def __init__(self, in_channels, nclass):
super().__init__()
self.nclass = nclass
inter_channels = in_channels // 4
self.inp = paddle.zeros(shape=(nclass, 300), dtype='float32')
self.inp = paddle.create_parameter(
shape=self.inp.shape,
dtype=str(self.inp.numpy().dtype),
default_initializer=paddle.nn.initializer.Assign(self.inp))
self.inp.stop_gradient = True
self.fc1 = nn.Sequential(nn.Linear(300, 128), nn.BatchNorm1D(128),
nn.ReLU())
self.fc2 = nn.Sequential(nn.Linear(128, 256), nn.BatchNorm1D(256),
nn.ReLU())
self.conv5 = layers.ConvBNReLU(in_channels,
inter_channels,
3,
padding=1,
bias_attr=False,
stride=1)
self.gloru = GlobalReasonUnit(in_channels=inter_channels,
num_state=256,
num_node=84,
nclass=nclass)
self.conv6 = nn.Sequential(nn.Dropout(0.1),
nn.Conv2D(inter_channels, nclass, 1))
def __init__(self,
num_layers,
emb_dim,
drop_ratio=0.5,
JK="last",
residual=False,
gnn_type='gin'):
'''
emb_dim (int): node embedding dimensionality
'''
super(GNN_node_Virtualnode, self).__init__()
self.num_layers = num_layers
self.drop_ratio = drop_ratio
self.JK = JK
### add residual connection or not
self.residual = residual
if self.num_layers < 2:
raise ValueError("Number of GNN layers must be greater than 1.")
self.atom_encoder = AtomEncoder(emb_dim)
### set the initial virtual node embedding to 0.
# self.virtualnode_embedding = paddle.nn.Embedding(1, emb_dim)
self.virtualnode_embedding = self.create_parameter(
shape=[1, emb_dim],
dtype='float32',
default_initializer=nn.initializer.Constant(value=0.0))
### List of GNNs
self.convs = []
### batch norms applied to node embeddings
self.batch_norms = []
### List of MLPs to transform virtual node at every layer
self.mlp_virtualnode_list = []
for layer in range(num_layers):
if gnn_type == 'gin':
self.convs.append(GINConv(emb_dim))
elif gnn_type == 'gcn':
self.convs.append(GCNConv(emb_dim))
else:
ValueError('Undefined GNN type called {}'.format(gnn_type))
self.batch_norms.append(paddle.nn.BatchNorm1D(emb_dim))
for layer in range(num_layers - 1):
self.mlp_virtualnode_list.append(
nn.Sequential(nn.Linear(emb_dim, emb_dim),
nn.BatchNorm1D(emb_dim), nn.ReLU(),
nn.Linear(emb_dim, emb_dim),
nn.BatchNorm1D(emb_dim), nn.ReLU()))
self.pool = gnn.GraphPool(pool_type="sum")
self.convs = nn.LayerList(self.convs)
self.batch_norms = nn.LayerList(self.batch_norms)
self.mlp_virtualnode_list = nn.LayerList(self.mlp_virtualnode_list)
def batch_norm_1d(num_channels):
"""tbd"""
if dist.get_world_size() > 1:
return nn.SyncBatchNorm.convert_sync_batchnorm(
nn.BatchNorm1D(num_channels))
else:
return nn.BatchNorm1D(num_channels)
def __init__(self,
name_scope='PointNet2_SSG_Clas_',
num_classes=16,
normal_channel=False):
super(PointNet2_SSG_Clas, self).__init__()
in_channel = 6 if normal_channel else 3
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstraction(npoint=512,
radius=0.2,
nsample=32,
in_channel=in_channel,
mlp=[64, 64, 128],
group_all=False)
self.sa2 = PointNetSetAbstraction(npoint=128,
radius=0.4,
nsample=64,
in_channel=128 + 3,
mlp=[128, 128, 256],
group_all=False)
self.sa3 = PointNetSetAbstraction(npoint=None,
radius=None,
nsample=None,
in_channel=256 + 3,
mlp=[256, 512, 1024],
group_all=True)
self.fc1 = nn.Linear(1024, 512)
self.bn1 = nn.BatchNorm1D(512)
self.drop1 = nn.Dropout(0.4)
self.fc2 = nn.Linear(512, 256)
self.bn2 = nn.BatchNorm1D(256)
