def __init__(self,
in_channels,
out_channels,
kernel_size=3,
dilation=1,
stride=2,
padding=1,
output_padding=1,
activation='PReLU',
bias=False,
dropout_prob=0.1):
super(UpsamplingBottleNeck, self).__init__()
internal_channels = in_channels // 4
self.conv_down = Sequential(
Conv2d(in_channels,
internal_channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias), BatchNorm2d(internal_channels),
PReLU() if activation == 'PReLU' else ReLU())
self.conv_main = Sequential(
ConvTranspose2d(internal_channels,
internal_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
output_padding=output_padding,
dilation=dilation,
bias=bias), BatchNorm2d(internal_channels),
PReLU() if activation == 'PReLU' else ReLU())
self.conv_up = Sequential(
Conv2d(internal_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias), BatchNorm2d(out_channels),
PReLU() if activation == 'PReLU' else ReLU())
self.main_conv = Sequential(
Conv2d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias), BatchNorm2d(out_channels))
self.mainmaxunpool = MaxUnpool2d(kernel_size=2, stride=2, padding=0)
self.regularizer = Dropout2d(p=dropout_prob)
self.out_activation = PReLU() if activation == 'PReLU' else ReLU()
def __init__(self,
vocab: Vocabulary,
sentence_encoder: SentenceEncoder,
tan_ffnn: FeedForward,
inject_predicate: bool = False,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None):
super(SpanToTanModel, self).__init__(vocab, regularizer)
self._sentence_encoder = sentence_encoder
self._tan_ffnn = tan_ffnn
self._inject_predicate = inject_predicate
self._span_extractor = EndpointSpanExtractor(
input_dim=self._sentence_encoder.get_output_dim(),
combination="x,y")
prediction_input_dim = (3 * self._sentence_encoder.get_output_dim()
) if self._inject_predicate else (
2 *
self._sentence_encoder.get_output_dim())
self._tan_pred = TimeDistributed(
Sequential(
Linear(prediction_input_dim, self._tan_ffnn.get_input_dim()),
ReLU(), self._tan_ffnn,
Linear(self._tan_ffnn.get_output_dim(),
self.vocab.get_vocab_size("tan-string-labels"))))
self._metric = BinaryF1()
def __init__(self,
input_dim=13,
num_classes=9,
d_model=64,
n_head=2,
n_layers=5,
d_inner=128,
activation="relu",
dropout=0.017998950510888446,
max_len=200):
super(PETransformerModel, self).__init__()
self.modelname = f"PeTransformerEncoder_input-dim={input_dim}_num-classes={num_classes}_" \
f"d-model={d_model}_d-inner={d_inner}_n-layers={n_layers}_n-head={n_head}_" \
f"dropout={dropout}"
encoder_layer = TransformerEncoderLayer(d_model, n_head, d_inner,
dropout, activation)
encoder_norm = LayerNorm(d_model)
self.inlinear = Linear(input_dim, d_model)
self.relu = ReLU()
self.transformerencoder = TransformerEncoder(encoder_layer, n_layers,
encoder_norm)
self.flatten = Flatten()
self.outlinear = Linear(d_model, num_classes)
self.pe = PositionalEncoding(d_model, max_len=max_len)
"""
def __init__(self,
vocab: Vocabulary,
sentence_encoder: SentenceEncoder,
question_encoder: SlotSequenceEncoder,
span_selector: PruningSpanSelector,
classify_invalids: bool = True,
invalid_hidden_dim: int = 100,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None):
super(QuestionToSpanModel, self).__init__(vocab, regularizer)
self._sentence_encoder = sentence_encoder
self._question_encoder = question_encoder
self._span_selector = span_selector
self._classify_invalids = classify_invalids
self._invalid_hidden_dim = invalid_hidden_dim
injected_embedding_dim = self._sentence_encoder.get_output_dim(
) + self._question_encoder.get_output_dim()
extra_input_dim = self._span_selector.get_extra_input_dim()
if injected_embedding_dim != extra_input_dim:
raise ConfigurationError(
