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Commit 9353be88 authored by Andi Gerken's avatar Andi Gerken
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Updated models, datasets, index

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automation/upload.sh
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python calculate_model_hashes.py
find /home/andi/blubber_workspace/model_server/public_html/ -type d -exec chmod 755 {} \;
find /home/andi/blubber_workspace/model_server/public_html/ -type f -exec chmod 655 {} \;
rsync -a --delete /home/andi/blubber_workspace/model_server/public_html/ login_zedat:public_html/model_server/
public_html/datasets.json
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......@@ -90,23 +90,5 @@
"max_files": 20
},
"notes": "<span style='color:darkred;'>Should be regenerated to fit the current fish_models version</span>"
},
"pascal_vanilla_couzin":{
"path": "datasets/pascal_vanilla_couzin.pickle",
"date": "21.12.2021",
"dataset_config": {
"data_path": "pascal_vanilla_couzin",
"output_strings":["actions", "poses"]
},
"notes": "<span style='color:darkred;'>Should be regenerated to fit the current fish_models version</span>"
},
"pascal_180fov_couzin":{
"path": "datasets/pascal_180fov_couzin.pickle",
"date": "21.12.2021",
"dataset_config": {
"data_path": "pascal_180fov_couzin",
"output_strings":["actions", "poses"]
},
"notes": "<span style='color:darkred;'>Should be regenerated to fit the current fish_models version</span>"
}
}
}
\ No newline at end of file
public_html/index.php
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......@@ -41,13 +41,13 @@
<th>Date</th>
<th>Dataset</th>
<th>Size</th>
<th>Generated Tracks</th>
<th>Generated Tracks</th>
<th>Evaluation</th>
<th>Video</th>
<th>Model</th>
</tr>
<?php
foreach($models_json as $model_name => $model_args){
foreach(array_reverse($models_json) as $model_name => $model_args){
echo "<tr>";
echo "<td>", $model_name, "</td>";
echo "<td>", $model_args["date"], "</td>";
......@@ -90,37 +90,89 @@
?>
</table>
<div>
<a href = "model_hashes.json">Model hashes (sha1)</a>
</div>
<div>
<h2>Models without training</h2>
Models which don't require training can be generated using the same <code>fish_models.ModelStorage.load_from_model_name("model.class.path(arg=42)")</code> function with the class path and arguments passed as a string (from version 0.3.7).
<ul>
<li><b>BasicFollowModel:</b> A model that always follows. The model turns can only operate with one partner fish in the tank. It will turn towards it smothly and has a constant speed. Normally distributed noise can be added to the turning angle.</br>
<a href='http://agerken.de/fish_models/models/basic_test_models.html#fish_models.models.basic_test_models.BasicFollowModel'><code>fish_models.models.basic_test_models.BasicFollowModel()</code></a>
<li><b>LiveFemaleFemaleConstantSpeedCouzinModel:</b> A constant speed couzin model with tuned parameters to fit the behavior of the live female female data. The behavior is non deterministic.</br>
Example tracks can be found in the documentation:</br>
<a href='http://agerken.de/fish_models/models/released/parametrized.html#fish_models.models.released.parametrized.LiveFemaleFemaleConstantSpeedCouzinModel'><code>fish_models.models.released.parametrized.LiveFemaleFemaleConstantSpeedCouzinModel()</code></a>
</ul>
<h1>Datasets</h1>
<table>
<tr>
<th>Name</th>
<th>Date</th>
<th>Data</th>
<th>Wallray</th>
<th>Fishbins</th>
<th>Output</th>
<th>Reduce dim</th>
<th>Max Files</th>
<th>Size</th>
<th>Download</th>
</tr>
<?php
<a href="model_hashes.json">Model hashes (sha1)</a>
</div>
<div>
<h2>Models without training</h2>
Models which don't require training can be generated using the same
<code>fish_models.ModelStorage.load_from_model_name("model.class.path(arg=42)")</code> function with the
class path and arguments passed as a string (from version 0.3.7).
