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Commit 7bcde8ad authored by Yuxin Wu's avatar Yuxin Wu
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update docs

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examples/FasterRCNN/README.md
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......@@ -13,7 +13,8 @@ with the support of:
## Dependencies
+ Python 3; TensorFlow >= 1.6 (1.4 or 1.5 can run but may crash due to a TF bug);
+ [pycocotools](https://github.com/pdollar/coco/tree/master/PythonAPI/pycocotools), OpenCV.
+ Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/ResNet/) from tensorpack model zoo.
+ Pre-trained [ImageNet ResNet model](http://models.tensorpack.com/FasterRCNN/)
from tensorpack model zoo. Use the models with "-AlignPadding".
+ COCO data. It needs to have the following directory structure:
```
COCO/DIR/
......@@ -37,7 +38,7 @@ To train:
./train.py --config \
MODE_MASK=True MODE_FPN=True \
DATA.BASEDIR=/path/to/COCO/DIR \
BACKBONE.WEIGHTS=/path/to/ImageNet-ResNet50.npz \
BACKBONE.WEIGHTS=/path/to/ImageNet-R50-Pad.npz \
```
Options can be changed by either the command line or the `config.py` file.
Recommended configurations are listed in the table below.
......@@ -50,13 +51,13 @@ To predict on an image (and show output in a window):
./train.py --predict input.jpg --load /path/to/model --config SAME-AS-TRAINING
```
Evaluate the performance of a model on COCO, and save results to json.
(Trained COCO models can be downloaded in [model zoo](http://models.tensorpack.com/FasterRCNN):
Evaluate the performance of a model on COCO.
(Several trained models can be downloaded in [model zoo](http://models.tensorpack.com/FasterRCNN):
```
./train.py --evaluate output.json --load /path/to/COCO-ResNet50-MaskRCNN.npz \
./train.py --evaluate output.json --load /path/to/COCO-R50C4-MaskRCNN-Standard.npz \
--config MODE_MASK=True DATA.BASEDIR=/path/to/COCO/DIR
```
Evaluation or prediction will need the same config used during training.
Evaluation or prediction will need the same `--config` used during training.
## Results
......@@ -69,8 +70,8 @@ MaskRCNN results contain both box and mask mAP.
| R50-C4 | 36.6 | 36.5 | 44h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=False` </details> |
| R50-FPN | 37.5 | 37.9<sup>[1](#ft1)</sup> | 28h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=False MODE_FPN=True` </details> |
| R50-C4 | 36.8/32.1 | | 39h on 8 P100s | <details><summary>quick</summary>`MODE_MASK=True FRCNN.BATCH_PER_IM=256`<br/>`TRAIN.LR_SCHEDULE=[150000,230000,280000]` </details> |
| R50-C4 | 37.8/33.1 | 37.8/32.8 | 45h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=True` </details> |
| R50-FPN | 38.1/34.9 | 38.6/34.5<sup>[1](#ft1)</sup> | 32h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True` </details> |
| R50-C4 | 37.8/33.1 | 37.8/32.8 | 49h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=True` </details> |
| R50-FPN | 38.2/34.9 | 38.6/34.5<sup>[1](#ft1)</sup> | 32h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True` </details> |
| R50-FPN | 38.5/34.8 | 38.6/34.2<sup>[2](#ft2)</sup> | 34h on 8 V100s | <details><summary>standard+ConvHead</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_head` </details> |
| R50-FPN | 39.5/35.2 | 39.5/34.4<sup>[2](#ft2)</sup> | 34h on 8 V100s | <details><summary>standard+ConvGNHead</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head` </details> |
| R101-C4 | 40.8/35.1 | | 63h on 8 V100s | <details><summary>standard</summary>`MODE_MASK=True `<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details> |
......
examples/FasterRCNN/config.py
View file @ 7bcde8ad
......@@ -68,10 +68,11 @@ _C.BACKBONE.NORM = 'FreezeBN' # options: FreezeBN, SyncBN
# Use a base model with TF-preferred padding mode,
# which may pad more pixels on right/bottom than top/left.
# TF_PAD_MODE=False is better for accuracy but will require a different base model.
# We will eventually switch to TF_PAD_MODE=False.
# See https://github.com/tensorflow/tensorflow/issues/18213
_C.BACKBONE.TF_PAD_MODE = True
# In tensorpack model zoo, ResNet models with TF_PAD_MODE=False are marked with "-AlignPadding".
# All other models under `ResNet/` in the model zoo are trained with TF_PAD_MODE=True.
_C.BACKBONE.TF_PAD_MODE = False
_C.BACKBONE.STRIDE_1X1 = False # True for MSRA models
# schedule -----------------------
......
tensorpack/graph_builder/model_desc.py
View file @ 7bcde8ad
......@@ -8,7 +8,6 @@ import tensorflow as tf
from ..utils import logger
from ..utils.argtools import memoized
from ..utils.develop import log_deprecated
from ..tfutils.gradproc import FilterNoneGrad
from ..tfutils.tower import get_current_tower_context
from ..input_source import InputSource
from ..models.regularize import regularize_cost_from_collection
......@@ -128,17 +127,16 @@ class ModelDescBase(object):
"""
Build the whole symbolic graph.
This is supposed to be part of the "tower function" when used with :class:`TowerTrainer`.
By default it will call :meth:`_build_graph` with a list of input tensors.
A subclass is expected to overwrite this method or the :meth:`_build_graph` method.
