def main():
root = "/home/fanyang/PycharmProjects/SignLanguage/data/tctodd"
id_sign, sign_id = id_2_sign(root)
train_samples, dev_samples = load_dataset(root, sign_id, hold_id=8)
train_dataset = SignLangDataset(sample_list=train_samples)
dev_dateset = SignLangDataset(sample_list=dev_samples)
# model
model = SimpleNN1DCNN()
model.load_state_dict(
torch.load('/home/fanyang/PycharmProjects/SignLanguage/ckpt/model.pkl'))
model.cuda()
for param in model.parameters():
param.requires_grad = False
vis = visdom.Visdom()
min_val, max_val = get_min_max_from_dataset(train_dataset)
# min: -0.4535120129585266, max: 1.0
print("min:{}, max:{}".format(min_val, max_val))
# exit()
vis.heatmap(X=train_dataset[0][0], opts={'xmin': min_val, 'xmax': max_val})
# [22, 90] label :0
inputs = Variable(torch.randn(1, 1, 22, 90), requires_grad=True).cuda()
nn.init.normal(inputs, std=.001)
inputs._grad_fn = None
model.optimizer = optim.SGD(params=[inputs], lr=1e-4)
for i in count():
logits = model(inputs)
logits = torch.squeeze(logits)
model.optimizer.zero_grad()
logits[0].backward(torch.FloatTensor([-1.]).cuda())
model.optimizer.step()
if (i + 1) % 50000 == 0:
vis.heatmap(X=torch.squeeze(inputs).cpu().data,
opts={'xmin': min_val, 'xmax': max_val,
'title': '%d-step' % (i + 1)})
print("step:%d" % (i + 1), "prob:%.7f" %
F.softmax(logits)[0].cpu().data.numpy()[0])
adjust_learning_rate(model.optimizer)
