def train_and_test(flags,
corruption_level=0,
gold_fraction=0.5,
get_C=uniform_mix_C):
np.random.seed(1)
torch.manual_seed(1)
torch.cuda.manual_seed(1)
C = get_C(corruption_level)
gold, silver = prepare_data(C, gold_fraction)
print("Gold shape = {}, Silver shape = {}".format(gold.images.shape,
silver.images.shape))
# TODO : test on whole set
test_x = torch.from_numpy(mnist.test.images[:500].reshape([-1, 1, 28, 28]))
test_y = torch.from_numpy(mnist.test.labels[:500]).type(torch.LongTensor)
print("Test shape = {}".format(test_x.shape))
model = LeNet()
optimizer = torch.optim.Adam([p for p in model.parameters()], lr=0.001)
for step in range(flags.num_steps):
x, y = silver.next_batch(flags.batch_size)
y, y_true = np.array([l[0] for l in y]), np.array([l[1] for l in y])
x_val, y_val = gold.next_batch(min(flags.batch_size, flags.nval))
x, y = torch.from_numpy(x.reshape(
[-1, 1, 28, 28])), torch.from_numpy(y).type(torch.LongTensor)
x_val, y_val = torch.from_numpy(x_val.reshape(
[-1, 1, 28, 28])), torch.from_numpy(y_val).type(torch.LongTensor)
# forward
if flags.method == "l2w":
ex_wts = reweight_autodiff(model, x, y, x_val, y_val)
logits, loss = model.loss(x, y, ex_wts)
if step % dbg_steps == 0:
tbrd.log_histogram("ex_wts", ex_wts, step=step)
tbrd.log_value("More_than_0.01",
sum([x > 0.01 for x in ex_wts]),
step=step)
tbrd.log_value("More_than_0.05",
sum([x > 0.05 for x in ex_wts]),
step=step)
tbrd.log_value("More_than_0.1",
sum([x > 0.1 for x in ex_wts]),
step=step)
mean_on_clean_labels = np.mean(
[ex_wts[i] for i in range(len(y)) if y[i] == y_true[i]])
mean_on_dirty_labels = np.mean(
[ex_wts[i] for i in range(len(y)) if y[i] != y_true[i]])
tbrd.log_value("mean_on_clean_labels",
mean_on_clean_labels,
step=step)
tbrd.log_value("mean_on_dirty_labels",
mean_on_dirty_labels,
step=step)
else:
logits, loss = model.loss(x, y)
print("Loss = {}".format(loss))
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
tbrd.log_value("loss", loss, step=step)
if step % dbg_steps == 0:
model.eval()
pred = torch.max(model.forward(test_x), 1)[1]
test_acc = torch.sum(torch.eq(pred, test_y)).item() / float(
test_y.shape[0])
model.train()
print("Test acc = {}.".format(test_acc))
tbrd.log_value("test_acc", test_acc, step=step)
dirs = os.listdir("./gcommands/train/")
dirs = sorted(dirs)
parser = argparse.ArgumentParser(
description='ConvNets for Speech Commands Recognition')
parser.add_argument('--wav_path',
default='gcommands/recordings/one/3.wav',
help='path to the audio file')
args = parser.parse_args()
path = args.wav_path
warnings.filterwarnings("ignore")
model = LeNet()
model.load_state_dict(torch.load("checkpoint/ckpt.t7"))
model.eval()
wav = spect_loader(path,
window_size=.02,
window_stride=.01,
normalize=True,
max_len=101,
window='hamming')
#print(wav.shape)
with torch.no_grad():
label = model.forward(wav.view(1, 1, 161, 101))
print(dirs[np.argmax(np.ravel(label.numpy()))])
