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VDGR/models/vilbert_dialog.py
Adnen Abdessaied 09fb25e339 Code release
2023-10-25 15:38:09 +02:00

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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BERT model."""
import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
from torch.nn.utils.weight_norm import weight_norm
from pytorch_transformers.modeling_bert import BertEmbeddings
from utils.data_utils import sequence_mask, to_data_list
import torch_geometric.nn as pyg_nn
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
from pytorch_pretrained_bert.file_utils import cached_path
import pdb
logger = logging.getLogger(__name__)
PRETRAINED_MODEL_ARCHIVE_MAP = {
"bert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased.tar.gz",
"bert-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased.tar.gz",
"bert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased.tar.gz",
"bert-large-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased.tar.gz",
"bert-base-multilingual-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased.tar.gz",
"bert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased.tar.gz",
"bert-base-chinese": "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese.tar.gz",
}
def load_tf_weights_in_bert(model, tf_checkpoint_path):
""" Load tf checkpoints in a pytorch model
"""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
print(
"Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
print("Converting TensorFlow checkpoint from {}".format(tf_path))
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
print("Loading TF weight {} with shape {}".format(name, shape))
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(n in ["adam_v", "adam_m"] for n in name):
print("Skipping {}".format("/".join(name)))
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
l = re.split(r"_(\d+)", m_name)
else:
l = [m_name]
if l[0] == "kernel" or l[0] == "gamma":
pointer = getattr(pointer, "weight")
elif l[0] == "output_bias" or l[0] == "beta":
pointer = getattr(pointer, "bias")
elif l[0] == "output_weights":
pointer = getattr(pointer, "weight")
else:
pointer = getattr(pointer, l[0])
if len(l) >= 2:
num = int(l[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
assert pointer.shape == array.shape
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
print("Initialize PyTorch weight {}".format(name))
pointer.data = torch.from_numpy(array)
return model
class GeLU(nn.Module):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
def __init__(self):
super(GeLU, self).__init__()
def forward(self, x):
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
Also see https://arxiv.org/abs/1606.08415
"""
return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
def swish(x):
return x * torch.sigmoid(x)
ACT2FN = {"GeLU": GeLU(), "gelu": gelu,
"relu": torch.nn.functional.relu, "swish": swish}
class BertConfig(object):
"""Configuration class to store the configuration of a `BertModel`.
"""
def __init__(
self,
vocab_size_or_config_json_file,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=2,
initializer_range=0.02,
v_feature_size=2048,
v_target_size=1601,
v_hidden_size=768,
v_num_hidden_layers=3,
v_num_attention_heads=12,
v_intermediate_size=3072,
bi_hidden_size=1024,
bi_num_attention_heads=16,
v_attention_probs_dropout_prob=0.1,
v_hidden_act="gelu",
v_hidden_dropout_prob=0.1,
v_initializer_range=0.2,
v_biattention_id=[0, 1],
t_biattention_id=[10, 11],
predict_feature=False,
fast_mode=False,
fixed_v_layer=0,
fixed_t_layer=0,
in_batch_pairs=False,
fusion_method="mul",
intra_gate=False,
with_coattention=True
):
"""Constructs BertConfig.
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_attention_heads: Number of attention heads for each attention layer in
the Transformer encoder.
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
hidden_dropout_prob: The dropout probabilitiy for all fully connected
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
probabilities.
max_position_embeddings: The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
type_vocab_size: The vocabulary size of the `token_type_ids` passed into
`BertModel`.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
assert len(v_biattention_id) == len(t_biattention_id)
assert max(v_biattention_id) < v_num_hidden_layers
assert max(t_biattention_id) < num_hidden_layers
if isinstance(vocab_size_or_config_json_file, str) or (
sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)
):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.v_feature_size = v_feature_size
self.v_hidden_size = v_hidden_size
self.v_num_hidden_layers = v_num_hidden_layers
self.v_num_attention_heads = v_num_attention_heads
self.v_intermediate_size = v_intermediate_size
self.v_attention_probs_dropout_prob = v_attention_probs_dropout_prob
self.v_hidden_act = v_hidden_act
self.v_hidden_dropout_prob = v_hidden_dropout_prob
self.v_initializer_range = v_initializer_range
self.v_biattention_id = v_biattention_id
self.t_biattention_id = t_biattention_id
self.v_target_size = v_target_size
self.bi_hidden_size = bi_hidden_size
self.bi_num_attention_heads = bi_num_attention_heads
self.predict_feature = predict_feature
self.fast_mode = fast_mode
self.fixed_v_layer = fixed_v_layer
self.fixed_t_layer = fixed_t_layer
self.in_batch_pairs = in_batch_pairs
self.fusion_method = fusion_method
self.intra_gate = intra_gate
self.with_coattention=with_coattention
else:
raise ValueError(
"First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)"
)
@classmethod
def from_dict(cls, json_object):
"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = BertConfig(vocab_size_or_config_json_file=-1)
for key, value in json_object.items():
config.__dict__[key] = value
return config
@classmethod
def from_json_file(cls, json_file):
"""Constructs a `BertConfig` from a json file of parameters."""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
return cls.from_dict(json.loads(text))
def __repr__(self):
return str(self.to_json_string())
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
try:
# from apex.normalization.fused_layer_norm import FusedLayerNorm as BertLayerNorm
import torch.nn.LayerNorm as BertLayerNorm
except ImportError:
# logger.info(
# "Better speed can be achieved with apex installed from https://www.github.com/nvidia/apex ."
# )
pass
class BertLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(BertLayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x + self.bias
class BertEmbeddingsDialog(nn.Module):
def __init__(self, config, device):
super(BertEmbeddingsDialog, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
max_seq_len = 256
d_model = config.hidden_size
pe = torch.zeros(max_seq_len, d_model)
for pos in range(max_seq_len):
for i in range(0, d_model, 2):
pe[pos, i] = \
math.sin(pos / (10000 ** ((2 * i)/d_model)))
pe[pos, i + 1] = \
math.cos(pos / (10000 ** ((2 * (i + 1))/d_model)))
self.pe = pe.to(device)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# add support for additional segment embeddings. Supporting 10 additional embedding as of now
self.token_type_embeddings_extension = nn.Embedding(10,config.hidden_size)
# adding specialized embeddings for sep tokens
self.sep_embeddings = nn.Embedding(50,config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.config = config
def forward(self, input_ids, sep_indices=None, sep_len=None, token_type_ids=None):
seq_length = input_ids.size(1)
position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
words_embeddings = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_ids_extension = token_type_ids - self.config.type_vocab_size
token_type_ids_extension_mask = (token_type_ids_extension >= 0).float()
token_type_ids_extension = (token_type_ids_extension.float() * token_type_ids_extension_mask).long()
token_type_ids_mask = (token_type_ids < self.config.type_vocab_size).float()
assert torch.sum(token_type_ids_extension_mask + token_type_ids_mask) == \
torch.numel(token_type_ids) == torch.numel(token_type_ids_mask)
token_type_ids = (token_type_ids.float() * token_type_ids_mask).long()
token_type_embeddings = self.token_type_embeddings(token_type_ids)
token_type_embeddings_extension = self.token_type_embeddings_extension(token_type_ids_extension)
token_type_embeddings = (token_type_embeddings * token_type_ids_mask.unsqueeze(-1)) + \
(token_type_embeddings_extension * token_type_ids_extension_mask.unsqueeze(-1))
embeddings = words_embeddings + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class BertSelfAttention(nn.Module):
def __init__(self, config):
super(BertSelfAttention, self).__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (
self.num_attention_heads,
self.attention_head_size,
)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer, attention_probs
class BertSelfOutput(nn.Module):
def __init__(self, config):
super(BertSelfOutput, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BertAttention(nn.Module):
def __init__(self, config):
super(BertAttention, self).__init__()
self.self = BertSelfAttention(config)
self.output = BertSelfOutput(config)
def forward(self, input_tensor, attention_mask):
self_output, attention_probs = self.self(input_tensor, attention_mask)
attention_output = self.output(self_output, input_tensor)
return attention_output, attention_probs
class BertIntermediate(nn.Module):
def __init__(self, config):
super(BertIntermediate, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str) or (
sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class BertOutput(nn.Module):
def __init__(self, config):
super(BertOutput, self).__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BertLayer(nn.Module):
def __init__(self, config):
super(BertLayer, self).__init__()
self.attention = BertAttention(config)
self.intermediate = BertIntermediate(config)
self.output = BertOutput(config)
def forward(self, hidden_states, attention_mask):
attention_output, attention_probs = self.attention(hidden_states, attention_mask)
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output, attention_probs
class TextGraphLayer(nn.Module):
def __init__(self, config):
super(TextGraphLayer, self).__init__()
self.config = config
self.gnn_act = ACT2FN[config.gnn_act]
self.num_q_gnn_layers = config.num_q_gnn_layers
self.num_h_gnn_layers = config.num_h_gnn_layers
self.q_gnn_layers = []
self.q_gnn_norm_layers = []
for _ in range(self.num_q_gnn_layers):
# Graph layers
self.q_gnn_layers.append(
pyg_nn.GATv2Conv(
config.hidden_size, config.hidden_size//config.num_gnn_attention_heads,
config.num_gnn_attention_heads,
dropout=config.gnn_dropout_prob,
edge_dim=config.q_gnn_edge_dim,
concat=True
)
)
# After each graph layer, a normalization layer is added
self.q_gnn_norm_layers.append(pyg_nn.PairNorm())
self.q_gnn_layers = nn.ModuleList(self.q_gnn_layers)
self.q_gnn_norm_layers = nn.ModuleList(self.q_gnn_norm_layers)
self.h_gnn_layers = []
self.h_gnn_norm_layers = []
for _ in range(self.num_h_gnn_layers):
self.h_gnn_layers.append(
pyg_nn.GATv2Conv(
config.hidden_size, config.hidden_size//config.num_gnn_attention_heads,
config.num_gnn_attention_heads,
dropout=config.gnn_dropout_prob,
concat=True
)
)
# After each graph layer, a normalization layer is added
self.h_gnn_norm_layers.append(pyg_nn.PairNorm())
self.h_gnn_layers = nn.ModuleList(self.h_gnn_layers)
self.h_gnn_norm_layers = nn.ModuleList(self.h_gnn_norm_layers)
self.h_gnn_dense_hub = nn.Linear(config.v_hidden_size, config.hidden_size)
self.h_gnn_layer_norm_hub = BertLayerNorm(config.hidden_size, eps=1e-12)
self.h_gnn_dropout_hub = nn.Dropout(config.gnn_dropout_prob)
q_dense_pooling = nn.Sequential(
nn.Linear(config.hidden_size, 1),
ACT2FN['GeLU'],
nn.Dropout(config.gnn_dropout_prob)
)
self.q_gnn_pooling = pyg_nn.GlobalAttention(q_dense_pooling)
h_dense_pooling = nn.Sequential(
nn.Linear(config.hidden_size, 1),
ACT2FN['GeLU'],
nn.Dropout(config.gnn_dropout_prob)
)
self.h_gnn_pooling = pyg_nn.GlobalAttention(h_dense_pooling)
def forward(
self, hidden_states, q_edge_indices, q_edge_attributes,
q_limits, h_edge_indices, h_sep_indices, v_hub,
len_q_gr=None, len_h_gr=None, len_h_sep=None):
device = hidden_states.device
batch_size, _, hidden_size = hidden_states.size()
if isinstance(q_edge_indices, list):
assert len(q_edge_indices) == len(q_edge_attributes) == q_limits.size(0) \
== len(h_edge_indices) == len(h_sep_indices) == batch_size
else:
assert q_edge_indices.size(0) == q_edge_attributes.size(0) == q_limits.size(0) \
== h_edge_indices.size(0) == h_sep_indices.size(0) == batch_size
if len_q_gr is not None:
q_edge_indices = [t.squeeze(0)[:, :l].long() for t, l in zip(torch.split(q_edge_indices, 1, dim=0), len_q_gr)]
q_edge_attributes = [t.squeeze(0)[:l, :] for t, l in zip(torch.split(q_edge_attributes, 1, dim=0), len_q_gr)]
h_edge_indices = [t.squeeze(0)[:, :l].long() for t, l in zip(torch.split(h_edge_indices, 1, dim=0), len_h_gr)]
