def __init__(self, in_dim=256, mlp_dim=1024, resolution=7, num_stages=1):
super(TwoFCHead, self).__init__()
self.in_dim = in_dim
self.mlp_dim = mlp_dim
self.num_stages = num_stages
fan = in_dim * resolution * resolution
self.fc6_list = []
self.fc6_relu_list = []
self.fc7_list = []
self.fc7_relu_list = []
for stage in range(num_stages):
fc6_name = 'fc6_{}'.format(stage)
fc7_name = 'fc7_{}'.format(stage)
fc6 = self.add_sublayer(
fc6_name,
nn.Linear(in_dim * resolution * resolution,
mlp_dim,
weight_attr=ParamAttr(initializer=XavierUniform(
fan_out=fan)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
fc6_relu = self.add_sublayer(fc6_name + 'act', ReLU())
fc7 = self.add_sublayer(
fc7_name,
nn.Linear(mlp_dim,
mlp_dim,
weight_attr=ParamAttr(initializer=XavierUniform()),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
fc7_relu = self.add_sublayer(fc7_name + 'act', ReLU())
self.fc6_list.append(fc6)
self.fc6_relu_list.append(fc6_relu)
self.fc7_list.append(fc7)
self.fc7_relu_list.append(fc7_relu)
def __init__(self, num_classes=10, classifier_activation='softmax'):
super(LeNet, self).__init__()
self.num_classes = num_classes
self.features = Sequential(Conv2d(1, 6, 3, stride=1,
padding=1), ReLU(),
Pool2D(2, 'max', 2),
Conv2d(6, 16, 5, stride=1, padding=0),
ReLU(), Pool2D(2, 'max', 2))
if num_classes > 0:
self.fc = Sequential(Linear(400, 120), Linear(120, 84),
Linear(84, 10),
Softmax()) #Todo: accept any activation
def __init__(self, num_classes=10):
super(LeNetDygraph, self).__init__()
self.num_classes = num_classes
self.features = Sequential(Conv2D(1, 6, 3, stride=1,
padding=1), ReLU(),
paddle.fluid.dygraph.Pool2D(2, 'max', 2),
Conv2D(6, 16, 5, stride=1, padding=0),
ReLU(),
paddle.fluid.dygraph.Pool2D(2, 'max', 2))
if num_classes > 0:
self.fc = Sequential(Linear(400, 120), Linear(120, 84),
Linear(84, 10))
def __init__(self, num_classes=10):
super(LeNetListInput, self).__init__()
self.num_classes = num_classes
self.cov = Conv2D(1, 6, 3, stride=1, padding=1)
for param in self.cov.parameters():
param.trainable = False
self.features = Sequential(self.cov, ReLU(),
paddle.fluid.dygraph.Pool2D(2, 'max', 2),
Conv2D(6, 16, 5, stride=1, padding=0),
ReLU(),
paddle.fluid.dygraph.Pool2D(2, 'max', 2))
if num_classes > 0:
self.fc = Sequential(Linear(400, 120), Linear(120, 84),
Linear(84, 10))
def __init__(self,
num_channels,
num_filters,
stride=1,
shortcut=True,
name=None):
super(BasicBlock, self).__init__(name)
self.conv0 = ConvBNLayer(num_channels=num_channels,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu',
name=name)
self.conv1 = ConvBNLayer(num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
act=None,
name=name)
if not shortcut:
self.short = ConvBNLayer(num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
stride=stride,
act=None,
name=name)
self.shortcut = shortcut
self.relu = ReLU()
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
zero_init_residual=True):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = BatchNorm2D(
planes,
weight_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)),
bias_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)))
self.relu = ReLU()
self.conv2 = conv3x3(planes, planes)
if zero_init_residual:
self.bn2 = BatchNorm2D(
planes,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Constant(value=0.0),
