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Commit 3b158095 authored by Ilya Mironov's avatar Ilya Mironov
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Merge branch 'master' of https://github.com/ilyamironov/models

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research/differential_privacy/pate/core_test.py
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......@@ -13,7 +13,7 @@
# limitations under the License.
# ==============================================================================
"""Tests for google3.experimental.brain.privacy.pate.pate."""
"""Tests for pate.core."""
from __future__ import absolute_import
from __future__ import division
......
research/differential_privacy/pate/smooth_sensitivity_test.py
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# limitations under the License.
# ==============================================================================
"""Tests for google3.experimental.brain.privacy.pate.pate_smooth_sensitivity."""
"""Tests for pate.smooth_sensitivity."""
from __future__ import absolute_import
from __future__ import division
......
research/marco/README.md 0 → 100644
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Automating the Evaluation of Crystallization Experiments
========================================================
This is a pretrained model described in the paper:
[Classification of crystallization outcomes using deep convolutional neural networks](https://arxiv.org/abs/1803.10342).
This model takes images of crystallization experiments as an input:
<img src="https://storage.googleapis.com/marco-168219-model/002s_C6_ImagerDefaults_9.jpg" alt="crystal sample" width="320" height="240" />
It classifies it as belonging to one of four categories: crystals, precipitate, clear, or 'others'.
The model is a variant of [Inception-v3](https://arxiv.org/abs/1512.00567) trained on data from the [MARCO](http://marco.ccr.buffalo.edu) repository.
Model
-----
The model can be downloaded from:
https://storage.googleapis.com/marco-168219-model/savedmodel.zip
Example
-------
1. Install TensorFlow and the [Google Cloud SDK](https://cloud.google.com/sdk/gcloud/).
2. Download and unzip the model:
```bash
unzip savedmodel.zip
```
3. A sample image can be downloaded from:
https://storage.googleapis.com/marco-168219-model/002s_C6_ImagerDefaults_9.jpg
Convert your image into a JSON request using:
```bash
python jpeg2json.py 002s_C6_ImagerDefaults_9.jpg > request.json
```
4. To issue a prediction, run:
```bash
gcloud ml-engine local predict --model-dir=savedmodel --json-instances=request.json
```
The request should return normalized scores for each class:
<pre>
CLASSES SCORES
[u'Crystals', u'Other', u'Precipitate', u'Clear'] [0.926338255405426, 0.026199858635663986, 0.026074528694152832, 0.021387407556176186]
</pre>
CloudML Endpoint
----------------
The model can also be accessed on [Google CloudML](https://cloud.google.com/ml-engine/) by issuing:
```bash
gcloud ml-engine predict --model marco_168219_model --json-instances request.json
```
Ask the author for access privileges to the CloudML instance.
Note
----
`002s_C6_ImagerDefaults_9.jpg` is a sample from the
[MARCO](http://marco.ccr.buffalo.edu) repository, contributed to the dataset under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
Author
------
[Vincent Vanhoucke](mailto:vanhoucke@google.com) (github: vincentvanhoucke)
research/marco/jpeg2json.py 0 → 100644
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#!/usr/bin/python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""jpeg2json.py: Converts a JPEG image into a json request to CloudML.
Usage:
python jpeg2json.py 002s_C6_ImagerDefaults_9.jpg > request.json
See:
https://cloud.google.com/ml-engine/docs/concepts/prediction-overview#online_prediction_input_data
"""
import base64
import sys
def to_json(data):
return '{"image_bytes":{"b64": "%s"}}' % base64.b64encode(data)
if __name__ == '__main__':
file = open(sys.argv[1]) if len(sys.argv) > 1 else sys.stdin
print(to_json(file.read()))
research/marco/request.json 0 → 100644
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{"image_bytes":{"b64": "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"}}
research/maskgan/README.md
View file @ 3b158095
......@@ -5,7 +5,7 @@ ______*](https://arxiv.org/abs/1801.07736) published at ICLR 2018.
## Requirements
* TensorFlow >= v1.3
* TensorFlow >= v1.5
## Instructions
......
research/maskgan/model_utils/variable_mapping.py
View file @ 3b158095
......@@ -163,52 +163,48 @@ def rnn_zaremba(hparams, model):
if v.op.name == str(model) + '/rnn/embedding'
][0]
lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
str(model) + '/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name == str(model) +
'/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
# Dictionary mapping.
if model == 'gen':
variable_mapping = {
'Model/embedding': embedding,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': lstm_b_1,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': lstm_b_1,
'Model/softmax_w': softmax_w,
'Model/softmax_b': softmax_b
}
else:
if FLAGS.dis_share_embedding:
variable_mapping = {
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': lstm_b_1
}
else:
variable_mapping = {
'Model/embedding': embedding,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': lstm_b_1
}
return variable_mapping
......@@ -356,24 +352,20 @@ def gen_encoder_seq2seq(hparams):
if v.op.name == 'gen/encoder/rnn/embedding'
][0]
encoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
encoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.data_set == 'ptb':
......@@ -385,24 +377,24 @@ def gen_encoder_seq2seq(hparams):
variable_mapping = {
str(model_str) + '/embedding':
encoder_embedding,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1
}
else:
variable_mapping = {
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1
}
return variable_mapping
......@@ -418,24 +410,20 @@ def gen_decoder_seq2seq(hparams):
if v.op.name == 'gen/decoder/rnn/embedding'
][0]
decoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
decoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
decoder_softmax_b = [
v for v in tf.trainable_variables()
......@@ -450,13 +438,13 @@ def gen_decoder_seq2seq(hparams):
variable_mapping = {
str(model_str) + '/embedding':
decoder_embedding,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1,
str(model_str) + '/softmax_b':
decoder_softmax_b
......@@ -487,34 +475,34 @@ def dis_fwd_bidirectional(hparams):
][0]
fw_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
fw_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
fw_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
fw_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/fw/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.dis_share_embedding:
variable_mapping = {
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': fw_lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': fw_lstm_b_1
}
else:
variable_mapping = {
'Model/embedding': embedding,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': fw_lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': fw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': fw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': fw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': fw_lstm_b_1
}
return variable_mapping
......@@ -537,26 +525,26 @@ def dis_bwd_bidirectional(hparams):
