def get_audio_feature_extractor(model_path, gpu=-1):
if gpu < 0:
device = torch.device("cpu")
model_dict = torch.load(model_path,
map_location=lambda storage, loc: storage)
else:
device = torch.device("cuda:" + str(gpu))
model_dict = torch.load(
model_path, map_location=lambda storage, loc: storage.cuda(gpu))
audio_rate = model_dict["audio_rate"]
audio_feat_len = model_dict['audio_feat_len']
rnn_gen_dim = model_dict['rnn_gen_dim']
aud_enc_dim = model_dict['aud_enc_dim']
video_rate = model_dict["video_rate"]
encoder = RNN(audio_feat_len,
aud_enc_dim,
rnn_gen_dim,
audio_rate,
init_kernel=0.005,
init_stride=0.001)
encoder.to(device)
encoder.load_state_dict(model_dict['encoder'])
overlap = audio_feat_len - 1.0 / video_rate
return encoder, {
"rate": audio_rate,
"feature length": audio_feat_len,
"overlap": overlap
}
class Scorer(object):
def __init__(self, char_list, model_path, rnn_type, ninp, nhid, nlayers,
device):
char_list = list(char_list) + ['sil_start', 'sil_end']
self.inv_vocab_map = dict([(i, c) for (i, c) in enumerate(char_list)])
self.vocab_map = dict([(c, i) for (i, c) in enumerate(char_list)])
self.criterion = nn.CrossEntropyLoss()
self.device = device
self.rnn = RNN(rnn_type, len(char_list), ninp, nhid,
nlayers).to(self.device)
self.rnn.load_state_dict(torch.load(model_path))
self.rnn.eval()
self.history = defaultdict(tuple)
def get_score(self, string):
if len(string) < 2:
return 0, self.rnn.init_hidden(1)
string_idx = map(lambda x: self.vocab_map[x], string)
input = string_idx[:-1]
grt = string_idx[1:]
input, grt = torch.LongTensor(input).to(
self.device), torch.LongTensor(grt).to(self.device)
input = input.view(1, input.size()[0])
init_hidden = self.rnn.init_hidden(1)
pred, hidden = self.rnn(input, init_hidden)
pred = pred.view(-1, pred.size(-1))
loss = self.criterion(pred, grt)
return -(len(string_idx) - 1) * loss.item(), hidden
def get_score_fast(self, strings):
strings = [''.join(x) for x in strings]
history_to_update = defaultdict(tuple)
scores = []
for string in strings:
if len(string) <= 2:
score, hidden_state = self.get_score(string)
scores.append(score)
history_to_update[string] = (score, hidden_state)
elif string in self.history:
history_to_update[string] = self.history[string]
scores.append(self.history[string][0])
elif string[:-1] in self.history:
score, hidden = self.history[string[:-1]]
input, grt = torch.LongTensor([
self.vocab_map[string[-2]]
]).view(1, 1).to(self.device), torch.LongTensor(
[self.vocab_map[string[-1]]]).to(self.device)
pred, hidden = self.rnn(input, hidden)
loss = self.criterion(pred.view(-1, pred.size(-1)), grt).item()
history_to_update[string] = (score - loss, hidden)
scores.append(score - loss)
else:
raise ValueError("%s not stored" % (string[:-1]))
self.history = history_to_update
return scores
def load_model(input_size):
model = RNN(input_size, hidden_size, num_layers)
# load on CPU only
checkpoint = torch.load('checkpoint.pt', map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
print(model)
print('model training loss', checkpoint['loss'])
print('model training epoch', checkpoint['epoch'])
return model
def creat_trunk_ply_by_nn(AE_model_dir1, AE_model_dir2, RNN_model_dir,
rawPc_ply_dir_list, device, threshold, save_dir):
rnn_model = RNN().to(device)
rnn_model.load_state_dict(torch.load(RNN_model_dir))
AE_model1 = autoencoder.AE_3d_conv().to(device)
AE_model1.load_state_dict(torch.load(AE_model_dir1))
rnn_in_feature = get_rnn_in_featur(AE_model1, device, rawPc_ply_dir_list)
rnn_out = get_rnn_out(rnn_model, rnn_in_feature)
# AE_model2 = autoencoder.AE_3d_conv().to(device)
# AE_model2.load_state_dict(torch.load(AE_model_dir2))
AE_decoder_out = get_AE_decoder_out(AE_model1, rnn_out)
#point_counts = len(AE_decoder_out[AE_decoder_out>0.2])
AE_decoder_out = torch.squeeze(AE_decoder_out)
print('try to save predicted point cloud(.ply) by NN ')
