├── .gitignore
├── .idea
    ├── misc.xml
    ├── modules.xml
    ├── vcs.xml
    ├── visual-interaction-networks-pytorch.iml
    └── workspace.xml
├── LICENSE
├── README.md
├── config.py
├── figures
    ├── 1.gif
    └── 2.png
├── logger.py
├── model.py
├── train.py
├── utils.py
├── vin.py
└── vin_dataset.py


/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *$py.class
  5 | 
  6 | # C extensions
  7 | *.so
  8 | 
  9 | # Distribution / packaging
 10 | .Python
 11 | env/
 12 | build/
 13 | develop-eggs/
 14 | dist/
 15 | downloads/
 16 | eggs/
 17 | .eggs/
 18 | lib/
 19 | lib64/
 20 | parts/
 21 | sdist/
 22 | var/
 23 | wheels/
 24 | *.egg-info/
 25 | .installed.cfg
 26 | *.egg
 27 | 
 28 | # PyInstaller
 29 | #  Usually these files are written by a python script from a template
 30 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 31 | *.manifest
 32 | *.spec
 33 | 
 34 | # Installer logs
 35 | pip-log.txt
 36 | pip-delete-this-directory.txt
 37 | 
 38 | # Unit test / coverage reports
 39 | htmlcov/
 40 | .tox/
 41 | .coverage
 42 | .coverage.*
 43 | .cache
 44 | nosetests.xml
 45 | coverage.xml
 46 | *.cover
 47 | .hypothesis/
 48 | 
 49 | # Translations
 50 | *.mo
 51 | *.pot
 52 | 
 53 | # Django stuff:
 54 | *.log
 55 | local_settings.py
 56 | 
 57 | # Flask stuff:
 58 | instance/
 59 | .webassets-cache
 60 | 
 61 | # Scrapy stuff:
 62 | .scrapy
 63 | 
 64 | # Sphinx documentation
 65 | docs/_build/
 66 | 
 67 | # PyBuilder
 68 | target/
 69 | 
 70 | # Jupyter Notebook
 71 | .ipynb_checkpoints
 72 | 
 73 | # pyenv
 74 | .python-version
 75 | 
 76 | # celery beat schedule file
 77 | celerybeat-schedule
 78 | 
 79 | # SageMath parsed files
 80 | *.sage.py
 81 | 
 82 | # dotenv
 83 | .env
 84 | 
 85 | # virtualenv
 86 | .venv
 87 | venv/
 88 | ENV/
 89 | 
 90 | # Spyder project settings
 91 | .spyderproject
 92 | .spyproject
 93 | 
 94 | # Rope project settings
 95 | .ropeproject
 96 | 
 97 | # mkdocs documentation
 98 | /site
 99 | 
100 | # mypy
101 | .mypy_cache/
102 | /vin/
103 | /checkpoint/
104 | /log/
105 | 


--------------------------------------------------------------------------------
/.idea/misc.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
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697 |           <caret line="160" column="0" lean-forward="false" selection-start-line="160" selection-start-column="0" selection-end-line="160" selection-end-column="0" />
698 |           <folding>
699 |             <element signature="e#0#37#0" expanded="true" />
700 |           </folding>
701 |         </state>
702 |       </provider>
703 |     </entry>
704 |     <entry file="file://$PROJECT_DIR$/README.md">
705 |       <provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
706 |         <state split_layout="SPLIT">
707 |           <first_editor relative-caret-position="195">
708 |             <caret line="13" column="15" lean-forward="false" selection-start-line="13" selection-start-column="15" selection-end-line="13" selection-end-column="15" />
709 |             <folding />
710 |           </first_editor>
711 |           <second_editor />
712 |         </state>
713 |       </provider>
714 |     </entry>
715 |   </component>
716 | </project>


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2017 Mahmoud Gemy
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Visual-Interaction-Networks
 2 | An implementation of Deepmind visual interaction networks in Pytorch.
 3 |  # Introduction
 4 | > For the purpose of understanding the challenge of relational reasoning. they publised VIN that involves predicting the future in a physical scene. From just a glance, humans can infer not only what objects are where, but also what will happen to them over the upcoming seconds, minutes and even longer in some cases. For example, if you kick a football against a wall, your brain predicts what will happen when the ball hits the wall and how their movements will be affected afterwards (the ball will ricochet at a speed proportional to the kick and - in most cases - the wall will remain where it is).
 5 | 
 6 | <div align="center">
 7 | 
 8 | <img align="center" hight="600" width="600" src="https://github.com/Mrgemy95/visual-interaction-networks-pytorch/blob/master/figures/1.gif?raw=true">
 9 | </div>
10 | 
11 | 
12 | ## Architecture
13 | <div align="center">
14 | <img hight="600" width="600" src="https://github.com/Mrgemy95/visual-interaction-networks-pytorch/blob/master/figures/2.png?raw=true">
15 | </div>
16 | 
17 | 
18 | ### Data
19 | I used [I@jaesik817](https://github.com/jaesik817/Interaction-networks_tensorflow) physics engine to generate the data.
