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Repository: WellyZhang/RAVEN
Branch: master
Commit: 77927ba3fe26
Files: 30
Total size: 192.3 KB
Directory structure:
gitextract_dccjdwzt/
├── .gitignore
├── LICENSE
├── README.md
├── assets/
│ ├── README.md
│ └── embedding.npy
├── requirements.txt
└── src/
├── dataset/
│ ├── AoT.py
│ ├── Attribute.py
│ ├── Rule.py
│ ├── __init__.py
│ ├── api.py
│ ├── build_tree.py
│ ├── const.py
│ ├── constraints.py
│ ├── main.py
│ ├── rendering.py
│ ├── sampling.py
│ ├── serialize.py
│ └── solver.py
└── model/
├── __init__.py
├── basic_model.py
├── cnn_lstm.py
├── cnn_mlp.py
├── const/
│ ├── __init__.py
│ └── const.py
├── fc_tree_net.py
├── main.py
├── resnet18.py
└── utility/
├── __init__.py
└── dataset_utility.py
================================================
FILE CONTENTS
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FILE: .gitignore
================================================
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var/
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*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
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# Unit test / coverage reports
htmlcov/
.tox/
.coverage
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.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/
.pytest_cache/
# Translations
*.mo
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# Django stuff:
*.log
local_settings.py
db.sqlite3
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instance/
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# mypy
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# vscode
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# experiments
/experiments
================================================
FILE: LICENSE
================================================
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DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
SUCH DAMAGES.
17. Interpretation of Sections 15 and 16.
If the disclaimer of warranty and limitation of liability provided
above cannot be given local legal effect according to their terms,
reviewing courts shall apply local law that most closely approximates
an absolute waiver of all civil liability in connection with the
Program, unless a warranty or assumption of liability accompanies a
copy of the Program in return for a fee.
END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
to attach them to the start of each source file to most effectively
state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year> <name of author>
This program is free software: you can redistribute it and/or modify
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but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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along with this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
================================================
FILE: README.md
================================================
# RAVEN
This repo contains code for our CVPR 2019 paper.
[RAVEN: A Dataset for <u>R</u>elational and <u>A</u>nalogical <u>V</u>isual r<u>E</u>aso<u>N</u>ing](http://wellyzhang.github.io/attach/cvpr19zhang.pdf)
Chi Zhang*, Feng Gao*, Baoxiong Jia, Yixin Zhu, Song-Chun Zhu
*Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2019
(* indicates equal contribution.)
Dramatic progress has been witnessed in basic vision tasks involving low-level perception, such as object recognition, detection, and tracking. Unfortunately, there is still an enormous performance gap between artificial vision systems and human intelligence in terms of higher-level vision problems, especially ones involving reasoning. Earlier attempts in equipping machines with high-level reasoning have hovered around Visual Question Answering (VQA), one typical task associating vision and language understanding. In this work, we propose a new dataset, built in the context of Raven's Progressive Matrices (RPM) and aimed at lifting machine intelligence by associating vision with structural, relational, and analogical reasoning in a hierarchical representation. Unlike previous works in measuring abstract reasoning using RPM, we establish a semantic link between vision and reasoning by providing structure representation. This addition enables a new type of abstract reasoning by jointly operating on the structure representation. Machine reasoning ability using modern computer vision is evaluated in this newly proposed dataset. Additionally, we also provide human performance as a reference. Finally, we show consistent improvement across all models by incorporating a simple neural module that combines visual understanding and structure reasoning.
# Dataset
The dataset is generated using the attributed stochastic image grammar. An example is shown below.
The grammatical design makes the dataset flexible and extendable. In total, we come up with 7 different figural configurations.
The dataset formatting document is in ```assets/README.md```. To download the dataset, please check [our project page](http://wellyzhang.github.io/project/raven.html#dataset).
# Performance
We show performance of models in the following table. For details, please check our [paper](http://wellyzhang.github.io/attach/cvpr19zhang.pdf).
| Method | Acc | Center | 2x2Grid | 3x3Grid | L-R | U-D | O-IC | O-IG |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| LSTM | 13.07% | 13.19% | 14.13% | 13.69% | 12.84% | 12.35% | 12.15% | 12.99% |
| WReN | 14.69% | 13.09% | 28.62% | 28.27% | 7.49% | 6.34% | 8.38% | 10.56% |
| CNN | 36.97% | 33.58% | 30.30% | 33.53% | 39.43% | 41.26% | 43.20% | 37.54% |
| ResNet | 53.43% | 52.82% | 41.86% | 44.29% | 58.77% | 60.16% | 63.19% | 53.12% |
| LSTM+DRT | 13.96% | 14.29% | 15.08% | 14.09% | 13.79% | 13.24% | 13.99% | 13.29% |
| WReN+DRT | 15.02% | 15.38% | 23.26% | 29.51% | 6.99% | 8.43% | 8.93% | 12.35% |
| CNN+DRT | 39.42% | 37.30% | 30.06% | 34.57% | 45.49% | 45.54% | 45.93% | 37.54% |
| ResNet+DRT | **59.56%** | **58.08%** | **46.53%** | **50.40%** | **65.82%** | **67.11%** | **69.09%** | **60.11%** |
| Human | 84.41% | 95.45% | 81.82% | 79.55% | 86.36% | 81.81% | 86.36% | 81.81% |
| Solver | 100% | 100% | 100% | 100% | 100% | 100% | 100% | 100% |
# Dependencies
**Important**
* Python 2.7
* OpenCV
* PyTorch
* CUDA and cuDNN expected
See ```requirements.txt``` for a full list of packages required.
# Usage
## Dataset Generation
Code to generate the dataset resides in the ```src/dataset``` folder. To generate a dataset, run
```
python src/dataset/main.py --num-samples <number of samples per configuration> --save-dir <directory to save the dataset>
```
Check the ```main.py``` file for a full list of arguments you can adjust.
## Benchmarking
Code to benchmark the dataset resides in ```src/model```. To run the code, first put ```assets/embedding.npy``` in the dataset folder as specified in the ```src/model/utility/dataset_utility.py```. Then run
```
python src/model/main.py --model <model name> --path <path to the dataset>
```
You can check the ```main.py``` file for a full list of arguments. This repo only supports ```Resnet18_MLP```, ```CNN_MLP```, and ```CNN_LSTM```. For WReN, please check the implementation in [the WReN repo](https://github.com/Fen9/WReN).
Note that for batch processing, we implement the DRT as a maximum tree of all possible tree structures and prune the branches during training based on an indicator.
# Citation
If you find the paper and/or the code helpful, please cite us.
```
@inproceedings{zhang2019raven,
title={RAVEN: A Dataset for Relational and Analogical Visual rEasoNing},
author={Zhang, Chi and Gao, Feng and Jia, Baoxiong and Zhu, Yixin and Zhu, Song-Chun},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
```
# Acknowledgement
We'd like to express our gratitude towards all the colleagues and anonymous reviewers for helping us improve the paper. The project is impossible to finish without the following open-source implementation.
* [WReN](https://github.com/Fen9/WReN)
================================================
FILE: assets/README.md
================================================
# Dataset Format
The dataset folder is organized as follows:
```
center_single/
RAVEN_0_train.npz
RAVEN_0_train.xml
...
RAVEN_6_val.npz
RAVEN_6_val.xml
...
RAVEN_8_test.npz
RAVEN_8_test.xml
...
distribute_four/
...
distribute_nine/
...
in_center_single_out_center_single/
...
in_distribute_four_out_center_single/
...
left_center_single_right_center_single/
...
up_center_single_down_center_single/
...
```
Note that each npz file comes with an xml file.
These 7 folders correspond to the 7 figure configurations in the paper. Specifically,
* Center = center_single
* 2x2Grid = distribute_four
* 3x3Grid = distribute_nine
* Left-Right = left_center_single_right_center_single
* Up-Down = up_center_single_down_center_single
* Out-InCenter = in_center_single_out_center_single
* Out-InGrid = in_distribute_four_out_center_single
## Naming
You might notice that the actual naming in this dataset is slightly different from what's reported in our paper. This is mostly due to the fact that things like **2x2** or **3x3** do not have corresponding word vectors. They are now **distribute_four** and **distribute_nine**. To make the paper concise, we also remove certain adjectives. **Center** was **Center_Single** and sometimes came with a component name.
As described in the paper, embeddings for each of them are obtained from pre-trained GloVe vectors and held fixed during training.
## NPZ file
Each npz file contains the following:
* image: a (16, 160, 160) array where all 16 figures in each problem are stacked on the first dimension. Note that first 8 figures compose the problem matrix and the last 8 figures are choices.
* target: the index of the correct answer in the answer set. Note that it starts from 0 and you should offset it by 8 if you want to retrieve it from the image array.
* structure: the tree structure annotation for the problem. It's serialized into a sequence using pre-order traversal.
* meta_matrix: similar to that in PGM. Detailed ordering could be found in ```src/dataset/const.py```.
* meta_target: bitwise-or of meta_matrix on all rows.
* meta_structure: it's similar to meta_matrix. Detailed ordering is in ```src/dataset/const.py```.
## XML file
Each xml file contains the following:
* Context panels and choice panels: each Panel could be further decomposed into Struct, Component, Layout, and Entity.
* Each layer comes with its name and id if necessary.
* Layout has its own attributes, whose values are indices into the value set (see also ```src/dataset/const.py```), except Position. Position is a list of slots entities could occupy, denoted by center and width/height.
* Entity's attributes follow the same annotation. The bbox is retrieved from the Position array in its parent Layout and the real_bbox is the actual bounding box, denoted by center and width/height. The mask is encoded using the run-length encoding. To decode it, use the ```rle_decode``` function in ```src/dataset/api.py```.
* Rules: rules are divided into groups, each of which applies to the corresponding component with the same id number.
* ```attr``` could be ```Number/Position``` when the rule is ```Constant``` as these two attributes are deeply coupled.
* When there is a rule on ```Number``` or ```Position```, we omit the rule on the other attribute, as it should be assumed **as is**, *i.e.*, following the rule on the other (could remain unchanged).
* Therefore, each rule group has 4 rules.
================================================
FILE: requirements.txt
================================================
numpy
scipy
matplotlib
pillow
scikit-image
opencv-contrib-python
tqdm
torch
torchvision
================================================
FILE: src/dataset/AoT.py
================================================
# -*- coding: utf-8 -*-
import copy
import numpy as np
from scipy.misc import comb
from Attribute import Angle, Color, Number, Position, Size, Type, Uniformity
from constraints import rule_constraint
class AoTNode(object):
"""Superclass of AoT.
"""
levels_next = {"Root": "Structure",
"Structure": "Component",
"Component": "Layout",
"Layout": "Entity"}
def __init__(self, name, level, node_type, is_pg=False):
self.name = name
self.level = level
self.node_type = node_type
self.children = []
self.is_pg = is_pg
def insert(self, node):
"""Used for public.
Arguments:
node(AoTNode): a node to insert
"""
assert isinstance(node, AoTNode)
assert self.node_type != "leaf"
assert node.level == self.levels_next[self.level]
self.children.append(node)
def _insert(self, node):
"""Used for private.
Arguments:
node(AoTNode): a node to insert
"""
assert isinstance(node, AoTNode)
assert self.node_type != "leaf"
assert node.level == self.levels_next[self.level]
self.children.append(node)
def _resample(self, change_number):
"""Resample the layout. If the number of entities change, resample also the
position distribution; otherwise only resample each attribute for each entity.
Arugments:
change_number(bool): whether to the number has been reset
"""
assert self.is_pg
if self.node_type == "and":
for child in self.children:
child._resample(change_number)
else:
self.children[0]._resample(change_number)
def __repr__(self):
return self.level + "." + self.name
def __str__(self):
return self.level + "." + self.name
class Root(AoTNode):
def __init__(self, name, is_pg=False):
super(Root, self).__init__(name, level="Root", node_type="or", is_pg=is_pg)
def sample(self):
"""The function returns a separate AoT that is correctly parsed.
Note that a new node is needed so that modification does not alter settings
in the original tree.
Returns:
new_node(Root): a newly instantiated node
"""
if self.is_pg:
raise ValueError("Could not sample on a PG")
new_node = Root(self.name, True)
selected = np.random.choice(self.children)
new_node.insert(selected._sample())
return new_node
def resample(self, change_number=False):
self._resample(change_number)
def prune(self, rule_groups):
"""Prune the AoT such that all branches satisfy the constraints.
Arguments:
rule_groups(list of list of Rule): each list of Rule applies to a component
Returns:
new_node(Root): a newly instantiated node with branches all satisfying the constraints;
None if no branches satisfy all the constraints
"""
new_node = Root(self.name)
for structure in self.children:
if len(structure.children) == len(rule_groups):
new_child = structure._prune(rule_groups)
if new_child is not None:
new_node.insert(new_child)
# during real execution, this should never happens
if len(new_node.children) == 0:
new_node = None
return new_node
def prepare(self):
"""This function prepares the AoT for rendering.
Returns:
structure.name(str): used for rendering structure
entities(list of Entity): used for rendering each entity
"""
assert self.is_pg
assert self.level == "Root"
structure = self.children[0]
components = []
for child in structure.children:
components.append(child)
entities = []
for component in components:
for child in component.children[0].children:
entities.append(child)
return structure.name, entities
def sample_new(self, component_idx, attr_name, min_level, max_level, root):
"""Sample a new configuration. This is used for generating answers.
Arguments:
component_idx(int): the component we will sample
attr_name(str): name of the attribute to sample
min_level(int): lower bound of value level for the attribute
max_level(int): upper bound of value level for the attribute
root(AoTNode): the answer AoT, used for storing previous value levels for each attribute
"""
assert self.is_pg
self.children[0]._sample_new(component_idx, attr_name, min_level, max_level, root.children[0])
class Structure(AoTNode):
def __init__(self, name, is_pg=False):
super(Structure, self).__init__(name, level="Structure", node_type="and", is_pg=is_pg)
def _sample(self):
if self.is_pg:
raise ValueError("Could not sample on a PG")
new_node = Structure(self.name, True)
for child in self.children:
new_node.insert(child._sample())
return new_node
def _prune(self, rule_groups):
new_node = Structure(self.name)
for i in range(len(self.children)):
child = self.children[i]
# if any of the components fails to satisfy the constraint
# the structure could not be chosen
new_child = child._prune(rule_groups[i])
if new_child is None:
return None
new_node.insert(new_child)
return new_node
def _sample_new(self, component_idx, attr_name, min_level, max_level, structure):
self.children[component_idx]._sample_new(attr_name, min_level, max_level, structure.children[component_idx])
class Component(AoTNode):
def __init__(self, name, is_pg=False):
super(Component, self).__init__(name, level="Component", node_type="or", is_pg=is_pg)
def _sample(self):
if self.is_pg:
raise ValueError("Could not sample on a PG")
new_node = Component(self.name, True)
selected = np.random.choice(self.children)
new_node.insert(selected._sample())
return new_node
def _prune(self, rule_group):
new_node = Component(self.name)
for child in self.children:
new_child = child._update_constraint(rule_group)
if new_child is not None:
new_node.insert(new_child)
if len(new_node.children) == 0:
new_node = None
return new_node
def _sample_new(self, attr_name, min_level, max_level, component):
self.children[0]._sample_new(attr_name, min_level, max_level, component.children[0])
class Layout(AoTNode):
"""Layout is the highest level of the hierarchy that has attributes (Number, Position and Uniformity).
To copy a Layout, please use deepcopy such that newly instantiated and separated attributes are created.
"""
def __init__(self, name, layout_constraint, entity_constraint,
orig_layout_constraint=None, orig_entity_constraint=None,
sample_new_num_count=None, is_pg=False):
super(Layout, self).__init__(name, level="Layout", node_type="and", is_pg=is_pg)
self.layout_constraint = layout_constraint
self.entity_constraint = entity_constraint
self.number = Number(min_level=layout_constraint["Number"][0], max_level=layout_constraint["Number"][1])
self.position = Position(pos_type=layout_constraint["Position"][0], pos_list=layout_constraint["Position"][1])
self.uniformity = Uniformity(min_level=layout_constraint["Uni"][0], max_level=layout_constraint["Uni"][1])
self.number.sample()
self.position.sample(self.number.get_value())
self.uniformity.sample()
# store initial layout_constraint and entity_constraint for answer generation
if orig_layout_constraint is None:
self.orig_layout_constraint = copy.deepcopy(self.layout_constraint)
else:
self.orig_layout_constraint = orig_layout_constraint
if orig_entity_constraint is None:
self.orig_entity_constraint = copy.deepcopy(self.entity_constraint)
else:
self.orig_entity_constraint = orig_entity_constraint
if sample_new_num_count is None:
self.sample_new_num_count = dict()
most_num = len(self.position.values)
for i in range(layout_constraint["Number"][0], layout_constraint["Number"][1] + 1):
self.sample_new_num_count[i] = [comb(most_num, i + 1), []]
else:
self.sample_new_num_count = sample_new_num_count
def add_new(self, *bboxes):
"""Add new entities into this level.
Arguments:
*bboxes(tuple of bbox): bboxes of new entities
"""
name = self.number.get_value()
uni = self.uniformity.get_value()
for i in range(len(bboxes)):
name += i
bbox = bboxes[i]
new_entity = copy.deepcopy(self.children[0])
new_entity.name = str(name)
new_entity.bbox = bbox
if not uni:
new_entity.resample()
self._insert(new_entity)
def resample(self, change_number=False):
self._resample(change_number)
def _sample(self):
"""Though Layout is an "and" node, we do not enumerate all possible configurations, but rather
we treat it as a sampling process such that different configurtions are sampled. After the
sampling, the lower level Entities are instantiated.
Returns:
new_node(Layout): a separated node with independent attributes
"""
pos = self.position.get_value()
new_node = copy.deepcopy(self)
new_node.is_pg = True
if self.uniformity.get_value():
node = Entity(name=str(0), bbox=pos[0], entity_constraint=self.entity_constraint)
new_node._insert(node)
for i in range(1, len(pos)):
bbox = pos[i]
node = copy.deepcopy(node)
node.name = str(i)
node.bbox = bbox
new_node._insert(node)
else:
for i in range(len(pos)):
bbox = pos[i]
node = Entity(name=str(i), bbox=bbox, entity_constraint=self.entity_constraint)
new_node._insert(node)
return new_node
def _resample(self, change_number):
"""Resample each attribute for every child.
This function is called across rows.
Arguments:
change_number(bool): whether to resample a number
"""
if change_number:
self.number.sample()
del self.children[:]
self.position.sample(self.number.get_value())
pos = self.position.get_value()
if self.uniformity.get_value():
node = Entity(name=str(0), bbox=pos[0], entity_constraint=self.entity_constraint)
self._insert(node)
for i in range(1, len(pos)):
bbox = pos[i]
node = copy.deepcopy(node)
node.name = str(i)
node.bbox = bbox
self._insert(node)
else:
for i in range(len(pos)):
bbox = pos[i]
node = Entity(name=str(i), bbox=bbox, entity_constraint=self.entity_constraint)
self._insert(node)
def _update_constraint(self, rule_group):
"""Update the constraint of the layout. If one constraint is not satisfied, return None
such that this structure is disgarded.
