Add class filtering to `LoadImagesAndLabels()` dataloader (#5172)

* Add train class filter feature to datasets.py

Allows for training on a subset of total classes if `include_class` list is defined on datasets.py L448:
```python
include_class = [] # filter labels to include only these classes (optional)
```

* segments fix

utils/datasets.py

@@ -437,10 +437,6 @@ class LoadImagesAndLabels(Dataset):
         self.shapes = np.array(shapes, dtype=np.float64)
         self.img_files = list(cache.keys())  # update
         self.label_files = img2label_paths(cache.keys())  # update
-        if single_cls:
-            for x in self.labels:
-                x[:, 0] = 0
-
         n = len(shapes)  # number of images
         bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
         nb = bi[-1] + 1  # number of batches
@@ -448,6 +444,20 @@ class LoadImagesAndLabels(Dataset):
         self.n = n
         self.indices = range(n)
 
+        # Update labels
+        include_class = []  # filter labels to include only these classes (optional)
+        include_class_array = np.array(include_class).reshape(1, -1)
+        for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
+            if include_class:
+                j = (label[:, 0:1] == include_class_array).any(1)
+                self.labels[i] = label[j]
+                if segment:
+                    self.segments[i] = segment[j]
+            if single_cls:  # single-class training, merge all classes into 0
+                self.labels[i][:, 0] = 0
+                if segment:
+                    self.segments[i][:, 0] = 0
+
         # Rectangular Training
         if self.rect:
             # Sort by aspect ratio