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transformer.py
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transformer.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
import time
from scipy.io import loadmat
import os
from encoder import *
from decoder import *
from utils import *
def epoch_time(start_time, end_time):
elapsed_time = end_time - start_time
elapsed_mins = int(elapsed_time / 60)
elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
return elapsed_mins, elapsed_secs
class Sees_model(nn.Module):
def __init__(self, device='cpu', model_path='defaulttrans.model'):
super(Sees_model, self).__init__()
print('Using device: {}'.format(device))
self.model_path = model_path
self.device = device
self.n_node = -1
self.support = None
self.train_ = [None, None]
self.val_ = [None, None]
self.test_ = [None, None]
def INIT(self, data_path):
self.read_data(data_path)
self.encoder = seesEncoder(INPUT_DIM,
HID_DIM,
N_LAYERS,
N_HEADS,
PF_DIM,
SEQ_LEN,
WALK_LEN,
self.n_node,
self.get_device())
self.decoder = seesDecoder(HID_DIM,
OUT_DIM,
SEQ_LEN,
self.get_device())
self.optimizer = torch.optim.Adam(self.parameters(), lr=LEARNING_RATE)
def get_device(self):
if self.device == 'gpu':
return torch.device('cuda')
else:
return torch.device('cpu')
def read_data(self, path):
train_path = path+'/train.npz'
val_path = path+'/val.npz'
test_path = path+'/test.npz'
net_path = path+'/net.mat'
net_path2 = path+'/N.npy'
if os.path.exists(net_path):
net = loadmat(net_path)
support = net['S']
else:
support = np.load(net_path2)
N_NODE = support.shape[0]
self.n_node = support.shape[0]
support[range(N_NODE),range(N_NODE)] = np.ones(N_NODE)
self.support = support
train_npz = np.load(train_path)
train_x_ = train_npz['x']
train_y_ = train_npz['y']
train_y_ = np.split(train_y_, 2, axis=-1)[0]
# train_x_, (data_len, seq_len, n_node, input_dim)
# train_y_, (data_len, seq_len, n_node, out_dim)
train_x_ = train_x_.transpose(2,0,1,3)
train_y_ = train_y_.transpose(2,0,1,3)
#print(train_x_[0][0].shape)
# train_x_, (n_node, data_len, seq_len, input_dim)
# train_y_, (n_node, data_len, seq_len, out_dim)
val_npz = np.load(val_path)
val_x_ = val_npz['x']
val_y_ = val_npz['y']
val_y_ = np.split(val_y_, 2, axis=-1)[0]
val_x_ = val_x_.transpose(2,0,1,3)
val_y_ = val_y_.transpose(2,0,1,3)
test_npz = np.load(test_path)
test_x_ = test_npz['x']
test_y_ = test_npz['y']
test_y_ = np.split(test_y_, 2, axis=-1)[0]
test_x_ = test_x_.transpose(2,0,1,3)
test_y_ = test_y_.transpose(2,0,1,3)
self.train_ = [train_x_, train_y_]
self.val_ = [val_x_, val_y_]
self.test_ = [test_x_, test_y_]
print('Training size: {}'.format(train_x_.shape[1]))
print('Validating size: {}'.format(val_x_.shape[1]))
print('Testing size: {}'.format(test_x_.shape[1]))
def gen_walk(self, start_idx, walk_len):
walk = [start_idx]
for i in range(walk_len):
neigh_weight = self.support[walk[-1]]
neigh_nonzero = neigh_weight.nonzero()[0]
sum_neigh = sum([float(neigh_weight[_]) for _ in neigh_nonzero])
neigh_prob = [neigh_weight[_]/sum_neigh for _ in neigh_nonzero]
r = random.random()
for j in range(len(neigh_nonzero)):
if r < sum(neigh_prob[:j+1]):
walk.append(neigh_nonzero[j])
break
return np.array(walk[1:])
def get_neigh(self, src_node, walk_len):
neighs = []
