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PaddleDetection

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PaddleDetection
未验证 提交 626d2fa1 编写于 作者: W wangxinxin08 提交者： GitHub
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support nhwc data_format in yolov3 (#2336)

上级 2b8638d1
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Showing with 146 addition and 37 deletion
dygraph/ppdet/modeling/architectures/meta_arch.py
浏览文件 @ 626d2fa1
......@@ -12,10 +12,14 @@ __all__ = ['BaseArch']
@register
class BaseArch(nn.Layer):
def __init__(self):
def __init__(self, data_format='NCHW'):
super(BaseArch, self).__init__()
self.data_format = data_format
def forward(self, inputs):
if self.data_format == 'NHWC':
image = inputs['image']
inputs['image'] = paddle.transpose(image, [0, 2, 3, 1])
self.inputs = inputs
self.model_arch()
......
dygraph/ppdet/modeling/architectures/yolo.py
浏览文件 @ 626d2fa1
......@@ -11,14 +11,16 @@ __all__ = ['YOLOv3']
@register
class YOLOv3(BaseArch):
__category__ = 'architecture'
__shared__ = ['data_format']
__inject__ = ['post_process']
def __init__(self,
backbone='DarkNet',
neck='YOLOv3FPN',
yolo_head='YOLOv3Head',
post_process='BBoxPostProcess'):
super(YOLOv3, self).__init__()
post_process='BBoxPostProcess',
data_format='NCHW'):
super(YOLOv3, self).__init__(data_format=data_format)
self.backbone = backbone
self.neck = neck
self.yolo_head = yolo_head
......
dygraph/ppdet/modeling/backbones/darknet.py
浏览文件 @ 626d2fa1
......@@ -35,7 +35,8 @@ class ConvBNLayer(nn.Layer):
norm_type='bn',
norm_decay=0.,
act="leaky",
name=None):
name=None,
data_format='NCHW'):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2D(
......@@ -46,9 +47,14 @@ class ConvBNLayer(nn.Layer):
padding=padding,
groups=groups,
weight_attr=ParamAttr(name=name + '.conv.weights'),
data_format=data_format,
bias_attr=False)
self.batch_norm = batch_norm(
ch_out, norm_type=norm_type, norm_decay=norm_decay, name=name)
ch_out,
norm_type=norm_type,
norm_decay=norm_decay,
name=name,
data_format=data_format)
self.act = act
def forward(self, inputs):
......@@ -68,7 +74,8 @@ class DownSample(nn.Layer):
padding=1,
norm_type='bn',
norm_decay=0.,
name=None):
name=None,
data_format='NCHW'):
super(DownSample, self).__init__()
......@@ -80,6 +87,7 @@ class DownSample(nn.Layer):
padding=padding,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=name)
self.ch_out = ch_out
......@@ -89,7 +97,13 @@ class DownSample(nn.Layer):
class BasicBlock(nn.Layer):
def __init__(self, ch_in, ch_out, norm_type='bn', norm_decay=0., name=None):
def __init__(self,
ch_in,
ch_out,
norm_type='bn',
norm_decay=0.,
name=None,
data_format='NCHW'):
super(BasicBlock, self).__init__()
self.conv1 = ConvBNLayer(
......@@ -100,6 +114,7 @@ class BasicBlock(nn.Layer):
padding=0,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=name + '.0')
self.conv2 = ConvBNLayer(
ch_in=ch_out,
......@@ -109,6 +124,7 @@ class BasicBlock(nn.Layer):
padding=1,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=name + '.1')
def forward(self, inputs):
......@@ -125,7 +141,8 @@ class Blocks(nn.Layer):
count,
norm_type='bn',
norm_decay=0.,
name=None):
name=None,
data_format='NCHW'):
super(Blocks, self).__init__()
self.basicblock0 = BasicBlock(
......@@ -133,6 +150,7 @@ class Blocks(nn.Layer):
ch_out,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=name + '.0')
self.res_out_list = []
for i in range(1, count):
......@@ -144,6 +162,7 @@ class Blocks(nn.Layer):
ch_out,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=block_name))
self.res_out_list.append(res_out)
self.ch_out = ch_out
......@@ -161,7 +180,7 @@ DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
@register
@serializable
class DarkNet(nn.Layer):
__shared__ = ['norm_type']
__shared__ = ['norm_type', 'data_format']
def __init__(self,
depth=53,
......@@ -169,7 +188,8 @@ class DarkNet(nn.Layer):
return_idx=[2, 3, 4],
num_stages=5,
norm_type='bn',
norm_decay=0.):
norm_decay=0.,
data_format='NCHW'):
super(DarkNet, self).__init__()
self.depth = depth
self.freeze_at = freeze_at
......@@ -185,6 +205,7 @@ class DarkNet(nn.Layer):
padding=1,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name='yolo_input')
self.downsample0 = DownSample(
......@@ -192,6 +213,7 @@ class DarkNet(nn.Layer):
ch_out=32 * 2,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name='yolo_input.downsample')
self._out_channels = []
......@@ -208,6 +230,7 @@ class DarkNet(nn.Layer):
stage,
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=name))
self.darknet_conv_block_list.append(conv_block)
if i in return_idx:
......@@ -221,6 +244,7 @@ class DarkNet(nn.Layer):
ch_out=32 * (2**(i + 2)),
norm_type=norm_type,
norm_decay=norm_decay,
data_format=data_format,
name=down_name))
self.downsample_list.append(downsample)
......
