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seqskip_train_seq1H_genlog128.py
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seqskip_train_seq1H_genlog128.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 11 00:45:08 2018
seq1H_genlog: seqeunce learning model 2stack conv enc
- q(x), l(x)
- non-autoregressive (not feeding predicted labels)
- instance Norm.
- G: GLU version
- H: Highway-net version
기존 버그 완전 수정!
@author: mimbres
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim.lr_scheduler import StepLR
from torch.backends import cudnn
import numpy as np
import glob, os
import argparse
from tqdm import trange, tqdm
from spotify_data_loader import SpotifyDataloader
from utils.eval import evaluate
from blocks.highway_dil_conv import HighwayDCBlock
cudnn.benchmark = True
parser = argparse.ArgumentParser(description="Sequence Skip Prediction")
parser.add_argument("-c","--config",type=str, default="./config_init_dataset.json")
parser.add_argument("-s","--save_path",type=str, default="./save/exp_seq1H_genlog128/")
parser.add_argument("-l","--load_continue_latest",type=str, default=None)
parser.add_argument("-spl","--use_suplog_as_feat", type=bool, default=True)
parser.add_argument("-pl","--use_predicted_label", type=bool, default=False)
parser.add_argument("-glu","--use_glu", type=bool, default=False)
parser.add_argument("-w","--class_num",type=int, default = 2)
parser.add_argument("-e","--epochs",type=int, default= 15)
parser.add_argument("-lr","--learning_rate", type=float, default = 0.001)
parser.add_argument("-b","--train_batch_size", type=int, default = 2048)
parser.add_argument("-tsb","--test_batch_size", type=int, default = 1024)
parser.add_argument("-g","--gpu",type=int, default=0)
args = parser.parse_args()
# Hyper Parameters
USE_SUPLOG = args.use_suplog_as_feat
USE_PRED_LABEL = args.use_predicted_label
USE_GLU = args.use_glu
INPUT_DIM = 72 if USE_SUPLOG else 31
CLASS_NUM = args.class_num
EPOCHS = args.epochs
LEARNING_RATE = args.learning_rate
TR_BATCH_SZ = args.train_batch_size
TS_BATCH_SZ = args.test_batch_size
GPU = args.gpu
# Model-save directory
MODEL_SAVE_PATH = args.save_path
os.makedirs(os.path.dirname(MODEL_SAVE_PATH), exist_ok=True)
hist_trloss = list()
hist_tracc = list()
hist_vloss = list()
hist_vacc = list()
hist_trloss_qlog = list()
hist_trloss_skip = list()
hist_vloss_qlog = list()
hist_vloss_skip = list()
np.set_printoptions(precision=3)
class SeqFeatEnc(nn.Module):
