fix: update NumPy compatibility for v1.24+ and fix deprecated type al…

req.txt

@@ -1,5 +1,5 @@
 llvmlite==0.38.1
-numpy==1.21.6
+numpy==1.26.4
 face_alignment==1.3.5
 imageio==2.19.3
 imageio-ffmpeg==0.4.7

requirements.txt

@@ -1,4 +1,4 @@
-numpy==1.23.4
+numpy==1.26.4
 face_alignment==1.3.5
 imageio==2.19.3
 imageio-ffmpeg==0.4.7

requirements3d.txt

@@ -1,4 +1,4 @@
-numpy==1.23.4
+numpy==1.26.4
 face_alignment==1.3.5
 imageio==2.19.3
 imageio-ffmpeg==0.4.7

src/face3d/models/arcface_torch/eval_ijbc.py

@@ -120,8 +120,8 @@ def divideIntoNstrand(listTemp, n):
 def read_template_media_list(path):
     # ijb_meta = np.loadtxt(path, dtype=str)
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int_)
+    medias = ijb_meta[:, 2].astype(np.int_)
     return templates, medias
 
 
@@ -132,10 +132,10 @@ def read_template_pair_list(path):
     # pairs = np.loadtxt(path, dtype=str)
     pairs = pd.read_csv(path, sep=' ', header=None).values
     # print(pairs.shape)
-    # print(pairs[:, 0].astype(np.int))
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    # print(pairs[:, 0].astype(np.int_))
+    t1 = pairs[:, 0].astype(np.int_)
+    t2 = pairs[:, 1].astype(np.int_)
+    label = pairs[:, 2].astype(np.int_)
     return t1, t2, label
 
 

src/face3d/models/arcface_torch/onnx_ijbc.py

@@ -77,16 +77,16 @@ def batchify_fn(data):
 
 def read_template_media_list(path):
     ijb_meta = pd.read_csv(path, sep=' ', header=None).values
-    templates = ijb_meta[:, 1].astype(np.int)
-    medias = ijb_meta[:, 2].astype(np.int)
+    templates = ijb_meta[:, 1].astype(np.int_)
+    medias = ijb_meta[:, 2].astype(np.int_)
     return templates, medias
 
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int_)
+    t2 = pairs[:, 1].astype(np.int_)
+    label = pairs[:, 2].astype(np.int_)
     return t1, t2, label
 
 

src/face3d/models/arcface_torch/torch2onnx.py

@@ -6,7 +6,7 @@
 def convert_onnx(net, path_module, output, opset=11, simplify=False):
     assert isinstance(net, torch.nn.Module)
     img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32)
-    img = img.astype(np.float)
+    img = img.astype(np.float64)
     img = (img / 255. - 0.5) / 0.5  # torch style norm
     img = img.transpose((2, 0, 1))
     img = torch.from_numpy(img).unsqueeze(0).float()

src/face3d/models/arcface_torch/utils/plot.py

@@ -18,9 +18,9 @@
 
 def read_template_pair_list(path):
     pairs = pd.read_csv(path, sep=' ', header=None).values
-    t1 = pairs[:, 0].astype(np.int)
-    t2 = pairs[:, 1].astype(np.int)
-    label = pairs[:, 2].astype(np.int)
+    t1 = pairs[:, 0].astype(np.int_)
+    t2 = pairs[:, 1].astype(np.int_)
+    label = pairs[:, 2].astype(np.int_)
     return t1, t2, label
 
 

src/face3d/util/my_awing_arch.py

@@ -15,7 +15,7 @@ def calculate_points(heatmaps):
     indexes = np.argmax(heatline, axis=2)
 
     preds = np.stack((indexes % W, indexes // W), axis=2)
-    preds = preds.astype(np.float, copy=False)
+    preds = preds.astype(np.float64, copy=False)
 
     inr = indexes.ravel()
 

src/face3d/util/preprocess.py

@@ -9,7 +9,11 @@
 from skimage import transform as trans
 import torch
 import warnings
-warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning) 
+# Handle NumPy 2.x compatibility - VisibleDeprecationWarning was removed
+try:
+    warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)
+except AttributeError:
+    warnings.filterwarnings("ignore", category=DeprecationWarning)
 warnings.filterwarnings("ignore", category=FutureWarning) 
 
 
@@ -98,6 +102,6 @@ def align_img(img, lm, lm3D, mask=None, target_size=224., rescale_factor=102.):
 
     # processing the image
     img_new, lm_new, mask_new = resize_n_crop_img(img, lm, t, s, target_size=target_size, mask=mask)
-    trans_params = np.array([w0, h0, s, t[0], t[1]])
+    trans_params = np.array([float(w0), float(h0), float(s), float(t[0]), float(t[1])])
 
     return trans_params, img_new, lm_new, mask_new