self.drop2 = nn.Dropout(0.4)
self.fc3 = nn.Linear(256, num_classes)
def __init__(self,
vocab_size,
emb_dim=256,
hidden_size=512,
kernel_size=9,
n_layers=32,
padding_idx=0,
dropout_rate=0.1,
epsilon=1e-6):
super(ResnetEncoderModel, self).__init__()
self.hidden_size = hidden_size
self.n_layers = n_layers
self.token_embedding = nn.Embedding(vocab_size,
emb_dim,
padding_idx=padding_idx)
max_pos_len = 3000
self.pos_embedding = nn.Embedding(max_pos_len,
emb_dim,
padding_idx=padding_idx)
self.padded_conv = nn.Sequential(
nn.BatchNorm1D(emb_dim, data_format="NLC"),
nn.ReLU(),
nn.Conv1D(in_channels=emb_dim, out_channels=hidden_size, kernel_size=kernel_size, padding="same", \
data_format="NLC", weight_attr=nn.initializer.KaimingNormal()),
nn.Dropout(p=dropout_rate)
)
self.residual_block_1 = nn.Sequential(
nn.BatchNorm1D(hidden_size, data_format="NLC"),
nn.ReLU(),
nn.Conv1D(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, padding="same", \
data_format="NLC", weight_attr=nn.initializer.KaimingNormal()),
nn.Dropout(p=dropout_rate),
nn.BatchNorm1D(hidden_size, data_format="NLC"),
nn.ReLU(),
nn.Conv1D(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, padding="same", \
data_format="NLC", weight_attr=nn.initializer.KaimingNormal()),
nn.Dropout(p=dropout_rate)
)
self.residual_block_n = nn.Sequential(
nn.BatchNorm1D(hidden_size, data_format="NLC"),
nn.ReLU(),
nn.Conv1D(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, dilation=2, \
padding="same", data_format="NLC", weight_attr=nn.initializer.KaimingNormal()),
nn.Dropout(p=dropout_rate),
nn.BatchNorm1D(hidden_size, data_format="NLC"),
nn.ReLU(),
nn.Conv1D(in_channels=hidden_size, out_channels=hidden_size, kernel_size=kernel_size, dilation=2, \
padding="same", data_format="NLC", weight_attr=nn.initializer.KaimingNormal()),
nn.Dropout(p=dropout_rate)
)
def __init__(self, label_list: list = None, load_checkpoint: str = None):
super(SpinalNet_ResNet101, self).__init__()
if label_list is not None:
self.labels = label_list
class_dim = len(self.labels)
else:
label_list = []
label_file = os.path.join(self.directory, 'label_list.txt')
files = open(label_file)
for line in files.readlines():
line = line.strip('\n')
label_list.append(line)
self.labels = label_list
class_dim = len(self.labels)
self.backbone = ResNet()
half_in_size = round(2048 / 2)
layer_width = 20
self.half_in_size = half_in_size
self.fc_spinal_layer1 = nn.Sequential(
nn.Dropout(p=0.5), nn.Linear(half_in_size, layer_width),
nn.BatchNorm1D(layer_width), nn.ReLU())
self.fc_spinal_layer2 = nn.Sequential(
nn.Dropout(p=0.5), nn.Linear(half_in_size + layer_width,
layer_width),
nn.BatchNorm1D(layer_width), nn.ReLU())
self.fc_spinal_layer3 = nn.Sequential(
nn.Dropout(p=0.5), nn.Linear(half_in_size + layer_width,
layer_width),
nn.BatchNorm1D(layer_width), nn.ReLU())
self.fc_spinal_layer4 = nn.Sequential(
nn.Dropout(p=0.5), nn.Linear(half_in_size + layer_width,
layer_width),
nn.BatchNorm1D(layer_width), nn.ReLU())
self.fc_out = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(layer_width * 4, class_dim),
)
if load_checkpoint is not None:
self.model_dict = paddle.load(load_checkpoint)[0]
self.set_dict(self.model_dict)
print("load custom checkpoint success")
else:
checkpoint = os.path.join(self.directory,
'spinalnet_res101.pdparams')
self.model_dict = paddle.load(checkpoint)
