clip-caption-reward
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379 lines
14 KiB
379 lines
14 KiB
# This file contains Transformer network
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# Most of the code is copied from http://nlp.seas.harvard.edu/2018/04/03/attention.html
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# The cfg name correspondance:
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# N=num_layers
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# d_model=input_encoding_size
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# d_ff=rnn_size
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# h is always 8
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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#from . import utils
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import copy
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import math
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import numpy as np
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#from .CaptionModel import CaptionModel
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#from .AttModel import sort_pack_padded_sequence, pad_unsort_packed_sequence, pack_wrapper, AttModel
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from captioning.models.CaptionModel import CaptionModel
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from captioning.models.AttModel import sort_pack_padded_sequence, pad_unsort_packed_sequence, pack_wrapper, AttModel
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def repeat_tensors(n, x):
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"""
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For a tensor of size Bx..., we repeat it n times, and make it Bnx...
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For collections, do nested repeat
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"""
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if torch.is_tensor(x):
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x = x.unsqueeze(1) # Bx1x...
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x = x.expand(-1, n, *([-1]*len(x.shape[2:]))) # Bxnx...
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x = x.reshape(x.shape[0]*n, *x.shape[2:]) # Bnx...
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elif type(x) is list or type(x) is tuple:
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x = [repeat_tensors(n, _) for _ in x]
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return x
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class EncoderDecoder(nn.Module):
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"""
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A standard Encoder-Decoder architecture. Base for this and many
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other models.
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"""
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def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
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super(EncoderDecoder, self).__init__()
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self.encoder = encoder
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self.decoder = decoder
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self.src_embed = src_embed
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self.tgt_embed = tgt_embed
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self.generator = generator
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def forward(self, src, tgt, src_mask, tgt_mask):
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"Take in and process masked src and target sequences."
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return self.decode(self.encode(src, src_mask), src_mask,
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tgt, tgt_mask)
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def encode(self, src, src_mask):
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return self.encoder(self.src_embed(src), src_mask)
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def decode(self, memory, src_mask, tgt, tgt_mask):
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return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)
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class Generator(nn.Module):
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"Define standard linear + softmax generation step."
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def __init__(self, d_model, vocab):
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super(Generator, self).__init__()
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self.proj = nn.Linear(d_model, vocab)
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def forward(self, x):
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return F.log_softmax(self.proj(x), dim=-1)
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def clones(module, N):
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"Produce N identical layers."
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return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])
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class Encoder(nn.Module):
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"Core encoder is a stack of N layers"
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def __init__(self, layer, N):
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super(Encoder, self).__init__()
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self.layers = clones(layer, N)
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self.norm = LayerNorm(layer.size)
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def forward(self, x, mask):
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"Pass the input (and mask) through each layer in turn."
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for layer in self.layers:
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x = layer(x, mask)
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return self.norm(x)
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class LayerNorm(nn.Module):
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"Construct a layernorm module (See citation for details)."
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def __init__(self, features, eps=1e-6):
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super(LayerNorm, self).__init__()
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self.a_2 = nn.Parameter(torch.ones(features))
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self.b_2 = nn.Parameter(torch.zeros(features))
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self.eps = eps
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def forward(self, x):
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mean = x.mean(-1, keepdim=True)
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std = x.std(-1, keepdim=True)
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return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
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class SublayerConnection(nn.Module):
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"""
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A residual connection followed by a layer norm.
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Note for code simplicity the norm is first as opposed to last.
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"""
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def __init__(self, size, dropout):
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super(SublayerConnection, self).__init__()
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self.norm = LayerNorm(size)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x, sublayer):
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"Apply residual connection to any sublayer with the same size."
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return x + self.dropout(sublayer(self.norm(x)))
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class EncoderLayer(nn.Module):
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"Encoder is made up of self-attn and feed forward (defined below)"
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def __init__(self, size, self_attn, feed_forward, dropout):
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super(EncoderLayer, self).__init__()
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self.self_attn = self_attn
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self.feed_forward = feed_forward
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self.sublayer = clones(SublayerConnection(size, dropout), 2)
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self.size = size
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def forward(self, x, mask):
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"Follow Figure 1 (left) for connections."
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x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
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return self.sublayer[1](x, self.feed_forward)
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class Decoder(nn.Module):
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"Generic N layer decoder with masking."
