Merge pull request #27 from sbelharbi/main

support numpy >= 1.20.0

FaceBoxes/utils/nms/cpu_nms.pyx

@@ -26,7 +26,7 @@ def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):
 
     cdef int ndets = dets.shape[0]
     cdef np.ndarray[np.int_t, ndim=1] suppressed = \
-            np.zeros((ndets), dtype=np.int)
+            np.zeros((ndets), dtype=np.int64)
 
     # nominal indices
     cdef int _i, _j

benchmark.py

@@ -169,8 +169,8 @@ def benchmark_aflw2000_params(params, data_param):
                     #plot the first 50 samples for validation
                     bkg = cv2.imread(img_list[i*batch_size+j],-1)
                     lm_sample = lm[j]
-                    c0 = np.clip((lm_sample[1,:]).astype(np.int), 0, 119)
-                    c1 = np.clip((lm_sample[0,:]).astype(np.int), 0, 119)
+                    c0 = np.clip((lm_sample[1,:]).astype(np.int64), 0, 119)
+                    c1 = np.clip((lm_sample[0,:]).astype(np.int64), 0, 119)
                     for y, x, in zip([c0,c0,c0-1,c0-1],[c1,c1-1,c1,c1-1]):
                         bkg[y, x, :] = np.array([233,193,133])
                     cv2.imwrite(f'./results/{i*batch_size+j}.png', bkg)
@@ -253,4 +253,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
+    main()

model_building.py

@@ -66,7 +66,7 @@ class SynergyNet(nn.Module):
 	def __init__(self, args):
 		super(SynergyNet, self).__init__()
 		self.triangles = sio.loadmat('./3dmm_data/tri.mat')['tri'] -1
-		self.triangles = torch.Tensor(self.triangles.astype(np.int)).long().cuda()
+		self.triangles = torch.Tensor(self.triangles.astype(np.int64)).long().cuda()
 		self.img_size = args.img_size
 		# Image-to-parameter
 		self.I2P = I2P(args)
@@ -170,7 +170,7 @@ class WrapUpSynergyNet(nn.Module):
 	def __init__(self):
 		super(WrapUpSynergyNet, self).__init__()
 		self.triangles = sio.loadmat('./3dmm_data/tri.mat')['tri'] -1
-		self.triangles = torch.Tensor(self.triangles.astype(np.int)).long()
+		self.triangles = torch.Tensor(self.triangles.astype(np.int64)).long()
 		args = types.SimpleNamespace()
 		args.arch = 'mobilenet_v2'
 		args.checkpoint_fp = 'pretrained/best.pth.tar'
@@ -306,4 +306,4 @@ def get_all_outputs(self, input):
 		return pts_res, vertices_lst, poses
 
 if __name__ == '__main__':
-	pass
+	pass

synergy3DMM.py

@@ -71,7 +71,7 @@ class SynergyNet(nn.Module):
 	def __init__(self):
 		super(SynergyNet, self).__init__()
 		self.triangles = sio.loadmat(os.path.join(prefix_path, '3dmm_data/tri.mat'))['tri'] -1
-		self.triangles = torch.Tensor(self.triangles.astype(np.int)).long()
+		self.triangles = torch.Tensor(self.triangles.astype(np.int64)).long()
 		args = types.SimpleNamespace()
 		args.arch = 'mobilenet_v2'
 		args.checkpoint_fp = os.path.join(prefix_path, 'pretrained/best.pth.tar')
@@ -207,4 +207,4 @@ def get_all_outputs(self, input):
 		return pts_res, vertices_lst, poses
 
 if __name__ == '__main__':
-	pass
+	pass