def __init__(self, input_dim, e_ch, #d_ch=256,
#h_io_chs=[256, 256, 256, 256, 256, 256, 256],
d_ch,
h_io_chs=[1,1,1,1,1,1,1],
h_k_szs=[2,2,2,2,2,1,1],
h_dils=[1,2,4,8,16,1,1],
# h_dils=[1,2,4,1,2,4,1,2,4,1,1,1,1], #이것도 Receptive Field가 20인데 왜 안되는걸까??????
use_glu=False):
super(SeqFeatEnc, self).__init__()
h_io_chs[:] = [n * d_ch for n in h_io_chs]
# Layers:
self.mlp = nn.Sequential(nn.Conv1d(input_dim,e_ch,1),
nn.ReLU(),
nn.Conv1d(e_ch,d_ch,1))
self.h_block = HighwayDCBlock(h_io_chs, h_k_szs, h_dils, causality=True, use_glu=use_glu)
return None
def forward(self, x):
# Input={{x_sup,x_que};{label_sup,label_que}} BxC*T (Bx(29+1)*20), audio feat dim=29, label dim=1, n_sup+n_que=20
# Input bx30x20
x = self.mlp(x) # bx128*20
x = self.h_block(x) #bx256*20, 여기서 attention 쓰려면 split 128,128
return x#x[:,:128,:]
class SeqClassifier(nn.Module):
def __init__(self, input_ch, e_ch,
h_io_chs=[1,1,1,1,1,1,1],
h_k_szs=[2,2,2,2,2,1,1],
h_dils=[1,2,4,8,16,1,1],
use_glu=False):
super(SeqClassifier, self).__init__()
h_io_chs[:] = [n * e_ch for n in h_io_chs]
self.front_1x1 = nn.Conv1d(input_ch, e_ch,1)
self.h_block = HighwayDCBlock(h_io_chs, h_k_szs, h_dils, causality=True, use_glu=use_glu)
self.last_1x1 = nn.Sequential(nn.Conv1d(e_ch,e_ch,1), nn.ReLU(),
nn.Conv1d(e_ch,e_ch,1), nn.ReLU())
self.classifier = nn.Sequential(nn.Conv1d(e_ch,e_ch,1), nn.ReLU(),
nn.Conv1d(e_ch,e_ch,1))#nn.Conv1d(e_ch,1,1))
def forward(self, x): # Input:bx256*20
x = self.front_1x1(x) # bx128*20
x = self.h_block(x) # bx128*20
x = self.last_1x1(x) # bx64*20
return self.classifier(x).squeeze(1) # bx20
class SeqModel(nn.Module):
def __init__(self, input_dim=INPUT_DIM, e_ch=128, d_ch=128, use_glu=USE_GLU):
super(SeqModel, self).__init__()
self.enc = SeqFeatEnc(input_dim=input_dim, e_ch=e_ch, d_ch=d_ch, use_glu=use_glu)
self.clf = SeqClassifier(input_ch=d_ch, e_ch=e_ch, use_glu=use_glu)
self.qlog_classifier = nn.Sequential(nn.Conv1d(e_ch,e_ch,1), nn.ReLU(),
nn.Conv1d(e_ch,41,1))#nn.Conv1d(e_ch,1,1))
self.skip_classifier = nn.Sequential(nn.Conv1d(e_ch,e_ch,1), nn.ReLU(),
nn.Conv1d(e_ch,1,1))#nn.Conv1d(e_ch,1,1))
def forward(self, x):
x = self.enc(x) # bx128x20
x = self.clf(x) # bx128x20
#x_qlog, x_skip = x[:,:41,:], x[:,41,:]
x_qlog = self.qlog_classifier(x) # bx41*20
x_skip = self.skip_classifier(x).squeeze(1) # bx20
return x_qlog, x_skip
#%%
def validate(mval_loader, SM, eval_mode, GPU):
tqdm.write("Validation...")
submit = []
gt = []
total_vloss = 0
total_vloss_qlog = 0
total_vloss_skip = 0
total_vcorrects = 0
total_vquery = 0
val_sessions_iter = iter(mval_loader)
for val_session in trange(len(val_sessions_iter), desc='val-sessions', position=2, ascii=True):
SM.eval()
x, labels, y_mask, num_items, index = val_sessions_iter.next() # FIXED 13.Dec. SEPARATE LOGS. QUERY SHOULT NOT INCLUDE LOGS
# Sample data for 'support' and 'query': ex) 15 items = 7 sup, 8 queries...
num_support = num_items[:,0].detach().numpy().flatten() # If num_items was odd number, query has one more item.