# Backward Discriminator Elements.
bw_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
bw_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
bw_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
bw_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
if v.op.name == 'dis/rnn/bw/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
variable_mapping = {
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights': bw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases': bw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights': bw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases': bw_lstm_b_1
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel': bw_lstm_w_0,
'Model/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias': bw_lstm_b_0,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel': bw_lstm_w_1,
'Model/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias': bw_lstm_b_1
}
return variable_mapping
......@@ -576,24 +564,20 @@ def dis_encoder_seq2seq(hparams):
## Encoder forward variables.
encoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
encoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.data_set == 'ptb':
......@@ -602,13 +586,13 @@ def dis_encoder_seq2seq(hparams):
model_str = 'model'
variable_mapping = {
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1
}
return variable_mapping
......@@ -624,24 +608,20 @@ def dis_decoder_seq2seq(hparams):
if v.op.name == 'dis/decoder/rnn/embedding'
][0]
decoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
decoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
if FLAGS.data_set == 'ptb':
......@@ -653,24 +633,24 @@ def dis_decoder_seq2seq(hparams):
variable_mapping = {
str(model_str) + '/embedding':
decoder_embedding,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1
}
else:
variable_mapping = {
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
str(model_str) + '/RNN/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1,
}
return variable_mapping
......@@ -688,24 +668,20 @@ def dis_seq2seq_vd(hparams):
## Encoder variables.
encoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
encoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
encoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
## Attention.
......@@ -721,43 +697,39 @@ def dis_seq2seq_vd(hparams):
## Decoder.
decoder_lstm_w_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_0 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias'
][0]
decoder_lstm_w_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel'
][0]
decoder_lstm_b_1 = [
v for v in tf.trainable_variables()
if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases'
v for v in tf.trainable_variables() if v.op.name ==
'dis/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias'
][0]
# Standard variable mappings.
variable_mapping = {
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
encoder_lstm_w_0,
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
'gen/encoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
encoder_lstm_b_0,
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
encoder_lstm_w_1,
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
'gen/encoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
encoder_lstm_b_1,
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/weights':
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/kernel':
decoder_lstm_w_0,
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/biases':
'gen/decoder/rnn/multi_rnn_cell/cell_0/basic_lstm_cell/bias':
decoder_lstm_b_0,
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/weights':
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/kernel':
decoder_lstm_w_1,
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/biases':
'gen/decoder/rnn/multi_rnn_cell/cell_1/basic_lstm_cell/bias':
decoder_lstm_b_1
}
......
research/minigo/README.md
View file @ 3b158095
# MiniGo
This is a simplified implementation of MiniGo based on the code provided by the authors: [MiniGo](https://github.com/tensorflow/minigo).
MiniGo is a minimalist Go engine modeled after AlphaGo Zero, built on MuGo. The current implementation consists of three main modules: the DualNet model, the Monte Carlo Tree Search (MCTS), and Go domain knowledge. Currently the **model** part is our focus.
MiniGo is a minimalist Go engine modeled after AlphaGo Zero, ["Mastering the Game of Go without Human
Knowledge"](https://www.nature.com/articles/nature24270). An useful one-diagram overview of Alphago Zero can be found in the [cheat sheet](https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0).
This implementation maintains the features of model training and validation, and also provides evaluation of two Go models.
The implementation of MiniGo consists of three main components: the DualNet model, the Monte Carlo Tree Search (MCTS), and Go domain knowledge. Currently, the **DualNet model** is our focus.
## DualNet Model
## DualNet Architecture
DualNet is the neural network used in MiniGo. It's based on residual blocks with two heads output. Following is a brief overview of the DualNet architecture.
### Input Features
The input to the neural network is a [board_size * board_size * 17] image stack
comprising 17 binary feature planes. 8 feature planes consist of binary values
indicating the presence of the current player's stones; A further 8 feature
planes represent the corresponding features for the opponent's stones; The final
feature plane represents the color to play, and has a constant value of either 1
if black is to play or 0 if white to play. Check `features.py` for more details.
if black is to play or 0 if white to play. Check [features.py](features.py) for more details.
### Neural Network Structure
In MiniGo implementation, the input features are processed by a residual tower
that consists of a single convolutional block followed by either 9 or 19
residual blocks.
......@@ -31,8 +36,9 @@ Each residual block applies the following modules sequentially to its input:
6. A skip connection that adds the input to the block
7. A rectifier non-linearity
Note: num_filter is 128 for 19 x 19 board size, and 32 for 9 x 9 board size.
Note: num_filter is 128 for 19 x 19 board size, and 32 for 9 x 9 board size in MiniGo implementation.
### Dual Heads Output
The output of the residual tower is passed into two separate "heads" for
computing the policy and value respectively. The policy head applies the
following modules:
......@@ -51,7 +57,7 @@ The value head applies the following modules:
6. A fully connected linear layer to a scalar
7. A tanh non-linearity outputting a scalar in the range [-1, 1]
The overall network depth, in the 10 or 20 block network, is 19 or 39
In MiniGo, the overall network depth, in the 10 or 20 block network, is 19 or 39
parameterized layers respectively for the residual tower, plus an additional 2
layers for the policy head and 3 layers for the value head.
......@@ -59,56 +65,74 @@ layers for the policy head and 3 layers for the value head.
This project assumes you have virtualenv, TensorFlow (>= 1.5) and two other Go-related
packages pygtp(>=0.4) and sgf (==0.5).
## Training Model
One iteration of reinforcement learning consists of the following steps:
- Bootstrap: initializes a random model
- Selfplay: plays games with the latest model, producing data used for training
One iteration of reinforcement learning (RL) consists of the following steps:
- Bootstrap: initializes a random DualNet model. If the estimator directory has exist, the model is initialized with the last checkpoint.
- Selfplay: plays games with the latest model or the best model so far identified by evaluation, producing data used for training
- Gather: groups games played with the same model into larger files of tfexamples.
- Train: trains a new model with the selfplay results from the most recent N
generations.
- Train: trains a new model with the selfplay results from the most recent N generations.
Run `minigo.py`.
To run the RL pipeline, issue the following command:
```
python minigo.py
python minigo.py --base_dir=$HOME/minigo/ --board_size=9 --batch_size=256
```
Arguments:
* `--base_dir`: Base directory for MiniGo data and models. If not specified, it's set as /tmp/minigo/ by default.
* `--board_size`: Go board size. It can be either 9 or 19. By default, it's 9.
* `--batch_size`: Batch size for model training. If not specified, it's calculated based on go board size.
Use the `--help` or `-h` flag to get a full list of possible arguments. Besides all these arguments, other parameters about RL pipeline and DualNet model can be found and configured in [model_params.py](model_params.py).