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def __init__(self, layer, N):
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super(Decoder, self).__init__()
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self.layers = clones(layer, N)
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self.norm = LayerNorm(layer.size)
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def forward(self, x, memory, src_mask, tgt_mask):
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for layer in self.layers:
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x = layer(x, memory, src_mask, tgt_mask)
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return self.norm(x)
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class DecoderLayer(nn.Module):
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"Decoder is made of self-attn, src-attn, and feed forward (defined below)"
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def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
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super(DecoderLayer, self).__init__()
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self.size = size
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self.self_attn = self_attn
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self.src_attn = src_attn
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self.feed_forward = feed_forward
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self.sublayer = clones(SublayerConnection(size, dropout), 3)
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def forward(self, x, memory, src_mask, tgt_mask):
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"Follow Figure 1 (right) for connections."
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m = memory
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x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
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x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
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return self.sublayer[2](x, self.feed_forward)
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def subsequent_mask(size):
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"Mask out subsequent positions."
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attn_shape = (1, size, size)
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subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
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return torch.from_numpy(subsequent_mask) == 0
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def attention(query, key, value, mask=None, dropout=None):
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"Compute 'Scaled Dot Product Attention'"
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d_k = query.size(-1)
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scores = torch.matmul(query, key.transpose(-2, -1)) \
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/ math.sqrt(d_k)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, float('-inf'))
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p_attn = F.softmax(scores, dim = -1)
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if dropout is not None:
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p_attn = dropout(p_attn)
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return torch.matmul(p_attn, value), p_attn
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class MultiHeadedAttention(nn.Module):
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def __init__(self, h, d_model, dropout=0.1):
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"Take in model size and number of heads."
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super(MultiHeadedAttention, self).__init__()
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assert d_model % h == 0
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# We assume d_v always equals d_k
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self.d_k = d_model // h
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self.h = h
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self.linears = clones(nn.Linear(d_model, d_model), 4)
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self.attn = None
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self.dropout = nn.Dropout(p=dropout)
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def forward(self, query, key, value, mask=None):
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"Implements Figure 2"
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if mask is not None:
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# Same mask applied to all h heads.
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mask = mask.unsqueeze(1)
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nbatches = query.size(0)
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# 1) Do all the linear projections in batch from d_model => h x d_k
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query, key, value = \
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[l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
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for l, x in zip(self.linears, (query, key, value))]
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# 2) Apply attention on all the projected vectors in batch.
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x, self.attn = attention(query, key, value, mask=mask,
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dropout=self.dropout)
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# 3) "Concat" using a view and apply a final linear.
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x = x.transpose(1, 2).contiguous() \
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.view(nbatches, -1, self.h * self.d_k)
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return self.linears[-1](x)
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class PositionwiseFeedForward(nn.Module):
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"Implements FFN equation."
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def __init__(self, d_model, d_ff, dropout=0.1):
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super(PositionwiseFeedForward, self).__init__()
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self.w_1 = nn.Linear(d_model, d_ff)
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self.w_2 = nn.Linear(d_ff, d_model)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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return self.w_2(self.dropout(F.relu(self.w_1(x))))
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class Embeddings(nn.Module):
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def __init__(self, d_model, vocab):
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super(Embeddings, self).__init__()
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self.lut = nn.Embedding(vocab, d_model)
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self.d_model = d_model
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def forward(self, x):
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return self.lut(x) * math.sqrt(self.d_model)
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class PositionalEncoding(nn.Module):
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"Implement the PE function."
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def __init__(self, d_model, dropout, max_len=5000):
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super(PositionalEncoding, self).__init__()
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self.dropout = nn.Dropout(p=dropout)
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# Compute the positional encodings once in log space.
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len).unsqueeze(1).float()
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div_term = torch.exp(torch.arange(0, d_model, 2).float() *
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-(math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0)
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self.register_buffer('pe', pe)
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def forward(self, x):
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x = x + self.pe[:, :x.size(1)]
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return self.dropout(x)
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class TransformerModel(AttModel):
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def make_model(self, src_vocab, tgt_vocab, N_enc=6, N_dec=6,
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d_model=512, d_ff=2048, h=8, dropout=0.1):
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"Helper: Construct a model from hyperparameters."
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c = copy.deepcopy
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attn = MultiHeadedAttention(h, d_model, dropout)
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ff = PositionwiseFeedForward(d_model, d_ff, dropout)
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position = PositionalEncoding(d_model, dropout)
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model = EncoderDecoder(
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Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N_enc),
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Decoder(DecoderLayer(d_model, c(attn), c(attn),
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c(ff), dropout), N_dec),
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lambda x:x, # nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
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nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
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Generator(d_model, tgt_vocab))
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# This was important from their code.
