Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

hyperparameter updates

This commit is contained in:
Glenn Jocher 2019-04-19 20:41:18 +02:00
parent b4fa1d90d0
commit f9d25f6d24
4 changed files with 74 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
detect.py
View File

@ -34,6 +34,22 @@ def detect(
else: # darknet format
_ = load_darknet_weights(model, weights)
# Fuse batchnorm
fuse = True
if fuse:
fused_list = nn.ModuleList()
for a in list(model.children())[0]:
for i, b in enumerate(a):
if isinstance(b, nn.modules.batchnorm.BatchNorm2d):
# fuse this bn layer with the previous conv2d layer
conv = a[i - 1]
fused = torch_utils.fuse_conv_and_bn(conv, b)
a = nn.Sequential(fused, *list(a.children())[i + 1:])
break
fused_list.append(a)
model.module_list = fused_list
#model_info(model) # yolov3-spp reduced from 225 to 152 layers
model.to(device).eval()
# Set Dataloader

46
models.py
View File

@ -63,10 +63,10 @@ def create_modules(module_defs):
anchors = [float(x) for x in module_def['anchors'].split(',')]
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
anchors = [anchors[i] for i in anchor_idxs]
nC = int(module_def['classes']) # number of classes
nc = int(module_def['classes']) # number of classes
img_size = int(hyperparams['height'])
# Define detection layer
yolo_layer = YOLOLayer(anchors, nC, img_size, yolo_layer_count, cfg=hyperparams['cfg'])
yolo_layer = YOLOLayer(anchors, nc, img_size, yolo_layer_count, cfg=hyperparams['cfg'])
modules.add_module('yolo_%d' % i, yolo_layer)
yolo_layer_count += 1
@ -100,12 +100,12 @@ class Upsample(nn.Module):
class YOLOLayer(nn.Module):
def __init__(self, anchors, nC, img_size, yolo_layer, cfg):
def __init__(self, anchors, nc, img_size, yolo_layer, cfg):
super(YOLOLayer, self).__init__()
self.anchors = torch.FloatTensor(anchors)
self.nA = len(anchors) # number of anchors (3)
self.nC = nC # number of classes (80)
self.na = len(anchors) # number of anchors (3)
self.nc = nc # number of classes (80)
self.img_size = 0
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
create_grids(self, 32, 1, device=device)
@ -115,35 +115,35 @@ class YOLOLayer(nn.Module):
if cfg.endswith('yolov3-tiny.cfg'):
stride *= 2
nG = int(img_size / stride) # number grid points
create_grids(self, img_size, nG)
ng = int(img_size / stride) # number grid points
create_grids(self, img_size, ng)
def forward(self, p, img_size, var=None):
if ONNX_EXPORT:
bs, nG = 1, self.nG # batch size, grid size
bs, ng = 1, self.ng # batch size, grid size
else:
bs, nG = p.shape[0], p.shape[-1]
bs, ng = p.shape[0], p.shape[-1]
if self.img_size != img_size:
create_grids(self, img_size, nG, p.device)
create_grids(self, img_size, ng, p.device)
# p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85) # (bs, anchors, grid, grid, classes + xywh)
p = p.view(bs, self.nA, self.nC + 5, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
p = p.view(bs, self.na, self.nc + 5, ng, ng).permute(0, 1, 3, 4, 2).contiguous() # prediction
if self.training:
return p
elif ONNX_EXPORT:
grid_xy = self.grid_xy.repeat((1, self.nA, 1, 1, 1)).view((1, -1, 2))
anchor_wh = self.anchor_wh.repeat((1, 1, nG, nG, 1)).view((1, -1, 2)) / nG
grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view((1, -1, 2))
anchor_wh = self.anchor_wh.repeat((1, 1, ng, ng, 1)).view((1, -1, 2)) / ng
# p = p.view(-1, 5 + self.nC)
# p = p.view(-1, 5 + self.nc)
# xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y
# wh = torch.exp(p[..., 2:4]) * anchor_wh[0] # width, height
# p_conf = torch.sigmoid(p[:, 4:5]) # Conf
# p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like conf
# return torch.cat((xy / nG, wh, p_conf, p_cls), 1).t()
# return torch.cat((xy / ng, wh, p_conf, p_cls), 1).t()
p = p.view(1, -1, 5 + self.nC)
p = p.view(1, -1, 5 + self.nc)
xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y
wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height
p_conf = torch.sigmoid(p[..., 4:5]) # Conf
@ -153,7 +153,7 @@ class YOLOLayer(nn.Module):
p_cls = torch.exp(p_cls).permute((2, 1, 0))
p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent
p_cls = p_cls.permute(2, 1, 0)
return torch.cat((xy / nG, wh, p_conf, p_cls), 2).squeeze().t()
return torch.cat((xy / ng, wh, p_conf, p_cls), 2).squeeze().t()
else: # inference
io = p.clone() # inference output
@ -165,7 +165,7 @@ class YOLOLayer(nn.Module):
io[..., :4] *= self.stride
# reshape from [1, 3, 13, 13, 85] to [1, 507, 85]
return io.view(bs, -1, 5 + self.nC), p
return io.view(bs, -1, 5 + self.nc), p
class Darknet(nn.Module):
@ -218,19 +218,19 @@ def get_yolo_layers(model):
return [i for i, x in enumerate(a) if x] # [82, 94, 106] for yolov3
def create_grids(self, img_size, nG, device='cpu'):
def create_grids(self, img_size, ng, device='cpu'):
self.img_size = img_size
self.stride = img_size / nG
self.stride = img_size / ng
# build xy offsets
grid_x = torch.arange(nG).repeat((nG, 1)).view((1, 1, nG, nG)).float()
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)
# 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.anchor_wh = self.anchor_vec.view(1, self.na, 1, 1, 2).to(device)
self.ng = torch.FloatTensor([ng]).to(device)
def load_darknet_weights(self, weights, cutoff=-1):