neigh_nonzero = self.support[src_node].nonzero()[0]
for node in neigh_nonzero:
neighs.append(node)
while len(neighs) < walk_len:
neighs.append(self.n_node)
return np.array(neighs[:walk_len])
def get_batch(self, pdata):
curr_idx = 0
c_i, c_j = pdata[0].shape[0], pdata[0].shape[1]
while curr_idx < c_i*c_j:
start_idx = curr_idx
end_idx = min(curr_idx + BATCH_SIZE, c_i*c_j)
src = []
src_mask = []
trg = []
walks = []
walk_src = []
self_node = []
for i in range(end_idx-start_idx):
s_i = int((start_idx+i)/c_j) # idx of node
s_j = (start_idx+i)%c_j # idx of data seq
src.append(pdata[0][s_i][s_j])
trg_full = pdata[1][s_i][s_j]
trg_first = np.split(trg_full, SEQ_LEN, axis=0)[0]
trg.append(trg_first)
src_mask.append([1 for _ in range(SEQ_LEN)])
#walk = self.gen_walk(s_i, WALK_LEN)
walk = self.get_neigh(s_i, WALK_LEN)
walks.append(walk)
walk_src.append([])
walk_src[i] = []
for w_idx in walk:
if w_idx == self.n_node:
walk_src[i].append([[0.0, 0.0] for _ in range(SEQ_LEN)])
else:
walk_src[i].append(pdata[0][w_idx][s_j])
#walk_src[i] = [self.pdata[w_idx][s_j] for w_idx in walk]
self_node.append(s_i)
curr_idx = end_idx
yield (torch.tensor(src, dtype=torch.float, device=self.get_device()),
torch.tensor(src_mask, dtype=torch.int64, device=self.get_device()),
torch.tensor(trg, dtype=torch.float, device=self.get_device()),
torch.tensor(walks, dtype=torch.int64, device=self.get_device()),
torch.tensor(walk_src, dtype=torch.float, device=self.get_device()),
torch.tensor(self_node, dtype=torch.int64, device=self.get_device()))
def get_train_batch(self):
curr_idx = 0
c_i, c_j = self.train_[0].shape[0], self.train_[0].shape[1]
while curr_idx < c_i*c_j:
start_idx = curr_idx
end_idx = min(curr_idx + BATCH_SIZE, c_i*c_j)
src = []
src_mask = []
trg = []
walks = []
walk_src = []
self_node = []
for i in range(end_idx-start_idx):
s_i = int((start_idx+i)/c_j) # idx of node
s_j = (start_idx+i)%c_j # idx of data seq
src.append(self.train_[0][s_i][s_j])
trg_full = self.train_[1][s_i][s_j]
trg_first = np.split(trg_full, SEQ_LEN, axis=0)[0]
trg.append(trg_first)
src_mask.append([1 for _ in range(SEQ_LEN)])
#walk = self.gen_walk(s_i, WALK_LEN)
walk = self.get_neigh(s_i, WALK_LEN)
walks.append(walk)
walk_src.append([])
walk_src[i] = [self.train_[0][w_idx][s_j] for w_idx in walk]
self_node.append(s_i)
curr_idx = end_idx
yield (torch.tensor(src, dtype=torch.float, device=self.get_device()),
torch.tensor(src_mask, dtype=torch.int64, device=self.get_device()),
torch.tensor(trg, dtype=torch.float, device=self.get_device()),
torch.tensor(walks, dtype=torch.int64, device=self.get_device()),
torch.tensor(walk_src, dtype=torch.float, device=self.get_device()),
torch.tensor(self_node, dtype=torch.int64, device=self.get_device()))
def get_val_batch(self):
curr_idx = 0
c_i, c_j = self.val_[0].shape[0], self.val_[0].shape[1]
while curr_idx < c_i*c_j:
start_idx = curr_idx
end_idx = min(curr_idx + BATCH_SIZE, c_i*c_j)
src = []
src_mask = []
trg = []
walks = []
walk_src = []
self_node = []
for i in range(end_idx-start_idx):
s_i = int((start_idx+i)/c_j) # idx of node
s_j = (start_idx+i)%c_j # idx of data seq
src.append(self.val_[0][s_i][s_j])
trg_full = self.val_[1][s_i][s_j]
trg_first = np.split(trg_full, SEQ_LEN, axis=0)[0]
trg.append(trg_first)
src_mask.append([1 for _ in range(SEQ_LEN)])