Suppose the base directory argument `base_dir` is `$HOME/minigo/` and we use 9 as the `board_size`. After model training, the following directories are created to store models and game data:
$HOME/minigo # base directory
│
├── 9_size # directory for 9x9 board size
│ │
│ ├── data
│ │ ├── holdout # holdout data for model validation
│ │ ├── selfplay # data generated by selfplay of each model
│ │ └── training_chunks # gatherd tf_examples for model training
│ │
│ ├── estimator_model_dir # estimator working directory
│ │
│ ├── trained_models # all the trained models
│ │
│ └── sgf # sgf (smart go files) folder
│ ├── 000000-bootstrap # model name
│ │ ├── clean # clean sgf files of model selfplay
│ │ └── full # full sgf files of model selfplay
│ ├── ...
│ └── evaluate # clean sgf files of model evaluation
│
└── ...
## Validating Model
Run `minigo.py` with `--validation` argument
To validate the trained model, issue the following command with `--validation` argument:
```
python minigo.py --validation
```
The `--validation` argument is to generate holdout dataset for model validation
## Evaluating MiniGo Models
Run `minigo.py` with `--evaluation` argument
python minigo.py --base_dir=$HOME/minigo/ --board_size=9 --batch_size=256 --validation
```
python minigo.py --evaluation
```
The `--evaluation` argument is to invoke the evaluation between the latest model and the current best model.
## Testing Pipeline
As the whole RL pipeline may takes hours to train even for a 9x9 board size, we provide a dummy model with a `--debug` mode for testing purpose.
Run `minigo.py` with `--debug` argument
```
python minigo.py --debug
```
The `--debug` argument is for testing purpose with a dummy model.
## Evaluating Models
The performance of two models are compared with evaluation step. Given two models, one plays black and the other plays white. They play several games (# of games can be configured by parameter `eval_games` in [model_params.py](model_params.py)), and the one wins by a margin of 55% will be the winner.
Validation and evaluation can also be tested with the dummy model by combing their corresponding arguments with `--debug`.
To test validation, run the following commands:
```
python minigo.py --debug --validation
```
To test evaluation, run the following commands:
To include the evaluation step in the RL pipeline, `--evaluation` argument can be specified to compare the performance of the `current_trained_model` and the `best_model_so_far`. The winner is used to update `best_model_so_far`. Run the following command to include evaluation step in the pipeline:
```
python minigo.py --debug --evaluation
```
To test both validation and evaluation, run the following commands:
```
python minigo.py --debug --validation --evaluation
python minigo.py --base_dir=$HOME/minigo/ --board_size=9 --batch_size=256 --evaluation
```
## MCTS and Go features (TODO)
Code clean up on MCTS and Go features.
## Testing Pipeline
As the whole RL pipeline may take hours to train even for a 9x9 board size, a `--test` argument is provided to test the pipeline quickly with a dummy neural network model.
To test the RL pipeline with a dummy model, issue the following command:
```
python minigo.py --base_dir=$HOME/minigo/ --board_size=9 --batch_size=256 --test
```
## Running Self-play Only
Self-play only option is provided to run selfplay step individually to generate training data in parallel. Issue the following command to run selfplay only with the latest trained model:
```
python minigo.py --selfplay
```
Other optional arguments:
* `--selfplay_model_name`: The name of the model used for selfplay only. If not specified, the latest trained model will be used for selfplay.
* `--selfplay_max_games`: The maximum number of games selfplay is required to generate. If not specified, the default parameter `max_games_per_generation` is used.
research/minigo/dualnet.py
View file @ 3b158095
......@@ -191,24 +191,24 @@ def export_model(working_dir, model_path):
tf.gfile.Copy(filename, destination_path)
def train(working_dir, tf_records, generation_num, params):
def train(working_dir, tf_records, generation, params):
"""Train the model for a specific generation.
Args:
working_dir: The model working directory to save model parameters,
drop logs, checkpoints, and so on.
tf_records: A list of tf_record filenames for training input.
generation_num: The generation to be trained.
generation: The generation to be trained.
params: hyperparams of the model.
Raises:
ValueError: if generation_num is not greater than 0.
ValueError: if generation is not greater than 0.
"""
if generation_num <= 0:
if generation <= 0:
raise ValueError('Model 0 is random weights')
estimator = tf.estimator.Estimator(
dualnet_model.model_fn, model_dir=working_dir, params=params)
max_steps = (generation_num * params.examples_per_generation
max_steps = (generation * params.examples_per_generation
// params.batch_size)
profiler_hook = tf.train.ProfilerHook(output_dir=working_dir, save_secs=600)
......
research/minigo/gtp_wrapper.py
View file @ 3b158095
......@@ -49,8 +49,7 @@ class GtpInterface(object):
def set_size(self, n):
if n != self.board_size:
raise ValueError((
'''Can't handle boardsize {n}!Restart with env var BOARD_SIZE={n}'''
).format(n=n))
"Can't handle boardsize {}! Please check the board size.").format(n))
def set_komi(self, komi):
self.komi = komi
......@@ -75,7 +74,7 @@ class GtpInterface(object):
self.position.flip_playerturn(mutate=True)
def make_move(self, color, vertex):
c = coords.from_pygtp(vertex)
c = coords.from_pygtp(self.board_size, vertex)
# let's assume this never happens for now.
# self.accomodate_out_of_turn(color)
return self.play_move(c)
......@@ -85,7 +84,7 @@ class GtpInterface(object):
move = self.suggest_move(self.position)
if self.should_resign():
return gtp.RESIGN
return coords.to_pygtp(move)
return coords.to_pygtp(self.board_size, move)
def final_score(self):
return self.position.result_string()
......
research/minigo/minigo.py
View file @ 3b158095
......@@ -66,7 +66,7 @@ def bootstrap(estimator_model_dir, trained_models_dir, params):
estimator_model_dir: tf.estimator model directory.
trained_models_dir: Dir to save the trained models. Here to export the first
bootstrapped generation.
params: An object of hyperparameters for the model.
params: A MiniGoParams instance of hyperparameters for the model.
"""
bootstrap_name = utils.generate_model_name(0)
_ensure_dir_exists(trained_models_dir)
......@@ -79,41 +79,23 @@ def bootstrap(estimator_model_dir, trained_models_dir, params):
dualnet.export_model(estimator_model_dir, bootstrap_model_path)
def selfplay(model_name, trained_models_dir, selfplay_dir, holdout_dir, sgf_dir,
params):
def selfplay(selfplay_dirs, selfplay_model, params):
"""Perform selfplay with a specific model.
