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update docs

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ppwwyyxx committed Aug 29, 2019
1 parent 47de91d commit f43309f0eb437c36b1249627ab29340c99d1dd3c
@@ -14,7 +14,7 @@ There are two ways to do inference during training.
"evaluate some tensors for each input, and aggregate the results in the end".
You can use the `InferenceRunner` interface with some `Inferencer`.
This will further support prefetch & data-parallel inference.

Currently this lacks documentation, but you can refer to examples
that uses `InferenceRunner` or custom `Inferencer` to learn more.

@@ -55,8 +55,8 @@ predictor = OfflinePredictor(pred_config)
output1_array, output2_array = predictor(input1_array, input2_array)
```

It's __common to use a different graph for inference__,
e.g., use NHWC format, support encoded image format, etc.
It's __common to use a different graph for inference__,
e.g., use NHWC format, support encoded image format, etc.
You can make these changes inside the `model` or `tower_func` in your `PredictConfig`.
The example in [examples/basics/export-model.py](../examples/basics/export-model.py) demonstrates such an altered inference graph.

@@ -90,7 +90,7 @@ you can also save your models into other formats after training, so it may be mo
- Removes all unnecessary operations (training-only ops, e.g., learning-rate) to compress the graph.

This creates a self-contained graph which includes all necessary information to run inference.

To load the saved graph, you can simply:
```python
graph_def = tf.GraphDef()
@@ -116,7 +116,7 @@ training:

1. The model (the graph): you've already written it yourself with TF symbolic functions.
Nothing about it is related to the tensorpack interface.
If you use tensorpack layers, they are mainly just wrappers around `tf.layers`.
If you use tensorpack layers, they are not so different from `tf.layers`.

2. The trained parameters: tensorpack saves them in standard TF checkpoint format.
Nothing about the format is related to tensorpack.
@@ -139,14 +139,16 @@ with TowerContext('', is_training=False):
```eval_rst
.. note:: **Do not use metagraph for inference!**
Metagraph is the wrong abstraction for a "model".
Tensorpack saves a metagraph during training. Users should not try to load it for inference.
Metagraph is the wrong abstraction for a "model".
It stores the entire graph which contains not only the mathematical model, but also all the
training settings (queues, iterators, summaries, evaluations, multi-gpu replications).
Therefore it is usually wrong to import a training metagraph for inference.
It's especially error-prone to load a metagraph on top of a non-empty graph.
The potential name conflicts between the current graph and the nodes in the
metagraph can lead to esoteric bugs or sometimes completely ruin the model.
It's especially error-prone to load a metagraph on top of a non-empty graph.
The potential name conflicts between the current graph and the nodes in the
metagraph can lead to esoteric bugs or sometimes completely ruin the model.
It's also very common to change the graph for inference.
For example, you may need a different data layout for CPU inference,
@@ -161,7 +163,7 @@ with TowerContext('', is_training=False):
You can just use `tf.train.Saver` for all the work.
Alternatively, use tensorpack's `get_model_loader(path).init(tf.get_default_session())`

Now, you've already built a graph for inference, and the checkpoint is also loaded.
Now, you've already built a graph for inference, and the checkpoint is also loaded.
You may now:

1. use `sess.run` to do inference
@@ -28,7 +28,7 @@ Some practicical notes:

### To test a model:

Download models from [model zoo](http://models.tensorpack.com/OpenAIGym/).
Download models from [model zoo](http://models.tensorpack.com/#OpenAIGym).

Watch the agent play:
`./train-atari.py --task play --env Breakout-v0 --load Breakout-v0.npz`
@@ -1,8 +1,7 @@

Example code to convert, load and run inference of some Caffe models.
Require caffe python bindings to be installed.
Converted models can also be found at [tensorpack model zoo](http://models.tensorpack.com).

Converted models can also be found at [tensorpack model zoo](http://models.tensorpack.com/#Caffe-Converted).
## AlexNet:

Download: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
@@ -46,7 +46,7 @@ In this implementation, quantized operations are all performed through `tf.float
+ Look at the docstring in `*-dorefa.py` to see detailed usage and performance.

Pretrained model for (1,4,32)-ResNet18 and several AlexNet are available at
[tensorpack model zoo](http://models.tensorpack.com/DoReFa-Net/).
[tensorpack model zoo](http://models.tensorpack.com/#DoReFa-Net).
They're provided in the format of numpy dictionary.
The __binary-weight 4-bit-activation ResNet-18__ model has 59.2% top-1 validation accuracy.

@@ -18,8 +18,8 @@ This is likely the best-performing open source TensorFlow reimplementation of th
## Dependencies
+ Python 3.3+; OpenCV
+ TensorFlow ≥ 1.6
+ pycocotools: `for i in cython 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'; do pip install $i; done`
+ Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/FasterRCNN/)
+ pycocotools/scipy: `for i in cython 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' scipy; do pip install $i; done`
+ Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/#FasterRCNN)
from tensorpack model zoo
+ [COCO data](http://cocodataset.org/#download). It needs to have the following directory structure:
```
@@ -83,24 +83,24 @@ prediction have to be run with the corresponding configs used in training.

These models are trained on train2017 and evaluated on val2017 using mAP@IoU=0.50:0.95.
Unless otherwise noted, all models are fine-tuned from ImageNet pre-trained R50/R101 models in
[tensorpack model zoo](http://models.tensorpack.com/FasterRCNN/),
[tensorpack model zoo](http://models.tensorpack.com/#FasterRCNN),
using 8 NVIDIA V100s.