tensor_to_ply(AE_decoder_out, threshold, save_dir)
def predicted_labels(sentence, hypothesis, classifier, network='best-GRU'):
if network == 'best-GRU':
# load model
vocab = ['Europeans', 'Germans', 'Italians', 'Romans', 'all', 'children', 'fear', 'hate', 'like', 'love', 'not',
'some']
rels = ['#', '<', '=', '>', '^', 'v', '|']
word_dim = 25
n_hidden = 128
cpr_dim = 75
model_path = '/Users/mathijs/Documents/Studie/MoL/thesis/mol_thesis/final_experiments/binary_fol_rnn/nobrackets/models/GRUbinary_2dets_4negs_train_0bracket_pairs1.pt'
net = RNN('GRU', vocab, rels, word_dim, n_hidden, cpr_dim)
net.load_state_dict(torch.load(model_path))
s = [sentence.split()]
_, hidden_vectors = net.rnn_forward(s, 1, hypothesis=hypothesis)
test_hiddens = np.array(hidden_vectors[0])
y_pred = classifier.predict(test_hiddens)
labels = np.array([y_pred])
return(labels)
n_epochs = 200
print_every = 500
frac_train = 0.90
n_hidden = 512
learning_rate = 0.001
model = RNN(20,n_hidden,2)
criterion = nn.NLLLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.8,nesterov=True)
if os.path.isfile('all_pos_148.ckpt'):
checkpoint = torch.load('all_pos_148.ckpt')
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
print("=> loaded checkpoint ")
with open(logfile_name,'a') as outfile:
outfile.write("=> loaded checkpoint\n")
#optimizer = torch.optim.Adam(model.parameters(),lr=learning_rate)
#optimizer = torch.optim.ASGD(model.parameters(),lr=learning_rate)
def train(category_tensor,line_tensor):
model.zero_grad()
hidden = model.init_hidden()
for i in range(line_tensor.size()[0]):
output, hidden = model(line_tensor[i],hidden)
# input after only good parts of vae taken
input_size = 50
lr = 1e-4
rnn = RNN(input_size, hidden_size)
optim = optim.Adam(rnn.parameters(), lr=lr, weight_decay=1e-6)
if use_cuda:
rnn.cuda()
rnn_epoch = 0
total_passes = 0
train_loss = []
test_loss = []
if args.rnn_model_loadpath is not None:
if os.path.exists(args.rnn_model_loadpath):
rnn_model_dict = torch.load(args.rnn_model_loadpath)
rnn.load_state_dict(rnn_model_dict['state_dict'])
optim.load_state_dict(rnn_model_dict['optimizer'])
rnn_epoch = rnn_model_dict['epoch']
try:
total_passes = rnn_model_dict['total_passes']
train_loss = rnn_model_dict['train_loss']
test_loss = rnn_model_dict['test_loss']
except:
print("could not load total passes")
print("loaded rnn from %s at epoch %s" %
(args.rnn_model_loadpath, rnn_epoch))
else:
print("could not find model at %s" % args.rnn_model_loadpath)
sys.exit()
else:
print("creating new model")
from data_loader import fetch_data
# Our stuff we imported
from gensim.models import Word2Vec
from collections import Counter
from rnn import RNN
# from ffnn import FFNN, convert_to_vector_representation, make_indices, make_vocab
directory = 'models_b/'
model_paths = [
'rnn_sgd_base.pt', 'rnn_rmsprop_base.pt', 'lstm_sgd_base.pt',
'lstm_rmsprop_base.pt'
]
model1 = RNN(32, 1, 64, True)
model1.load_state_dict(torch.load(os.path.join(directory, model_paths[0])))
model2 = RNN(32, 1, 64, True)
model2.load_state_dict(torch.load(os.path.join(directory, model_paths[1])))
model3 = RNN(32, 1, 64, False)
model3.load_state_dict(torch.load(os.path.join(directory, model_paths[2])))
model4 = RNN(32, 1, 64, False)
model4.load_state_dict(torch.load(os.path.join(directory, model_paths[3])))
models = [model1, model2, model3, model4]
print('models succesfuly loaded')
# Load trained word embeddings
train_data, valid_data = fetch_data()
wv_model = Word2Vec.load("word2vec.model")
validation_samples = []
# load data
data_file = '/Users/mathijs/Documents/Studie/MoL/thesis/mol_thesis/data/binary/2dets_4negs/partial_bracketing/binary_2dets_4negs_train_0bracket_all.txt'
data = dat.SentencePairsDataset(data_file)
data.load_data(sequential=True)
vocab = [
'Europeans', 'Germans', 'Italians', 'Romans', 'all', 'children', 'fear',
'hate', 'like', 'love', 'not', 'some'
]
