New Dataset Generator, How to differentiate the loss function?

.gitignore

@@ -0,0 +1,71 @@
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
+.idea/**/dataSources/
+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
+# .idea/compiler.xml
+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
+# mpeltonen/sbt-idea plugin
+.idea_modules/
+
+# JIRA plugin
+atlassian-ide-plugin.xml
+
+# Cursive Clojure plugin
+.idea/replstate.xml
+
+# Crashlytics plugin (for Android Studio and IntelliJ)
+com_crashlytics_export_strings.xml
+crashlytics.properties
+crashlytics-build.properties
+fabric.properties
+
+# Editor-based Rest Client
+.idea/httpRequests
+
+# Android studio 3.1+ serialized cache file
+.idea/caches/build_file_checksums.ser

.idea/deployment.xml

@@ -1,18 +1,11 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="PublishConfigData" autoUpload="On explicit save action" serverName="traj_gen-AiMachine">
+  <component name="PublishConfigData" autoUpload="On explicit save action" serverName="steffen@aimachine:22">
     <serverData>
-      <paths name="ErLoWa-AiMachine">
+      <paths name="steffen@aimachine:22">
         <serverdata>
           <mappings>
-            <mapping local="$PROJECT_DIR$" web="/" />
-          </mappings>
-        </serverdata>
-      </paths>
-      <paths name="traj_gen-AiMachine">
-        <serverdata>
-          <mappings>
-            <mapping deploy="/hom_traj_gen" local="$PROJECT_DIR$" web="/" />
+            <mapping deploy="\" local="$PROJECT_DIR$" />
           </mappings>
         </serverdata>
       </paths>

.idea/dictionaries/steffen.xml

@@ -3,8 +3,10 @@
     <words>
       <w>conv</w>
       <w>homotopic</w>
+      <w>hparams</w>
       <w>hyperparamter</w>
       <w>numlayers</w>
+      <w>traj</w>
     </words>
   </dictionary>
 </component>

.idea/hom_traj_gen.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="traj_gen@AiMachine" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Remote Python 3.7.6 (sftp://steffen@aimachine:22/home/steffen/envs/traj_gen/bin/python)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="traj_gen@AiMachine" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Remote Python 3.7.6 (sftp://steffen@aimachine:22/home/steffen/envs/traj_gen/bin/python)" project-jdk-type="Python SDK" />
 </project>

dataset/dataset.py

@@ -1,28 +1,31 @@
 import shelve
 from pathlib import Path
+from typing import Union
 
 import torch
+from random import choice
 from torch.utils.data import ConcatDataset, Dataset
 
 from lib.objects.map import Map
 from lib.preprocessing.generator import Generator
 
 
-class TrajDataset(Dataset):
+class TrajPairDataset(Dataset):
     def __init__(self, data):
-        super(TrajDataset, self).__init__()
+        super(TrajPairDataset, self).__init__()
         self.alternatives = data['alternatives']
         self.trajectory = data['trajectory']
         self.labels = data['labels']
+        self.mapname = data['map']['name'][4:] if data['map']['name'].startswith('map_') else data['map']['name']
 
     def __len__(self):
         return len(self.alternatives)
 
     def __getitem__(self, item):
-        return self.trajectory.vertices, self.alternatives[item].vertices, self.labels[item]
+        return self.trajectory.vertices, self.alternatives[item].vertices, self.labels[item], self.mapname
 
 
-class DataSetMapping(Dataset):
+class DatasetMapping(Dataset):
     def __init__(self, dataset, mapping):
         self._dataset = dataset
         self._mapping = mapping
@@ -34,12 +37,12 @@ class DataSetMapping(Dataset):
         return self._dataset[self._mapping[item]]
 
 
-class TrajData(object):
+class TrajPairData(object):
     @property
     def name(self):
         return self.__class__.__name__
 
-    def __init__(self, data_root, mapname='tate_sw', trajectories=1000, alternatives=10,
+    def __init__(self, data_root, map_root: Union[Path, str] = '', mapname='tate_sw', trajectories=1000, alternatives=10,
                  train_val_test_split=(0.6, 0.2, 0.2), rebuild=False, equal_samples=True, **_):
 
         self.rebuild = rebuild
@@ -49,13 +52,13 @@ class TrajData(object):
         self.mapname = mapname
         self.train_split, self.val_split, self.test_split = train_val_test_split
         self.data_root = Path(data_root)
+        self.maps_root = Path(data_root) if data_root else Path() / 'res' / 'maps'
         self._dataset = None
         self._dataset, self._train_map, self._val_map, self._test_map = self._load_dataset()
 
