def __init__(self,
feature_vocabulary,
embedding_size,
tower_dims=[128, 64, 32],
drop_prob=[0.1, 0.3, 0.3]):
super(AITM, self).__init__()
self.feature_vocabulary = feature_vocabulary
self.feature_names = sorted(list(feature_vocabulary.keys()))
self.embedding_size = embedding_size
self.embedding_dict = nn.LayerList()
self.__init_weight()
self.tower_input_size = len(feature_vocabulary) * embedding_size
self.click_tower = Tower(self.tower_input_size, tower_dims, drop_prob)
self.conversion_tower = Tower(self.tower_input_size, tower_dims,
drop_prob)
self.attention_layer = Attention(tower_dims[-1])
self.info_layer = nn.Sequential(nn.Linear(tower_dims[-1], 32),
nn.ReLU(), nn.Dropout(drop_prob[-1]))
self.click_layer = nn.Sequential(nn.Linear(tower_dims[-1], 1),
nn.Sigmoid())
self.conversion_layer = nn.Sequential(nn.Linear(tower_dims[-1], 1),
nn.Sigmoid())
def __init__(self, channel, reduction=16):
super(SCSEBlock, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.channel_excitation = nn.Sequential(
nn.Conv2D(channel, int(channel//reduction), kernel_size=1, stride=1, padding=0),
nn.ReLU(),
nn.Conv2D(int(channel//reduction), channel, kernel_size=1, stride=1, padding=0),
nn.Sigmoid()
)
self.spatial_se = nn.Sequential(
nn.Conv2D(channel, 1, kernel_size=1, stride=1, padding=0),
nn.Sigmoid()
)
def __init__(self, in_channel, mid_channel, out_channel, fuse):
super().__init__()
self.conv1 = nn.Conv2D(
in_channel,
mid_channel,
kernel_size=1,
bias_attr=False,
weight_attr=ParamAttr(initializer=KaimingNormal()))
self.conv1_bn = nn.BatchNorm2D(mid_channel)
self.conv2 = nn.Conv2D(
mid_channel,
out_channel,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False,
weight_attr=ParamAttr(initializer=KaimingNormal()))
self.conv2_bn = nn.BatchNorm2D(out_channel)
if fuse:
self.att_conv = nn.Sequential(
nn.Conv2D(mid_channel * 2, 2, kernel_size=1),
nn.Sigmoid(),
)
else:
self.att_conv = None
def __init_weight(self):
self.num_users = self.dataset.n_users
self.num_items = self.dataset.m_items
self.latent_dim = self.config['latent_dim_rec']
self.n_layers = self.config['lightGCN_n_layers']
self.lgn = LightGCNonv(self.n_layers)
self.embedding_user = nn.Embedding(num_embeddings=self.num_users,
embedding_dim=self.latent_dim)
self.embedding_item = nn.Embedding(num_embeddings=self.num_items,
embedding_dim=self.latent_dim)
if self.config['pretrain'] == 0:
emb_item_weight = np.random.normal(
0, 0.1,
self.embedding_item.weight.numpy().shape).astype(np.float32)
emb_user_weight = np.random.normal(
0, 0.1,
self.embedding_user.weight.numpy().shape).astype(np.float32)
else:
emb_item_weight = np.load('item_embedding.npy').astype(np.float32)
emb_user_weight = np.load('item_embedding.npy').astype(np.float32)
self.embedding_item.weight.set_value(emb_item_weight)
self.embedding_user.weight.set_value(emb_user_weight)
self.f = nn.Sigmoid()
num_nodes = self.dataset.n_users + self.dataset.m_items
edges = paddle.to_tensor(self.dataset.trainEdge, dtype='int64')
self.Graph = pgl.Graph(num_nodes=num_nodes, edges=edges)
print(f"lgn is already to go(dropout:{self.config['dropout']})")
self.lgn.train()
def __init__(self, in_channels, channels, se_ratio=12):
super(SE, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.fc = nn.Sequential(
nn.Conv2D(in_channels, channels // se_ratio, kernel_size=1, padding=0),
nn.BatchNorm2D(channels // se_ratio), nn.ReLU(),
nn.Conv2D(channels // se_ratio, channels, kernel_size=1, padding=0), nn.Sigmoid())
def __init__(self, input_dim, num_layers):
super(Highway, self).__init__()
self._num_layers = num_layers
self._highway_layers = []
