v0.8.0

CHANGELOG.md

@@ -1,7 +1,12 @@
+## Version 0.8.0
+  - Add ``get_features`` method to ``SimpleConvNet2d`` for extracting encoded features.
+  - Add ``get_features`` method to ``ConvNet2d`` for extracting encoded features.
+  - Added a demo using a multiple-instance-learning attention model.
+
 ## Version 0.7.0
   - Changed ``AutoEncoder2d`` demo to use ovarian histology images.
 
-## Varsion 0.6.1
+## Version 0.6.1
   - Added residual blocks as optional block style to all relevant models.
   - Changed the ``UNet`` demo to use a nuclei segmentation data set.
 

demos/autoencoder-demo.ipynb

@@ -475,7 +475,7 @@
     "from numpy import floor\n",
     "\n",
     "\n",
-    "total_steps = 2 * 10 ** 4\n",
+    "total_steps = 2 * 10**4\n",
     "\n",
     "interval = 500\n",
     "\n",

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "torch_tools"
-version = "0.7.0"
+version = "0.8.0"
 description = "PyTorch and other tools"
 authors = [
   { name="Jim Denholm", email="j.denholm.2017@gmail.com" },

src/torch_tools/datasets/_shapes_dataset.py

@@ -1,19 +1,21 @@
 """Synthetic dataset object."""
-from typing import Tuple
+from typing import Tuple, Optional, Dict, Callable, List
 
 from torch import from_numpy, Tensor  # pylint: disable=no-name-in-module
 from torch.utils.data import Dataset
 
-from numpy import ones, float32, ndarray, array
+from torchvision.transforms import Compose  # type: ignore
+
+from numpy import ndarray, array, where, full
 from numpy.random import default_rng
 
-from skimage.draw import disk, rectangle  # pylint: disable=no-name-in-module
+from skimage.morphology import star, square, octagon, disk
 
 
 class ShapesDataset(Dataset):
     """Synthetic dataset which produces images withs spots and squares.
 
-    *Warning—*this dataset object is untested.
+    *Warning*—this dataset object is untested.
 
     Parameters
     ----------
@@ -29,33 +31,67 @@ class ShapesDataset(Dataset):
         The length of the data set.
     image_size : int, optional
         The length of the square images.
+    input_tfms : Compose, optional
+        A composition of transforms to apply to the input.
+    target_tfms : Compose, optional
+        A composition of transforms to apply to the target.
+    seed : int
+        Integer seed for numpy's default rng.
 
     Notes
     -----
     The images have white backrounds and the shapes have randomly selected
     RGB colours on [0, 1)^{3}.
 
+    To get the indices of each shape, use, for example
+
+    >>> data_set = ShapesDataset()
+    >>> spot_index = data_set.target_names.index("spot")
+    >>> star_index = data_set.target_names.index("star")
+
+    To print the classes as a list, use
+
+    >>> print(data_set.target_names)
+
 
     """
 
     def __init__(  # pylint: disable=too-many-arguments
         self,
-        spots_prob: float = 0.5,
+        spot_prob: float = 0.5,
         square_prob: float = 0.5,
-        num_spots: int = 10,
-        num_squares: int = 10,
+        star_prob: float = 0.5,
+        octagon_prob: float = 0.5,
+        num_shapes: int = 3,
         length: int = 1000,
         image_size: int = 256,
+        input_tfms: Optional[Compose] = None,
+        target_tfms: Optional[Compose] = None,
+        seed: int = 666,
     ):
         """Build ``ShapesDataset``."""
         self._len = length
-        self._spot_prob = spots_prob
-        self._square_prob = square_prob
-        self._num_spots = num_spots
-        self._num_squares = num_squares
+        self._num_shapes = num_shapes
+
+        self._probs = {
+            "square": square_prob,
+            "spot": spot_prob,
+            "star": star_prob,
+            "octagon": octagon_prob,
+        }
+
         self._img_size = image_size
+        self._x_tfms = input_tfms
+        self._y_tfms = target_tfms
+
+        self._rng = default_rng(seed=seed)
 