h_sep_indices = [t.squeeze(0)[:l].long() for t, l in zip(torch.split(h_sep_indices, 1, dim=0), len_h_sep)]
else:
q_edge_indices = [t.squeeze(0) for t in torch.split(q_edge_indices, 1, dim=0)]
q_edge_attributes = [t.squeeze(0) for t in torch.split(q_edge_attributes, 1, dim=0)]
h_edge_indices = [t.squeeze(0) for t in torch.split(h_edge_indices, 1, dim=0)]
h_sep_indices = [t.squeeze(0).long() for t in torch.split(h_sep_indices, 1, dim=0)]
gnn_hidden_states = hidden_states.clone().detach()
# Extract the history and question node features (without the hub node)
h_node_feats = []
q_node_feats = []
q_limits = q_limits.tolist()
q_tok_indices_extended = []
h_sep_indices_extended = []
for i, (h_sep_idx, q_limit) in enumerate(zip(h_sep_indices, q_limits)):
batch_data = gnn_hidden_states[i, :, :].clone().detach()
h_sep_idx = h_sep_idx.unsqueeze(-1).repeat(1, hidden_size)
h_sep_indices_extended.append(h_sep_idx)
h_node_feats.append(torch.gather(batch_data, 0, h_sep_idx))
q_tok_idx = torch.arange(q_limit[0], q_limit[1]).unsqueeze(-1).repeat(1, hidden_size).to(device)
q_tok_indices_extended.append(q_tok_idx)
q_node_feats.append(torch.gather(batch_data, 0, q_tok_idx))
# if self.use_hub_nodes:
# Map v_hub to the correct vector space
v_hub = self.h_gnn_dense_hub(v_hub)
v_hub = self.h_gnn_layer_norm_hub(v_hub)
v_hub = self.h_gnn_dropout_hub(v_hub)
# Add the hub node to the history nodes
v_hub = torch.split(v_hub, 1, dim=0)
h_node_feats = [torch.cat((h, x), dim=0) for h, x in zip(h_node_feats, v_hub)]
# Create the history graph data and pass them through the GNNs
pg_hist_data = [Data(x=x, edge_index=idx) for x, idx in zip(h_node_feats, h_edge_indices)]
pg_hist_loader = DataLoader(pg_hist_data, batch_size=batch_size, shuffle=False)
for data in pg_hist_loader:
x_h, edge_index_h, h_gnn_batch_idx = data.x, data.edge_index, data.batch
for i in range(self.num_h_gnn_layers):
# Normalization
x_h = self.h_gnn_norm_layers[i](x_h, h_gnn_batch_idx)
# Graph propagation
x_h = self.h_gnn_layers[i](x_h, edge_index_h, edge_attr=None)
# Activation
x_h = self.gnn_act(x_h) + x_h
x_h = self.gnn_act(x_h)
h_hub = self.h_gnn_pooling(x_h, h_gnn_batch_idx)
# Add the hub nodes
h_hub_split = torch.split(h_hub, 1, dim=0)
q_node_feats = [torch.cat((q, x), dim=0) for q, x in zip(q_node_feats, h_hub_split)]
# Create the question graph data and pass them through the GNNs
pg_ques_data = [Data(x=x, edge_index=idx, edge_attr=attr) for x, idx, attr in zip(q_node_feats, q_edge_indices, q_edge_attributes)]
pg_ques_loader = DataLoader(pg_ques_data, batch_size=batch_size, shuffle=False)
for data in pg_ques_loader:
x_q, edge_index_q, edge_attr_q, q_gnn_batch_idx = data.x, data.edge_index, data.edge_attr, data.batch
for i in range(self.num_q_gnn_layers):
# Normalization
x_q = self.q_gnn_norm_layers[i](x_q, q_gnn_batch_idx)
# GNN propagation
x_q = self.q_gnn_layers[i](x_q, edge_index_q, edge_attr=edge_attr_q)
# Activation
x_q = self.gnn_act(x_q) + x_q
x_q = self.gnn_act(x_q)
q_hub = self.q_gnn_pooling(x_q, q_gnn_batch_idx)
# Reshape the node features
h_node_feats = to_data_list(x_h, h_gnn_batch_idx)
q_node_feats = to_data_list(x_q, q_gnn_batch_idx)
# Update the text tokens with the graph feats
zipped_data = zip(h_node_feats, h_sep_indices_extended, q_node_feats, q_tok_indices_extended)
for i, (h_node_feat, h_sep_idx, q_node_feat, q_tok_idx) in enumerate(zipped_data):
gnn_hidden_states[i].scatter(0, h_sep_idx, h_node_feat[:-1])
gnn_hidden_states[i].scatter(0, q_tok_idx, q_node_feat[:-1])
final_hidden_states = 0.5 * (hidden_states + gnn_hidden_states)
return final_hidden_states, h_hub, q_hub
class BertImageSelfAttention(nn.Module):
def __init__(self, config):
super(BertImageSelfAttention, self).__init__()
if config.v_hidden_size % config.v_num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.v_hidden_size, config.v_num_attention_heads)
)
self.num_attention_heads = config.v_num_attention_heads
self.attention_head_size = int(
config.v_hidden_size / config.v_num_attention_heads
)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.v_hidden_size, self.all_head_size)
self.key = nn.Linear(config.v_hidden_size, self.all_head_size)
self.value = nn.Linear(config.v_hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.v_attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (
self.num_attention_heads,
self.attention_head_size,
)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, hidden_states, attention_mask):
mixed_query_layer = self.query(hidden_states)
mixed_key_layer = self.key(hidden_states)
mixed_value_layer = self.value(hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer)
key_layer = self.transpose_for_scores(mixed_key_layer)
value_layer = self.transpose_for_scores(mixed_value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer, attention_probs
class BertImageSelfOutput(nn.Module):
def __init__(self, config):
super(BertImageSelfOutput, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.v_hidden_size)
self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.v_hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BertImageAttention(nn.Module):
def __init__(self, config):
super(BertImageAttention, self).__init__()
self.self = BertImageSelfAttention(config)
self.output = BertImageSelfOutput(config)
def forward(self, input_tensor, attention_mask):
self_output, attention_probs = self.self(input_tensor, attention_mask)
attention_output = self.output(self_output, input_tensor)
return attention_output, attention_probs
class BertImageIntermediate(nn.Module):
def __init__(self, config):
super(BertImageIntermediate, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.v_intermediate_size)
if isinstance(config.v_hidden_act, str) or (
sys.version_info[0] == 2 and isinstance(config.v_hidden_act, unicode)
):
self.intermediate_act_fn = ACT2FN[config.v_hidden_act]
else:
self.intermediate_act_fn = config.v_hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class BertImageOutput(nn.Module):
def __init__(self, config):
super(BertImageOutput, self).__init__()
self.dense = nn.Linear(config.v_intermediate_size, config.v_hidden_size)
self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.v_hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BertImageLayer(nn.Module):
def __init__(self, config):
super(BertImageLayer, self).__init__()
self.attention = BertImageAttention(config)
self.intermediate = BertImageIntermediate(config)
self.output = BertImageOutput(config)
def forward(self, hidden_states, attention_mask):
attention_output, attention_probs = self.attention(hidden_states, attention_mask)
