Kurz vorm durchdrehen

datasets/trajectory_dataset.py

@@ -18,10 +18,12 @@ class TrajDataset(Dataset):
     def map_shape(self):
         return self.map.as_array.shape
 
-    def __init__(self, *args, maps_root: Union[Path, str] = '', mapname='tate_sw',
+    def __init__(self, *args, maps_root: Union[Path, str] = '', mapname='tate_sw', normalized=True,
-                 length=100000, mode='separated_arrays', embedding_size=None, preserve_equal_samples=False, **kwargs):
+                 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']
+        self.normalized = normalized
         self.preserve_equal_samples = preserve_equal_samples
         self.mode = mode
         self.mapname = mapname if mapname.endswith('.bmp') else f'{mapname}.bmp'
@@ -58,6 +60,10 @@ class TrajDataset(Dataset):
             trajectory = trajectory.draw_in_array(self.map_shape)
             alternative = alternative.draw_in_array(self.map_shape)
             if self.mode == 'separated_arrays':
+                if self.normalized:
+                    map_array = map_array / V.WHITE
+                    trajectory = trajectory / V.WHITE
+                    alternative = alternative / V.WHITE
                 return (map_array, trajectory, label), alternative
             else:
                 return np.concatenate((map_array, trajectory, alternative)), label
@@ -86,8 +92,9 @@ class TrajData(object):
     def name(self):
         return self.__class__.__name__
 
-    def __init__(self, map_root, length=100000, mode='separated_arrays', **_):
+    def __init__(self, map_root, length=100000, mode='separated_arrays', normalized=True, **_):
 
+        self.normalized = normalized
         self.mode = mode
         self.maps_root = Path(map_root)
         self.length = length
@@ -100,7 +107,7 @@ class TrajData(object):
         # 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]))))
         return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
-                                          mode=self.mode, embedding_size=max_map_size,
+                                          mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
                                           preserve_equal_samples=True)
                               for map_file in map_files])
 

lib/models/generators/cnn.py

@@ -27,20 +27,21 @@ class CNNRouteGeneratorModel(LightningBaseModule):
     def training_step(self, batch_xy, batch_nb, *args, **kwargs):
         batch_x, alternative = batch_xy
         generated_alternative, z, mu, logvar = self(batch_x)
-        mse_loss = self.criterion(generated_alternative, alternative)
+        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 /= reduce(mul, self.in_shape)
 
-        loss = (kld_loss + mse_loss) / 2
+        loss = (kld_loss + element_wise_loss) / 2
-        return dict(loss=loss, log=dict(loss=loss, mse_loss=mse_loss, kld_loss=kld_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, alternative = batch_xy
+        batch_x, _ = batch_xy
         map_array, trajectory, label = batch_x
 
         generated_alternative, z, mu, logvar = self(batch_x)
@@ -48,18 +49,12 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         return dict(batch_nb=batch_nb,  label=label, generated_alternative=generated_alternative, pred_label=-1)
 
     def _test_val_epoch_end(self, outputs, test=False):
-
-        labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
-
-        if test:
-            self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf())
-        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)
+        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
 
         return dict(epoch=self.current_epoch)
 
@@ -81,69 +76,88 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         if not issubclassed:
             # Dataset
             self.dataset = TrajData(self.hparams.data_param.map_root, mode='separated_arrays',
-                                    length=self.hparams.data_param.dataset_length)
+                                    length=self.hparams.data_param.dataset_length, normalized=True)
             self.criterion = nn.MSELoss()
 
         # Additional Attributes
         self.in_shape = self.dataset.map_shapes_max
         # Todo: Better naming and size in Parameters
-        self.feature_dim = 10
+        self.feature_dim = self.hparams.model_param.lat_dim * 10
-        self.lat_dim = self.feature_dim + self.feature_dim + 1
+        self.feature_mixed_dim = self.feature_dim + self.feature_dim + 1
 
         # NN Nodes
         ###################################################
         #
         # Utils
-        self.relu = nn.ReLU()
+        self.activation = nn.ReLU()
         self.sigmoid = nn.Sigmoid()
 