20 | 
21 | Just run the `physics_engine.py`
22 | 
23 | 
24 | ## Usage
25 | ### Main Dependencies
26 | ``` 
27 | Python 3.5
28 | pytorch 0.3
29 | numpy 1.13.1
30 | ```
31 | 
32 | ### RUN
33 | - Edit configration file to meet your need.
34 | - Run `vin.py`
35 | 
36 | ### References
37 | * https://github.com/jaesik817/visual-interaction-networks_tensorflow
38 | 


--------------------------------------------------------------------------------
/config.py:
--------------------------------------------------------------------------------
 1 | 
 2 | 
 3 | 
 4 | 
 5 | 
 6 | 
 7 | 
 8 | class VinConfig():
 9 |     No = 3  # the number of object
10 |     img_folder = "./vin/img/"  # image folder
11 |     data_folder = "./vin/data/"  # data folder
12 |     frame_num = 50  # The Number of Saved Frame per each simul
13 |     frame_step = 1  # The difference between saved frames
14 |     roll_num = 20  # The Number of Rollout
15 |     set_num = 10  # The Number of set
16 |     weight=32
17 |     height=32
18 |     col_dim=4
19 |     batch_size=4
20 |     checkpoint_dir="./checkpoint/"
21 |     load=False
22 |     log_dir="./log"


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/figures/1.gif:
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https://raw.githubusercontent.com/MrGemy95/visual-interaction-networks-pytorch/27ae3ffd0c68643d61a6dda9235c80b5c20165ac/figures/1.gif


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/figures/2.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/MrGemy95/visual-interaction-networks-pytorch/27ae3ffd0c68643d61a6dda9235c80b5c20165ac/figures/2.png


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/logger.py:
--------------------------------------------------------------------------------
  1 | # Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
  2 | 
  3 | import numpy as np
  4 | import scipy.misc
  5 | import os
  6 | import tensorflow as tf
  7 | try:
  8 |     from StringIO import StringIO
  9 | except ImportError:
 10 |     from io import BytesIO
 11 | 
 12 | 
 13 | class Logger(object):
 14 |     def __init__(self, log_dir):
 15 |         import tensorflow as tf
 16 | 
 17 |         self.summary_writer = tf.summary.FileWriter(log_dir)
 18 |         self.summary_ops = {}
 19 | 
 20 |     def graph_summary(self, graph):
 21 |         self.summary_writer.add_graph(graph)
 22 |         self.summary_writer.flush()
 23 | 
 24 |     def scalar_summary(self, tag, value, step, scope):
 25 |         summary = tf.Summary(value=[tf.Summary.Value(tag=os.path.join(scope, tag), simple_value=value)])
 26 |         self.summary_writer.add_summary(summary, step)
 27 |         self.summary_writer.flush()
 28 | 
 29 |     def image_summary(self, tag, images, step, scope, max_output=4, random_summarization=False):
 30 |         """Log a list of images."""
 31 |         assert len(images.shape) == 4, "the input image shape should be in form [batch,hight,width,channels]"
 32 |         img_summaries = []
 33 |         if random_summarization:
 34 |             idxs = np.random.choice(images.shape[0], min(max_output, images.shape[0]))
 35 |             images = images[idxs]
 36 |         else:
 37 |             images = images[:max_output]
 38 |         if images.shape[-1]==1:
 39 |             images=np.squeeze(images)
 40 |         for i in range(images.shape[0]):
 41 |             img=images[i]
 42 |             try:
 43 |                 s = StringIO()
 44 |             except:
 45 |                 s = BytesIO()
 46 |             scipy.misc.toimage(img).save(s, format="png")
 47 | 
 48 |             # Create an Image object
 49 |             img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
 50 |                                        height=img.shape[0],
 51 |                                        width=img.shape[1])
 52 |             # Create a Summary value
 53 |             img_summaries.append(tf.Summary.Value(tag=os.path.join(scope, '%s/%d' % (tag, i)), image=img_sum))
 54 | 
 55 |         # Create and write Summary
 56 |         summary = tf.Summary(value=img_summaries)
 57 |         self.summary_writer.add_summary(summary, step)
 58 |         self.summary_writer.flush()
 59 | 
 60 |     def histo_summary(self, tag, values, step, scope, bins=1000, ):
 61 |         """Log a histogram of the tensor of values."""