<ul>
<li><b>BasicFollowModel:</b> A model that always follows. The model turns can only operate with one
partner fish in the tank. It will turn towards it smothly and has a constant speed. Normally
distributed noise can be added to the turning angle.</br>
<a
href='http://agerken.de/fish_models/models/basic_test_models.html#fish_models.models.basic_test_models.BasicFollowModel'><code>fish_models.models.basic_test_models.BasicFollowModel()</code></a>
<li><b>LiveFemaleFemaleConstantSpeedCouzinModel:</b> A constant speed couzin model with tuned parameters
to fit the behavior of the live female female data. The behavior is non deterministic.</br>
Example tracks can be found in the documentation:</br>
<a
href='http://agerken.de/fish_models/models/released/parametrized.html#fish_models.models.released.parametrized.LiveFemaleFemaleConstantSpeedCouzinModel'><code>fish_models.models.released.parametrized.LiveFemaleFemaleConstantSpeedCouzinModel()</code></a>
</ul>
<h1>Raw Data</h1>
<table>
<tr>
<th>Name</th>
<th>Author</th>
<th>Date</th>
<th>Tracking</th>
<th>Size</th>
<th>Folders [Files]</th>
<th>Evaluation</th>
<th>Download</th>
</tr>
<?php
foreach($raw_data_json as $raw_data_name => $rdarg){
if($_GET["raw_data"] == $raw_data_name){
echo "<tr id=$raw_data_name class=active>";
}else{
echo "<tr id=$raw_data_name>";
}
echo "<td>$raw_data_name</td>";
echo "<td>",$rdarg['author'],"</td>";
echo "<td>",$rdarg['date'],"</td>";
echo "<td>",$rdarg['tracking'],"</td>";
echo "<td>",round(filesize($rdarg['path']) / pow(1024, 2),1) ,"MB</td>";
echo "<td><code>";
$folder_str = "";
foreach($rdarg['folders'] as $folder){
$folder_str = $folder_str."$folder: [".(count(scandir($rdarg['path_unpacked']."/$folder")) - 2)."], ";
}
echo substr($folder_str, 0, -2);
echo "</code></td>";
if(file_exists("evaluation/raw_data/".$raw_data_name)){
echo "<td><a href='raw_data_details.php?raw_data=", $raw_data_name, "'>Show</a></td>";
}else{
echo"<td></td>";
}
echo "<td><a href='",$rdarg['path'],"'>Download</a></td>";
echo "</tr>";
if(array_key_exists("notes",$rdarg)){
echo "<tr><td></td><td colspan=9 class='textrow'>".$rdarg["notes"]."</td></tr>";
}
}
?>
</table>
<h1>Datasets (Deprecated?)</h1>
<table>
<tr>
<th>Name</th>
<th>Date</th>
<th>Data</th>
<th>Wallray</th>
<th>Fishbins</th>
<th>Output</th>
<th>Reduce dim</th>
<th>Max Files</th>
<th>Size</th>
<th>Download</th>
</tr>
<?php
foreach($datasets_json as $dataset_name => $darg){
if($_GET["dset"] == $dataset_name){
echo "<tr id=$dataset_name class=active>";
......@@ -154,53 +206,8 @@
}
}
?>
</table>
<h1>Raw Data</h1>
<table>
<tr>
<th>Name</th>
<th>Author</th>
<th>Date</th>
<th>Tracking</th>
<th>Size</th>
<th>Folders [Files]</th>
<th>Evaluation</th>
<th>Download</th>
</tr>
<?php
foreach($raw_data_json as $raw_data_name => $rdarg){
if($_GET["raw_data"] == $raw_data_name){
echo "<tr id=$raw_data_name class=active>";
}else{
echo "<tr id=$raw_data_name>";
}
echo "<td>$raw_data_name</td>";
echo "<td>",$rdarg['author'],"</td>";
echo "<td>",$rdarg['date'],"</td>";
echo "<td>",$rdarg['tracking'],"</td>";
echo "<td>",round(filesize($rdarg['path']) / pow(1024, 2),1) ,"MB</td>";
echo "<td><code>";
$folder_str = "";
foreach($rdarg['folders'] as $folder){
