def __init__(self, vocab, config, logger, random):
super().__init__(config, random)
self.config = config
self.logger = logger
self.encoder = vocab.encoder()
self.simmat = modules.InteractionMatrix()
channels = self.encoder.emb_views()
self.ngrams = nn.ModuleList()
for ng in range(config['mingram'], config['maxgram'] + 1):
self.ngrams.append(
ConvMax2d(ng,
config['nfilters'],
k=config['kmax'],
channels=channels))
if config['combine'] == 'dense32':
num_feats = len(self.ngrams) * config['kmax'] + (1 if config['idf']
else 0)
self.combination = DenseCombination(
[config['qlen'] * num_feats, 32, 32, 1], config['shuf'])
elif config['combine'] == 'sum':
self.combination = SumCombination(
len(self.ngrams) * config['kmax'] +
(1 if config['idf'] else 0))
elif config['combine'] == 'sumnorm':
self.combination = SumCombination(
len(self.ngrams) * config['kmax'] +
(1 if config['idf'] else 0), False)
else:
raise ValueError('unknown combine `{combine}`'.format(**config))
self.path = util.path_model(self)
def __init__(self, vocab, config, logger, random):
super().__init__(config, random)
self.logger = logger
self.encoder = vocab.encoder()
self.simmat = modules.InteractionMatrix()
self.kernels = modules.RbfKernelBank.from_strs(config['mus'], config['sigmas'], dim=1, requires_grad=config['grad_kernels'])
self.combine = nn.Linear(self.kernels.count() * self.encoder.emb_views(), 1)
def __init__(self, vocab, config, logger, random):
super().__init__(config, random)
self.logger = logger
self.encoder = vocab.encoder()
self.simmat = modules.InteractionMatrix()
self.conv = nn.Conv2d(1, config['nfilters'], (1, config['dlen']))
self.combine1 = nn.Linear(config['qlen'] * config['nfilters'], 300)
self.combine2 = nn.Linear(300, 300)
self.dropout = nn.Dropout(0.2)
self.combine3 = nn.Linear(300, 1)
def __init__(self, vocab, config, logger, random):
super().__init__(config, random)
self.config = config
self.logger = logger
self.encoder = vocab.encoder()
self.simmat = modules.InteractionMatrix()
self.conv = nn.Conv2d(self.encoder.emb_views(), config['nfilters'],
(config['conv_q'], config['conv_d']))
self.pool = nn.MaxPool2d((config['pool_q'], config['pool_d']))
if config['combine'] == 'dense128':
self.combine1 = nn.Linear(
(config['qlen'] // config['pool_q']) *
(config['dlen'] // config['pool_d'] - 1) * config['nfilters'],
128)
self.combine2 = nn.Linear(128, 1)
def initialize(self, random):
super().initialize(random)
self.encoder = self.vocab.encoder()
if not self.encoder.static():
self.logger.warn(
'In most cases, using vocab.train=True will not have an effect on DRMM '
'because the histogram is not differentiable. An exception might be if '
'the gradient is proped back by another means, e.g. BERT [CLS] token.'
)
self.simmat = modules.InteractionMatrix()
self.histogram = {
'count': CountHistogram,
'norm': NormalizedHistogram,
'logcount': LogCountHistogram
}[self.histType](self.nbins)
channels = self.encoder.emb_views()
self.hidden_1 = nn.Linear(self.nbins * channels, self.hidden)
self.hidden_2 = nn.Linear(self.hidden, 1)
self.combine = {
'idf': IdfCombination,
'sum': SumCombination
}[self.combine]()
def __init__(self, vocab, config, logger, random):
super().__init__(config, random)
self.logger = logger
self.embed = vocab.encoder()
self.simmat = modules.InteractionMatrix()
self.padding, self.convs = nn.ModuleList(), nn.ModuleList()
for conv_size in range(1, config['max_ngram'] + 1):
if conv_size > 1:
self.padding.append(nn.ConstantPad1d((0, conv_size - 1), 0))
else:
self.padding.append(nn.Sequential()) # identity
self.convs.append(nn.ModuleList())
if self.combine_channels:
self.convs[-1].append(
nn.Conv1d(self.embed.dim() * self.embed.emb_views(),
config['conv_filters'], conv_size))
else:
for _ in range(self.embed.emb_views()):
self.convs[-1].append(
nn.Conv1d(self.embed.dim(), config['conv_filters'],
conv_size))
if self.pretrained_kernels:
kernels = wordvec_vocab._SOURCES[vocab.config['source']][
vocab.config['variant']](logger, get_kernels=True)
for conv, weight, bias in zip(self.convs, *kernels):
conv[0].weight.data = torch.from_numpy(weight).float()
conv[0].bias.data = torch.from_numpy(bias).float()
self.kernels = modules.RbfKernelBank.from_strs(
config['mus'],
config['sigmas'],
dim=1,
requires_grad=config['grad_kernels'])
channels = config['max_ngram']**2 if config['crossmatch'] else config[
'max_ngram']
if not self.combine_channels:
channels *= self.embed.emb_views(
)**2 if config['crossmatch'] else self.embed.emb_views()
self.combine = nn.Linear(self.kernels.count() * channels, 1)
def __init__(self, vocab, config, logger, random):
super().__init__(config, random)
self.logger = logger
self.encoder = vocab.encoder()
if not self.encoder.static():
logger.warn(
'In most cases, using vocab.train=True will not have an effect on DRMM '
'because the histogram is not differentiable. An exception might be if '
'the gradient is proped back by another means, e.g. BERT [CLS] token.'
)
self.simmat = modules.InteractionMatrix()
self.histogram = {
'count': CountHistogram,
'norm': NormalizedHistogram,
'logcount': LogCountHistogram
}[self.config['histo']](config['nbins'])
channels = self.encoder.emb_views()
self.hidden_1 = nn.Linear(config['nbins'] * channels, config['hidden'])
self.hidden_2 = nn.Linear(config['hidden'], 1)
self.combine = {
'idf': IdfCombination,
'sum': SumCombination
}[config['combine']]()