Args:
model_name: The name of the model used for selfplay.
trained_models_dir: The path to the model files.
selfplay_dir: Where to write the games. Set as 'base_dir/data/selfplay/'.
holdout_dir: Where to write the holdout data. Set as
'base_dir/data/holdout/'.
sgf_dir: Where to write the sgf (Smart Game Format) files. Set as
'base_dir/sgf/'.
params: An object of hyperparameters for the model.
selfplay_dirs: A dict to specify the directories used in selfplay.
selfplay_dirs = {
'output_dir': output_dir,
'holdout_dir': holdout_dir,
'clean_sgf': clean_sgf,
'full_sgf': full_sgf
}
selfplay_model: The actual Dualnet runner for selfplay.
params: A MiniGoParams instance of hyperparameters for the model.
"""
print('Playing a game with model {}'.format(model_name))
# Set paths for the model with 'model_name'
model_path = os.path.join(trained_models_dir, model_name)
output_dir = os.path.join(selfplay_dir, model_name)
holdout_dir = os.path.join(holdout_dir, model_name)
# clean_sgf is to write sgf file without comments.
# full_sgf is to write sgf file with comments.
clean_sgf = os.path.join(sgf_dir, model_name, 'clean')
full_sgf = os.path.join(sgf_dir, model_name, 'full')
_ensure_dir_exists(output_dir)
_ensure_dir_exists(holdout_dir)
_ensure_dir_exists(clean_sgf)
_ensure_dir_exists(full_sgf)
with utils.logged_timer('Loading weights from {} ... '.format(model_path)):
network = dualnet.DualNetRunner(model_path, params)
with utils.logged_timer('Playing game'):
player = selfplay_mcts.play(
params.board_size, network, params.selfplay_readouts,
params.board_size, selfplay_model, params.selfplay_readouts,
params.selfplay_resign_threshold, params.simultaneous_leaves,
params.selfplay_verbose)
......@@ -124,8 +106,8 @@ def selfplay(model_name, trained_models_dir, selfplay_dir, holdout_dir, sgf_dir,
os.path.join(dir_sgf, '{}.sgf'.format(output_name)), 'w') as f:
f.write(player.to_sgf(use_comments=use_comments))
_write_sgf_data(clean_sgf, use_comments=False)
_write_sgf_data(full_sgf, use_comments=True)
_write_sgf_data(selfplay_dirs['clean_sgf'], use_comments=False)
_write_sgf_data(selfplay_dirs['full_sgf'], use_comments=True)
game_data = player.extract_data()
tf_examples = preprocessing.make_dataset_from_selfplay(game_data, params)
......@@ -133,10 +115,10 @@ def selfplay(model_name, trained_models_dir, selfplay_dir, holdout_dir, sgf_dir,
# Hold out 5% of games for evaluation.
if random.random() < params.holdout_pct:
fname = os.path.join(
holdout_dir, ('{}'+_TF_RECORD_SUFFIX).format(output_name))
selfplay_dirs['holdout_dir'], output_name + _TF_RECORD_SUFFIX)
else:
fname = os.path.join(
output_dir, ('{}'+_TF_RECORD_SUFFIX).format(output_name))
selfplay_dirs['output_dir'], output_name + _TF_RECORD_SUFFIX)
preprocessing.write_tf_examples(fname, tf_examples)
......@@ -148,7 +130,7 @@ def gather(selfplay_dir, training_chunk_dir, params):
selfplay_dir: Where to look for games. Set as 'base_dir/data/selfplay/'.
training_chunk_dir: where to put collected games. Set as
'base_dir/data/training_chunks/'.
params: An object of hyperparameters for the model.
params: A MiniGoParams instance of hyperparameters for the model.
"""
# Check the selfplay data from the most recent 50 models.
_ensure_dir_exists(training_chunk_dir)
......@@ -196,22 +178,22 @@ def gather(selfplay_dir, training_chunk_dir, params):
f.write('\n'.join(sorted(already_processed)))
def train(trained_models_dir, estimator_model_dir, training_chunk_dir, params):
def train(trained_models_dir, estimator_model_dir, training_chunk_dir,
generation, params):
"""Train the latest model from gathered data.
Args:
trained_models_dir: Where to export the completed generation.
estimator_model_dir: tf.estimator model directory.
training_chunk_dir: Directory where gathered training chunks are.
params: An object of hyperparameters for the model.
generation: Which generation you are training.
params: A MiniGoParams instance of hyperparameters for the model.
"""
model_num, model_name = utils.get_latest_model(trained_models_dir)
print('Initializing from model {}'.format(model_name))
new_model_name = utils.generate_model_name(model_num + 1)
new_model_name = utils.generate_model_name(generation)
print('New model will be {}'.format(new_model_name))
save_file = os.path.join(trained_models_dir, new_model_name)
new_model = os.path.join(trained_models_dir, new_model_name)
print('Training on gathered game data...')
tf_records = sorted(
tf.gfile.Glob(os.path.join(training_chunk_dir, '*'+_TF_RECORD_SUFFIX)))
tf_records = tf_records[
......@@ -219,8 +201,8 @@ def train(trained_models_dir, estimator_model_dir, training_chunk_dir, params):
print('Training from: {} to {}'.format(tf_records[0], tf_records[-1]))
with utils.logged_timer('Training'):
dualnet.train(estimator_model_dir, tf_records, model_num + 1, params)
dualnet.export_model(estimator_model_dir, save_file)
dualnet.train(estimator_model_dir, tf_records, generation, params)
dualnet.export_model(estimator_model_dir, new_model)
def validate(trained_models_dir, holdout_dir, estimator_model_dir, params):
......@@ -230,7 +212,7 @@ def validate(trained_models_dir, holdout_dir, estimator_model_dir, params):
trained_models_dir: Directories where the completed generations/models are.
holdout_dir: Directories where holdout data are.
estimator_model_dir: tf.estimator model directory.
params: An object of hyperparameters for the model.
params: A MiniGoParams instance of hyperparameters for the model.
