Fix float zeros format (#5491)

* Fix float zeros format

* 255 to integer

utils/activations.py

@@ -19,7 +19,7 @@ class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
     @staticmethod
     def forward(x):
         # return x * F.hardsigmoid(x)  # for torchscript and CoreML
-        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
+        return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0  # for torchscript, CoreML and ONNX
 
 
 # Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------

utils/augmentations.py

@@ -124,7 +124,7 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
 
 def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
                        border=(0, 0)):
-    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
+    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
     # targets = [cls, xyxy]
 
     height = im.shape[0] + border[0] * 2  # shape(h,w,c)

utils/autoanchor.py

@@ -34,10 +34,10 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
 
     def metric(k):  # compute metric
         r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
         best = x.max(1)[0]  # best_x
-        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1. / thr).float().mean()  # best possible recall
+        aat = (x > 1 / thr).float().sum(1).mean()  # anchors above threshold
+        bpr = (best > 1 / thr).float().mean()  # best possible recall
         return bpr, aat
 
     anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1)  # current anchors
@@ -80,12 +80,12 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
     """
     from scipy.cluster.vq import kmeans
 
-    thr = 1. / thr
+    thr = 1 / thr
     prefix = colorstr('autoanchor: ')
 
     def metric(k, wh):  # compute metrics
         r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        x = torch.min(r, 1 / r).min(2)[0]  # ratio metric
         # x = wh_iou(wh, torch.tensor(k))  # iou metric
         return x, x.max(1)[0]  # x, best_x
 

utils/datasets.py

@@ -634,13 +634,13 @@ class LoadImagesAndLabels(Dataset):
         n = len(shapes) // 4
         img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
 
-        ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
-        wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
-        s = torch.tensor([[1, 1, .5, .5, .5, .5]])  # scale
+        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
+        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
+        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # scale
         for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
             i *= 4
             if random.random() < 0.5:
-                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
+                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', align_corners=False)[
                     0].type(img[i].type())
                 l = label[i]
             else:

utils/general.py

@@ -802,7 +802,7 @@ def apply_classifier(x, model, img, im0):
 
                 im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
                 im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
-                im /= 255.0  # 0 - 255 to 0.0 - 1.0
+                im /= 255  # 0 - 255 to 0.0 - 1.0
                 ims.append(im)
 
             pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction

utils/loss.py

@@ -108,7 +108,7 @@ class ComputeLoss:
             BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
         det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
+        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7
         self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
         self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
         for k in 'na', 'nc', 'nl', 'anchors':
@@ -129,7 +129,7 @@ class ComputeLoss:
                 ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
 
                 # Regression
-                pxy = ps[:, :2].sigmoid() * 2. - 0.5
+                pxy = ps[:, :2].sigmoid() * 2 - 0.5
                 pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
                 pbox = torch.cat((pxy, pwh), 1)  # predicted box
                 iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
@@ -189,15 +189,15 @@ class ComputeLoss:
             if nt:
                 # Matches
                 r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
+                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
                 # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                 t = t[j]  # filter
 
                 # Offsets
                 gxy = t[:, 2:4]  # grid xy
                 gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
+                j, k = ((gxy % 1 < g) & (gxy > 1)).T
+                l, m = ((gxi % 1 < g) & (gxi > 1)).T
                 j = torch.stack((torch.ones_like(j), j, k, l, m))
                 t = t.repeat((5, 1, 1))[j]
                 offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]

utils/plots.py

@@ -155,7 +155,7 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
     if isinstance(targets, torch.Tensor):
         targets = targets.cpu().numpy()
     if np.max(images[0]) <= 1:
-        images *= 255.0  # de-normalise (optional)
+        images *= 255  # de-normalise (optional)
     bs, _, h, w = images.shape  # batch size, _, height, width
     bs = min(bs, max_subplots)  # limit plot images
     ns = np.ceil(bs ** 0.5)  # number of subplots (square)

utils/torch_utils.py

@@ -111,7 +111,7 @@ def profile(input, ops, n=10, device=None):
         for m in ops if isinstance(ops, list) else [ops]:
             m = m.to(device) if hasattr(m, 'to') else m  # device
             m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
-            tf, tb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
+            tf, tb, t = 0, 0, [0, 0, 0]  # dt forward, backward
             try:
                 flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPs
             except:
@@ -177,7 +177,7 @@ def find_modules(model, mclass=nn.Conv2d):
 
 def sparsity(model):
     # Return global model sparsity
-    a, b = 0., 0.
+    a, b = 0, 0
     for p in model.parameters():
         a += p.numel()
         b += (p == 0).sum()
@@ -336,7 +336,7 @@ class ModelEMA:
             for k, v in self.ema.state_dict().items():
                 if v.dtype.is_floating_point:
                     v *= d
-                    v += (1. - d) * msd[k].detach()
+                    v += (1 - d) * msd[k].detach()
 
     def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
         # Update EMA attributes