def load_pretrained_uncond(data_type):
####################################################
# default parameters, do not change
hidden_enc_dim = 256
hidden_dec_dim = 256
n_layers = 1
num_gaussian = 20
dropout_p = 0.2
batch_size = 50
latent_dim = 64
rnn_dir = 2 # 1 for unidirection, 2 for bi-direction
bi_mode = 2 # bidirectional mode:- 1 for addition 2 for concatenation
cond_gen = False
if not cond_gen:
rnn_dir = 1
bi_mode = 1
####################################################
encoder = encoder_skrnn(input_size = 5, hidden_size = hidden_enc_dim, hidden_dec_size=hidden_dec_dim,\
dropout_p = dropout_p,n_layers = n_layers, batch_size = batch_size, latent_dim = latent_dim,\
device = device, cond_gen= cond_gen, bi_mode= bi_mode, rnn_dir = rnn_dir).to(device)
decoder = decoder_skrnn(input_size = 5, hidden_size = hidden_dec_dim, num_gaussian = num_gaussian,\
dropout_p = dropout_p, n_layers = n_layers, batch_size = batch_size,\
latent_dim = latent_dim, device = device, cond_gen= cond_gen).to(device)
if data_type == 'cat':
encoder.load_state_dict(
torch.load('saved_model/UncondEnc_cat.pt',
map_location='cuda:0')['model'])
decoder.load_state_dict(
torch.load('saved_model/UncondDec_cat.pt',
map_location='cuda:0')['model'])
data_enc, data_dec, max_seq_len = get_data(data_type='cat')
else:
encoder.load_state_dict(
torch.load('saved_model/UncondEnc_kanji.pt')['model'])
decoder.load_state_dict(
torch.load('saved_model/UncondDec_kanji.pt')['model'])
data_enc, data_dec, max_seq_len = get_data(data_type='kanji')
return encoder, decoder, hidden_enc_dim, latent_dim, max_seq_len, cond_gen, bi_mode, device
print_every = batch_size * 200 # print loss after this much iteration, change the multiplier aacording to dataset
plot_every = 1 # plot the strokes using current trained model
rnn_dir = 2 # 1 for unidirection, 2 for bi-direction
bi_mode = 2 # bidirectional mode:- 1 for addition 2 for concatenation
cond_gen = False # use either unconditional or conditional generation
data_type = 'cat' # 'cat' and 'kanji'
if not cond_gen:
weight_kl = 0.0
rnn_dir = 1
bi_mode = 1
encoder = encoder_skrnn(input_size = 5, hidden_size = hidden_enc_dim, hidden_dec_size=hidden_dec_dim,\
dropout_p = dropout_p,n_layers = n_layers, batch_size = batch_size, latent_dim = latent_dim,\
device = device, cond_gen= cond_gen, bi_mode= bi_mode, rnn_dir = rnn_dir).to(device)
decoder = decoder_skrnn(input_size = 5, hidden_size = hidden_dec_dim, num_gaussian = num_gaussian,\
dropout_p = dropout_p, n_layers = n_layers, batch_size = batch_size,\
latent_dim = latent_dim, device = device, cond_gen= cond_gen).to(device)
encoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)
decoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate)
data_enc, data_dec, max_seq_len = get_data(data_type=data_type)
num_mini_batch = len(data_dec) - (len(data_dec) % batch_size)
for epoch in range(epochs):
start = time.time()