def __init__(self, opts, img_fc_dim, img_fc_use_batchnorm, img_dropout, img_feat_input_dim,
rnn_hidden_size, rnn_dropout, max_len, fc_bias=True, max_navigable=16):
super(Configuring, self).__init__()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.max_navigable = max_navigable
self.feature_size = img_feat_input_dim
self.hidden_size = rnn_hidden_size
self.max_len = max_len
proj_navigable_kwargs = {
'input_dim': img_feat_input_dim,
'hidden_dims': img_fc_dim,
'use_batchnorm': img_fc_use_batchnorm,
'dropout': img_dropout,
'fc_bias': fc_bias,
'relu': opts.mlp_relu
}
self.proj_navigable_mlp = build_mlp(**proj_navigable_kwargs)
self.h0_fc = nn.Linear(rnn_hidden_size, img_fc_dim[-1], bias=fc_bias)
self.h1_fc = nn.Linear(rnn_hidden_size, rnn_hidden_size, bias=fc_bias)
self.soft_attn = SoftAttention()
self.dropout = nn.Dropout(p=rnn_dropout)
self.lstm = nn.LSTMCell(img_fc_dim[-1] * 2 + rnn_hidden_size, rnn_hidden_size)
self.lang_position = PositionalEncoding(rnn_hidden_size, dropout=0.1, max_len=max_len)
self.logit_fc = nn.Linear(rnn_hidden_size * 2, img_fc_dim[-1])
self.h2_fc_lstm = nn.Linear(rnn_hidden_size + img_fc_dim[-1], rnn_hidden_size, bias=fc_bias)
self.r_linear = nn.Linear(rnn_hidden_size + 128, 2)
self.sm = nn.Softmax(dim=1)
self.num_predefined_action = 1
self.state_attention = StateAttention()
self.config_fc = nn.Linear(768, 512, bias=False)
if opts.monitor_sigmoid:
self.critic = nn.Sequential(
#nn.Linear(max_len + rnn_hidden_size, 1),
nn.Linear(10 + rnn_hidden_size, 1),
nn.Sigmoid()
)
else:
self.critic = nn.Sequential(
# nn.Linear(max_len + rnn_hidden_size, 1),
nn.Linear(10 + rnn_hidden_size, 1),
nn.Tanh()
)
def __init__(self,
opts,
img_fc_dim,
img_fc_use_batchnorm,
img_dropout,
img_feat_input_dim,
rnn_hidden_size,
rnn_dropout,
max_len,
fc_bias=True,
max_navigable=16):
super(Regretful, self).__init__()
self.opts = opts
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.max_navigable = max_navigable
proj_navigable_kwargs = {
'input_dim': img_feat_input_dim,
'hidden_dims': img_fc_dim,
'use_batchnorm': img_fc_use_batchnorm,
'dropout': img_dropout,
'fc_bias': fc_bias
}
self.proj_navigable_mlp = build_mlp(**proj_navigable_kwargs)
self.h0_fc = nn.Linear(rnn_hidden_size, img_fc_dim[-1])
self.h1_fc = nn.Linear(rnn_hidden_size, rnn_hidden_size, bias=fc_bias)
self.positional_encoding = PositionalEncoding(rnn_hidden_size,
dropout=0.1,
max_len=max_len)
self.soft_attn = SoftAttention()
self.dropout = nn.Dropout(p=rnn_dropout)
self.lstm = nn.LSTMCell(img_fc_dim[-1] * 2 + rnn_hidden_size,
rnn_hidden_size)
self.logit_fc = nn.Linear(rnn_hidden_size * 2, img_fc_dim[-1])
self.h2_fc_lstm = nn.Linear(rnn_hidden_size + img_fc_dim[-1],
rnn_hidden_size,
bias=fc_bias)
self.critic_fc = nn.Linear(max_len + rnn_hidden_size, 1)
self.tanh = nn.Tanh()
self.sigmoid = nn.Sigmoid()
self.critic_valueDiff_fc = nn.Linear(1, 2)
self.relu = nn.ReLU(inplace=True)
self.softmax = nn.Softmax(dim=1)
self.move_fc = nn.Linear(img_fc_dim[-1],
img_fc_dim[-1] + opts.tiled_len)
self.num_predefined_action = 1
def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5, device='cpu'):
super(TransformerModel, self).__init__()
self.device = device
from torch.nn import TransformerEncoder, TransformerEncoderLayer
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, self.device, dropout).to(self.device)
encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout).to(self.device)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers).to(self.device)
self.ninp = ninp
self.decoder = nn.Linear(ninp, ntoken)
'''Since you are using the CrossEntropyLoss, you shouldn't do the log_softmax in the above line. You can