regularizer=paddle.regularizer.L1Decay(0.0)),
bias_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)))
else:
self.bn2 = BatchNorm2D(
planes,
weight_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)),
bias_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)))
self.downsample = downsample
self.stride = stride
def make_layers(cfg, batch_norm=False):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [Pool2D(pool_size=2, pool_stride=2)]
else:
if batch_norm:
conv2d = Conv2d(in_channels, v, kernel_size=3, padding=1)
layers += [conv2d, BatchNorm(v), ReLU()]
else:
conv2d = Conv2d(in_channels, v, kernel_size=3, padding=1)
layers += [conv2d, ReLU()]
in_channels = v
return Sequential(*layers)
def __init__(self,
in_channels,
out_channels,
filter_size,
pool_size,
pool_stride,
pool_padding=0,
conv_stride=1,
conv_padding=0,
conv_dilation=1,
conv_groups=1,
weight_attr=None,
bias_attr=None):
super(SimpleImgConvPool, self).__init__()
# Conv2D网络的初始化
self._conv2d = Conv2D(in_channels=in_channels,
out_channels=out_channels,
kernel_size=filter_size,
stride=conv_stride,
padding=conv_padding,
dilation=conv_dilation,
groups=conv_groups,
weight_attr=weight_attr,
bias_attr=bias_attr)
# ReLU激活的初始化
self._relu = ReLU()
# Pool2D网络的初始化
self._pool2d = MaxPool2D(kernel_size=pool_size,
stride=pool_stride,
padding=pool_padding)
def __init__(self,
input_channels,
output_channels,
filter_size,
stride,
padding,
stdv,
groups=1,
act=None,
name=None):
super(ConvPoolLayer, self).__init__()
self.relu = ReLU() if act == "relu" else None
self._conv = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=ParamAttr(
name=name + "_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name=name + "_offset", initializer=Uniform(-stdv, stdv)))
self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
def __init__(self,
mask_roi_extractor=None,
num_convs=0,
feat_in=2048,
feat_out=256,
mask_num_stages=1,
share_bbox_feat=False):
super(MaskFeat, self).__init__()
self.num_convs = num_convs
self.feat_in = feat_in
self.feat_out = feat_out
self.mask_roi_extractor = mask_roi_extractor
self.mask_num_stages = mask_num_stages
self.share_bbox_feat = share_bbox_feat
self.upsample_module = []
fan_conv = feat_out * 3 * 3
fan_deconv = feat_out * 2 * 2
for i in range(self.mask_num_stages):
name = 'stage_{}'.format(i)
mask_conv = Sequential()
for j in range(self.num_convs):
conv_name = 'mask_inter_feat_{}'.format(j + 1)
mask_conv.add_sublayer(
conv_name,
Conv2D(in_channels=feat_in if j == 0 else feat_out,
out_channels=feat_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=KaimingNormal(
fan_in=fan_conv)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
mask_conv.add_sublayer(conv_name + 'act', ReLU())
mask_conv.add_sublayer(
'conv5_mask',
Conv2DTranspose(
in_channels=self.feat_in,
out_channels=self.feat_out,
kernel_size=2,
stride=2,
weight_attr=ParamAttr(initializer=KaimingNormal(
fan_in=fan_deconv)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
mask_conv.add_sublayer('conv5_mask' + 'act', ReLU())
upsample = self.add_sublayer(name, mask_conv)
self.upsample_module.append(upsample)
def __init__(self):
super(Model, self).__init__()
with supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2,
4]) as ofa_super:
models = []
models += [nn.Conv2D(1, 6, 3)]
models += [ReLU()]
models += [nn.Pool2D(2, 'max', 2)]
models += [nn.Conv2D(6, 16, 5, padding=0)]
