further sfd improvements, update req

conda/meta.yaml

@@ -24,6 +24,7 @@ requirements:
     - scipy
     - opencv
     - tqdm
+    - numba
 
 about:
   home: https://github.com/1adrianb/face-alignment

face_alignment/detection/sfd/bbox.py

@@ -1,19 +1,6 @@
 import math
 import numpy as np
-
-def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh):
-    xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1
-    dx, dy = (xc - axc) / aww, (yc - ayc) / ahh
-    dw, dh = math.log(ww / aww), math.log(hh / ahh)
-    return dx, dy, dw, dh
-
-
-def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh):
-    xc, yc = dx * aww + axc, dy * ahh + ayc
-    ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh
-    x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2
-    return x1, y1, x2, y2
-
+from numba import jit
 
 def nms(dets, thresh):
     if 0 == len(dets):
@@ -62,7 +49,7 @@ def encode(matched, priors, variances):
     # return target for smooth_l1_loss
     return np.concatenate([g_cxcy, g_wh], 1)  # [num_priors,4]
 
-
+@jit(nopython=True)
 def decode(loc, priors, variances):
     """Decode locations from predictions using priors to undo
     the encoding we did for offset regression at train time.

face_alignment/detection/sfd/detect.py

@@ -3,6 +3,8 @@ import torch.nn.functional as F
 
 import cv2
 import numpy as np
+from numba import jit
+from numba.typed import List
 
 from .bbox import *
 
@@ -26,7 +28,7 @@ def batch_detect(net, img_batch, device):
     if 'cuda' in device:
         torch.backends.cudnn.benchmark = True
 
-    BB = img_batch.size(0)
+    batch_size = img_batch.size(0)
     img_batch = img_batch.to(device, dtype=torch.float32)
 
     img_batch = img_batch.flip(-3)  # RGB to BGR
@@ -38,12 +40,16 @@ def batch_detect(net, img_batch, device):
     for i in range(len(olist) // 2):
         olist[i * 2] = F.softmax(olist[i * 2], dim=1)
 
-    bboxlists = []
-
     olist = [oelem.data.cpu().numpy() for oelem in olist]
-    variances = [0.1, 0.2]
+    
+    bboxlists = get_predictions(List(olist), batch_size)
+    return bboxlists
 
-    for j in range(BB):
+@jit(nopython=True)
+def get_predictions(olist, batch_size):
+    bboxlists = []
+    variances = [0.1, 0.2]
+    for j in range(batch_size):
         bboxlist = []
         for i in range(len(olist) // 2):
             ocls, oreg = olist[i * 2], olist[i * 2 + 1]
@@ -52,16 +58,17 @@ def batch_detect(net, img_batch, device):
             for Iindex, hindex, windex in poss:
                 axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride
                 score = ocls[j, 1, hindex, windex]
-                loc = oreg[j, :, hindex, windex].resize(1, 4)
+                loc = oreg[j, :, hindex, windex].copy().reshape(1, 4)
                 priors = np.array([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]])
                 box = decode(loc, priors, variances)
-                x1, y1, x2, y2 = box[0] * 1.0
+                x1, y1, x2, y2 = box[0]
                 bboxlist.append([x1, y1, x2, y2, score])
 
         bboxlists.append(bboxlist)
 
     bboxlists = np.array(bboxlists)
-    return bboxlists
+    return bboxlists  
+    
 
 
 def flip_detect(net, img, device):

face_alignment/detection/sfd/net_s3fd.py

@@ -68,40 +68,40 @@ class s3fd(nn.Module):
         self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
 
     def forward(self, x):
-        h = F.relu(self.conv1_1(x))
-        h = F.relu(self.conv1_2(h))
+        h = F.relu(self.conv1_1(x), inplace=True)
+        h = F.relu(self.conv1_2(h), inplace=True)
         h = F.max_pool2d(h, 2, 2)
 
-        h = F.relu(self.conv2_1(h))
-        h = F.relu(self.conv2_2(h))
+        h = F.relu(self.conv2_1(h), inplace=True)
+        h = F.relu(self.conv2_2(h), inplace=True)
         h = F.max_pool2d(h, 2, 2)
 
-        h = F.relu(self.conv3_1(h))
-        h = F.relu(self.conv3_2(h))
-        h = F.relu(self.conv3_3(h))
+        h = F.relu(self.conv3_1(h), inplace=True)
+        h = F.relu(self.conv3_2(h), inplace=True)
+        h = F.relu(self.conv3_3(h), inplace=True)
         f3_3 = h
         h = F.max_pool2d(h, 2, 2)
 
-        h = F.relu(self.conv4_1(h))
-        h = F.relu(self.conv4_2(h))
-        h = F.relu(self.conv4_3(h))
+        h = F.relu(self.conv4_1(h), inplace=True)
+        h = F.relu(self.conv4_2(h), inplace=True)
+        h = F.relu(self.conv4_3(h), inplace=True)
         f4_3 = h
         h = F.max_pool2d(h, 2, 2)
 
-        h = F.relu(self.conv5_1(h))
-        h = F.relu(self.conv5_2(h))
-        h = F.relu(self.conv5_3(h))
+        h = F.relu(self.conv5_1(h), inplace=True)
+        h = F.relu(self.conv5_2(h), inplace=True)
+        h = F.relu(self.conv5_3(h), inplace=True)
         f5_3 = h
         h = F.max_pool2d(h, 2, 2)
 
-        h = F.relu(self.fc6(h))
-        h = F.relu(self.fc7(h))
+        h = F.relu(self.fc6(h), inplace=True)
+        h = F.relu(self.fc7(h), inplace=True)
         ffc7 = h
-        h = F.relu(self.conv6_1(h))
-        h = F.relu(self.conv6_2(h))
+        h = F.relu(self.conv6_1(h), inplace=True)
+        h = F.relu(self.conv6_2(h), inplace=True)
         f6_2 = h
-        h = F.relu(self.conv7_1(h))
-        h = F.relu(self.conv7_2(h))
+        h = F.relu(self.conv7_1(h), inplace=True)
+        h = F.relu(self.conv7_2(h), inplace=True)
         f7_2 = h
 
         f3_3 = self.conv3_3_norm(f3_3)

requirements.txt

@@ -1,3 +1,4 @@
 opencv-python
 scipy>=0.17.0
 scikit-image
+numba

setup.py

@@ -42,6 +42,7 @@ requirements = [
     'scikit-image',
     'opencv-python',
     'tqdm',
+    'numba',
     'enum34;python_version<"3.4"'
 ]