Fix float zeros format (#5491)

* Fix float zeros format

* 255 to integer

detect.py

         else:
             img = torch.from_numpy(img).to(device)
             img = img.half() if half else img.float()  # uint8 to fp16/32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        img /= 255  # 0 - 255 to 0.0 - 1.0
         if len(img.shape) == 3:
             img = img[None]  # expand for batch dim
         t2 = time_sync()

export.py

 
         model.train()  # CoreML exports should be placed in model.train() mode
         ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
-        ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255.0, bias=[0, 0, 0])])
+        ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
         ct_model.save(f)
 
         LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')

models/common.py

         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'):
     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

         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

         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, :],
 
             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)
     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

 
                 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))

train.py

 
     # Model parameters
     nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)
-    hyp['box'] *= 3. / nl  # scale to layers
-    hyp['cls'] *= nc / 80. * 3. / nl  # scale to classes and layers
-    hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl  # scale to image size and layers
+    hyp['box'] *= 3 / nl  # scale to layers
+    hyp['cls'] *= nc / 80 * 3 / nl  # scale to classes and layers
+    hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
     hyp['label_smoothing'] = opt.label_smoothing
     model.nc = nc  # attach number of classes to model
     model.hyp = hyp  # attach hyperparameters to model
         optimizer.zero_grad()
         for i, (imgs, targets, paths, _) in pbar:  # batch -------------------------------------------------------------
             ni = i + nb * epoch  # number integrated batches (since train start)
-            imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0
+            imgs = imgs.to(device, non_blocking=True).float() / 255  # uint8 to float32, 0-255 to 0.0-1.0
 
             # Warmup
             if ni <= nw:

utils/activations.py

     @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

 
 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

 
     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
     """
     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

         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

 
                 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

             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':
                 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)
             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

     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

         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:
 
 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()
             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

val.py

         t1 = time_sync()
         img = img.to(device, non_blocking=True)
         img = img.half() if half else img.float()  # uint8 to fp16/32
-        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        img /= 255  # 0 - 255 to 0.0 - 1.0
         targets = targets.to(device)
         nb, _, height, width = img.shape  # batch size, channels, height, width
         t2 = time_sync()