Refactor `Detect()` anchors for ONNX <> OpenCV DNN compatibility (#4833)
* refactor anchors and anchor_grid in Detect Layer * fix CI failures by adding compatibility * fix tf failure * fix different devices errors * Cleanup * fix anchors overwriting issue * better refactoring * Remove self.anchor_grid shape check (redundant with self.grid check) Also PEP8 / 120 line width * Convert _make_grid() from static to dynamic method * Remove anchor_grid.to(device) clone() should already clone to same device as self.anchors * fix different devices error Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/5136/head
Jebastin Nadar
GitHub
parent
153873e9e4
commit
9d75e42f98
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@ -295,6 +295,8 @@ class AutoShape(nn.Module):
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m = self.model.model[-1] # Detect()
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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@torch.no_grad()
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@ -102,6 +102,10 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
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for m in model.modules():
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if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
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m.inplace = inplace # pytorch 1.7.0 compatibility
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if type(m) is Detect:
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if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility
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delattr(m, 'anchor_grid')
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setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
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elif type(m) is Conv:
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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@ -193,7 +193,7 @@ class TFDetect(keras.layers.Layer):
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self.na = len(anchors[0]) // 2 # number of anchors
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self.grid = [tf.zeros(1)] * self.nl # init grid
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self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
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self.anchor_grid = tf.reshape(tf.convert_to_tensor(w.anchor_grid.numpy(), dtype=tf.float32),
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self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
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[self.nl, 1, -1, 1, 2])
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self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
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self.training = False # set to False after building model
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@ -44,9 +44,8 @@ class Detect(nn.Module):
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self.nl = len(anchors) # number of detection layers
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self.na = len(anchors[0]) // 2 # number of anchors
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self.grid = [torch.zeros(1)] * self.nl # init grid
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a = torch.tensor(anchors).float().view(self.nl, -1, 2)
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self.register_buffer('anchors', a) # shape(nl,na,2)
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self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
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self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
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self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
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self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
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self.inplace = inplace # use in-place ops (e.g. slice assignment)
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@ -59,7 +58,7 @@ class Detect(nn.Module):
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if not self.training: # inference
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if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
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self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
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self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
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y = x[i].sigmoid()
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if self.inplace:
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@ -67,16 +66,19 @@ class Detect(nn.Module):
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y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
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else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
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xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
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wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh
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wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
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y = torch.cat((xy, wh, y[..., 4:]), -1)
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z.append(y.view(bs, -1, self.no))
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return x if self.training else (torch.cat(z, 1), x)
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@staticmethod
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def _make_grid(nx=20, ny=20):
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yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
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return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
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def _make_grid(self, nx=20, ny=20, i=0):
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d = self.anchors[i].device
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yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
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grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
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anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
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.view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
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return grid, anchor_grid
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class Model(nn.Module):
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@ -239,6 +241,8 @@ class Model(nn.Module):
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if isinstance(m, Detect):
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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@ -15,13 +15,12 @@ from utils.general import colorstr
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def check_anchor_order(m):
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# Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
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a = m.anchor_grid.prod(-1).view(-1) # anchor area
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a = m.anchors.prod(-1).view(-1) # anchor area
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da = a[-1] - a[0] # delta a
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ds = m.stride[-1] - m.stride[0] # delta s
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if da.sign() != ds.sign(): # same order
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print('Reversing anchor order')
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m.anchors[:] = m.anchors.flip(0)
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m.anchor_grid[:] = m.anchor_grid.flip(0)
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def check_anchors(dataset, model, thr=4.0, imgsz=640):
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@ -41,12 +40,12 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
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bpr = (best > 1. / thr).float().mean() # best possible recall
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return bpr, aat
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anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors
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bpr, aat = metric(anchors)
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anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1) # current anchors
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bpr, aat = metric(anchors.cpu().view(-1, 2))
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print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
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if bpr < 0.98: # threshold to recompute
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print('. Attempting to improve anchors, please wait...')
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na = m.anchor_grid.numel() // 2 # number of anchors
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na = m.anchors.numel() // 2 # number of anchors
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try:
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anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
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except Exception as e:
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@ -54,7 +53,6 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
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new_bpr = metric(anchors)[0]
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if new_bpr > bpr: # replace anchors
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anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
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m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference
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m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
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check_anchor_order(m)
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print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
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