self.set_dict(self.model_dict)
print("load pretrained checkpoint success")
def __init__(self, dim, conv_type="gin"):
super(VNAgg, self).__init__()
self.conv_type = conv_type
if "gin" in conv_type:
self.mlp = nn.Sequential(
MLP(dim, dim), nn.BatchNorm1D(dim), nn.ReLU())
elif "gcn" in conv_type:
self.W0 = nn.Linear(dim, dim)
self.W1 = nn.Linear(dim, dim)
self.nl_bn = nn.Sequential(nn.BatchNorm1D(dim), nn.ReLU())
else:
raise NotImplementedError('Unrecognised model conv : {}'.format(
conv_type))
def __init__(self,
name_scope='PointNet2_MSG_Seg_',
num_classes=16,
num_parts=50,
normal_channel=False):
super(PointNet2_MSG_Seg, self).__init__()
if normal_channel:
additional_channel = 3
else:
additional_channel = 0
self.num_classes = num_classes
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstractionMsg(
512, [0.1, 0.2, 0.4], [32, 64, 128], 3 + additional_channel,
[[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2 = PointNetSetAbstractionMsg(
128, [0.4, 0.8], [64, 128], 128 + 128 + 64,
[[128, 128, 256], [128, 196, 256]])
self.sa3 = PointNetSetAbstraction(npoint=None,
radius=None,
nsample=None,
in_channel=512 + 3,
mlp=[256, 512, 1024],
group_all=True)
self.fp3 = PointNetFeaturePropagation(in_channel=1536, mlp=[256, 256])
self.fp2 = PointNetFeaturePropagation(in_channel=576, mlp=[256, 128])
self.fp1 = PointNetFeaturePropagation(in_channel=150 +
additional_channel,
mlp=[128, 128])
self.conv1 = nn.Conv1D(128, 128, 1)
self.bn1 = nn.BatchNorm1D(128)
self.drop1 = nn.Dropout(0.5)
self.conv2 = nn.Conv1D(128, num_parts, 1)
def __init__(self,
inp_dim,
hidden_dim,
num_layers,
batch_norm=False,
dropout=0.):
super(MLP, self).__init__()
layer_list = OrderedDict()
in_dim = inp_dim
for l in range(num_layers):
layer_list['fc{}'.format(l)] = nn.Linear(in_dim, hidden_dim)
if l < num_layers - 1:
if batch_norm:
layer_list['norm{}'.format(l)] = nn.BatchNorm1D(
num_features=hidden_dim)
layer_list['relu{}'.format(l)] = nn.LeakyReLU()
if dropout > 0:
layer_list['drop{}'.format(l)] = nn.Dropout(p=dropout)
in_dim = hidden_dim
if num_layers > 0:
self.network = nn.Sequential()
for i in layer_list:
self.network.add_sublayer(i, layer_list[i])
else:
self.network = nn.Identity()
def __init__(self, config):
super(GINEncoder, self).__init__()
self.hidden_size = config['hidden_size']
self.num_layers = config['num_layers']
self.embed_dim = config['embed_dim']
self.atom_type_num = config['atom_type_num']
self.chirality_tag_num = config['chirality_tag_num']
self.bond_type_num = config['bond_type_num']
self.bond_direction_num = config['bond_direction_num']
self.readout = config['readout']
self.activation = config['activation']
self.atom_names = config['atom_names']
self.bond_names = config['bond_names']
self.atom_embedding = AtomEmbedding(self.atom_names, self.embed_dim)
self.gin_list = nn.LayerList()
self.norm_list = nn.LayerList()
for layer_id in range(self.num_layers):
self.gin_list.append(
pgl.nn.GINConv(self.embed_dim,
self.embed_dim,
activation=self.activation))
self.norm_list.append(nn.BatchNorm1D(self.embed_dim))
if self.readout == 'mean':
self.graph_pool = MeanPool()
else:
self.graph_pool = pgl.nn.GraphPool(pool_type=self.readout)
def __init__(self,
dim,
dropout=0.5,
activation=F.relu,
virtual_node=False,
virtual_node_agg=True,
k=4,
last_layer=False,
conv_type='gin',
edge_embedding=None):
super().__init__()