Arguments:
rule_group(list of Rule): all rules to apply to this layout
Returns:
Layout(Layout): a new Layout node with independent attributes
"""
num_min = self.layout_constraint["Number"][0]
num_max = self.layout_constraint["Number"][1]
uni_min = self.layout_constraint["Uni"][0]
uni_max = self.layout_constraint["Uni"][1]
type_min = self.entity_constraint["Type"][0]
type_max = self.entity_constraint["Type"][1]
size_min = self.entity_constraint["Size"][0]
size_max = self.entity_constraint["Size"][1]
color_min = self.entity_constraint["Color"][0]
color_max = self.entity_constraint["Color"][1]
new_constraints = rule_constraint(rule_group, num_min, num_max,
uni_min, uni_max,
type_min, type_max,
size_min, size_max,
color_min, color_max)
new_layout_constraint, new_entity_constraint = new_constraints
new_num_min = new_layout_constraint["Number"][0]
new_num_max = new_layout_constraint["Number"][1]
if new_num_min > new_num_max:
return None
new_uni_min = new_layout_constraint["Uni"][0]
new_uni_max = new_layout_constraint["Uni"][1]
if new_uni_min > new_uni_max:
return None
new_type_min = new_entity_constraint["Type"][0]
new_type_max = new_entity_constraint["Type"][1]
if new_type_min > new_type_max:
return None
new_size_min = new_entity_constraint["Size"][0]
new_size_max = new_entity_constraint["Size"][1]
if new_size_min > new_size_max:
return None
new_color_min = new_entity_constraint["Color"][0]
new_color_max = new_entity_constraint["Color"][1]
if new_color_min > new_color_max:
return None
new_layout_constraint = copy.deepcopy(self.layout_constraint)
new_layout_constraint["Number"][:] = [new_num_min, new_num_max]
new_layout_constraint["Uni"][:] = [new_uni_min, new_uni_max]
new_entity_constraint = copy.deepcopy(self.entity_constraint)
new_entity_constraint["Type"][:] = [new_type_min, new_type_max]
new_entity_constraint["Size"][:] = [new_size_min, new_size_max]
new_entity_constraint["Color"][:] = [new_color_min, new_color_max]
return Layout(self.name, new_layout_constraint, new_entity_constraint,
self.orig_layout_constraint, self.orig_entity_constraint,
self.sample_new_num_count)
def reset_constraint(self, attr):
attr_name = attr.lower()
instance = getattr(self, attr_name)
instance.min_level = self.layout_constraint[attr][0]
instance.max_level = self.layout_constraint[attr][1]
def _sample_new(self, attr_name, min_level, max_level, layout):
if attr_name == "Number":
while True:
value_level = self.number.sample_new(min_level, max_level)
if layout.sample_new_num_count[value_level][0] == 0:
continue
new_num = self.number.get_value(value_level)
new_value_idx = self.position.sample_new(new_num)
set_new_value_idx = set(new_value_idx)
if set_new_value_idx not in layout.sample_new_num_count[value_level][1]:
layout.sample_new_num_count[value_level][0] -= 1
layout.sample_new_num_count[value_level][1].append(set_new_value_idx)
break
self.number.set_value_level(value_level)
self.position.set_value_idx(new_value_idx)
pos = self.position.get_value()
del self.children[:]
for i in range(len(pos)):
bbox = pos[i]
node = Entity(name=str(i), bbox=bbox, entity_constraint=self.entity_constraint)
self._insert(node)
elif attr_name == "Position":
new_value_idx = self.position.sample_new(self.number.get_value())
layout.position.previous_values.append(new_value_idx)
self.position.set_value_idx(new_value_idx)
pos = self.position.get_value()
for i in range(len(pos)):
bbox = pos[i]
self.children[i].bbox = bbox
elif attr_name == "Type":
for index in range(len(self.children)):
new_value_level = self.children[index].type.sample_new(min_level, max_level)
self.children[index].type.set_value_level(new_value_level)
layout.children[index].type.previous_values.append(new_value_level)
elif attr_name == "Size":
for index in range(len(self.children)):
new_value_level = self.children[index].size.sample_new(min_level, max_level)
self.children[index].size.set_value_level(new_value_level)
layout.children[index].size.previous_values.append(new_value_level)
elif attr_name == "Color":
for index in range(len(self.children)):
new_value_level = self.children[index].color.sample_new(min_level, max_level)
self.children[index].color.set_value_level(new_value_level)
layout.children[index].color.previous_values.append(new_value_level)
else:
raise ValueError("Unsupported operation")
class Entity(AoTNode):
def __init__(self, name, bbox, entity_constraint):
super(Entity, self).__init__(name, level="Entity", node_type="leaf", is_pg=True)
# Attributes
# Sample each attribute such that the value lies in the admissible range
# Otherwise, random sample
self.entity_constraint = entity_constraint
self.bbox = bbox
self.type = Type(min_level=entity_constraint["Type"][0], max_level=entity_constraint["Type"][1])
self.type.sample()
self.size = Size(min_level=entity_constraint["Size"][0], max_level=entity_constraint["Size"][1])
self.size.sample()
self.color = Color(min_level=entity_constraint["Color"][0], max_level=entity_constraint["Color"][1])
self.color.sample()
self.angle = Angle(min_level=entity_constraint["Angle"][0], max_level=entity_constraint["Angle"][1])
self.angle.sample()
def reset_constraint(self, attr, min_level, max_level):
attr_name = attr.lower()
self.entity_constraint[attr][:] = [min_level, max_level]
instance = getattr(self, attr_name)
instance.min_level = min_level
instance.max_level = max_level
def resample(self):
self.type.sample()
self.size.sample()
self.color.sample()
self.angle.sample()
================================================
FILE: src/dataset/Attribute.py
================================================
# -*- coding: utf-8 -*-
import numpy as np
from const import (ANGLE_MAX, ANGLE_MIN, ANGLE_VALUES, COLOR_MAX, COLOR_MIN,
COLOR_VALUES, NUM_MAX, NUM_MIN, NUM_VALUES, SIZE_MAX,
SIZE_MIN, SIZE_VALUES, TYPE_MAX, TYPE_MIN, TYPE_VALUES,
UNI_MAX, UNI_MIN, UNI_VALUES)
class Attribute(object):
"""Super-class for all attributes. This should not be instantiated.
In the sub-class, each attribute should have a pre-defined value set
and a member to indicate the index in the value set. This design enables
setting a value by modifying the index only. Also, each instance should
come with value index boundaries, set as min_level and max_level. Boundaries
are good when we want to set constraints on the value set.
Before accessing the value, we should sample a value level by calling
the sample function.
"""
def __init__(self, name):
self.name = name
self.level = "Attribute"
# memory to store previous values
self.previous_values = []
def sample(self):
pass
def get_value(self):
pass
def set_value(self):
pass
def __repr__(self):
return self.level + "." + self.name
def __str__(self):
return self.level + "." + self.name
class Number(Attribute):
def __init__(self, min_level=NUM_MIN, max_level=NUM_MAX):
super(Number, self).__init__("Number")
self.value_level = 0
self.values = NUM_VALUES
self.min_level = min_level
self.max_level = max_level
def sample(self, min_level=NUM_MIN, max_level=NUM_MAX):
# min_level: min level index
# max_level: max level index
min_level = max(self.min_level, min_level)
max_level = min(self.max_level, max_level)
self.value_level = np.random.choice(range(min_level, max_level + 1))
def sample_new(self, min_level=None, max_level=None, previous_values=None):
"""Sample new values for generating the answer set.
Returns:
new_idx(int): a new value_level
"""
if min_level is None or max_level is None:
values = range(self.min_level, self.max_level + 1)
else:
values = range(min_level, max_level + 1)
if not previous_values:
available = set(values) - set(self.previous_values) - set([self.value_level])
else:
available = set(values) - set(previous_values) - set([self.value_level])
new_idx = np.random.choice(list(available))
return new_idx
def get_value_level(self):
return self.value_level
def set_value_level(self, value_level):
self.value_level = value_level
def get_value(self, value_level=None):
if value_level is None:
value_level = self.value_level
return self.values[value_level]
class Type(Attribute):
def __init__(self, min_level=TYPE_MIN, max_level=TYPE_MAX):
super(Type, self).__init__("Type")
self.value_level = 0
self.values = TYPE_VALUES
self.min_level = min_level
self.max_level = max_level
def sample(self, min_level=TYPE_MIN, max_level=TYPE_MAX):
min_level = max(self.min_level, min_level)
max_level = min(self.max_level, max_level)
self.value_level = np.random.choice(range(min_level, max_level + 1))
def sample_new(self, min_level=None, max_level=None, previous_values=None):
if min_level is None or max_level is None:
values = range(self.min_level, self.max_level + 1)
else:
values = range(min_level, max_level + 1)
if not previous_values:
available = set(values) - set(self.previous_values) - set([self.value_level])
else:
available = set(values) - set(previous_values) - set([self.value_level])
new_idx = np.random.choice(list(available))
return new_idx
def get_value_level(self):
return self.value_level
def set_value_level(self, value_level):
self.value_level = value_level
def get_value(self, value_level=None):
if value_level is None:
value_level = self.value_level
return self.values[value_level]
class Size(Attribute):
def __init__(self, min_level=SIZE_MIN, max_level=SIZE_MAX):
super(Size, self).__init__("Size")
self.value_level = 3
self.values = SIZE_VALUES
self.min_level = min_level
self.max_level = max_level
def sample(self, min_level=SIZE_MIN, max_level=SIZE_MAX):
min_level = max(self.min_level, min_level)
max_level = min(self.max_level, max_level)
self.value_level = np.random.choice(range(min_level, max_level + 1))
def sample_new(self, min_level=None, max_level=None, previous_values=None):
if min_level is None or max_level is None:
values = range(self.min_level, self.max_level + 1)
else:
values = range(min_level, max_level + 1)
if not previous_values:
available = set(values) - set(self.previous_values) - set([self.value_level])
else:
available = set(values) - set(previous_values) - set([self.value_level])
new_idx = np.random.choice(list(available))
return new_idx
def get_value_level(self):
return self.value_level
def set_value_level(self, value_level):
self.value_level = value_level
def get_value(self, value_level=None):
if value_level is None:
value_level = self.value_level
return self.values[value_level]
class Color(Attribute):
def __init__(self, min_level=COLOR_MIN, max_level=COLOR_MAX):
super(Color, self).__init__("Color")
self.value_level = 0
self.values = COLOR_VALUES
self.min_level = min_level
self.max_level = max_level
def sample(self, min_level=COLOR_MIN, max_level=COLOR_MAX):
min_level = max(self.min_level, min_level)
max_level = min(self.max_level, max_level)
self.value_level = np.random.choice(range(min_level, max_level + 1))
def sample_new(self, min_level=None, max_level=None, previous_values=None):
if min_level is None or max_level is None:
values = range(self.min_level, self.max_level + 1)
else:
values = range(min_level, max_level + 1)
if not previous_values:
available = set(values) - set(self.previous_values) - set([self.value_level])
else:
available = set(values) - set(previous_values) - set([self.value_level])
new_idx = np.random.choice(list(available))
return new_idx
def get_value_level(self):
return self.value_level
def set_value_level(self, value_level):
self.value_level = value_level
def get_value(self, value_level=None):
if value_level is None:
value_level = self.value_level
return self.values[value_level]
class Angle(Attribute):
def __init__(self, min_level=ANGLE_MIN, max_level=ANGLE_MAX):
super(Angle, self).__init__("Angle")
self.value_level = 3
self.values = ANGLE_VALUES
self.min_level = min_level
self.max_level = max_level
def sample(self, min_level=ANGLE_MIN, max_level=ANGLE_MAX):
min_level = max(self.min_level, min_level)
max_level = min(self.max_level, max_level)
self.value_level = np.random.choice(range(min_level, max_level + 1))
def sample_new(self, min_level=None, max_level=None, previous_values=None):
if min_level is None or max_level is None:
values = range(self.min_level, self.max_level + 1)
else:
values = range(min_level, max_level + 1)
if not previous_values:
available = set(values) - set(self.previous_values) - set([self.value_level])
else:
available = set(values) - set(previous_values) - set([self.value_level])
new_idx = np.random.choice(list(available))
return new_idx
def get_value_level(self):
return self.value_level
def set_value_level(self, value_level):
self.value_level = value_level
def get_value(self, value_level=None):
if value_level is None:
value_level = self.value_level
return self.values[value_level]
class Uniformity(Attribute):
def __init__(self, min_level=UNI_MIN, max_level=UNI_MAX):
super(Uniformity, self).__init__("Uniformity")
self.value_level = 0
self.values = UNI_VALUES
self.min_level = min_level
self.max_level = max_level
def sample(self):
self.value_level = np.random.choice(range(self.min_level, self.max_level + 1))
def sample_new(self):
# Should not resample uniformity
pass
def set_value_level(self, value_level):
self.value_level = value_level
def get_value_level(self):
return self.value_level
def get_value(self, value_level=None):
if value_level is None:
value_level = self.value_level
return self.values[value_level]
class Position(Attribute):
"""Position is a special case. There are the planar position and
the angular position. Planar position allows translation in the plane
while angular Position performs roration around an axis penperdicular to the plane.
"""
def __init__(self, pos_type, pos_list):
"""Instantiate the Position attribute by passing a position type
and a pre-defined position distribution on the plane. This attribute
is strongly coupled with Number and hence value index boundaries are
not needed.
Arguments:
pos_type(str): either "planar" or "angular
pos_list(list of list of numbers): actual distribution on the plane
"""
super(Position, self).__init__("Position")
# planar: [x_c, y_c, max_w, max_h]
# angular: [x_c, y_c, max_w, max_h, x_r, y_r, omega]
assert pos_type in ("planar", "angular")
self.pos_type = pos_type
self.values = pos_list
self.value_idx = None
def sample(self, num):
"""Sample multiple positions at the same time.
Arguments:
num(int): the number of positions to sample
"""
length = len(self.values)
assert num <= length
self.value_idx = np.random.choice(range(length), num, False)
def sample_new(self, num, previous_values=None):
# Here sample new relies on probability
length = len(self.values)
if not previous_values:
constraints = self.previous_values
else:
constraints = previous_values
while True:
finished = True
new_value_idx = np.random.choice(length, num, False)
if set(new_value_idx) == set(self.value_idx):
continue
for previous_value in constraints:
if set(new_value_idx) == set(previous_value):
finished = False
break
if finished:
break
return new_value_idx
def sample_add(self, num):
"""Sample additional number of positions.
Arguments:
num(int): the number of additional positions to sample
Returns:
ret(tuple of position): new positions to add to the layout
"""
ret = []
available = set(range(len(self.values))) - set(self.value_idx)
idxes_2_add = np.random.choice(list(available), num, False)
for index in idxes_2_add:
self.value_idx = np.insert(self.value_idx, 0, index)
ret.append(self.values[index])
return ret
def get_value_idx(self):
return self.value_idx
def set_value_idx(self, value_idx):
# Note that after sampling self.value_idx is a Numpy array
self.value_idx = value_idx
def get_value(self, value_idx=None):
if value_idx is None:
value_idx = self.value_idx
ret = []
for idx in value_idx:
ret.append(self.values[idx])
return ret
def remove(self, bbox):
# Note that after sampling self.value_idx is a Numpy array
idx = self.values.index(bbox)
np_idx = np.where(self.value_idx == idx)[0][0]
self.value_idx = np.delete(self.value_idx, np_idx)
================================================
FILE: src/dataset/Rule.py
================================================
# -*- coding: utf-8 -*-
import copy
import numpy as np
from const import COLOR_MAX, COLOR_MIN
def Rule_Wrapper(name, attr, param, component_idx):
ret = None
if name == "Constant":
ret = Constant(name, attr, param, component_idx)
elif name == "Progression":
ret = Progression(name, attr, param, component_idx)
elif name == "Arithmetic":
ret = Arithmetic(name, attr, param, component_idx)
elif name == "Distribute_Three":
ret = Distribute_Three(name, attr, param, component_idx)
else:
raise ValueError("Unsupported Rule")
return ret
class Rule(object):
"""General API for a rule.
Priority order: Rule on Number/Position always comes first
"""
def __init__(self, name, attr, params, component_idx=0):
"""Instantiate a rule by its name, attribute, paramter list and the component it applies to.
Each rule should be applied to all entities in a component.
Arguments:
name(str): pre-defined name of the rule
attr(str): pre-defined name of the attribute
params(list): a list of possible parameters for it to sample
component_idx(int): the index of the component to apply the rule
"""
self.name = name
self.attr = attr
self.params = params
self.component_idx = component_idx
self.value = 0
self.sample()
def sample(self):
"""Sample a parameter from the parameter list.
"""
if self.params is not None:
self.value = np.random.choice(self.params)
def apply_rule(self, aot, in_aot=None):
"""Apply the rule to a component in the AoT.
Arguments:
aot(AoTNode): an AoT for reference
in_aot(AoTNode): an AoT to apply the rule
Returns:
second_aot(AoTNode): a modified AoT
"""
# Root -> Structure -> Component -> Layout -> Entity
pass
class Constant(Rule):
"""Unary operator. Nothing changes.
"""
def __init__(self, name, attr, param, component_idx):
super(Constant, self).__init__(name, attr, param, component_idx)
def apply_rule(self, aot, in_aot=None):
if in_aot is None:
in_aot = aot
return copy.deepcopy(in_aot)
class Progression(Rule):
"""Unary operator. Attribute difference on two consequetive Panels remains the same.
"""
def __init__(self, name, attr, param, component_idx):
super(Progression, self).__init__(name, attr, param, component_idx)
# Flag to trigger consistency of the attribute in the first column
self.first_col = True
def apply_rule(self, aot, in_aot=None):
current_layout = aot.children[0].children[self.component_idx].children[0]
if in_aot is None:
in_aot = aot
second_aot = copy.deepcopy(in_aot)
second_layout = second_aot.children[0].children[self.component_idx].children[0]
if self.attr == "Number":
second_layout.number.set_value_level(second_layout.number.get_value_level() + self.value)
second_layout.position.sample(second_layout.number.get_value())
pos = second_layout.position.get_value()
del second_layout.children[:]
for i in range(len(pos)):
entity = copy.deepcopy(current_layout.children[0])
entity.name = str(i)
entity.bbox = pos[i]
if not current_layout.uniformity.get_value():
entity.resample()
second_layout.insert(entity)
elif self.attr == "Position":
second_pos_idx = (second_layout.position.get_value_idx() + self.value) % len(second_layout.position.values)
second_layout.position.set_value_idx(second_pos_idx)
second_bbox = second_layout.position.get_value()
for i in range(len(second_bbox)):
second_layout.children[i].bbox = second_bbox[i]
elif self.attr == "Type":
old_value_level = current_layout.children[0].type.get_value_level()
# enforce value consistency
if self.first_col and not current_layout.uniformity.get_value():
for entity in current_layout.children:
entity.type.set_value_level(old_value_level)
for entity in second_layout.children:
entity.type.set_value_level(old_value_level + self.value)
elif self.attr == "Size":
old_value_level = current_layout.children[0].size.get_value_level()
# enforce value consistency
if self.first_col and not current_layout.uniformity.get_value():
for entity in current_layout.children:
entity.size.set_value_level(old_value_level)
for entity in second_layout.children:
entity.size.set_value_level(old_value_level + self.value)
elif self.attr == "Color":
old_value_level = current_layout.children[0].color.get_value_level()
# enforce value consistency
if self.first_col and not current_layout.uniformity.get_value():
for entity in current_layout.children:
entity.color.set_value_level(old_value_level)
for entity in second_layout.children:
entity.color.set_value_level(old_value_level + self.value)
else:
raise ValueError("Unsupported attriubute")
self.first_col = not self.first_col
return second_aot
class Arithmetic(Rule):
"""Binary operator. Basically: Panel_3 = Panel_1 + Panel_2.
For Position: + means SET_UNION and - SET_DIFF.