 62 |         counts, bin_edges = np.histogram(values, bins=bins)
 63 |         # Fill the fields of the histogram proto
 64 |         hist = tf.HistogramProto()
 65 |         hist.min = float(np.min(values))
 66 |         hist.max = float(np.max(values))
 67 |         hist.num = int(np.prod(values.shape))
 68 |         hist.sum = float(np.sum(values))
 69 |         hist.sum_squares = float(np.sum(values ** 2))
 70 | 
 71 |         # Drop the start of the first bin
 72 |         bin_edges = bin_edges[1:]
 73 | 
 74 |         # Add bin edges and counts
 75 |         for edge in bin_edges:
 76 |             hist.bucket_limit.append(edge)
 77 |         for c in counts:
 78 |             hist.bucket.append(c)
 79 | 
 80 |         # Create and write Summary
 81 |         summary = tf.Summary(value=[tf.Summary.Value(tag=os.path.join(scope, tag), histo=hist)])
 82 |         self.summary_writer.add_summary(summary, step)
 83 |         self.summary_writer.flush()
 84 | 
 85 |     # summarize tenorflow tenosrs or images or merged summary, but this requires tensorflow session run
 86 |     def summarize(self, sess, step, scope='train', summaries_dict=None, summaries_merged=None):
 87 |         """
 88 |         Add the summaries to tensorboard
 89 |         :param step:
 90 |         :param summaries_dict:
 91 |         :param summaries_merged:
 92 |         :return:
 93 |         """
 94 |         with tf.variable_scope(scope):
 95 |             if summaries_dict is not None:
 96 |                 summary_list = []
 97 |                 for tag, value in summaries_dict.items():
 98 |                     if tag not in self.summary_ops:
 99 |                         self.summary_placeholders[tag] = tf.placeholder('float32', value.shape, name=tag)
100 |                         if len(value.shape) <= 1:
101 |                             self.summary_ops[tag] = tf.summary.scalar(tag, self.summary_placeholders[tag])
102 |                         else:
103 |                             self.summary_ops[tag] = tf.summary.image(tag, self.summary_placeholders[tag])
104 | 
105 |                     summary_list.append(sess.run(self.summary_ops[tag], {self.summary_placeholders[tag]: value}))
106 | 
107 |                 for summary in summary_list:
108 |                     self.summary_writer.add_summary(summary, step)
109 |             if summaries_merged is not None:
110 |                 self.summary_writer.add_summary(summaries_merged, step)
111 |             self.summary_writer.flush()


--------------------------------------------------------------------------------
/model.py:
--------------------------------------------------------------------------------
  1 | from __future__ import print_function
  2 | 
  3 | import torch
  4 | from torch.autograd import Variable
  5 | import torch.nn as nn
  6 | import torch.nn.functional as F
  7 | 
  8 | class Net(nn.Module):
  9 |     def __init__(self, config):
 10 |         super(Net, self).__init__()
 11 |         self.config = config
 12 |         self.pool = nn.MaxPool2d(2, 2)
 13 | 
 14 |         # visual encoder modules
 15 |         self.conv1 = nn.Conv2d(10, 128, 3, padding=1)
 16 |         self.conv2 = nn.Conv2d(128, 128, 3, padding=1)
 17 |         self.conv3 = nn.Conv2d(128, 128, 3, padding=1)
 18 |         self.conv4 = nn.Conv2d(128, 128, 3, padding=1)
 19 |         self.conv5 = nn.Conv2d(128, 128, 3, padding=1)
 20 |         # shared linear layer to convert the tensors to shape N_obj*64
 21 |         self.fc1 = nn.Linear(128, 3 * 64)
 22 |         # shared MLP layer to output the encoded state code N_obj*64
 23 |         self.fc2 = nn.Linear(64 * 2, 64)
 24 |         self.fc3 = nn.Linear(64, 64)
 25 |         self.fc4 = nn.Linear(64, 64)
 26 |         # end of visual encoder
 27 | 
 28 |         # dynamic predictor modules
 29 |         self.self_cores = {}
 30 |         for i in range(3):
 31 |             self.self_cores[i] = []
 32 |             self.self_cores[i].append(nn.Linear(64, 64).double().cuda())
 33 |             self.self_cores[i].append(nn.Linear(64, 64).double().cuda())
 34 |         self.rel_cores = {}
 35 |         for i in range(3):
 36 |             self.rel_cores[i] = []
 37 |             self.rel_cores[i].append(nn.Linear(64 * 2, 64).double().cuda())
 38 |             self.rel_cores[i].append(nn.Linear(64, 64).double().cuda())
 39 |             self.rel_cores[i].append(nn.Linear(64, 64).double().cuda())