num_query = num_items[:,1].detach().numpy().flatten()
batch_sz = num_items.shape[0]
# x: bx70*20
x = x.permute(0,2,1)
# Prepare ground truth log and label, y
y_qlog = x[:,:41,:].clone() # bx41*20
y_skip = labels.clone() #bx20
y_mask_qlog = y_mask.unsqueeze(1).repeat(1,41,1) #bx41*20
y_mask_skip = y_mask #bx20
# log shift: bx41*20
log_shift = torch.zeros(batch_sz,41,20)
log_shift[:,:,1:] = x[:,:41,:-1]
log_shift[:,:,11:] = 0 # DELETE LOG QUE
# labels_shift: bx1*20(model can only observe past labels)
labels_shift = torch.zeros(batch_sz,1,20)
labels_shift[:,0,1:] = labels[:,:-1].float()
labels_shift[:,0,11:] = 0 #!!! NOLABEL for previous QUERY
# support/query state labels: bx1*20
sq_state = torch.zeros(batch_sz,1,20)
sq_state[:,0,:11] = 1
# Pack x: bx72*20 (or bx32*20 if not using sup_logs)
x = torch.cat((log_shift, x[:,41:,:], labels_shift, sq_state), 1).cuda(GPU) # x: bx72*20
if USE_PRED_LABEL is True:
# Predict
li = 70 # the label's dimension indice
_x = x[:,:,:11].clone() # bx72*11
for q in range(11,20):
y_hat_qlog, y_hat_skip = SM(Variable(_x, requires_grad=False)) # will be bx11 at the first round
# Append next features
_x = torch.cat((_x, x[:,:,q].unsqueeze(2)), 2) # now bx72*12
_x[:,li,q] = torch.sigmoid(y_hat_skip[:,-1]) # replace with predicted label
_x[:,:41,q] = torch.sigmoid(y_hat_qlog[:,-1])
y_hat_qlog, y_hat_skip = SM(Variable(_x, requires_grad=False)) # y_hat(final): bx20
del _x
else:
y_hat_qlog, y_hat_skip = SM(x) # y_hat_qlog: bx41*20, y_hat_skip: b*20
# Calcultate BCE loss
loss_qlog = F.binary_cross_entropy_with_logits(input=y_hat_qlog.cuda(GPU)*y_mask_qlog.cuda(GPU),
target=y_qlog.cuda(GPU)*y_mask_qlog.cuda(GPU))
loss_skip = F.binary_cross_entropy_with_logits(input=y_hat_skip.cuda(GPU)*y_mask_skip.cuda(GPU),
target=y_skip.cuda(GPU)*y_mask_skip.cuda(GPU))
loss = loss_qlog + loss_skip
total_vloss_qlog += loss_qlog.item()
total_vloss_skip += loss_skip.item()
total_vloss += loss.item()
# Decision
y_prob = torch.sigmoid(y_hat_skip.detach()*y_mask_skip.cuda(GPU)).cpu().numpy() # bx20
y_pred = (y_prob[:,10:]>=0.5).astype(np.int) # bx10
y_numpy = y_skip[:,10:].numpy() # bx10
# Acc
total_vcorrects += np.sum((y_pred==y_numpy)*y_mask_skip[:,10:].numpy())
total_vquery += np.sum(num_query)
# Restore GPU memory
del loss, loss_qlog, loss_skip, y_hat_qlog, y_hat_skip
# Eval, Submission
if eval_mode is not 0:
for b in np.arange(batch_sz):
submit.append(y_pred[b,:num_query[b]].flatten())
gt.append(y_numpy[b,:num_query[b]].flatten())
if (val_session+1)%400 == 0:
sample_sup = labels[0,(10-num_support[0]):10].long().numpy().flatten()
sample_que = y_numpy[0,:num_query[0]].astype(int)
sample_pred = y_pred[0,:num_query[0]]
sample_prob = y_prob[0,10:10+num_query[0]]
tqdm.write("S:" + np.array2string(sample_sup) +'\n'+
"Q:" + np.array2string(sample_que) + '\n' +
"P:" + np.array2string(sample_pred) + '\n' +
"prob:" + np.array2string(sample_prob))
tqdm.write("val_session:{0:} vloss(qlog|skip):{1:.6f}({2:.6f}|{3:.6f}) vacc:{4:.4f}".format(val_session,
total_vloss/total_vquery, total_vloss_qlog/total_vquery,
total_vloss_skip/total_vquery, total_vcorrects/total_vquery))
# Avg.Acc (skip labels only, log-generation acc is not implemented yet!)
if eval_mode==1:
aacc = evaluate(submit, gt)
tqdm.write("AACC={0:.6f}, FirstAcc={1:.6f}".format(aacc[0], aacc[1]))
hist_vloss.append(total_vloss/total_vquery)
hist_vloss_qlog.append(total_vloss_qlog/total_vquery)
hist_vloss_skip.append(total_vloss_skip/total_vquery)
hist_vacc.append(total_vcorrects/total_vquery)
return submit
# Main
def main():
# Trainset stats: 2072002577 items from 124950714 sessions
print('Initializing dataloader...')