self.edge_embed = edge_embedding
self.conv_type = conv_type
if conv_type == 'gin+':
self.conv = GINEPLUS(MLP(dim, dim), dim, k=k)
elif conv_type == 'gcn':
self.conv = gnn.GCNConv(dim, dim)
self.norm = nn.BatchNorm1D(dim)
self.act = activation or None
self.last_layer = last_layer
self.dropout_ratio = dropout
self.virtual_node = virtual_node
self.virtual_node_agg = virtual_node_agg
if self.virtual_node and self.virtual_node_agg:
self.vn_aggregator = VNAgg(dim, conv_type=conv_type)
def __init__(self, num_classes=751):
super(Net, self).__init__()
# 3 128 64
self.conv = nn.Sequential(
nn.Conv2D(3, 64, 3, stride=1, padding=1),
nn.BatchNorm2D(64),
nn.ReLU(),
# nn.Conv2d(32,32,3,stride=1,padding=1),
# nn.BatchNorm2d(32),
# nn.ReLU(inplace=True),
nn.MaxPool2D(3, 2, padding=1),
)
# 32 64 32
self.layer1 = make_layers(64, 64, 2, False)
# 32 64 32
self.layer2 = make_layers(64, 128, 2, True)
# 64 32 16
self.layer3 = make_layers(128, 256, 2, True)
# 128 16 8
self.layer4 = make_layers(256, 512, 2, True)
# 256 8 4
self.avgpool = nn.AvgPool2D((8, 4), 1)
# 256 1 1
self.classifier = nn.Sequential(
nn.Linear(512, 256),
nn.BatchNorm1D(256),
nn.ReLU(),
nn.Dropout(),
nn.Linear(256, num_classes),
)
def __init__(self, block, depth):
super(ResNet, self).__init__()
layer_cfg = {
18: [2, 2, 2, 2],
34: [3, 4, 6, 3],
50: [3, 4, 6, 3],
101: [3, 4, 23, 3],
152: [3, 8, 36, 3]
}
layers = layer_cfg[depth]
self._norm_layer = nn.BatchNorm2D
self.inplanes = 64
self.dilation = 1
self.conv1 = nn.Conv2D(1,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias_attr=False)
self.bn1 = self._norm_layer(self.inplanes)
self.relu = nn.ReLU()
self.maxpool = nn.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.pool = nn.AdaptiveMaxPool2D((1, 1))
self.bn4 = nn.BatchNorm2D(512 * block.expansion)
self.dropout = nn.Dropout()
self.fc5 = nn.Linear(512 * block.expansion, 512)
self.bn5 = nn.BatchNorm1D(512)
def __init__(self,
block,
layers,
num_filters,
feature_dim,
encoder_type='SAP',
n_mels=40,
log_input=True,
**kwargs):
super(ResNetSE, self).__init__()
print('Embedding size is %d, encoder %s.' %
(feature_dim, encoder_type))
self.inplanes = num_filters[0]
self.encoder_type = encoder_type
self.n_mels = n_mels
self.log_input = log_input
self.conv1 = nn.Conv2D(1,
num_filters[0],
kernel_size=3,
stride=1,
padding=1)
self.relu = nn.ReLU()
self.bn1 = nn.BatchNorm2D(num_filters[0])
self.layer1 = self._make_layer(block, num_filters[0], layers[0])
self.layer2 = self._make_layer(block,
num_filters[1],
layers[1],
stride=(2, 2))
self.layer3 = self._make_layer(block,
num_filters[2],
layers[2],
stride=(2, 2))
self.layer4 = self._make_layer(block,
num_filters[3],
layers[3],
stride=(2, 2))
outmap_size = int(self.n_mels / 8)
self.attention = nn.Sequential(
nn.Conv1D(num_filters[3] * outmap_size, 128, kernel_size=1),
nn.ReLU(),
nn.BatchNorm1D(128),
nn.Conv1D(128, num_filters[3] * outmap_size, kernel_size=1),
nn.Softmax(axis=2),
)
if self.encoder_type == "SAP":
out_dim = num_filters[3] * outmap_size
elif self.encoder_type == "ASP":
out_dim = num_filters[3] * outmap_size * 2
else:
raise ValueError('Undefined encoder')
self.fc = nn.Linear(out_dim, feature_dim)
def __init__(self, model_config={}):
super(PretrainGNNModel, self).__init__()
self.embed_dim = model_config.get('embed_dim', 300)
self.dropout_rate = model_config.get('dropout_rate', 0.5)