#walk = self.gen_walk(s_i, WALK_LEN)
walk = self.get_neigh(s_i, WALK_LEN)
walks.append(walk)
walk_src.append([])
walk_src[i] = [self.val_[0][w_idx][s_j] for w_idx in walk]
self_node.append(s_i)
curr_idx = end_idx
yield (torch.tensor(src, dtype=torch.float, device=self.get_device()),
torch.tensor(src_mask, dtype=torch.int64, device=self.get_device()),
torch.tensor(trg, dtype=torch.float, device=self.get_device()),
torch.tensor(walks, dtype=torch.int64, device=self.get_device()),
torch.tensor(walk_src, dtype=torch.float, device=self.get_device()),
torch.tensor(self_node, dtype=torch.int64, device=self.get_device()))
def get_test_batch(self):
curr_idx = 0
c_i, c_j = self.test_[0].shape[0], self.test_[0].shape[1]
while curr_idx < c_i*c_j:
start_idx = curr_idx
end_idx = min(curr_idx + BATCH_SIZE, c_i*c_j)
src = []
src_mask = []
trg = []
walks = []
walk_src = []
self_node = []
for i in range(end_idx-start_idx):
s_i = int((start_idx+i)/c_j) # idx of node
s_j = (start_idx+i)%c_j # idx of data seq
src.append(self.test_[0][s_i][s_j])
trg_full = self.test_[1][s_i][s_j]
trg_first = np.split(trg_full, SEQ_LEN, axis=0)[0]
trg.append(trg_first)
src_mask.append([1 for _ in range(SEQ_LEN)])
#walk = self.gen_walk(s_i, WALK_LEN)
walk = self.get_neigh(s_i, WALK_LEN)
walks.append(walk)
walk_src.append([])
walk_src[i] = [self.test_[0][w_idx][s_j] for w_idx in walk]
self_node.append(s_i)
curr_idx = end_idx
yield (torch.tensor(src, dtype=torch.float, device=self.get_device()),
torch.tensor(src_mask, dtype=torch.int64, device=self.get_device()),
torch.tensor(trg, dtype=torch.float, device=self.get_device()),
torch.tensor(walks, dtype=torch.int64, device=self.get_device()),
torch.tensor(walk_src, dtype=torch.float, device=self.get_device()),
torch.tensor(self_node, dtype=torch.int64, device=self.get_device()))
def forward(self, src, src_mask, walk, walk_src):
src, src_mask = self.encoder(src, src_mask, walk, walk_src)
src = self.decoder(src)
return src
def calculate_loss(self, this_batch):
src, src_mask, trg, walk, walk_src, self_node = this_batch
src = self.forward(src, src_mask, walk, walk_src)
loss_func = nn.MSELoss()
src = src.view(src.shape[0], OUT_DIM)
trg = trg.view(trg.shape[0], OUT_DIM)
mse = loss_func(src, trg)
return torch.sqrt(mse)
def run_step(self, this_batch):
self.optimizer.zero_grad()
loss = self.calculate_loss(this_batch)
loss.backward()
torch.nn.utils.clip_grad_norm_(self.parameters(), CLIP)
self.optimizer.step()
return float(loss.cpu())
def run_training(self):
best_loss = float('inf')
start_time = time.time()
ready_to_stop = 0
for epoch in range(MAX_EPOCHES):
if ready_to_stop > PATIENCE:
print('Terminating... No more improvement...')
break
self.train()
batch_data = self.get_batch(self.train_)
train_loss = 0
batch_num = 0
for this_batch in batch_data:
batch_num += 1
train_loss += self.run_step(this_batch)
train_loss /= batch_num
self.eval()
batch_data = self.get_batch(self.val_)
val_loss = 0
batch_num = 0
for this_batch in batch_data:
batch_num += 1
val_loss += self.calculate_loss(this_batch)
val_loss /= batch_num
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if val_loss <= best_loss:
best_loss = val_loss
torch.save(self, self.model_path)
ready_to_stop = 0
else:
ready_to_stop += 1
print('Training...')