models += [ReLU()]
models += [nn.Pool2D(2, 'max', 2)]
models += [
nn.Linear(784, 120),
nn.Linear(120, 84),
nn.Linear(84, 10)
]
models = ofa_super.convert(models)
self.models = paddle.nn.Sequential(*models)
def __init__(self, num_classes=10):
super(ImperativeLenetWithSkipQuant, self).__init__()
conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
fc_b3_attr = fluid.ParamAttr(name="fc_b_3")
self.conv2d_0 = Conv2D(in_channels=1,
out_channels=6,
kernel_size=3,
stride=1,
padding=1,
weight_attr=conv2d_w1_attr,
bias_attr=conv2d_b1_attr)
self.conv2d_0.skip_quant = True
self.batch_norm_0 = BatchNorm2D(6)
self.relu_0 = ReLU()
self.pool2d_0 = MaxPool2D(kernel_size=2, stride=2)
self.conv2d_1 = Conv2D(in_channels=6,
out_channels=16,
kernel_size=5,
stride=1,
padding=0,
weight_attr=conv2d_w2_attr,
bias_attr=conv2d_b2_attr)
self.conv2d_1.skip_quant = False
self.batch_norm_1 = BatchNorm2D(16)
self.relu6_0 = ReLU6()
self.pool2d_1 = MaxPool2D(kernel_size=2, stride=2)
self.linear_0 = Linear(in_features=400,
out_features=120,
weight_attr=fc_w1_attr,
bias_attr=fc_b1_attr)
self.linear_0.skip_quant = True
self.leaky_relu_0 = LeakyReLU()
self.linear_1 = Linear(in_features=120,
out_features=84,
weight_attr=fc_w2_attr,
bias_attr=fc_b2_attr)
self.linear_1.skip_quant = False
self.sigmoid_0 = Sigmoid()
self.linear_2 = Linear(in_features=84,
out_features=num_classes,
weight_attr=fc_w3_attr,
bias_attr=fc_b3_attr)
self.linear_2.skip_quant = False
self.softmax_0 = Softmax()
def __init__(self):
super(ModelConv, self).__init__()
with supernet(kernel_size=(3, 5, 7),
channel=((4, 8, 12), (8, 12, 16), (8, 12, 16),
(8, 12, 16))) as ofa_super:
models = []
models += [nn.Conv2D(3, 4, 3, padding=1)]
models += [nn.InstanceNorm2D(4)]
models += [ReLU()]
models += [nn.Conv2D(4, 4, 3, groups=4)]
models += [nn.InstanceNorm2D(4)]
models += [ReLU()]
models += [nn.Conv2DTranspose(4, 4, 3, groups=4, padding=1)]
models += [nn.BatchNorm2D(4)]
models += [ReLU()]
models += [nn.Conv2D(4, 3, 3)]
models += [ReLU()]
models = ofa_super.convert(models)
models += [
Block(SuperSeparableConv2D(3,
6,
1,
padding=1,
candidate_config={'channel': (3, 6)}),
fixed=True)
]
with supernet(kernel_size=(3, 5, 7),
expand_ratio=(1, 2, 4)) as ofa_super:
models1 = []
models1 += [nn.Conv2D(6, 4, 3)]
models1 += [nn.BatchNorm2D(4)]
models1 += [ReLU()]
models1 += [nn.Conv2D(4, 4, 3, groups=2)]
models1 += [nn.InstanceNorm2D(4)]
models1 += [ReLU()]
models1 += [nn.Conv2DTranspose(4, 4, 3, groups=2)]
models1 += [nn.BatchNorm2D(4)]
models1 += [ReLU()]
models1 += [nn.Conv2DTranspose(4, 4, 3)]
models1 += [nn.BatchNorm2D(4)]
models1 += [ReLU()]
models1 += [nn.Conv2DTranspose(4, 4, 1)]
models1 += [nn.BatchNorm2D(4)]
models1 += [ReLU()]
models1 = ofa_super.convert(models1)
models += models1
self.models = paddle.nn.Sequential(*models)
def __init__(self):
super(ModelCase6, self).__init__()
self.bn1 = BatchNorm2D(3)
self.relu1 = ReLU()
self.fc1 = paddle.nn.Linear(3 * 16 * 16, 3 * 16 * 16)
self.dp = paddle.nn.Dropout(p=0.5)
self.lstm = paddle.nn.LSTM(1536,
10,
direction='bidirectional',
num_layers=2)
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = AdaptiveAvgPool2D(1)
self.fc1 = Conv2D(channels,
channels // reduction,
kernel_size=1,
padding=0,
bias_attr=False)
self.relu = ReLU()
self.fc2 = Conv2D(channels // reduction,
channels,
kernel_size=1,