self.norm_type = model_config.get('norm_type', 'batch_norm')
self.graph_norm = model_config.get('graph_norm', False)
self.residual = model_config.get('residual', False)
self.layer_num = model_config.get('layer_num', 5)
self.gnn_type = model_config.get('gnn_type', 'gin')
self.JK = model_config.get('JK', 'last')
self.readout = model_config.get('readout', 'mean')
self.atom_names = model_config['atom_names']
self.bond_names = model_config['bond_names']
self.atom_embedding = AtomEmbedding(self.atom_names, self.embed_dim)
self.bond_embedding_list = nn.LayerList()
self.gnn_list = nn.LayerList()
self.norm_list = nn.LayerList()
self.graph_norm_list = nn.LayerList()
self.dropout_list = nn.LayerList()
for layer_id in range(self.layer_num):
self.bond_embedding_list.append(BondEmbedding(self.bond_names, self.embed_dim))
if self.gnn_type == 'gin':
self.gnn_list.append(GIN(self.embed_dim))
else:
raise ValueError(self.gnn_type)
if self.norm_type == 'batch_norm':
self.norm_list.append(nn.BatchNorm1D(self.embed_dim))
elif self.norm_type == 'layer_norm':
self.norm_list.append(nn.LayerNorm(self.embed_dim))
else:
raise ValueError(self.norm_type)
if self.graph_norm:
self.graph_norm_list.append(GraphNorm()) # TODO: pgl.nn.GraphNorm not implemented in pgl==2.1.2
self.dropout_list.append(nn.Dropout(self.dropout_rate))
# TODO: use self-implemented MeanPool due to pgl bug.
if self.readout == 'mean':
self.graph_pool = MeanPool()
else:
self.graph_pool = pgl.nn.GraphPool(pool_type=self.readout)
print('[PretrainGNNModel] embed_dim:%s' % self.embed_dim)
print('[PretrainGNNModel] dropout_rate:%s' % self.dropout_rate)
print('[PretrainGNNModel] norm_type:%s' % self.norm_type)
print('[PretrainGNNModel] graph_norm:%s' % self.graph_norm)
print('[PretrainGNNModel] residual:%s' % self.residual)
print('[PretrainGNNModel] layer_num:%s' % self.layer_num)
print('[PretrainGNNModel] gnn_type:%s' % self.gnn_type)
print('[PretrainGNNModel] JK:%s' % self.JK)
print('[PretrainGNNModel] readout:%s' % self.readout)
print('[PretrainGNNModel] atom_names:%s' % str(self.atom_names))
print('[PretrainGNNModel] bond_names:%s' % str(self.bond_names))
def __init__(self, a, b, bias=True, std=0.02):
super().__init__()
self.add_sublayer('bn', nn.BatchNorm1D(a))
l = nn.Linear(a, b, bias_attr=bias)
trunc_normal_(l.weight)
if bias:
zeros_(l.bias)
self.add_sublayer('l', l)
def __init__(self,
inplanes=256,
planes=256,
kernel_size=9,
dilation=1,
dropout_rate=0.1):
super(ResnetBasicBlock, self).__init__()
self.conv1 = nn.Conv1D(in_channels=inplanes, out_channels=planes, kernel_size=kernel_size, dilation=dilation, \
padding="same", data_format="NLC", weight_attr=nn.initializer.KaimingNormal())
self.bn1 = nn.BatchNorm1D(planes, data_format="NLC")
self.gelu1 = nn.GELU()
self.dropout1 = nn.Dropout(p=dropout_rate)
self.conv2 = nn.Conv1D(in_channels=planes, out_channels=planes, kernel_size=kernel_size, dilation=dilation, \
padding="same", data_format="NLC", weight_attr=nn.initializer.KaimingNormal())
self.bn2 = nn.BatchNorm1D(planes, data_format="NLC")
self.gelu2 = nn.GELU()
self.dropout2 = nn.Dropout(p=dropout_rate)
def __init__(self, model_name='ResNet50', last_stride=1):
super(ResNetEmbedding, self).__init__()
assert model_name in ['ResNet50', 'ResNet101'
], "Unsupported ReID arch: {}".format(model_name)
self.base = eval(model_name)(last_conv_stride=last_stride)
