Offline Datasets res net optionality

datasets/paired_dataset.py

@@ -6,7 +6,7 @@ import torch
 from torch.utils.data import Dataset, ConcatDataset
 
 from datasets.utils import DatasetMapping
-from lib.modules.model_parts import Generator
+from lib.preprocessing.generator import Generator
 from lib.objects.map import Map
 
 

datasets/trajectory_dataset.py

@@ -2,15 +2,50 @@ import shelve
 from pathlib import Path
 from typing import Union, List
 
+import multiprocessing as mp
+
 import torch
 from random import choice
 from torch.utils.data import ConcatDataset, Dataset
 import numpy as np
+from tqdm import tqdm
 
 from lib.objects.map import Map
 import lib.variables as V
 from PIL import Image
 
+from lib.utils.tools import write_to_shelve
+
+
+class TrajDataShelve(Dataset):
+
+    @property
+    def map_shape(self):
+        return self[0][0].shape
+
+    def __init__(self, file_path, **kwargs):
+        super(TrajDataShelve, self).__init__()
+        self._mutex = mp.Lock()
+        self.file_path = str(file_path)
+
+
+    def __len__(self):
+        self._mutex.acquire()
+        with shelve.open(self.file_path) as d:
+            length = len(d)
+            self._mutex.release()
+        return length
+
+    def seed(self):
+        pass
+
+    def __getitem__(self, item):
+        self._mutex.acquire()
+        with shelve.open(self.file_path) as d:
+            sample = d[str(item)]
+            self._mutex.release()
+        return sample
+
 
 class TrajDataset(Dataset):
 
@@ -22,14 +57,15 @@ class TrajDataset(Dataset):
                  length=100000, mode='separated_arrays', embedding_size=None, preserve_equal_samples=False,
                  **kwargs):
         super(TrajDataset, self).__init__()
-        assert mode.lower() in ['vectors', 'all_in_map', 'separated_arrays', 'just_route']
+        assert mode.lower() in ['generator_all_in_map', 'generator_hom_all_in_map'
+                                                        'classifier_all_in_map']
         self.normalized = normalized
         self.preserve_equal_samples = preserve_equal_samples
         self.mode = mode
         self.mapname = mapname if mapname.endswith('.bmp') else f'{mapname}.bmp'
         self.maps_root = maps_root
         self._len = length
-        self.last_label = -1
+        self.last_label = V.ALTERNATIVE if 'hom' in self.mode else choice([-1, V.ALTERNATIVE, V.HOMOTOPIC])
 
         self.map = Map(self.mapname).from_image(self.maps_root / self.mapname, embedding_size=embedding_size)
 
@@ -39,6 +75,7 @@ class TrajDataset(Dataset):
     def __getitem__(self, item):
 
         if self.mode.lower() == 'just_route':
+            raise NotImplementedError
             trajectory = self.map.get_random_trajectory()
             trajectory_space = trajectory.draw_in_array(self.map.shape)
             label = choice([0, 1])
@@ -54,37 +91,41 @@ class TrajDataset(Dataset):
             else:
                 break
 
-        self.last_label = label
+        self.last_label = label if self.mode != ['generator_hom_all_in_map'] else V.ALTERNATIVE
-        if self.mode.lower() in ['all_in_map', 'separated_arrays']:
+        if self.mode.lower() in ['classifier_all_in_map', 'generator_all_in_map']:
             map_array = self.map.as_array
             trajectory = trajectory.draw_in_array(self.map_shape)
             alternative = alternative.draw_in_array(self.map_shape)
-            if self.mode == 'separated_arrays':
+            label_as_array = np.full_like(map_array, label)
             if self.normalized:
                 map_array = map_array / V.WHITE
                 trajectory = trajectory / V.WHITE
                 alternative = alternative / V.WHITE
-                return (map_array, trajectory, label), alternative
+            if self.mode == 'generator_all_in_map':
-            else:
+                return np.concatenate((map_array, trajectory, label_as_array)), alternative
+            elif self.mode == 'classifier_all_in_map':
                 return np.concatenate((map_array, trajectory, alternative)), label
 
-        elif self.mode == 'vectors':
+        elif self.mode == '_vectors':
+            raise NotImplementedError
             return trajectory.vertices, alternative.vertices, label, self.mapname
 
-        else:
+        raise ValueError(f'Mode was: {self.mode}')
-            raise ValueError
+
+    def seed(self, seed):
+        self.map.seed(seed)
 
 
 class TrajData(object):
     @property
     def map_shapes(self):
-        return [dataset.map_shape for dataset in self._dataset.datasets]
+        return [dataset.map_shape for dataset in self._train_dataset.datasets]
 
     @property
     def map_shapes_max(self):
         shapes = self.map_shapes
         shape_list = list(map(max, zip(*shapes)))
-        if self.mode in ['separated_arrays', 'all_in_map']:
+        if '_all_in_map' in self.mode:
             shape_list[0] += 2
         return shape_list
 
@@ -92,36 +133,81 @@ class TrajData(object):
     def name(self):
         return self.__class__.__name__
 