"Sum of pred rep and question embedding dim %s did not match span selector injection dim of %s"
% (injected_embedding_dim, extra_input_dim))
if self._classify_invalids:
self._invalid_pred = Sequential(
Linear(extra_input_dim, self._invalid_hidden_dim), ReLU(),
Linear(self._invalid_hidden_dim, 1))
self._invalid_metric = BinaryF1()
def mini_vgg_features():
model = Sequential(
# 32 x 32
Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 16 x 16
Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 8 x 8
Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),
# 4 x 4
)
return model
def mini_vgg_decoder():
model = Sequential(
# 8 x 8
UpsamplingBilinear2d(scale_factor=2),
ConvTranspose2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
ConvTranspose2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
# 16 x 16
UpsamplingBilinear2d(scale_factor=2),
ConvTranspose2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
# 32 x 32
UpsamplingBilinear2d(scale_factor=2),
ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
ReLU(),
ConvTranspose2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
Tanh(),
)
return model
def __init__(self,
input_dim: int,
extra_input_dim: int = 0,
span_hidden_dim: int = 100,
span_ffnn: FeedForward = None,
classifier: SetClassifier = SetBinaryClassifier(),
span_decoding_threshold: float = 0.05,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None):
super(SpanSelector, self).__init__()
self._input_dim = input_dim
self._extra_input_dim = extra_input_dim
self._span_hidden_dim = span_hidden_dim
self._span_ffnn = span_ffnn
self._classifier = classifier
self._span_decoding_threshold = span_decoding_threshold
self._span_hidden = SpanRepAssembly(self._input_dim, self._input_dim, self._span_hidden_dim)
if self._span_ffnn is not None:
if self._span_ffnn.get_input_dim() != self._span_hidden_dim:
raise ConfigurationError(
"Span hidden dim %s must match span classifier FFNN input dim %s" % (
self._span_hidden_dim, self._span_ffnn.get_input_dim()
)
)
self._span_scorer = TimeDistributed(
torch.nn.Sequential(
ReLU(),
self._span_ffnn,
Linear(self._span_ffnn.get_output_dim(), 1)))
else:
self._span_scorer = TimeDistributed(
torch.nn.Sequential(
ReLU(),
Linear(self._span_hidden_dim, 1)))
self._span_pruner = Pruner(self._span_scorer)
if self._extra_input_dim > 0:
self._extra_input_lin = Linear(self._extra_input_dim, self._span_hidden_dim)
def __init__(self,
in_channels,
out_channels,
kernel_size=3,
dilation=1,
stride=1,
padding=1,
activation='PReLU',
bias=False,
asymmetric=False,
dropout_prob=0):
super(RegularBottleNeck, self).__init__()
internal_channels = in_channels // 4
self.conv_down = Sequential(
Conv2d(in_channels,
internal_channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias), BatchNorm2d(internal_channels),
PReLU() if activation == 'PReLU' else ReLU())
if asymmetric is False:
self.conv_main = Sequential(
Conv2d(internal_channels,
internal_channels,
kernel_size=kernel_size,
dilation=dilation,
stride=stride,
padding=padding,
bias=bias), BatchNorm2d(internal_channels),
PReLU() if activation == 'PReLU' else ReLU())
else:
self.conv_main = Sequential(
Conv2d(internal_channels,
internal_channels,
kernel_size=(kernel_size, 1),
dilation=dilation,
stride=stride,
padding=(padding, 0),
bias=bias), BatchNorm2d(internal_channels),
PReLU() if activation == 'PReLU' else ReLU(),
Conv2d(internal_channels,
internal_channels,
kernel_size=(1, kernel_size),
dilation=dilation,
stride=stride,
padding=(0, padding),
bias=bias), BatchNorm2d(internal_channels),
PReLU() if activation == 'PReLU' else ReLU())
self.conv_up = Sequential(
Conv2d(internal_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=bias), BatchNorm2d(out_channels),