"""
def __init__(self, name, attr, param, component_idx):
super(Arithmetic, self).__init__(name, attr, param, component_idx)
self.memory = []
self.color_count = 0
self.color_white_alarm = False
def apply_rule(self, aot, in_aot=None):
current_layout = aot.children[0].children[self.component_idx].children[0]
if in_aot is None:
in_aot = aot
second_aot = copy.deepcopy(in_aot)
second_layout = second_aot.children[0].children[self.component_idx].children[0]
if self.attr == "Number":
# the third col
if len(self.memory) > 0:
first_layout_number_level = self.memory.pop()
if self.value > 0:
total = first_layout_number_level + 1 + current_layout.number.get_value()
else:
total = first_layout_number_level + 1 - current_layout.number.get_value()
second_layout.number.set_value_level(total - 1)
# the second col
else:
old_value_level = current_layout.number.get_value_level()
self.memory.append(old_value_level)
if self.value > 0:
num_max_level_orig = sum(current_layout.layout_constraint["Number"]) + 1
new_num_max_level = num_max_level_orig - old_value_level - 1
second_layout.layout_constraint["Number"][1] = new_num_max_level
else:
num_min_level_orig = (second_layout.layout_constraint["Number"][0] - 1) / 2
new_num_max_level = old_value_level - num_min_level_orig - 1
second_layout.layout_constraint["Number"][:] = [num_min_level_orig, new_num_max_level]
second_layout.reset_constraint("Number")
second_layout.number.sample()
second_layout.position.sample(second_layout.number.get_value())
pos = second_layout.position.get_value()
del second_layout.children[:]
for i in range(len(pos)):
entity = copy.deepcopy(current_layout.children[0])
entity.name = str(i)
entity.bbox = pos[i]
if not current_layout.uniformity.get_value():
entity.resample()
second_layout.insert(entity)
elif self.attr == "Position":
# ADD is interpreted as SET_UNION; SUB is interpreted as SET_DIFF
# the third col
if len(self.memory) > 0:
first_layout_value_idx = self.memory.pop()
if self.value > 0:
new_pos_idx = set(first_layout_value_idx) | set(current_layout.position.get_value_idx())
else:
new_pos_idx = set(first_layout_value_idx) - set(current_layout.position.get_value_idx())
second_layout.number.set_value_level(len(new_pos_idx) - 1)
second_layout.position.set_value_idx(np.array(list(new_pos_idx)))
# the second col
else:
current_layout_value_idx = current_layout.position.get_value_idx()
self.memory.append(current_layout_value_idx)
while True:
second_layout.number.sample()
second_layout.position.sample(second_layout.number.get_value())
# if UNION, not a subset; otherwise not clearly a union
if self.value > 0:
if not (set(current_layout_value_idx) >= set(second_layout.position.get_value_idx())):
break
# if DIFF, not a subset; otherwise no entities left
else:
if not (set(current_layout_value_idx) <= set(second_layout.position.get_value_idx())):
break
pos = second_layout.position.get_value()
del second_layout.children[:]
for i in range(len(pos)):
entity = copy.deepcopy(current_layout.children[0])
entity.name = str(i)
entity.bbox = pos[i]
if not current_layout.uniformity.get_value():
entity.resample()
second_layout.insert(entity)
elif self.attr == "Size":
if len(self.memory) > 0:
first_layout_size_level = self.memory.pop()
if self.value > 0:
new_size_value_level = first_layout_size_level + \
current_layout.children[0].size.get_value_level() + 1
else:
new_size_value_level = first_layout_size_level - \
current_layout.children[0].size.get_value_level() - 1
for entity in second_layout.children:
entity.size.set_value_level(new_size_value_level)
else:
# make sure of value consistency
old_value_level = current_layout.children[0].size.get_value_level()
self.memory.append(old_value_level)
if not current_layout.uniformity.get_value():
for entity in current_layout.children:
entity.size.set_value_level(old_value_level)
if self.value > 0:
size_max_level_orig = sum(current_layout.entity_constraint["Size"]) + 1
new_size_max_level = size_max_level_orig - old_value_level - 1
# deepcopy breaks the link of constraints between Layout and Entity
# Need to reset each attribute
second_layout.entity_constraint["Size"][1] = new_size_max_level
else:
size_min_level_orig = (current_layout.entity_constraint["Size"][0] - 1) / 2
new_size_max_level = old_value_level - size_min_level_orig - 1
second_layout.entity_constraint["Size"] = [size_min_level_orig, new_size_max_level]
new_size_min_level, new_size_max_level = second_layout.entity_constraint["Size"]
the_child = second_layout.children[0]
the_child.reset_constraint("Size", new_size_min_level, new_size_max_level)
the_child.size.sample()
new_size_value_level = the_child.size.get_value_level()
for idx in range(1, len(second_layout.children)):
entity = second_layout.children[idx]
entity.reset_constraint("Size", new_size_min_level, new_size_max_level)
entity.size.set_value_level(new_size_value_level)
elif self.attr == "Color":
self.color_count += 1
if len(self.memory) > 0:
first_layout_color_level = self.memory.pop()
if self.value > 0:
new_color_value_level = first_layout_color_level + \
current_layout.children[0].color.get_value_level()
else:
new_color_value_level = first_layout_color_level - \
current_layout.children[0].color.get_value_level()
for entity in second_layout.children:
entity.color.set_value_level(new_color_value_level)
else:
# Logic here: C_12 and C_22 could not be both 0, otherwise it's impossible to distinguish + and -
# If C_12 == 0, we set an alarm
# Under this alarm, if C_21 == MAX and ADD rule, then resample C_21 to ensure C_22 could be other than 0
# Similarly, if C_21 == 0 and SUB rule, then resample C_21 to ensure C_22 could be other than 0
# Finally, loop until C_22 is not 0
# make sure of value consistency
old_value_level = current_layout.children[0].color.get_value_level()
# the third time you apply this rule and find C_21 == MAX/0 if +/-
reset_current_layout = False
if self.color_count == 3 and self.color_white_alarm:
if self.value > 0 and old_value_level == COLOR_MAX:
old_value_level = current_layout.children[0].color.sample_new()
reset_current_layout = True
if self.value < 0 and old_value_level == COLOR_MIN:
old_value_level = current_layout.children[0].color.sample_new()
reset_current_layout = True
self.memory.append(old_value_level)
if reset_current_layout or not current_layout.uniformity.get_value():
for entity in current_layout.children:
entity.color.set_value_level(old_value_level)
if self.value > 0:
color_max_level_orig = sum(current_layout.entity_constraint["Color"])
new_color_max_level = color_max_level_orig - old_value_level
second_layout.entity_constraint["Color"][1] = new_color_max_level
else:
color_min_level_orig = second_layout.entity_constraint["Color"][0] / 2
new_color_max_level = old_value_level
second_layout.entity_constraint["Color"][:] = [color_min_level_orig, new_color_max_level]
new_color_min_level, new_color_max_level = second_layout.entity_constraint["Color"]
the_child = second_layout.children[0]
the_child.reset_constraint("Color", new_color_min_level, new_color_max_level)
the_child.color.sample()
new_color_value_level = the_child.color.get_value_level()
# the first time you apply this rule and get C_12 == 0
# set the alarm
if self.color_count == 1:
self.color_white_alarm = (new_color_value_level == 0)
if self.color_count == 3 and self.color_white_alarm and new_color_value_level == 0:
new_color_value_level = the_child.color.sample_new()
the_child.color.set_value_level(new_color_value_level)
for idx in range(1, len(second_layout.children)):
entity = second_layout.children[idx]
entity.reset_constraint("Color", new_color_min_level, new_color_max_level)
entity.color.set_value_level(new_color_value_level)
else:
raise ValueError("Unsupported attriubute")
return second_aot
class Distribute_Three(Rule):
"""Ternay operator. Three values across the columns form a fixed set.
"""
def __init__(self, name, attr, param, component_idx):
super(Distribute_Three, self).__init__(name, attr, param, component_idx)
self.value_levels = []
self.count = 0
def apply_rule(self, aot, in_aot=None):
current_layout = aot.children[0].children[self.component_idx].children[0]
if in_aot is None:
in_aot = aot
second_aot = copy.deepcopy(in_aot)
second_layout = second_aot.children[0].children[self.component_idx].children[0]
if self.attr == "Number":
if self.count == 0:
all_value_levels = range(current_layout.layout_constraint["Number"][0],
current_layout.layout_constraint["Number"][1] + 1)
current_value_level = current_layout.number.get_value_level()
idx = all_value_levels.index(current_value_level)
all_value_levels.pop(idx)
three_value_levels = np.random.choice(all_value_levels, 2, False)
three_value_levels = np.insert(three_value_levels, 0, current_value_level)
self.value_levels.append(three_value_levels[[0, 1, 2]])
if np.random.uniform() >= 0.5:
self.value_levels.append(three_value_levels[[1, 2, 0]])
self.value_levels.append(three_value_levels[[2, 0, 1]])
else:
self.value_levels.append(three_value_levels[[2, 0, 1]])
self.value_levels.append(three_value_levels[[1, 2, 0]])
second_layout.number.set_value_level(self.value_levels[0][1])
else:
row, col = divmod(self.count, 2)
if col == 0:
current_layout.number.set_value_level(self.value_levels[row][0])
current_layout.resample()
second_aot = copy.deepcopy(aot)
second_layout = second_aot.children[0].children[self.component_idx].children[0]
second_layout.number.set_value_level(self.value_levels[row][1])
else:
second_layout.number.set_value_level(self.value_levels[row][2])
second_layout.position.sample(second_layout.number.get_value())
pos = second_layout.position.get_value()
del second_layout.children[:]
for i in range(len(pos)):
entity = copy.deepcopy(current_layout.children[0])
entity.name = str(i)
entity.bbox = pos[i]
if not current_layout.uniformity.get_value():
entity.resample()
second_layout.insert(entity)
self.count = (self.count + 1) % 6
elif self.attr == "Position":
if self.count == 0:
# sample new does not change value_level/value_idx
num = current_layout.number.get_value()
pos_0 = current_layout.position.get_value_idx()
pos_1 = current_layout.position.sample_new(num)
pos_2 = current_layout.position.sample_new(num, [pos_1])
three_value_levels = np.array([pos_0, pos_1, pos_2])
self.value_levels.append(three_value_levels[[0, 1, 2]])
if np.random.uniform() >= 0.5:
self.value_levels.append(three_value_levels[[1, 2, 0]])
self.value_levels.append(three_value_levels[[2, 0, 1]])
else:
self.value_levels.append(three_value_levels[[2, 0, 1]])
self.value_levels.append(three_value_levels[[1, 2, 0]])
second_layout.position.set_value_idx(self.value_levels[0][1])
else:
row, col = divmod(self.count, 2)
if col == 0:
current_layout.number.set_value_level(len(self.value_levels[row][0]) - 1)
current_layout.resample()
current_layout.position.set_value_idx(self.value_levels[row][0])
pos = current_layout.position.get_value()
for i in range(len(pos)):
entity = current_layout.children[i]
entity.bbox = pos[i]
second_aot = copy.deepcopy(aot)
second_layout = second_aot.children[0].children[self.component_idx].children[0]
second_layout.position.set_value_idx(self.value_levels[row][1])
else:
second_layout.position.set_value_idx(self.value_levels[row][2])
pos = second_layout.position.get_value()
for i in range(len(pos)):
entity = second_layout.children[i]
entity.bbox = pos[i]
self.count = (self.count + 1) % 6
elif self.attr == "Type":
if self.count == 0:
all_value_levels = range(current_layout.entity_constraint["Type"][0],
current_layout.entity_constraint["Type"][1] + 1)
# if np.random.uniform() >= 0.5 and 0 not in all_value_levels:
# all_value_levels.insert(0, 0)
three_value_levels = np.random.choice(all_value_levels, 3, False)
np.random.shuffle(three_value_levels)
self.value_levels.append(three_value_levels[[0, 1, 2]])
if np.random.uniform() >= 0.5:
self.value_levels.append(three_value_levels[[1, 2, 0]])
self.value_levels.append(three_value_levels[[2, 0, 1]])
else:
self.value_levels.append(three_value_levels[[2, 0, 1]])
self.value_levels.append(three_value_levels[[1, 2, 0]])
for entity in current_layout.children:
entity.type.set_value_level(self.value_levels[0][0])
for entity in second_layout.children:
entity.type.set_value_level(self.value_levels[0][1])
else:
row, col = divmod(self.count, 2)
if col == 0:
value_level = self.value_levels[row][0]
for entity in current_layout.children:
entity.type.set_value_level(value_level)
value_level = self.value_levels[row][1]
for entity in second_layout.children:
entity.type.set_value_level(value_level)
else:
value_level = self.value_levels[row][2]
for entity in second_layout.children:
entity.type.set_value_level(value_level)
self.count = (self.count + 1) % 6
elif self.attr == "Size":
if self.count == 0:
all_value_levels = range(current_layout.entity_constraint["Size"][0],
current_layout.entity_constraint["Size"][1] + 1)
three_value_levels = np.random.choice(all_value_levels, 3, False)
self.value_levels.append(three_value_levels[[0, 1, 2]])
if np.random.uniform() >= 0.5:
self.value_levels.append(three_value_levels[[1, 2, 0]])
self.value_levels.append(three_value_levels[[2, 0, 1]])
else:
self.value_levels.append(three_value_levels[[2, 0, 1]])
self.value_levels.append(three_value_levels[[1, 2, 0]])
for entity in current_layout.children:
entity.size.set_value_level(self.value_levels[0][0])
for entity in second_layout.children:
entity.size.set_value_level(self.value_levels[0][1])
else:
row, col = divmod(self.count, 2)
if col == 0:
value_level = self.value_levels[row][0]
for entity in current_layout.children:
entity.size.set_value_level(value_level)
value_level = self.value_levels[row][1]
for entity in second_layout.children:
entity.size.set_value_level(value_level)
else:
value_level = self.value_levels[row][2]
for entity in second_layout.children:
entity.size.set_value_level(value_level)
self.count = (self.count + 1) % 6
elif self.attr == "Color":
if self.count == 0:
all_value_levels = range(current_layout.entity_constraint["Color"][0],
current_layout.entity_constraint["Color"][1] + 1)
three_value_levels = np.random.choice(all_value_levels, 3, False)
self.value_levels.append(three_value_levels[[0, 1, 2]])
if np.random.uniform() >= 0.5:
self.value_levels.append(three_value_levels[[1, 2, 0]])
self.value_levels.append(three_value_levels[[2, 0, 1]])
else:
self.value_levels.append(three_value_levels[[2, 0, 1]])
self.value_levels.append(three_value_levels[[1, 2, 0]])
for entity in current_layout.children:
entity.color.set_value_level(self.value_levels[0][0])
for entity in second_layout.children:
entity.color.set_value_level(self.value_levels[0][1])
else:
row, col = divmod(self.count, 2)
if col == 0:
value_level = self.value_levels[row][0]
for entity in current_layout.children:
entity.color.set_value_level(value_level)
value_level = self.value_levels[row][1]
for entity in second_layout.children:
entity.color.set_value_level(value_level)
else:
value_level = self.value_levels[row][2]
for entity in second_layout.children:
entity.color.set_value_level(value_level)
self.count = (self.count + 1) % 6
else:
raise ValueError("Unsupported attriubute")
return second_aot
================================================
FILE: src/dataset/__init__.py
================================================
""" RAVEN dataset generation code
Author: Chi Zhang
Data: 05/14/2019
Contact: chi.zhang@ucla.edu
"""
================================================
FILE: src/dataset/api.py
================================================
# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import cv2
import numpy as np
from const import DEFAULT_WIDTH, IMAGE_SIZE
from rendering import render_entity
class Bunch:
"""Dummy class"""
def __init__(self, **kwds):
self.__dict__.update(kwds)
def get_real_bbox(entity_bbox, entity_type, entity_size, entity_angle):
assert entity_type != "none"
center = (int(entity_bbox[1] * IMAGE_SIZE), int(entity_bbox[0] * IMAGE_SIZE))
M = cv2.getRotationMatrix2D(center, entity_angle, 1)
unit = min(entity_bbox[2], entity_bbox[3]) * IMAGE_SIZE / 2
delta = DEFAULT_WIDTH * 1.5 / IMAGE_SIZE
if entity_type == "circle":
radius = unit * entity_size
real_bbox = [center[1] * 1.0 / IMAGE_SIZE, center[0] * 1.0 / IMAGE_SIZE, 2 * radius / IMAGE_SIZE + delta, 2 * radius / IMAGE_SIZE + delta]
else:
if entity_type == "triangle":
dl = int(unit * entity_size)
homo_pts = np.array([[center[0], center[1] - dl, 1],
[center[0] + int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0), 1],
[center[0] - int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0), 1]],
np.int32)
if entity_type == "square":
dl = int(unit / 2 * np.sqrt(2) * entity_size)
homo_pts = np.array([[center[0] - dl, center[1] - dl, 1],
[center[0] - dl, center[1] + dl, 1],
[center[0] + dl, center[1] + dl, 1],
[center[0] + dl, center[1] - dl, 1]],
np.int32)
if entity_type == "pentagon":
dl = int(unit * entity_size)
homo_pts = np.array([[center[0], center[1] - dl, 1],
[center[0] - int(dl * np.cos(np.pi / 10)), center[1] - int(dl * np.sin(np.pi / 10)), 1],
[center[0] - int(dl * np.sin(np.pi / 5)), center[1] + int(dl * np.cos(np.pi / 5)), 1],
[center[0] + int(dl * np.sin(np.pi / 5)), center[1] + int(dl * np.cos(np.pi / 5)), 1],
[center[0] + int(dl * np.cos(np.pi / 10)), center[1] - int(dl * np.sin(np.pi / 10)), 1]],
np.int32)
if entity_type == "hexagon":
dl = int(unit * entity_size)
homo_pts = np.array([[center[0], center[1] - dl, 1],
[center[0] - int(dl / 2.0 * np.sqrt(3)), center[1] - int(dl / 2.0), 1],
[center[0] - int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0), 1],
[center[0], center[1] + dl, 1],
[center[0] + int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0), 1],
[center[0] + int(dl / 2.0 * np.sqrt(3)), center[1] - int(dl / 2.0), 1]],
np.int32)
after_pts = np.dot(M, homo_pts.T)
min_x = min(after_pts[1, :]) / IMAGE_SIZE
max_x = max(after_pts[1, :]) / IMAGE_SIZE
min_y = min(after_pts[0, :]) / IMAGE_SIZE
max_y = max(after_pts[0, :]) / IMAGE_SIZE
real_bbox = [(min_x + max_x) / 2, (min_y + max_y) / 2, max_x - min_x + delta, max_y - min_y + delta]
return list(np.round(real_bbox, 4))
def get_mask(entity_bbox, entity_type, entity_size, entity_angle):
dummy_entity = Bunch()
dummy_entity.bbox = entity_bbox
dummy_entity.type = Bunch(get_value=lambda : entity_type)
dummy_entity.size = Bunch(get_value=lambda : entity_size)
dummy_entity.color = Bunch(get_value=lambda : 0)
dummy_entity.angle = Bunch(get_value=lambda : entity_angle)
mask = render_entity(dummy_entity) / 255
return mask
# ref: https://www.kaggle.com/stainsby/fast-tested-rle
# ref: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
def rle_encode(img):
'''
img: numpy array, 1 - mask, 0 - background
Returns run length as string formated
'''
pixels = img.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return "[" + ",".join(str(x) for x in runs) + "]"
def rle_decode(mask_rle, shape):
'''
mask_rle: run-length as string formated (start length)
shape: (height,width) of array to return
Returns numpy array, 1 - mask, 0 - background
'''
s = mask_rle[1:-1].split(",")
starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + lengths
img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
for lo, hi in zip(starts, ends):
img[lo:hi] = 1
return img.reshape(shape)
================================================
FILE: src/dataset/build_tree.py
================================================
# -*- coding: utf-8 -*-
from AoT import Component, Layout, Root, Structure
from constraints import (gen_entity_constraint, gen_layout_constraint,
rule_constraint)
def build_center_single():
# Build AoT here
root = Root("Scene")
# Singleton struct
struct = Structure("Singleton")
# Singleton comp
comp = Component("Grid")
# Center_Single layout
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.5, 0.5, 1, 1)],
num_min=0,
num_max=0)
layout = Layout("Center_Single", layout_constraint, entity_constraint)
comp.insert(layout)
struct.insert(comp)
root.insert(struct)
return root
def build_distribute_four():
# Build AoT here
root = Root("Scene")
# Singleton struct
struct = Structure("Singleton")
# Singleton comp
comp = Component("Grid")
# Distribute_Four
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.25, 0.25, 0.5, 0.5),
(0.25, 0.75, 0.5, 0.5),
(0.75, 0.25, 0.5, 0.5),
(0.75, 0.75, 0.5, 0.5)],
num_min=0,
num_max=3)
layout = Layout("Distribute_Four", layout_constraint, entity_constraint)
comp.insert(layout)
struct.insert(comp)
root.insert(struct)
return root
def build_distribute_nine():
# Build AoT here
root = Root("Scene")
# Singleton struct
struct = Structure("Singleton")
# Singleton comp
comp = Component("Grid")