-    _rng = default_rng(seed=123)
+    _shapes: Dict[str, Callable] = {
+        "square": lambda x: square(2 * x),
+        "star": star,
+        "octagon": lambda x: octagon(x, x),
+        "spot": disk,
+    }
 
     def __len__(self) -> int:
         """Return the length of the dataset.
@@ -78,9 +114,9 @@ def __getitem__(self, idx: int):
 
         Returns
         -------
-        Tensor
+        img : Tensor
             An RGB image of shape (c, H, W).
-        Tensor
+        tgt : Tensor
             Target vector:
 
                 — If there are no spots or squares, [0.0, 0.0]
@@ -89,71 +125,54 @@ def __getitem__(self, idx: int):
                 — If there are both, [1.0, 1.0]
 
         """
-        return self._create_image()
+        img, tgt = self._create_image()
 
-    def _add_spots(self, image: ndarray) -> bool:
+        if self._x_tfms is not None:
+            img = self._x_tfms(img)
+
+        if self._y_tfms is not None:
+            tgt = self._y_tfms(tgt)
+
+        return img, tgt
+
+    def _add_shape(self, image: ndarray, shape: str) -> bool:
         """Add spots to ``image``.
 
         Parameters
         ----------
         image : ndarray
             RGB image.
+        shape : str
+            Name of the shape to include.
+
 
         Returns
         -------
         include_spots : bool
             Whether or not the spots were added.
 
         """
-        include_spots = self._rng.random() <= self._spot_prob
+        include_shape = self._rng.random() <= self._probs[shape]
 
-        if include_spots:
-            for _ in range(self._num_spots):
-                colour = self._rng.random(size=3)
+        if include_shape:
+            for _ in range(self._num_shapes):
+                colour = self._rng.random(size=(1, 3))
                 radius = self._img_size // 20
-                centre = self._rng.integers(
-                    radius,
-                    self._img_size - radius,
-                    size=2,
-                )
-
-                rows, cols = disk(centre, radius)
-
-                image[rows, cols, :] = colour
+                shape_arr = self._shapes[shape](radius)
 
-        return include_spots
-
-    def _add_squares(self, image: ndarray) -> bool:
-        """Add spots to ``image``.
-
-        Parameters
-        ----------
-        image : ndarray
-            RGB image.
-
-        Returns
-        -------
-        include_squares : bool
-            Whether or no squares were included.
-
-        """
-        include_squares = self._rng.random() <= self._square_prob
-
-        if include_squares:
-            for _ in range(self._num_spots):
-                colour = self._rng.random(size=3)
-                length = self._img_size // 20
-                origin = self._rng.integers(
+                # pylint: disable=unbalanced-tuple-unpacking
+                rows, cols = where(shape_arr == 1)
+                left, top = self._rng.integers(
                     0,
-                    self._img_size - (2 * length),
+                    self._img_size - len(shape_arr),
                     size=2,
                 )
 
-                rows, cols = rectangle(origin, origin + (2 * length))
+                rows, cols = rows + top, cols + left
 
-                image[rows, cols, :] = colour
+                image[rows, cols] = colour
 
-        return include_squares
+        return include_shape
 
     def _create_image(self) -> Tuple[Tensor, Tensor]:
         """Create image.
@@ -171,12 +190,31 @@ def _create_image(self) -> Tuple[Tensor, Tensor]:
                 — If there are both, [1.0, 1.0]
 
         """
-        image = ones((self._img_size, self._img_size, 3), dtype=float32)
+        # image = ones((self._img_size, self._img_size, 3), dtype=float32)
+
+        image = full(
+            (self._img_size, self._img_size, 3),
+            fill_value=self._rng.random(size=(1, 3)),
+        )
 
