Exemplos de text_objseg_upsample32s em Python

Exemplo n.º 1

embed_dim = 1000
lstm_dim = 1000
mlp_hidden_dims = 500

################################################################################
# segmentation network
################################################################################

# Inputs
text_seq_batch = tf.placeholder(tf.int32, [T, N])  # one batch per sentence
imcrop_batch = tf.placeholder(tf.float32, [N, 512, 512, 3])

_ = segmodel.text_objseg_upsample32s(text_seq_batch,
                                     imcrop_batch,
                                     num_vocab,
                                     embed_dim,
                                     lstm_dim,
                                     mlp_hidden_dims,
                                     vgg_dropout=False,
                                     mlp_dropout=False)

load_var = {
    var.op.name: var
    for var in tf.all_variables()
    if not var.op.name.startswith('classifier/upsample32s')
}
snapshot_loader = tf.train.Saver(load_var)
with tf.variable_scope('classifier', reuse=True):
    upsample32s_w = tf.get_variable('upsample32s/weights')
    init_upsample32s_w = tf.assign(upsample32s_w,
                                   segmodel.generate_bilinear_filter(32))

Exemplo n.º 2

Arquivo: init_referit_seg_highres_from_lowres.py Projeto: BenJamesbabala/text_objseg

num_vocab = 8803
embed_dim = 1000
lstm_dim = 1000
mlp_hidden_dims = 500

################################################################################
# segmentation network
################################################################################

# Inputs
text_seq_batch = tf.placeholder(tf.int32, [T, N])  # one batch per sentence
imcrop_batch = tf.placeholder(tf.float32, [N, 512, 512, 3])

_ = segmodel.text_objseg_upsample32s(text_seq_batch, imcrop_batch,
    num_vocab, embed_dim, lstm_dim, mlp_hidden_dims,
    vgg_dropout=False, mlp_dropout=False)

load_var = {var.op.name: var for var in tf.all_variables()
            if not var.op.name.startswith('classifier/upsample32s')}
snapshot_loader = tf.train.Saver(load_var)
with tf.variable_scope('classifier', reuse=True):
    upsample32s_w = tf.get_variable('upsample32s/weights')
    init_upsample32s_w = tf.assign(upsample32s_w, segmodel.generate_bilinear_filter(32))

snapshot_saver = tf.train.Saver()
with tf.Session() as sess:
    snapshot_loader.restore(sess, lowres_model)
    sess.run(init_upsample32s_w)
    snapshot_saver.save(sess, highres_model)