 40 | 
 41 |         self.affector = {}
 42 |         for i in range(3):
 43 |             self.affector[i] = []
 44 |             self.affector[i].append(nn.Linear(64, 64).double().cuda())
 45 |             self.affector[i].append(nn.Linear(64, 64).double().cuda())
 46 |             self.affector[i].append(nn.Linear(64, 64).double().cuda())
 47 |         self.out = {}
 48 |         for i in range(3):
 49 |             self.out[i] = []
 50 |             self.out[i].append(nn.Linear(64 + 64, 64).double().cuda())
 51 |             self.out[i].append(nn.Linear(64, 64).double().cuda())
 52 |         self.aggregator1 = nn.Linear(64 * 3, 64)
 53 |         self.aggregator2 = nn.Linear(64, 64)
 54 |         self.state_decoder = nn.Linear(64, 4)
 55 | 
 56 |     def core(self, s, core_idx):
 57 |         objects = torch.chunk(s, 3, 1)
 58 | 
 59 |         s_reshaped = s.view(-1, 64)
 60 |         self_sd_h1 = F.relu(self.self_cores[core_idx][0](s_reshaped))
 61 |         self_sd_h2 = self.self_cores[core_idx][1](self_sd_h1) + self_sd_h1
 62 |         self_dynamic = self_sd_h2.view(-1, 3, 64)
 63 | 
 64 |         rel_combination = []
 65 |         for i in range(6):
 66 |             row_idx = int(i / (2));
 67 |             col_idx = int(i % (2));
 68 |             rel_combination.append(torch.cat([objects[row_idx], objects[col_idx]], 1))
 69 |         rel_combination = torch.cat(rel_combination)
 70 |         rel_combination=rel_combination.view(-1,64*2)
 71 |         rel_sd_h1 = F.relu(self.rel_cores[core_idx][0](rel_combination))
 72 |         rel_sd_h2 = F.relu(self.rel_cores[core_idx][1](rel_sd_h1) + rel_sd_h1)
 73 |         rel_sd_h3 = self.rel_cores[core_idx][2](rel_sd_h2) + rel_sd_h2
 74 |         rel_objects = torch.chunk(rel_sd_h3, 6)
 75 |         obj1 = rel_objects[0] + rel_objects[1]
 76 |         obj2 = rel_objects[2] + rel_objects[3]
 77 |         obj3 = rel_objects[4] + rel_objects[5]
 78 |         rel_dynamic = torch.stack([obj1, obj2, obj3], 1)
 79 |         dynamic_pred = self_dynamic + rel_dynamic
 80 |         dynamic_pred = dynamic_pred.view(-1, 64)
 81 |         aff1 = F.relu(self.affector[core_idx][0](dynamic_pred))
 82 |         aff2 = F.relu(self.affector[core_idx][1](aff1) + aff1)
 83 |         aff3 = self.affector[core_idx][2](aff2) + aff2
 84 |         aff3 = aff3.view(-1, 3, 64)
 85 |         aff_s = torch.cat([aff3, s], 2)
 86 |         aff_s = aff_s.view(-1, 64 + 64)
 87 | 
 88 |         out1 = F.relu(self.out[core_idx][0](aff_s))
 89 |         out2 = self.out[core_idx][1](out1) + out1
 90 |         out2 = out2.view(-1, 3, 64)
 91 | 
 92 |         return out2
 93 | 
 94 |     def forward(self, x, x_cor, y_cor):
 95 |         f1, f2, f3, f4, f5, f6 = torch.chunk(x, 6,1)
 96 |         f1, f2, f3, f4, f5, f6=f1.squeeze(), f2.squeeze(), f3.squeeze(), f4.squeeze(), f5.squeeze(), f6.squeeze()
 97 |         f1f2 = torch.cat([f1, f2], 1)
 98 |         f2f3 = torch.cat([f2, f3], 1)
 99 |         f3f4 = torch.cat([f3, f4], 1)
100 |         f4f5 = torch.cat([f4, f5], 1)
101 |         f5f6 = torch.cat([f5, f6], 1)
102 | 
103 |         pairs = torch.cat([f1f2, f2f3, f3f4, f4f5, f5f6])
104 |         pairs = torch.cat([pairs, x_cor, y_cor], dim=1)
105 |         ve_h1 = F.relu(self.conv1(pairs))
106 |         ve_h1 = self.pool(ve_h1)
107 |         ve_h2 = F.relu(self.conv2(ve_h1) + ve_h1)
108 |         ve_h2 = self.pool(ve_h2)
109 |         ve_h3 = F.relu(self.conv3(ve_h2) + ve_h2)
110 |         ve_h3 = self.pool(ve_h3)
111 |         ve_h4 = F.relu(self.conv4(ve_h3) + ve_h3)
112 |         ve_h4 = self.pool(ve_h4)
113 |         ve_h5 = F.relu(self.conv5(ve_h4) + ve_h4)
114 |         ve_h5 = self.pool(ve_h5)
115 |         unit_pairs = ve_h5.view(-1, 128)
116 |         # p1,p2,p3,p4,p5=torch.chunk(unit_pairs, 5)
117 |         encoded_pairs = self.fc1(unit_pairs)
118 |         p1, p2, p3, p4, p5 = torch.chunk(encoded_pairs, 5)
119 |         p1 = p1.view(-1, 3, 64)
120 |         p2 = p2.view(-1, 3, 64)
121 |         p3 = p3.view(-1, 3, 64)
122 |         p4 = p4.view(-1, 3, 64)
123 |         p5 = p5.view(-1, 3, 64)
124 | 
125 |         pair1 = torch.cat([p1, p2], 2)
126 |         pair2 = torch.cat([p2, p3], 2)
127 |         pair3 = torch.cat([p3, p4], 2)
128 |         pair4 = torch.cat([p4, p5], 2)
129 | 
130 |         diff_pairs = torch.cat([pair1, pair2, pair3, pair4])
131 |         diff_pairs = diff_pairs.view(-1, 64 * 2)
132 |         shared_h1 = F.relu(self.fc2(diff_pairs))