     def _build_data_on_demand(self):
-        maps_root = Path() / 'res' / 'maps'
-        map_object = Map(self.mapname).from_image(maps_root / f'{self.mapname}.bmp')
-        assert maps_root.exists()
+        map_object = Map(self.mapname).from_image(self.maps_root / f'{self.mapname}.bmp')
+        assert self.maps_root.exists()
         dataset_file = Path(self.data_root) / f'{self.mapname}.pik'
         if dataset_file.exists() and self.rebuild:
             dataset_file.unlink()
@@ -68,7 +71,7 @@ class TrajData(object):
     def _load_dataset(self):
         assert self._build_data_on_demand()
         with shelve.open(str(self.data_root / f'{self.mapname}.pik')) as d:
-            dataset = ConcatDataset([TrajDataset(d[key]) for key in d.keys() if key != 'map'])
+            dataset = ConcatDataset([TrajPairDataset(d[key]) for key in d.keys() if key != 'map'])
             indices = torch.randperm(len(dataset))
 
             train_size = int(len(dataset) * self.train_split)
@@ -82,15 +85,50 @@ class TrajData(object):
 
     @property
     def train_dataset(self):
-        return DataSetMapping(self._dataset, self._train_map)
+        return DatasetMapping(self._dataset, self._train_map)
 
     @property
     def val_dataset(self):
-        return DataSetMapping(self._dataset, self._val_map)
+        return DatasetMapping(self._dataset, self._val_map)
 
     @property
     def test_dataset(self):
-        return DataSetMapping(self._dataset, self._test_map)
+        return DatasetMapping(self._dataset, self._test_map)
 
     def get_datasets(self):
         return self.train_dataset, self.val_dataset, self.test_dataset
+
+
+class TrajDataset(Dataset):
+
+    def __init__(self, data_root, maps_root: Union[Path, str] = '', mapname='tate_sw', length=100.000, **_):
+        super(TrajDataset, self).__init__()
+        self.mapname = mapname
+        self.maps_root = maps_root
+        self.data_root = data_root
+        self._len = length
+
+        self._map_obj = Map(self.mapname).from_image(self.maps_root / f'{self.mapname}.bmp')
+
+    def __len__(self):
+        return self._len
+
+    def __getitem__(self, item):
+        trajectory = self._map_obj.get_random_trajectory()
+        label = choice([0, 1])
+        return trajectory.vertices, None, label, self.mapname
+
+    @property
+    def train_dataset(self):
+        return self
+
+    @property
+    def val_dataset(self):
+        return self
+
+    @property
+    def test_dataset(self):
+        return self
+
+    def get_datasets(self):
+        return self, self, self

lib/models/blocks.py

@@ -12,7 +12,8 @@ import pytorch_lightning as pl
 ###################
 from torch.utils.data import DataLoader
 
-from dataset.dataset import TrajData
+from dataset.dataset import TrajDataset
+from lib.objects.map import MapStorage
 
 
 class Flatten(nn.Module):
@@ -77,7 +78,8 @@ class LightningBaseModule(pl.LightningModule, ABC):
         # Data loading
         # =============================================================================
         # Dataset
-        self.dataset = TrajData('data')
+        self.dataset = TrajDataset('data')
+        self.map_storage = MapStorage(self.hparams.data_param.map_root)
 
     def size(self):
         return self.shape
@@ -176,6 +178,17 @@ class MergingLayer(nn.Module):
         return
 
 
+class FlipTensor(nn.Module):
+    def __init__(self, dim=-2):
+        super(FlipTensor, self).__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        idx = [i for i in range(x.size(self.dim) - 1, -1, -1)]
+        idx = torch.as_tensor(idx).long()
+        inverted_tensor = x.index_select(self.dim, idx)
+        return inverted_tensor
+
 #
 # Sub - Modules
 ###################

lib/models/cnn.py

@@ -3,9 +3,7 @@ from lib.models.blocks import RecurrentModule, ConvModule, DeConvModule, Generat
 
 class CNNRouteGeneratorModel(LightningBaseModule):
 
-    @classmethod
-    def name(cls):
-        pass
+    name = 'CNNRouteGenerator'
 