# Distribute_Nine
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.16, 0.16, 0.33, 0.33),
(0.16, 0.5, 0.33, 0.33),
(0.16, 0.83, 0.33, 0.33),
(0.5, 0.16, 0.33, 0.33),
(0.5, 0.5, 0.33, 0.33),
(0.5, 0.83, 0.33, 0.33),
(0.83, 0.16, 0.33, 0.33),
(0.83, 0.5, 0.33, 0.33),
(0.83, 0.83, 0.33, 0.33)],
num_min=0,
num_max=8)
layout = Layout("Distribute_Nine", layout_constraint, entity_constraint)
comp.insert(layout)
struct.insert(comp)
root.insert(struct)
return root
def build_left_center_single_right_center_single():
# Build AoT here
root = Root("Scene")
# Left-Right Structure
struct = Structure("Left_Right")
# Left Component
comp_left = Component("Left")
# Left_Center_Single
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.5, 0.25, 0.5, 0.5)],
num_min=0,
num_max=0)
layout = Layout("Left_Center_Single", layout_constraint, entity_constraint)
comp_left.insert(layout)
# Right Component
comp_right = Component("Right")
# Right_Center_Single
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.5, 0.75, 0.5, 0.5)],
num_min=0,
num_max=0)
layout = Layout("Right_Center_Single", layout_constraint, entity_constraint)
comp_right.insert(layout)
struct.insert(comp_left)
struct.insert(comp_right)
root.insert(struct)
return root
def build_up_center_single_down_center_single():
# Build AoT here
root = Root("Scene")
# Up-Down Structure
struct = Structure("Up_Down")
# Left Component
comp_up = Component("Up")
# Up_Center_Single
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.25, 0.5, 0.5, 0.5)],
num_min=0,
num_max=0)
layout = Layout("Up_Center_Single", layout_constraint, entity_constraint)
comp_up.insert(layout)
# Down Component
comp_down = Component("Down")
# Down_Center_Single
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.75, 0.5, 0.5, 0.5)],
num_min=0,
num_max=0)
layout = Layout("Down_Center_Single", layout_constraint, entity_constraint)
comp_down.insert(layout)
struct.insert(comp_up)
struct.insert(comp_down)
root.insert(struct)
return root
def build_in_center_single_out_center_single():
# Build AoT here
root = Root("Scene")
# In-Out Structure
struct = Structure("Out_In")
# Out Component
comp_out = Component("Out")
# Out_One
entity_constraint = gen_entity_constraint(type_min=1,
size_min=3,
color_max=0)
layout_constraint = gen_layout_constraint("planar",
[(0.5, 0.5, 1, 1)],
num_min=0,
num_max=0)
layout = Layout("Out_Center_Single", layout_constraint, entity_constraint)
comp_out.insert(layout)
# In Component
comp_in = Component("In")
# In_Center_Single
entity_constraint = gen_entity_constraint(type_min=1)
layout_constraint = gen_layout_constraint("planar",
[(0.5, 0.5, 0.33, 0.33)],
num_min=0,
num_max=0)
layout = Layout("In_Center_Single", layout_constraint, entity_constraint)
comp_in.insert(layout)
struct.insert(comp_out)
struct.insert(comp_in)
root.insert(struct)
return root
def build_in_distribute_four_out_center_single():
# Build AoT here
root = Root("Scene")
# In-Out Structure
struct = Structure("Out_In")
# Out Component
comp_out = Component("Out")
# Out_One
entity_constraint = gen_entity_constraint(type_min=1,
size_min=3,
color_max=0)
layout_constraint = gen_layout_constraint("planar",
[(0.5, 0.5, 1, 1)],
num_min=0,
num_max=0)
layout = Layout("Out_Center_Single", layout_constraint, entity_constraint)
comp_out.insert(layout)
# In Component
comp_in = Component("In")
# In_Four
entity_constraint = gen_entity_constraint(type_min=1, size_min=2)
layout_constraint = gen_layout_constraint("planar",
[(0.42, 0.42, 0.15, 0.15),
(0.42, 0.58, 0.15, 0.15),
(0.58, 0.42, 0.15, 0.15),
(0.58, 0.58, 0.15, 0.15)],
num_min=0,
num_max=3)
layout = Layout("In_Distribute_Four", layout_constraint, entity_constraint)
comp_in.insert(layout)
struct.insert(comp_out)
struct.insert(comp_in)
root.insert(struct)
return root
================================================
FILE: src/dataset/const.py
================================================
# -*- coding: utf-8 -*-
# Maximum number of components in a RPM
MAX_COMPONENTS = 2
# Canvas parameters
IMAGE_SIZE = 160
CENTER = (IMAGE_SIZE / 2, IMAGE_SIZE / 2)
DEFAULT_RADIUS = IMAGE_SIZE / 4
DEFAULT_WIDTH = 2
# Attribute parameters
# Number
NUM_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9]
NUM_MIN = 0
NUM_MAX = len(NUM_VALUES) - 1
# Uniformity
UNI_VALUES = [False, False, False, True]
UNI_MIN = 0
UNI_MAX = len(UNI_VALUES) - 1
# Type
TYPE_VALUES = ["none", "triangle", "square", "pentagon", "hexagon", "circle"]
TYPE_MIN = 0
TYPE_MAX = len(TYPE_VALUES) - 1
# Size
SIZE_VALUES = [0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
SIZE_MIN = 0
SIZE_MAX = len(SIZE_VALUES) - 1
# Color
COLOR_VALUES = [255, 224, 196, 168, 140, 112, 84, 56, 28, 0]
COLOR_MIN = 0
COLOR_MAX = len(COLOR_VALUES) - 1
# Angle: self-rotation
ANGLE_VALUES = [-135, -90, -45, 0, 45, 90, 135, 180]
ANGLE_MIN = 0
ANGLE_MAX = len(ANGLE_VALUES) - 1
META_TARGET_FORMAT = ["Constant", "Progression", "Arithmetic", "Distribute_Three", "Number", "Position", "Type", "Size", "Color"]
META_STRUCTURE_FORMAT = ["Singleton", "Left_Right", "Up_Down", "Out_In", "Left", "Right", "Up", "Down", "Out", "In", "Grid", "Center_Single", "Distribute_Four", "Distribute_Nine", "Left_Center_Single", "Right_Center_Single", "Up_Center_Single", "Down_Center_Single", "Out_Center_Single", "In_Center_Single", "In_Distribute_Four"]
# Rule, Attr, Param
# The design encodes rule priority order: Number/Position always comes first
# Number and Position could not both be sampled
# Progression on Number: Number on each Panel +1/2 or -1/2
# Progression on Position: Entities on each Panel roll over the layout
# Arithmetic on Number: Numeber on the third Panel = Number on first +/- Number on second (1 for + and -1 for -)
# Arithmetic on Position: 1 for SET_UNION and -1 for SET_DIFF
# Distribute_Three on Number: Three numbers through each row
# Distribute_Three on Position: Three positions (same number) through each row
# Constant on Number/Position: Nothing changes
# Progression on Type: Type progression defined as the number of edges on each entity (Triangle, Square, Pentagon, Hexagon, Circle)
# Distribute_Three on Type: Three types through each row
# Constant on Type: Nothing changes
# Progression on Size: Size on each entity +1/2 or -1/2
# Arithmetic on Size: Size on the third Panel = Size on the first +/- Size on the second (1 for + and -1 for -)
# Distribute_Three on Size: Three sizes through each row
# Constant on Size: Nothing changes
# Progression on Color: Color +1/2 or -1/2
# Arithmetic on Color: Color on the third Panel = Color on the first +/- Color on the second (1 for + and -1 for -)
# Distribute_Three on Color: Three colors through each row
# Constant on Color: Nothing changes
# Note that all rules on Type, Size and Color enforce value consistency in a panel
RULE_ATTR = [[["Progression", "Number", [-2, -1, 1, 2]],
["Progression", "Position", [-2, -1, 1, 2]],
["Arithmetic", "Number", [1, -1]],
["Arithmetic", "Position", [1, -1]],
["Distribute_Three", "Number", None],
["Distribute_Three", "Position", None],
["Constant", "Number/Position", None]],
[["Progression", "Type", [-2, -1, 1, 2]],
["Distribute_Three", "Type", None],
["Constant", "Type", None]],
[["Progression", "Size", [-2, -1, 1, 2]],
["Arithmetic", "Size", [1, -1]],
["Distribute_Three", "Size", None],
["Constant", "Size", None]],
[["Progression", "Color", [-2, -1, 1, 2]],
["Arithmetic", "Color", [1, -1]],
["Distribute_Three", "Color", None],
["Constant", "Color", None]]]
================================================
FILE: src/dataset/constraints.py
================================================
# -*- coding: utf-8 -*-
from const import (ANGLE_MAX, ANGLE_MIN, COLOR_MAX, COLOR_MIN, NUM_MAX,
NUM_MIN, SIZE_MAX, SIZE_MIN, TYPE_MAX, TYPE_MIN, UNI_MAX,
UNI_MIN)
def gen_layout_constraint(pos_type, pos_list,
num_min=NUM_MIN, num_max=NUM_MAX,
uni_min=UNI_MIN, uni_max=UNI_MAX):
constraint = {"Number": [num_min, num_max],
"Position": [pos_type, pos_list[:]],
"Uni": [uni_min, uni_max]}
return constraint
def gen_entity_constraint(type_min=TYPE_MIN, type_max=TYPE_MAX,
size_min=SIZE_MIN, size_max=SIZE_MAX,
color_min=COLOR_MIN, color_max=COLOR_MAX,
angle_min=ANGLE_MIN, angle_max=ANGLE_MAX):
constraint = {"Type": [type_min, type_max],
"Size": [size_min, size_max],
"Color": [color_min, color_max],
"Angle": [angle_min, angle_max]}
return constraint
def rule_constraint(rule_list, num_min, num_max,
uni_min, uni_max,
type_min, type_max,
size_min, size_max,
color_min, color_max):
"""Generate constraints given the rules and the original constraints
from layout and entity. Note that each attribute has at most one rule
applied on it.
Arguments:
rule_list(ordered list of Rule): all rules applied to this layout
others (int): boundary levels for each attribute in a layout; note that
num_max + 1 == len(layout.position.values)
Returns:
layout_constraint(dict): a new layout constraint
entity_constraint(dict): a new entity constraint
"""
assert len(rule_list) > 0
for rule in rule_list:
if rule.name == "Progression":
# rule.value: add/sub how many levels
if rule.attr == "Number":
if rule.value > 0:
num_max = num_max - rule.value * 2
else:
num_min = num_min - rule.value * 2
if rule.attr == "Position":
# Progression here means moving in Layout slots in order
abs_value = abs(rule.value)
num_max = num_max - abs_value * 2
if rule.attr == "Type":
if rule.value > 0:
type_max = type_max - rule.value * 2
else:
type_min = type_min - rule.value * 2
if rule.attr == "Size":
if rule.value > 0:
size_max = size_max - rule.value * 2
else:
size_min = size_min - rule.value * 2
if rule.attr == "Color":
if rule.value > 0:
color_max = color_max - rule.value * 2
else:
color_min = color_min - rule.value * 2
if rule.name == "Arithmetic":
# rule.value > 0 if add col_0 + col_1
# rule.value < 0 if sub col_0 - col_1
if rule.attr == "Number":
if rule.value > 0:
num_max = num_max - num_min - 1
else:
num_min = 2 * num_min + 1
if rule.attr == "Position":
# SET_UNION
# at least two position configurations
if rule.value > 0:
num_max = num_max - 1
# num_min makes sure of overlap
# at least two configurations
# SET_DIFF
else:
num_min = (num_max + 2) / 2 - 1
num_max = num_max - 1
if rule.attr == "Size":
if rule.value > 0:
size_max = size_max - size_min - 1
else:
size_min = 2 * size_min + 1
if rule.attr == "Color":
# at least two different colors
if color_max - color_min < 1:
color_max = color_min - 1
else:
if rule.value > 0:
color_max = color_max - color_min
if rule.value < 0:
color_min = 2 * color_min
if rule.name == "Distribute_Three":
# if less than 3 values, invalidate it
if rule.attr == "Number":
if num_max - num_min + 1 < 3:
num_max = num_min - 1
if rule.attr == "Position":
# max number allowed in the layout should be >= 3
if num_max + 1 < 3:
num_max = num_min - 1
# num_max + 1 == len(layout.position.values)
# C_{num_max + 1}^{num_value} >= 3
# C_{num_max + 1} = num_max + 1 >= 3
# hence only need to constrain num_max: num_max = num_max - 1
# Check Yang Hui’s Triangle (Pascal's Triangle): https://www.varsitytutors.com/hotmath/hotmath_help/topics/yang-huis-triangle
else:
num_max = num_max - 1
if rule.attr == "Type":
if type_max - type_min + 1 < 3:
type_max = type_min - 1
if rule.attr == "Size":
if size_max - size_min + 1 < 3:
size_max = size_min - 1
if rule.attr == "Color":
if color_max - color_min + 1 < 3:
color_max = color_min - 1
return gen_layout_constraint(None, [],
num_min, num_max,
uni_min, uni_max), \
gen_entity_constraint(type_min, type_max,
size_min, size_max,
color_min, color_max)
================================================
FILE: src/dataset/main.py
================================================
# -*- coding: utf-8 -*-
import argparse
import copy
import os
import random
import sys
import numpy as np
from tqdm import trange
from build_tree import (build_center_single, build_distribute_four,
build_distribute_nine,
build_in_center_single_out_center_single,
build_in_distribute_four_out_center_single,
build_left_center_single_right_center_single,
build_up_center_single_down_center_single)
from const import IMAGE_SIZE, RULE_ATTR
from rendering import (generate_matrix, generate_matrix_answer, imsave, imshow,
render_panel)
from Rule import Rule_Wrapper
from sampling import sample_attr, sample_attr_avail, sample_rules
from serialize import dom_problem, serialize_aot, serialize_rules
from solver import solve
def merge_component(dst_aot, src_aot, component_idx):
src_component = src_aot.children[0].children[component_idx]
dst_aot.children[0].children[component_idx] = src_component
def fuse(args, all_configs):
random.seed(args.seed)
np.random.seed(args.seed)
acc = 0
for k in trange(args.num_samples * len(all_configs)):
if k < args.num_samples * (1 - args.val - args.test):
set_name = "train"
elif k < args.num_samples * (1 - args.test):
set_name = "val"
else:
set_name = "test"
tree_name = random.choice(all_configs.keys())
root = all_configs[tree_name]
while True:
rule_groups = sample_rules()
new_root = root.prune(rule_groups)
if new_root is not None:
break
start_node = new_root.sample()
row_1_1 = copy.deepcopy(start_node)
for l in range(len(rule_groups)):
rule_group = rule_groups[l]
rule_num_pos = rule_group[0]
row_1_2 = rule_num_pos.apply_rule(row_1_1)
row_1_3 = rule_num_pos.apply_rule(row_1_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_1_2 = rule.apply_rule(row_1_1, row_1_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_1_3 = rule.apply_rule(row_1_2, row_1_3)
if l == 0:
to_merge = [row_1_1, row_1_2, row_1_3]
else:
merge_component(to_merge[1], row_1_2, l)
merge_component(to_merge[2], row_1_3, l)
row_1_1, row_1_2, row_1_3 = to_merge
row_2_1 = copy.deepcopy(start_node)
row_2_1.resample(True)
for l in range(len(rule_groups)):
rule_group = rule_groups[l]
rule_num_pos = rule_group[0]
row_2_2 = rule_num_pos.apply_rule(row_2_1)
row_2_3 = rule_num_pos.apply_rule(row_2_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_2_2 = rule.apply_rule(row_2_1, row_2_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_2_3 = rule.apply_rule(row_2_2, row_2_3)
if l == 0:
to_merge = [row_2_1, row_2_2, row_2_3]
else:
merge_component(to_merge[1], row_2_2, l)
merge_component(to_merge[2], row_2_3, l)
row_2_1, row_2_2, row_2_3 = to_merge
row_3_1 = copy.deepcopy(start_node)
row_3_1.resample(True)
for l in range(len(rule_groups)):
rule_group = rule_groups[l]
rule_num_pos = rule_group[0]
row_3_2 = rule_num_pos.apply_rule(row_3_1)
row_3_3 = rule_num_pos.apply_rule(row_3_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_3_2 = rule.apply_rule(row_3_1, row_3_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_3_3 = rule.apply_rule(row_3_2, row_3_3)
if l == 0:
to_merge = [row_3_1, row_3_2, row_3_3]
else:
merge_component(to_merge[1], row_3_2, l)
merge_component(to_merge[2], row_3_3, l)
row_3_1, row_3_2, row_3_3 = to_merge
imgs = [render_panel(row_1_1),
render_panel(row_1_2),
render_panel(row_1_3),
render_panel(row_2_1),
render_panel(row_2_2),
render_panel(row_2_3),
render_panel(row_3_1),
render_panel(row_3_2),
np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)]
context = [row_1_1, row_1_2, row_1_3, row_2_1, row_2_2, row_2_3, row_3_1, row_3_2]
modifiable_attr = sample_attr_avail(rule_groups, row_3_3)
answer_AoT = copy.deepcopy(row_3_3)
candidates = [answer_AoT]
for j in range(7):
component_idx, attr_name, min_level, max_level = sample_attr(modifiable_attr)
answer_j = copy.deepcopy(answer_AoT)
answer_j.sample_new(component_idx, attr_name, min_level, max_level, answer_AoT)
candidates.append(answer_j)
random.shuffle(candidates)
answers = []
for candidate in candidates:
answers.append(render_panel(candidate))
# imsave(generate_matrix_answer(imgs + answers), "./experiments/fuse/{}.jpg".format(k))
image = imgs[0:8] + answers
target = candidates.index(answer_AoT)
predicted = solve(rule_groups, context, candidates)
meta_matrix, meta_target = serialize_rules(rule_groups)
structure, meta_structure = serialize_aot(start_node)
np.savez("{}/RAVEN_{}_{}.npz".format(args.save_dir, k, set_name), image=image,
target=target,
predict=predicted,
meta_matrix=meta_matrix,
meta_target=meta_target,
structure=structure,
meta_structure=meta_structure)
with open("{}/RAVEN_{}_{}.xml".format(args.save_dir, k, set_name), "w") as f:
dom = dom_problem(context + candidates, rule_groups)
f.write(dom)
if target == predicted:
acc += 1
print "Accuracy: {}".format(float(acc) / (args.num_samples * len(all_configs)))
def separate(args, all_configs):
random.seed(args.seed)
np.random.seed(args.seed)
for key in all_configs.keys():
acc = 0
for k in trange(args.num_samples):
count_num = k % 10
if count_num < (10 - args.val - args.test):
set_name = "train"
elif count_num < (10 - args.test):
set_name = "val"
else:
set_name = "test"
root = all_configs[key]
while True:
rule_groups = sample_rules()
new_root = root.prune(rule_groups)
if new_root is not None:
break
start_node = new_root.sample()
row_1_1 = copy.deepcopy(start_node)
for l in range(len(rule_groups)):
rule_group = rule_groups[l]
rule_num_pos = rule_group[0]
row_1_2 = rule_num_pos.apply_rule(row_1_1)
row_1_3 = rule_num_pos.apply_rule(row_1_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_1_2 = rule.apply_rule(row_1_1, row_1_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_1_3 = rule.apply_rule(row_1_2, row_1_3)
if l == 0:
to_merge = [row_1_1, row_1_2, row_1_3]
else:
merge_component(to_merge[1], row_1_2, l)
merge_component(to_merge[2], row_1_3, l)
row_1_1, row_1_2, row_1_3 = to_merge
row_2_1 = copy.deepcopy(start_node)
row_2_1.resample(True)
for l in range(len(rule_groups)):
rule_group = rule_groups[l]
rule_num_pos = rule_group[0]
row_2_2 = rule_num_pos.apply_rule(row_2_1)
row_2_3 = rule_num_pos.apply_rule(row_2_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_2_2 = rule.apply_rule(row_2_1, row_2_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_2_3 = rule.apply_rule(row_2_2, row_2_3)
if l == 0:
to_merge = [row_2_1, row_2_2, row_2_3]
else:
merge_component(to_merge[1], row_2_2, l)
merge_component(to_merge[2], row_2_3, l)
row_2_1, row_2_2, row_2_3 = to_merge
row_3_1 = copy.deepcopy(start_node)
row_3_1.resample(True)
for l in range(len(rule_groups)):
rule_group = rule_groups[l]
rule_num_pos = rule_group[0]
row_3_2 = rule_num_pos.apply_rule(row_3_1)
row_3_3 = rule_num_pos.apply_rule(row_3_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_3_2 = rule.apply_rule(row_3_1, row_3_2)
for i in range(1, len(rule_group)):
rule = rule_group[i]
row_3_3 = rule.apply_rule(row_3_2, row_3_3)
if l == 0:
to_merge = [row_3_1, row_3_2, row_3_3]
else:
merge_component(to_merge[1], row_3_2, l)
merge_component(to_merge[2], row_3_3, l)
row_3_1, row_3_2, row_3_3 = to_merge
imgs = [render_panel(row_1_1),
render_panel(row_1_2),
render_panel(row_1_3),
render_panel(row_2_1),
render_panel(row_2_2),
render_panel(row_2_3),
render_panel(row_3_1),
render_panel(row_3_2),
np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)]
context = [row_1_1, row_1_2, row_1_3, row_2_1, row_2_2, row_2_3, row_3_1, row_3_2]
modifiable_attr = sample_attr_avail(rule_groups, row_3_3)
answer_AoT = copy.deepcopy(row_3_3)
candidates = [answer_AoT]
for j in range(7):
component_idx, attr_name, min_level, max_level = sample_attr(modifiable_attr)
answer_j = copy.deepcopy(answer_AoT)
answer_j.sample_new(component_idx, attr_name, min_level, max_level, answer_AoT)
candidates.append(answer_j)
random.shuffle(candidates)
answers = []
for candidate in candidates:
answers.append(render_panel(candidate))
# imsave(generate_matrix_answer(imgs + answers), "./experiments/{}/{}.jpg".format(key, k))
image = imgs[0:8] + answers
target = candidates.index(answer_AoT)
predicted = solve(rule_groups, context, candidates)
meta_matrix, meta_target = serialize_rules(rule_groups)
structure, meta_structure = serialize_aot(start_node)
np.savez("{}/{}/RAVEN_{}_{}.npz".format(args.save_dir, key, k, set_name), image=image,
target=target,
predict=predicted,
meta_matrix=meta_matrix,
meta_target=meta_target,
structure=structure,
meta_structure=meta_structure)
with open("{}/{}/RAVEN_{}_{}.xml".format(args.save_dir, key, k, set_name), "w") as f:
dom = dom_problem(context + candidates, rule_groups)
f.write(dom)
if target == predicted:
acc += 1
print "Accuracy of {}: {}".format(key, float(acc) / args.num_samples)
def main():
main_arg_parser = argparse.ArgumentParser(description="parser for RAVEN")
main_arg_parser.add_argument("--num-samples", type=int, default=20000,
help="number of samples for each component configuration")
main_arg_parser.add_argument("--save-dir", type=str, default="~/Datasets/",
help="path to folder where the generated dataset will be saved.")