133 |         shared_h2 = F.relu(self.fc3(shared_h1) + shared_h1)
134 |         shared_h3 = self.fc4(shared_h2) + shared_h2
135 |         state_codes = shared_h3.view(-1, 3, 64)
136 |         s1, s2, s3, s4 = torch.chunk(state_codes, 4)
137 |         rolls = []
138 |         for i in range(20):
139 |             c1 = self.core(s1, 0)
140 |             c2 = self.core(s2, 1)
141 |             c3 = self.core(s3, 2)
142 |             all_c = torch.cat([c1, c2, c3], 2)
143 |             all_c = all_c.view(-1, 64 * 3)
144 |             aggregator1 = F.relu(self.aggregator1(all_c))
145 |             aggregator2 = self.aggregator2(aggregator1)
146 |             aggregator2 = aggregator2.view(-1, 3, 64)
147 |             rolls.append(aggregator2)
148 |             s1, s2, s3, s4 = s2, s3, s4, aggregator2
149 |         rollouts=torch.stack(rolls)
150 |         rollouts=rollouts.view(80,3,64)
151 |         rollouts=torch.cat([s1,s2,s3,s4,rollouts])
152 |         rollouts=rollouts.view(-1,64)
153 |         state_decoder=self.state_decoder(rollouts)
154 |         state_decoder=state_decoder.view(-1,3,4)
155 |         aux_out=state_decoder[:4*4]
156 |         aux_out=aux_out.view(4,4,3,4)
157 | 
158 | 
159 | 
160 |         net_out=state_decoder[4*4:]
161 |         net_out=net_out.view(-1,20,3,4)
162 |         net_out2=torch.chunk(net_out, 20,1)
163 | 
164 |         net_out2=net_out2[:8]
165 |         return net_out2,aux_out,net_out
166 | 
167 | 
168 |     def num_flat_features(self, x):
169 |         size = x.size()[1:]  # all dimensions except the batch dimension
170 |         num_features = 1
171 |         for s in size:
172 |             num_features *= s
173 |         return num_features
174 | 


--------------------------------------------------------------------------------
/train.py:
--------------------------------------------------------------------------------
  1 | from vin_dataset import VinDataset, VinTestDataset, ToTensor, ToTensorV2
  2 | from torchvision import transforms, datasets
  3 | from torch.utils.data import DataLoader
  4 | from torch.autograd import Variable
  5 | import torch.optim as optim
  6 | from torch import nn
  7 | import numpy as np
  8 | import torch
  9 | from logger import Logger
 10 | from utils import create_dir
 11 | import os
 12 | from utils import make_image2
 13 | 
 14 | 
 15 | class Trainer():
 16 |     def __init__(self, config, net):
 17 |         self.net = net
 18 |         self.config = config
 19 |         create_dir(self.config.checkpoint_dir)
 20 | 
 21 |         dataset = VinDataset(self.config, transform=ToTensor())
 22 |         test_dataset = VinTestDataset(self.config, transform=ToTensorV2())
 23 |         self.dataloader = DataLoader(dataset, batch_size=self.config.batch_size,
 24 |                                      shuffle=True, num_workers=4)
 25 |         self.test_dataloader = DataLoader(test_dataset, batch_size=1,
 26 |                                           shuffle=True, num_workers=1)
 27 |         self.optimizer = optim.Adam(self.net.parameters(), lr=0.0005)
 28 |         self.logger = Logger(self.config.log_dir)
 29 |         self.construct_cors()
 30 |         self.save()
 31 |         if config.load:
 32 |             self.load()
 33 | 
 34 |     def save(self):
 35 |         torch.save(self.net.state_dict(), os.path.join(self.config.checkpoint_dir, "checkpoint"))
 36 | 
 37 |     def load(self):
 38 |         self.net.load_state_dict(torch.load(os.path.join(self.config.checkpoint_dir, "checkpoint")))
 39 | 
 40 |     def construct_cors(self):
 41 |         # x-cor and y-cor setting
 42 |         nx, ny = (self.config.weight, self.config.height)
 43 |         x = np.linspace(0, 1, nx)
 44 |         y = np.linspace(0, 1, ny)
 45 |         xv, yv = np.meshgrid(x, y)
 46 |         xv = np.reshape(xv, [self.config.height, self.config.weight, 1])
 47 |         yv = np.reshape(yv, [self.config.height, self.config.weight, 1])
 48 |         xcor = np.zeros((self.config.batch_size * 5, self.config.height, self.config.weight, 1), dtype=float)
 49 |         ycor = np.zeros((self.config.batch_size * 5, self.config.height, self.config.weight, 1), dtype=float)
 50 |         for i in range(self.config.batch_size * 5):
 51 |             xcor[i] = xv
 52 |             ycor[i] = yv
 53 |         xcor = xcor.transpose((0, 3, 1, 2))
 54 |         ycor = ycor.transpose((0, 3, 1, 2))