"""
model_num, _ = utils.get_latest_model(trained_models_dir)
......@@ -251,6 +233,11 @@ def validate(trained_models_dir, holdout_dir, estimator_model_dir, params):
tf_records.extend(
tf.gfile.Glob(os.path.join(record_dir, '*'+_TF_RECORD_SUFFIX)))
if not tf_records:
print('No holdout dataset for validation! '
'Please check your holdout directory: {}'.format(holdout_dir))
return
print('The length of tf_records is {}.'.format(len(tf_records)))
first_tf_record = os.path.basename(tf_records[0])
last_tf_record = os.path.basename(tf_records[-1])
......@@ -259,21 +246,22 @@ def validate(trained_models_dir, holdout_dir, estimator_model_dir, params):
dualnet.validate(estimator_model_dir, tf_records, params)
def evaluate(trained_models_dir, black_model_name, white_model_name,
def evaluate(black_model_name, black_net, white_model_name, white_net,
evaluate_dir, params):
"""Evaluate with two models.
With the model name, construct two DualNetRunners to play as black and white
in a Go match. Two models play several names, and the model that wins by a
margin of 55% will be the winner.
With two DualNetRunners to play as black and white in a Go match. Two models
play several games, and the model that wins by a margin of 55% will be the
winner.
Args:
trained_models_dir: Directories where the completed generations/models are.
black_model_name: The name of the model playing black.
black_net: The DualNetRunner model for black
white_model_name: The name of the model playing white.
white_net: The DualNetRunner model for white.
evaluate_dir: Where to write the evaluation results. Set as
'base_dir/sgf/evaluate/''
params: An object of hyperparameters for the model.
'base_dir/sgf/evaluate/'.
params: A MiniGoParams instance of hyperparameters for the model.
Returns:
The model name of the winner.
......@@ -281,19 +269,6 @@ def evaluate(trained_models_dir, black_model_name, white_model_name,
Raises:
ValueError: if neither `WHITE` or `BLACK` is returned.
"""
black_model = os.path.join(trained_models_dir, black_model_name)
white_model = os.path.join(trained_models_dir, white_model_name)
print('Evaluate models between {} and {}'.format(
black_model_name, white_model_name))
_ensure_dir_exists(evaluate_dir)
with utils.logged_timer('Loading weights'):
black_net = dualnet.DualNetRunner(black_model, params)
white_net = dualnet.DualNetRunner(white_model, params)
with utils.logged_timer('{} games'.format(params.eval_games)):
winner = evaluation.play_match(
params, black_net, white_net, params.eval_games,
......@@ -305,38 +280,122 @@ def evaluate(trained_models_dir, black_model_name, white_model_name,
return black_model_name if winner == go.BLACK_NAME else white_model_name
def _set_params_from_board_size(board_size):
"""Set hyperparameters from board size."""
def _set_params(flags):
"""Set hyperparameters from board size.
Args:
flags: Flags from Argparser.
Returns:
An MiniGoParams instance of hyperparameters.
"""
params = model_params.MiniGoParams()
k = utils.round_power_of_two(board_size ** 2 / 3)
k = utils.round_power_of_two(flags.board_size ** 2 / 3)
params.num_filters = k # Number of filters in the convolution layer
params.fc_width = 2 * k # Width of each fully connected layer
params.num_shared_layers = board_size # Number of shared trunk layers
params.board_size = board_size # Board size
params.num_shared_layers = flags.board_size # Number of shared trunk layers
params.board_size = flags.board_size # Board size
# How many positions can fit on a graphics card. 256 for 9s, 16 or 32 for 19s.
if FLAGS.board_size == 9:
params.batch_size = 256
if flags.batch_size is None:
if flags.board_size == 9:
params.batch_size = 256
else:
params.batch_size = 32
else:
params.batch_size = 32
params.batch_size = flags.batch_size
return params
def _prepare_selfplay(
model_name, trained_models_dir, selfplay_dir, holdout_dir, sgf_dir, params):
"""Set directories and load the network for selfplay.
Args:
model_name: The name of the model for self-play
trained_models_dir: Directories where the completed generations/models are.
selfplay_dir: Where to write the games. Set as 'base_dir/data/selfplay/'.
holdout_dir: Where to write the holdout data. Set as
'base_dir/data/holdout/'.
sgf_dir: Where to write the sgf (Smart Game Format) files. Set as
'base_dir/sgf/'.
params: A MiniGoParams instance of hyperparameters for the model.
Returns:
The directories and network model for selfplay.
"""
# Set paths for the model with 'model_name'
model_path = os.path.join(trained_models_dir, model_name)
output_dir = os.path.join(selfplay_dir, model_name)
holdout_dir = os.path.join(holdout_dir, model_name)
# clean_sgf is to write sgf file without comments.
# full_sgf is to write sgf file with comments.
clean_sgf = os.path.join(sgf_dir, model_name, 'clean')
full_sgf = os.path.join(sgf_dir, model_name, 'full')
_ensure_dir_exists(output_dir)
_ensure_dir_exists(holdout_dir)
_ensure_dir_exists(clean_sgf)
_ensure_dir_exists(full_sgf)
selfplay_dirs = {
'output_dir': output_dir,
'holdout_dir': holdout_dir,
'clean_sgf': clean_sgf,
'full_sgf': full_sgf
}
# cache the network model for self-play
with utils.logged_timer('Loading weights from {} ... '.format(model_path)):
network = dualnet.DualNetRunner(model_path, params)
return selfplay_dirs, network
def run_selfplay(selfplay_model, selfplay_games, dirs, params):
"""Run selfplay to generate training data.
Args:
selfplay_model: The model name for selfplay.
selfplay_games: The number of selfplay games.
dirs: A MiniGoDirectory instance of directories used in each step.
params: A MiniGoParams instance of hyperparameters for the model.
"""
selfplay_dirs, network = _prepare_selfplay(
selfplay_model, dirs.trained_models_dir, dirs.selfplay_dir,
dirs.holdout_dir, dirs.sgf_dir, params)
print('Self-play with model: {}'.format(selfplay_model))
for _ in range(selfplay_games):
selfplay(selfplay_dirs, network, params)
def main(_):
"""Run the reinforcement learning loop."""
tf.logging.set_verbosity(tf.logging.INFO)
params = _set_params_from_board_size(FLAGS.board_size)
params = _set_params(FLAGS)
# A dummy model for debug/testing purpose with fewer games and iterations
if FLAGS.debug:
if FLAGS.test:
params = model_params.DummyMiniGoParams()
base_dir = FLAGS.base_dir + str(FLAGS.board_size) + '_size_dummy/'
else:
# Set directories for models and datasets
base_dir = FLAGS.base_dir + str(FLAGS.board_size) + '_size/'
# Set directories for models and datasets
base_dir = FLAGS.base_dir + str(FLAGS.board_size) + '_board_size/'
dirs = utils.MiniGoDirectory(base_dir)
# Run selfplay only if user specifies the argument.
if FLAGS.selfplay:
selfplay_model_name = FLAGS.selfplay_model_name or utils.get_latest_model(
dirs.trained_models_dir)[1]
max_games = FLAGS.selfplay_max_games or params.max_games_per_generation
run_selfplay(selfplay_model_name, max_games, dirs, params)
return
# Run the RL pipeline
# if no models have been trained, start from bootstrap model
if os.path.isdir(base_dir) is False:
if not os.path.isdir(dirs.trained_models_dir):
print('No trained model exists! Starting from Bootstrap...')
print('Creating random initial weights...')
bootstrap(dirs.estimator_model_dir, dirs.trained_models_dir, params)
......@@ -345,50 +404,51 @@ def main(_):
print('Start from the last checkpoint...')
