Fix float zeros format (#5491)

* Fix float zeros format

* 255 to integer

models/common.py

@@ -339,7 +339,7 @@ class AutoShape(nn.Module):
         x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs]  # pad
         x = np.stack(x, 0) if n > 1 else x[0][None]  # stack
         x = np.ascontiguousarray(x.transpose((0, 3, 1, 2)))  # BHWC to BCHW
-        x = torch.from_numpy(x).to(p.device).type_as(p) / 255.  # uint8 to fp16/32
+        x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32
         t.append(time_sync())
 
         with amp.autocast(enabled=p.device.type != 'cpu'):
@@ -362,7 +362,7 @@ class Detections:
     def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
         super().__init__()
         d = pred[0].device  # device
-        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1., 1.], device=d) for im in imgs]  # normalizations
+        gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs]  # normalizations
         self.imgs = imgs  # list of images as numpy arrays
         self.pred = pred  # list of tensors pred[0] = (xyxy, conf, cls)
         self.names = names  # class names

models/experimental.py

@@ -32,7 +32,7 @@ class Sum(nn.Module):
         self.weight = weight  # apply weights boolean
         self.iter = range(n - 1)  # iter object
         if weight:
-            self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True)  # layer weights
+            self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True)  # layer weights
 
     def forward(self, x):
         y = x[0]  # no weight

models/tf.py

@@ -98,7 +98,7 @@ class TFFocus(keras.layers.Layer):
         self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
 
     def call(self, inputs):  # x(b,w,h,c) -> y(b,w/2,h/2,4c)
-        # inputs = inputs / 255.  # normalize 0-255 to 0-1
+        # inputs = inputs / 255  # normalize 0-255 to 0-1
         return self.conv(tf.concat([inputs[:, ::2, ::2, :],
                                     inputs[:, 1::2, ::2, :],
                                     inputs[:, ::2, 1::2, :],
@@ -227,7 +227,7 @@ class TFDetect(keras.layers.Layer):
 
             if not self.training:  # inference
                 y = tf.sigmoid(x[i])
-                xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
+                xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                 wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
                 # Normalize xywh to 0-1 to reduce calibration error
                 xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
@@ -414,7 +414,7 @@ def representative_dataset_gen(dataset, ncalib=100):
     for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
         input = np.transpose(img, [1, 2, 0])
         input = np.expand_dims(input, axis=0).astype(np.float32)
-        input /= 255.0
+        input /= 255
         yield [input]
         if n >= ncalib:
             break

models/yolo.py

@@ -60,10 +60,10 @@ class Detect(nn.Module):
 
                 y = x[i].sigmoid()
                 if self.inplace:
-                    y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
+                    y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                     y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                 else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
-                    xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
+                    xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i]  # xy
                     wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                     y = torch.cat((xy, wh, y[..., 4:]), -1)
                 z.append(y.view(bs, -1, self.no))