Merge pull request #18 from BloodAxe/develop

Release 0.1.3

Author: BloodAxe

.appveyor.yml

@@ -14,7 +14,7 @@ environment:
 install:
   - '%PYTHON%\python.exe -m pip install -U pip wheel setuptools'
   - '%PYTHON%\python.exe -m pip install .[tests]'
-  - '%PYTHON%\python.exe -m pip install flake8 flake8-docstrings'
+  - '%PYTHON%\python.exe -m pip install "pydocstyle<4.0.0" flake8 flake8-docstrings'
 
 test_script:
   - '%PYTHON%\python.exe -m pytest'

.travis.yml

@@ -18,7 +18,7 @@ matrix:
 install:
 - if [[ "$TEST_MODE" == "DOCS" ]]; then pip install -q .; fi
 - if [[ "$TEST_MODE" != "DOCS" ]]; then pip install -q .[tests]; fi
-- if [[ "$TEST_MODE" != "DOCS" ]]; then pip install flake8 flake8-docstrings; fi
+- if [[ "$TEST_MODE" != "DOCS" ]]; then pip install "pydocstyle<4.0.0" flake8 flake8-docstrings; fi
 script:
 - if [[ "$TEST_MODE" != "DOCS" ]]; then pytest; fi
 - if [[ "$TEST_MODE" != "DOCS" ]]; then flake8; fi

pytorch_toolbelt/__init__.py

@@ -1,3 +1,3 @@
 from __future__ import absolute_import
 
-__version__ = '0.1.2'
+__version__ = '0.1.3'

pytorch_toolbelt/losses/focal.py

@@ -8,19 +8,19 @@
 
 
 class BinaryFocalLoss(_Loss):
-    def __init__(self, alpha=0.5, gamma=2, ignore=None, reduction='mean', reduced=False, threshold=0.5):
+    def __init__(self, alpha=0.5, gamma=2, ignore_index=None, reduction='mean', reduced=False, threshold=0.5):
         """
 
         :param alpha:
         :param gamma:
-        :param ignore:
+        :param ignore_index:
         :param reduced:
         :param threshold:
         """
         super().__init__()
         self.alpha = alpha
         self.gamma = gamma
-        self.ignore = ignore
+        self.ignore_index = ignore_index
         if reduced:
             self.focal_loss = partial(reduced_focal_loss, gamma=gamma, threshold=threshold, reduction=reduction)
         else:
@@ -32,9 +32,9 @@ def forward(self, label_input, label_target):
         label_target = label_target.view(-1)
         label_input = label_input.view(-1)
 
-        if self.ignore is not None:
+        if self.ignore_index is not None:
             # Filter predictions with ignore label from loss computation
-            not_ignored = label_target != self.ignore
+            not_ignored = label_target != self.ignore_index
             label_input = label_input[not_ignored]
             label_target = label_target[not_ignored]
 
@@ -43,28 +43,32 @@ def forward(self, label_input, label_target):
 
 
 class FocalLoss(_Loss):
-    def __init__(self, alpha=0.5, gamma=2, ignore=None):
+    def __init__(self, alpha=0.5, gamma=2, ignore_index=None):
+        """
+        Focal loss for multi-class problem.
+
+        :param alpha:
+        :param gamma:
+        :param ignore_index: If not None, targets with given index are ignored
+        """
         super().__init__()
         self.alpha = alpha
         self.gamma = gamma
-        self.ignore = ignore
+        self.ignore_index = ignore_index
 
     def forward(self, label_input, label_target):
-        """Compute focal loss for multi-class problem.
-        Ignores anchors having -1 target label
-        """
         num_classes = label_input.size(1)
         loss = 0
 
         # Filter anchors with -1 label from loss computation
-        if self.ignore is not None:
-            not_ignored = label_target != self.ignore
+        if self.ignore_index is not None:
+            not_ignored = label_target != self.ignore_index
 
         for cls in range(num_classes):
             cls_label_target = (label_target == cls).long()
             cls_label_input = label_input[:, cls, ...]
 