A subclass is expected to overwrite this method.
Args:
args ([tf.Tensor]): tensors that matches the list of inputs defined by ``inputs()``.
Returns:
In general it returns nothing, but a subclass (e.g.
:class:`ModelDesc`) may require it to return necessary information
(e.g. cost) to build the trainer.
In general it returns nothing, but a subclass
may require it to return necessary information to build the trainer.
For example, `SingleCostTrainer` expect this method to return the cost tensor.
"""
if len(args) == 1:
arg = args[0]
......@@ -230,51 +228,12 @@ class ModelDesc(ModelDescBase):
def _build_graph_get_cost(self, *inputs):
"""
Used internally by trainers to get the final cost for optimization.
Used internally by trainers to get the final cost for optimization in a backward-compatible way.
"""
ret = self.build_graph(*inputs)
if not get_current_tower_context().is_training:
return None # this is the tower function, could be called for inference
if isinstance(ret, tf.Tensor): # the preferred way
assert ret.shape.ndims == 0, "Cost must be a scalar, but found a tensor of shape {}!".format(ret.shape)
_check_unused_regularization()
return ret
else: # the old way
else: # the old way, for compatibility
return self.get_cost()
# TODO this is deprecated and only used for v1 trainers
def _build_graph_get_grads(self, *inputs):
"""
Build the graph from inputs and return the grads.
Returns:
[(grad, var)]
"""
ctx = get_current_tower_context()
cost = self._build_graph_get_cost(*inputs)
if not ctx.is_training:
return None # this is the tower function, could be called for inference
if ctx.has_own_variables:
varlist = ctx.get_collection_in_tower(tf.GraphKeys.TRAINABLE_VARIABLES)
else:
varlist = tf.trainable_variables()
opt = self.get_optimizer()
grads = opt.compute_gradients(
cost, var_list=varlist,
gate_gradients=False, colocate_gradients_with_ops=True)
grads = FilterNoneGrad().process(grads)
return grads
def _check_unused_regularization():
coll = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
unconsumed_reg = []
for c in coll:
if len(c.consumers()) == 0:
unconsumed_reg.append(c)
if unconsumed_reg:
logger.warn("The following tensors appear in REGULARIZATION_LOSSES collection but has no "
"consumers! You may have forgotten to add regularization to total cost.")
logger.warn("Unconsumed regularization: {}".format(', '.join([x.name for x in unconsumed_reg])))
tensorpack/train/interface.py
View file @ 7bcde8ad
# -*- coding: utf-8 -*-
# File: interface.py
import tensorflow as tf
from ..input_source import (
InputSource, FeedInput, QueueInput, StagingInput, DummyConstantInput)
from ..utils import logger
......@@ -79,6 +81,7 @@ def launch_train_with_config(config, trainer):
trainer.setup_graph(
inputs_desc, input,
model._build_graph_get_cost, model.get_optimizer)
_check_unused_regularization()
trainer.train_with_defaults(
callbacks=config.callbacks,
monitors=config.monitors,
......@@ -88,3 +91,15 @@ def launch_train_with_config(config, trainer):
starting_epoch=config.starting_epoch,
max_epoch=config.max_epoch,
extra_callbacks=config.extra_callbacks)
def _check_unused_regularization():
coll = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
unconsumed_reg = []
for c in coll:
if len(c.consumers()) == 0:
unconsumed_reg.append(c)
if unconsumed_reg:
logger.warn("The following tensors appear in REGULARIZATION_LOSSES collection but have no "
"consumers! You may have forgotten to add regularization to total cost.")
logger.warn("Unconsumed regularization: {}".format(', '.join([x.name for x in unconsumed_reg])))
tensorpack/train/tower.py
View file @ 7bcde8ad
......@@ -129,14 +129,14 @@ class SingleCostTrainer(TowerTrainer):
Base class for single-cost trainer.
Single-cost trainer has a :meth:`setup_graph` method which takes
(inputs_desc, input, get_cost_fn, get_opt_fn), and build the training operations from them.
(inputs_desc, input, get_cost_fn, get_opt_fn), and build the training graph from them.
To use a :class:`SingleCostTrainer` object, call `trainer.setup_graph(...); trainer.train(...)`.
"""
COLOCATE_GRADIENTS_WITH_OPS = True
"""
See `tf.gradients`. This might affect performance when backward op does
See `tf.gradients`. It sometimes can heavily affect performance when backward op does
not support the device of forward op.
"""
......@@ -159,7 +159,7 @@ class SingleCostTrainer(TowerTrainer):
optimizer. Will only be called once.
Note:
`get_cost_fn` will be the tower function.
`get_cost_fn` will be part of the tower function.
It must follows the `rules of tower function.
<http://tensorpack.readthedocs.io/en/latest/tutorial/trainer.html#tower-trainer>`_.
"""
......@@ -188,15 +188,18 @@ class SingleCostTrainer(TowerTrainer):
def _make_get_grad_fn(self, input, get_cost_fn, get_opt_fn):
"""
Internal use only.
Returns:
a get_grad_fn for GraphBuilder to use.
"""
# internal use only
assert input.setup_done()
def get_grad_fn():
ctx = get_current_tower_context()
cost = get_cost_fn(*input.get_input_tensors())
assert isinstance(cost, tf.Tensor), cost
assert cost.shape.ndims == 0, "Cost must be a scalar, but found {}!".format(cost)
if not ctx.is_training:
return None # this is the tower function, could be called for inference
......
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