_, best_model_so_far = utils.get_latest_model(dirs.trained_models_dir)
for rl_iter in range(params.max_iters_per_pipeline):
print('RL_iteration: {}'.format(rl_iter))
# Self-play with the best model to generate training data
run_selfplay(
best_model_so_far, params.max_games_per_generation, dirs, params)
# Self-play to generate at least params.max_games_per_generation games
selfplay(best_model_so_far, dirs.trained_models_dir, dirs.selfplay_dir,
dirs.holdout_dir, dirs.sgf_dir, params)
games = tf.gfile.Glob(
os.path.join(dirs.selfplay_dir, best_model_so_far, '*.zz'))
while len(games) < params.max_games_per_generation:
selfplay(best_model_so_far, dirs.trained_models_dir, dirs.selfplay_dir,
dirs.holdout_dir, dirs.sgf_dir, params)
if FLAGS.validation:
params = model_params.DummyValidationParams()
selfplay(best_model_so_far, dirs.trained_models_dir, dirs.selfplay_dir,
dirs.holdout_dir, dirs.sgf_dir, params)
games = tf.gfile.Glob(
os.path.join(dirs.selfplay_dir, best_model_so_far, '*.zz'))
# gather selfplay data for training
print('Gathering game output...')
gather(dirs.selfplay_dir, dirs.training_chunk_dir, params)
# train the next generation model
model_num, _ = utils.get_latest_model(dirs.trained_models_dir)
print('Training on gathered game data...')
train(dirs.trained_models_dir, dirs.estimator_model_dir,
dirs.training_chunk_dir, params)
dirs.training_chunk_dir, model_num + 1, params)
# validate the latest model if needed
if FLAGS.validation:
print('Validating on the holdout game data...')
validate(dirs.trained_models_dir, dirs.holdout_dir,
dirs.estimator_model_dir, params)
_, current_model = utils.get_latest_model(dirs.trained_models_dir)
if FLAGS.evaluation: # Perform evaluation if needed
print('Evaluating the latest model...')
print('Evaluate models between {} and {}'.format(
best_model_so_far, current_model))
black_model = os.path.join(dirs.trained_models_dir, best_model_so_far)
white_model = os.path.join(dirs.trained_models_dir, current_model)
_ensure_dir_exists(dirs.evaluate_dir)
with utils.logged_timer('Loading weights'):
black_net = dualnet.DualNetRunner(black_model, params)
white_net = dualnet.DualNetRunner(white_model, params)
best_model_so_far = evaluate(
dirs.trained_models_dir, best_model_so_far, current_model,
best_model_so_far, black_net, current_model, white_net,
dirs.evaluate_dir, params)
print('Winner: {}!'.format(best_model_so_far))
print('Winner of evaluation: {}!'.format(best_model_so_far))
else:
best_model_so_far = current_model
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# flags to run the RL pipeline
parser.add_argument(
'--base_dir',
type=str,
......@@ -402,18 +462,45 @@ if __name__ == '__main__':
metavar='N',
choices=[9, 19],
help='Go board size. The default size is 9.')
parser.add_argument(
'--batch_size',
type=int,
default=None,
metavar='BS',
help='Batch size for training. The default size is None')
# Test the pipeline with a dummy model
parser.add_argument(
'--test',
action='store_true',
help='A boolean to test RL pipeline with a dummy model.')
# Run RL pipeline with the validation step
parser.add_argument(
'--validation',
action='store_true',
help='A boolean to specify validation in the RL pipeline.')
# Run RL pipeline with the evaluation step
parser.add_argument(
'--evaluation',
action='store_true',
help='A boolean to specify evaluation in the RL pipeline.')
# self-play only
parser.add_argument(
'--debug',
'--selfplay',
action='store_true',
help='A boolean to indicate debug mode for testing purpose.')
help='A boolean to run self-play only.')
parser.add_argument(
'--validation',
action='store_true',
help='A boolean to explicitly generate holdout data for validation.')
'--selfplay_model_name',
type=str,
default=None,
metavar='SM',
help='The model used for self-play only.')
parser.add_argument(
'--selfplay_max_games',
type=int,
default=None,
metavar='SMG',
help='The number of game data self-play only needs to generate')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
research/minigo/model_params.py
View file @ 3b158095
......@@ -18,7 +18,7 @@
class MiniGoParams(object):
"""Parameters for MiniGo."""
# Go params
# Go board size
board_size = 9
# RL pipeline
......@@ -51,6 +51,7 @@ class MiniGoParams(object):
# the number of simultaneous leaves in MCTS
simultaneous_leaves = 8
# holdout data for validation
holdout_pct = 0.05 # How many games to hold out for validation
holdout_generation = 50 # How many recent generations/models for holdout data
......@@ -63,7 +64,7 @@ class MiniGoParams(object):
# AGZ used the most recent 500k games, which, assuming 250 moves/game = 125M
train_window_size = 125000000
# evaluation
# evaluation with two models
eval_games = 50 # The number of games to play in evaluation
eval_readouts = 100 # How many readouts to make per move in evaluation
eval_verbose = 1 # How verbose the players should be in evaluation
......
research/minigo/utils.py
View file @ 3b158095
......@@ -205,7 +205,7 @@ class MiniGoDirectory(object):
"""The class to set up directories of MiniGo."""
def __init__(self, base_dir):
self.trained_models_dir = os.path.join(base_dir, 'models')
self.trained_models_dir = os.path.join(base_dir, 'trained_models')
self.estimator_model_dir = os.path.join(base_dir, 'estimator_model_dir/')
self.selfplay_dir = os.path.join(base_dir, 'data/selfplay/')
self.holdout_dir = os.path.join(base_dir, 'data/holdout/')
......
research/object_detection/README.md
View file @ 3b158095
......@@ -90,6 +90,15 @@ reporting an issue.