-    def __init__(self, map_root, length=100000, mode='separated_arrays', normalized=True, **_):
+    def __init__(self, map_root, length=100000, mode='separated_arrays', normalized=True, preprocessed=False, **_):
-
+        self.preprocessed = preprocessed
         self.normalized = normalized
         self.mode = mode
         self.maps_root = Path(map_root)
         self.length = length
-        self._dataset = self._load_datasets()
+        self._test_dataset = self._load_datasets('train')
+        self._val_dataset = self._load_datasets('val')
+        self._train_dataset = self._load_datasets('test')
+
+    def _load_datasets(self, dataset_type=''):
 
-    def _load_datasets(self):
         map_files = list(self.maps_root.glob('*.bmp'))
         equal_split = int(self.length // len(map_files)) or 1
 
         # find max image size among available maps:
         max_map_size = (1, ) + tuple(reversed(tuple(map(max, *[Image.open(map_file).size for map_file in map_files]))))
+
+        if self.preprocessed:
+            preprocessed_map_files = list(self.maps_root.glob('*.pik'))
+            preprocessed_map_names = [p.name for p in preprocessed_map_files]
+            datasets = []
+            for map_file in map_files:
+                new_pik_name = f'{dataset_type}_{str(map_file.name)[:-3]}.pik'
+                if dataset_type != 'train':
+                    equal_split *= 0.01
+                if not [f'{new_pik_name[:-3]}.bmp' in preprocessed_map_names]:
+                    traj_dataset = TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
+                                               mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
+                                               preserve_equal_samples=True)
+                    self.dump_n(map_file.parent / new_pik_name, traj_dataset, n=equal_split)
+
+                dataset = TrajDataShelve(map_file.parent / new_pik_name)
+                datasets.append(dataset)
+            return ConcatDataset(datasets)
         return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
                                           mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
                                           preserve_equal_samples=True)
                               for map_file in map_files])
 
+    def kill_em_all(self):
+        for pik_file in self.maps_root.glob('*.pik'):
+            pik_file.unlink()
+            print(pik_file.name, ' was deleted.')
+        print('Done.')
+
+    def seed(self, seed):
+        for dataset in [x.datasets for x in [self._train_dataset, self._test_dataset, self.val_dataset]]:
+            dataset.seed(seed)
+
+    def dump_n(self, file_path, traj_dataset: TrajDataset, n=100000):
+        assert str(file_path).endswith('.pik')
+        processes = mp.cpu_count() - 1
+        mutex = mp.Lock()
+        with mp.Pool(processes) as pool:
+            async_results = [pool.apply_async(traj_dataset.__getitem__, kwds=dict(item=i)) for i in range(n)]
+
+            for result_obj in tqdm(async_results, total=n, desc=f'Generating {n} Samples'):
+                sample = result_obj.get()
+                mutex.acquire()
+                write_to_shelve(file_path, sample)
+                mutex.release()
+        print(f'{n} samples sucessfully dumped to "{file_path}"!')
+
     @property
     def train_dataset(self):
-        return self._dataset
+        return self._train_dataset
 
     @property
     def val_dataset(self):
-        return self._dataset
+        return self._val_dataset
 
     @property
     def test_dataset(self):
-        return self._dataset
+        return self._test_dataset
 
     def get_datasets(self):
-        return self._dataset, self._dataset, self._dataset
+        return self._train_dataset, self._val_dataset, self._test_dataset

lib/models/generators/cnn.py

@@ -1,4 +1,5 @@
-from random import choice
+from random import choices, seed
+import numpy as np
 
 import torch
 from functools import reduce
@@ -36,28 +37,36 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         # kld_loss /= reduce(mul, self.in_shape)
         # kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
 
-        loss = (kld_loss + element_wise_loss) / 2
+        loss = kld_loss + element_wise_loss
         return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
 
     def _test_val_step(self, batch_xy, batch_nb, *args):
         batch_x, _ = batch_xy
-        map_array, trajectory, label = batch_x
+        map_array = batch_x[:, 0].unsqueeze(1)
+        trajectory = batch_x[:, 1].unsqueeze(1)
+        labels = batch_x[:, 2].unsqueeze(1).max(dim=-1).values.max(-1).values
 
-        generated_alternative, z, mu, logvar = self(batch_x)
+        _, mu, _ = self.encode(batch_x)
-
+        generated_alternative = self.generate(mu)
-        return dict(batch_nb=batch_nb,  label=label, generated_alternative=generated_alternative, pred_label=-1)
+        return dict(maps=map_array, trajectories=trajectory, batch_nb=batch_nb,  labels=labels,
+                    generated_alternative=generated_alternative, pred_label=-1)
 
     def _test_val_epoch_end(self, outputs, test=False):
-        maps, trajectories, labels, val_restul_dict = self.generate_random()
+        val_restul_dict = self.generate_random()
 
         from lib.visualization.generator_eval import GeneratorVisualizer
-        g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
+        g = GeneratorVisualizer(**val_restul_dict)
         fig = g.draw()
         self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
         plt.clf()
 
         return dict(epoch=self.current_epoch)
 