dygraph/ppdet/modeling/heads/yolo_head.py
浏览文件 @ 626d2fa1
......@@ -16,7 +16,7 @@ def _de_sigmoid(x, eps=1e-7):
@register
class YOLOv3Head(nn.Layer):
__shared__ = ['num_classes']
__shared__ = ['num_classes', 'data_format']
__inject__ = ['loss']
def __init__(self,
......@@ -26,7 +26,8 @@ class YOLOv3Head(nn.Layer):
num_classes=80,
loss='YOLOv3Loss',
iou_aware=False,
iou_aware_factor=0.4):
iou_aware_factor=0.4,
data_format='NCHW'):
super(YOLOv3Head, self).__init__()
self.num_classes = num_classes
self.loss = loss
......@@ -36,6 +37,7 @@ class YOLOv3Head(nn.Layer):
self.parse_anchor(anchors, anchor_masks)
self.num_outputs = len(self.anchors)
self.data_format = data_format
self.yolo_outputs = []
for i in range(len(self.anchors)):
......@@ -53,6 +55,7 @@ class YOLOv3Head(nn.Layer):
kernel_size=1,
stride=1,
padding=0,
data_format=data_format,
weight_attr=ParamAttr(name=name + '.conv.weights'),
bias_attr=ParamAttr(
name=name + '.conv.bias', regularizer=L2Decay(0.))))
......@@ -73,6 +76,8 @@ class YOLOv3Head(nn.Layer):
yolo_outputs = []
for i, feat in enumerate(feats):
yolo_output = self.yolo_outputs[i](feat)
if self.data_format == 'NHWC':
yolo_output = paddle.transpose(yolo_output, [0, 3, 1, 2])
yolo_outputs.append(yolo_output)
if self.training:
......
dygraph/ppdet/modeling/necks/yolo_fpn.py
浏览文件 @ 626d2fa1
......@@ -26,7 +26,7 @@ __all__ = ['YOLOv3FPN', 'PPYOLOFPN']
class YoloDetBlock(nn.Layer):
def __init__(self, ch_in, channel, norm_type, name):
def __init__(self, ch_in, channel, norm_type, name, data_format='NCHW'):
super(YoloDetBlock, self).__init__()
self.ch_in = ch_in
self.channel = channel
......@@ -51,6 +51,7 @@ class YoloDetBlock(nn.Layer):
filter_size=filter_size,
padding=(filter_size - 1) // 2,
norm_type=norm_type,
data_format=data_format,
name=name + post_name))
self.tip = ConvBNLayer(
......@@ -59,6 +60,7 @@ class YoloDetBlock(nn.Layer):
filter_size=3,
padding=1,
norm_type=norm_type,
data_format=data_format,
name=name + '.tip')
def forward(self, inputs):
......@@ -68,7 +70,14 @@ class YoloDetBlock(nn.Layer):
class SPP(nn.Layer):
def __init__(self, ch_in, ch_out, k, pool_size, norm_type, name):
def __init__(self,
ch_in,
ch_out,
k,
pool_size,
norm_type,
name,
data_format='NCHW'):
super(SPP, self).__init__()
self.pool = []
for size in pool_size:
......@@ -78,10 +87,17 @@ class SPP(nn.Layer):
kernel_size=size,
stride=1,
padding=size // 2,
data_format=data_format,
ceil_mode=False))
self.pool.append(pool)
self.conv = ConvBNLayer(
ch_in, ch_out, k, padding=k // 2, norm_type=norm_type, name=name)
ch_in,
ch_out,
k,
padding=k // 2,
norm_type=norm_type,
name=name,
data_format=data_format)
def forward(self, x):
outs = [x]
......@@ -93,30 +109,46 @@ class SPP(nn.Layer):
class DropBlock(nn.Layer):
def __init__(self, block_size, keep_prob, name):
def __init__(self, block_size, keep_prob, name, data_format='NCHW'):
super(DropBlock, self).__init__()
self.block_size = block_size
self.keep_prob = keep_prob
self.name = name
self.data_format = data_format
def forward(self, x):
if not self.training or self.keep_prob == 1:
return x
else:
gamma = (1. - self.keep_prob) / (self.block_size**2)
for s in x.shape[2:]:
if self.data_format == 'NCHW':
shape = x.shape[2:]
else:
shape = x.shape[1:3]
for s in shape:
gamma *= s / (s - self.block_size + 1)
matrix = paddle.cast(paddle.rand(x.shape, x.dtype) < gamma, x.dtype)
mask_inv = F.max_pool2d(
matrix, self.block_size, stride=1, padding=self.block_size // 2)
matrix,
self.block_size,
stride=1,
padding=self.block_size // 2,
data_format=self.data_format)
mask = 1. - mask_inv
y = x * mask * (mask.numel() / mask.sum())
return y
class CoordConv(nn.Layer):
def __init__(self, ch_in, ch_out, filter_size, padding, norm_type, name):
def __init__(self,
ch_in,
ch_out,
filter_size,
padding,
norm_type,
name,
data_format='NCHW'):
super(CoordConv, self).__init__()
self.conv = ConvBNLayer(
ch_in + 2,
......@@ -124,36 +156,53 @@ class CoordConv(nn.Layer):
filter_size=filter_size,
padding=padding,
norm_type=norm_type,
data_format=data_format,
name=name)
self.data_format = data_format
def forward(self, x):
b = x.shape[0]
h = x.shape[2]
w = x.shape[3]
if self.data_format == 'NCHW':
h = x.shape[2]
w = x.shape[3]
else:
h = x.shape[1]
w = x.shape[2]
gx = paddle.arange(w, dtype='float32') / (w - 1.) * 2.0 - 1.
gx = gx.reshape([1, 1, 1, w]).expand([b, 1, h, w])
if self.data_format == 'NCHW':
gx = gx.reshape([1, 1, 1, w]).expand([b, 1, h, w])
else:
gx = gx.reshape([1, 1, w, 1]).expand([b, h, w, 1])
gx.stop_gradient = True
gy = paddle.arange(h, dtype='float32') / (h - 1.) * 2.0 - 1.
gy = gy.reshape([1, 1, h, 1]).expand([b, 1, h, w])
if self.data_format == 'NCHW':
gy = gy.reshape([1, 1, h, 1]).expand([b, 1, h, w])
else:
gy = gy.reshape([1, h, 1, 1]).expand([b, h, w, 1])
gy.stop_gradient = True
y = paddle.concat([x, gx, gy], axis=1)
if self.data_format == 'NCHW':
y = paddle.concat([x, gx, gy], axis=1)
else:
y = paddle.concat([x, gx, gy], axis=-1)
y = self.conv(y)
return y
class PPYOLODetBlock(nn.Layer):
def __init__(self, cfg, name):
def __init__(self, cfg, name, data_format='NCHW'):
super(PPYOLODetBlock, self).__init__()
self.conv_module = nn.Sequential()
for idx, (conv_name, layer, args, kwargs) in enumerate(cfg[:-1]):
kwargs.update(name='{}.{}'.format(name, conv_name))
kwargs.update(
name='{}.{}'.format(name, conv_name), data_format=data_format)
self.conv_module.add_sublayer(conv_name, layer(*args, **kwargs))
conv_name, layer, args, kwargs = cfg[-1]
kwargs.update(name='{}.{}'.format(name, conv_name))
kwargs.update(
name='{}.{}'.format(name, conv_name), data_format=data_format)
self.tip = layer(*args, **kwargs)
def forward(self, inputs):
......@@ -165,9 +214,12 @@ class PPYOLODetBlock(nn.Layer):
@register
@serializable
class YOLOv3FPN(nn.Layer):
__shared__ = ['norm_type']
__shared__ = ['norm_type', 'data_format']
def __init__(self, in_channels=[256, 512, 1024], norm_type='bn'):
def __init__(self,
in_channels=[256, 512, 1024],
norm_type='bn',
data_format='NCHW'):
super(YOLOv3FPN, self).__init__()
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
......@@ -176,6 +228,7 @@ class YOLOv3FPN(nn.Layer):