either switch to the NLLLoss or remove the log_softmax to fix this. '''
self.init_weights()
def __init__(
self,
opts,
img_fc_dim,
img_fc_use_batchnorm,
img_dropout,
img_feat_input_dim,
rnn_hidden_size,
rnn_dropout,
max_len,
film_size=2048,
fc_bias=True,
max_navigable=16,
conv_hidden=2048,
num_resblocks=8,
):
super(SelfMonitoring, self).__init__()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.max_navigable = max_navigable
self.feature_size = img_feat_input_dim
self.hidden_size = rnn_hidden_size
self.max_len = max_len
proj_navigable_kwargs = {
"input_dim": img_feat_input_dim,
"hidden_dims": img_fc_dim,
"use_batchnorm": img_fc_use_batchnorm,
"dropout": img_dropout,
"fc_bias": fc_bias,
"relu": opts.mlp_relu,
}
self.proj_navigable_mlp = build_mlp(**proj_navigable_kwargs)
self.h0_fc = nn.Linear(rnn_hidden_size, img_fc_dim[-1], bias=fc_bias)
self.h1_fc = nn.Linear(rnn_hidden_size, rnn_hidden_size, bias=fc_bias)
self.soft_attn = SoftAttention()
self.dropout = nn.Dropout(p=rnn_dropout)
self.lstm = nn.LSTMCell(img_fc_dim[-1] * 2 + rnn_hidden_size,
rnn_hidden_size)
self.lang_position = PositionalEncoding(rnn_hidden_size,
dropout=0.1,
max_len=max_len)
self.logit_fc = nn.Linear(film_size, img_fc_dim[-1])
self.h2_fc_lstm = nn.Linear(rnn_hidden_size + img_fc_dim[-1],
rnn_hidden_size,
bias=fc_bias)
if opts.monitor_sigmoid:
self.critic = nn.Sequential(
nn.Linear(max_len + rnn_hidden_size, 1), nn.Sigmoid())
else:
self.critic = nn.Sequential(
nn.Linear(max_len + rnn_hidden_size, 1), nn.Tanh())
self.num_predefined_action = 1
# EDIT: add FiLM
self.resnet = torch.hub.load("pytorch/vision:v0.5.0",
"resnet152",
pretrained=True)
self.resnet = nn.Sequential(*list(self.resnet.children())[:-2])
self.film_gen = FiLMGenerator(
context_size=rnn_hidden_size,
num_resblocks=num_resblocks,
conv_hidden=conv_hidden,
)
self.film = FiLMedResBlocks(
num_blocks=num_resblocks,
conv_hidden=conv_hidden,
with_batch_norm=True,
)
self.film_tail = nn.AdaptiveAvgPool2d(1)
def __init__(self,
opts,
img_fc_dim,
img_fc_use_batchnorm,
img_dropout,
img_feat_input_dim,
rnn_hidden_size,
rnn_dropout,
max_len,
fc_bias=True,
max_navigable=16):
super(SelfMonitoring, self).__init__()
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.max_navigable = max_navigable
self.feature_size = img_feat_input_dim
self.hidden_size = rnn_hidden_size
self.max_len = max_len
proj_navigable_kwargs = {
'input_dim': img_feat_input_dim,
'hidden_dims': img_fc_dim,
'use_batchnorm': img_fc_use_batchnorm,
'dropout': img_dropout,
'fc_bias': fc_bias,
'relu': opts.mlp_relu
}
self.proj_navigable_mlp = build_mlp(**proj_navigable_kwargs)
self.h0_fc = nn.Linear(rnn_hidden_size, img_fc_dim[-1], bias=fc_bias)
self.h1_fc = nn.Linear(rnn_hidden_size, rnn_hidden_size, bias=fc_bias)
self.soft_attn = SoftAttention()
self.dropout = nn.Dropout(p=rnn_dropout)
self.lstm = nn.LSTMCell(img_fc_dim[-1] * 2 + rnn_hidden_size,
rnn_hidden_size)
self.lstm = nn.LSTMCell(2587, rnn_hidden_size)
self.lang_position = PositionalEncoding(rnn_hidden_size,
dropout=0.1,
max_len=max_len)
self.logit_fc = nn.Linear(rnn_hidden_size * 2, img_fc_dim[-1])
self.h2_fc_lstm = nn.Linear(rnn_hidden_size + img_fc_dim[-1],
rnn_hidden_size,
bias=fc_bias)
if opts.monitor_sigmoid: #NOT HERE
self.critic = nn.Sequential(
nn.Linear(max_len + rnn_hidden_size, 1), nn.Sigmoid())
else: #THIS
self.critic = nn.Sequential(
nn.Linear(max_len + rnn_hidden_size, 1), nn.Tanh())
self.num_predefined_action = 1
self.object_t_size = 17 # object_size
self.place_t_size = 10 # place_Size
self.object_attention_layer = ObjectDotAttention(
rnn_hidden_size, self.object_t_size)
self.place_attention_layer = VisualSoftDotAttention(
rnn_hidden_size, self.place_t_size)