-        spots = self._add_spots(image)
-        squares = self._add_squares(image)
+        targets = []
+        for key in self._shapes:
+            targets.append(self._add_shape(image, key))
 
         return (
-            from_numpy(image).permute(2, 0, 1),
-            from_numpy(array([spots, squares])).float(),
+            from_numpy(image).permute(2, 0, 1).float(),
+            from_numpy(array(targets)).float(),
         )
+
+    @property
+    def target_names(self) -> List[str]:
+        """Return a list of target names order by their one-hot indices.
+
+        Returns
+        -------
+        List[str]
+            A list of the names of the shapes, ordered by their one-hot
+            indices.
+
+        """
+        return list(self._shapes.keys())

src/torch_tools/models/_conv_net_2d.py

@@ -176,6 +176,23 @@ def forward(self, batch: Tensor, frozen_encoder: bool = False) -> Tensor:
         pool_out = self.pool(encoder_out)
         return self.dense_layers(pool_out)
 
+    def get_features(self, batch: Tensor) -> Tensor:
+        """Return the features produced by the encoder and pool.
+
+        Parameters
+        ----------
+        batch : Tensor
+            A mini-batch of image-like inputs.
+
+        Returns
+        -------
+        Tensor
+            The encoded features for the items in ``batch``.
+
+        """
+        encoder_out = self.backbone(batch)
+        return self.pool(encoder_out)
+
 
 def _conv_config(conv: Conv2d) -> Dict[str, Any]:
     """Return a dictionary with the `conv`'s instantiation arguments.

src/torch_tools/models/_simple_conv_2d.py

@@ -1,6 +1,7 @@
 """A simple two-dimensional convolutional neural network."""
 from typing import Optional, Dict, Any
 
+from torch import Tensor
 from torch.nn import Sequential, Flatten
 
 from torch_tools.models._encoder_2d import Encoder2d
@@ -107,6 +108,24 @@ def __init__(
             ),
         )
 
+    def get_features(self, batch: Tensor) -> Tensor:
+        """Get the features produced by the encoder and adaptive poool.
+
+        Parameters
+        ----------
+        batch : Tensor
+            A mini-batch of image-like inputs.
+
+        Returns
+        -------
+        Tensor
+            The features for each item in ``batch``.
+
+        """
+        feats = self[0](batch)
+        feats = self[1](feats)
+        return self[2](feats)
+
     _dn_args: Dict[str, Any] = {
         "hidden_sizes": None,
         "input_bnorm": False,

tests/models/test_conv_net_2d_call.py

@@ -64,3 +64,15 @@ def test_call_works_with_all_kinds_of_pools():
 
     model = ConvNet2d(out_feats=1, pool_style="avg-max-concat")
     _ = model(rand(10, 3, 50, 50))
+
+
+def test_the_returned_shapes_of_get_features_method():
+    """Test the shapes returned by the ``get_features`` method."""
+    model = ConvNet2d(10, encoder_style="resnet18", pool_style="avg")
+    assert model.get_features(rand(10, 3, 100, 100)).shape == (10, 512)
+
+    model = ConvNet2d(10, encoder_style="resnet34", pool_style="avg")
+    assert model.get_features(rand(10, 3, 100, 100)).shape == (10, 512)
+
+    model = ConvNet2d(10, encoder_style="resnet50", pool_style="avg")
+    assert model.get_features(rand(10, 3, 100, 100)).shape == (10, 2048)

tests/models/test_simple_conv_2d_call.py

@@ -54,3 +54,21 @@ def test_simple_conv_net_2d_call():
         )
 
         assert model(rand(10, in_chans, 100, 100)).shape == (10, out_feats)
+
+
+def test_the_returned_shapes_of_get_features_method():
+    """Test the shapes returned by the ``get_features`` method."""
+    for feats_start, num_blocks in zip([16, 32], [3, 4, 5]):
+        model = SimpleConvNet2d(
+            3,
+            10,
+            features_start=feats_start,
+            num_blocks=num_blocks,
+        )
+
+        batch = rand(10, 3, 100, 100)
+
+        assert model.get_features(batch).shape == (
+            10,
+            feats_start * (2 ** (num_blocks - 1)),
+        )