$folder_str = $folder_str."$folder: [".(count(scandir($rdarg['path_unpacked']."/$folder")) - 2)."], ";
}
echo substr($folder_str, 0, -2);
echo "</code></td>";
if(file_exists("evaluation/raw_data/".$raw_data_name)){
echo "<td><a href='raw_data_details.php?raw_data=", $raw_data_name, "'>Show</a></td>";
}else{
echo"<td></td>";
}
echo "<td><a href='",$rdarg['path'],"'>Download</a></td>";
echo "</tr>";
if(array_key_exists("notes",$rdarg)){
echo "<tr><td></td><td colspan=9 class='textrow'>".$rdarg["notes"]."</td></tr>";
}
}
?>
</table>
</div>
</table>
</div>
</body>
</html>
public_html/model_hashes.json
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{"ReplayModel_v0.zip": "2e131cadbf30d78156a5ba4d0e2af0aeafdd6bc3", "GRU_with_conv_15fov.zip": "1be32ccdf9f28d428e11724d8e6f61e6fb1b16af", "KNNModel_v0.zip": "9ffa06b66a1519b7a44b325baddb5ed12e79ac33", "GRU_with_conv_51fov.zip": "f8f4d3729b4c94dfd8ff176802df7d7c1e142d4e", "GRU_without_conv_51_fov.zip": "c16a383de8dca96b5f4758fc66c70535985a0f15", "ClusterModel_v1_0.zip": "6148edf3820ba1a3227b192bd15a0f83d2c26d46", "ClassificationModel_v0.zip": "8945a0156dec3bc2a7269060c5b0a51d0525f0db", "ConvLinearDropoutClassifier_v0.zip": "8fc3561a93774f9187ae33ce9ed8ee226943533f", "ConvLinearDropoutClassifier_v1_trial_399.zip": "ec8fabecef782fc2e0f741dba7e0e2b9e711b2da", "ConvLinearDropoutClassifier_v1_trial_405.zip": "e283c494115e815ec8f5f064070bf0087654212b", "ClusterModel_v1_1.zip": "2de7e594c695b558644e19abd56d279bfb093fce", "ClusterModel_v0.zip": "52d184745f1f7879f7244845588376c314ef8b4a"}
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{"ClusterModel_v0.zip": "52d184745f1f7879f7244845588376c314ef8b4a", "ClusterModel_v1_1.zip": "2de7e594c695b558644e19abd56d279bfb093fce", "ConvLinearDropoutClassifier_v1_trial_405.zip": "e283c494115e815ec8f5f064070bf0087654212b", "ConvLinearDropoutClassifier_v0.zip": "8fc3561a93774f9187ae33ce9ed8ee226943533f", "GRU_with_conv_51fov.zip": "f8f4d3729b4c94dfd8ff176802df7d7c1e142d4e", "ConvLinearDropoutClassifier_v1_trial_399.zip": "ec8fabecef782fc2e0f741dba7e0e2b9e711b2da", "ClassificationModel_v0.zip": "8945a0156dec3bc2a7269060c5b0a51d0525f0db", "GRU_with_conv_15fov.zip": "1be32ccdf9f28d428e11724d8e6f61e6fb1b16af", "ClusterModel_v1_0.zip": "6148edf3820ba1a3227b192bd15a0f83d2c26d46", "ConvLinearDropoutClassifier_v1_full_dataset.zip": "c95e411b80ed5d2f2aaa1faf865dde3f86a313db", "KNNModel_v0.zip": "9ffa06b66a1519b7a44b325baddb5ed12e79ac33", "GRU_without_conv_51_fov.zip": "c16a383de8dca96b5f4758fc66c70535985a0f15", "ReplayModel_v0.zip": "2e131cadbf30d78156a5ba4d0e2af0aeafdd6bc3"}
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public_html/models.json
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......@@ -98,5 +98,15 @@
"live_female_female/cherry_train",
"live_female_female/cherry_test"
]
},
"ConvLinearDropoutClassifier_v1_full_dataset": {
"path": "models/ConvLinearDropoutClassifier_v1_full_dataset.zip",
"date": "10.05.2022",
"notes": "The ConvLinearDropoutClsasifier trained to the full live_female_female dataset.",
"dataset": "live_female_female",
"evaluation_folders": [
"live_female_female/train",
"live_female_female/test"
]
}
}
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}
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