upload train code

CTAI_model/utils/dice_loss.py

@@ -0,0 +1,38 @@
+import numpy as np
+
+
+def dice(im1, im2):
+    """
+    Computes the Dice coefficient, a measure of set similarity.
+    Parameters
+    ----------
+    im1 : array-like, bool
+        Any array of arbitrary size. If not boolean, will be converted.
+    im2 : array-like, bool
+        Any other array of identical size. If not boolean, will be converted.
+    Returns
+    -------
+    dice : float
+        Dice coefficient as a float on range [0,1].
+        Maximum similarity = 1
+        No similarity = 0
+
+    Notes
+    -----
+    The order of inputs for `dice` is irrelevant. The result will be
+    identical if `im1` and `im2` are switched.
+    """
+    im1 = np.asarray(im1).astype(np.bool)
+    im2 = np.asarray(im2).astype(np.bool)
+
+    if im1.shape != im2.shape:
+        raise ValueError("Shape mismatch: im1 and im2 must have the same shape.")
+
+    # 俩都为全黑
+    if not (im1.any() or im2.any()):
+        return 1.0
+
+    # Compute Dice coefficient
+    intersection = np.logical_and(im1, im2)
+    res = 2. * intersection.sum() / (im1.sum() + im2.sum())
+    return np.round(res, 5)