main_arg_parser.add_argument("--seed", type=int, default=1234,
help="random seed for dataset generation")
main_arg_parser.add_argument("--fuse", type=int, default=0,
help="whether to fuse different configurations")
main_arg_parser.add_argument("--val", type=float, default=2,
help="the proportion of the size of validation set")
main_arg_parser.add_argument("--test", type=float, default=2,
help="the proportion of the size of test set")
args = main_arg_parser.parse_args()
all_configs = {"center_single": build_center_single(),
"distribute_four": build_distribute_four(),
"distribute_nine": build_distribute_nine(),
"left_center_single_right_center_single": build_left_center_single_right_center_single(),
"up_center_single_down_center_single": build_up_center_single_down_center_single(),
"in_center_single_out_center_single": build_in_center_single_out_center_single(),
"in_distribute_four_out_center_single": build_in_distribute_four_out_center_single()}
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
if args.fuse:
if not os.path.exists(os.path.join(args.save_dir, "fuse")):
os.mkdir(os.path.join(args.save_dir, "fuse"))
fuse(args, all_configs)
else:
for key in all_configs.keys():
if not os.path.exists(os.path.join(args.save_dir, key)):
os.mkdir(os.path.join(args.save_dir, key))
separate(args, all_configs)
if __name__ == "__main__":
main()
================================================
FILE: src/dataset/rendering.py
================================================
# -*- coding: utf-8 -*-
import cv2
import numpy as np
from PIL import Image
from AoT import Root
from const import CENTER, DEFAULT_WIDTH, IMAGE_SIZE
def imshow(array):
image = Image.fromarray(array)
image.show()
def imsave(array, filepath):
image = Image.fromarray(array)
image.save(filepath)
def generate_matrix(array_list):
# row-major array_list
assert len(array_list) <= 9
img_grid = np.zeros((IMAGE_SIZE * 3, IMAGE_SIZE * 3), np.uint8)
for idx in range(len(array_list)):
i, j = divmod(idx, 3)
img_grid[i * IMAGE_SIZE:(i + 1) * IMAGE_SIZE, j * IMAGE_SIZE:(j + 1) * IMAGE_SIZE] = array_list[idx]
# draw grid
for x in [0.33, 0.67]:
img_grid[int(x * IMAGE_SIZE * 3) - 1:int(x * IMAGE_SIZE * 3) + 1, :] = 0
for y in [0.33, 0.67]:
img_grid[:, int(y * IMAGE_SIZE * 3) - 1:int(y * IMAGE_SIZE * 3) + 1] = 0
return img_grid
def generate_answers(array_list):
assert len(array_list) <= 8
img_grid = np.zeros((IMAGE_SIZE * 2, IMAGE_SIZE * 4), np.uint8)
for idx in range(len(array_list)):
i, j = divmod(idx, 4)
img_grid[i * IMAGE_SIZE:(i + 1) * IMAGE_SIZE, j * IMAGE_SIZE:(j + 1) * IMAGE_SIZE] = array_list[idx]
# draw grid
for x in [0.5]:
img_grid[int(x * IMAGE_SIZE * 2) - 1:int(x * IMAGE_SIZE * 2) + 1, :] = 0
for y in [0.25, 0.5, 0.75]:
img_grid[:, int(y * IMAGE_SIZE * 4) - 1:int(y * IMAGE_SIZE * 4) + 1] = 0
return img_grid
def generate_matrix_answer(array_list):
# row-major array_list
assert len(array_list) <= 18
img_grid = np.zeros((IMAGE_SIZE * 6, IMAGE_SIZE * 3), np.uint8)
for idx in range(len(array_list)):
i, j = divmod(idx, 3)
img_grid[i * IMAGE_SIZE:(i + 1) * IMAGE_SIZE, j * IMAGE_SIZE:(j + 1) * IMAGE_SIZE] = array_list[idx]
# draw grid
for x in [0.33, 0.67, 1.00, 1.33, 1.67]:
img_grid[int(x * IMAGE_SIZE * 3), :] = 0
for y in [0.33, 0.67]:
img_grid[:, int(y * IMAGE_SIZE * 3)] = 0
return img_grid
def merge_matrix_answer(matrix, answer):
matrix_image = generate_matrix(matrix)
answer_image = generate_answers(answer)
img_grid = np.ones((IMAGE_SIZE * 5 + 20, IMAGE_SIZE * 4), np.uint8) * 255
img_grid[:IMAGE_SIZE * 3, int(0.5 * IMAGE_SIZE):int(3.5 * IMAGE_SIZE)] = matrix_image
img_grid[-(IMAGE_SIZE * 2):, :] = answer_image
return img_grid
def render_panel(root):
# Decompose the panel into a structure and its entities
assert isinstance(root, Root)
canvas = np.ones((IMAGE_SIZE, IMAGE_SIZE), np.uint8) * 255
structure, entities = root.prepare()
structure_img = render_structure(structure)
background = np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)
# note left components entities are in the lower layer
for entity in entities:
entity_img = render_entity(entity)
background = layer_add(background, entity_img)
background = layer_add(background, structure_img)
return canvas - background
def render_structure(structure_name):
ret = None
if structure_name == "Left_Right":
ret = np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)
ret[:, int(0.5 * IMAGE_SIZE)] = 255.0
elif structure_name == "Up_Down":
ret = np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)
ret[int(0.5 * IMAGE_SIZE), :] = 255.0
else:
ret = np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)
return ret
def render_entity(entity):
entity_bbox = entity.bbox
entity_type = entity.type.get_value()
entity_size = entity.size.get_value()
entity_color = entity.color.get_value()
entity_angle = entity.angle.get_value()
img = np.zeros((IMAGE_SIZE, IMAGE_SIZE), np.uint8)
# planar position: [x, y, w, h]
# angular position: [x, y, w, h, x_c, y_c, omega]
# center: (columns, rows)
center = (int(entity_bbox[1] * IMAGE_SIZE), int(entity_bbox[0] * IMAGE_SIZE))
if entity_type == "triangle":
unit = min(entity_bbox[2], entity_bbox[3]) * IMAGE_SIZE / 2
dl = int(unit * entity_size)
pts = np.array([[center[0], center[1] - dl],
[center[0] + int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0)],
[center[0] - int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0)]],
np.int32)
pts = pts.reshape((-1, 1, 2))
color = 255 - entity_color
width = DEFAULT_WIDTH
draw_triangle(img, pts, color, width)
elif entity_type == "square":
unit = min(entity_bbox[2], entity_bbox[3]) * IMAGE_SIZE / 2
dl = int(unit / 2 * np.sqrt(2) * entity_size)
pt1 = (center[0] - dl, center[1] - dl)
pt2 = (center[0] + dl, center[1] + dl)
color = 255 - entity_color
width = DEFAULT_WIDTH
draw_square(img, pt1, pt2, color, width)
elif entity_type == "pentagon":
unit = min(entity_bbox[2], entity_bbox[3]) * IMAGE_SIZE / 2
dl = int(unit * entity_size)
pts = np.array([[center[0], center[1] - dl],
[center[0] - int(dl * np.cos(np.pi / 10)), center[1] - int(dl * np.sin(np.pi / 10))],
[center[0] - int(dl * np.sin(np.pi / 5)), center[1] + int(dl * np.cos(np.pi / 5))],
[center[0] + int(dl * np.sin(np.pi / 5)), center[1] + int(dl * np.cos(np.pi / 5))],
[center[0] + int(dl * np.cos(np.pi / 10)), center[1] - int(dl * np.sin(np.pi / 10))]],
np.int32)
pts = pts.reshape((-1, 1, 2))
color = 255 - entity_color
width = DEFAULT_WIDTH
draw_pentagon(img, pts, color, width)
elif entity_type == "hexagon":
unit = min(entity_bbox[2], entity_bbox[3]) * IMAGE_SIZE / 2
dl = int(unit * entity_size)
pts = np.array([[center[0], center[1] - dl],
[center[0] - int(dl / 2.0 * np.sqrt(3)), center[1] - int(dl / 2.0)],
[center[0] - int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0)],
[center[0], center[1] + dl],
[center[0] + int(dl / 2.0 * np.sqrt(3)), center[1] + int(dl / 2.0)],
[center[0] + int(dl / 2.0 * np.sqrt(3)), center[1] - int(dl / 2.0)]],
np.int32)
pts = pts.reshape((-1, 1, 2))
color = 255 - entity_color
width = DEFAULT_WIDTH
draw_hexagon(img, pts, color, width)
elif entity_type == "circle":
# Minus because of the way we show the image. See: render_panel's return
color = 255 - entity_color
unit = min(entity_bbox[2], entity_bbox[3]) * IMAGE_SIZE / 2
radius = int(unit * entity_size)
width = DEFAULT_WIDTH
draw_circle(img, center, radius, color, width)
elif entity_type == "none":
pass
# angular
if len(entity_bbox) > 4:
# [x, y, w, h, x_c, y_c, omega]
entity_angle = entity_bbox[6]
center = (int(entity_bbox[5] * IMAGE_SIZE), int(entity_bbox[4] * IMAGE_SIZE))
img = rotate(img, entity_angle, center=center)
# planar
else:
img = rotate(img, entity_angle, center=center)
# img = shift(img, *entity_position)
return img
def shift(img, dx, dy):
M = np.array([[1, 0, dx], [0, 1, dy]], np.float32)
img = cv2.warpAffine(img, M, (IMAGE_SIZE, IMAGE_SIZE), flags=cv2.INTER_LINEAR)
return img
def rotate(img, angle, center=CENTER):
M = cv2.getRotationMatrix2D(center, angle, 1)
img = cv2.warpAffine(img, M, (IMAGE_SIZE, IMAGE_SIZE), flags=cv2.INTER_LINEAR)
return img
def scale(img, tx, ty, center=CENTER):
M = np.array([[tx, 0, center[0] * (1 - tx)], [0, ty, center[1] * (1 - ty)]], np.float32)
img = cv2.warpAffine(img, M, (IMAGE_SIZE, IMAGE_SIZE), flags=cv2.INTER_LINEAR)
return img
def layer_add(lower_layer_np, higher_layer_np):
# higher_layer_np is superimposed on lower_layer_np
# new_np = lower_layer_np.copy()
# lower_layer_np is modified
lower_layer_np[higher_layer_np > 0] = 0
return lower_layer_np + higher_layer_np
# Draw primitives
def draw_triangle(img, pts, color, width):
# if filled
if color != 0:
# fill the interior
cv2.fillConvexPoly(img, pts, color)
# draw the edge
cv2.polylines(img, [pts], True, 255, width)
# if not filled
else:
cv2.polylines(img, [pts], True, 255, width)
def draw_square(img, pt1, pt2, color, width):
# if filled
if color != 0:
# fill the interior
cv2.rectangle(img,
pt1,
pt2,
color,
-1)
# draw the edge
cv2.rectangle(img,
pt1,
pt2,
255,
width)
# if not filled
else:
cv2.rectangle(img,
pt1,
pt2,
255,
width)
def draw_pentagon(img, pts, color, width):
# if filled
if color != 0:
# fill the interior
cv2.fillConvexPoly(img, pts, color)
# draw the edge
cv2.polylines(img, [pts], True, 255, width)
# if not filled
else:
cv2.polylines(img, [pts], True, 255, width)
def draw_hexagon(img, pts, color, width):
# if filled
if color != 0:
# fill the interior
cv2.fillConvexPoly(img, pts, color)
# draw the edge
cv2.polylines(img, [pts], True, 255, width)
# if not filled
else:
cv2.polylines(img, [pts], True, 255, width)
def draw_circle(img, center, radius, color, width):
# if filled
if color != 0:
# fill the interior
cv2.circle(img,
center,
radius,
color,
-1)
# draw the edge
cv2.circle(img,
center,
radius,
255,
width)
# if not filled
else:
cv2.circle(img,
center,
radius,
255,
width)
================================================
FILE: src/dataset/sampling.py
================================================
# -*- coding: utf-8 -*-
import numpy as np
from scipy.misc import comb
from const import MAX_COMPONENTS, RULE_ATTR
from Rule import Rule_Wrapper
def sample_rules():
"""First sample # components; for each component, sample a rule on each attribute.
"""
num_components = np.random.randint(1, MAX_COMPONENTS + 1)
all_rules = []
for i in range(num_components):
all_rules_component = []
for j in range(len(RULE_ATTR)):
idx = np.random.choice(len(RULE_ATTR[j]))
name_attr_param = RULE_ATTR[j][idx]
all_rules_component.append(Rule_Wrapper(name_attr_param[0], name_attr_param[1], name_attr_param[2], component_idx=i))
all_rules.append(all_rules_component)
return all_rules
# pay attention to Position Arithmetic, new entities (resample)
def sample_attr_avail(rule_groups, row_3_3):
"""Sample available attributes whose values could be modified.
Arguments:
rule_groups(list of list of Rule): a list of rules to apply to the component
row_3_3(AoTNode): the answer AoT
Returns:
ret(list of list): [component_idx, attr, available_times, constraints]
"""
ret = []
for i in range(len(rule_groups)):
rule_group = rule_groups[i]
start_node_layout = row_3_3.children[0].children[i].children[0]
row_3_3_layout = row_3_3.children[0].children[i].children[0]
uni = row_3_3_layout.uniformity.get_value()
# Number/Position
# If Rule on Number: Only change Number
# If Rule on Position: Both Number and Position could be changed
rule = rule_group[0]
num = row_3_3_layout.number.get_value()
most_num = len(start_node_layout.position.values)
if rule.attr == "Number":
num_times = 0
min_level = start_node_layout.orig_layout_constraint["Number"][0]
max_level = start_node_layout.orig_layout_constraint["Number"][1]
for k in range(min_level, max_level + 1):
if k + 1 != num:
num_times += comb(most_num, k + 1)
if num_times > 0:
ret.append([i, "Number", num_times, min_level, max_level])
# Constant or on Position
else:
num_times = 0
min_level = start_node_layout.orig_layout_constraint["Number"][0]
max_level = start_node_layout.orig_layout_constraint["Number"][1]
for k in range(min_level, max_level + 1):
if k + 1 != num:
num_times += comb(most_num, k + 1)
if num_times > 0:
ret.append([i, "Number", num_times, min_level, max_level])
pos_times = comb(most_num, row_3_3_layout.number.get_value())
pos_times -= 1
if pos_times > 0:
ret.append([i, "Position", pos_times, None, None])
# Type, Size, Color
for j in range(1, len(rule_group)):
rule = rule_group[j]
rule_attr = rule.attr
min_level = start_node_layout.orig_entity_constraint[rule_attr][0]
max_level = start_node_layout.orig_entity_constraint[rule_attr][1]
if rule.name == "Constant":
if uni or rule_group[0].name == "Constant" or \
(rule_group[0].attr == "Position" and
(rule_group[0].name == "Progression" or rule_group[0].name == "Distribute_Three")):
times = max_level - min_level + 1
times = times - 1
if times > 0:
ret.append([i, rule_attr, times, min_level, max_level])
else:
times = max_level - min_level + 1
times = times - 1
if times > 0:
ret.append([i, rule_attr, times, min_level, max_level])
return ret
def sample_attr(attrs_list):
"""Given the attr_avail list, sample one attribute to modify the value.