 55 |         self.xcor, self.ycor = Variable(torch.from_numpy(xcor)).cuda(), Variable(torch.from_numpy(ycor)).cuda()
 56 | 
 57 |     def train(self):
 58 |         total_step = 0
 59 |         df = Variable(torch.ones(1)).double().cuda()
 60 |         for epoch in range(100):  # loop over the dataset multiple times
 61 |             running_loss = 0.0
 62 |             for i, data in enumerate(self.dataloader, 0):
 63 |                 total_step += 1
 64 |                 data = data[0]
 65 |                 if data['image'].size()[0] < self.config.batch_size:
 66 |                     print(data['image'].size()[0])
 67 |                     continue
 68 |                 inputs, output_label, output_S_label = data['image'], data['output_label'], data[
 69 |                     'output_S_label']
 70 | 
 71 |                 inputs, output_label, output_S_label = Variable(inputs).cuda(), Variable(output_label).cuda(), Variable(
 72 |                     output_S_label).cuda()
 73 | 
 74 |                 self.optimizer.zero_grad()
 75 | 
 76 |                 net_out, aux_out, _ = self.net(inputs, self.xcor, self.ycor)
 77 | 
 78 |                 # loss and optimizer
 79 |                 loss = nn.MSELoss()
 80 |                 mse = df * loss(net_out[0], output_label[:, 0])
 81 | 
 82 |                 for i in range(1, 8):
 83 |                     mse += (df ** (i + 1)) * loss(net_out[i], output_label[:, i])
 84 |                 mse = mse / 8;
 85 |                 ve_loss = loss(aux_out, output_S_label)
 86 |                 total_loss = mse + ve_loss
 87 |                 total_loss.backward()
 88 | 
 89 |                 self.optimizer.step()
 90 |                 # tensorboard_logging
 91 |                 self.logger.scalar_summary("mse", mse.data[0], total_step, "train")
 92 |                 self.logger.scalar_summary("ve_loss", ve_loss.data[0], total_step, "train")
 93 |                 self.logger.scalar_summary("total_loss", total_loss.data[0], total_step, "train")
 94 |             print("epoch ", epoch, " Finished")
 95 |             print("testing................")
 96 |             self.test()
 97 |         print('Finished Training')
 98 | 
 99 |     def test(self):
100 |         test_data = None
101 |         for i, data in enumerate(self.test_dataloader, 0):
102 |             test_data = data
103 | 
104 |             data = test_data[0]
105 |             inputs, output_label, output_S_label, xy_origin, xy_estimated = data['image'][0], data['output_label'], data[
106 |                 'output_S_label'], data['xy_origin'], data['xy_estimated']
107 |             xy_origin = Variable(xy_origin).data.cpu().numpy()[0]
108 |             xy_estimated = Variable(xy_estimated).data.cpu().numpy()[0]
109 |             inputs, output_label, output_S_label = Variable(inputs).cuda(), Variable(output_label).cuda(), Variable(
110 |                 output_S_label).cuda()
111 | 
112 |             out, aux_out, posi = self.net(inputs[:4], self.xcor, self.ycor)
113 |             velo = posi[0][:, :, 2:4];
114 |             xy_estimated[0] = output_S_label[0][3][3][:, :2].data.cpu().numpy() + (velo[0] * 0.01).data.cpu().numpy()
115 |             for i in range(1, len([0])):
116 |                 xy_estimated[i] = xy_estimated[i - 1] + velo[i] * 0.01
117 | 
118 |             # Saving
119 |             print("Image Making")
120 |             make_image2(xy_origin, self.config.img_folder + "../results/", "true")
121 |             make_image2(xy_estimated, self.config.img_folder + "../results/", "modeling")
122 |             print("Done")
123 |             yield


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/utils.py:
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 1 | import os
 2 | import matplotlib.pyplot as plt
 3 | def create_dir(path):
 4 |     if not os.path.exists(path):
 5 |         os.makedirs(path)
 6 | def make_image2(xy,img_folder,prefix):
 7 |   if not os.path.exists(img_folder):
 8 |     os.makedirs(img_folder);
 9 |   fig_num=len(xy);
10 |   mydpi=100;
11 |   for i in range(fig_num):
12 |     #fig = plt.figure(figsize=(32/mydpi,32/mydpi))
13 |     fig = plt.figure(figsize=(128/mydpi,128/mydpi))
14 |     plt.xlim(-200, 200)
15 |     plt.ylim(-200, 200)
16 |     plt.axis('off');
17 |     color=['r','b','g','k','y','m','c'];
18 |     for j in range(len(xy[0])):
19 |       #plt.scatter(xy[i,j,1],xy[i,j,0],c=color[j%len(color)],s=0.5);