+    def on_epoch_start(self):
+        self.dataset.seed(self.logger.version)
+        # torch.random.manual_seed(self.logger.version)
+        # np.random.seed(self.logger.version)
+
     def validation_step(self, *args):
         return self._test_val_step(*args)
 
@@ -75,14 +84,18 @@ class CNNRouteGeneratorModel(LightningBaseModule):
 
         if not issubclassed:
             # Dataset
-            self.dataset = TrajData(self.hparams.data_param.map_root, mode='separated_arrays',
+            self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
+                                    preprocessed=self.hparams.data_param.use_preprocessed,
                                     length=self.hparams.data_param.dataset_length, normalized=True)
             self.criterion = nn.MSELoss()
 
-        # Additional Attributes
+        # Additional Attributes                               #
+        #######################################################
         self.in_shape = self.dataset.map_shapes_max
-        # Todo: Better naming and size in Parameters
+        self.use_res_net = self.hparams.model_param.use_res_net
-        self.feature_dim = self.hparams.model_param.lat_dim * 10
+        self.lat_dim = self.hparams.model_param.lat_dim
+        self.feature_dim = self.lat_dim * 10
+        ########################################################
 
         # NN Nodes
         ###################################################
@@ -93,82 +106,100 @@ class CNNRouteGeneratorModel(LightningBaseModule):
 
         #
         # Map Encoder
-        self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
+        self.enc_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
                                      conv_filters=self.hparams.model_param.filters[0],
                                      use_norm=self.hparams.model_param.use_norm,
                                      use_bias=self.hparams.model_param.use_bias)
 
-        self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
+        self.enc_res_1 = ResidualModule(self.enc_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
                                         conv_padding=2, conv_filters=self.hparams.model_param.filters[0],
                                         use_norm=self.hparams.model_param.use_norm,
                                         use_bias=self.hparams.model_param.use_bias)
-        self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
+        self.enc_conv_1a = ConvModule(self.enc_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[1],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
                                       conv_filters=self.hparams.model_param.filters[1],
                                       use_norm=self.hparams.model_param.use_norm,
                                       use_bias=self.hparams.model_param.use_bias)
 
-        self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
+        self.enc_res_2 = ResidualModule(self.enc_conv_1b.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
                                         conv_padding=2, conv_filters=self.hparams.model_param.filters[1],
                                         use_norm=self.hparams.model_param.use_norm,
                                         use_bias=self.hparams.model_param.use_bias)
-        self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
+        self.enc_conv_2a = ConvModule(self.enc_res_2.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[2],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_2b = ConvModule(self.enc_conv_2a.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
                                       conv_filters=self.hparams.model_param.filters[2],
                                       use_norm=self.hparams.model_param.use_norm,
                                       use_bias=self.hparams.model_param.use_bias)
 
-        self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
+        self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
                                         conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
                                         use_norm=self.hparams.model_param.use_norm,
                                         use_bias=self.hparams.model_param.use_bias)
-        self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=11, conv_stride=1, conv_padding=0,
+        self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[2],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
                                       conv_filters=self.hparams.model_param.filters[2],
                                       use_norm=self.hparams.model_param.use_norm,
                                       use_bias=self.hparams.model_param.use_bias)
 
-        self.map_flat = Flatten(self.map_conv_3.shape)
+        self.enc_flat = Flatten(self.enc_conv_3b.shape)
-        self.map_lin = nn.Linear(reduce(mul, self.map_conv_3.shape), self.feature_dim)
+        self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
 
         #
         # Mixed Encoder
-        self.mixed_lin = nn.Linear(self.feature_dim, self.feature_dim)
+        self.enc_lin_2 = nn.Linear(self.feature_dim, self.feature_dim)
-        self.mixed_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
+        self.enc_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
 
         #
         # Variational Bottleneck
-        self.mu = nn.Linear(self.feature_dim, self.hparams.model_param.lat_dim)
+        self.mu = nn.Linear(self.feature_dim, self.lat_dim)
-        self.logvar = nn.Linear(self.feature_dim, self.hparams.model_param.lat_dim)
+        self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
 
         #
         # Alternative Generator
-        self.alt_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
+        self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
-        # Todo Fix This Hack!!!!
-        reshape_shape = (1, self.map_conv_3.shape[1], self.map_conv_3.shape[2])
 
-        self.alt_lin_2 = nn.Linear(self.feature_dim, reduce(mul, reshape_shape))
+        self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
 
-        self.reshape_to_map = Flatten(reduce(mul, reshape_shape), reshape_shape)
+        self.reshape_to_last_conv = Flatten(self.enc_flat.shape, self.enc_conv_3b.shape)
 