rels = ['#', '<', '=', '>', '^', 'v', '|']
word_dim = 25
n_hidden = 128
cpr_dim = 75
model_path = '/Users/mathijs/Documents/Studie/MoL/thesis/mol_thesis/final_experiments/binary_fol_rnn/nobrackets/models/GRUbinary_2dets_4negs_train_0bracket_pairs1.pt'
net = RNN('GRU', vocab, rels, word_dim, n_hidden, cpr_dim)
net.load_state_dict(torch.load(model_path))
net.eval()
shuffle_samples = False
batch_size = 50
batches = dat.BatchData(data, batch_size, shuffle_samples)
batches.create_batches()
for batch_idx in range(batches.num_batches):
print('Batch %i / %i' % (batch_idx, batches.num_batches))
inputs = batches.batched_data[batch_idx]
net(inputs)
class SDFA():
def __init__(self, model_path, gpu=-1):
self.model_path = model_path
if gpu < 0:
self.device = torch.device("cpu")
model_dict = torch.load(self.model_path,
map_location=lambda storage, loc: storage)
self.fa = face_alignment.FaceAlignment(
face_alignment.LandmarksType._2D,
device="cpu",
flip_input=False)
else:
self.device = torch.device("cuda:" + str(gpu))
model_dict = torch.load(
self.model_path,
map_location=lambda storage, loc: storage.cuda(gpu))
self.fa = face_alignment.FaceAlignment(
face_alignment.LandmarksType._2D,
device="cuda:" + str(gpu),
flip_input=False)
self.stablePntsIDs = [33, 36, 39, 42, 45]
self.mean_face = model_dict["mean_face"]
self.img_size = model_dict["img_size"]
self.audio_rate = model_dict["audio_rate"]
self.video_rate = model_dict["video_rate"]
self.audio_feat_len = model_dict['audio_feat_len']
self.audio_feat_samples = model_dict['audio_feat_samples']
self.id_enc_dim = model_dict['id_enc_dim']
self.rnn_gen_dim = model_dict['rnn_gen_dim']
self.aud_enc_dim = model_dict['aud_enc_dim']
self.aux_latent = model_dict['aux_latent']
self.sequential_noise = model_dict['sequential_noise']
self.conversion_dict = {'s16': np.int16, 's32': np.int32}
self.img_transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((self.img_size[0], self.img_size[1])),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
self.encoder = RNN(self.audio_feat_len,
self.aud_enc_dim,
self.rnn_gen_dim,
self.audio_rate,
init_kernel=0.005,
init_stride=0.001)
self.encoder.to(self.device)
self.encoder.load_state_dict(model_dict['encoder'])
self.encoder_id = ImageEncoder(code_size=self.id_enc_dim,
img_size=self.img_size)
self.encoder_id.to(self.device)
self.encoder_id.load_state_dict(model_dict['encoder_id'])
skip_channels = list(self.encoder_id.channels)
skip_channels.reverse()
self.generator = Generator(
self.img_size,
self.rnn_gen_dim,
condition_size=self.id_enc_dim,
num_gen_channels=self.encoder_id.channels[-1],
skip_channels=skip_channels,
aux_size=self.aux_latent,
sequential_noise=self.sequential_noise)
self.generator.to(self.device)
self.generator.load_state_dict(model_dict['generator'])
self.encoder.eval()
self.encoder_id.eval()
self.generator.eval()
def save_video(self,
video,
audio,
path,
overwrite=True,
experimental_ffmpeg=False,
scale=None):
if not os.path.isabs(path):
path = os.getcwd() + "/" + path
with tempdir() as dirpath:
writer = sio.FFmpegWriter(dirpath + "/tmp.avi",
inputdict={
'-r': str(self.video_rate) + "/1",
},
outputdict={
'-r': str(self.video_rate) + "/1",
})
for i in range(video.shape[0]):
frame = np.rollaxis(video[i, :, :, :], 0, 3)
if scale is not None:
frame = tf.rescale(frame,
scale,
anti_aliasing=True,
multichannel=True,
mode='reflect')
writer.writeFrame(frame)
writer.close()
wav.write(dirpath + "/tmp.wav", self.audio_rate, audio)
in1 = ffmpeg.input(dirpath + "/tmp.avi")
in2 = ffmpeg.input(dirpath + "/tmp.wav")
if experimental_ffmpeg:
out = ffmpeg.output(in1['v'],
in2['a'],
path,
strict='-2',
loglevel="panic")
else:
out = ffmpeg.output(in1['v'], in2['a'], path, loglevel="panic")
if overwrite:
out = out.overwrite_output()
out.run()
def preprocess_img(self, img):
src = self.fa.get_landmarks(img)[0][self.stablePntsIDs, :]
dst = self.mean_face[self.stablePntsIDs, :]