         #
         # Map Encoder
         self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
-                                     conv_filters=self.hparams.model_param.filters[0])
+                                     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=3, conv_stride=1,
-                                        conv_padding=1, conv_filters=self.hparams.model_param.filters[0])
+                                        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_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
-                                     conv_filters=self.hparams.model_param.filters[1])
+                                     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=3, conv_stride=1,
-                                        conv_padding=1, conv_filters=self.hparams.model_param.filters[1])
+                                        conv_padding=1, 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=3, conv_stride=1, conv_padding=0,
-                                     conv_filters=self.hparams.model_param.filters[2])
+                                     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=3, conv_stride=1,
-                                        conv_padding=1, conv_filters=self.hparams.model_param.filters[2])
+                                        conv_padding=1, 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=5, conv_stride=1, conv_padding=0,
-                                     conv_filters=self.hparams.model_param.filters[2]*2)
+                                     conv_filters=self.hparams.model_param.filters[2]*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.map_lin = nn.Linear(reduce(mul, self.map_conv_3.shape), self.feature_dim)
 
         #
         # Trajectory Encoder
         self.traj_conv_1 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=0,
-                                      conv_filters=self.hparams.model_param.filters[0])
+                                      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.traj_conv_2 = ConvModule(self.traj_conv_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
-                                      conv_filters=self.hparams.model_param.filters[0])
+                                      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.traj_conv_3 = ConvModule(self.traj_conv_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
-                                      conv_filters=self.hparams.model_param.filters[0])
+                                      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.traj_flat = Flatten(self.traj_conv_3.shape)
-
         self.traj_lin = nn.Linear(reduce(mul, self.traj_conv_3.shape), self.feature_dim)
 
         #
         # Mixed Encoder
-        self.mixed_lin = nn.Linear(self.lat_dim, self.lat_dim)
+        self.mixed_lin = nn.Linear(self.feature_mixed_dim, self.feature_mixed_dim)
+        self.mixed_norm = nn.BatchNorm1d(self.feature_mixed_dim) if self.hparams.model_param.use_norm else lambda x: x
 
         #
         # Variational Bottleneck
-        self.mu = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
+        self.mu = nn.Linear(self.feature_mixed_dim, self.hparams.model_param.lat_dim)
-        self.logvar = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
+        self.logvar = nn.Linear(self.feature_mixed_dim, self.hparams.model_param.lat_dim)
 
         #
         # Alternative Generator
@@ -153,13 +167,17 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         self.reshape_to_map = Flatten(reduce(mul, self.traj_conv_3.shape), self.traj_conv_3.shape)
 
         self.alt_deconv_1 = DeConvModule(self.traj_conv_3.shape, self.hparams.model_param.filters[2],
-                                         conv_padding=0, conv_kernel=5, conv_stride=1)
+                                         conv_padding=0, conv_kernel=5, 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],
-                                         conv_padding=0, conv_kernel=3, conv_stride=1)
+                                         conv_padding=0, conv_kernel=3, 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],
-                                         conv_padding=1, conv_kernel=3, conv_stride=1)
+                                         conv_padding=1, conv_kernel=3, conv_stride=1,
+                                         use_norm=self.hparams.model_param.use_norm)
         self.alt_deconv_out = DeConvModule(self.alt_deconv_3.shape, 1, activation=None,
-                                           conv_padding=1, conv_kernel=3, conv_stride=1)
+                                           conv_padding=1, conv_kernel=3, conv_stride=1,
+                                           use_norm=self.hparams.model_param.use_norm)
 
     def forward(self, batch_x):
         #
@@ -173,7 +191,6 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         #
         # Generate
         alt_tensor = self.generate(z)
-
         return alt_tensor, z, mu, logvar
 
     @staticmethod
@@ -184,13 +201,16 @@ class CNNRouteGeneratorModel(LightningBaseModule):
 
     def generate(self, z):
         alt_tensor = self.alt_lin_1(z)
+        alt_tensor = self.activation(alt_tensor)
         alt_tensor = self.alt_lin_2(alt_tensor)
+        alt_tensor = self.activation(alt_tensor)
         alt_tensor = self.reshape_to_map(alt_tensor)
         alt_tensor = self.alt_deconv_1(alt_tensor)
         alt_tensor = self.alt_deconv_2(alt_tensor)
         alt_tensor = self.alt_deconv_3(alt_tensor)
         alt_tensor = self.alt_deconv_out(alt_tensor)
-        # alt_tensor = self.sigmoid(alt_tensor)
+        # alt_tensor = self.activation(alt_tensor)
+        alt_tensor = self.sigmoid(alt_tensor)
         return alt_tensor
 