-        self.alt_deconv_1 = DeConvModule(reshape_shape, self.hparams.model_param.filters[2],
+        self.gen_deconv_1a = DeConvModule(self.enc_conv_3b.shape, self.hparams.model_param.filters[2],
-                                         conv_padding=0, conv_kernel=13, conv_stride=1,
+                                          conv_padding=0, conv_kernel=11, conv_stride=1,
                                           use_norm=self.hparams.model_param.use_norm)
-        self.alt_deconv_2 = DeConvModule(self.alt_deconv_1.shape, self.hparams.model_param.filters[1],
+        self.gen_deconv_1b = DeConvModule(self.gen_deconv_1a.shape, self.hparams.model_param.filters[2],
+                                          conv_padding=0, conv_kernel=9, conv_stride=2,
+                                          use_norm=self.hparams.model_param.use_norm)
+
+        self.gen_deconv_2a = DeConvModule(self.gen_deconv_1b.shape, self.hparams.model_param.filters[1],
                                           conv_padding=0, conv_kernel=7, conv_stride=1,
                                           use_norm=self.hparams.model_param.use_norm)
-        self.alt_deconv_3 = DeConvModule(self.alt_deconv_2.shape, self.hparams.model_param.filters[0],
+        self.gen_deconv_2b = DeConvModule(self.gen_deconv_2a.shape, self.hparams.model_param.filters[1],
+                                          conv_padding=0, conv_kernel=7, conv_stride=1,
+                                          use_norm=self.hparams.model_param.use_norm)
+
+        self.gen_deconv_3a = DeConvModule(self.gen_deconv_2b.shape, self.hparams.model_param.filters[0],
                                           conv_padding=1, conv_kernel=5, conv_stride=1,
                                           use_norm=self.hparams.model_param.use_norm)
-        self.alt_deconv_out = DeConvModule(self.alt_deconv_3.shape, 1, activation=None,
+        self.gen_deconv_3b = DeConvModule(self.gen_deconv_3a.shape, self.hparams.model_param.filters[0],
-                                           conv_padding=1, conv_kernel=3, conv_stride=1,
+                                          conv_padding=1, conv_kernel=4, conv_stride=1,
+                                          use_norm=self.hparams.model_param.use_norm)
+
+        self.gen_deconv_out = DeConvModule(self.gen_deconv_3b.shape, 1, activation=None,
+                                           conv_padding=0, conv_kernel=3, conv_stride=1,
                                            use_norm=self.hparams.model_param.use_norm)
 
     def forward(self, batch_x):
-        #
-        # Sorting the Input
-        map_array, trajectory, label = batch_x
-
         #
         # Encode
-        z, mu, logvar = self.encode(map_array, trajectory, label)
+        z, mu, logvar = self.encode(batch_x)
 
         #
         # Generate
@@ -181,42 +212,26 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         eps = torch.randn_like(std)
         return mu + eps * std
 
-    def generate(self, z):
+    def encode(self, batch_x):
-        alt_tensor = self.alt_lin_1(z)
+        combined_tensor = self.enc_conv_0(batch_x)
-        alt_tensor = self.activation(alt_tensor)
+        combined_tensor = self.enc_res_1(combined_tensor) if self.use_res_net else combined_tensor
-        alt_tensor = self.alt_lin_2(alt_tensor)
+        combined_tensor = self.enc_conv_1a(combined_tensor)
-        alt_tensor = self.activation(alt_tensor)
+        combined_tensor = self.enc_conv_1b(combined_tensor)
-        alt_tensor = self.reshape_to_map(alt_tensor)
+        combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
-        alt_tensor = self.alt_deconv_1(alt_tensor)
+        combined_tensor = self.enc_conv_2a(combined_tensor)
-        alt_tensor = self.alt_deconv_2(alt_tensor)
+        combined_tensor = self.enc_conv_2b(combined_tensor)
-        alt_tensor = self.alt_deconv_3(alt_tensor)
+        combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
-        alt_tensor = self.alt_deconv_out(alt_tensor)
+        combined_tensor = self.enc_conv_3a(combined_tensor)
-        # alt_tensor = self.activation(alt_tensor)
+        combined_tensor = self.enc_conv_3b(combined_tensor)
-        alt_tensor = self.sigmoid(alt_tensor)
-        return alt_tensor
 
-    def encode(self, map_array, trajectory, label):
+        combined_tensor = self.enc_flat(combined_tensor)
-        label_array = torch.cat([torch.full((1, 1, self.in_shape[1], self.in_shape[2]), x.item())
+        combined_tensor = self.enc_lin_1(combined_tensor)
-                                 for x in label], dim=0)
+        combined_tensor = self.enc_lin_2(combined_tensor)
-        label_array = self._move_to_model_device(label_array)
-        combined_tensor = torch.cat((map_array, trajectory, label_array), dim=1)
-        combined_tensor = self.map_conv_0(combined_tensor)
-        combined_tensor = self.map_res_1(combined_tensor)
-        combined_tensor = self.map_conv_1(combined_tensor)
-        combined_tensor = self.map_res_2(combined_tensor)
-        combined_tensor = self.map_conv_2(combined_tensor)
-        combined_tensor = self.map_res_3(combined_tensor)
-        combined_tensor = self.map_conv_3(combined_tensor)
 
-        combined_tensor = self.map_flat(combined_tensor)
+        combined_tensor = self.enc_norm(combined_tensor)
-        combined_tensor = self.map_lin(combined_tensor)
-
-        combined_tensor = self.mixed_lin(combined_tensor)
-
-        combined_tensor = self.mixed_norm(combined_tensor)
         combined_tensor = self.activation(combined_tensor)
-        combined_tensor = self.mixed_lin(combined_tensor)
+        combined_tensor = self.enc_lin_2(combined_tensor)
-        combined_tensor = self.mixed_norm(combined_tensor)
+        combined_tensor = self.enc_norm(combined_tensor)
         combined_tensor = self.activation(combined_tensor)
 