## Release information
### April 30, 2018
We have released a Faster R-CNN detector with ResNet-101 feature extractor trained on [AVA](https://research.google.com/ava/) v2.1.
Compared with other commonly used object detectors, it changes the action classification loss function to per-class Sigmoid loss to handle boxes with multiple labels.
The model is trained on the training split of AVA v2.1 for 1.5M iterations, it achieves mean AP of 11.25% over 60 classes on the validation split of AVA v2.1.
For more details please refer to this [paper](https://arxiv.org/abs/1705.08421).
<b>Thanks to contributors</b>: Chen Sun, David Ross
### April 2, 2018
Supercharge your mobile phones with the next generation mobile object detector!
......
research/object_detection/builders/box_predictor_builder.py
View file @ 3b158095
......@@ -80,12 +80,14 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes):
num_classes=num_classes,
conv_hyperparams_fn=conv_hyperparams_fn,
depth=conv_box_predictor.depth,
num_layers_before_predictor=(conv_box_predictor.
num_layers_before_predictor),
num_layers_before_predictor=(
conv_box_predictor.num_layers_before_predictor),
kernel_size=conv_box_predictor.kernel_size,
box_code_size=conv_box_predictor.box_code_size,
class_prediction_bias_init=conv_box_predictor.class_prediction_bias_init
)
class_prediction_bias_init=conv_box_predictor.
class_prediction_bias_init,
use_dropout=conv_box_predictor.use_dropout,
dropout_keep_prob=conv_box_predictor.dropout_keep_probability)
return box_predictor_object
if box_predictor_oneof == 'mask_rcnn_box_predictor':
......@@ -113,7 +115,9 @@ def build(argscope_fn, box_predictor_config, is_training, num_classes):
mask_rcnn_box_predictor.mask_prediction_conv_depth),
masks_are_class_agnostic=(
mask_rcnn_box_predictor.masks_are_class_agnostic),
predict_keypoints=mask_rcnn_box_predictor.predict_keypoints)
predict_keypoints=mask_rcnn_box_predictor.predict_keypoints,
share_box_across_classes=(
mask_rcnn_box_predictor.share_box_across_classes))
return box_predictor_object
if box_predictor_oneof == 'rfcn_box_predictor':
......
research/object_detection/builders/box_predictor_builder_test.py
View file @ 3b158095
......@@ -317,6 +317,7 @@ class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
use_dropout: true
dropout_keep_probability: 0.8
box_code_size: 3
share_box_across_classes: true
}
"""
hyperparams_proto = hyperparams_pb2.Hyperparams()
......@@ -338,6 +339,7 @@ class MaskRCNNBoxPredictorBuilderTest(tf.test.TestCase):
self.assertEqual(box_predictor.num_classes, 90)
self.assertTrue(box_predictor._is_training)
self.assertEqual(box_predictor._box_code_size, 3)
self.assertEqual(box_predictor._share_box_across_classes, True)
def test_build_default_mask_rcnn_box_predictor(self):
box_predictor_proto = box_predictor_pb2.BoxPredictor()
......
research/object_detection/builders/losses_builder.py
View file @ 3b158095
......@@ -121,6 +121,10 @@ def build_faster_rcnn_classification_loss(loss_config):
config = loss_config.weighted_softmax
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
if loss_type == 'weighted_logits_softmax':
config = loss_config.weighted_logits_softmax
return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
logit_scale=config.logit_scale)
# By default, Faster RCNN second stage classifier uses Softmax loss
# with anchor-wise outputs.
......@@ -193,6 +197,11 @@ def _build_classification_loss(loss_config):
return losses.WeightedSoftmaxClassificationLoss(
logit_scale=config.logit_scale)
if loss_type == 'weighted_logits_softmax':
config = loss_config.weighted_logits_softmax
return losses.WeightedSoftmaxClassificationAgainstLogitsLoss(
logit_scale=config.logit_scale)
if loss_type == 'bootstrapped_sigmoid':
config = loss_config.bootstrapped_sigmoid
return losses.BootstrappedSigmoidClassificationLoss(
......
research/object_detection/builders/losses_builder_test.py
View file @ 3b158095
......@@ -207,6 +207,24 @@ class ClassificationLossBuilderTest(tf.test.TestCase):
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss))
def test_build_weighted_logits_softmax_classification_loss(self):
losses_text_proto = """
classification_loss {
weighted_logits_softmax {
}
}
localization_loss {
weighted_l2 {
}
}
"""
losses_proto = losses_pb2.Loss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss, _, _, _, _ = losses_builder.build(losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
def test_build_weighted_softmax_classification_loss_with_logit_scale(self):
losses_text_proto = """
classification_loss {
......@@ -442,6 +460,19 @@ class FasterRcnnClassificationLossBuilderTest(tf.test.TestCase):
self.assertTrue(isinstance(classification_loss,
losses.WeightedSoftmaxClassificationLoss))
def test_build_logits_softmax_loss(self):
losses_text_proto = """
weighted_logits_softmax {
}
"""
losses_proto = losses_pb2.ClassificationLoss()
text_format.Merge(losses_text_proto, losses_proto)
classification_loss = losses_builder.build_faster_rcnn_classification_loss(
losses_proto)
self.assertTrue(
isinstance(classification_loss,
losses.WeightedSoftmaxClassificationAgainstLogitsLoss))
def test_build_softmax_loss_by_default(self):
losses_text_proto = """
"""
......
research/object_detection/core/box_predictor.py
View file @ 3b158095
......@@ -308,7 +308,8 @@ class MaskRCNNBoxPredictor(BoxPredictor):
mask_prediction_num_conv_layers=2,
mask_prediction_conv_depth=256,
masks_are_class_agnostic=False,
predict_keypoints=False):
predict_keypoints=False,
share_box_across_classes=False):
"""Constructor.
Args:
......@@ -341,7 +342,8 @@ class MaskRCNNBoxPredictor(BoxPredictor):
masks_are_class_agnostic: Boolean determining if the mask-head is
class-agnostic or not.
predict_keypoints: Whether to predict keypoints insde detection boxes.
share_box_across_classes: Whether to share boxes across classes rather
than use a different box for each class.