padding=0,
bias_attr=False)
self.sigmoid = Sigmoid()
def __init__(self, input_size, block, layers, zero_init_residual=True):
super(ResNet, self).__init__()
assert input_size[0] in [
112, 224
], "input_size should be [112, 112] or [224, 224]"
self.inplanes = 64
self.zero_init_residual = zero_init_residual
self.conv1 = Conv2D(3,
64,
kernel_size=7,
stride=2,
padding=3,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.KaimingNormal()))
self.bn1 = BatchNorm2D(
64,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Constant(value=1),
regularizer=paddle.regularizer.L1Decay(0.0)),
bias_attr=paddle.ParamAttr(
initializer=nn.initializer.Constant(value=0),
regularizer=paddle.regularizer.L1Decay(0.0)))
self.relu = ReLU()
self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.bn_o1 = BatchNorm2D(
2048,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Constant(value=0.0),
regularizer=paddle.regularizer.L1Decay(0.0)),
bias_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)))
self.dropout = Dropout()
if input_size[0] == 112:
self.fc = Linear(2048 * 4 * 4, 512)
else:
self.fc = Linear(2048 * 8 * 8, 512)
self.bn_o2 = BatchNorm1D(
512,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Constant(value=0.0),
regularizer=paddle.regularizer.L1Decay(0.0)),
bias_attr=paddle.ParamAttr(
regularizer=paddle.regularizer.L1Decay(0.0)))
def __init__(self, mode="train"):
ClasModel = paddle.nn.Sequential(
Conv2D(3, 6, (3, 3)),
BatchNorm2D(6),
ReLU(),
Conv2D(6, 6, (3, 3)),
BatchNorm2D(6),
ReLU(),
MaxPool2D((2, 2)),
Conv2D(6, 12, (3, 3)),
BatchNorm2D(12),
ReLU(),
Conv2D(12, 12, (3, 3)),
BatchNorm2D(12),
ReLU(),
MaxPool2D((2, 2)),
Conv2D(12, 8, (3, 3)),
BatchNorm2D(8),
ReLU(),
Conv2D(8, 8, (3, 3)),
BatchNorm2D(8),
ReLU(),
MaxPool2D((2, 2)),
Flatten(),
Linear(128, 128),
ReLU(),
Linear(128, 32),
ReLU(),
Linear(32, 2),
Softmax(),
)
input = InputSpec([None, 3, 64, 64], "float32", "x")
label = InputSpec([None, 1], "int32", "label")
model = paddle.Model(ClasModel, inputs=input, labels=label)
model.prepare(
paddle.optimizer.Adam(parameters=ClasModel.parameters()),
paddle.nn.CrossEntropyLoss(),
paddle.metric.Accuracy(),
)
self.model = model
if mode == "predict":
self.load_weight()
def __init__(self, num_classes=10):
super(ImperativeLenet, self).__init__()
conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
fc_b3_attr = fluid.ParamAttr(name="fc_b_3")
self.features = Sequential(
Conv2D(in_channels=1,
out_channels=6,
kernel_size=3,
stride=1,
padding=1,
weight_attr=conv2d_w1_attr,
bias_attr=False), BatchNorm2D(6), ReLU(),
MaxPool2D(kernel_size=2, stride=2),
Conv2D(in_channels=6,
out_channels=16,
kernel_size=5,
stride=1,
padding=0,
weight_attr=conv2d_w2_attr,
bias_attr=conv2d_b2_attr), BatchNorm2D(16), PReLU(),
MaxPool2D(kernel_size=2, stride=2))
self.fc = Sequential(
Linear(in_features=400,
out_features=120,
weight_attr=fc_w1_attr,
bias_attr=fc_b1_attr), LeakyReLU(),
Linear(in_features=120,
out_features=84,
weight_attr=fc_w2_attr,
bias_attr=fc_b2_attr), Sigmoid(),
Linear(in_features=84,
out_features=num_classes,
weight_attr=fc_w3_attr,
bias_attr=fc_b3_attr), Softmax())
self.add = paddle.nn.quant.add()
self.quant_stub = paddle.nn.quant.QuantStub()
def __init__(self):
super(ModelConv2, self).__init__()
with supernet(expand_ratio=(1, 2, 4)) as ofa_super:
models = []
models += [
nn.Conv2DTranspose(