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output, attention_probs
class ImageGraphLayer(nn.Module):
def __init__(self, config):
super(ImageGraphLayer, self).__init__()
self.config = config
self.gnn_act = ACT2FN[config.gnn_act]
self.num_gnn_layers = config.num_v_gnn_layers
self.gnn_layers = []
self.gnn_norm_layers = []
for _ in range(self.num_gnn_layers):
self.gnn_layers.append(
pyg_nn.GATv2Conv(
config.v_hidden_size, config.v_hidden_size//config.num_gnn_attention_heads,
config.num_gnn_attention_heads,
dropout=config.gnn_dropout_prob,
edge_dim=config.v_gnn_edge_dim,
concat=True
)
)
# After each graph layer, a normalization layer is added
self.gnn_norm_layers.append(pyg_nn.PairNorm())
self.gnn_layers = nn.ModuleList(self.gnn_layers)
self.gnn_norm_layers = nn.ModuleList(self.gnn_norm_layers)
self.gnn_dense_hub = nn.Linear(config.hidden_size, config.v_hidden_size)
self.gnn_layer_norm_hub = BertLayerNorm(config.v_hidden_size, eps=1e-12)
self.gnn_dropout_hub = nn.Dropout(config.gnn_dropout_prob)
dense_pooling = nn.Sequential(
nn.Linear(config.v_hidden_size, 1),
ACT2FN['GeLU'],
nn.Dropout(config.gnn_dropout_prob)
)
self.gnn_pooling = pyg_nn.GlobalAttention(dense_pooling)
def forward(
self, hidden_states, edge_indices, edge_attributes, hub_states,
len_img_gr=None):
# assert hub_states is not None
gnn_hidden_states = hidden_states.clone().detach()
batch_size, num_img_reg, v_hidden_size = hidden_states.size()
node_feats = hidden_states.clone().detach()
# Remave the [IMG] feats
node_feats = node_feats[:, 1:]
node_feats = torch.split(node_feats, 1, dim=0)
if len_img_gr is not None:
edge_indices = [t.squeeze(0)[:, :l].long() for t, l in zip(torch.split(edge_indices, 1, dim=0), len_img_gr)]
edge_attributes = [t.squeeze(0)[:l, :] for t, l in zip(torch.split(edge_attributes, 1, dim=0), len_img_gr)]
# Concat the hub states
hub_states = self.gnn_dense_hub(hub_states)
hub_states = self.gnn_dropout_hub(hub_states)
hub_states = self.gnn_layer_norm_hub(hub_states)
hub_states = torch.split(hub_states, 1, dim=0)
node_feats = [torch.cat((x.squeeze(0), h), dim=0)
for x, h in zip(node_feats, hub_states)]
pg_data = [Data(x, idx, attr) for x, idx, attr in zip(
node_feats, edge_indices, edge_attributes)]
pg_dataloader = DataLoader(
pg_data, batch_size=batch_size, shuffle=False)
# Gnn forward pass
for data in pg_dataloader:
x, edge_index, edge_attr, gnn_batch_idx = data.x, data.edge_index, data.edge_attr, data.batch
for i in range(self.num_gnn_layers):
# Normalization
x = self.gnn_norm_layers[i](x, gnn_batch_idx)
# GNN propagation
x = self.gnn_layers[i](x, edge_index, edge_attr=edge_attr)
# Activation
x = self.gnn_act(x) + x
x = self.gnn_act(x)
# Reshape the output of the GNN to batch_size x num_img_reg x hidden_dim
v_hub = self.gnn_pooling(x, gnn_batch_idx)
x = x.view(batch_size, num_img_reg, v_hidden_size)
gnn_hidden_states[:, 1:, :] = x[:, :-1, :]
final_hidden_states = 0.5 * (hidden_states + gnn_hidden_states)
return final_hidden_states, v_hub
class BertBiAttention(nn.Module):
def __init__(self, config):
super(BertBiAttention, self).__init__()
if config.bi_hidden_size % config.bi_num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.bi_hidden_size, config.bi_num_attention_heads)
)
self.num_attention_heads = config.bi_num_attention_heads
self.attention_head_size = int(
config.bi_hidden_size / config.bi_num_attention_heads
)
self.all_head_size = self.num_attention_heads * self.attention_head_size
# self.scale = nn.Linear(1, self.num_attention_heads, bias=False)
# self.scale_act_fn = ACT2FN['relu']
self.query1 = nn.Linear(config.v_hidden_size, self.all_head_size)
self.key1 = nn.Linear(config.v_hidden_size, self.all_head_size)
self.value1 = nn.Linear(config.v_hidden_size, self.all_head_size)
# self.logit1 = nn.Linear(config.hidden_size, self.num_attention_heads)
self.dropout1 = nn.Dropout(config.v_attention_probs_dropout_prob)
self.query2 = nn.Linear(config.hidden_size, self.all_head_size)
self.key2 = nn.Linear(config.hidden_size, self.all_head_size)
self.value2 = nn.Linear(config.hidden_size, self.all_head_size)
# self.logit2 = nn.Linear(config.hidden_size, self.num_attention_heads)
self.dropout2 = nn.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (
self.num_attention_heads,
self.attention_head_size,
)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(self, input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask=None, use_co_attention_mask=False):
# for vision input.
mixed_query_layer1 = self.query1(input_tensor1)
mixed_key_layer1 = self.key1(input_tensor1)
mixed_value_layer1 = self.value1(input_tensor1)
# mixed_logit_layer1 = self.logit1(input_tensor1)
query_layer1 = self.transpose_for_scores(mixed_query_layer1)
key_layer1 = self.transpose_for_scores(mixed_key_layer1)
value_layer1 = self.transpose_for_scores(mixed_value_layer1)
# logit_layer1 = self.transpose_for_logits(mixed_logit_layer1)
# for text input:
mixed_query_layer2 = self.query2(input_tensor2)
mixed_key_layer2 = self.key2(input_tensor2)
mixed_value_layer2 = self.value2(input_tensor2)
# mixed_logit_layer2 = self.logit2(input_tensor2)
query_layer2 = self.transpose_for_scores(mixed_query_layer2)
key_layer2 = self.transpose_for_scores(mixed_key_layer2)
value_layer2 = self.transpose_for_scores(mixed_value_layer2)
# logit_layer2 = self.transpose_for_logits(mixed_logit_layer2)
# Take the dot product between "query2" and "key1" to get the raw attention scores for value 1.
attention_scores1 = torch.matmul(query_layer2, key_layer1.transpose(-1, -2))
attention_scores1 = attention_scores1 / math.sqrt(self.attention_head_size)
attention_scores1 = attention_scores1 + attention_mask1
if use_co_attention_mask:
attention_scores1 = attention_scores1 + co_attention_mask.permute(0,1,3,2)
# Normalize the attention scores to probabilities.
attention_probs1 = nn.Softmax(dim=-1)(attention_scores1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs1 = self.dropout1(attention_probs1)
context_layer1 = torch.matmul(attention_probs1, value_layer1)
context_layer1 = context_layer1.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape1 = context_layer1.size()[:-2] + (self.all_head_size,)
context_layer1 = context_layer1.view(*new_context_layer_shape1)
# Take the dot product between "query1" and "key2" to get the raw attention scores for value 2.
attention_scores2 = torch.matmul(query_layer1, key_layer2.transpose(-1, -2))
attention_scores2 = attention_scores2 / math.sqrt(self.attention_head_size)
# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
# we can comment this line for single flow.
attention_scores2 = attention_scores2 + attention_mask2
if use_co_attention_mask:
attention_scores2 = attention_scores2 + co_attention_mask
# Normalize the attention scores to probabilities.
attention_probs2 = nn.Softmax(dim=-1)(attention_scores2)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs2 = self.dropout2(attention_probs2)
context_layer2 = torch.matmul(attention_probs2, value_layer2)
context_layer2 = context_layer2.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape2 = context_layer2.size()[:-2] + (self.all_head_size,)
context_layer2 = context_layer2.view(*new_context_layer_shape2)
return context_layer1, context_layer2, (attention_probs1, attention_probs2)
class BertBiOutput(nn.Module):
def __init__(self, config):
super(BertBiOutput, self).__init__()
self.dense1 = nn.Linear(config.bi_hidden_size, config.v_hidden_size)
self.LayerNorm1 = BertLayerNorm(config.v_hidden_size, eps=1e-12)
self.dropout1 = nn.Dropout(config.v_hidden_dropout_prob)
self.q_dense1 = nn.Linear(config.bi_hidden_size, config.v_hidden_size)
self.q_dropout1 = nn.Dropout(config.v_hidden_dropout_prob)
self.dense2 = nn.Linear(config.bi_hidden_size, config.hidden_size)
self.LayerNorm2 = BertLayerNorm(config.hidden_size, eps=1e-12)
self.dropout2 = nn.Dropout(config.hidden_dropout_prob)
self.q_dense2 = nn.Linear(config.bi_hidden_size, config.hidden_size)
self.q_dropout2 = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states1, input_tensor1, hidden_states2, input_tensor2):
context_state1 = self.dense1(hidden_states1)
context_state1 = self.dropout1(context_state1)
context_state2 = self.dense2(hidden_states2)
context_state2 = self.dropout2(context_state2)
hidden_states1 = self.LayerNorm1(context_state1 + input_tensor1)
hidden_states2 = self.LayerNorm2(context_state2 + input_tensor2)
return hidden_states1, hidden_states2
class BertConnectionLayer(nn.Module):
def __init__(self, config):
super(BertConnectionLayer, self).__init__()
self.biattention = BertBiAttention(config)
self.biOutput = BertBiOutput(config)
self.v_intermediate = BertImageIntermediate(config)
self.v_output = BertImageOutput(config)
self.t_intermediate = BertIntermediate(config)
self.t_output = BertOutput(config)
def forward(self, input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask=None, use_co_attention_mask=False):
bi_output1, bi_output2, co_attention_probs = self.biattention(
input_tensor1, attention_mask1, input_tensor2, attention_mask2, co_attention_mask, use_co_attention_mask
)
attention_output1, attention_output2 = self.biOutput(bi_output2, input_tensor1, bi_output1, input_tensor2)
intermediate_output1 = self.v_intermediate(attention_output1)
layer_output1 = self.v_output(intermediate_output1, attention_output1)
intermediate_output2 = self.t_intermediate(attention_output2)
layer_output2 = self.t_output(intermediate_output2, attention_output2)
return layer_output1, layer_output2, co_attention_probs
class BertEncoder(nn.Module):
def __init__(self, config):
super(BertEncoder, self).__init__()
# in the bert encoder, we need to extract three things here.
# text bert layer: BertLayer
# vision bert layer: BertImageLayer
# Bi-Attention: Given the output of two bertlayer, perform bi-directional
# attention and add on two layers.
self.FAST_MODE = config.fast_mode
self.with_coattention = config.with_coattention
self.v_biattention_id = config.v_biattention_id
self.t_biattention_id = config.t_biattention_id
self.in_batch_pairs = config.in_batch_pairs
self.fixed_t_layer = config.fixed_t_layer
self.fixed_v_layer = config.fixed_v_layer
self.t_gnn_ids = config.t_gnn_ids
self.v_gnn_ids = config.v_gnn_ids
v_layer = BertImageLayer(config)
connect_layer = BertConnectionLayer(config)
self.layer = []
for _ in range(config.num_hidden_layers):
self.layer.append(BertLayer(config))
self.layer = nn.ModuleList(self.layer)
txt_graph_layer = TextGraphLayer(config)
self.t_gnns = nn.ModuleList([txt_graph_layer for _ in range(len(self.t_gnn_ids))])
self.v_layer = nn.ModuleList(
[copy.deepcopy(v_layer) for _ in range(config.v_num_hidden_layers)]
)
img_graph_layer = ImageGraphLayer(config)
self.v_gnns = nn.ModuleList([img_graph_layer for _ in range(len(self.v_gnn_ids))])
self.c_layer = nn.ModuleList(
[copy.deepcopy(connect_layer)
for _ in range(len(config.v_biattention_id))]
)
def forward(
self,
txt_embedding,
image_embedding,
txt_attention_mask,
image_attention_mask,
image_edge_indices,
image_edge_attributes,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
co_attention_mask=None,
output_all_encoded_layers=True,
output_all_attention_masks=False,
len_img_gr=None, len_q_gr=None, len_h_gr=None, len_h_sep=None
):
v_start = 0
t_start = 0
count = 0
all_encoder_layers_t = []
all_encoder_layers_v = []
all_attention_mask_t = []
all_attnetion_mask_v = []
all_attention_mask_c = []
batch_size, num_words, t_hidden_size = txt_embedding.size()
_, num_regions, v_hidden_size = image_embedding.size()
# self.pool_feats(txt_embedding)
use_co_attention_mask = False
# Init the v_hub with the [IMG]-token embedding
v_hub = image_embedding[:, 0, :].clone().detach()
q_hub = None
for v_layer_id, t_layer_id in zip(self.v_biattention_id, self.t_biattention_id):
v_end = v_layer_id
t_end = t_layer_id
assert self.fixed_t_layer <= t_end
assert self.fixed_v_layer <= v_end
for idx in range(v_start, self.fixed_v_layer):
with torch.no_grad():
image_embedding, image_attention_probs = self.v_layer[idx](
image_embedding, image_attention_mask)
v_start = self.fixed_v_layer
if output_all_attention_masks:
all_attnetion_mask_v.append(image_attention_probs)
for idx in range(v_start, v_end):
# Perfrom graph message passing and aggr. if applicable
if idx in self.v_gnn_ids:
assert q_hub is not None
v_gnn_layer_idx = self.v_gnn_ids.index(idx)
image_embedding, v_hub = self.v_gnns[v_gnn_layer_idx](
image_embedding,
image_edge_indices,
image_edge_attributes,
q_hub,
len_img_gr=len_img_gr,
)
# Perform standard bert self-attention
image_embedding, image_attention_probs = self.v_layer[idx](
image_embedding, image_attention_mask)
if output_all_attention_masks:
all_attnetion_mask_v.append(image_attention_probs)
for idx in range(t_start, self.fixed_t_layer):
with torch.no_grad():
txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
t_start = self.fixed_t_layer
if output_all_attention_masks:
all_attention_mask_t.append(txt_attention_probs)
for idx in range(t_start, t_end):
# Perfrom graph message passing and aggr. if applicable
if idx in self.t_gnn_ids:
t_gnn_layer_idx = self.t_gnn_ids.index(idx)
txt_embedding, h_hub, q_hub = self.t_gnns[t_gnn_layer_idx](
txt_embedding,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
v_hub,
len_q_gr=len_q_gr,
len_h_gr=len_h_gr,
len_h_sep=len_h_sep
)
# Perform standard bert self-attention
txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
if output_all_attention_masks:
all_attention_mask_t.append(txt_attention_probs)
if count == 0 and self.in_batch_pairs:
# new batch size is the batch_size ^2
image_embedding = image_embedding.unsqueeze(0).expand(batch_size, batch_size, num_regions, v_hidden_size).contiguous(
).view(batch_size*batch_size, num_regions, v_hidden_size)
image_attention_mask = image_attention_mask.unsqueeze(0).expand(
batch_size, batch_size, 1, 1, num_regions).contiguous().view(batch_size*batch_size, 1, 1, num_regions)
txt_embedding = txt_embedding.unsqueeze(1).expand(batch_size, batch_size, num_words, t_hidden_size).contiguous(
).view(batch_size*batch_size, num_words, t_hidden_size)
txt_attention_mask = txt_attention_mask.unsqueeze(1).expand(
batch_size, batch_size, 1, 1, num_words).contiguous().view(batch_size*batch_size, 1, 1, num_words)
co_attention_mask = co_attention_mask.unsqueeze(1).expand(
batch_size, batch_size, 1, num_regions, num_words).contiguous().view(batch_size*batch_size, 1, num_regions, num_words)
if self.with_coattention:
# do the bi attention.
image_embedding, txt_embedding, co_attention_probs = self.c_layer[count](
image_embedding, image_attention_mask, txt_embedding, txt_attention_mask, co_attention_mask, use_co_attention_mask)
# use_co_attention_mask = False
if output_all_attention_masks:
all_attention_mask_c.append(co_attention_probs)
v_start = v_end
t_start = t_end
count += 1
if output_all_encoded_layers:
all_encoder_layers_t.append(txt_embedding)
all_encoder_layers_v.append(image_embedding)
for idx in range(v_start, len(self.v_layer)):
# Perfrom graph message passing and aggr. if applicable
if idx in self.v_gnn_ids:
v_gnn_layer_idx = self.v_gnn_ids.index(idx)
image_embedding, v_hub = self.v_gnns[v_gnn_layer_idx](
image_embedding,
image_edge_indices,
image_edge_attributes,
q_hub,
len_img_gr=len_img_gr
)
# Perform standard bert self-attention
image_embedding, image_attention_probs = self.v_layer[idx](
image_embedding, image_attention_mask)
if output_all_attention_masks:
all_attnetion_mask_v.append(image_attention_probs)
for idx in range(t_start, len(self.layer)):
# Perfrom graph message passing and aggr. if applicable
if idx in self.t_gnn_ids:
t_gnn_layer_idx = self.t_gnn_ids.index(idx)
txt_embedding, h_hub, q_hub = self.t_gnns[t_gnn_layer_idx](
txt_embedding,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
v_hub,
len_q_gr=len_q_gr,
len_h_gr=len_h_gr,
len_h_sep=len_h_sep
)
# Perform standard bert self-attention
txt_embedding, txt_attention_probs = self.layer[idx](txt_embedding, txt_attention_mask)
if output_all_attention_masks:
all_attention_mask_t.append(txt_attention_probs)
# add the end part to finish.
if not output_all_encoded_layers:
all_encoder_layers_t.append(txt_embedding)
all_encoder_layers_v.append(image_embedding)
return all_encoder_layers_t, all_encoder_layers_v, (all_attention_mask_t, all_attnetion_mask_v, all_attention_mask_c), (h_hub, q_hub, v_hub)
class BertTextPooler(nn.Module):
def __init__(self, config):
super(BertTextPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class BertImagePooler(nn.Module):
def __init__(self, config):
super(BertImagePooler, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.bi_hidden_size)
self.activation = nn.ReLU()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super(BertPredictionHeadTransform, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str) or (
sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class BertImgPredictionHeadTransform(nn.Module):
def __init__(self, config):
super(BertImgPredictionHeadTransform, self).__init__()
self.dense = nn.Linear(config.v_hidden_size, config.v_hidden_size)
if isinstance(config.hidden_act, str) or (
sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)
):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.v_hidden_act
self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class BertLMPredictionHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(BertLMPredictionHead, self).__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(
bert_model_embedding_weights.size(1),
bert_model_embedding_weights.size(0),
bias=False,
)
self.decoder.weight = bert_model_embedding_weights
self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states) + self.bias
return hidden_states
class BertOnlyMLMHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(BertOnlyMLMHead, self).__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class BertOnlyNSPHead(nn.Module):
def __init__(self, config):
super(BertOnlyNSPHead, self).__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
class BertPreTrainingHeads(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super(BertPreTrainingHeads, self).__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
self.bi_seq_relationship = nn.Linear(config.bi_hidden_size, 2)
self.imagePredictions = BertImagePredictionHead(config)
self.fusion_method = config.fusion_method
self.dropout = nn.Dropout(0.1)
def forward(
self, sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
):
if self.fusion_method == 'sum':
pooled_output = self.dropout(pooled_output_t + pooled_output_v)
elif self.fusion_method == 'mul':
pooled_output = self.dropout(pooled_output_t * pooled_output_v)
else:
assert False
prediction_scores_t = self.predictions(sequence_output_t)
seq_relationship_score = self.bi_seq_relationship(pooled_output)
prediction_scores_v = self.imagePredictions(sequence_output_v)
return prediction_scores_t, prediction_scores_v, seq_relationship_score
class BertImagePredictionHead(nn.Module):
def __init__(self, config):
super(BertImagePredictionHead, self).__init__()
self.transform = BertImgPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.v_hidden_size, config.v_target_size)
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class BertPreTrainedModel(nn.Module):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
"""
def __init__(self, config, device='cuda:0', default_gpu=True, *inputs, **kwargs):
super(BertPreTrainedModel, self).__init__()
if not isinstance(config, BertConfig):
raise ValueError(
"Parameter config in `{}(config)` should be an instance of class `BertConfig`. "
"To create a model from a Google pretrained model use "
"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
self.__class__.__name__, self.__class__.__name__
)
)
self.config = config
def init_bert_weights(self, module):
""" Initialize the weights.
"""
if isinstance(module, (nn.Linear, nn.Embedding)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
elif isinstance(module, BertLayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path,
config,
device,
use_apex=False,
default_gpu=True,
state_dict=None,
cache_dir=None,
from_tf=False,
*inputs,
**kwargs
):
"""
Instantiate a BertPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name_or_path: either:
- a str with the name of a pre-trained model to load selected in the list of:
. `bert-base-uncased`
. `bert-large-uncased`
. `bert-base-cased`
. `bert-large-cased`
. `bert-base-multilingual-uncased`
. `bert-base-multilingual-cased`
. `bert-base-chinese`
- a path or url to a pretrained model archive containing:
. `bert_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a BertForPreTraining instance
- a path or url to a pretrained model archive containing:
. `bert_config.json` a configuration file for the model
. `model.chkpt` a TensorFlow checkpoint
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of Google pre-trained models
*inputs, **kwargs: additional input for the specific Bert class
(ex: num_labels for BertForSequenceClassification)
"""
CONFIG_NAME = "bert_config.json"
WEIGHTS_NAME = "pytorch_model.bin"
TF_WEIGHTS_NAME = "model.ckpt"
if pretrained_model_name_or_path in PRETRAINED_MODEL_ARCHIVE_MAP:
archive_file = PRETRAINED_MODEL_ARCHIVE_MAP[pretrained_model_name_or_path]
else:
archive_file = pretrained_model_name_or_path
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
"associated to this path or url.".format(
pretrained_model_name_or_path,
", ".join(PRETRAINED_MODEL_ARCHIVE_MAP.keys()),
archive_file,
)
)
return None
if default_gpu:
if resolved_archive_file == archive_file:
logger.info("loading archive file {}".format(archive_file))
else:
logger.info(
"loading archive file {} from cache at {}".format(
archive_file, resolved_archive_file
)
)
tempdir = None
if os.path.isdir(resolved_archive_file) or from_tf:
serialization_dir = resolved_archive_file
elif resolved_archive_file[-3:] == 'bin':
serialization_dir = '/'.join(resolved_archive_file.split('/')[:-1])
WEIGHTS_NAME = resolved_archive_file.split('/')[-1]
else:
# Extract archive to temp dir
tempdir = tempfile.mkdtemp()
logger.info(
"extracting archive file {} to temp dir {}".format(
resolved_archive_file, tempdir
)
)
with tarfile.open(resolved_archive_file, "r:gz") as archive:
archive.extractall(tempdir)
serialization_dir = tempdir
# Load config
# config_file = os.path.join(serialization_dir, CONFIG_NAME)
# config = BertConfig.from_json_file(config_file)
if default_gpu:
# cancel output
# logger.info("Model config {}".format(config))
pass
# Instantiate model.
model = cls(config, device, use_apex, *inputs, **kwargs)
if state_dict is None and not from_tf:
weights_path = os.path.join(serialization_dir, WEIGHTS_NAME)
map_location = {'cuda:0': device}
state_dict = torch.load(
weights_path,
map_location=map_location
)
if 'state_dict' in dir(state_dict):
state_dict = state_dict.state_dict()
if tempdir:
# Clean up temp dir
shutil.rmtree(tempdir)
if from_tf:
# Directly load from a TensorFlow checkpoint
weights_path = os.path.join(serialization_dir, TF_WEIGHTS_NAME)
return load_tf_weights_in_bert(model, weights_path)
# Load from a PyTorch state_dict
old_keys = []
new_keys = []
for key in state_dict.keys():
new_key = None
if "gamma" in key:
new_key = key.replace("gamma", "weight")
if "beta" in key:
new_key = key.replace("beta", "bias")
if new_key:
old_keys.append(key)
new_keys.append(new_key)
for old_key, new_key in zip(old_keys, new_keys):
state_dict[new_key] = state_dict.pop(old_key)
missing_keys = []
unexpected_keys = []
error_msgs = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, "_metadata", None)
state_dict = state_dict.copy()
if metadata is not None:
state_dict._metadata = metadata
def load(module, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
module._load_from_state_dict(
state_dict,
prefix,
local_metadata,
True,
missing_keys,
unexpected_keys,
error_msgs,
)
for name, child in module._modules.items():
if child is not None:
load(child, prefix + name + ".")
start_prefix = ""
if not hasattr(model, "bert") and any(
s.startswith("bert.") for s in state_dict.keys()
):
start_prefix = "bert."
load(model, prefix=start_prefix)
if len(missing_keys) > 0 and default_gpu:
logger.info(
"Weights of {} not initialized from pretrained model: {}".format(
model.__class__.__name__, missing_keys
)
)
if len(unexpected_keys) > 0 and default_gpu:
logger.info(
"Weights from pretrained model not used in {}: {}".format(
model.__class__.__name__, unexpected_keys
)
)
if len(error_msgs) > 0 and default_gpu:
raise RuntimeError(
"Error(s) in loading state_dict for {}:\n\t{}".format(
model.__class__.__name__, "\n\t".join(error_msgs)
)
)
return model
class BertModel(BertPreTrainedModel):
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
Params:
config: a BertConfig class instance with the configuration to build a new model
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
Outputs: Tuple of (encoded_layers, pooled_output)
`encoded_layers`: controled by `output_all_encoded_layers` argument:
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
to the last attention block of shape [batch_size, sequence_length, hidden_size],
`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
classifier pretrained on top of the hidden state associated to the first character of the
input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = modeling.BertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = modeling.BertModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
def __init__(self, config, device, use_apex=False):
super(BertModel, self).__init__(config, device)
# initilize word embedding
self.embeddings = BertEmbeddingsDialog(config, device)
# initlize the vision embedding
self.v_embeddings = BertImageEmbeddings(config)
self.encoder = BertEncoder(config)
self.t_pooler = BertTextPooler(config)
self.v_pooler = BertImagePooler(config)
self.use_apex = use_apex
self.apply(self.init_bert_weights)
def forward(
self,
input_txt,
input_imgs,
image_loc,
image_edge_indices,
image_edge_attributes,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
sep_indices=None,
sep_len=None,
token_type_ids=None,
attention_mask=None,
image_attention_mask=None,
co_attention_mask=None,
output_all_encoded_layers=False,
output_all_attention_masks=False,
):
if attention_mask is None:
attention_mask = torch.ones_like(input_txt)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_txt)
if image_attention_mask is None:
image_attention_mask = torch.ones(
input_imgs.size(0), input_imgs.size(1)
).type_as(input_txt)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_image_attention_mask = image_attention_mask.unsqueeze(1).unsqueeze(2)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
if self.use_apex:
dtype = dtype=next(self.parameters()).dtype
else:
dtype = torch.float32
extended_attention_mask = extended_attention_mask.to(dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
extended_image_attention_mask = extended_image_attention_mask.to(dtype) # fp16 compatibility
extended_image_attention_mask = (1.0 - extended_image_attention_mask) * -10000.0
if co_attention_mask is None:
co_attention_mask = torch.zeros(input_txt.size(0), input_imgs.size(1), input_txt.size(1)).type_as(extended_image_attention_mask)
extended_co_attention_mask = co_attention_mask.unsqueeze(1)
# extended_co_attention_mask = co_attention_mask.unsqueeze(-1)
extended_co_attention_mask = extended_co_attention_mask * 5.0
extended_co_attention_mask = extended_co_attention_mask.to(dtype) # fp16 compatibility
embedding_output = self.embeddings(input_txt, token_type_ids=token_type_ids, sep_indices=sep_indices, sep_len=sep_len)
v_embedding_output = self.v_embeddings(input_imgs, image_loc)
encoded_layers_t, encoded_layers_v, all_attention_mask, hub_feats = self.encoder(
embedding_output,
v_embedding_output,
extended_attention_mask,
extended_image_attention_mask,
image_edge_indices,
image_edge_attributes,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
co_attention_mask=extended_co_attention_mask,
output_all_encoded_layers=output_all_encoded_layers,
output_all_attention_masks=output_all_attention_masks,
)
sequence_output_t = encoded_layers_t[-1]
sequence_output_v = encoded_layers_v[-1]
pooled_output_t = self.t_pooler(sequence_output_t)
pooled_output_v = self.v_pooler(sequence_output_v)
if not output_all_encoded_layers:
encoded_layers_t = encoded_layers_t[-1]
encoded_layers_v = encoded_layers_v[-1]
return encoded_layers_t, encoded_layers_v, pooled_output_t, pooled_output_v, all_attention_mask, hub_feats
class BertImageEmbeddings(nn.Module):
"""Construct the embeddings from image, spatial location (omit now) and token_type embeddings.