         #
@@ -226,19 +241,31 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         z = self.reparameterize(mu, logvar)
         return z, mu, logvar
 
-    def generate_random(self, n=6):
+    def generate(self, z):
-        maps = [self.map_storage[choice(self.map_storage.keys_list)] for _ in range(n)]
+        alt_tensor = self.gen_lin_1(z)
+        alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.gen_lin_2(alt_tensor)
+        alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.reshape_to_last_conv(alt_tensor)
+        alt_tensor = self.gen_deconv_1a(alt_tensor)
+        alt_tensor = self.gen_deconv_1b(alt_tensor)
+        alt_tensor = self.gen_deconv_2a(alt_tensor)
+        alt_tensor = self.gen_deconv_2b(alt_tensor)
+        alt_tensor = self.gen_deconv_3a(alt_tensor)
+        alt_tensor = self.gen_deconv_3b(alt_tensor)
+        alt_tensor = self.gen_deconv_out(alt_tensor)
+        # alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.sigmoid(alt_tensor)
+        return alt_tensor
 
-        trajectories = [x.get_random_trajectory() for x in maps]
+    def generate_random(self, n=12):
-        trajectories = [x.draw_in_array(self.map_storage.max_map_size) for x in trajectories]
-        trajectories = [torch.as_tensor(x, dtype=torch.float32) for x in trajectories] * 2
-        trajectories = self._move_to_model_device(torch.stack(trajectories))
 
-        maps = [torch.as_tensor(x.as_array, dtype=torch.float32) for x in maps] * 2
+        samples, alternatives = zip(*[self.dataset.test_dataset[choice]
-        maps = self._move_to_model_device(torch.stack(maps))
+                                      for choice in choices(range(self.dataset.length), k=n)])
+        samples = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in samples]))
+        alternatives = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in alternatives]))
 
-        labels = self._move_to_model_device(torch.as_tensor([0] * n + [1] * n))
+        return self._test_val_step((samples, alternatives), -9999)
-        return maps, trajectories, labels, self._test_val_step(((maps, trajectories, labels), None), -9999)
 
 
 class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
@@ -329,11 +356,12 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
         self._disc = disc_model
 
     def __init__(self, *params):
+        raise NotImplementedError
         super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
 
         self._disc = None
 
         self.criterion = nn.BCELoss()
 
-        self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
+        self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
                                 length=self.hparams.data_param.dataset_length, normalized=True)