tform = tf.estimate_transform('similarity', src, dst)
warped = tf.warp(img,
inverse_map=tform.inverse,
output_shape=self.img_size)
warped = warped * 255
warped = warped.astype('uint8')
return warped
def _cut_sequence_(self, seq, cutting_stride, pad_samples):
pad_left = torch.zeros(pad_samples // 2, 1)
pad_right = torch.zeros(pad_samples - pad_samples // 2, 1)
seq = torch.cat((pad_left, seq), 0)
seq = torch.cat((seq, pad_right), 0)
stacked = seq.narrow(0, 0, self.audio_feat_samples).unsqueeze(0)
iterations = (seq.size()[0] -
self.audio_feat_samples) // cutting_stride + 1
for i in range(1, iterations):
stacked = torch.cat(
(stacked,
seq.narrow(0, i * cutting_stride,
self.audio_feat_samples).unsqueeze(0)))
return stacked.to(self.device)
def _broadcast_elements_(self, batch, repeat_no):
total_tensors = []
for i in range(0, batch.size()[0]):
total_tensors += [torch.stack(repeat_no * [batch[i]])]
return torch.stack(total_tensors)
def __call__(self, img, audio, fs=None, aligned=False):
if isinstance(img, str):
frm = Image.open(img)
frm.thumbnail((400, 400))
frame = np.array(frm)
else:
frame = img
if not aligned:
frame = self.preprocess_img(frame)
if isinstance(audio, str):
info = mediainfo(audio)
fs = int(info['sample_rate'])
audio = np.array(
AudioSegment.from_file(audio,
info['format_name']).set_channels(
1).get_array_of_samples())
if info['sample_fmt'] in self.conversion_dict:
audio = audio.astype(self.conversion_dict[info['sample_fmt']])
else:
if max(audio) > np.iinfo(np.int16).max:
audio = audio.astype(np.int32)
else:
audio = audio.astype(np.int16)
if audio.ndim > 1 and audio.shape[1] > 1:
audio = audio[:, 0]
max_value = np.iinfo(audio.dtype).max
if fs != self.audio_rate:
seq_length = audio.shape[0]
speech = torch.from_numpy(
signal.resample(
audio, int(seq_length * self.audio_rate / float(fs))) /
float(max_value)).float()
speech = speech.view(-1, 1)
else:
audio = torch.from_numpy(audio / float(max_value)).float()
speech = audio.view(-1, 1)
frame = self.img_transform(frame).to(self.device)
cutting_stride = int(self.audio_rate / float(self.video_rate))
audio_seq_padding = self.audio_feat_samples - cutting_stride
audio_feat_seq = self._cut_sequence_(speech, cutting_stride,
audio_seq_padding)
frame = frame.unsqueeze(0)
audio_feat_seq = audio_feat_seq.unsqueeze(0)
audio_feat_seq_length = audio_feat_seq.size()[1]
z = self.encoder(audio_feat_seq, [audio_feat_seq_length])
noise = torch.FloatTensor(1, audio_feat_seq_length,
self.aux_latent).normal_(0, 0.33).to(
self.device)
z_id, skips = self.encoder_id(frame, retain_intermediate=True)
skip_connections = []
for skip_variable in skips:
skip_connections.append(
self._broadcast_elements_(skip_variable,
z.size()[1]))
skip_connections.reverse()
z_id = self._broadcast_elements_(z_id, z.size()[1])
gen_video = self.generator(z, c=z_id, aux=noise, skip=skip_connections)
returned_audio = ((2**15) * speech.detach().cpu().numpy()).astype(
np.int16)
gen_video = 125 * gen_video.squeeze().detach().cpu().numpy() + 125
return gen_video, returned_audio
from cuda_check import device
from parse import *
from rnn import RNN
from util import *
usefulness, problemlemmas = get_usefulness_problemslemmas()
all_letters = string.printable
n_letters = len(all_letters)
# Output is just a float, the proof length ratios
output_size = 1
n_hidden = 128
model = RNN(n_letters, n_hidden, output_size)
state_dict = torch.load('./test2models/training.pt')
model.load_state_dict(state_dict)
model.to(device)
criterion = nn.MSELoss()
def eval_model(usefulness_tensor, line_tensor):
hidden = model.initHidden()
output = None
model.zero_grad()
for i in range(line_tensor.size()[0]):
output, hidden = model(line_tensor[i], hidden)
loss = criterion(output, usefulness_tensor)
cnn.cuda()
rnn.cuda()
loss_fn.cuda()
else:
raise ValueError('Please specify a valid device from ["cpu", "gpu"].')