     def encode(self, map_array, trajectory, label):
@@ -211,23 +231,26 @@ class CNNRouteGeneratorModel(LightningBaseModule):
         traj_tensor = self.traj_lin(traj_tensor)
 
         mixed_tensor = torch.cat((map_tensor, traj_tensor, label.float().unsqueeze(-1)), dim=1)
-        mixed_tensor = self.relu(mixed_tensor)
+        mixed_tensor = self.mixed_norm(mixed_tensor)
+        mixed_tensor = self.activation(mixed_tensor)
         mixed_tensor = self.mixed_lin(mixed_tensor)
-        mixed_tensor = self.relu(mixed_tensor)
+        mixed_tensor = self.mixed_norm(mixed_tensor)
+        mixed_tensor = self.activation(mixed_tensor)
 
         #
         # Parameter and Sampling
         mu = self.mu(mixed_tensor)
         logvar = self.logvar(mixed_tensor)
+        # logvar = torch.clamp(logvar, min=0, max=10)
         z = self.reparameterize(mu, logvar)
         return z, mu, logvar
 
     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] * 2
+        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]
+        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
@@ -294,14 +317,14 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
         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())
+            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)
+        self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
 
         return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
 
@@ -330,4 +353,4 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
         self.criterion = nn.BCELoss()
 
         self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
-                                length=self.hparams.data_param.dataset_length)
+                                length=self.hparams.data_param.dataset_length, normalized=True)

lib/modules/blocks.py

@@ -4,11 +4,11 @@ import torch
 from torch import nn
 from lib.modules.utils import AutoPad, Interpolate
 
+
 #
 # Sub - Modules
 ###################
 
-
 class ConvModule(nn.Module):
 
     @property
@@ -60,7 +60,7 @@ class DeConvModule(nn.Module):
     def __init__(self, in_shape, conv_filters=3, conv_kernel=5, conv_stride=1, conv_padding=0,
                  dropout: Union[int, float] = 0, autopad=False,
                  activation: Union[None, nn.Module] = nn.ReLU, interpolation_scale=None,
-                 use_bias=True, normalize=False):
+                 use_bias=True, use_norm=False):
         super(DeConvModule, self).__init__()
         in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
         self.padding = conv_padding
@@ -70,7 +70,7 @@ class DeConvModule(nn.Module):
 
         self.autopad = AutoPad() if autopad else lambda x: x
         self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
-        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else lambda x: x
+        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if use_norm else lambda x: x
         self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
         self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, conv_kernel, bias=use_bias,
                                           padding=self.padding, stride=self.stride)

lib/modules/model_parts.py

@@ -24,7 +24,7 @@ class Generator(nn.Module):
         self.lat_dim = lat_dim
         self.dropout = dropout
         self.l1 = nn.Linear(self.lat_dim, reduce(mul, re_shape), bias=use_bias)
-        # re_shape = (self.lat_dim // reduce(mul, re_shape[1:]), ) + tuple(re_shape[1:])
+        # re_shape = (self.feature_mixed_dim // reduce(mul, re_shape[1:]), ) + tuple(re_shape[1:])
 
         self.flat = Flatten(to=re_shape)
 

lib/modules/utils.py

@@ -67,6 +67,23 @@ class AutoPad(nn.Module):
         return x
 
 
+class WeightInit:
+
+    def __init__(self, in_place_init_function):
+        self.in_place_init_function = in_place_init_function
+
+    def __call__(self, m):
+        if hasattr(m, 'weight'):
+            if isinstance(m.weight, torch.Tensor):
+                if m.weight.ndim < 2:
+                    m.weight.data.fill_(0.01)
+                else:
+                    self.in_place_init_function(m.weight)
+        if hasattr(m, 'bias'):
+            if isinstance(m.bias, torch.Tensor):
+                m.bias.data.fill_(0.01)
+
+
 class LightningBaseModule(pl.LightningModule, ABC):
 
     @classmethod
@@ -128,15 +145,9 @@ class LightningBaseModule(pl.LightningModule, ABC):
     def test_epoch_end(self, outputs):
         raise NotImplementedError
 