If the available times becomes zero, delete it.
Arguments:
attrs_list(list of list): a flat component of available attributes
to change the values; consisting of different component indexes
"""
attr_idx = np.random.choice(len(attrs_list))
component_idx, attr_name, _, min_level, max_level = attrs_list[attr_idx]
attrs_list[attr_idx][2] -= 1
if attrs_list[attr_idx][2] == 0:
del attrs_list[attr_idx]
return component_idx, attr_name, min_level, max_level
================================================
FILE: src/dataset/serialize.py
================================================
# -*- coding: utf-8 -*-
import json
import xml.etree.ElementTree as ET
import numpy as np
from const import META_STRUCTURE_FORMAT
from api import get_real_bbox, get_mask, rle_encode
def n_tree_serialize(aot):
assert aot.is_pg
ret = ""
if aot.level == "Layout":
return aot.name + "./"
else:
ret += aot.name + "."
for child in aot.children:
x = n_tree_serialize(child)
ret += x
ret += "."
ret += "/"
return ret
def serialize_aot(aot):
"""Meta Structure format
META_STRUCTURE_FORMAT provided by const.py
"""
n_tree = n_tree_serialize(aot)
meta_structure = np.zeros(len(META_STRUCTURE_FORMAT), np.uint8)
split = n_tree.split(".")
for node in split:
try:
node_index = META_STRUCTURE_FORMAT.index(node)
meta_structure[node_index] = 1
except ValueError:
continue
return split, meta_structure
def serialize_rules(rule_groups):
"""Meta matrix format
["Constant", "Progression", "Arithmetic", "Distribute_Three", "Number", "Position", "Type", "Size", "Color"]
"""
meta_matrix = np.zeros((8, 9), np.uint8)
counter = 0
for rule_group in rule_groups:
for rule in rule_group:
if rule.name == "Constant":
meta_matrix[counter, 0] = 1
elif rule.name == "Progression":
meta_matrix[counter, 1] = 1
elif rule.name == "Arithmetic":
meta_matrix[counter, 2] = 1
else:
meta_matrix[counter, 3] = 1
if rule.attr == "Number/Position":
meta_matrix[counter, 4] = 1
meta_matrix[counter, 5] = 1
elif rule.attr == "Number":
meta_matrix[counter, 4] = 1
elif rule.attr == "Position":
meta_matrix[counter, 5] = 1
elif rule.attr == "Type":
meta_matrix[counter, 6] = 1
elif rule.attr == "Size":
meta_matrix[counter, 7] = 1
else:
meta_matrix[counter, 8] = 1
counter += 1
return meta_matrix, np.bitwise_or.reduce(meta_matrix)
def dom_problem(instances, rule_groups):
data = ET.Element("Data")
panels = ET.SubElement(data, "Panels")
for i in range(len(instances)):
panel = instances[i]
panel_i = ET.SubElement(panels, "Panel")
struct = panel.children[0]
struct_i = ET.SubElement(panel_i, "Struct")
struct_i.set("name", struct.name)
for j in range(len(struct.children)):
component = struct.children[j]
component_j = ET.SubElement(struct_i, "Component")
component_j.set("id", str(j))
component_j.set("name", component.name)
layout = component.children[0]
layout_k = ET.SubElement(component_j, "Layout")
layout_k.set("name", layout.name)
layout_k.set("Number", str(layout.number.get_value_level()))
layout_k.set("Position", json.dumps(layout.position.values))
layout_k.set("Uniformity", str(layout.uniformity.get_value_level()))
for l in range(len(layout.children)):
entity = layout.children[l]
entity_l = ET.SubElement(layout_k, "Entity")
entity_bbox = entity.bbox
entity_type = entity.type.get_value()
entity_size = entity.size.get_value()
entity_angle = entity.angle.get_value()
entity_l.set("bbox", json.dumps(entity_bbox))
entity_l.set("real_bbox", json.dumps(get_real_bbox(entity_bbox, entity_type, entity_size, entity_angle)))
entity_l.set("mask", rle_encode(get_mask(entity_bbox, entity_type, entity_size, entity_angle)))
entity_l.set("Type", str(entity.type.get_value_level()))
entity_l.set("Size", str(entity.size.get_value_level()))
entity_l.set("Color", str(entity.color.get_value_level()))
entity_l.set("Angle", str(entity.angle.get_value_level()))
rules = ET.SubElement(data, "Rules")
for i in range(len(rule_groups)):
rule_group = rule_groups[i]
rule_group_i = ET.SubElement(rules, "Rule_Group")
rule_group_i.set("id", str(i))
for rule in rule_group:
rule_j = ET.SubElement(rule_group_i, "Rule")
rule_j.set("name", rule.name)
rule_j.set("attr", rule.attr)
return ET.tostring(data)
================================================
FILE: src/dataset/solver.py
================================================
# -*- coding: utf-8 -*-
import numpy as np
def solve(rule_groups, context, candidates):
"""Search-based Heuristic Solver.
Arguments:
rule_groups(list of list of Rule): rules that apply to each component
context(list of AoTNode): a list of context AoTs in a row-major order;
should be of length 8
candidates(list of AoTNode): a list of candidate answer AoTs;
should be of length 8
Returns:
ans(int): index of the correct answer in the candidates
"""
satisfied = [0] * len(candidates)
for i in range(len(candidates)):
candidate = candidates[i]
# note that rule.component_idx should be the same as j
for j in range(len(rule_groups)):
rule_group = rule_groups[j]
rule_num_pos = rule_group[0]
satisfied[i] += check_num_pos(rule_num_pos, context, candidate)
regenerate = False
if rule_num_pos.attr == "Number" or rule_num_pos.name == "Arithmetic":
regenerate = True
rule_type = rule_group[1]
satisfied[i] += check_entity(rule_type, context, candidate, "Type", regenerate)
rule_size = rule_group[2]
satisfied[i] += check_entity(rule_size, context, candidate, "Size", regenerate)
rule_color = rule_group[3]
satisfied[i] += check_entity(rule_color, context, candidate, "Color", regenerate)
satisfied = np.array(satisfied)
answer_set = np.where(satisfied == max(satisfied))[0]
return np.random.choice(answer_set)
def check_num_pos(rule_num_pos, context, candidate):
"""Check whether Rule on layout attribute is satisfied.
Arguments:
rule_num_pos(Rule): the rule to check
context(list of AoTNode): the 8 context figures
candidate(AoTNode): the candidate AoT
Returns:
ret(int): 0 if failure, 1 if success
"""
ret = 0
component_idx = rule_num_pos.component_idx
row_3_1_layout = context[6].children[0].children[component_idx].children[0]
row_3_2_layout = context[7].children[0].children[component_idx].children[0]
candidate_layout = candidate.children[0].children[component_idx].children[0]
if rule_num_pos.name == "Constant":
set_row_3_1_pos = set(row_3_1_layout.position.get_value_idx())
set_row_3_2_pos = set(row_3_2_layout.position.get_value_idx())
set_candidate_pos = set(candidate_layout.position.get_value_idx())
# note that set equal only when len(Number) equal and content equal
if set_candidate_pos == set_row_3_1_pos and set_candidate_pos == set_row_3_2_pos:
ret = 1
elif rule_num_pos.name == "Progression":
if rule_num_pos.attr == "Number":
row_3_1_num = row_3_1_layout.number.get_value_level()
row_3_2_num = row_3_2_layout.number.get_value_level()
candidate_num = candidate_layout.number.get_value_level()
if row_3_2_num * 2 == row_3_1_num + candidate_num:
ret = 1
else:
row_3_1_pos = row_3_1_layout.position.get_value_idx()
row_3_2_pos = row_3_2_layout.position.get_value_idx()
candidate_pos = candidate_layout.position.get_value_idx()
most_num = len(candidate_layout.position.values)
diff = rule_num_pos.value
if (set((row_3_1_pos + diff) % most_num) == set(row_3_2_pos)) and \
(set((row_3_2_pos + diff) % most_num) == set(candidate_pos)):
ret = 1
elif rule_num_pos.name == "Arithmetic":
mode = rule_num_pos.value
if rule_num_pos.attr == "Number":
row_3_1_num = row_3_1_layout.number.get_value()
row_3_2_num = row_3_2_layout.number.get_value()
candidate_num = candidate_layout.number.get_value()
if mode > 0 and (candidate_num == row_3_1_num + row_3_2_num):
ret = 1
if mode < 0 and (candidate_num == row_3_1_num - row_3_2_num):
ret = 1
else:
row_3_1_pos = row_3_1_layout.position.get_value_idx()
row_3_2_pos = row_3_2_layout.position.get_value_idx()
candidate_pos = candidate_layout.position.get_value_idx()
if mode > 0 and (set(candidate_pos) == set(row_3_1_pos) | set(row_3_2_pos)):
ret = 1
if mode < 0 and (set(candidate_pos) == set(row_3_1_pos) - set(row_3_2_pos)):
ret = 1
else:
three_values = rule_num_pos.value_levels[2]
if rule_num_pos.attr == "Number":
row_3_1_num = row_3_1_layout.number.get_value_level()
row_3_2_num = row_3_2_layout.number.get_value_level()
candidate_num = candidate_layout.number.get_value_level()
if row_3_1_num == three_values[0] and \
row_3_2_num == three_values[1] and \
candidate_num == three_values[2]:
ret = 1
else:
row_3_1_pos = row_3_1_layout.position.get_value_idx()
row_3_2_pos = row_3_2_layout.position.get_value_idx()
candidate_pos = candidate_layout.position.get_value_idx()
if set(row_3_1_pos) == set(three_values[0]) and \
set(row_3_2_pos) == set(three_values[1]) and \
set(candidate_pos) == set(three_values[2]):
ret = 1
return ret
def check_consistency(candidate, attr, component_idx):
candidate_layout = candidate.children[0].children[component_idx].children[0]
entity_0 = candidate_layout.children[0]
attr_name = attr.lower()
entity_0_value = getattr(entity_0, attr_name).get_value_level()
for i in range(1, len(candidate_layout.children)):
entity_i = candidate_layout.children[i]
entity_i_value = getattr(entity_i, attr_name).get_value_level()
if entity_i_value != entity_0_value:
return False
return True
def check_entity(rule, context, candidate, attr, regenerate):
"""Check whether Rule on entity attribute is satisfied.
Arguments:
rule(Rule): the rule to check
context(list of AoTNode): the 8 context figures
candidate(AoTNode): the candidate AoT
attr(str): attribute name
Returns:
ret(int): 0 if failure, 1 if success
"""
ret = 0
component_idx = rule.component_idx
row_3_1_layout = context[6].children[0].children[component_idx].children[0]
row_3_2_layout = context[7].children[0].children[component_idx].children[0]
candidate_layout = candidate.children[0].children[component_idx].children[0]
uni = candidate_layout.uniformity.get_value()
attr_name = attr.lower()
if rule.name == "Constant":
if uni:
if check_consistency(candidate, attr, component_idx):
if getattr(candidate_layout.children[0], attr_name).get_value_level() == \
getattr(row_3_2_layout.children[0], attr_name).get_value_level():
ret = 1
else:
row_3_1_num = row_3_1_layout.number.get_value_level()
row_3_2_num = row_3_2_layout.number.get_value_level()
candidate_num = candidate_layout.number.get_value_level()
if (row_3_1_num == row_3_2_num) and (row_3_2_num == candidate_num):
if regenerate:
ret = 1
else:
flag = True
for i in range(len(candidate_layout.children)):
if not (getattr(candidate_layout.children[i], attr_name).get_value_level() ==
getattr(row_3_2_layout.children[i], attr_name).get_value_level()):
flag = False
break
if flag:
ret = 1
else:
ret = 1
elif rule.name == "Progression":
if check_consistency(candidate, attr, component_idx):
row_3_1_value = getattr(row_3_1_layout.children[0], attr_name).get_value_level()
row_3_2_value = getattr(row_3_2_layout.children[0], attr_name).get_value_level()
candidate_value = getattr(candidate_layout.children[0], attr_name).get_value_level()
if row_3_2_value * 2 == row_3_1_value + candidate_value:
ret = 1
elif rule.name == "Arithmetic":
if check_consistency(candidate, attr, component_idx):
row_3_1_value = getattr(row_3_1_layout.children[0], attr_name).get_value_level()
row_3_2_value = getattr(row_3_2_layout.children[0], attr_name).get_value_level()
candidate_value = getattr(candidate_layout.children[0], attr_name).get_value_level()
if rule.value > 0:
if attr == "Color":
if candidate_value == row_3_1_value + row_3_2_value:
ret = 1
else:
if candidate_value == row_3_1_value + row_3_2_value + 1:
ret = 1
if rule.value < 0:
if attr == "Color":
if candidate_value == row_3_1_value - row_3_2_value:
ret = 1
else:
if candidate_value == row_3_1_value - row_3_2_value - 1:
ret = 1
else:
if check_consistency(candidate, attr, component_idx):
row_3_1_value = getattr(row_3_1_layout.children[0], attr_name).get_value_level()
row_3_2_value = getattr(row_3_2_layout.children[0], attr_name).get_value_level()
candidate_value = getattr(candidate_layout.children[0], attr_name).get_value_level()
three_values = rule.value_levels[2]
if row_3_1_value == three_values[0] and \
row_3_2_value == three_values[1] and \
candidate_value == three_values[2]:
ret = 1
return ret
================================================
FILE: src/model/__init__.py
================================================
""" RAVEN benchmarking code
Author: Chi Zhang
Data: 05/14/2019
Contact: chi.zhang@ucla.edu
"""
================================================
FILE: src/model/basic_model.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicModel(nn.Module):
def __init__(self, args):
super(BasicModel, self).__init__()
self.name = args.model
def load_model(self, path, epoch):
state_dict = torch.load(path+'{}_epoch_{}.pth'.format(self.name, epoch))['state_dict']
self.load_state_dict(state_dict)
def save_model(self, path, epoch, acc, loss):
torch.save({'state_dict': self.state_dict(), 'acc': acc, 'loss': loss}, path+'{}_epoch_{}.pth'.format(self.name, epoch))
def compute_loss(self, output, target, meta_target, meta_structure):
pass
def train_(self, image, target, meta_target, meta_structure, embedding, indicator):
self.optimizer.zero_grad()
output = self(image, embedding, indicator)
loss = self.compute_loss(output, target, meta_target, meta_structure)
loss.backward()
self.optimizer.step()
pred = output[0].data.max(1)[1]
correct = pred.eq(target.data).cpu().sum().numpy()
accuracy = correct * 100.0 / target.size()[0]
return loss.item(), accuracy
def validate_(self, image, target, meta_target, meta_structure, embedding, indicator):
with torch.no_grad():
output = self(image, embedding, indicator)
loss = self.compute_loss(output, target, meta_target, meta_structure)
pred = output[0].data.max(1)[1]
correct = pred.eq(target.data).cpu().sum().numpy()
accuracy = correct * 100.0 / target.size()[0]
return loss.item(), accuracy
def test_(self, image, target, meta_target, meta_structure, embedding, indicator):
with torch.no_grad():
output = self(image, embedding, indicator)
pred = output[0].data.max(1)[1]
correct = pred.eq(target.data).cpu().sum().numpy()
accuracy = correct * 100.0 / target.size()[0]
return accuracy
================================================
FILE: src/model/cnn_lstm.py
================================================
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from basic_model import BasicModel
from fc_tree_net import FCTreeNet
class conv_module(nn.Module):
def __init__(self):
super(conv_module, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=2)
self.batch_norm1 = nn.BatchNorm2d(16)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(16, 16, kernel_size=3, stride=2)
self.batch_norm2 = nn.BatchNorm2d(16)
self.relu2 = nn.ReLU()
self.conv3 = nn.Conv2d(16, 16, kernel_size=3, stride=2)
self.batch_norm3 = nn.BatchNorm2d(16)
self.relu3 = nn.ReLU()
self.conv4 = nn.Conv2d(16, 16, kernel_size=3, stride=2)
self.batch_norm4 = nn.BatchNorm2d(16)
self.relu4 = nn.ReLU()
def forward(self, x):
x = self.conv1(x)
x = self.relu1(self.batch_norm1(x))
x = self.conv2(x)
x = self.relu2(self.batch_norm2(x))
x = self.conv3(x)
x = self.relu3(self.batch_norm3(x))
x = self.conv4(x)
x = self.relu4(self.batch_norm4(x))
return x.view(-1, 16, 16*4*4)
class lstm_module(nn.Module):
def __init__(self):
super(lstm_module, self).__init__()
self.lstm = nn.LSTM(input_size=16*4*4, hidden_size=96, num_layers=1)
self.dropout = nn.Dropout(0.5)
self.fc = nn.Linear(96, 8)
def forward(self, x):
x = x.permute(1, 0, 2)
hidden, _ = self.lstm(x)
score = self.fc(hidden[-1, :, :])
return score
class CNN_LSTM(BasicModel):
def __init__(self, args):
super(CNN_LSTM, self).__init__(args)
self.conv = conv_module()
self.lstm = lstm_module()
self.fc_tree_net = FCTreeNet(in_dim=300, img_dim=256)
self.optimizer = optim.Adam(self.parameters(), lr=args.lr, betas=(args.beta1, args.beta2), eps=args.epsilon)
def compute_loss(self, output, target, meta_target, meta_structure):
pred = output[0]
loss = F.cross_entropy(pred, target)
return loss
def forward(self, x, embedding, indicator):
alpha = 1.0
features = self.conv(x.view(-1, 1, 80, 80))
features_tree = self.fc_tree_net(features, embedding, indicator)
features_tree = features_tree.view(-1, 16, 256)
final_features = features + alpha * features_tree
score = self.lstm(final_features)
return score, None
================================================
FILE: src/model/cnn_mlp.py
================================================
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from basic_model import BasicModel
from fc_tree_net import FCTreeNet
class conv_module(nn.Module):
def __init__(self):
super(conv_module, self).__init__()
self.conv1 = nn.Conv2d(16, 32, kernel_size=3, stride=2)
self.batch_norm1 = nn.BatchNorm2d(32)
self.relu1 = nn.ReLU()
self.conv2 = nn.Conv2d(32, 32, kernel_size=3, stride=2)
self.batch_norm2 = nn.BatchNorm2d(32)
self.relu2 = nn.ReLU()
self.conv3 = nn.Conv2d(32, 32, kernel_size=3, stride=2)
self.batch_norm3 = nn.BatchNorm2d(32)
self.relu3 = nn.ReLU()
self.conv4 = nn.Conv2d(32, 32, kernel_size=3, stride=2)
self.batch_norm4 = nn.BatchNorm2d(32)
self.relu4 = nn.ReLU()
def forward(self, x):
x = self.conv1(x)
x = self.relu1(self.batch_norm1(x))
x = self.conv2(x)
x = self.relu2(self.batch_norm2(x))
x = self.conv3(x)
x = self.relu3(self.batch_norm3(x))
x = self.conv4(x)
x = self.relu4(self.batch_norm4(x))
return x.view(-1, 32*4*4)
class mlp_module(nn.Module):
def __init__(self):
super(mlp_module, self).__init__()
self.fc1 = nn.Linear(32*4*4, 512)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(512, 8)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.relu1(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
class CNN_MLP(BasicModel):
def __init__(self, args):
super(CNN_MLP, self).__init__(args)
self.conv = conv_module()
self.mlp = mlp_module()
self.fc_tree_net = FCTreeNet(in_dim=300, img_dim=512)
self.optimizer = optim.Adam(self.parameters(), lr=args.lr, betas=(args.beta1, args.beta2), eps=args.epsilon)
def compute_loss(self, output, target, meta_target, meta_structure):
pred = output[0]
loss = F.cross_entropy(pred, target)
return loss
def forward(self, x, embedding, indicator):
alpha = 1.0
features = self.conv(x.view(-1, 16, 80, 80))
features_tree = features.view(-1, 1, 512)
features_tree = self.fc_tree_net(features_tree, embedding, indicator)
final_features = features + alpha * features_tree
score = self.mlp(final_features)
return score, None
================================================
FILE: src/model/const/__init__.py
================================================
from const import *
================================================
FILE: src/model/const/const.py
================================================
# -*- coding: utf-8 -*-
# Maximum number of components in a RPM
MAX_COMPONENTS = 2
# Canvas parameters
IMAGE_SIZE = 160
CENTER = (IMAGE_SIZE / 2, IMAGE_SIZE / 2)
DEFAULT_RADIUS = IMAGE_SIZE / 4
DEFAULT_WIDTH = 2
# Attribute parameters
# Number
NUM_VALUES = [1, 2, 3, 4, 5, 6, 7, 8, 9]
NUM_MIN = 0
NUM_MAX = len(NUM_VALUES) - 1
# Uniformity
UNI_VALUES = [False, False, False, True]
UNI_MIN = 0
UNI_MAX = len(UNI_VALUES) - 1
# Type
TYPE_VALUES = ["none", "triangle", "square", "pentagon", "hexagon", "circle"]
TYPE_MIN = 0
TYPE_MAX = len(TYPE_VALUES) - 1
# Size
SIZE_VALUES = [0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
SIZE_MIN = 0
SIZE_MAX = len(SIZE_VALUES) - 1
# Color
COLOR_VALUES = [255, 224, 196, 168, 140, 112, 84, 56, 28, 0]
COLOR_MIN = 0
COLOR_MAX = len(COLOR_VALUES) - 1
# Angle: self-rotation
ANGLE_VALUES = [-135, -90, -45, 0, 45, 90, 135, 180]
ANGLE_MIN = 0
ANGLE_MAX = len(ANGLE_VALUES) - 1
META_STRUCTURE_FORMAT = ["Singleton", "Left_Right", "Up_Down", "Out_In", "Left", "Right", \
"Up","Down", "Out", "In", "Grid", "Center_Single", "Distribute_Four",
"Distribute_Nine", "Left_Center_Single", "Right_Center_Single", \
"Up_Center_Single", "Down_Center_Single", "Out_Center_Single", "In_Center_Single", "In_Distribute_Four"]
# Rule, Attr, Param
# The design encodes rule priority order: Number/Position always comes first
# Number and Position could not both be sampled
# Progression on Number: Number on each Panel +1/2 or -1/2
# Progression on Position: Entities on each Panel roll over the layout
# Arithmetic on Number: Numeber on the third Panel = Number on first +/- Number on second (1 for + and -1 for -)
# Arithmetic on Position: 1 for SET_UNION and -1 for SET_DIFF
# Distribute_Three on Number: Three numbers through each row
# Distribute_Three on Position: Three positions (same number) through each row
# Constant on Number/Position: Nothing changes
# Progression on Type: Type progression defined as the number of edges on each entity (Triangle, Square, Pentagon, Hexagon, Circle)
# Distribute_Three on Type: Three types through each row
# Constant on Type: Nothing changes
# Progression on Size: Size on each entity +1/2 or -1/2
# Arithmetic on Size: Size on the third Panel = Size on the first +/- Size on the second (1 for + and -1 for -)
# Distribute_Three on Size: Three sizes through each row
# Constant on Size: Nothing changes
# Progression on Color: Color +1/2 or -1/2
# Arithmetic on Color: Color on the third Panel = Color on the first +/- Color on the second (1 for + and -1 for -)
# Distribute_Three on Color: Three colors through each row
# Constant on Color: Nothing changes
# Note that all rules on Type, Size and Color enforce value consistency in a panel
RULE_ATTR = [[["Progression", "Number", [-2, -1, 1, 2]],
["Progression", "Position", [-2, -1, 1, 2]],
["Arithmetic", "Number", [1, -1]],
["Arithmetic", "Position", [1, -1]],
["Distribute_Three", "Number", None],
["Distribute_Three", "Position", None],
["Constant", "Number/Position", None]],
[["Progression", "Type", [-2, -1, 1, 2]],
["Distribute_Three", "Type", None],
["Constant", "Type", None]],
[["Progression", "Size", [-2, -1, 1, 2]],
["Arithmetic", "Size", [1, -1]],
["Distribute_Three", "Size", None],
["Constant", "Size", None]],
[["Progression", "Color", [-2, -1, 1, 2]],
["Arithmetic", "Color", [1, -1]],
["Distribute_Three", "Color", None],
["Constant", "Color", None]]]
================================================
FILE: src/model/fc_tree_net.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class FCTreeNet(torch.nn.Module):
def __init__(self, in_dim=300, img_dim=256, use_cuda=True):
'''
initialization for TreeNet model, basically a ChildSumLSTM model
with non-linear activation embedding for different nodes in the AoG.