print('Loaded the models to the %s.' % (params['device'].upper()))
if params['is_training']:
if params['resume_training']:
print("Loading the model - %s" %
(params['resume_model_train'] + '.ckpt'))
state_dict = torch.load(
os.path.join(params['output_dir'],
params['resume_model_train'] + '.ckpt'))
cnn.load_state_dict(state_dict['encoder_state_dict'])
rnn.load_state_dict(state_dict['decoder_state_dict'])
optimizer.load_state_dict(state_dict['optimizer_state_dict'])
print("Models loaded.")
cnn.train()
rnn.train()
start_time = time.time()
print('Training started.')
for epoch in range(params['num_epochs']):
print("Epoch %d started." % (epoch + 1))
train_loss = []
for idx, (_, image, caption,
from rnn import RNN
from ffnn import FFNN, convert_to_vector_representation, make_indices, make_vocab
# Load models
base = ('rnn_sgd_base.pt', 'ffnn_sgd_base.pt')
hx2 = ('rnn_sgd_hx2.pt', 'ffnn_sgd_hx2.pt')
lx2 = ('rnn_sgd_lx2.pt', 'ffnn_sgd_lx2.pt')
files = (base, hx2, lx2)
directory = 'models_part_a/'
base_models = []
#RNN SGD BASE
path = directory + base[0]
model = RNN(32, 1, 64, True)
model.load_state_dict(torch.load(path))
base_models.append(model)
#FFNN SGD BASE
path = directory + base[1]
model = FFNN(97305, 32, 1)
model.load_state_dict(torch.load(path))
base_models.append(model)
hx2_models = []
#RNN SGD hx2
path = directory + hx2[0]
model = RNN(64, 1, 64, True)
model.load_state_dict(torch.load(path))
hx2_models.append(model)
#FFNN SGD hx2
path = directory + hx2[1]
HL = 1
HN1 = 5
EPOCHS = 30
BATCH_SIZE = 8
LR = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008, 0.0009, 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
MODELS = {}
rnn = RNN(3, 5, 12, HN1, HL)
init_state = copy.deepcopy(rnn.state_dict())
for lr in LR:
MSEs = []
for index, subset in enumerate(subset_train_list):
subset.value = np.array(subset.value)
subset_test_list[index].value = np.array(subset_test_list[index].value)
rnn.load_state_dict(init_state)
training_inputs = subset.value[:, 0:5]
training_labels = subset.value[:, 5:]
test_inputs = subset_test_list[index].value[:, 0:5]
test_labels = subset_test_list[index].value[:, 5:]
training_inputs = np.split(training_inputs, 505)
training_labels = np.split(training_labels, 505)
test_inputs = np.array([test_inputs])
test_labels = np.array([test_labels])
train(rnn, training_inputs, training_labels, EPOCHS, lr, BATCH_SIZE)
avg_mse = test(test_inputs, test_labels, rnn)
MSEs.append(avg_mse)
except FileNotFoundError as e:
print(e)
print("Please download the input data from 'https://cs.stanford.edu/people/karpathy/char-rnn/'")
print("Place it in the 'input' directory")
exit(1)
input_length = len(input_text)
net = RNN(input_size=ALPHABET_SIZE,
hidden_size=HIDDEN_SIZE,
output_size=ALPHABET_SIZE)
if MODEL_SAVE_PATH.exists():
print("Loading trained model from file")
net.load_state_dict(torch.load(MODEL_SAVE_PATH))
net.train()
optimizer = optim.RMSprop(net.parameters())
hidden_state = torch.zeros(BATCH_SIZE, HIDDEN_SIZE)
total_loss = 0.0
print("Starting to train char RNN")
i = 0
last_print = 0
while i < input_length:
if i + BATCH_SIZE >= input_length:
# TODO: pad last batch to `BATCH_SIZE`
def main(args):
config_file = args.config
test = args.test
cfg = Config(config_file)
tr = None
if test is None:
tr = DataSet(cfg.tr_data, cfg)