-    def init_weights(self):
+    def init_weights(self, in_place_init_func_=nn.init.xavier_uniform_):
-        def _weight_init(m):
+        weight_initializer = WeightInit(in_place_init_function=in_place_init_func_)
-            if hasattr(m, 'weight'):
+        self.apply(weight_initializer)
-                if isinstance(m.weight, torch.Tensor):
-                    torch.nn.init.xavier_uniform_(m.weight)
-            if hasattr(m, 'bias'):
-                if isinstance(m.bias, torch.Tensor):
-                    m.bias.data.fill_(0.01)
-        self.apply(_weight_init)
 
     # Dataloaders
     # ================================================================================

lib/utils/config.py

@@ -8,6 +8,7 @@ from pathlib import Path
 from lib.models.generators.cnn import CNNRouteGeneratorModel, CNNRouteGeneratorDiscriminated
 from lib.models.homotopy_classification.cnn_based import ConvHomDetector
 from lib.utils.model_io import ModelParameters
+from lib.utils.transforms import AsArray
 
 
 def is_jsonable(x):

lib/utils/logging.py

@@ -5,10 +5,12 @@ from pytorch_lightning.loggers.neptune import NeptuneLogger
 from pytorch_lightning.loggers.test_tube import TestTubeLogger
 
 from lib.utils.config import Config
-
+import numpy as np
 
 class Logger(LightningLoggerBase):
 
+    media_dir = 'media'
+
     @property
     def experiment(self):
         if self.debug:
@@ -84,7 +86,9 @@ class Logger(LightningLoggerBase):
 
     def log_image(self, name, image, **kwargs):
         self.neptunelogger.log_image(name, image, **kwargs)
-        image.savefig(self.log_dir / name)
+        step = kwargs.get('step', None)
+        name = f'{step}_{name}' if step is not None else name
+        image.savefig(self.log_dir / self.media_dir / name)
 
     def save(self):
         self.testtubelogger.save()

lib/utils/transforms.py

@@ -8,5 +8,4 @@ class AsArray(object):
 
     def __call__(self, x):
         array = np.zeros((self.width, self.height))
-
         return array

lib/variables.py

@@ -5,3 +5,5 @@ HOMOTOPIC = 1
 ALTERNATIVE = 0
 WHITE = 255
 BLACK = 0
+
+DPI = 100

lib/visualization/generator_eval.py

@@ -1,36 +1,49 @@
-import torch
-
 import matplotlib.pyplot as plt
 from mpl_toolkits.axisartist.axes_grid import ImageGrid
-from tqdm import tqdm
+import lib.variables as V
-from typing import List
 
 
 class GeneratorVisualizer(object):
 
     def __init__(self, maps, trajectories, labels, val_result_dict):
         # val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
-        self.generated_alternatives = val_result_dict['generated_alternative']
+        self.alternatives = val_result_dict['generated_alternative']
-        self.pred_labels = val_result_dict['pred_label']
         self.labels = labels
         self.trajectories = trajectories
         self.maps = 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)
+        self._rows = len(self.column_dict_list[0])
 
     def _build_column_dict_list(self):
-        dict_list = []
+        trajectories = []
-        for idx in range(self.maps.shape[0]):
+        non_hom_alternatives = []
-            image = (self.maps[idx] + self.trajectories[idx] + self.generated_alternatives[idx]).cpu().numpy().squeeze()
+        hom_alternatives = []
-            label = int(self.labels[idx])
+
-            dict_list.append(dict(image=image, label=label))
+        for idx in range(self.alternatives.shape[0]):
-        half_size = int(len(dict_list) // 2)
+            image = (self.alternatives[idx]).cpu().numpy().squeeze()
-        return dict_list[:half_size], dict_list[half_size:]
+            label = self.labels[idx].item()
+            if label == V.HOMOTOPIC:
+                hom_alternatives.append(dict(image=image, label='Homotopic'))
+            else:
+                non_hom_alternatives.append(dict(image=image, label='NonHomotopic'))
+        for idx in range(max(len(hom_alternatives), len(non_hom_alternatives))):
+            image = (self.maps[idx] + self.trajectories[idx]).cpu().numpy().squeeze()
+            label = 'original'
+            trajectories.append(dict(image=image, label=label))
+
+        return trajectories, hom_alternatives, non_hom_alternatives
 