-            if self.ignore is not None:
+            if self.ignore_index is not None:
                 cls_label_target = cls_label_target[not_ignored]
                 cls_label_input = cls_label_input[not_ignored]
 

pytorch_toolbelt/utils/catalyst/metrics.py

@@ -19,12 +19,21 @@
            'JaccardScoreCallback']
 
 
-def pixel_accuracy(outputs, targets):
-    """Compute the pixel accuracy
+def pixel_accuracy(outputs: torch.Tensor,
+                   targets: torch.Tensor, ignore_index=None):
     """
-    outputs = (outputs.detach() > 0).float()
+    Compute the pixel accuracy
+    """
+    outputs = outputs.detach()
+    targets = targets.detach()
+    if ignore_index is not None:
+        mask = targets != ignore_index
+        outputs = outputs[mask]
+        targets = targets[mask]
+
+    outputs = (outputs > 0).float()
 
-    correct = float(torch.sum(outputs == targets.detach()))
+    correct = float(torch.sum(outputs == targets))
     total = targets.numel()
     return correct / total
 
@@ -38,16 +47,18 @@ def __init__(
             input_key: str = "targets",
             output_key: str = "logits",
             prefix: str = "accuracy",
+            ignore_index=None
     ):
         """
         :param input_key: input key to use for iou calculation;
             specifies our `y_true`.
         :param output_key: output key to use for iou calculation;
             specifies our `y_pred`
+        :param ignore_index: same meaning as in nn.CrossEntropyLoss
         """
         super().__init__(
             prefix=prefix,
-            metric_fn=pixel_accuracy,
+            metric_fn=partial(pixel_accuracy, ignore_index=ignore_index),
             input_key=input_key,
             output_key=output_key,
         )
@@ -64,20 +75,23 @@ def __init__(
             input_key: str = "targets",
             output_key: str = "logits",
             prefix: str = "confusion_matrix",
-            class_names=None
+            class_names=None,
+            ignore_index=None
     ):
         """
         :param input_key: input key to use for precision calculation;
             specifies our `y_true`.
         :param output_key: output key to use for precision calculation;
             specifies our `y_pred`.
+        :param ignore_index: same meaning as in nn.CrossEntropyLoss
         """
         self.prefix = prefix
         self.class_names = class_names
         self.output_key = output_key
         self.input_key = input_key
         self.outputs = []
         self.targets = []
+        self.ignore_index = ignore_index
 
     def on_loader_start(self, state):
         self.outputs = []
@@ -89,6 +103,11 @@ def on_batch_end(self, state: RunnerState):
 
         outputs = np.argmax(outputs, axis=1)
 
+        if self.ignore_index is not None:
+            mask = targets != self.ignore_index
+            outputs = outputs[mask]
+            targets = targets[mask]
+
         self.outputs.extend(outputs)
         self.targets.extend(targets)
 
@@ -124,7 +143,8 @@ def __init__(
             self,
             input_key: str = "targets",
             output_key: str = "logits",
-            prefix: str = "macro_f1"
+            prefix: str = "macro_f1",
+            ignore_index=None
     ):
         """
         :param input_key: input key to use for precision calculation;
@@ -138,13 +158,21 @@ def __init__(
         self.input_key = input_key
         self.outputs = []
         self.targets = []
+        self.ignore_index = ignore_index
 
     def on_batch_end(self, state: RunnerState):
         outputs = to_numpy(state.output[self.output_key])
         targets = to_numpy(state.input[self.input_key])
+
         num_classes = outputs.shape[1]
+        outputs = np.argmax(outputs, axis=1)
+
+        if self.ignore_index is not None:
+            mask = targets != self.ignore_index
+            outputs = outputs[mask]
+            targets = targets[mask]
 
-        outputs = [np.eye(num_classes)[y] for y in np.argmax(outputs, axis=1)]
+        outputs = [np.eye(num_classes)[y] for y in outputs]
         targets = [np.eye(num_classes)[y] for y in targets]
 
         self.outputs.extend(outputs)

pytorch_toolbelt/utils/fs.py

@@ -10,7 +10,7 @@
 
 def has_image_ext(fname: str):
     name, ext = os.path.splitext(fname)
-    return ext.lower() in {'.bmp', '.png', '.jpeg', '.jpg', '.tiff'}
+    return ext.lower() in {'.bmp', '.png', '.jpeg', '.jpg', '.tiff', 'tif'}
 
 
 def find_in_dir(dirname: str):