for i in range(num_layers):
paramAttr = paddle.ParamAttr(
initializer=I.Normal(mean=0.0, std=1.0 / np.sqrt(input_dim)))
paramAttr_b = paddle.ParamAttr(initializer=I.Constant(value=-2.0))
carry_linear = nn.Linear(input_dim,
input_dim,
weight_attr=paramAttr,
bias_attr=paramAttr_b)
self.add_sublayer('carry_linear_{}'.format(i), carry_linear)
paramAttr = paddle.ParamAttr(
initializer=I.Normal(mean=0.0, std=1.0 / np.sqrt(input_dim)))
transform_linear = nn.Linear(input_dim,
input_dim,
weight_attr=paramAttr)
self.add_sublayer('transform_linear_{}'.format(i),
transform_linear)
self._highway_layers.append([carry_linear, transform_linear])
self._relu = nn.ReLU()
self._sigmoid = nn.Sigmoid()
def __init__(self,
num_channels,
num_filters,
reduction_ratio,
name=None,
data_format="NCHW"):
super(SELayer, self).__init__()
self.data_format = data_format
self.pool2d_gap = AdaptiveAvgPool2D(1, data_format=self.data_format)
self._num_channels = num_channels
med_ch = int(num_channels / reduction_ratio)
stdv = 1.0 / math.sqrt(num_channels * 1.0)
self.squeeze = Linear(num_channels,
med_ch,
weight_attr=ParamAttr(
initializer=Uniform(-stdv, stdv),
name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
self.relu = nn.ReLU()
stdv = 1.0 / math.sqrt(med_ch * 1.0)
self.excitation = Linear(
med_ch,
num_filters,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv),
name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
self.sigmoid = nn.Sigmoid()
def __init__(self, in_channels, out_channels, size=(8, 8)):
super(AttentionModule_stage3_cifar, self).__init__()
self.first_residual_blocks = ResidualBlock(in_channels, out_channels)
self.trunk_branches = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.middle_2r_blocks = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.conv1_1_blocks = nn.Sequential(
nn.BatchNorm2D(out_channels), nn.ReLU(),
nn.Conv2D(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False), nn.BatchNorm2D(out_channels),
nn.ReLU(),
nn.Conv2D(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False), nn.Sigmoid())
self.last_blocks = ResidualBlock(in_channels, out_channels)
def __init__(self, args, dataset):
super(LightGCN, self).__init__()
self.args = args
self.dataset = dataset
self.num_users = self.dataset.n_users
self.num_items = self.dataset.m_items
num_nodes = self.dataset.n_users + self.dataset.m_items
self.latent_dim = self.args.recdim
self.n_layers = self.args.n_layers
self.lightgcn = LightGCN_Layer(self.n_layers)
# self.lightgcn = LightGCNonv(self.n_layers)
self.embedding_user = nn.Embedding(
num_embeddings=self.num_users, embedding_dim=self.latent_dim)
self.embedding_item = nn.Embedding(
num_embeddings=self.num_items, embedding_dim=self.latent_dim)
emb_item_weight = np.random.normal(
0, 0.1,
self.embedding_item.weight.numpy().shape).astype(np.float32)
emb_user_weight = np.random.normal(
0, 0.1,
self.embedding_user.weight.numpy().shape).astype(np.float32)
self.embedding_item.weight.set_value(emb_item_weight)
self.embedding_user.weight.set_value(emb_user_weight)
self.f = nn.Sigmoid()
edges = paddle.to_tensor(self.dataset.trainEdge, dtype='int64')
self.Graph = pgl.Graph(num_nodes=num_nodes, edges=edges)
self.lightgcn.train()
def __init__(self, channel, reduction=8):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.fc = nn.Sequential(nn.Linear(channel, channel // reduction),
nn.ReLU(),
nn.Linear(channel // reduction, channel),
nn.Sigmoid())
def __init__(self,
in_channels=3,
num_classes=2,
is_batchnorm=True,
is_deepsup=False,
is_CGM=False):
super(UNet3Plus, self).__init__()