print('Epoch: {} ---- Time: {}m {}s'.format(epoch, epoch_mins, epoch_secs))
print('Training loss: {}'.format(train_loss))
print('Validating loss: {}'.format(val_loss))
print('------------------------------------------')
# del
def get_test_loss(self, this_batch):
src, src_mask, trg, walk, walk_src = this_batch
src = self.forward(src, src_mask, walk, walk_src)
loss_func = nn.MSELoss()
src = torch.split(src, 1, dim=1)
trg = torch.split(trg, 1, dim=1)
mse = []
for i in range(SEQ_LEN):
mse.append(torch.sqrt(loss_func(src[i].squeeze(1), trg[i].squeeze(1))))
return [_.cpu() for _ in mse]
def run_testing(self):
self = torch.load(self.model_path)
start_time = time.time()
self.eval()
batch_data = self.get_batch(self.test_)
test_loss = 0.0
batch_num = 0
for this_batch in batch_data:
batch_num += 1
test_loss += self.calculate_loss(this_batch)
#mse_loss = self.get_test_loss(this_batch)
#test_loss = [test_loss[i] + mse_loss[i] for i in range(SEQ_LEN)]
#test_loss = [test_loss[i]/batch_num for i in range(SEQ_LEN)]
test_loss /= batch_num
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
print('Testing...')
print('Time: {}m {}s'.format(epoch_mins, epoch_secs))
print('Testing loss: {}'.format(test_loss))
#for ts in range(SEQ_LEN):
# print('Testing loss on SEQ {}: {}'.format(ts,test_loss[ts]))
print('-------------------------------------------------')
return test_loss
if __name__ == '__main__':
data_path = 'motion/boxing'
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
random.seed(SEED)
np.random.seed(SEED)
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
if torch.cuda.is_available():
model = Sees_model('gpu')
model.INIT(data_path)
model.cuda()
for c in model.children():
c.cuda()
else:
model = Sees_model('cpu')
model.INIT(data_path)
model.cpu()
for c in model.children():
c.cpu()
model.run_training()
test_rmse = model.run_testing()
print('Data: {}, Parameters: [{}, {}, {}, {}]'.format(data_path,N_LAYERS, N_HEADS, HID_DIM, MAX_EPOCHES))
print('Testing loss: {}'.format(test_rmse))
'''
Data: motion/wave, Parameters: [2, 2, 8, 400]
Testing loss: 1.1848671436309814
Data: motion/jump, Parameters: [2, 2, 8, 400]
Testing loss: 0.878345251083374
Data: motion/mawashigeri, Parameters: [2, 2, 8, 200]
Testing loss: 1.0291368961334229
Data: motion/catch, Parameters: [2, 4, 16, 400]
Testing loss: 1.146062970161438
--------------------------------------------
[n_layers, n_heads, hid_dim, n_epoch]
Finacial [2, 2, 8, 50]
Testing loss: 0.13379737734794617
bike [2, 2, 8, 50]
Testing loss: 7.2491936683654785
jump [2, 2, 8, 50]
Testing loss: 3.028826951980591
jump [2, 2, 8, 100]
Testing loss: 2.0881550312042236
jump [2, 2, 8, 200] 28m
Testing loss: 1.044929027557373
jump [2, 2, 8, 300] 35m 248 converge
Testing loss: 0.7960168123245239
jump [2, 2, 16, 100] 16m27s
Testing loss: 1.9511746168136597
catch [2, 2, 16, 100] 71 converge
Testing loss: 1.1857619285583496
catch [2, 2, 8, 200] 118 converge
Testing loss: 0.9847110509872437
boxing [2, 2, 16, 100]
Testing loss: 11.136903762817383
boxing [2, 2, 16, 400] 197 converge
Testing loss: 4.6241326332092285
mawashigeri [2, 2, 8, 400] 60 converge
Testing loss: 1.9586907625198364
mawashigeri [2, 2, 8, 400] 65 converge
Testing loss: 1.6870359182357788
mawashigeri [2 2 8 400]
Testing loss: 0.7203245759010315
mawashigeri [2, 2, 32, 400]
Testing loss: 0.5256958603858948
wave [2, 2, 16, 400] 285 converge
Testing loss: 2.1978421211242676
wave [2 2 8 400] 193
Testing loss: 1.8339669704437256
Data: motion/wave, Parameters: [2, 4, 8, 400]
Testing loss: 1.1494758129119873
Data: motion/wave, Parameters: [2, 4, 16, 400] 84
Testing loss: 1.4789971113204956
Data: motion/wave, Parameters: [2, 2, 16, 400] 82
Testing loss: 1.2042409181594849
Data: motion/wave, Parameters: [2, 2, 32, 400] 56
Testing loss: 1.7742559909820557
Data: motion/wave, Parameters: [2, 2, 8, 400]
Testing loss: 0.5825341939926147
Data: motion/wave, Parameters: [2, 2, 8, 200]
Testing loss: 1.5185561180114746
'''