Shared weigths for all LSTM cells.
:param in_dim: input feature dimension for word embedding (from string to vector space)
:param img_dim: dimension of the input image feature, should be (panel_pair_number * img_feature_dim (e.g. 512 or 256))
'''
super(FCTreeNet, self).__init__()
self.in_dim = in_dim
self.img_dim = img_dim
self.fc = nn.Linear(self.in_dim, self.in_dim)
self.leaf = nn.Linear(self.in_dim + self.img_dim, self.img_dim)
self.middle = nn.Linear(self.in_dim + self.img_dim, self.img_dim)
self.merge = nn.Linear(self.in_dim + self.img_dim, self.img_dim)
self.root = nn.Linear(self.in_dim + self.img_dim, self.img_dim)
self.relu = nn.ReLU()
def forward(self, image_feature, input, indicator):
'''
Forward funciton for TreeNet model
:param input: input should be (batch_size * 6 * input_word_embedding_dimension), got from the embedding vector
:param indicator: indicating whether the input is of structure with branches (batch_size * 1)
:param image_feature: input dictionary for each node, primarily feature, for example (batch_size * 16 (panel_pair_number) * feature_dim (output from CNN))
:return:
'''
# image_feature = image_feature.view(-1, 16, image_feature.size(2))
input = self.fc(input.view(-1, input.size(-1)))
input = input.view(-1, 6, input.size(-1))
input = input.unsqueeze(1).repeat(1, image_feature.size(1), 1, 1)
indicator = indicator.unsqueeze(1).repeat(1, image_feature.size(1), 1).view(-1, 1)
leaf_left = input[:, :, 3, :].view(-1, input.size(-1)) # (batch_size * panel_pair_num) * input_word_embedding_dimension
leaf_right = input[:, :, 5, :].view(-1, input.size(-1))
inter_left = input[:, :, 2, :].view(-1, input.size(-1))
inter_right = input[:, :, 4, :].view(-1, input.size(-1))
merge = input[:, :, 1, :].view(-1, input.size(-1))
root = input[:, :, 0, :].view(-1, input.size(-1))
# concating image_feature and word_embeddings for leaf node inputs
leaf_left = torch.cat((leaf_left, image_feature.view(-1, image_feature.size(-1))), dim=-1)
leaf_right = torch.cat((leaf_right, image_feature.view(-1, image_feature.size(-1))), dim=-1)
out_leaf_left = self.leaf(leaf_left)
out_leaf_right = self.leaf(leaf_right)
out_leaf_left = self.relu(out_leaf_left)
out_leaf_right = self.relu(out_leaf_right)
out_left = self.middle(torch.cat((inter_left, out_leaf_left), dim=-1))
out_right = self.middle(torch.cat((inter_right, out_leaf_right), dim=-1))
out_left = self.relu(out_left)
out_right = self.relu(out_right)
out_right = torch.mul(out_right, indicator)
merge_input = torch.cat((merge, out_left + out_right), dim=-1)
out_merge = self.merge(merge_input)
out_merge = self.relu(out_merge)
out_root = self.root(torch.cat((root, out_merge), dim=-1))
out_root = self.relu(out_root)
# size ((batch_size * panel_pair) * feature_dim)
return out_root
================================================
FILE: src/model/main.py
================================================
import os
import numpy as np
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from utility import dataset, ToTensor
from cnn_mlp import CNN_MLP
from cnn_lstm import CNN_LSTM
from resnet18 import Resnet18_MLP
parser = argparse.ArgumentParser(description='our_model')
parser.add_argument('--model', type=str, default='Resnet18_MLP')
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--seed', type=int, default=12345)
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--load_workers', type=int, default=16)
parser.add_argument('--resume', type=bool, default=False)
parser.add_argument('--path', type=str, default='/home/chizhang/Datasets/RAVEN-10000/')
parser.add_argument('--save', type=str, default='./experiments/checkpoint/')
parser.add_argument('--img_size', type=int, default=224)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--beta1', type=float, default=0.9)
parser.add_argument('--beta2', type=float, default=0.999)
parser.add_argument('--epsilon', type=float, default=1e-8)
parser.add_argument('--meta_alpha', type=float, default=0.0)
parser.add_argument('--meta_beta', type=float, default=0.0)
args = parser.parse_args()
args.cuda = torch.cuda.is_available()
torch.cuda.set_device(args.device)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if not os.path.exists(args.save):
os.makedirs(args.save)
train = dataset(args.path, "train", args.img_size, transform=transforms.Compose([ToTensor()]))
valid = dataset(args.path, "val", args.img_size, transform=transforms.Compose([ToTensor()]))
test = dataset(args.path, "test", args.img_size, transform=transforms.Compose([ToTensor()]))
trainloader = DataLoader(train, batch_size=args.batch_size, shuffle=True, num_workers=16)
validloader = DataLoader(valid, batch_size=args.batch_size, shuffle=False, num_workers=16)
testloader = DataLoader(test, batch_size=args.batch_size, shuffle=False, num_workers=16)
if args.model == "CNN_MLP":
model = CNN_MLP(args)
elif args.model == "CNN_LSTM":
model = CNN_LSTM(args)
elif args.model == "Resnet18_MLP":
model = Resnet18_MLP(args)
if args.resume:
model.load_model(args.save, 0)
print('Loaded model')
if args.cuda:
model = model.cuda()
def train(epoch):
model.train()
train_loss = 0
accuracy = 0
loss_all = 0.0
acc_all = 0.0
counter = 0
for batch_idx, (image, target, meta_target, meta_structure, embedding, indicator) in enumerate(trainloader):
counter += 1
if args.cuda:
image = image.cuda()
target = target.cuda()
meta_target = meta_target.cuda()
meta_structure = meta_structure.cuda()
embedding = embedding.cuda()
indicator = indicator.cuda()
loss, acc = model.train_(image, target, meta_target, meta_structure, embedding, indicator)
print('Train: Epoch:{}, Batch:{}, Loss:{:.6f}, Acc:{:.4f}.'.format(epoch, batch_idx, loss, acc))
loss_all += loss
acc_all += acc
if counter > 0:
print("Avg Training Loss: {:.6f}".format(loss_all/float(counter)))
def validate(epoch):
model.eval()
val_loss = 0
accuracy = 0
loss_all = 0.0
acc_all = 0.0
counter = 0
for batch_idx, (image, target, meta_target, meta_structure, embedding, indicator) in enumerate(validloader):
counter += 1
if args.cuda:
image = image.cuda()
target = target.cuda()
meta_target = meta_target.cuda()
meta_structure = meta_structure.cuda()
embedding = embedding.cuda()
indicator = indicator.cuda()
loss, acc = model.validate_(image, target, meta_target, meta_structure, embedding, indicator)
# print('Validate: Epoch:{}, Batch:{}, Loss:{:.6f}, Acc:{:.4f}.'.format(epoch, batch_idx, loss, acc))
loss_all += loss
acc_all += acc
if counter > 0:
print("Total Validation Loss: {:.6f}, Acc: {:.4f}".format(loss_all/float(counter), acc_all/float(counter)))
return loss_all/float(counter), acc_all/float(counter)
def test(epoch):
model.eval()
accuracy = 0
acc_all = 0.0
counter = 0
for batch_idx, (image, target, meta_target, meta_structure, embedding, indicator) in enumerate(testloader):
counter += 1
if args.cuda:
image = image.cuda()
target = target.cuda()
meta_target = meta_target.cuda()
meta_structure = meta_structure.cuda()
embedding = embedding.cuda()
indicator = indicator.cuda()
acc = model.test_(image, target, meta_target, meta_structure, embedding, indicator)
# print('Test: Epoch:{}, Batch:{}, Acc:{:.4f}.'.format(epoch, batch_idx, acc))
acc_all += acc
if counter > 0:
print("Total Testing Acc: {:.4f}".format(acc_all / float(counter)))
return acc_all/float(counter)
def main():
for epoch in range(0, args.epochs):
train(epoch)
avg_loss, avg_acc = validate(epoch)
test(epoch)
model.save_model(args.save, epoch, avg_acc, avg_loss)
if __name__ == '__main__':
main()
================================================
FILE: src/model/resnet18.py
================================================
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.models as models
from basic_model import BasicModel
from fc_tree_net import FCTreeNet
class identity(nn.Module):
def __init__(self):
super(identity, self).__init__()
def forward(self, x):
return x
class mlp_module(nn.Module):
def __init__(self):
super(mlp_module, self).__init__()
self.fc1 = nn.Linear(512, 512)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(512, 8+9+21)
self.dropout = nn.Dropout(0.5)
def forward(self, x):
x = self.relu1(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
class Resnet18_MLP(BasicModel):
def __init__(self, args):
super(Resnet18_MLP, self).__init__(args)
self.resnet18 = models.resnet18(pretrained=False)
self.resnet18.conv1 = nn.Conv2d(16, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.resnet18.fc = identity()
self.mlp = mlp_module()
self.fc_tree_net = FCTreeNet(in_dim=300, img_dim=512)
self.optimizer = optim.Adam(self.parameters(), lr=args.lr, betas=(args.beta1, args.beta2), eps=args.epsilon)
self.meta_alpha = args.meta_alpha
self.meta_beta = args.meta_beta
def compute_loss(self, output, target, meta_target, meta_structure):
pred, meta_target_pred, meta_struct_pred = output[0], output[1], output[2]
target_loss = F.cross_entropy(pred, target)
meta_target_pred = torch.chunk(meta_target_pred, chunks=9, dim=1)
meta_target = torch.chunk(meta_target, chunks=9, dim=1)
meta_target_loss = 0.
for idx in range(0, 9):
meta_target_loss += F.binary_cross_entropy(F.sigmoid(meta_target_pred[idx]), meta_target[idx])
meta_struct_pred = torch.chunk(meta_struct_pred, chunks=21, dim=1)
meta_structure = torch.chunk(meta_structure, chunks=21, dim=1)
meta_struct_loss = 0.
for idx in range(0, 21):
meta_struct_loss += F.binary_cross_entropy(F.sigmoid(meta_struct_pred[idx]), meta_structure[idx])
loss = target_loss + self.meta_alpha*meta_struct_loss/21. + self.meta_beta*meta_target_loss/9.
return loss
def forward(self, x, embedding, indicator):
alpha = 1.0
features = self.resnet18(x.view(-1, 16, 224, 224))
features_tree = features.view(-1, 1, 512)
features_tree = self.fc_tree_net(features_tree, embedding, indicator)
final_features = features + alpha * features_tree
output = self.mlp(final_features)
pred = output[:,0:8]
meta_target_pred = output[:,8:17]
meta_struct_pred = output[:,17:38]
return pred, meta_target_pred, meta_struct_pred
================================================
FILE: src/model/utility/__init__.py
================================================
from dataset_utility import *
================================================
FILE: src/model/utility/dataset_utility.py
================================================
import os
import glob
import numpy as np
from scipy import misc
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
class ToTensor(object):
def __call__(self, sample):
return torch.tensor(sample, dtype=torch.float32)
class dataset(Dataset):
def __init__(self, root_dir, dataset_type, img_size, transform=None, shuffle=False):
self.root_dir = root_dir
self.transform = transform
self.file_names = [f for f in glob.glob(os.path.join(root_dir, "*", "*.npz")) \
if dataset_type in f]
self.img_size = img_size
self.embeddings = np.load(os.path.join(root_dir, 'embedding.npy'), allow_pickle=True)
self.shuffle = shuffle
def __len__(self):
return len(self.file_names)
def __getitem__(self, idx):
data_path = self.file_names[idx]
data = np.load(data_path)
image = data["image"].reshape(16, 160, 160)
target = data["target"]
structure = data["structure"]
meta_target = data["meta_target"]
meta_structure = data["meta_structure"]
if self.shuffle:
context = image[:8, :, :]
choices = image[8:, :, :]
indices = range(8)
np.random.shuffle(indices)
new_target = indices.index(target)
new_choices = choices[indices, :, :]
image = np.concatenate((context, new_choices))
target = new_target
resize_image = []
for idx in range(0, 16):
resize_image.append(misc.imresize(image[idx,:,:], (self.img_size, self.img_size)))
resize_image = np.stack(resize_image)
# image = resize(image, (16, 128, 128))
# meta_matrix = data["mata_matrix"]
embedding = torch.zeros((6, 300), dtype=torch.float)
indicator = torch.zeros(1, dtype=torch.float)
element_idx = 0
for element in structure:
if element != '/':
embedding[element_idx, :] = torch.tensor(self.embeddings.item().get(element), dtype=torch.float)
element_idx += 1
if element_idx == 6:
indicator[0] = 1.