20 |       plt.scatter(xy[i,j,1],xy[i,j,0],c=color[j%len(color)],s=5);
21 |     fig.savefig(img_folder+prefix+"_"+str(i)+".png",dpi=mydpi);


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/vin.py:
--------------------------------------------------------------------------------
 1 | from model import Net
 2 | from config import VinConfig
 3 | import tensorflow as tf
 4 | 
 5 | 
 6 | def main():
 7 |     from  train import Trainer
 8 |     net=Net(VinConfig)
 9 |     net=net.cuda()
10 |     net=net.double()
11 |     trainer=Trainer(VinConfig,net)
12 |     trainer.train()
13 | 
14 | 
15 | 
16 | 
17 | if __name__ == '__main__':
18 |     main()


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/vin_dataset.py:
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  1 | from torch.utils.data import Dataset
  2 | import os
  3 | import numpy as np
  4 | import matplotlib.image as mpimg
  5 | import torch
  6 | 
  7 | 
  8 | class VinDataset(Dataset):
  9 |     """Face Landmarks dataset."""
 10 | 
 11 |     def __init__(self, config, transform=None):
 12 | 
 13 |         self.config = config
 14 |         self.transform = transform
 15 | 
 16 |         total_img = np.zeros((self.config.set_num, int(self.config.frame_num), self.config.height, self.config.weight,
 17 |                               self.config.col_dim))
 18 |         for i in range(self.config.set_num):
 19 |             for j in range(int(self.config.frame_num)):
 20 |                 total_img[i, j] = mpimg.imread(self.config.img_folder + "train/" + str(i) + '_' + str(j) + '.png')[:, :,
 21 |                                   :self.config.col_dim]
 22 | 
 23 |         total_data = np.zeros((self.config.set_num, int(self.config.frame_num), self.config.No * 5))
 24 |         for i in range(self.config.set_num):
 25 |             f = open(self.config.data_folder + "train/" + str(i) + ".csv", "r")
 26 |             total_data[i] = [line[:-1].split(",") for line in f.readlines()]
 27 |         total_data = np.reshape(total_data, [self.config.set_num, int(self.config.frame_num), self.config.No, 5])
 28 | 
 29 |         # reshape img and data
 30 |         input_img = np.zeros((self.config.set_num * (int(self.config.frame_num) - 14 + 1), 6, self.config.height,
 31 |                               self.config.weight, self.config.col_dim)
 32 |                              )
 33 |         output_label = np.zeros((self.config.set_num * (int(self.config.frame_num) - 14 + 1), 8, self.config.No, 4)
 34 |                                 )
 35 |         output_S_label = np.zeros((self.config.set_num * (int(self.config.frame_num) - 14 + 1), 4, self.config.No, 4)
 36 |                                   )
 37 |         for i in range(self.config.set_num):
 38 |             for j in range(int(self.config.frame_num) - 14 + 1):
 39 |                 input_img[i * (int(self.config.frame_num) - 14 + 1) + j] = total_img[i, j:j + 6]
 40 |                 output_label[i * (int(self.config.frame_num) - 14 + 1) + j] = np.reshape(total_data[i, j + 6:j + 14],
 41 |                                                                                          [8, self.config.No, 5])[
 42 |                                                                               :, :, 1:5]
 43 |                 output_S_label[i * (int(self.config.frame_num) - 14 + 1) + j] = np.reshape(total_data[i, j + 2:j + 6],
 44 |                                                                                            [4, self.config.No, 5])[:, :,
 45 |                                                                                 1:5]
 46 | 
 47 |         # shuffle
 48 |         tr_data_num = int(len(input_img) * 1)
 49 |         total_idx = np.arange(len(input_img))
 50 |         np.random.shuffle(total_idx)
 51 |         self.tr_data = input_img[total_idx]
 52 |         self.tr_label = output_label[total_idx]
 53 |         self.tr_S_label = output_S_label[total_idx]
 54 | 
 55 |     def __len__(self):
 56 |         return len(self.tr_data)
 57 | 
 58 |     def __getitem__(self, idx):
 59 | 
 60 |         sample = {'image': self.tr_data[idx], 'output_label': self.tr_label[idx],
 61 |                   'output_S_label': self.tr_S_label[idx]}
 62 | 
 63 |         if self.transform:
 64 |             sample = self.transform(sample),
 65 | 
 66 |         return sample
 67 | 
 68 | 
 69 | class ToTensor(object):
 70 |     """Convert ndarrays in sample to Tensors."""