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# Initialize parameters with Glorot / fan_avg.
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for p in model.parameters():
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if p.dim() > 1:
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nn.init.xavier_uniform_(p)
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return model
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def __init__(self, opt):
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super(TransformerModel, self).__init__(opt)
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self.opt = opt
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# self.config = yaml.load(open(opt.config_file))
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self.N_enc = getattr(opt, 'N_enc', opt.num_layers)
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self.N_dec = getattr(opt, 'N_dec', opt.num_layers)
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self.d_model = getattr(opt, 'd_model', opt.input_encoding_size)
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self.d_ff = getattr(opt, 'd_ff', opt.rnn_size)
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self.h = getattr(opt, 'num_att_heads', 8)
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self.dropout = getattr(opt, 'dropout', 0.1)
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delattr(self, 'att_embed')
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self.att_embed = nn.Sequential(*(
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((nn.BatchNorm1d(self.att_feat_size),) if self.use_bn else ())+
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(nn.Linear(self.att_feat_size, self.d_model),
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nn.ReLU(),
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nn.Dropout(self.drop_prob_lm))+
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((nn.BatchNorm1d(self.d_model),) if self.use_bn==2 else ())))
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delattr(self, 'embed')
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self.embed = lambda x : x
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delattr(self, 'fc_embed')
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self.fc_embed = lambda x : x
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delattr(self, 'logit')
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del self.ctx2att
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tgt_vocab = self.vocab_size + 1
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self.model = self.make_model(0, tgt_vocab,
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N_enc=self.N_enc,
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N_dec=self.N_dec,
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d_model=self.d_model,
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d_ff=self.d_ff,
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h=self.h,
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dropout=self.dropout)
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def logit(self, x): # unsafe way
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return self.model.generator.proj(x)
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def init_hidden(self, bsz):
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return []
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def _prepare_feature(self, fc_feats, att_feats, att_masks):
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att_feats, seq, att_masks, seq_mask = self._prepare_feature_forward(att_feats, att_masks)
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memory = self.model.encode(att_feats, att_masks)
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return fc_feats[...,:0], att_feats[...,:0], memory, att_masks
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def _prepare_feature_forward(self, att_feats, att_masks=None, seq=None):
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att_feats, att_masks = self.clip_att(att_feats, att_masks)
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att_feats = pack_wrapper(self.att_embed, att_feats, att_masks)
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if att_masks is None:
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att_masks = att_feats.new_ones(att_feats.shape[:2], dtype=torch.long)
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att_masks = att_masks.unsqueeze(-2)
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if seq is not None:
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# crop the last one
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# seq = seq[:,:-1]
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seq_mask = (seq.data != self.eos_idx) & (seq.data != self.pad_idx)
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seq_mask[:,0] = 1 # bos
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seq_mask = seq_mask.unsqueeze(-2)
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seq_mask = seq_mask & subsequent_mask(seq.size(-1)).to(seq_mask)
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seq_per_img = seq.shape[0] // att_feats.shape[0]
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if seq_per_img > 1:
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att_feats, att_masks = utils.repeat_tensors(seq_per_img,
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[att_feats, att_masks]
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)
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else:
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seq_mask = None
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return att_feats, seq, att_masks, seq_mask
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def _forward(self, fc_feats, att_feats, seq, att_masks=None):
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if seq.ndim == 3: # B * seq_per_img * seq_len
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seq = seq.reshape(-1, seq.shape[2])
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att_feats, seq, att_masks, seq_mask = self._prepare_feature_forward(att_feats, att_masks, seq)
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out = self.model(att_feats, seq, att_masks, seq_mask)
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outputs = self.model.generator(out)
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return outputs
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# return torch.cat([_.unsqueeze(1) for _ in outputs], 1)
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def core(self, it, fc_feats_ph, att_feats_ph, memory, state, mask):
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"""
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state = [ys.unsqueeze(0)]
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"""
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if len(state) == 0:
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ys = it.unsqueeze(1)
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else:
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ys = torch.cat([state[0][0], it.unsqueeze(1)], dim=1)
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out = self.model.decode(memory, mask,
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ys,
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subsequent_mask(ys.size(1))
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.to(memory.device))
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return out[:, -1], [ys.unsqueeze(0)]
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