Raises:
ValueError: If predict_instance_masks is true but conv_hyperparams is not
......@@ -362,6 +364,7 @@ class MaskRCNNBoxPredictor(BoxPredictor):
self._mask_prediction_conv_depth = mask_prediction_conv_depth
self._masks_are_class_agnostic = masks_are_class_agnostic
self._predict_keypoints = predict_keypoints
self._share_box_across_classes = share_box_across_classes
if self._predict_keypoints:
raise ValueError('Keypoint prediction is unimplemented.')
if ((self._predict_instance_masks or self._predict_keypoints) and
......@@ -403,10 +406,14 @@ class MaskRCNNBoxPredictor(BoxPredictor):
flattened_image_features = slim.dropout(flattened_image_features,
keep_prob=self._dropout_keep_prob,
is_training=self._is_training)
number_of_boxes = 1
if not self._share_box_across_classes:
number_of_boxes = self._num_classes
with slim.arg_scope(self._fc_hyperparams_fn()):
box_encodings = slim.fully_connected(
flattened_image_features,
self._num_classes * self._box_code_size,
number_of_boxes * self._box_code_size,
activation_fn=None,
scope='BoxEncodingPredictor')
class_predictions_with_background = slim.fully_connected(
......@@ -415,7 +422,7 @@ class MaskRCNNBoxPredictor(BoxPredictor):
activation_fn=None,
scope='ClassPredictor')
box_encodings = tf.reshape(
box_encodings, [-1, 1, self._num_classes, self._box_code_size])
box_encodings, [-1, 1, number_of_boxes, self._box_code_size])
class_predictions_with_background = tf.reshape(
class_predictions_with_background, [-1, 1, self._num_classes + 1])
return box_encodings, class_predictions_with_background
......@@ -778,7 +785,9 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
num_layers_before_predictor,
box_code_size,
kernel_size=3,
class_prediction_bias_init=0.0):
class_prediction_bias_init=0.0,
use_dropout=False,
dropout_keep_prob=0.8):
"""Constructor.
Args:
......@@ -796,6 +805,8 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
kernel_size: Size of final convolution kernel.
class_prediction_bias_init: constant value to initialize bias of the last
conv2d layer before class prediction.
use_dropout: Whether to apply dropout to class prediction head.
dropout_keep_prob: Probability of keeping activiations.
"""
super(WeightSharedConvolutionalBoxPredictor, self).__init__(is_training,
num_classes)
......@@ -805,6 +816,8 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
self._box_code_size = box_code_size
self._kernel_size = kernel_size
self._class_prediction_bias_init = class_prediction_bias_init
self._use_dropout = use_dropout
self._dropout_keep_prob = dropout_keep_prob
def _predict(self, image_features, num_predictions_per_location_list):
"""Computes encoded object locations and corresponding confidences.
......@@ -867,6 +880,7 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
num_predictions_per_location * self._box_code_size,
[self._kernel_size, self._kernel_size],
activation_fn=None, stride=1, padding='SAME',
normalizer_fn=None,
scope='BoxEncodingPredictor')
for i in range(self._num_layers_before_predictor):
......@@ -877,11 +891,15 @@ class WeightSharedConvolutionalBoxPredictor(BoxPredictor):
stride=1,
padding='SAME',
scope='ClassPredictionTower/conv2d_{}'.format(i))
if self._use_dropout:
class_predictions_net = slim.dropout(
class_predictions_net, keep_prob=self._dropout_keep_prob)
class_predictions_with_background = slim.conv2d(
class_predictions_net,
num_predictions_per_location * num_class_slots,
[self._kernel_size, self._kernel_size],
activation_fn=None, stride=1, padding='SAME',
normalizer_fn=None,
biases_initializer=tf.constant_initializer(
self._class_prediction_bias_init),
scope='ClassPredictor')
......
research/object_detection/core/box_predictor_test.py
View file @ 3b158095
......@@ -70,6 +70,33 @@ class MaskRCNNBoxPredictorTest(tf.test.TestCase):
self.assertAllEqual(box_encodings_shape, [2, 1, 5, 4])
self.assertAllEqual(class_predictions_with_background_shape, [2, 1, 6])
def test_get_boxes_with_five_classes_share_box_across_classes(self):
image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
is_training=False,
num_classes=5,
fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
share_box_across_classes=True
)
box_predictions = mask_box_predictor.predict(
[image_features], num_predictions_per_location=[1],
scope='BoxPredictor')
box_encodings = box_predictions[box_predictor.BOX_ENCODINGS]
class_predictions_with_background = box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND]
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
(box_encodings_shape,
class_predictions_with_background_shape) = sess.run(
[tf.shape(box_encodings),
tf.shape(class_predictions_with_background)])
self.assertAllEqual(box_encodings_shape, [2, 1, 1, 4])
self.assertAllEqual(class_predictions_with_background_shape, [2, 1, 6])
def test_value_error_on_predict_instance_masks_with_no_conv_hyperparms(self):
with self.assertRaises(ValueError):
box_predictor.MaskRCNNBoxPredictor(
......@@ -403,9 +430,14 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
}
}
initializer {
truncated_normal_initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
train: true,
}
"""
text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
return hyperparams_builder.build(conv_hyperparams, is_training=True)
......@@ -434,6 +466,27 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4])
self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
def test_bias_predictions_to_background_with_sigmoid_score_conversion(self):
def graph_fn(image_features):
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=True,
num_classes=2,
conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(),
depth=32,
num_layers_before_predictor=1,
class_prediction_bias_init=-4.6,
box_code_size=4)
box_predictions = conv_box_predictor.predict(
[image_features], num_predictions_per_location=[5],
scope='BoxPredictor')
class_predictions = tf.concat(box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1)
return (tf.nn.sigmoid(class_predictions),)
image_features = np.random.rand(4, 8, 8, 64).astype(np.float32)
class_predictions = self.execute(graph_fn, [image_features])
self.assertAlmostEqual(np.mean(class_predictions), 0.01, places=3)
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location(
self):
......@@ -524,19 +577,19 @@ class WeightSharedConvolutionalBoxPredictorTest(test_case.TestCase):
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_0/biases'),
'BoxEncodingPredictionTower/conv2d_0/BatchNorm/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictionTower/conv2d_1/biases'),
'BoxEncodingPredictionTower/conv2d_1/BatchNorm/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_0/biases'),
'ClassPredictionTower/conv2d_0/BatchNorm/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'ClassPredictionTower/conv2d_1/biases'),
'ClassPredictionTower/conv2d_1/BatchNorm/beta'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
'BoxEncodingPredictor/weights'),
('BoxPredictor/WeightSharedConvolutionalBoxPredictor/'
......
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