self.gap = nn.AdaptiveAvgPool2D(output_size=1)
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
self.bn = nn.BatchNorm1D(self.in_planes, bias_attr=False)
def __init__(self, in_dim, out_dim):
super(MLP, self).__init__()
self.main = [
nn.Linear(in_dim, 2 * in_dim), nn.BatchNorm1D(2 * in_dim),
nn.ReLU()
]
self.main.append(nn.Linear(2 * in_dim, out_dim))
self.main = nn.Sequential(*self.main)
def __init__(self, in_channel, mlp):
super(PointNetFeaturePropagation, self).__init__()
self.mlp_convs = []
self.mlp_bns = []
last_channel = in_channel
for out_channel in mlp:
self.mlp_convs.append(nn.Conv1D(last_channel, out_channel, 1))
self.mlp_bns.append(nn.BatchNorm1D(out_channel))
last_channel = out_channel
def __init__(self, i_size: int, h_size: int):
super().__init__()
hidden_size = h_size * 3
self.fw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.fw_bn = nn.BatchNorm1D(hidden_size,
bias_attr=None,
data_format='NLC')
self.bw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
self.bw_bn = nn.BatchNorm1D(hidden_size,
bias_attr=None,
data_format='NLC')
self.fw_cell = nn.GRUCell(input_size=hidden_size, hidden_size=h_size)
self.bw_cell = nn.GRUCell(input_size=hidden_size, hidden_size=h_size)
self.fw_rnn = nn.RNN(self.fw_cell, is_reverse=False,
time_major=False) # [B, T, D]
self.bw_rnn = nn.RNN(self.fw_cell, is_reverse=True,
time_major=False) # [B, T, D]
def __init__(self, a, b, bn_weight_init=1):
super().__init__()
self.add_sublayer('c', nn.Linear(a, b, bias_attr=False))
bn = nn.BatchNorm1D(b)
if bn_weight_init == 0:
zeros_(bn.weight)
else:
ones_(bn.weight)
zeros_(bn.bias)
self.add_sublayer('bn', bn)
def __init__(self, a, b, bn_weight_init=1, resolution=-100000):
super().__init__()
self.add_sublayer("c", nn.Linear(a, b, bias_attr=False))
bn = nn.BatchNorm1D(b)
Constant(bn_weight_init)(bn.weight)
zeros_(bn.bias)
self.add_sublayer("bn", bn)
def __init__(self, in_channels=3, out_channels=3, kernel_size=3, *args):
super(Block, self).__init__()
self.nn = nn.Sequential(
nn.Conv1D(in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding=0,
bias_attr=False),
nn.BatchNorm1D(num_features=out_channels), nn.LeakyReLU(.2),
nn.Dropout(p=.2))
def __init__(self,
vocab_size,
emb_dim=128,
hidden_size=256,
kernel_size=9,
n_layers=35,
padding_idx=0,
dropout_rate=0.1,
epsilon=1e-6):
super(ResnetEncoderModel, self).__init__()
self.hidden_size = hidden_size
self.n_layers = n_layers
self.token_embedding = nn.Embedding(vocab_size,
emb_dim,
padding_idx=padding_idx)
max_pos_len = 3000
self.pos_embedding = nn.Embedding(max_pos_len,
emb_dim,
padding_idx=padding_idx)
self.layer_norm = nn.BatchNorm1D(emb_dim, data_format="NLC")
self.dropout = nn.Dropout(dropout_rate)
self.padded_conv = nn.Sequential(
nn.Conv1D(in_channels=emb_dim, out_channels=hidden_size, kernel_size=kernel_size, padding="same", \
data_format="NLC", weight_attr=nn.initializer.KaimingNormal()),
nn.BatchNorm1D(hidden_size, data_format="NLC"),
nn.GELU(),
nn.Dropout(p=dropout_rate)
)
self.residual_block_1 = ResnetBasicBlock(inplanes=hidden_size,
planes=hidden_size,
kernel_size=kernel_size,
dropout_rate=dropout_rate)
self.residual_block_n = nn.Sequential()
for i in range(1, n_layers):
self.residual_block_n.add_sublayer("residual_block_%d" % i, \
ResnetBasicBlock(inplanes=hidden_size, planes=hidden_size, kernel_size=kernel_size, dilation=2, dropout_rate=dropout_rate))