 71 | 
 72 |     def __call__(self, sample):
 73 |         image, output_label, output_S_label = sample['image'], sample['output_label'], sample[
 74 |             'output_S_label']
 75 | 
 76 |         # swap color axis because
 77 |         # numpy image: H x W x C
 78 |         # torch image: C X H X W
 79 |         image = image.transpose((0, 3, 1, 2))
 80 |         sample['image']=torch.from_numpy(image)
 81 |         sample['output_label']=torch.from_numpy(output_label)
 82 |         sample['output_S_label']=torch.from_numpy(output_S_label)
 83 |         return sample
 84 | 
 85 | class ToTensorV2(object):
 86 |     """Convert ndarrays in sample to Tensors."""
 87 | 
 88 |     def __call__(self, sample):
 89 |         image, output_label, output_S_label = sample['image'], sample['output_label'], sample[
 90 |             'output_S_label']
 91 | 
 92 |         # swap color axis because
 93 |         # numpy image: H x W x C
 94 |         # torch image: C X H X W
 95 |         image = image.transpose((0, 1, 4, 2,3))
 96 |         sample['image']=torch.from_numpy(image)
 97 |         sample['output_label']=torch.from_numpy(output_label)
 98 |         sample['output_S_label']=torch.from_numpy(output_S_label)
 99 |         return sample
100 | 
101 | 
102 | class VinTestDataset(Dataset):
103 |     """Face Landmarks dataset."""
104 | 
105 |     def __init__(self, config, transform=None):
106 | 
107 |         self.config = config
108 |         self.transform = transform
109 | 
110 |     def __len__(self):
111 |         return 1
112 | 
113 |     def __getitem__(self, idx):
114 | 
115 |         total_img = np.zeros((self.config.set_num, int(self.config.frame_num), self.config.height, self.config.weight,
116 |                               self.config.col_dim))
117 |         for i in range(self.config.set_num):
118 |             for j in range(int(self.config.frame_num)):
119 |                 total_img[i, j] = mpimg.imread(self.config.img_folder + "train/" + str(i) + '_' + str(j) + '.png')[:, :,
120 |                                   :self.config.col_dim]
121 |         ts_img = np.zeros(
122 |             (1, int(self.config.frame_num), self.config.height, self.config.weight, self.config.col_dim),
123 |             dtype=float)
124 |         for i in range(1):
125 |             for j in range(int(self.config.frame_num)):
126 |                 ts_img[i, j] = mpimg.imread(self.config.img_folder + "test/" + str(i) + "_" + str(j) + '.png')[:, :,
127 |                                :self.config.col_dim]
128 |         ts_data = np.zeros((1, int(self.config.frame_num), self.config.No * 5), dtype=float)
129 |         for i in range(1):
130 |             f = open(self.config.data_folder + "test/" + str(i) + ".csv", "r")
131 |             ts_data[i] = [line[:-1].split(",") for line in f.readlines()]
132 | 
133 |         # reshape img and data
134 |         input_img = np.zeros(
135 |             (1 * (int(self.config.frame_num) - 14 + 1), 6, self.config.height, self.config.weight,
136 |              self.config.col_dim),
137 |             dtype=float);
138 |         output_label = np.zeros((1 * (int(self.config.frame_num) - 14 + 1), 8, self.config.No, 4), dtype=float)
139 |         output_S_label = np.zeros((1 * (int(self.config.frame_num) - 14 + 1), 4, self.config.No, 4), dtype=float)
140 |         for i in range(1):
141 |             for j in range(int(self.config.frame_num) - 14 + 1):
142 |                 input_img[i * (int(self.config.frame_num) - 14 + 1) + j] = total_img[i, j:j + 6]
143 |                 output_label[i * (int(self.config.frame_num) - 14 + 1) + j] = np.reshape(ts_data[i, j + 6:j + 14],
144 |                                                                                          [8, self.config.No, 5])[:,
145 |                                                                               :, 1:5]
146 |                 output_S_label[i * (int(self.config.frame_num) - 14 + 1) + j] = np.reshape(ts_data[i, j + 2:j + 6],
147 |                                                                                            [4, self.config.No, 5])[
148 |                                                                                 :,
149 |                                                                                 :, 1:5]
150 | 
151 |         xy_origin = output_label[:(int(self.config.frame_num) - 14 + 1 - 4 + 1), 0, :, 0:2]
152 |         xy_estimated = np.zeros((self.config.roll_num, self.config.No, 2), dtype=float)
153 | 
154 |         sample = {'image': input_img, 'output_label': output_label,
155 |                   'output_S_label': output_S_label,'xy_origin':xy_origin,'xy_estimated':xy_estimated}
156 | 
157 |         if self.transform:
158 |             sample = self.transform(sample),
159 | 
160 |         return sample
161 | 
162 | 


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