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seqskip_test_GenLog1H_Skip1eH.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 31 17:44:49 2018
GenLog1H_Skip1eH: predict Skip labels by using que-logs generated with pre-trained seq1H_genlog model.
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, time
import argparse
from tqdm import trange, tqdm
from spotify_data_loader import SpotifyDataloader
from utils.eval import evaluate
from blocks.highway_glu_dil_conv_v2 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_GenLog1H_Skip1eH/")
parser.add_argument("-gf","--load_generator_fpath",type=str, default="./save/exp_seq1H_genlog128/check_14_48811.pth")
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
GENERATOR_LOAD_PATH = args.load_generator_fpath
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
# Generator
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
###
class SeqEncoder2(nn.Module):
def __init__(self, input_ch, e_ch,
h_k_szs=[2,2,2,3,1,1], #h_k_szs=[2,2,5,1,1],
h_dils=[1,2,4,8,1,1],
causality=True,
use_glu=False):
super(SeqEncoder2, self).__init__()
h_io_chs = [e_ch]*len(h_k_szs)
self.front_1x1 = nn.Conv1d(input_ch, e_ch,1)
self.h_block = HighwayDCBlock(h_io_chs, h_k_szs, h_dils, causality=causality, use_glu=use_glu)
self.mid_1x1 = nn.Sequential(nn.Conv1d(e_ch,e_ch,1), nn.ReLU(),
nn.Conv1d(e_ch,e_ch,1), nn.ReLU())
self.last_1x1 = nn.Sequential(nn.Conv1d(e_ch,e_ch,1))
def forward(self, x): # Input:bx(input_dim)*20
x = self.front_1x1(x) # bx128*20
x = self.h_block(x) # bx128*20
x = self.mid_1x1(x) # bx128*20
return self.last_1x1(x) # bx128*20
# Model for predicting skip labels
class SeqModel2(nn.Module):
def __init__(self, input_dim=INPUT_DIM, e_ch=128, d_ch=128, use_glu=USE_GLU):
super(SeqModel2, self).__init__()
self.e_ch = e_ch
self.d_ch = d_ch
self.enc = SeqEncoder2(input_ch=input_dim, e_ch=e_ch,
h_k_szs=[2,2,2,3,1,1], #h_k_szs=[2,2,2,3,1,1],
h_dils=[1,2,4,8,1,1], #h_dils=[1,2,4,8,1,1],
use_glu=use_glu) # bx128*10
self.feature = nn.Sequential(nn.Conv1d(d_ch,d_ch,1), nn.ReLU(),
nn.Conv1d(d_ch,d_ch,1), nn.ReLU())
self.classifier = nn.Conv1d(d_ch,1,1)
def forward(self, x):
x = self.enc(x) # bx128*10
x = self.feature(x)
x = self.classifier(x).squeeze(1) # bx256*10 --> b*10
return x# bx20
#%%
def validate(mval_loader, SM, SMG, eval_mode, GPU):
tqdm.write("Validation...")
submit = []
gt = []
total_vloss = 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)
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_1 = Variable(torch.cat((log_shift, x[:,41:,:], labels_shift, sq_state), 1)).cuda(GPU) # x: bx72*20
# Pre-trained Generator: forward & get qlog^
y_hat_qlog, _ = SMG(x_1) # y_hat: b*20
x_feat_T = torch.zeros(batch_sz, 72, 20)
x_feat_T[:,:70,:] = x.clone()
x_feat_T[:, 70,:10] = 1 # Sup/Que state indicator
x_feat_T[:, 71,:10] = labels[:,:10].clone()
x_feat_S = x_feat_T.clone()
x_feat_S[:, :41, 10:] = y_hat_qlog[:,:,10:].clone() # remove que-log
x_feat_S = Variable(x_feat_S).cuda(GPU)
del y_hat_qlog, x_1
# y
y = labels.clone() # bx20
# y_mask
y_mask_que = y_mask.clone()
y_mask_que[:,:10] = 0
y_hat = SM(x_feat_S)
# Calcultate BCE loss
loss = F.binary_cross_entropy_with_logits(input=y_hat*y_mask_que.cuda(GPU), target=y.cuda(GPU)*y_mask_que.cuda(GPU))
total_vloss += loss.item()
# Decision
y_prob = torch.sigmoid(y_hat*y_mask_que.cuda(GPU)).detach().cpu().numpy() # bx20
y_pred = (y_prob[:,10:]>0.5).astype(np.int) # bx10
y_numpy = labels[:,10:].numpy() # bx10
# Acc
total_vcorrects += np.sum((y_pred==y_numpy)*y_mask_que[:,10:].numpy())
total_vquery += np.sum(num_query)
# Restore GPU memory
del loss, y_hat
# 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:{1:.6f} vacc:{2:.4f}".format(val_session,
total_vloss/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_vacc.append(total_vcorrects/total_vquery)
return submit
def save_submission(output, output_path):
os.makedirs(os.path.dirname(output_path), exist_ok=True)
with open(output_path,"w") as f:
for l in tqdm(output, ascii=True):
line = ''.join(map(str,l))
f.write(line + '\n')
tqdm.write('submission saved to {}'.format(output_path))
return
# 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, 124000000), # 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=(124000000, 124950714),#104965071),#(99965071, 124950714), # 20%를 테스트
# batch_size=TS_BATCH_SZ,
# shuffle=False,
# seq_mode=True)
mtest_loader = SpotifyDataloader(config_fpath=args.config,
mtrain_mode=False, # False = testset for submission
#data_sel=(0, 100),
batch_size=2048,
shuffle=False,
seq_mode=True)
# Load Generator net
SMG = SeqModel().cuda(GPU)
checkpoint = torch.load(GENERATOR_LOAD_PATH, map_location='cuda:{}'.format(GPU))
tqdm.write("Loading saved teacher model from '{0:}'... loss: {1:.6f}".format(GENERATOR_LOAD_PATH,checkpoint['loss']))
SMG.load_state_dict(checkpoint['SM_state'])
SMG.cuda(GPU).eval();
# Init neural net
SM = SeqModel2().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']
# Validation
submission = validate(mtest_loader, SM, SMG, eval_mode=2, GPU=GPU)
if len(submission)!=31251398: print("WARNING: submission size not matches.");
# Save
fpath = MODEL_SAVE_PATH+ "/submission_{}.txt".format(time.strftime('%Y%m%d_%Hh%Mm'))
tqdm.write("Saving...")
save_submission(submission, fpath)
tqdm.write("Succesfully saved submission file: {}".format(fpath) )
if __name__ == '__main__':
main()