mtrain_loader = SpotifyDataloader(config_fpath=args.config,
mtrain_mode=True,
data_sel=(0, 99965071), # 80% 트레인
batch_size=TR_BATCH_SZ,
shuffle=True,
seq_mode=True) # seq_mode implemented
mval_loader = SpotifyDataloader(config_fpath=args.config,
mtrain_mode=True, # True, because we use part of trainset as testset
data_sel=(99965071, 101065071),#104965071),#(99965071, 124950714), # 20%를 테스트
batch_size=TS_BATCH_SZ,
shuffle=False,
seq_mode=True)
# Init neural net
SM = SeqModel().cuda(GPU)
SM_optim = torch.optim.Adam(SM.parameters(), lr=LEARNING_RATE)
SM_scheduler = StepLR(SM_optim, step_size=1, gamma=0.8)
# Load checkpoint
if args.load_continue_latest is None:
START_EPOCH = 0
else:
latest_fpath = max(glob.iglob(MODEL_SAVE_PATH + "check*.pth"),key=os.path.getctime)
checkpoint = torch.load(latest_fpath, map_location='cuda:{}'.format(GPU))
tqdm.write("Loading saved model from '{0:}'... loss: {1:.6f}".format(latest_fpath,checkpoint['loss']))
SM.load_state_dict(checkpoint['SM_state'])
SM_optim.load_state_dict(checkpoint['SM_opt_state'])
SM_scheduler.load_state_dict(checkpoint['SM_sch_state'])
START_EPOCH = checkpoint['ep']
# Train
for epoch in trange(START_EPOCH, EPOCHS, desc='epochs', position=0, ascii=True):
tqdm.write('Train...')
tr_sessions_iter = iter(mtrain_loader)
total_corrects = 0
total_query = 0
total_trloss_qlog = 0
total_trloss_skip = 0
total_trloss = 0
for session in trange(len(tr_sessions_iter), desc='sessions', position=1, ascii=True):
SM.train();
x, labels, y_mask, num_items, index = tr_sessions_iter.next() # FIXED 13.Dec. SEPARATE LOGS. QUERY SHOULT NOT INCLUDE LOGS
# Sample data for 'support' and 'query': ex) 15 items = 7 sup, 8 queries...
num_support = num_items[:,0].detach().numpy().flatten() # If num_items was odd number, query has one more item.
num_query = num_items[:,1].detach().numpy().flatten()
batch_sz = num_items.shape[0]
# x: bx70*20
x = x.permute(0,2,1)
# Prepare ground truth log and label, y
y_qlog = x[:,:41,:].clone() # bx41*20
y_skip = labels.clone() #bx20
y_mask_qlog = y_mask.unsqueeze(1).repeat(1,41,1) #bx41*20
y_mask_skip = y_mask #bx20
# log shift: bx41*20
log_shift = torch.zeros(batch_sz,41,20)
log_shift[:,:,1:] = x[:,:41,:-1]
log_shift[:,:,11:] = 0 # DELETE LOG QUE
# labels_shift: bx1*20(model can only observe past labels)
labels_shift = torch.zeros(batch_sz,1,20)
labels_shift[:,0,1:] = labels[:,:-1].float()
labels_shift[:,0,11:] = 0 #!!! NOLABEL for previous QUERY
# support/query state labels: bx1*20
sq_state = torch.zeros(batch_sz,1,20)
sq_state[:,0,:11] = 1
# Pack x: bx72*20 (or bx32*20 if not using sup_logs)
x = Variable(torch.cat((log_shift, x[:,41:,:], labels_shift, sq_state), 1)).cuda(GPU) # x: bx72*20
# Forward & update
y_hat_qlog, y_hat_skip = SM(x) # y_hat: b*20
# Calcultate BCE loss
loss_qlog = F.binary_cross_entropy_with_logits(input=y_hat_qlog.cuda(GPU)*y_mask_qlog.cuda(GPU),
