Optimize fa

face_alignment/api.py

@@ -5,7 +5,6 @@
 
 from .utils import *
 
-
 class LandmarksType(IntEnum):
     """Enum class defining the type of landmarks to detect.
 
@@ -27,8 +26,8 @@ class NetworkSize(IntEnum):
 
 
 class FaceAlignment:
-    def __init__(self, landmarks_type, face_align_model_path, depth_pred_model_path, network_size=NetworkSize.LARGE,
-                 device='cuda', flip_input=False, face_detector='sfd', face_detector_kwargs=None, verbose=False):
+    def __init__(self, landmarks_type, face_align_model_path, depth_pred_model_path=None, network_size=NetworkSize.LARGE,
+                 device='cuda', flip_input=False, face_detector=None, face_detector_kwargs=None, verbose=False):
         self.device = device
         self.flip_input = flip_input
         self.landmarks_type = landmarks_type
@@ -51,10 +50,13 @@ def __init__(self, landmarks_type, face_align_model_path, depth_pred_model_path,
             torch.backends.cudnn.benchmark = True
 
         # Get the face detector
-        face_detector_module = __import__('face_alignment.detection.' + face_detector,
-                                          globals(), locals(), [face_detector], 0)
-        face_detector_kwargs = face_detector_kwargs or {}
-        self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose, **face_detector_kwargs)
+        if face_detector:
+            face_detector_module = __import__('face_alignment.detection.' + face_detector,
+                                            globals(), locals(), [face_detector], 0)
+            face_detector_kwargs = face_detector_kwargs or {}
+            self.face_detector = face_detector_module.FaceDetector(device=device, verbose=verbose, **face_detector_kwargs)
+        else:
+            self.face_detector = None
 
         # Initialise the face alignemnt networks
         if landmarks_type == LandmarksType._2D:
@@ -116,7 +118,10 @@ def get_landmarks_from_image(self, image_or_path, detected_faces=None, return_bb
         image = get_image(image_or_path)
 
         if detected_faces is None:
-            detected_faces = self.face_detector.detect_from_image(image.copy())
+            try:
+                detected_faces = self.face_detector.detect_from_image(image.copy())
+            except:
+                raise Exception(f"A list of bounding boxes or a face_detector method is needed.")
 
         if len(detected_faces) == 0:
             warnings.warn("No faces were detected.")
@@ -127,23 +132,30 @@ def get_landmarks_from_image(self, image_or_path, detected_faces=None, return_bb
 
         landmarks = []
         landmarks_scores = []
-        for i, d in enumerate(detected_faces):
+        for i, d in enumerate(detected_faces): 
             center = torch.tensor(
                 [d[2] - (d[2] - d[0]) / 2.0, d[3] - (d[3] - d[1]) / 2.0])
-            center[1] = center[1] - (d[3] - d[1]) * 0.12
-            scale = (d[2] - d[0] + d[3] - d[1]) / self.face_detector.reference_scale
+            if self.face_detector:
+                center[1] = center[1] - (d[3] - d[1]) * 0.12
+                scale = (d[2] - d[0] + d[3] - d[1]) / self.face_detector.reference_scale
+            else:
+                # Everywhere the variable scale is multiplied by 200 for no reason, if we divide here by 200 it works
+                scale = (d[2]-d[0])/200
 
             inp = crop(image, center, scale)
             inp = torch.from_numpy(inp.transpose(
                 (2, 0, 1))).float()
-
+            
             inp = inp.to(self.device)
             inp.div_(255.0).unsqueeze_(0)
 
+            # Disabling optimizations: https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md#disabling-optimizations
+            torch._C._set_graph_executor_optimize(False)
             out = self.face_alignment_net(inp).detach()
             if self.flip_input:
                 out += flip(self.face_alignment_net(flip(inp)).detach(), is_label=True)
             out = out.cpu().numpy()
+            torch._C._set_graph_executor_optimize(True)
 
             pts, pts_img, scores = get_preds_fromhm(out, center.numpy(), scale)
             pts, pts_img = torch.from_numpy(pts), torch.from_numpy(pts_img)
@@ -156,18 +168,19 @@ def get_landmarks_from_image(self, image_or_path, detected_faces=None, return_bb
                     if pts[i, 0] > 0 and pts[i, 1] > 0:
                         heatmaps[i] = draw_gaussian(
                             heatmaps[i], pts[i], 2)
+
                 heatmaps = torch.from_numpy(
                     heatmaps).unsqueeze_(0)
 
                 heatmaps = heatmaps.to(self.device)
                 depth_pred = self.depth_prediciton_net(
                     torch.cat((inp, heatmaps), 1)).data.cpu().view(68, 1)
                 pts_img = torch.cat(
-                    (pts_img, depth_pred * (1.0 / (256.0 / (200.0 * scale)))), 1)
+                    (pts_img, depth_pred * (1.0 / (256.0 / (200.0 * scale)))), 1)  
 
             landmarks.append(pts_img.numpy())
             landmarks_scores.append(scores)
-
+        
         if not return_bboxes:
             detected_faces = None
         if not return_landmark_score:

face_alignment/utils.py

@@ -145,7 +145,6 @@ def crop(image, center, scale, resolution=256.0):
     return newImg
 
 
-@jit(nopython=True)
 def transform_np(point, center, scale, resolution, invert=False):
     """Generate and affine transformation matrix.
 
@@ -203,7 +202,6 @@ def get_preds_fromhm(hm, center=None, scale=None):
     return preds, preds_orig, scores
 
 
-@jit(nopython=True)
 def _get_preds_fromhm(hm, idx, center=None, scale=None):
     """Obtain (x,y) coordinates given a set of N heatmaps and the
     coresponding locations of the maximums. If the center
@@ -234,7 +232,7 @@ def _get_preds_fromhm(hm, idx, center=None, scale=None):
                 preds[i, j] += np.sign(diff) * 0.25
 
     preds -= 0.5
-
+    
     preds_orig = np.zeros_like(preds)
     if center is not None and scale is not None:
         for i in range(B):

setup.py

@@ -33,8 +33,8 @@ def find_version(*file_paths):
 with open(path.join(here, 'README.md'), encoding='utf-8') as readme_file:
     long_description = readme_file.read()
 
-VERSION = find_version('face_alignment', '__init__.py')
-
+# VERSION = find_version('face_alignment', '__init__.py')
+VERSION = '2.0.0' #TODO Remove hardcoded value 
 requirements = [
     'torch',
     'numpy',