Visualization approach 1

viz/viz_latent.py

@@ -1,6 +1,3 @@
-from collections import defaultdict
-from tqdm import tqdm
-
 from sklearn.manifold import TSNE
 from sklearn.decomposition import PCA
 
@@ -12,57 +9,52 @@ from run_models import *
 sns.set()
 
 
-def search_for_weights(folder):
-    while not os.path.exists(folder):
-        if len(os.path.split(folder)) >= 50:
-            raise FileNotFoundError(f'The folder "{folder}" could not be found')
-        folder = os.path.join(os.pardir, folder)
-    for element in os.scandir(folder):
-        if os.path.exists(element):
-            if element.is_dir():
-                search_for_weights(element.path)
-            elif element.is_file() and element.name.endswith('.ckpt'):
-                load_and_predict(element)
-            else:
-                continue
-
-
 def load_and_predict(path_like_element):
-    if any([x.name.endswith('.png') for x in os.scandir(os.path.dirname(path_like_element))]):
-        return
-
     # Define Loop to search for models and folder with visualizations
-    model = globals()[path_like_element.path.split(os.sep)[-3]]
+    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.path,
-        tags_csv=os.path.join(os.path.dirname(path_like_element), 'default', 'version_0', 'meta_tags.csv'),
+        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
     )
 
     # 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
-        dataset = DataContainer(os.path.join(os.pardir, 'data'), 5, 5)
+
+        # 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
-        prediction_dict = defaultdict(list)
-        for i in tqdm(range(len(dataset)), total=len(dataset)):
-            p_X = pretrained_model(dataset[i].unsqueeze(0))
-            for idx in range(len(p_X) - 1):
-                prediction_dict[idx].append(p_X[idx])
+        test_pred = [pretrained_model(test_sample)[:-1] for test_sample in dataloader][0]
 
-    predictions = [torch.cat(prediction).detach().numpy() for prediction in prediction_dict.values()]
-    for idx, prediction in enumerate(predictions):
+    for idx, prediction in enumerate(test_pred):
         plot, _ = viz_latent(prediction)
-        plot.savefig(os.path.join(os.path.dirname(path_like_element), f'latent_space_{idx}.png'))
+        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:
@@ -91,4 +83,4 @@ def viz_latent(prediction):
 
 if __name__ == '__main__':
     path = 'output'
-    search_for_weights(path)
+    search_for_weights(search_for_weights, path)