updates

detect.py

@@ -55,11 +55,13 @@ def detect(
         t = time.time()
         save_path = str(Path(output) / Path(path).name)
 
+        if ONNX_EXPORT:
+            img = torch.zeros((1, 3, 416, 416))
+            torch.onnx.export(model, img, 'weights/export.onnx', verbose=True)
+            return
+
         # Get detections
         img = torch.from_numpy(img).unsqueeze(0).to(device)
-        if ONNX_EXPORT:
-            torch.onnx.export(model, img, 'weights/model.onnx', verbose=True)
-            return
         pred, _ = model(img)
         detections = non_max_suppression(pred, conf_thres, nms_thres)[0]
 

models.py

@@ -5,7 +5,7 @@ import torch.nn.functional as F
 from utils.parse_config import *
 from utils.utils import *
 
-ONNX_EXPORT = False
+ONNX_EXPORT = True
 
 
 def create_modules(module_defs):
@@ -234,18 +234,20 @@ def get_yolo_layers(model):
 
 
 def create_grids(self, img_size, ng, device='cpu'):
+    nx, ny = ng, ng  # x and y grid size
     self.img_size = img_size
-    self.stride = img_size / ng
+    self.stride = img_size / nx
 
     # build xy offsets
-    grid_x = torch.arange(ng).repeat((ng, 1)).view((1, 1, ng, ng)).float()
-    grid_y = grid_x.permute(0, 1, 3, 2)
-    self.grid_xy = torch.stack((grid_x, grid_y), 4).to(device)
+    yv, xv = torch.meshgrid([torch.arange(nx), torch.arange(ny)])
+    self.grid_xy = torch.stack((xv, yv), 2).to(device).float().view((1, 1, nx, ny, 2))
 
     # build wh gains
     self.anchor_vec = self.anchors.to(device) / self.stride
     self.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2).to(device)
-    self.ng = torch.FloatTensor([ng]).to(device)
+    self.ng = torch.Tensor([ng]).to(device)
+    self.nx = nx
+    self.ny = ny
 
 
 def load_darknet_weights(self, weights, cutoff=-1):