Performance in [Detectron](https://github.com/facebookresearch/Detectron/) can be reproduced.

| Backbone | mAP<br/>(box;mask) | Detectron mAP <sup>[1](#ft1)</sup><br/> (box;mask) | Time <br/>(on 8 V100s) | Configurations <br/> (click to expand) |
| - | - | - | - | - |
| R50-C4 | 34.1 | | 7.5h | <details><summary>super quick</summary>`MODE_MASK=False FRCNN.BATCH_PER_IM=64`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=600 PREPROC.MAX_SIZE=1024`<br/>`TRAIN.LR_SCHEDULE=[140000,180000,200000]` </details> |
| R50-C4 | 35.6 | 34.8 | 23h | <details><summary>standard</summary>`MODE_MASK=False` </details> |
| R50-FPN | 37.5 | 36.7 | 11h | <details><summary>standard</summary>`MODE_MASK=False MODE_FPN=True` </details> |
| R50-C4 | 36.2;31.8 [:arrow_down:][R50C41x] | 35.8;31.4 | 23.5h | <details><summary>standard</summary>this is the default, no changes in config needed </details> |
| R50-FPN | 38.2;34.8 | 37.7;33.9 | 13.5h | <details><summary>standard</summary>`MODE_FPN=True` </details> |
| R50-FPN | 38.9;35.4 [:arrow_down:][R50FPN2x] | 38.6;34.5 | 25h | <details><summary>2x</summary>`MODE_FPN=True`<br/>`TRAIN.LR_SCHEDULE=2x` </details> |
| R50-FPN-GN | 40.4;36.3 [:arrow_down:][R50FPN2xGN] | 40.3;35.7 | 31h | <details><summary>2x+GN</summary>`MODE_FPN=True`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head` <br/>`TRAIN.LR_SCHEDULE=2x` |
| R50-FPN | 41.7;36.2 | | 17h | <details><summary>+Cascade</summary>`MODE_FPN=True FPN.CASCADE=True` </details> |
| R101-C4 | 40.1;34.6 [:arrow_down:][R101C41x] | | 28h | <details><summary>standard</summary>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` </details> |
| R101-FPN | 40.7;36.8 [:arrow_down:][R101FPN1x] | 40.0;35.9 | 18h | <details><summary>standard</summary>`MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` </details> |
| R101-FPN | 46.6;40.3 [:arrow_down:][R101FPN3xCasAug] <sup>[2](#ft2)</sup> | | 69h | <details><summary>3x+Cascade+TrainAug</summary>`MODE_FPN=True FPN.CASCADE=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`<br/>`TRAIN.LR_SCHEDULE=3x` </details> |
| R50-C4 | 34.1 | | 7h | <details><summary>super quick</summary>`MODE_MASK=False FRCNN.BATCH_PER_IM=64`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=600 PREPROC.MAX_SIZE=1024`<br/>`TRAIN.LR_SCHEDULE=[140000,180000,200000]` </details> |
| R50-C4 | 35.6 | 34.8 | 22.5h | <details><summary>standard</summary>`MODE_MASK=False` </details> |
| R50-FPN | 37.5 | 36.7 | 10.5h | <details><summary>standard</summary>`MODE_MASK=False MODE_FPN=True` </details> |
| R50-C4 | 36.2;31.8 [:arrow_down:][R50C41x] | 35.8;31.4 | 23h | <details><summary>standard</summary>this is the default, no changes in config needed </details> |
| R50-FPN | 38.2;34.8 | 37.7;33.9 | 12.5h | <details><summary>standard</summary>`MODE_FPN=True` </details> |
| R50-FPN | 38.9;35.4 [:arrow_down:][R50FPN2x] | 38.6;34.5 | 24h | <details><summary>2x</summary>`MODE_FPN=True`<br/>`TRAIN.LR_SCHEDULE=2x` </details> |
| R50-FPN-GN | 40.4;36.3 [:arrow_down:][R50FPN2xGN] | 40.3;35.7 | 29h | <details><summary>2x+GN</summary>`MODE_FPN=True`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head` <br/>`TRAIN.LR_SCHEDULE=2x` |
| R50-FPN | 41.7;36.2 | | 16h | <details><summary>+Cascade</summary>`MODE_FPN=True FPN.CASCADE=True` </details> |
| R101-C4 | 40.1;34.6 [:arrow_down:][R101C41x] | | 27h | <details><summary>standard</summary>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` </details> |
| R101-FPN | 40.7;36.8 [:arrow_down:][R101FPN1x] | 40.0;35.9 | 17h | <details><summary>standard</summary>`MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]` </details> |
| R101-FPN | 46.6;40.3 [:arrow_down:][R101FPN3xCasAug] <sup>[2](#ft2)</sup> | | 64h | <details><summary>3x+Cascade+TrainAug</summary>`MODE_FPN=True FPN.CASCADE=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`<br/>`TRAIN.LR_SCHEDULE=3x` </details> |
| R101-FPN-GN<br/>(From Scratch) | 47.7;41.7 [:arrow_down:][R101FPN9xGNCasAugScratch] <sup>[3](#ft3)</sup> | 47.4;40.5 | 28h (on 64 V100s) | <details><summary>9x+GN+Cascade+TrainAug</summary>`MODE_FPN=True FPN.CASCADE=True`<br/>`BACKBONE.RESNET_NUM_BLOCKS=[3,4,23,3]`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head`<br/>`PREPROC.TRAIN_SHORT_EDGE_SIZE=[640,800]`<br/>`TRAIN.LR_SCHEDULE=9x`<br/>`BACKBONE.FREEZE_AT=0`</details> |

[R50C41x]: http://models.tensorpack.com/FasterRCNN/COCO-MaskRCNN-R50C41x.npz

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