29
utils/torch_utils.py
View File

@ -26,3 +26,32 @@ def select_device(force_cpu=False):
(i, x[i].name, x[i].total_memory / c))
return device
def fuse_conv_and_bn(conv, bn):
# https://tehnokv.com/posts/fusing-batchnorm-and-conv/
with torch.no_grad():
# init
fusedconv = torch.nn.Conv2d(
conv.in_channels,
conv.out_channels,
kernel_size=conv.kernel_size,
stride=conv.stride,
padding=conv.padding,
bias=True
)
# prepare filters
w_conv = conv.weight.clone().view(conv.out_channels, -1)
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
# prepare spatial bias
if conv.bias is not None:
b_conv = conv.bias
else:
b_conv = torch.zeros(conv.weight.size(0))
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
fusedconv.bias.copy_(b_conv + b_bn)
return fusedconv

12
utils/utils.py
View File

@ -291,7 +291,7 @@ def build_targets(model, targets):
# iou of targets-anchors
t, a = targets, []
gwh = targets[:, 4:6] * layer.nG
gwh = targets[:, 4:6] * layer.ng
if nt:
iou = [wh_iou(x, gwh) for x in layer.anchor_vec]
iou, a = torch.stack(iou, 0).max(0) # best iou and anchor
@ -304,7 +304,7 @@ def build_targets(model, targets):
# Indices
b, c = t[:, :2].long().t() # target image, class
gxy = t[:, 2:4] * layer.nG
gxy = t[:, 2:4] * layer.ng
gi, gj = gxy.long().t() # grid_i, grid_j
indices.append((b, a, gj, gi))
@ -318,7 +318,7 @@ def build_targets(model, targets):
# Class
tcls.append(c)
if c.shape[0]:
assert c.max() <= layer.nC, 'Target classes exceed model classes'
assert c.max() <= layer.nc, 'Target classes exceed model classes'
return txy, twh, tcls, indices
@ -442,12 +442,12 @@ def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
def coco_class_count(path='../coco/labels/train2014/'):
# Histogram of occurrences per class
nC = 80 # number classes
x = np.zeros(nC, dtype='int32')
nc = 80 # number classes
x = np.zeros(nc, dtype='int32')
files = sorted(glob.glob('%s/*.*' % path))
for i, file in enumerate(files):
labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
x += np.bincount(labels[:, 0].astype('int32'), minlength=nC)
x += np.bincount(labels[:, 0].astype('int32'), minlength=nc)
print(i, len(files))