lib/models/generators/recurrent.py

@@ -0,0 +1,348 @@
+from random import choice
+
+import torch
+from functools import reduce
+from operator import mul
+
+from torch import nn
+from torch.optim import Adam
+
+from datasets.trajectory_dataset import TrajData
+from lib.evaluation.classification import ROCEvaluation
+from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
+from lib.modules.utils import LightningBaseModule, Flatten
+
+import matplotlib.pyplot as plt
+
+
+class CNNRouteGeneratorModel(LightningBaseModule):
+
+    name = 'CNNRouteGenerator'
+
+    def configure_optimizers(self):
+        return Adam(self.parameters(), lr=self.hparams.train_param.lr)
+
+    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+        batch_x, alternative = batch_xy
+        generated_alternative, z, mu, logvar = self(batch_x)
+        element_wise_loss = self.criterion(generated_alternative, alternative)
+        # see Appendix B from VAE paper:
+        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+        # https://arxiv.org/abs/1312.6114
+        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+
+        kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+        # Dimensional Resizing TODO: Does This make sense? Sanity Check it!
+        # kld_loss /= reduce(mul, self.in_shape)
+        # kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
+
+        loss = (kld_loss + element_wise_loss) / 2
+        return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
+
+    def _test_val_step(self, batch_xy, batch_nb, *args):
+        batch_x, alternative = batch_xy
+        map_array = batch_x[0]
+        trajectory = batch_x[1]
+        label = batch_x[2].max()
+
+        z, _, _ = self.encode(batch_x)
+        generated_alternative = self.generate(z)
+
+        return dict(map_array=map_array, trajectory=trajectory, batch_nb=batch_nb,  label=label,
+                    generated_alternative=generated_alternative, pred_label=-1, alternative=alternative
+                    )
+
+    def _test_val_epoch_end(self, outputs, test=False):
+        maps, trajectories, labels, val_restul_dict = self.generate_random()
+
+        from lib.visualization.generator_eval import GeneratorVisualizer
+        g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
+        fig = g.draw()
+        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
+        plt.clf()
+
+        return dict(epoch=self.current_epoch)
+
+    def validation_step(self, *args):
+        return self._test_val_step(*args)
+
+    def validation_epoch_end(self, outputs: list):
+        return self._test_val_epoch_end(outputs)
+
+    def test_step(self, *args):
+        return self._test_val_step(*args)
+
+    def test_epoch_end(self, outputs):
+        return self._test_val_epoch_end(outputs, test=True)
+
+    def __init__(self, *params, issubclassed=False):
+        super(CNNRouteGeneratorModel, self).__init__(*params)
+
+        if not issubclassed:
+            # Dataset
+            self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
+                                    length=self.hparams.data_param.dataset_length, normalized=True)
+            self.criterion = nn.MSELoss()
+
+        # Additional Attributes                               #
+        #######################################################
+        self.map_shape = self.dataset.map_shapes_max
+        self.trajectory_features = 4
+        self.res_net = self.hparams.model_param.use_res_net
+        self.lat_dim = self.hparams.model_param.lat_dim
+        self.feature_dim = self.lat_dim * 10
+        ########################################################
+
+        # NN Nodes
+        ###################################################
+        #
+        # Utils
+        self.activation = nn.ReLU()
+        self.sigmoid = nn.Sigmoid()
+
+        #
+        # Map Encoder
+        self.enc_conv_0 = ConvModule(self.map_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
+                                     conv_filters=self.hparams.model_param.filters[0],
+                                     use_norm=self.hparams.model_param.use_norm,
+                                     use_bias=self.hparams.model_param.use_bias)
+
+        self.enc_res_1 = ResidualModule(self.enc_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
+                                        conv_padding=2, conv_filters=self.hparams.model_param.filters[0],
+                                        use_norm=self.hparams.model_param.use_norm,
+                                        use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_1a = ConvModule(self.enc_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[1],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[1],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+
+        self.enc_res_2 = ResidualModule(self.enc_conv_1b.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
+                                        conv_padding=2, conv_filters=self.hparams.model_param.filters[1],
+                                        use_norm=self.hparams.model_param.use_norm,
+                                        use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_2a = ConvModule(self.enc_res_2.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[2],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_2b = ConvModule(self.enc_conv_2a.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[2],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+
+        self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
+                                        conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
+                                        use_norm=self.hparams.model_param.use_norm,
+                                        use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[2],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+        self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
+                                      conv_filters=self.hparams.model_param.filters[2],
+                                      use_norm=self.hparams.model_param.use_norm,
+                                      use_bias=self.hparams.model_param.use_bias)
+
+        # Trajectory Encoder
+        self.env_gru_1 = nn.GRU(input_size=self.trajectory_features, hidden_size=self.feature_dim,
+                                num_layers=3, batch_first=True)
+
+        self.enc_flat = Flatten(self.enc_conv_3b.shape)
+        self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
+
+        #
+        # Mixed Encoder
+        self.enc_lin_2 = nn.Linear(self.feature_dim, self.feature_dim)
+        self.enc_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
+
+        #
+        # Variational Bottleneck
+        self.mu = nn.Linear(self.feature_dim, self.lat_dim)
+        self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
+
+        #
+        # Alternative Generator
+        self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
+
+        self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
+
+        self.gen_gru_x = nn.GRU(None, None, batch_first=True)
+
+
+
+    def forward(self, batch_x):
+        #
+        # Encode
+        z, mu, logvar = self.encode(batch_x)
+
+        #
+        # Generate
+        alt_tensor = self.generate(z)
+        return alt_tensor, z, mu, logvar
+
+    @staticmethod
+    def reparameterize(mu, logvar):
+        std = torch.exp(0.5 * logvar)
+        eps = torch.randn_like(std)
+        return mu + eps * std
+
+    def encode(self, batch_x):
+        combined_tensor = self.enc_conv_0(batch_x)
+        combined_tensor = self.enc_res_1(combined_tensor) if self.use_res_net else combined_tensor
+        combined_tensor = self.enc_conv_1a(combined_tensor)
+        combined_tensor = self.enc_conv_1b(combined_tensor)
+        combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
+        combined_tensor = self.enc_conv_2a(combined_tensor)
+        combined_tensor = self.enc_conv_2b(combined_tensor)
+        combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
+        combined_tensor = self.enc_conv_3a(combined_tensor)
+        combined_tensor = self.enc_conv_3b(combined_tensor)
+
+        combined_tensor = self.enc_flat(combined_tensor)
+        combined_tensor = self.enc_lin_1(combined_tensor)
+        combined_tensor = self.enc_lin_2(combined_tensor)
+
+        combined_tensor = self.enc_norm(combined_tensor)
+        combined_tensor = self.activation(combined_tensor)
+        combined_tensor = self.enc_lin_2(combined_tensor)
+        combined_tensor = self.enc_norm(combined_tensor)
+        combined_tensor = self.activation(combined_tensor)
+
+        #
+        # Parameter and Sampling
+        mu = self.mu(combined_tensor)
+        logvar = self.logvar(combined_tensor)
+        z = self.reparameterize(mu, logvar)
+        return z, mu, logvar
+
+    def generate(self, z):
+        alt_tensor = self.gen_lin_1(z)
+        alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.gen_lin_2(alt_tensor)
+        alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.reshape_to_last_conv(alt_tensor)
+        alt_tensor = self.gen_deconv_1a(alt_tensor)
+        alt_tensor = self.gen_deconv_1b(alt_tensor)
+        alt_tensor = self.gen_deconv_2a(alt_tensor)
+        alt_tensor = self.gen_deconv_2b(alt_tensor)
+        alt_tensor = self.gen_deconv_3a(alt_tensor)
+        alt_tensor = self.gen_deconv_3b(alt_tensor)
+        alt_tensor = self.gen_deconv_out(alt_tensor)
+        # alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.sigmoid(alt_tensor)
+        return alt_tensor
+
+    def generate_random(self, n=6):
+        maps = [self.map_storage[choice(self.map_storage.keys_list)] for _ in range(n)]
+
+        trajectories = [x.get_random_trajectory() for x in maps]
+        trajectories = [x.draw_in_array(self.map_storage.max_map_size) for x in trajectories]
+        trajectories = [torch.as_tensor(x, dtype=torch.float32) for x in trajectories] * 2
+        trajectories = self._move_to_model_device(torch.stack(trajectories))
+
+        maps = [torch.as_tensor(x.as_array, dtype=torch.float32) for x in maps] * 2
+        maps = self._move_to_model_device(torch.stack(maps))
+
+        labels = self._move_to_model_device(torch.as_tensor([0] * n + [1] * n))
+        return maps, trajectories, labels, self._test_val_step(((maps, trajectories, labels), None), -9999)
+
+
+class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
+
+    name = 'CNNRouteGeneratorDiscriminated'
+
+    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+        batch_x, label = batch_xy
+
+        generated_alternative, z, mu, logvar = self(batch_x)
+        map_array, trajectory = batch_x
+
+        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
+        pred_label = self.discriminator(map_stack)
+        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
+
+        # see Appendix B from VAE paper:
+        # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
+        # https://arxiv.org/abs/1312.6114
+        # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
+        kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+        # Dimensional Resizing
+        kld_loss /= reduce(mul, self.in_shape)
+
+        loss = (kld_loss + discriminated_bce_loss) / 2
+        return dict(loss=loss, log=dict(loss=loss,
+                                        discriminated_bce_loss=discriminated_bce_loss,
+                                        kld_loss=kld_loss)
+                    )
+
+    def _test_val_step(self, batch_xy, batch_nb, *args):
+        batch_x, label = batch_xy
+
+        generated_alternative, z, mu, logvar = self(batch_x)
+        map_array, trajectory = batch_x
+
+        map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
+        pred_label = self.discriminator(map_stack)
+
+        discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
+        return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
+                    pred_label=pred_label, label=label, generated_alternative=generated_alternative)
+
+    def validation_step(self, *args):
+        return self._test_val_step(*args)
+
+    def validation_epoch_end(self, outputs: list):
+        return self._test_val_epoch_end(outputs)
+
+    def _test_val_epoch_end(self, outputs, test=False):
+        evaluation = ROCEvaluation(plot_roc=True)
+        pred_label = torch.cat([x['pred_label'] for x in outputs])
+        labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
+        mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
+
+        # Sci-py call ROC eval call is eval(true_label, prediction)
+        roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
+        if test:
+            # self.logger.log_metrics(score_dict)
+            self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
+        plt.clf()
+
+        maps, trajectories, labels, val_restul_dict = self.generate_random()
+
+        from lib.visualization.generator_eval import GeneratorVisualizer
+        g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
+        fig = g.draw()
+        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
+        plt.clf()
+
+        return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
+
+    def test_step(self, *args):
+        return self._test_val_step(*args)
+
+    def test_epoch_end(self, outputs):
+        return self._test_val_epoch_end(outputs, test=True)
+
+    @property
+    def discriminator(self):
+        if self._disc is None:
+            raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
+        return self._disc
+
+    def set_discriminator(self, disc_model):
+        if self._disc is not None:
+            raise RuntimeError('Discriminator has already been set... What are trying to do?')
+        self._disc = disc_model
+
+    def __init__(self, *params):
+        super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
+
+        self._disc = None
+
+        self.criterion = nn.BCELoss()
+
+        self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
+                                length=self.hparams.data_param.dataset_length, normalized=True)

