Visualization approach n

viz/viz_latent.py

@@ -1,12 +1,12 @@
-from sklearn.manifold import TSNE
-from sklearn.decomposition import PCA
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-
+import warnings
+warnings.filterwarnings('ignore', category=FutureWarning)
+import torch
+from dataset import DataContainer
+from viz.utils import search_for_weights, Printer
 from run_models import *
 
-sns.set()
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 
 def load_and_predict(path_like_element):
@@ -14,73 +14,39 @@ def load_and_predict(path_like_element):
     splitpath = path_like_element.split(os.sep)
     base_dir = os.path.join(*splitpath[:4])
     model = globals()[splitpath[2]]
-    print(f'... loading model named: "{Model.name}" from timestamp: {splitpath[3]}')
     pretrained_model = model.load_from_metrics(
         weights_path=path_like_element,
         tags_csv=os.path.join(base_dir, 'default', 'version_0', 'meta_tags.csv'),
         on_gpu=True if torch.cuda.is_available() else False,
-        map_location=None
+        # map_location=None
     )
+    print(f'... loading model named: "{model.name}" from timestamp: {splitpath[3]}')
 
     # Init model and freeze its weights ( for faster inference)
     pretrained_model = pretrained_model.to(device)
     pretrained_model.eval()
     pretrained_model.freeze()
 
-    with torch.no_grad():
+    # Load the data for prediction
 
-        # Load the data for prediction
+    # TODO!!!!!!!!!:
+    # Hier müssen natürlich auch die date parameter geladen werden!
+    # Muss ich die val-sets automatisch neu setzen, also immer auf refresh haben, wenn ich validieren möchte?
+    # Was ist denn eigentlich mein Val Dataset?
+    # Hab ich irgendwo eine ganze karte?
+    # Wie sorge ich dafür, dass gewisse karten, also größenverhältnisse usw nicht überrepräsentiert sind?
+    dataset = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
 
-        # TODO!!!!!!!!!: 
-        # Hier müssen natürlich auch die date parameter geladen werden!
-        # Muss ich die val-sets automatisch neu setzen, also immer auf refresh haben, wenn ich validieren möchte?
-        # Was ist denn eigentlich mein Val Dataset?
-        # Hab ich irgendwo eine ganze karte?
-        # Wie sorge ich dafür, dass gewisse karten, also größenverhältnisse usw nicht überrepräsentiert sind?
-        dataset = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
-        dataloader = DataLoader(dataset, shuffle=True, batch_size=len(dataset))
+    # Do the inference
+    # test_pred = [pretrained_model(test_sample)[:-1] for test_sample in dataloader][0]
 
-        # Do the inference
-        test_pred = [pretrained_model(test_sample)[:-1] for test_sample in dataloader][0]
-
-    for idx, prediction in enumerate(test_pred):
-        plot, _ = viz_latent(prediction)
-        plot.savefig(os.path.join(base_dir, f'latent_space_{idx}.png'))
-
-
-def viz_latent(prediction):
-    try:
-        prediction = prediction.cpu()
-        prediction = prediction.numpy()
-    except AttributeError:
-        pass
-
-    if prediction.shape[-1] <= 1:
-        raise ValueError('How did this happen?')
-    elif prediction.shape[-1] == 2:
-        ax = sns.scatterplot(x=prediction[:, 0], y=prediction[:, 1])
-        try:
-            plt.show()
-        except:
-            pass
-        return ax.figure, (ax)
-    else:
-        fig, axs = plt.subplots(ncols=2)
-        plots = []
-        for idx, dim_reducer in enumerate([PCA, TSNE]):
-            predictions_reduced = dim_reducer(n_components=2).fit_transform(prediction)
-            plot = sns.scatterplot(x=predictions_reduced[:, 0], y=predictions_reduced[:, 1],
-                                         ax=axs[idx])
-            plot.set_title(dim_reducer.__name__)
-            plots.append(plot)
-
-        try:
-            plt.show()
-        except:
-            pass
-        return fig, (*plots, )
+    p = Printer(pretrained_model)
+    # Important:
+    # Use all given valdiation samples, even if they relate to differnt maps. This is important since we want to have a
+    # view on the complete latent space, not just in relation to a single basemap, which would be a major bias.
+    p.print_possible_latent_spaces(dataset, save=os.path.join(base_dir, f'latent_space'))
 
 
 if __name__ == '__main__':
     path = 'output'
-    search_for_weights(search_for_weights, path)
+    search_for_weights(load_and_predict, path, file_type='latent')