4, 4, 3, weight_attr=paddle.ParamAttr(name='conv1_w'))
]
models += [
nn.BatchNorm2D(
4,
weight_attr=paddle.ParamAttr(name='bn1_w'),
bias_attr=paddle.ParamAttr(name='bn1_b'))
]
models += [ReLU()]
models += [nn.Conv2D(4, 4, 3)]
models += [nn.BatchNorm2D(4)]
models += [ReLU()]
models = ofa_super.convert(models)
with supernet(channel=((4, 6, 8), (4, 6, 8))) as ofa_super:
models1 = []
models1 += [nn.Conv2DTranspose(4, 4, 3)]
models1 += [nn.BatchNorm2D(4)]
models1 += [ReLU()]
models1 += [nn.Conv2DTranspose(4, 4, 3)]
models1 += [nn.BatchNorm2D(4)]
models1 += [ReLU()]
models1 = ofa_super.convert(models1)
models += models1
with supernet(kernel_size=(3, 5, 7)) as ofa_super:
models2 = []
models2 += [nn.Conv2D(4, 4, 3)]
models2 += [nn.BatchNorm2D(4)]
models2 += [ReLU()]
models2 += [nn.Conv2DTranspose(4, 4, 3)]
models2 += [nn.BatchNorm2D(4)]
models2 += [ReLU()]
models2 += [nn.Conv2D(4, 4, 3)]
models2 += [nn.BatchNorm2D(4)]
models2 += [ReLU()]
models2 = ofa_super.convert(models2)
models += models2
self.models = paddle.nn.Sequential(*models)
def SeperableConv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0):
"""Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d.
"""
return Sequential(
Conv2D(in_channels=in_channels,
out_channels=in_channels,
kernel_size=kernel_size,
groups=in_channels,
stride=stride,
padding=padding),
ReLU(),
Conv2D(in_channels=in_channels,
out_channels=out_channels,
kernel_size=1),
)
def __init__(self,
num_channels,
filter_size,
num_filters,
stride,
padding,
num_groups=1):
super().__init__()
self.conv = Conv2D(in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(initializer=KaimingNormal()),
bias_attr=False)
self.bn = BatchNorm(num_filters)
self.relu = ReLU()
def __init__(self, in_channels, out_channels):
super(Decoder, self).__init__()
self.relus = LayerList([ReLU() for i in range(2)])
self.conv_transpose_01 = Conv2DTranspose(in_channels,
out_channels,
kernel_size=3,
padding=1)
self.conv_transpose_02 = Conv2DTranspose(out_channels,
out_channels,
kernel_size=3,
padding=1)
self.bns = LayerList([BatchNorm2D(out_channels) for i in range(2)])
self.upsamples = LayerList(
[Upsample(scale_factor=2.0) for i in range(2)])
self.residual_conv = Conv2D(in_channels,
out_channels,
kernel_size=1,
padding='same')
def __init__(self, in_channels, out_channels):
super(Encoder, self).__init__()
self.relus = LayerList([ReLU() for i in range(2)])
self.separable_conv_01 = SeparableConv2D(in_channels,
out_channels,
kernel_size=3,
padding='same')
self.bns = LayerList([BatchNorm2D(out_channels) for i in range(2)])
self.separable_conv_02 = SeparableConv2D(out_channels,
out_channels,
kernel_size=3,
padding='same')
self.pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.residual_conv = Conv2D(in_channels,
out_channels,
kernel_size=1,
stride=2,
padding='same')
def __init__(self, layer_num, ch_out, name=None):
super(ShortCut, self).__init__()
shortcut_conv = Sequential()
ch_in = ch_out * 2
for i in range(layer_num):
fan_out = 3 * 3 * ch_out
std = math.sqrt(2. / fan_out)
in_channels = ch_in if i == 0 else ch_out
shortcut_name = name + '.conv.{}'.format(i)
shortcut_conv.add_sublayer(
shortcut_name,
Conv2D(in_channels=in_channels,
out_channels=ch_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0, std)),
bias_attr=ParamAttr(learning_rate=2.,
regularizer=L2Decay(0.))))