target=y_qlog.cuda(GPU)*y_mask_qlog.cuda(GPU))
loss_skip = F.binary_cross_entropy_with_logits(input=y_hat_skip.cuda(GPU)*y_mask_skip.cuda(GPU),
target=y_skip.cuda(GPU)*y_mask_skip.cuda(GPU))
loss = loss_qlog + loss_skip
total_trloss_qlog += loss_qlog.item()
total_trloss_skip += loss_skip.item()
total_trloss += loss.item()
SM.zero_grad()
loss.backward()
# Gradient Clipping
#torch.nn.utils.clip_grad_norm_(SM.parameters(), 0.5)
SM_optim.step()
# Decision
y_prob = torch.sigmoid(y_hat_skip.detach()*y_mask_skip.cuda(GPU)).cpu().numpy() # bx20
y_pred = (y_prob[:,10:]>=0.5).astype(np.int) # bx10
y_numpy = y_skip[:,10:].numpy() # bx10
# Label Acc*
total_corrects += np.sum((y_pred==y_numpy)*y_mask_skip[:,10:].numpy())
total_query += np.sum(num_query)
# # Log generation Acc*
# y_qlog_mask = y_mask[:,:41,10:]
# Restore GPU memory
del loss, loss_qlog, loss_skip, y_hat_qlog, y_hat_skip
if (session+1)%500 == 0:
hist_trloss_qlog.append(total_trloss_qlog/500) #!
hist_trloss_skip.append(total_trloss_skip/500) #!
hist_trloss.append(total_trloss/500)
hist_tracc.append(total_corrects/total_query)
# Prepare display
sample_sup = labels[0,(10-num_support[0]):10].long().numpy().flatten()
sample_que = y_numpy[0,:num_query[0]].astype(int)
sample_pred = y_pred[0,:num_query[0]]
sample_prob = y_prob[0,10:10+num_query[0]]
tqdm.write("S:" + np.array2string(sample_sup) +'\n'+
"Q:" + np.array2string(sample_que) + '\n' +
"P:" + np.array2string(sample_pred) + '\n' +
"prob:" + np.array2string(sample_prob))
tqdm.write("tr_session:{0:} tr_loss(qlog|skip):{1:.6f}({2:.6f}|{3:.6f}) tr_acc:{4:.4f}".format(session,
hist_trloss[-1], hist_trloss_qlog[-1], hist_trloss_skip[-1], hist_tracc[-1]))
total_corrects = 0
total_query = 0
total_trloss = 0
total_trloss_qlog = 0
total_trloss_skip = 0
if (session+1)%8000 == 0:
# Validation
validate(mval_loader, SM, eval_mode=True, GPU=GPU)
# Save
torch.save({'ep': epoch, 'sess':session, 'SM_state': SM.state_dict(),'loss': hist_trloss[-1],
'hist_trloss_qlog': hist_trloss_qlog, 'hist_trloss_skip': hist_trloss_skip, 'hist_vacc': hist_vacc,
'hist_vloss': hist_vloss, 'hist_trloss': hist_trloss, 'SM_opt_state': SM_optim.state_dict(),
'SM_sch_state': SM_scheduler.state_dict()}, MODEL_SAVE_PATH + "check_{0:}_{1:}.pth".format(epoch, session))
# Validation
validate(mval_loader, SM, eval_mode=True, GPU=GPU)
# Save
torch.save({'ep': epoch, 'sess':session, 'SM_state': SM.state_dict(),'loss': hist_trloss[-1],
'hist_trloss_qlog': hist_trloss_qlog, 'hist_trloss_skip': hist_trloss_skip, 'hist_vacc': hist_vacc,
'hist_vloss': hist_vloss, 'hist_trloss': hist_trloss, 'SM_opt_state': SM_optim.state_dict(),
'SM_sch_state': SM_scheduler.state_dict()}, MODEL_SAVE_PATH + "check_{0:}_{1:}.pth".format(epoch, session))
SM_scheduler.step()
if __name__ == '__main__':
main()