self._out_channels = []
self.yolo_blocks = []
self.routes = []
self.data_format = data_format
for i in range(self.num_blocks):
name = 'yolo_block.{}'.format(i)
in_channel = in_channels[-i - 1]
......@@ -187,6 +240,7 @@ class YOLOv3FPN(nn.Layer):
in_channel,
channel=512 // (2**i),
norm_type=norm_type,
data_format=data_format,
name=name))
self.yolo_blocks.append(yolo_block)
# tip layer output channel doubled
......@@ -203,6 +257,7 @@ class YOLOv3FPN(nn.Layer):
stride=1,
padding=0,
norm_type=norm_type,
data_format=data_format,
name=name))
self.routes.append(route)
......@@ -212,13 +267,17 @@ class YOLOv3FPN(nn.Layer):
yolo_feats = []
for i, block in enumerate(blocks):
if i > 0:
block = paddle.concat([route, block], axis=1)
if self.data_format == 'NCHW':
block = paddle.concat([route, block], axis=1)
else:
block = paddle.concat([route, block], axis=-1)
route, tip = self.yolo_blocks[i](block)
yolo_feats.append(tip)
if i < self.num_blocks - 1:
route = self.routes[i](route)
route = F.interpolate(route, scale_factor=2.)
route = F.interpolate(
route, scale_factor=2., data_format=self.data_format)
return yolo_feats
......@@ -234,9 +293,13 @@ class YOLOv3FPN(nn.Layer):
@register
@serializable
class PPYOLOFPN(nn.Layer):
__shared__ = ['norm_type']
__shared__ = ['norm_type', 'data_format']
def __init__(self, in_channels=[512, 1024, 2048], norm_type='bn', **kwargs):
def __init__(self,
in_channels=[512, 1024, 2048],
norm_type='bn',
data_format='NCHW',
**kwargs):
super(PPYOLOFPN, self).__init__()
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
......@@ -332,6 +395,7 @@ class PPYOLOFPN(nn.Layer):
stride=1,
padding=0,
norm_type=norm_type,
data_format=data_format,
name=name))
self.routes.append(route)
......@@ -341,13 +405,17 @@ class PPYOLOFPN(nn.Layer):
yolo_feats = []
for i, block in enumerate(blocks):
if i > 0:
block = paddle.concat([route, block], axis=1)
if self.data_format == 'NCHW':
block = paddle.concat([route, block], axis=1)
else:
block = paddle.concat([route, block], axis=-1)
route, tip = self.yolo_blocks[i](block)
yolo_feats.append(tip)
if i < self.num_blocks - 1:
route = self.routes[i](route)
route = F.interpolate(route, scale_factor=2.)
route = F.interpolate(
route, scale_factor=2., data_format=self.data_format)
return yolo_feats
......
dygraph/ppdet/modeling/ops.py
浏览文件 @ 626d2fa1
......@@ -44,7 +44,12 @@ __all__ = [
]
def batch_norm(ch, norm_type='bn', norm_decay=0., initializer=None, name=None):
def batch_norm(ch,
norm_type='bn',
norm_decay=0.,
initializer=None,
name=None,
data_format='NCHW'):
bn_name = name + '.bn'
if norm_type == 'sync_bn':
batch_norm = nn.SyncBatchNorm
......@@ -58,7 +63,8 @@ def batch_norm(ch, norm_type='bn', norm_decay=0., initializer=None, name=None):
initializer=initializer,
regularizer=L2Decay(norm_decay)),
bias_attr=ParamAttr(
name=bn_name + '.offset', regularizer=L2Decay(norm_decay)))
name=bn_name + '.offset', regularizer=L2Decay(norm_decay)),
data_format=data_format)
@paddle.jit.not_to_static
......
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