# parameters
self.is_deepsup = True if is_CGM else is_deepsup
self.is_CGM = is_CGM
# internal definition
self.filters = [64, 128, 256, 512, 1024]
self.cat_channels = self.filters[0]
self.cat_blocks = 5
self.up_channels = self.cat_channels * self.cat_blocks
# layers
self.encoder = Encoder(in_channels, self.filters, is_batchnorm)
self.decoder = Decoder(self.filters, self.cat_channels,
self.up_channels)
if self.is_deepsup:
self.deepsup = DeepSup(self.up_channels, self.filters, num_classes)
if self.is_CGM:
self.cls = nn.Sequential(
nn.Dropout(p=0.5), nn.Conv2D(self.filters[4], 2, 1),
nn.AdaptiveMaxPool2D(1), nn.Sigmoid())
else:
self.outconv1 = nn.Conv2D(
self.up_channels, num_classes, 3, padding=1)
# initialise weights
for sublayer in self.sublayers():
if isinstance(sublayer, nn.Conv2D):
kaiming_normal_init(sublayer.weight)
elif isinstance(sublayer, (nn.BatchNorm, nn.SyncBatchNorm)):
kaiming_normal_init(sublayer.weight)
def __init__(self,
num_filters=64,
kernel_size=3,
stride=1,
padding=1,
dilation=1,
deformable_groups=8,
extra_offset_mask=True):
super(DCNPack, self).__init__()
self.extra_offset_mask = extra_offset_mask
self.deformable_groups = deformable_groups
self.num_filters = num_filters
if isinstance(kernel_size, int):
self.kernel_size = [kernel_size, kernel_size]
self.conv_offset_mask = nn.Conv2D(in_channels=self.num_filters,
out_channels=self.deformable_groups *
3 * self.kernel_size[0] *
self.kernel_size[1],
kernel_size=self.kernel_size,
stride=stride,
padding=padding)
self.total_channels = self.deformable_groups * 3 * self.kernel_size[
0] * self.kernel_size[1]
self.split_channels = self.total_channels // 3
self.dcn = DeformableConv_dygraph(
num_filters=self.num_filters,
filter_size=self.kernel_size,
dilation=dilation,
stride=stride,
padding=padding,
deformable_groups=self.deformable_groups)
# self.dcn = DeformConv2D(in_channels=self.num_filters,out_channels=self.num_filters,kernel_size=self.kernel_size,stride=stride,padding=padding,dilation=dilation,deformable_groups=self.deformable_groups,groups=1) # to be compiled
self.sigmoid = nn.Sigmoid()
def __init__(self, encoding_model):
super(UIE, self).__init__()
self.encoder = encoding_model
hidden_size = self.encoder.config["hidden_size"]
self.linear_start = paddle.nn.Linear(hidden_size, 1)
self.linear_end = paddle.nn.Linear(hidden_size, 1)
self.sigmoid = nn.Sigmoid()
def __init__(self, channels, reduction):
super(SEModule, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.fc1 = nn.Conv2D(channels, channels // reduction, kernel_size=1, padding=0, bias_attr=False)
self.relu = nn.ReLU()
self.fc2 = nn.Conv2D(channels // reduction, channels, kernel_size=1, padding=0, bias_attr=False)
self.sigmoid = nn.Sigmoid()
def __init__(self,
dnn_units=[8, 64, 16],
dnn_activation='sigmoid',
weight_normalization=False,
name=None):
super().__init__()
self.dnn_units = dnn_units
self.dnn_activation = 'sigmoid'
self.weight_normalization = weight_normalization
self.name = name
layer_list = []
#bn_list = []
for i in range(len(dnn_units) - 1):
dnn_layer = nn.Linear(in_features=self.dnn_units[i]
if i != 0 else self.dnn_units[i] * 4,
out_features=self.dnn_units[i + 1],
weight_attr=self._weight_init())
self.add_sublayer(self.name + f'linear_{i}', dnn_layer)
layer_list.append(dnn_layer)
#layer_list.append(copy.deepcopy(dnn_layer))
#bn_layer = nn.BatchNorm(50)
#self.add_sublayer(self.name + f'bn_{i}', bn_layer)
#bn_list.append(bn_layer)
#bn_list.append(copy.deepcopy(bn_layer))
#self.bn_layer = nn.LayerList(bn_list)
self.layers = nn.LayerList(layer_list)
self.dnn = nn.Linear(self.dnn_units[-1],