"""
def __init__(self, config):
super(BertImageEmbeddings, self).__init__()
self.image_embeddings = nn.Linear(config.v_feature_size, config.v_hidden_size)
self.image_location_embeddings = nn.Linear(5, config.v_hidden_size)
self.LayerNorm = BertLayerNorm(config.v_hidden_size, eps=1e-12)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids, input_loc):
img_embeddings = self.image_embeddings(input_ids)
loc_embeddings = self.image_location_embeddings(input_loc)
embeddings = self.LayerNorm(img_embeddings+loc_embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class BertForMultiModalPreTraining(BertPreTrainedModel):
"""BERT model with multi modal pre-training heads.
"""
def __init__(self, config, device, use_apex=False):
super(BertForMultiModalPreTraining, self).__init__(config, device)
self.bert = BertModel(config, device, use_apex)
self.cls = BertPreTrainingHeads(
config, self.bert.embeddings.word_embeddings.weight
)
self.apply(self.init_bert_weights)
self.predict_feature = config.predict_feature
self.loss_fct = CrossEntropyLoss(ignore_index=-1)
print("model's option for predict_feature is ", config.predict_feature)
if self.predict_feature:
self.vis_criterion = nn.MSELoss(reduction="none")
else:
self.vis_criterion = nn.KLDivLoss(reduction="none")
def forward(
self,
input_ids,
image_feat,
image_loc,
image_edge_indices,
image_edge_attributes,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
sep_indices=None,
sep_len=None,
token_type_ids=None,
attention_mask=None,
image_attention_mask=None,
masked_lm_labels=None,
image_label=None,
image_target = None,
next_sentence_label=None,
output_all_attention_masks=False
):
# in this model, we first embed the images.
sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, all_attention_mask, hub_nodes = self.bert(
input_ids,
image_feat,
image_loc,
image_edge_indices,
image_edge_attributes,
question_edge_indices,
question_edge_attributes,
question_limits,
history_edge_indices,
history_sep_indices,
sep_indices=sep_indices,
sep_len=sep_len,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
image_attention_mask=image_attention_mask,
output_all_encoded_layers=False,
output_all_attention_masks=output_all_attention_masks
)
prediction_scores_t, prediction_scores_v, seq_relationship_score = self.cls(
sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
)
if masked_lm_labels is not None and next_sentence_label is not None and image_target is not None:
# prediction_scores_v = prediction_scores_v[:, 1:]
if self.predict_feature:
img_loss = self.vis_criterion(prediction_scores_v, image_target)
masked_img_loss = torch.sum(
img_loss * (image_label == 1).unsqueeze(2).float()
) / max(torch.sum((image_label == 1).unsqueeze(2).expand_as(img_loss)),1)
else:
img_loss = self.vis_criterion(
F.log_softmax(prediction_scores_v, dim=2), image_target
)
masked_img_loss = torch.sum(
img_loss * (image_label == 1).unsqueeze(2).float()
) / max(torch.sum((image_label == 1)), 0)
# masked_img_loss = torch.sum(img_loss) / (img_loss.shape[0] * img_loss.shape[1])
masked_lm_loss = self.loss_fct(
prediction_scores_t.view(-1, self.config.vocab_size),
masked_lm_labels.view(-1),
)
next_sentence_loss = self.loss_fct(
seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
)
# total_loss = masked_lm_loss + next_sentence_loss + masked_img_loss
return masked_lm_loss.unsqueeze(0), masked_img_loss.unsqueeze(0), next_sentence_loss.unsqueeze(0), sequence_output_t, prediction_scores_t, seq_relationship_score, hub_nodes
else:
return prediction_scores_t, prediction_scores_v, seq_relationship_score, sequence_output_t, all_attention_mask, hub_nodes
def get_text_embedding(
self,
input_ids,
image_feat,
image_loc,
sep_indices=None,
sep_len=None,
token_type_ids=None,
attention_mask=None,
image_attention_mask=None,
output_all_attention_masks=False
):
# in this model, we first embed the images.
sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, all_attention_mask = self.bert(
input_ids,
image_feat,
image_loc,
sep_indices=sep_indices,
sep_len=sep_len,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
image_attention_mask=image_attention_mask,
output_all_encoded_layers=False,
output_all_attention_masks=output_all_attention_masks
)
return sequence_output_t # [batch_size, num_words, 768]
class VILBertForVLTasks(BertPreTrainedModel):
def __init__(self, config, device, num_labels, use_apex=False, dropout_prob=0.1, default_gpu=True):
super(VILBertForVLTasks, self).__init__(config)
self.num_labels = num_labels
self.bert = BertModel(config, device, use_apex)
self.use_apex = use_apex
self.dropout = nn.Dropout(dropout_prob)
self.cls = BertPreTrainingHeads(
config, self.bert.embeddings.word_embeddings.weight
)
self.vil_prediction = SimpleClassifier(config.bi_hidden_size, config.bi_hidden_size*2, num_labels, 0.5)
# self.vil_prediction = nn.Linear(config.bi_hidden_size, num_labels)
self.vil_logit = nn.Linear(config.bi_hidden_size, 1)
self.vision_logit = nn.Linear(config.v_hidden_size, 1)
self.linguisic_logit = nn.Linear(config.hidden_size, 1)
self.fusion_method = config.fusion_method
self.apply(self.init_bert_weights)
def forward(
self,
input_txt,
input_imgs,
image_loc,
token_type_ids=None,
attention_mask=None,
image_attention_mask=None,
co_attention_mask=None,
output_all_encoded_layers=False,
):
sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v, _ = self.bert(
input_txt,
input_imgs,
image_loc,
token_type_ids,
attention_mask,
image_attention_mask,
co_attention_mask,
output_all_encoded_layers=False,
)
vil_prediction = 0
vil_logit = 0
vil_binary_prediction = 0
vision_prediction = 0
vision_logit = 0
linguisic_prediction = 0
linguisic_logit = 0
linguisic_prediction, vision_prediction, vil_binary_prediction = self.cls(
sequence_output_t, sequence_output_v, pooled_output_t, pooled_output_v
)
if self.fusion_method == 'sum':
pooled_output = self.dropout(pooled_output_t + pooled_output_v)
elif self.fusion_method == 'mul':
pooled_output = self.dropout(pooled_output_t * pooled_output_v)
else:
assert False
vil_prediction = self.vil_prediction(pooled_output)
vil_logit = self.vil_logit(pooled_output)
if self.use_apex:
dtype = next(self.parameters()).dtype
else:
dtype = torch.float32
vision_logit = self.vision_logit(self.dropout(sequence_output_v)) + ((1.0 - image_attention_mask)* -10000.0).unsqueeze(2).to(dtype)
linguisic_logit = self.linguisic_logit(self.dropout(sequence_output_t))
return vil_prediction, vil_logit, vil_binary_prediction, vision_prediction, vision_logit, linguisic_prediction, linguisic_logit
class SimpleClassifier(nn.Module):
def __init__(self, in_dim, hid_dim, out_dim, dropout):
super(SimpleClassifier, self).__init__()
layers = [
weight_norm(nn.Linear(in_dim, hid_dim), dim=None),
nn.ReLU(),
nn.Dropout(dropout, inplace=True),
weight_norm(nn.Linear(hid_dim, out_dim), dim=None)
]
self.main = nn.Sequential(*layers)
def forward(self, x):
logits = self.main(x)
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