update np.int32 and np.float32

Author: adityapp

alignment/coordinate_reg/image_infer.py

@@ -15,7 +15,7 @@
     color = (200, 160, 75)
     for face in faces:
         lmk = face.landmark_2d_106
-        lmk = np.round(lmk).astype(np.int)
+        lmk = np.round(lmk).astype(np.int32)
         for i in range(lmk.shape[0]):
             p = tuple(lmk[i])
             cv2.circle(tim, p, 1, color, 1, cv2.LINE_AA)

alignment/heatmap/metric.py

@@ -51,7 +51,7 @@ def cal_nme(self, label, pred_label):
                     ind_gt = np.array(ind_gt)
                 else:
                     ind_gt = label[b][p]
-                    #ind_gt = ind_gt.astype(np.int)
+                    #ind_gt = ind_gt.astype(np.int32)
                     #print(ind_gt)
                 heatmap_pred = pred_label[b][p]
                 heatmap_pred = cv2.resize(

alignment/synthetics/datasets/augs.py

@@ -24,7 +24,7 @@ def apply(self, image, border_size_limit, **params):
         border_size[2] *= image.shape[1]
         border_size[1] *= image.shape[0]
         border_size[3] *= image.shape[0]
-        border_size = border_size.astype(np.int)
+        border_size = border_size.astype(np.int32)
         image[:,:border_size[0],:] = self.fill_value
         image[:border_size[1],:,:] = self.fill_value
         image[:,-border_size[2]:,:] = self.fill_value

alignment/synthetics/test_synthetics.py

@@ -85,10 +85,10 @@
     outf.write(' ')
     outf.write(' '.join(["%.5f"%x for x in kps.flatten()]))
     outf.write("\n")
-    box = bbox.astype(np.int)
+    box = bbox.astype(np.int32)
     color = (0, 0, 255)
     cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
-    kps = kps.astype(np.int)
+    kps = kps.astype(np.int32)
     #print(landmark.shape)
     for l in range(kps.shape[0]):
         color = (0, 0, 255)

alignment/synthetics/tools/prepare_synthetics.py

@@ -48,11 +48,11 @@
     _scale = output_size  / (max(w, h)*1.5)
     aimg, M = face_align.transform(dimg, center, output_size, _scale, rotate)
     pred = face_align.trans_points(pred, M)
-    #box = bbox.astype(np.int)
+    #box = bbox.astype(np.int32)
     #color = (0, 0, 255)
     #cv2.rectangle(dimg, (box[0], box[1]), (box[2], box[3]), color, 2)
 
-    #kps = pred.astype(np.int)
+    #kps = pred.astype(np.int32)
     #for l in range(kps.shape[0]):
     #    color = (0, 0, 255)
     #    cv2.circle(aimg, (kps[l][0], kps[l][1]), 1, color, 2)

detection/retinaface/rcnn/PY_OP/cascade_refine.py

@@ -127,8 +127,8 @@ def assign_anchor_fpn(self,
         if gt_boxes.size > 0:
             # overlap between the anchors and the gt boxes
             # overlaps (ex, gt)
-            overlaps = bbox_overlaps(anchors.astype(np.float),
-                                     gt_boxes.astype(np.float))
+            overlaps = bbox_overlaps(anchors.astype(np.float32),
+                                     gt_boxes.astype(np.float32))
             argmax_overlaps = overlaps.argmax(axis=1)
             #print('AAA', argmax_overlaps.shape)
             max_overlaps = overlaps[np.arange(num_anchors), argmax_overlaps]
@@ -344,13 +344,13 @@ def forward(self, is_train, req, in_data, out_data, aux):
                 assert anchors_t1.shape[0] == self.ori_anchors.shape[0]
 
                 #for i in range(_gt_boxes.shape[0]):
-                #  box = _gt_boxes[i].astype(np.int)
+                #  box = _gt_boxes[i].astype(np.int32)
                 #  print('%d: gt%d'%(self.nbatch, i), box)
                 #  #color = (0,0,255)
                 #  #cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)
                 #for i in range(anchors_t1.shape[0]):
-                #  box1 = self.ori_anchors[i].astype(np.int)
-                #  box2 = anchors_t1[i].astype(np.int)
+                #  box1 = self.ori_anchors[i].astype(np.int32)
+                #  box2 = anchors_t1[i].astype(np.int32)
                 #  print('%d %d: anchorscompare %d'%(self.nbatch, self.stride, i), box1, box2)
                 #color = (255,255,0)
                 #cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]), color, 2)

detection/retinaface/rcnn/core/loader.py

@@ -247,7 +247,7 @@ def get_batch(self):
                 print('DEBUG SHAPE', data['data'].shape,
                       label['gt_boxes'].shape)
 
-                box = label['gt_boxes'].copy()[0][0:4].astype(np.int)
+                box = label['gt_boxes'].copy()[0][0:4].astype(np.int32)
                 cv2.rectangle(img, (box[0], box[1]), (box[2], box[3]),
                               (0, 255, 0), 2)
                 filename = './debugout/%d.png' % (self._debug_id)

detection/retinaface/rcnn/core/tester.py

@@ -238,8 +238,8 @@ def test_proposals(predictor, test_data, imdb, roidb, vis=False):
                                                             gt_boxes[:, 1] + 1)
         num_pos += gt_boxes.shape[0]
 
-        overlaps = bbox_overlaps(boxes.astype(np.float),
-                                 gt_boxes.astype(np.float))
+        overlaps = bbox_overlaps(boxes.astype(np.float32),
+                                 gt_boxes.astype(np.float32))
         #print(im_info, gt_boxes.shape, boxes.shape, overlaps.shape, file=sys.stderr)
 
         _gt_overlaps = np.zeros((gt_boxes.shape[0]))

detection/retinaface/rcnn/cython/bbox.pyx

@@ -9,7 +9,7 @@ cimport cython
 import numpy as np
 cimport numpy as np
 
-DTYPE = np.float
+DTYPE = np.float32
 ctypedef np.float_t DTYPE_t
 
 def bbox_overlaps_cython(

detection/retinaface/rcnn/cython/cpu_nms.pyx

@@ -14,7 +14,7 @@ cdef inline np.float32_t max(np.float32_t a, np.float32_t b):
 cdef inline np.float32_t min(np.float32_t a, np.float32_t b):
     return a if a <= b else b
 
-def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):
+def cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float32 thresh):
     cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0]
     cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1]
     cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2]
@@ -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.int32)
 
     # nominal indices
     cdef int _i, _j