# if meta_target.dtype == np.int8:
# meta_target = meta_target.astype(n
gitextract_dccjdwzt/
├── .gitignore
├── LICENSE
├── README.md
├── assets/
│ ├── README.md
│ └── embedding.npy
├── requirements.txt
└── src/
├── dataset/
│ ├── AoT.py
│ ├── Attribute.py
│ ├── Rule.py
│ ├── __init__.py
│ ├── api.py
│ ├── build_tree.py
│ ├── const.py
│ ├── constraints.py
│ ├── main.py
│ ├── rendering.py
│ ├── sampling.py
│ ├── serialize.py
│ └── solver.py
└── model/
├── __init__.py
├── basic_model.py
├── cnn_lstm.py
├── cnn_mlp.py
├── const/
│ ├── __init__.py
│ └── const.py
├── fc_tree_net.py
├── main.py
├── resnet18.py
└── utility/
├── __init__.py
└── dataset_utility.py
SYMBOL INDEX (212 symbols across 18 files)
FILE: src/dataset/AoT.py
class AoTNode (line 13) | class AoTNode(object):
method __init__ (line 22) | def __init__(self, name, level, node_type, is_pg=False):
method insert (line 29) | def insert(self, node):
method _insert (line 39) | def _insert(self, node):
method _resample (line 49) | def _resample(self, change_number):
method __repr__ (line 62) | def __repr__(self):
method __str__ (line 65) | def __str__(self):
class Root (line 69) | class Root(AoTNode):
method __init__ (line 71) | def __init__(self, name, is_pg=False):
method sample (line 74) | def sample(self):
method resample (line 88) | def resample(self, change_number=False):
method prune (line 91) | def prune(self, rule_groups):
method prepare (line 110) | def prepare(self):
method sample_new (line 128) | def sample_new(self, component_idx, attr_name, min_level, max_level, r...
class Structure (line 141) | class Structure(AoTNode):
method __init__ (line 143) | def __init__(self, name, is_pg=False):
method _sample (line 146) | def _sample(self):
method _prune (line 154) | def _prune(self, rule_groups):
method _sample_new (line 166) | def _sample_new(self, component_idx, attr_name, min_level, max_level, ...
class Component (line 170) | class Component(AoTNode):
method __init__ (line 172) | def __init__(self, name, is_pg=False):
method _sample (line 175) | def _sample(self):
method _prune (line 183) | def _prune(self, rule_group):
method _sample_new (line 193) | def _sample_new(self, attr_name, min_level, max_level, component):
class Layout (line 197) | class Layout(AoTNode):
method __init__ (line 202) | def __init__(self, name, layout_constraint, entity_constraint,
method add_new (line 231) | def add_new(self, *bboxes):
method resample (line 248) | def resample(self, change_number=False):
method _sample (line 251) | def _sample(self):
method _resample (line 277) | def _resample(self, change_number):
method _update_constraint (line 303) | def _update_constraint(self, rule_group):
method reset_constraint (line 360) | def reset_constraint(self, attr):
method _sample_new (line 366) | def _sample_new(self, attr_name, min_level, max_level, layout):
class Entity (line 414) | class Entity(AoTNode):
method __init__ (line 416) | def __init__(self, name, bbox, entity_constraint):
method reset_constraint (line 432) | def reset_constraint(self, attr, min_level, max_level):
method resample (line 439) | def resample(self):
FILE: src/dataset/Attribute.py
class Attribute (line 12) | class Attribute(object):
method __init__ (line 24) | def __init__(self, name):
method sample (line 30) | def sample(self):
method get_value (line 33) | def get_value(self):
method set_value (line 36) | def set_value(self):
method __repr__ (line 39) | def __repr__(self):
method __str__ (line 42) | def __str__(self):
class Number (line 46) | class Number(Attribute):
method __init__ (line 48) | def __init__(self, min_level=NUM_MIN, max_level=NUM_MAX):
method sample (line 55) | def sample(self, min_level=NUM_MIN, max_level=NUM_MAX):
method sample_new (line 62) | def sample_new(self, min_level=None, max_level=None, previous_values=N...
method get_value_level (line 78) | def get_value_level(self):
method set_value_level (line 81) | def set_value_level(self, value_level):
method get_value (line 84) | def get_value(self, value_level=None):
class Type (line 90) | class Type(Attribute):
method __init__ (line 92) | def __init__(self, min_level=TYPE_MIN, max_level=TYPE_MAX):
method sample (line 99) | def sample(self, min_level=TYPE_MIN, max_level=TYPE_MAX):
method sample_new (line 104) | def sample_new(self, min_level=None, max_level=None, previous_values=N...
method get_value_level (line 116) | def get_value_level(self):
method set_value_level (line 119) | def set_value_level(self, value_level):
method get_value (line 122) | def get_value(self, value_level=None):
class Size (line 128) | class Size(Attribute):
method __init__ (line 130) | def __init__(self, min_level=SIZE_MIN, max_level=SIZE_MAX):
method sample (line 137) | def sample(self, min_level=SIZE_MIN, max_level=SIZE_MAX):
method sample_new (line 142) | def sample_new(self, min_level=None, max_level=None, previous_values=N...
method get_value_level (line 154) | def get_value_level(self):
method set_value_level (line 157) | def set_value_level(self, value_level):
method get_value (line 160) | def get_value(self, value_level=None):
class Color (line 166) | class Color(Attribute):
method __init__ (line 168) | def __init__(self, min_level=COLOR_MIN, max_level=COLOR_MAX):
method sample (line 175) | def sample(self, min_level=COLOR_MIN, max_level=COLOR_MAX):
method sample_new (line 180) | def sample_new(self, min_level=None, max_level=None, previous_values=N...
method get_value_level (line 192) | def get_value_level(self):
method set_value_level (line 195) | def set_value_level(self, value_level):
method get_value (line 198) | def get_value(self, value_level=None):
class Angle (line 204) | class Angle(Attribute):
method __init__ (line 206) | def __init__(self, min_level=ANGLE_MIN, max_level=ANGLE_MAX):
method sample (line 213) | def sample(self, min_level=ANGLE_MIN, max_level=ANGLE_MAX):
method sample_new (line 218) | def sample_new(self, min_level=None, max_level=None, previous_values=N...
method get_value_level (line 230) | def get_value_level(self):
method set_value_level (line 233) | def set_value_level(self, value_level):
method get_value (line 236) | def get_value(self, value_level=None):
class Uniformity (line 242) | class Uniformity(Attribute):
method __init__ (line 244) | def __init__(self, min_level=UNI_MIN, max_level=UNI_MAX):
method sample (line 251) | def sample(self):
method sample_new (line 254) | def sample_new(self):
method set_value_level (line 258) | def set_value_level(self, value_level):
method get_value_level (line 261) | def get_value_level(self):
method get_value (line 264) | def get_value(self, value_level=None):
class Position (line 270) | class Position(Attribute):
method __init__ (line 276) | def __init__(self, pos_type, pos_list):
method sample (line 293) | def sample(self, num):
method sample_new (line 302) | def sample_new(self, num, previous_values=None):
method sample_add (line 322) | def sample_add(self, num):
method get_value_idx (line 337) | def get_value_idx(self):
method set_value_idx (line 340) | def set_value_idx(self, value_idx):
method get_value (line 344) | def get_value(self, value_idx=None):
method remove (line 352) | def remove(self, bbox):
FILE: src/dataset/Rule.py
function Rule_Wrapper (line 11) | def Rule_Wrapper(name, attr, param, component_idx):
class Rule (line 26) | class Rule(object):
method __init__ (line 31) | def __init__(self, name, attr, params, component_idx=0):
method sample (line 47) | def sample(self):
method apply_rule (line 53) | def apply_rule(self, aot, in_aot=None):
class Constant (line 65) | class Constant(Rule):
method __init__ (line 69) | def __init__(self, name, attr, param, component_idx):
method apply_rule (line 72) | def apply_rule(self, aot, in_aot=None):
class Progression (line 78) | class Progression(Rule):
method __init__ (line 82) | def __init__(self, name, attr, param, component_idx):
method apply_rule (line 87) | def apply_rule(self, aot, in_aot=None):
class Arithmetic (line 141) | class Arithmetic(Rule):
method __init__ (line 146) | def __init__(self, name, attr, param, component_idx):
method apply_rule (line 152) | def apply_rule(self, aot, in_aot=None):
class Distribute_Three (line 326) | class Distribute_Three(Rule):
method __init__ (line 330) | def __init__(self, name, attr, param, component_idx):
method apply_rule (line 335) | def apply_rule(self, aot, in_aot=None):
FILE: src/dataset/api.py
class Bunch (line 12) | class Bunch:
method __init__ (line 14) | def __init__(self, **kwds):
function get_real_bbox (line 18) | def get_real_bbox(entity_bbox, entity_type, entity_size, entity_angle):
function get_mask (line 67) | def get_mask(entity_bbox, entity_type, entity_size, entity_angle):
function rle_encode (line 80) | def rle_encode(img):
function rle_decode (line 92) | def rle_decode(mask_rle, shape):
FILE: src/dataset/build_tree.py
function build_center_single (line 9) | def build_center_single():
function build_distribute_four (line 34) | def build_distribute_four():
function build_distribute_nine (line 62) | def build_distribute_nine():
function build_left_center_single_right_center_single (line 95) | def build_left_center_single_right_center_single():
function build_up_center_single_down_center_single (line 133) | def build_up_center_single_down_center_single():
function build_in_center_single_out_center_single (line 171) | def build_in_center_single_out_center_single():
function build_in_distribute_four_out_center_single (line 211) | def build_in_distribute_four_out_center_single():
FILE: src/dataset/constraints.py
function gen_layout_constraint (line 9) | def gen_layout_constraint(pos_type, pos_list,
function gen_entity_constraint (line 18) | def gen_entity_constraint(type_min=TYPE_MIN, type_max=TYPE_MAX,
function rule_constraint (line 29) | def rule_constraint(rule_list, num_min, num_max,
FILE: src/dataset/main.py
function merge_component (line 28) | def merge_component(dst_aot, src_aot, component_idx):
function fuse (line 33) | def fuse(args, all_configs):
function separate (line 161) | def separate(args, all_configs):
function main (line 290) | def main():
FILE: src/dataset/rendering.py
function imshow (line 12) | def imshow(array):
function imsave (line 17) | def imsave(array, filepath):
function generate_matrix (line 22) | def generate_matrix(array_list):
function generate_answers (line 37) | def generate_answers(array_list):
function generate_matrix_answer (line 51) | def generate_matrix_answer(array_list):
function merge_matrix_answer (line 66) | def merge_matrix_answer(matrix, answer):
function render_panel (line 74) | def render_panel(root):
function render_structure (line 89) | def render_structure(structure_name):
function render_entity (line 102) | def render_entity(entity):
function shift (line 183) | def shift(img, dx, dy):
function rotate (line 189) | def rotate(img, angle, center=CENTER):
function scale (line 195) | def scale(img, tx, ty, center=CENTER):
function layer_add (line 201) | def layer_add(lower_layer_np, higher_layer_np):
function draw_triangle (line 210) | def draw_triangle(img, pts, color, width):
function draw_square (line 222) | def draw_square(img, pt1, pt2, color, width):
function draw_pentagon (line 246) | def draw_pentagon(img, pts, color, width):
function draw_hexagon (line 258) | def draw_hexagon(img, pts, color, width):
function draw_circle (line 270) | def draw_circle(img, center, radius, color, width):
FILE: src/dataset/sampling.py
function sample_rules (line 11) | def sample_rules():
function sample_attr_avail (line 26) | def sample_attr_avail(rule_groups, row_3_3):
function sample_attr (line 91) | def sample_attr(attrs_list):
FILE: src/dataset/serialize.py
function n_tree_serialize (line 13) | def n_tree_serialize(aot):
function serialize_aot (line 28) | def serialize_aot(aot):
function serialize_rules (line 44) | def serialize_rules(rule_groups):
function dom_problem (line 77) | def dom_problem(instances, rule_groups):
FILE: src/dataset/solver.py
function solve (line 7) | def solve(rule_groups, context, candidates):
function check_num_pos (line 40) | def check_num_pos(rule_num_pos, context, candidate):
function check_consistency (line 116) | def check_consistency(candidate, attr, component_idx):
function check_entity (line 129) | def check_entity(rule, context, candidate, attr, regenerate):
FILE: src/model/basic_model.py
class BasicModel (line 5) | class BasicModel(nn.Module):
method __init__ (line 6) | def __init__(self, args):
method load_model (line 10) | def load_model(self, path, epoch):
method save_model (line 14) | def save_model(self, path, epoch, acc, loss):
method compute_loss (line 17) | def compute_loss(self, output, target, meta_target, meta_structure):
method train_ (line 20) | def train_(self, image, target, meta_target, meta_structure, embedding...
method validate_ (line 31) | def validate_(self, image, target, meta_target, meta_structure, embedd...
method test_ (line 40) | def test_(self, image, target, meta_target, meta_structure, embedding,...
FILE: src/model/cnn_lstm.py
class conv_module (line 11) | class conv_module(nn.Module):
method __init__ (line 12) | def __init__(self):
method forward (line 27) | def forward(self, x):
class lstm_module (line 38) | class lstm_module(nn.Module):
method __init__ (line 39) | def __init__(self):
method forward (line 45) | def forward(self, x):
class CNN_LSTM (line 51) | class CNN_LSTM(BasicModel):
method __init__ (line 52) | def __init__(self, args):
method compute_loss (line 59) | def compute_loss(self, output, target, meta_target, meta_structure):
method forward (line 64) | def forward(self, x, embedding, indicator):
FILE: src/model/cnn_mlp.py
class conv_module (line 11) | class conv_module(nn.Module):
method __init__ (line 12) | def __init__(self):
method forward (line 27) | def forward(self, x):
class mlp_module (line 38) | class mlp_module(nn.Module):
method __init__ (line 39) | def __init__(self):
method forward (line 46) | def forward(self, x):
class CNN_MLP (line 52) | class CNN_MLP(BasicModel):
method __init__ (line 53) | def __init__(self, args):
method compute_loss (line 60) | def compute_loss(self, output, target, meta_target, meta_structure):
method forward (line 65) | def forward(self, x, embedding, indicator):
FILE: src/model/fc_tree_net.py
class FCTreeNet (line 7) | class FCTreeNet(torch.nn.Module):
method __init__ (line 8) | def __init__(self, in_dim=300, img_dim=256, use_cuda=True):
method forward (line 27) | def forward(self, image_feature, input, indicator):
FILE: src/model/main.py
function train (line 65) | def train(epoch):
function validate (line 89) | def validate(epoch):
function test (line 114) | def test(epoch):
function main (line 136) | def main():
FILE: src/model/resnet18.py
class identity (line 12) | class identity(nn.Module):
method __init__ (line 13) | def __init__(self):
method forward (line 16) | def forward(self, x):
class mlp_module (line 19) | class mlp_module(nn.Module):
method __init__ (line 20) | def __init__(self):
method forward (line 27) | def forward(self, x):
class Resnet18_MLP (line 33) | class Resnet18_MLP(BasicModel):
method __init__ (line 34) | def __init__(self, args):
method compute_loss (line 45) | def compute_loss(self, output, target, meta_target, meta_structure):
method forward (line 63) | def forward(self, x, embedding, indicator):
FILE: src/model/utility/dataset_utility.py
class ToTensor (line 11) | class ToTensor(object):
method __call__ (line 12) | def __call__(self, sample):
class dataset (line 15) | class dataset(Dataset):
method __init__ (line 16) | def __init__(self, root_dir, dataset_type, img_size, transform=None, s...
method __len__ (line 25) | def __len__(self):
method __getitem__ (line 28) | def __getitem__(self, idx):
Condensed preview — 30 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (204K chars).
[
{
"path": ".gitignore",
"chars": 1248,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi"
},
{
"path": "LICENSE",
"chars": 35149,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 5887,
"preview": "# RAVEN\n\nThis repo contains code for our CVPR 2019 paper.\n\n[RAVEN: A Dataset for <u>R</u>elational and <u>A</u>nalogical"
},
{
"path": "assets/README.md",
"chars": 3509,
"preview": "# Dataset Format\n\nThe dataset folder is organized as follows:\n\n```\ncenter_single/\n RAVEN_0_train.npz\n RAVEN_0_trai"
},
{
"path": "requirements.txt",
"chars": 87,
"preview": "numpy\nscipy\nmatplotlib\npillow\nscikit-image\nopencv-contrib-python\ntqdm\ntorch\ntorchvision"
},
{
"path": "src/dataset/AoT.py",
"chars": 18967,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport copy\n\nimport numpy as np\nfrom scipy.misc import comb\n\nfrom Attribute import Angle, Colo"
},
{
"path": "src/dataset/Attribute.py",
"chars": 12574,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport numpy as np\n\nfrom const import (ANGLE_MAX, ANGLE_MIN, ANGLE_VALUES, COLOR_MAX, COLOR_MI"
},
{
"path": "src/dataset/Rule.py",
"chars": 26912,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport copy\n\nimport numpy as np\n\nfrom const import COLOR_MAX, COLOR_MIN\n\n\ndef Rule_Wrapper(nam"
},
{
"path": "src/dataset/__init__.py",
"chars": 101,
"preview": "\"\"\" RAVEN dataset generation code\n\nAuthor: Chi Zhang\nData: 05/14/2019\nContact: chi.zhang@ucla.edu\n\"\"\""
},
{
"path": "src/dataset/api.py",
"chars": 4819,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport xml.etree.ElementTree as ET\n\nimport cv2\nimport numpy as np\nfrom const import DEFAULT_WI"
},
{
"path": "src/dataset/build_tree.py",
"chars": 8377,
"preview": "# -*- coding: utf-8 -*-\n\n\nfrom AoT import Component, Layout, Root, Structure\nfrom constraints import (gen_entity_constra"
},
{
"path": "src/dataset/const.py",
"chars": 3744,
"preview": "# -*- coding: utf-8 -*-\n\n\n# Maximum number of components in a RPM\nMAX_COMPONENTS = 2\n\n# Canvas parameters\nIMAGE_SIZE = 1"
},
{
"path": "src/dataset/constraints.py",
"chars": 5913,
"preview": "# -*- coding: utf-8 -*-\n\n\nfrom const import (ANGLE_MAX, ANGLE_MIN, COLOR_MAX, COLOR_MIN, NUM_MAX,\n NUM"
},
{
"path": "src/dataset/main.py",
"chars": 15017,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport argparse\nimport copy\nimport os\nimport random\nimport sys\n\nimport numpy as np\nfrom tqdm i"
},
{
"path": "src/dataset/rendering.py",
"chars": 10184,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport cv2\nimport numpy as np\nfrom PIL import Image\n\nfrom AoT import Root\nfrom const import CE"
},
{
"path": "src/dataset/sampling.py",
"chars": 4499,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport numpy as np\nfrom scipy.misc import comb\n\nfrom const import MAX_COMPONENTS, RULE_ATTR\nfr"
},
{
"path": "src/dataset/serialize.py",
"chars": 4565,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport json\nimport xml.etree.ElementTree as ET\n\nimport numpy as np\n\nfrom const import META_STR"
},
{
"path": "src/dataset/solver.py",
"chars": 9937,
"preview": "# -*- coding: utf-8 -*-\n\n\nimport numpy as np\n\n\ndef solve(rule_groups, context, candidates):\n \"\"\"Search-based Heuristi"
},
{
"path": "src/model/__init__.py",
"chars": 95,
"preview": "\"\"\" RAVEN benchmarking code\n\nAuthor: Chi Zhang\nData: 05/14/2019\nContact: chi.zhang@ucla.edu\n\"\"\""
},
{
"path": "src/model/basic_model.py",
"chars": 1939,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass BasicModel(nn.Module):\n def __init__(self, "
},
{
"path": "src/model/cnn_lstm.py",
"chars": 2493,
"preview": "import numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom"
},
{
"path": "src/model/cnn_mlp.py",
"chars": 2467,
"preview": "import numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom"
},
{
"path": "src/model/const/__init__.py",
"chars": 20,
"preview": "from const import *\n"
},
{
"path": "src/model/const/const.py",
"chars": 3693,
"preview": "# -*- coding: utf-8 -*-\n\n\n# Maximum number of components in a RPM\nMAX_COMPONENTS = 2\n\n# Canvas parameters\nIMAGE_SIZE = 1"
},
{
"path": "src/model/fc_tree_net.py",
"chars": 3565,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n\nclass FCTreeNet(torch.nn.Module)"
},
{
"path": "src/model/main.py",
"chars": 5355,
"preview": "import os\nimport numpy as np\nimport argparse\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch."
},
{
"path": "src/model/resnet18.py",
"chars": 2837,
"preview": "import numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimpor"
},
{
"path": "src/model/utility/__init__.py",
"chars": 29,
"preview": "from dataset_utility import *"
},
{
"path": "src/model/utility/dataset_utility.py",
"chars": 2932,
"preview": "import os\nimport glob\nimport numpy as np\nfrom scipy import misc\n\nimport torch\nfrom torch.utils.data import Dataset, Data"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the WellyZhang/RAVEN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 30 files (192.3 KB), approximately 46.1k tokens, and a symbol index with 212 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.