1,
weight_attr=self._weight_init())
self.activation = nn.Sigmoid()
self.soft = nn.Softmax()
def __init__(self, planes, r=16):
super(SEBlock, self).__init__()
self.squeeze = nn.AdaptiveAvgPool2D(1)
self.excitation = nn.Sequential(nn.Linear(planes, planes // r),
nn.ReLU(),
nn.Linear(planes // r, planes),
nn.Sigmoid())
def __init__(self, channels_img, features_d):
super(Discriminator, self).__init__()
# Input : N x C x 256 x 256
self.disc = nn.Sequential(
nn.Conv2D( # 128 x 128
channels_img,
features_d,
kernel_size=4,
stride=2,
padding=1,
weight_attr=paddle.ParamAttr(initializer=conv_initializer())),
nn.LeakyReLU(0.2),
self._block(features_d, features_d * 2, 4, 2, 1), # 64 x 64
self._block(features_d * 2, features_d * 4, 4, 2, 1), # 32 x 32
self._block(features_d * 4, features_d * 8, 4, 2, 1), # 16 x 16
self._block(features_d * 8, features_d * 16, 4, 2, 1), # 8 x 8
self._block(features_d * 16, features_d * 32, 4, 2, 1), # 4 x 4
nn.Conv2D(
features_d * 32,
1,
kernel_size=4,
stride=2,
padding=0, # 1 x 1
weight_attr=paddle.ParamAttr(initializer=conv_initializer())),
nn.Sigmoid(),
)
def __init__(self, in_channels, out_channels, size=(8, 8)):
super(AttentionModule_stage2_cifar, self).__init__()
self.first_residual_blocks = ResidualBlock(in_channels, out_channels)
self.trunk_branches = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.mpool1 = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) # 4*4
self.middle_2r_blocks = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.interpolation1 = nn.UpsamplingBilinear2D(size=size) # 8*8
self.conv1_1_blocks = nn.Sequential(
nn.BatchNorm2D(out_channels), nn.ReLU(),
nn.Conv2D(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False), nn.BatchNorm2D(out_channels),
nn.ReLU(),
nn.Conv2D(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False), nn.Sigmoid())
self.last_blocks = ResidualBlock(in_channels, out_channels)
def __init__(self, num_classes=10):
super(ImperativeLenet, self).__init__()
self.features = nn.Sequential(
nn.Conv2D(
in_channels=1,
out_channels=6,
kernel_size=3,
stride=1,
padding=1,
bias_attr=False),
nn.BatchNorm2D(6),
nn.ReLU(),
nn.MaxPool2D(
kernel_size=2, stride=2),
nn.Conv2D(
in_channels=6,
out_channels=16,
kernel_size=5,
stride=1,
padding=0),
nn.BatchNorm2D(16),
nn.PReLU(),
nn.MaxPool2D(
kernel_size=2, stride=2))
self.fc = nn.Sequential(
nn.Linear(
in_features=400, out_features=120),
nn.LeakyReLU(),
nn.Linear(
in_features=120, out_features=84),
nn.Sigmoid(),
nn.Linear(
in_features=84, out_features=num_classes),
nn.Softmax())
def __init__(self,
in_channels,
out_channels,
size1=(56, 56),
size2=(28, 28),
size3=(14, 14)):
super(AttentionModule_stage1, self).__init__()
self.first_residual_blocks = ResidualBlock(in_channels, out_channels)
self.trunk_branches = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.mpool1 = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.softmax1_blocks = ResidualBlock(in_channels, out_channels)
self.skip1_connection_residual_block = ResidualBlock(
in_channels, out_channels)
self.mpool2 = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.softmax2_blocks = ResidualBlock(in_channels, out_channels)
self.skip2_connection_residual_block = ResidualBlock(
in_channels, out_channels)
self.mpool3 = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
self.softmax3_blocks = nn.Sequential(
ResidualBlock(in_channels, out_channels),
ResidualBlock(in_channels, out_channels))
self.interpolation3 = nn.UpsamplingBilinear2D(size=size3)
self.softmax4_blocks = ResidualBlock(in_channels, out_channels)