     def draw(self):
-        fig = plt.figure()
+        padding = 0.25
+        additional_size = self._cols * padding + 3 * padding
+        width = (self._map_width * self._cols) / V.DPI + additional_size
+        height = (self._map_height * self._rows) / V.DPI + additional_size
+        fig = plt.figure(figsize=(width, height), dpi=V.DPI)
         grid = ImageGrid(fig, 111,  # similar to subplot(111)
-                         nrows_ncols=(len(self.column_dict_list[0]), len(self.column_dict_list)),
+                         nrows_ncols=(self._rows, self._cols),
-                         axes_pad=0.2,  # pad between axes in inch.
+                         axes_pad=padding,  # pad between axes in inch.
                          )
 
         for idx in range(len(grid.axes_all)):
@@ -40,4 +53,5 @@ class GeneratorVisualizer(object):
             grid[idx].imshow(current_image)
             grid[idx].title.set_text(current_label)
         fig.cbar_mode = 'single'
+        fig.tight_layout()
         return fig

main.py

@@ -28,14 +28,16 @@ main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
 
 # Main Parameters
 main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--main_eval", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
 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_dataset_length", type=int, default=10000, help="")
+main_arg_parser.add_argument("--data_dataset_length", type=int, default=100000, help="")
 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="")
+
 
 # Transformations
 main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
@@ -43,16 +45,16 @@ main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, defa
 # Transformations
 main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
 main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
-main_arg_parser.add_argument("--train_epochs", type=int, default=10, help="")
+main_arg_parser.add_argument("--train_epochs", type=int, default=20, help="")
-main_arg_parser.add_argument("--train_batch_size", type=int, default=256, help="")
+main_arg_parser.add_argument("--train_batch_size", type=int, default=164, help="")
 main_arg_parser.add_argument("--train_lr", type=float, default=0.002, help="")
 
 # Model
 main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerator", help="")
-main_arg_parser.add_argument("--model_activation", type=str, default="relu", help="")
+main_arg_parser.add_argument("--model_activation", type=str, default="elu", help="")
 main_arg_parser.add_argument("--model_filters", type=str, default="[32, 16, 4]", help="")
 main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
-main_arg_parser.add_argument("--model_lat_dim", type=int, default=2, help="")
+main_arg_parser.add_argument("--model_lat_dim", type=int, default=4, 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_dropout", type=float, default=0.00, help="")
@@ -93,10 +95,10 @@ def run_lightning_loop(config_obj):
         # =============================================================================
         # Init
         model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
-        model.init_weights()
+        model.init_weights(torch.nn.init.xavier_normal_)
         if model.name == 'CNNRouteGeneratorDiscriminated':
             # ToDo: Make this dependent on the used seed
-            path = Path(Path(config_obj.train.outpath) / 'classifier_cnn' / 'trained')
+            path = Path(Path(config_obj.train.outpath) / 'classifier_cnn' / 'version_0')
             disc_model = SavedLightningModels.load_checkpoint(path).restore()
             model.set_discriminator(disc_model)
 
@@ -107,14 +109,14 @@ def run_lightning_loop(config_obj):
                           weights_save_path=logger.log_dir,
                           gpus=[0] if torch.cuda.is_available() else None,
                           check_val_every_n_epoch=1,
+                          num_sanity_val_steps=0,
                           # row_log_interval=(model.n_train_batches * 0.1),  # TODO: Better Value / Setting
                           # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting
                           checkpoint_callback=checkpoint_callback,
                           logger=logger,
+                          val_percent_check=0.05,
                           fast_dev_run=config_obj.main.debug,
-                          early_stop_callback=None,
+                          early_stop_callback=None
-                          val_percent_check=0.10,
-                          num_sanity_val_steps=1,
                           )
 
         # Train It
@@ -125,7 +127,8 @@ def run_lightning_loop(config_obj):
         model.save_to_disk(logger.log_dir)
 
         # Evaluate It
-        trainer.test()
+        if config_obj.main.eval:
+            trainer.test()
 
     return model
 

multi_run.py

@@ -18,7 +18,7 @@ if __name__ == '__main__':
     # use_bias, activation, model, use_norm, max_epochs, filters
     cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, model_activation='leaky_relu',
                           model_type='classifier_cnn', model_filters=[16, 32, 64], data_batchsize=512)
-    # use_bias, activation, model, use_norm, max_epochs, sr, lat_dim, filters
+    # use_bias, activation, model, use_norm, max_epochs, sr, feature_mixed_dim, filters
 
     for arg_dict in [cnn_classifier]:
         for seed in range(5):