lib/models/homotopy_classification/cnn_based.py

@@ -60,7 +60,7 @@ class ConvHomDetector(LightningBaseModule):
         super(ConvHomDetector, self).__init__(hparams)
 
         # Dataset
-        self.dataset = TrajData(self.hparams.data_param.map_root, mode='all_in_map', )
+        self.dataset = TrajData(self.hparams.data_param.map_root, mode='classifier_all_in_map', )
 
         # Additional Attributes
         self.map_shape = self.dataset.map_shapes_max

lib/modules/utils.py

@@ -22,7 +22,7 @@ class Flatten(nn.Module):
         try:
             x = torch.randn(self.in_shape).unsqueeze(0)
             output = self(x)
-            return output.shape[1:]
+            return output.shape[1:] if len(output.shape[1:]) > 1 else output.shape[-1]
         except Exception as e:
             print(e)
             return -1

lib/objects/map.py

@@ -1,10 +1,9 @@
-import shelve
 from collections import UserDict
 from pathlib import Path
 
 import copy
 from math import sqrt
-from random import choice
+from random import Random
 
 import numpy as np
 
@@ -53,8 +52,12 @@ class Map(object):
             assert array_like_map_representation.ndim == 3
         self.map_array: np.ndarray = array_like_map_representation
         self.name = name
+        self.prng = Random()
         pass
 