self.interpolation2 = nn.UpsamplingBilinear2D(size=size2)
self.softmax5_blocks = ResidualBlock(in_channels, out_channels)
self.interpolation1 = nn.UpsamplingBilinear2D(size=size1)
self.softmax6_blocks = nn.Sequential(
nn.BatchNorm2D(out_channels), nn.ReLU(),
nn.Conv2D(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False), nn.BatchNorm2D(out_channels),
nn.ReLU(),
nn.Conv2D(out_channels,
out_channels,
kernel_size=1,
stride=1,
bias_attr=False), nn.Sigmoid())
self.last_blocks = ResidualBlock(in_channels, out_channels)
def __init__(self, in_chan, out_chan, *args, **kwargs):
super(AttentionRefinementModule, self).__init__()
self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
self.conv_atten = nn.Conv2D(out_chan,
out_chan,
kernel_size=1,
bias_attr=False)
self.bn_atten = nn.BatchNorm(out_chan)
self.sigmoid_atten = nn.Sigmoid()
def __init__(self, inplanes, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.fc = nn.Sequential(
nn.Linear(inplanes, inplanes // reduction, bias_attr=False),
nn.ReLU(),
nn.Linear(inplanes // reduction, inplanes, bias_attr=False),
nn.Sigmoid(),
)
def __init__(self, input_nc=6, ndf=64):
super(NLayerDiscriminator, self).__init__()
self.layers = nn.Sequential(
nn.Conv2D(input_nc, ndf, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(0.2), ConvBlock(ndf, ndf * 2),
ConvBlock(ndf * 2, ndf * 4), ConvBlock(ndf * 4, ndf * 8, stride=1),
nn.Conv2D(ndf * 8, 1, kernel_size=4, stride=1, padding=1),
nn.Sigmoid())
def __init__(self):
super(Discriminator, self).__init__()
self.dis == nn.Sequential(
nn.Conv2D(1, 64, 4, 2, 1, bias_attr=False), nn.LeakyReLU(0.2),
nn.Conv2D(64, 64 * 2, 4, 2, 1, bias_attr=False),
nn.BatchNorm2D(64 * 2), nn.LeakyReLU(0.2),
nn.Conv2D(64 * 2, 64 * 4, 4, 2, 1, bias_attr=False),
nn.BatchNorm2D(64 * 4), nn.LeakyReLU(0.2),
nn.Conv2D(64 * 4, 1, 4, 1, 0, bias_attr=False), nn.Sigmoid())
def __init__(self, in_channels, reduction=16):
super(SELayer, self).__init__()
assert reduction >= 16
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.fc = nn.Sequential(
nn.Linear(in_channels, in_channels//reduction),
nn.ReLU(),
nn.Linear(in_channels // reduction, in_channels),
nn.Sigmoid()
)
def __init__(self, in_ch, out_ch):
super(AttenHead, self).__init__()
# bottleneck channels for seg and attn heads
bot_ch = 256
self.atten_head = nn.Sequential(
layers.ConvBNReLU(in_ch, bot_ch, 3, padding=1, bias_attr=False),
layers.ConvBNReLU(bot_ch, bot_ch, 3, padding=1, bias_attr=False),
nn.Conv2D(bot_ch, out_ch, kernel_size=(1, 1), bias_attr=False),
nn.Sigmoid())
def __init__(self, act=None, axis=-1):
super().__init__()
if act is not None:
assert act in ["softmax", "sigmoid"]
if act == "softmax":
self.act = nn.Softmax(axis=axis)
elif act == "sigmoid":
self.act = nn.Sigmoid()
else:
self.act = None
def __init__(self, in_planes, ratio=16):
super(ChannelAttention, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2D(1)
self.max_pool = nn.AdaptiveMaxPool2D(1)
self.fc1 = nn.Conv2D(in_planes, in_planes // 16, 1)
self.relu1 = nn.ReLU()
self.fc2 = nn.Conv2D(in_planes // 16, in_planes, 1)
self.sigmoid = nn.Sigmoid()
def __init__(self):
super(Discriminator, self).__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(img_shape)), 512),
nn.LeakyReLU(0.2),
nn.Linear(512, 256),
nn.LeakyReLU(0.2),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def __init__(self, act='softmax', axis=-1, reduction='mean'):
super().__init__()
assert act in ['softmax', 'sigmoid', None]
self.reduction = reduction
if act == 'softmax':
self.act = nn.Softmax(axis=axis)
elif act == 'sigmoid':
self.act = nn.Sigmoid()
else:
self.act = None
