scores,
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loc,
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prior_box,
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prior_box_var,
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background_label=0,
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nms_threshold=0.3,
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nms_top_k=400,
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keep_top_k=200,
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score_threshold=0.01,
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nms_eta=1.0):
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"""
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**Detection Output Layer**
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This layer applies the NMS to the output of network and computes the
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predict bounding box location. The output's shape of this layer could
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be zero if there is no valid bounding box.
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Args:
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scores(Variable): A 3-D Tensor with shape [N, C, M] represents the
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predicted confidence predictions. N is the batch size, C is the
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class number, M is number of bounding boxes. For each category
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there are total M scores which corresponding M bounding boxes.
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loc(Variable): A 3-D Tensor with shape [N, M, 4] represents the
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predicted locations of M bounding bboxes. N is the batch size,
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and each bounding box has four coordinate values and the layout
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is [xmin, ymin, xmax, ymax].
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prior_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes,
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each box is represented as [xmin, ymin, xmax, ymax],
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[xmin, ymin] is the left top coordinate of the anchor box,
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if the input is image feature map, they are close to the origin
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of the coordinate system. [xmax, ymax] is the right bottom
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coordinate of the anchor box.
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prior_box_var(Variable): A 2-D Tensor with shape [M, 4] holds M group
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of variance.
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background_label(float): The index of background label,
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the background label will be ignored. If set to -1, then all
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categories will be considered.
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nms_threshold(float): The threshold to be used in NMS.
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nms_top_k(int): Maximum number of detections to be kept according
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to the confidences aftern the filtering detections based on
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score_threshold.
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keep_top_k(int): Number of total bboxes to be kept per image after
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NMS step. -1 means keeping all bboxes after NMS step.
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score_threshold(float): Threshold to filter out bounding boxes with
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low confidence score. If not provided, consider all boxes.
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nms_eta(float): The parameter for adaptive NMS.
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Returns:
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The detected bounding boxes which are a Tensor.
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Examples:
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.. code-block:: python
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pb = layers.data(name='prior_box', shape=[10, 4],
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append_batch_size=False, dtype='float32')
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pbv = layers.data(name='prior_box_var', shape=[10, 4],
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append_batch_size=False, dtype='float32')
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loc = layers.data(name='target_box', shape=[21, 4],
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append_batch_size=False, dtype='float32')
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scores = layers.data(name='scores', shape=[2, 21, 10],
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append_batch_size=False, dtype='float32')
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nmsed_outs = fluid.layers.detection_output(scores=scores,
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loc=loc,
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prior_box=pb,
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prior_box_var=pbv)
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"""
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helper = LayerHelper("detection_output", **locals())
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decoded_box = helper.create_tmp_variable(dtype=loc.dtype)
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helper.append_op(
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type="box_coder",
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inputs={
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'PriorBox': prior_box,
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'PriorBoxVar': prior_box_var,
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'TargetBox': loc
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},
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outputs={'OutputBox': decoded_box},
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attrs={'code_type': 'decode_center_size'})
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nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
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helper.append_op(
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type="multiclass_nms",
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inputs={'Scores': scores,
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'BBoxes': decoded_box},
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outputs={'Out': nmsed_outs},
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attrs={
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'background_label': 0,
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'nms_threshold': nms_threshold,
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'nms_top_k': nms_top_k,
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'keep_top_k': keep_top_k,
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'score_threshold': score_threshold,
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'nms_eta': 1.0
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})
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return nmsed_outs
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@ -0,0 +1,53 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import paddle.v2.fluid.layers as layers
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from paddle.v2.fluid.framework import Program, program_guard
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class TestBook(unittest.TestCase):
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def test_detection_output(self):
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program = Program()
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with program_guard(program):
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pb = layers.data(
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name='prior_box',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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pbv = layers.data(
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name='prior_box_var',
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shape=[10, 4],
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append_batch_size=False,
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dtype='float32')
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loc = layers.data(
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name='target_box',
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shape=[20, 4],
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append_batch_size=False,
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dtype='float32')
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scores = layers.data(
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name='scores',
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shape=[2, 20, 10],
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append_batch_size=False,
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dtype='float32')
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out = layers.detection_output(
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scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
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self.assertIsNotNone(out)
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print(str(program))
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if __name__ == '__main__':
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unittest.main()
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Reference in new issue