if i < layer_num - 1:
shortcut_conv.add_sublayer(shortcut_name + '.act', ReLU())
self.shortcut = self.add_sublayer('short', shortcut_conv)
def __init__(self,
num_channels,
num_filters,
stride=1,
shortcut=True,
dilation=1,
padding=None,
name=None):
super(BottleneckBlock, self).__init__(name)
self.conv0 = ConvBNLayer(num_channels=num_channels,
num_filters=num_filters,
filter_size=1,
act='relu')
# name=name)
self.conv1 = ConvBNLayer(num_channels=num_filters,
num_filters=num_filters,
filter_size=3,
stride=stride,
padding=padding,
act='relu',
dilation=dilation)
# name=name)
self.conv2 = ConvBNLayer(num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
stride=1)
# name=name)
if not shortcut:
self.short = ConvBNLayer(num_channels=num_channels,
num_filters=num_filters * 4,
filter_size=1,
stride=stride)
# name=name)
self.shortcut = shortcut
self.num_channel_out = num_filters * 4
self.relu = ReLU()
self.data = data
self.transform = vt.Compose([vt.ToTensor()])
def __getitem__(self, index):
data = cv2.imread(osp.join(self.data_path, self.data[index][0]))
data = self.transform(data)
label = self.data[index][1]
return data, label
def __len__(self):
return len(self.data)
from paddle.nn import Conv2D, BatchNorm2D, ReLU, Softmax, MaxPool2D, Flatten, Linear
ClasModel = paddle.nn.Sequential(Conv2D(3, 6, (3, 3)), BatchNorm2D(6), ReLU(),
Conv2D(6, 6, (3, 3)), BatchNorm2D(6), ReLU(),
MaxPool2D((2, 2)), Conv2D(6, 12, (3, 3)),
BatchNorm2D(12), ReLU(),
Conv2D(12, 12,
(3, 3)), BatchNorm2D(12), ReLU(),
MaxPool2D((2, 2)), Conv2D(12, 8, (3, 3)),
BatchNorm2D(8), ReLU(), Conv2D(8, 8, (3, 3)),
BatchNorm2D(8), ReLU(), MaxPool2D((2, 2)),
Flatten(), Linear(128, 128), ReLU(),
Linear(128, 32), ReLU(), Linear(32, 2),
Softmax())
train_dataset = HumanClasDataset(mode="train")
eval_dataset = HumanClasDataset(mode="eval")
def create_Mb_Tiny_RFB_fd(num_classes, is_test=False, device="cuda"):
base_net = Mb_Tiny_RFB(2)
base_net_model = base_net.model # disable dropout layer
source_layer_indexes = [8, 11, 13]
extras = LayerList([
Sequential(
Conv2D(in_channels=base_net.base_channel * 16,
out_channels=base_net.base_channel * 4,
kernel_size=1), ReLU(),
SeperableConv2d(in_channels=base_net.base_channel * 4,
out_channels=base_net.base_channel * 16,
kernel_size=3,
stride=2,
padding=1), ReLU())
])
regression_headers = LayerList([
SeperableConv2d(in_channels=base_net.base_channel * 4,
out_channels=3 * 4,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 8,
out_channels=2 * 4,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 16,
out_channels=2 * 4,
kernel_size=3,
padding=1),
Conv2D(in_channels=base_net.base_channel * 16,
out_channels=3 * 4,
kernel_size=3,
padding=1)
])
classification_headers = LayerList([
SeperableConv2d(in_channels=base_net.base_channel * 4,
out_channels=3 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 8,
out_channels=2 * num_classes,
kernel_size=3,
padding=1),
SeperableConv2d(in_channels=base_net.base_channel * 16,
out_channels=2 * num_classes,
kernel_size=3,
padding=1),
Conv2D(in_channels=base_net.base_channel * 16,
out_channels=3 * num_classes,
kernel_size=3,
padding=1)
])
return SSD(num_classes,
base_net_model,
source_layer_indexes,
extras,
classification_headers,
regression_headers,
is_test=is_test,
config=config,
device=device)
def __init__(self, num_channels, num_nodes):
super(GCNModule, self).__init__()
self.conv1 = Conv1D(num_nodes, num_nodes, kernel_size=1)
self.relu = ReLU()
self.conv2 = Conv1D(num_channels, num_channels, kernel_size=1)
def __init__(self):
super(ModelCase4, self).__init__()
self.bn1 = BatchNorm2D(3)
self.ln1 = LayerNorm([3 * 16 * 16])
self.relu1 = ReLU()
self.fc1 = paddle.nn.Linear(3 * 16 * 16, 3 * 16 * 16)