te = DataSet(cfg.te_data, cfg, sub_sample=1)
tr0 = DataSet([cfg.tr_data[0]], cfg, sub_sample=1)
cfg.att = te.sz[1]
else:
if test == 'te':
te = DataSet([cfg.te_data[0]], cfg)
else:
te = DataSet([cfg.tr_data[0]], cfg)
cfg.att = te.sz[1]
iterations = 10000
loop = cfg.loop
print "input attribute", cfg.att, "LR", cfg.lr, 'feature', cfg.feature_len
n_att = cfg.att
n_length = cfg.feature_len
n_hidden = 256
n_output = cfg.num_output
mrnn = RNN(n_att, n_length, n_hidden, n_output, cfg.lr)
# print("Model's state_dict:")
# for param_tensor in mrnn.state_dict():
# print(param_tensor, "\t", mrnn.state_dict()[param_tensor].size())
hidden = ToTensor(np.ones(n_hidden).astype(np.float32))
if test:
mrnn.load_state_dict(torch.load(cfg.netTest[:-3]))
run_test(mrnn, te, cfg, hidden)
tr_loss, tr_median = run_test(mrnn, te, cfg, hidden)
for a in range(len(tr_loss)):
print a, tr_loss[a], tr_median[a]
exit(0)
if cfg.renetFile:
mrnn.load_state_dict(torch.load(cfg.renetFile[:-3]))
t00 = datetime.datetime.now()
T = 0
T_err=0
for a in range(iterations):
tr_pre_data = tr.prepare(multi=1)
while tr_pre_data:
for b in tr_pre_data:
length = len(b[0])
x = ToTensor(b[0].reshape(length, cfg.feature_len, cfg.att).astype(np.float32))
y = ToTensor(b[1].astype(np.float32))
err = mrnn.train(y, x, hidden)
if a%loop==0 and a>0:
t1 = datetime.datetime.now()
print a, (t1 - t00).total_seconds()/3600.0, T_err/T
T_err=0
T = 0
torch.save(mrnn.state_dict(), cfg.netFile[:-3])
T_err += err
T += 1
tr_pre_data = tr.get_next()
def main():
cuda = int(torch.cuda.is_available()) - 1
# define fields
TEXT = data.Field(lower=True, init_token="<start>", eos_token="<end>")
LABEL = data.Field(sequential=False, unk_token=None)
# data paths
data_path = './data/'
train_path = 'train.tsv'
val_path = 'val.tsv'
test_path = 'test.tsv'
# hyperparams
hidden_size = 64
num_classes = 2
num_layers = 2
num_dir = 2
batch_size = 8
emb_dim = 300
dropout = .2
net_type = 'lstm'
embfix = False
# build dataset splits
train, val, test = data.TabularDataset.splits(path=data_path,
train=train_path,
validation=val_path,
test=test_path,
format='tsv',
fields=[('text', TEXT),
('label', LABEL)])
# build vocabs
TEXT.build_vocab(train, vectors=GloVe(name='6B', dim=emb_dim), min_freq=2)
prevecs = TEXT.vocab.vectors
#TEXT.build_vocab(train, min_freq=3)
LABEL.build_vocab(train)
num_classes = len(LABEL.vocab)
input_size = len(TEXT.vocab)
# build iterators
train_iter = data.BucketIterator(train,
batch_size=batch_size,
sort_key=lambda x: len(x.text),
train=True)
val_iter = data.Iterator(val,
batch_size=batch_size,
repeat=False,
train=False,
sort=False,
shuffle=False)
test_iter = data.Iterator(test,
batch_size=batch_size,
repeat=False,
train=False,
sort=False,
shuffle=False) #batch_size = len(test)
model = RNN(input_size=input_size,
hidden_size=hidden_size,
num_classes=num_classes,
prevecs=prevecs,
num_layers=num_layers,
num_dir=num_dir,
batch_size=batch_size,
emb_dim=emb_dim,
embfix=embfix,
dropout=dropout,
net_type=net_type)
model.load_state_dict(torch.load('./models/e16_100.0.pt'))
print('type(model):', type(model))
print('model:', model)
val_acc, val_preds, _ = evaluate(val_iter, model, TEXT, LABEL)
print('val_acc:', val_acc)
test_acc, test_preds, _ = evaluate(test_iter, model, TEXT, LABEL)
print('test_acc:', test_acc)