     def configure_optimizers(self):
         pass

lib/models/full.py

@@ -0,0 +1,49 @@
+from lib.models.blocks import RecurrentModule, ConvModule, DeConvModule, Generator, LightningBaseModule
+from lib.models.losses import BinaryHomotopicLoss
+from lib.objects.map import Map
+from lib.objects.trajectory import Trajectory
+
+import torch
+import torch.functional as F
+import torch.nn as nn
+
+nn.MSELoss
+
+class LinearRouteGeneratorModel(LightningBaseModule):
+
+    name = 'LinearRouteGenerator'
+
+    def configure_optimizers(self):
+        pass
+
+    def validation_step(self, *args, **kwargs):
+        pass
+
+    def validation_end(self, outputs):
+        pass
+
+    def training_step(self, batch, batch_nb, *args, **kwargs):
+        # Type Annotation
+        traj_x: Trajectory
+        traj_o: Trajectory
+        label_x: int
+        map_name: str
+        map_x: Map
+        # Batch unpacking
+        traj_x, traj_o, label_x, map_name = batch
+        map_x = self.map_storage[map_name]
+        pred_y = self(map_x, traj_x, label_x)
+
+        loss = self.loss(traj_x, pred_y)
+        return dict(loss=loss, log=dict(loss=loss))
+
+    def test_step(self, *args, **kwargs):
+        pass
+
+    def __init__(self, *params):
+        super(LinearRouteGeneratorModel, self).__init__(*params)
+
+        self.loss = BinaryHomotopicLoss(self.map_storage)
+
+    def forward(self, map_x, traj_x, label_x):
+        pass

lib/models/losses.py

@@ -0,0 +1,21 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+import pytorch_lightning as pl
+
+from lib.models.blocks import FlipTensor
+from lib.objects.map import MapStorage
+
+
+class BinaryHomotopicLoss(nn.Module):
+    def __init__(self, map_storage: MapStorage):
+        super(BinaryHomotopicLoss, self).__init__()
+        self.map_storage = map_storage
+        self.flipper = FlipTensor()
+
+    def forward(self, x:torch.Tensor, y: torch.Tensor, mapnames: str):
+        y_flipepd = self.flipper(y)
+        circle = torch.cat((x, y_flipepd), dim=-1)
+        masp = self.map_storage[mapname].are
+
+

lib/objects/map.py

@@ -1,4 +1,7 @@
+import shelve
 from pathlib import Path
+from collections import UserDict
+
 
 import copy
 from math import sqrt
@@ -130,3 +133,30 @@ class Map(object):
         # https: // matplotlib.org / api / pyplot_summary.html?highlight = colormaps
         img = ax.imshow(self.as_array, cmap='Greys_r')
         return dict(img=img, fig=fig, ax=ax)
+
+
+class MapStorage(object):
+
+    def __init__(self, map_root, load_all=False):
+        self.data = dict()
+        self.map_root = Path(map_root)
+        if load_all:
+            for map_file in self.map_root.glob('*.bmp'):
+                _ = self[map_file.name]
+
+    def __getitem__(self, item):
+        if item in hasattr(self, item):
+            return self.__getattribute__(item)
+        else:
+            with shelve.open(self.map_root / f'{item}.pik', flag='r') as d:
+                self.__setattr__(item, d['map']['map'])
+        return self[item]
+
+
+
+
+
+
+
+
+

lib/preprocessing/generator.py

@@ -2,15 +2,10 @@ import multiprocessing as mp
 import pickle
 import shelve
 from collections import defaultdict
-from functools import partial
 
 from pathlib import Path
-from typing import Union
-
-from tqdm import trange
 
 from lib.objects.map import Map
-from lib.utils.parallel import run_n_in_parallel
 
 
 class Generator:
@@ -109,7 +104,7 @@ class Generator:
                                               trajectory=trajectory,
                                               labels=labels)
             if 'map' not in f:
-                f['map'] = dict(map=self.map, name=f'map_{self.map.name}')
+                f['map'] = dict(map=self.map, name=self.map.name)
 
     @staticmethod
     def _remove_unequal(hom_dict):

main.py

@@ -10,7 +10,7 @@ import warnings
 from pytorch_lightning import Trainer
 from torch.utils.data import DataLoader
 
-from dataset.dataset import TrajData
+from dataset.dataset import TrajPairData
 from lib.utils.config import Config
 from lib.utils.logging import Logger
 
@@ -32,7 +32,8 @@ main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
 # Data Parameters
 main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
 main_arg_parser.add_argument("--data_batchsize", type=int, default=100, help="")
-main_arg_parser.add_argument("--data_root", type=str, default='../data/rpoot', help="")
+main_arg_parser.add_argument("--data_root", type=str, default='/data/', help="")
+main_arg_parser.add_argument("--map_root", type=str, default='/res/maps', help="")
 
 # Transformations
 main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
@@ -65,7 +66,7 @@ config = Config.read_namespace(args)
 # TESTING ONLY #
 # =============================================================================
 hparams = config.model_paramters
-dataset = TrajData('data', mapname='tate', alternatives=10000, trajectories=2500)
+dataset = TrajPairData('data', mapname='tate', alternatives=10000, trajectories=2500)
 dataloader = DataLoader(dataset=dataset.train_dataset, shuffle=True,
                         batch_size=hparams.data_param.batchsize,
                         num_workers=hparams.data_param.worker)