self.apply(self.init_weights)
def __init__(self,
in_channels=2,
edge_importance_weighting=True,
data_bn=True,
layout='fsd10',
strategy='spatial',
**kwargs):
super(STGCN, self).__init__()
self.data_bn = data_bn
# load graph
self.graph = Graph(
layout=layout,
strategy=strategy,
)
A = paddle.to_tensor(self.graph.A, dtype='float32')
self.register_buffer('A', A)
# build networks
spatial_kernel_size = A.shape[0]
temporal_kernel_size = 9
kernel_size = (temporal_kernel_size, spatial_kernel_size)
self.data_bn = nn.BatchNorm1D(in_channels *
A.shape[1]) if self.data_bn else iden
kwargs0 = {k: v for k, v in kwargs.items() if k != 'dropout'}
self.st_gcn_networks = nn.LayerList((
st_gcn_block(in_channels,
64,
kernel_size,
1,
residual=False,
**kwargs0),
st_gcn_block(64, 64, kernel_size, 1, **kwargs),
st_gcn_block(64, 64, kernel_size, 1, **kwargs),
st_gcn_block(64, 64, kernel_size, 1, **kwargs),
st_gcn_block(64, 128, kernel_size, 2, **kwargs),
st_gcn_block(128, 128, kernel_size, 1, **kwargs),
st_gcn_block(128, 128, kernel_size, 1, **kwargs),
st_gcn_block(128, 256, kernel_size, 2, **kwargs),
st_gcn_block(256, 256, kernel_size, 1, **kwargs),
st_gcn_block(256, 256, kernel_size, 1, **kwargs),
))
# initialize parameters for edge importance weighting
if edge_importance_weighting:
self.edge_importance = nn.ParameterList([
self.create_parameter(
shape=self.A.shape,
default_initializer=nn.initializer.Constant(1))
for i in self.st_gcn_networks
])
else:
self.edge_importance = [1] * len(self.st_gcn_networks)
self.pool = nn.AdaptiveAvgPool2D(output_size=(1, 1))
def __init__(self, a, b, bias_attr=True, std=0.02):
super().__init__()
self.add_sublayer("bn", nn.BatchNorm1D(a))
l = nn.Linear(a, b, bias_attr=bias_attr)
TruncatedNormal(std=std)(l.weight)
if bias_attr:
zeros_(l.bias)
self.add_sublayer("l", l)
def __init__(self,
in_channels,
hid_channels,
out_channels,
with_avg_pool=True):
super(NonLinearNeckV2, self).__init__()
self.with_avg_pool = with_avg_pool
if with_avg_pool:
self.avgpool = nn.AdaptiveAvgPool2D((1, 1))
self.mlp = nn.Sequential(nn.Linear(in_channels, hid_channels),
nn.BatchNorm1D(hid_channels), nn.ReLU(),
nn.Linear(hid_channels, out_channels))
def __init__(self,
name_scope='PointNet2_MSG_Clas_',
num_classes=16,
normal_channel=False):
super(PointNet2_MSG_Clas, self).__init__()
in_channel = 3 if normal_channel else 0
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstractionMsg(
512, [0.1, 0.2, 0.4], [16, 32, 128], in_channel,
[[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2 = PointNetSetAbstractionMsg(
128, [0.2, 0.4, 0.8], [32, 64, 128], 320,
[[64, 64, 128], [128, 128, 256], [128, 128, 256]])
self.sa3 = PointNetSetAbstraction(None, None, None, 640 + 3,
[256, 512, 1024], True)
self.fc1 = nn.Linear(1024, 512)
self.bn1 = nn.BatchNorm1D(512)
self.drop1 = nn.Dropout(0.4)
self.fc2 = nn.Linear(512, 256)
self.bn2 = nn.BatchNorm1D(256)
self.drop2 = nn.Dropout(0.5)
self.fc3 = nn.Linear(256, num_classes)
def __init__(self, in_channels, num_codes):
super().__init__()
self.encoding_project = layers.ConvBNReLU(
in_channels,
in_channels,
1,
)
self.encoding = nn.Sequential(
Encoding(channels=in_channels, num_codes=num_codes),
nn.BatchNorm1D(num_codes),
nn.ReLU(),
)
self.fc = nn.Sequential(
nn.Linear(in_channels, in_channels),
nn.Sigmoid(),
)