PReLU() if activation == 'PReLU' else ReLU())
self.regularizer = Dropout2d(p=dropout_prob)
self.out_activation = PReLU() if activation == 'PReLU' else ReLU()
def __init__(self,
in_channels,
out_channels,
activation='PReLU',
bias=False):
super(InitialBlock, self).__init__()
self.conv = Conv2d(in_channels=in_channels,
out_channels=out_channels - 3,
kernel_size=3,
stride=2,
padding=1)
self.maxpooling = MaxPool2d(kernel_size=2, stride=2, padding=0)
self.bnActivate = Sequential(
BatchNorm2d(out_channels),
PReLU() if activation == 'PReLU' else ReLU())
def __init__(self,
input_dim=13,
num_classes=9,
sequencelength=13,
d_model=64,
n_head=1,
n_layers=3,
d_inner=256,
activation="relu",
dropout=0.39907201621346594):
super(TransformerModel, self).__init__()
self.modelname = f"TransformerEncoder_input-dim={input_dim}_num-classes={num_classes}_" \
f"d-model={d_model}_d-inner={d_inner}_n-layers={n_layers}_n-head={n_head}_" \
f"dropout={dropout}"
encoder_layer = TransformerEncoderLayer(d_model, n_head, d_inner,
dropout, activation)
encoder_norm = LayerNorm(d_model)
self.sequential = Sequential(
Linear(input_dim, d_model), ReLU(),
TransformerEncoder(encoder_layer, n_layers, encoder_norm),
Flatten(), ReLU(), Linear(d_model * sequencelength, num_classes))
def __init__(self, features):
super(bna, self).__init__()
self.batchnorm = BatchNorm2d(features)
self.activate = ReLU(inplace=True)
def __init__(
self,
input_dim: int,
extra_input_dim: int = 0,
span_hidden_dim: int = 100,
span_ffnn: FeedForward = None,
objective: str = "binary",
gold_span_selection_policy: str = "union",
pruning_ratio: float = 2.0,
skip_metrics_during_training: bool = True,
# metric: SpanMetric = SpanMetric(),
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None):
super(PruningSpanSelector, self).__init__()
self._input_dim = input_dim
self._span_hidden_dim = span_hidden_dim
self._extra_input_dim = extra_input_dim
self._span_ffnn = span_ffnn
self._pruning_ratio = pruning_ratio
self._objective = objective
self._gold_span_selection_policy = gold_span_selection_policy
self._skip_metrics_during_training = skip_metrics_during_training
if objective not in objective_values:
raise ConfigurationError(
"QA objective must be one of the following: " +
str(qa_objective_values))
if gold_span_selection_policy not in gold_span_selection_policy_values:
raise ConfigurationError(
"QA span selection policy must be one of the following: " +
str(qa_objective_values))
if objective == "multinomial" and gold_span_selection_policy == "weighted":
raise ConfigurationError(
"Cannot use weighted span selection policy with multinomial objective."
)
# self._metric = metric
self._span_hidden = SpanRepAssembly(input_dim, input_dim,
self._span_hidden_dim)
if self._span_ffnn is not None:
if self._span_ffnn.get_input_dim() != self._span_hidden_dim:
raise ConfigurationError(
"Span hidden dim %s must match span classifier FFNN input dim %s"
% (self._span_hidden_dim, self._span_ffnn.get_input_dim()))
self._span_scorer = TimeDistributed(
torch.nn.Sequential(
ReLU(), self._span_ffnn,
Linear(self._span_ffnn.get_output_dim(), 1)))
else:
self._span_scorer = TimeDistributed(
torch.nn.Sequential(ReLU(), Linear(self._span_hidden_dim, 1)))
self._span_pruner = Pruner(self._span_scorer)
if self._extra_input_dim > 0:
self._extra_input_lin = Linear(self._extra_input_dim,
self._span_hidden_dim)
def __init__(self, c_in, c_out, filter_size, stride=1, padding=0, **kwargs):
super(Conv2dBN, self).__init__()
self.conv = Conv2d(c_in, c_out, filter_size, stride=stride, padding=padding, **kwargs)
self.bn = BatchNorm2d(c_out)
self.relu = ReLU()