+    def seed(self, seed):
+        self.prng.seed(seed)
+
     def __setattr__(self, key, value):
         super(Map, self).__setattr__(key, value)
         if key == 'map_array' and self.map_array is not None:
@@ -102,7 +105,7 @@ class Map(object):
         return trajectory
 
     def get_valid_position(self):
-        valid_position = choice(list(self._G.nodes))
+        valid_position = self.prng.choice(list(self._G.nodes))
         return valid_position
 
     def get_trajectory_from_vertices(self, *args):

lib/preprocessing/generator.py

@@ -20,6 +20,8 @@ class Generator:
 
         self.data_root = Path(data_root)
 
+
+
     def generate_n_trajectories_m_alternatives(self, n, m, datafile_name, processes=0, **kwargs):
         datafile_name = datafile_name if datafile_name.endswith('.pik') else f'{str(datafile_name)}.pik'
         kwargs.update(n=m)

lib/utils/tools.py

@@ -0,0 +1,22 @@
+import pickle
+import shelve
+from pathlib import Path
+
+
+def write_to_shelve(file_path, value):
+    check_path(file_path)
+    file_path.parent.mkdir(exist_ok=True, parents=True)
+    with shelve.open(str(file_path), protocol=pickle.HIGHEST_PROTOCOL) as f:
+        new_key = str(len(f))
+        f[new_key] = value
+
+
+def load_from_shelve(file_path, key):
+    check_path(file_path)
+    with shelve.open(str(file_path)) as d:
+        return d[key]
+
+
+def check_path(file_path):
+    assert isinstance(file_path, Path)
+    assert str(file_path).endswith('.pik')

lib/visualization/generator_eval.py

@@ -5,12 +5,13 @@ import lib.variables as V
 
 class GeneratorVisualizer(object):
 
-    def __init__(self, maps, trajectories, labels, val_result_dict):
+    def __init__(self, **kwargs):
         # val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
-        self.alternatives = val_result_dict['generated_alternative']
+        self.alternatives = kwargs.get('generated_alternative')
-        self.labels = labels
+        self.labels = kwargs.get('labels')
-        self.trajectories = trajectories
+        self.trajectories = kwargs.get('trajectories')
-        self.maps = maps
+        self.maps = kwargs.get('maps')
+
         self._map_width, self._map_height = self.maps[0].squeeze().shape
         self.column_dict_list = self._build_column_dict_list()
         self._cols = len(self.column_dict_list)
@@ -24,10 +25,13 @@ class GeneratorVisualizer(object):
         for idx in range(self.alternatives.shape[0]):
             image = (self.alternatives[idx]).cpu().numpy().squeeze()
             label = self.labels[idx].item()
+            # Dirty and Quick hack incomming.
             if label == V.HOMOTOPIC:
                 hom_alternatives.append(dict(image=image, label='Homotopic'))
+                non_hom_alternatives.append(None)
             else:
                 non_hom_alternatives.append(dict(image=image, label='NonHomotopic'))
+                hom_alternatives.append(None)
         for idx in range(max(len(hom_alternatives), len(non_hom_alternatives))):
             image = (self.maps[idx] + self.trajectories[idx]).cpu().numpy().squeeze()
             label = 'original'
@@ -48,10 +52,13 @@ class GeneratorVisualizer(object):
 
         for idx in range(len(grid.axes_all)):
             row, col = divmod(idx, len(self.column_dict_list))
+            if self.column_dict_list[col][row] is not None:
                 current_image = self.column_dict_list[col][row]['image']
                 current_label = self.column_dict_list[col][row]['label']
                 grid[idx].imshow(current_image)
                 grid[idx].title.set_text(current_label)
+            else:
+                continue
         fig.cbar_mode = 'single'
         fig.tight_layout()
         return fig

main.py

@@ -37,6 +37,7 @@ main_arg_parser.add_argument("--data_dataset_length", type=int, default=100000,
 main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
 main_arg_parser.add_argument("--data_map_root", type=str, default='res/shapes', help="")
 main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
 
 
 # Transformations
@@ -55,9 +56,10 @@ main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerato
 main_arg_parser.add_argument("--model_activation", type=str, default="elu", help="")
 main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
 main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
-main_arg_parser.add_argument("--model_lat_dim", type=int, default=4, help="")
+main_arg_parser.add_argument("--model_lat_dim", type=int, default=8, help="")
 main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
 main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--model_use_res_net", type=strtobool, default=False, help="")
 main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
 
 # Project
@@ -115,7 +117,7 @@ def run_lightning_loop(config_obj):
                           # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting
                           checkpoint_callback=checkpoint_callback,
                           logger=logger,
-                          val_percent_check=0.05,
+                          val_percent_check=0.025,
                           fast_dev_run=config_obj.main.debug,
                           early_stop_callback=None
                           )