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seqskip_train_rnbc2sepUE256v2L.py
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seqskip_train_rnbc2sepUE256v2L.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 11 00:45:08 2018
rnbc2 sep UserEmb(80+40)cv2
RN with batch + classifier
@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
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_rnbc2_sepUE256v2L_all/")
parser.add_argument("-l","--load_continue_latest",type = str, default = None)
parser.add_argument("-w","--class_num",type = int, default = 2)
parser.add_argument("-e","--epochs",type = int, default= 10)
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("-g","--gpu",type=int, default=0)
args = parser.parse_args()
# Hyper Parameters
CLASS_NUM = args.class_num
EPOCHS = args.epochs
LEARNING_RATE = args.learning_rate
TR_BATCH_SZ = args.train_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()
np.set_printoptions(precision=3)
#Feature encoder:
class MLP(nn.Module):
def __init__(self, input_sz, hidden_sz, output_sz):
super(MLP, self).__init__()
self.fc1 = nn.Linear(input_sz, hidden_sz)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_sz, output_sz)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
# Model: Relation Nets
class RelationNetwork(nn.Module):
"""docstring for RelationNetwork"""
def __init__(self,
num_sup_max=10, num_que_max=10,
in_feat_sup_sz=64, in_feat_que_sz=64,
in_log_sup_sz=41, in_log_que_sz=0,
in_label_sup_sz=3, ue_sz=128, ue_sz2=128): # 64,64,41,0,3
super(RelationNetwork, self).__init__()
self.num_sup_max = num_sup_max
self.num_que_max = num_que_max
self.in_feat_sup_sz = in_feat_sup_sz
self.in_feat_que_sz = in_feat_que_sz
self.in_log_sup_sz = in_log_sup_sz
self.in_log_que_sz = in_log_que_sz
self.in_label_sup_sz = in_label_sup_sz
self.ue_sz = ue_sz
self.ue_sz2 = ue_sz2
self.layer1_input_sz = in_feat_sup_sz + in_feat_que_sz + ue_sz # SEP RELATION: we drop log and labels here.
self.layer1 = nn.Sequential(
nn.Linear(self.layer1_input_sz, 256), # bx8x7x1*128 -> bx8x7x1*512
nn.LayerNorm(256),
nn.ReLU())
self.layer2 = nn.Sequential(
nn.Linear(256, 128),
nn.LayerNorm(128),
nn.ReLU())
self.fc1 = nn.Linear(128+ue_sz2,64)
self.fc2 = nn.Linear(64,1)
# Option: user embedding layer
self.u_emb1 = nn.Sequential(
nn.Linear(108,ue_sz), # bx7x1*108 --> bx7x1*16
nn.LayerNorm(ue_sz),
nn.ReLU(),
nn.Conv1d(10,1,1,bias=False)) # 1x1Conv --> bx1x1*16
self.u_emb2 = nn.Sequential(
nn.Linear(108,ue_sz2), # bx7x1*108 --> bx7x1*40
nn.LayerNorm(ue_sz2),
nn.ReLU(),
nn.Conv1d(10,1,1,bias=False), # 1x1Conv --> bx1x1*40
nn.ReLU())
# Option: classifier
self.classifier = nn.Linear(10,1)
def forward(self, x_sup, x_que, x_log_sup, y_log_que, label_sup):
# x_feat_sup: bx7x1*64, x_feat_que: bx8x1*64
# x_log_sup: bx7*41, label_sup:bx10*3
######### User Emb:(x_feat_sup, log_sup, label_sup)
ue_source = torch.cat((x_sup.squeeze(2), x_log_sup, label_sup), 2) # bx7*108
ue = self.u_emb1(ue_source) # bx1x16, 16D user embedding
######### Audio relation: (audio_sup, audio_que, 유저embedding)
audio_relation_pairs = torch.cat((x_sup.unsqueeze(1).repeat(1,10,1,1,1),
x_que.unsqueeze(2).repeat(1,1,10,1,1),
ue.view(-1,1,1,1,self.ue_sz).repeat(1,10,10,1,1) ), 4) # bx8x7x1*144
# metric relation: (audio relation, 유저emb)
out = self.layer1(audio_relation_pairs) # bx7x8x1*512
out = self.layer2(out) # bx7x8x1*256
###
#out = torch.cat((out, _ext_sup), 4) # bx8x7x1*264
ue2 = self.u_emb2(ue_source).view(-1,1,1,1,self.ue_sz2).repeat(1,10,10,1,1)
out = torch.cat((out, ue2), 4) #bx8x7x1*(256+ue_sz2)
out = F.relu(self.fc1(out)) # -> bx7x8x1*64
out = torch.sigmoid(self.fc2(out)) # -> bx7x8*1
out = out.view(-1,10,10)
out = self.classifier(out) # bx8x1
out = out.view(-1,10) # bx8
return out
#%%
def validate(mval_loader, FeatEnc, RN, eval_mode):
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):
FeatEnc.eval(); RN.eval();
x_sup, x_que, x_log_sup, x_log_que, label_sup, label_que, num_items, index = val_sessions_iter.next() # FIXED 13.Dec. SEPARATE LOGS. QUERY SHOULT NOT INCLUDE LOGS
x_sup, x_que = Variable(x_sup).cuda(GPU), Variable(x_que).cuda(GPU)
x_log_sup, x_log_que = Variable(x_log_sup).cuda(GPU), Variable(x_log_que).cuda(GPU)
label_sup = Variable(label_sup).cuda(GPU)
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_sup = x_sup.unsqueeze(2) # 1x7*29 --> 1x7x1*29
x_que = x_que.unsqueeze(2) # 1x8*29 --> 1x8x1*29
x_feat_sup = FeatEnc(x_sup) # 1x7x1*64
x_feat_que = FeatEnc(x_que) # 1x8x1*64
y_hat = RN(x_feat_sup, x_feat_que, x_log_sup, x_log_que, label_sup) # bx8
y_gt = label_que[:,:,1]
y_mask = np.zeros((batch_sz,10), dtype=np.float32)
for b in np.arange(batch_sz):
y_mask[b,:num_query[b]] = 1
y_mask = torch.FloatTensor(y_mask).cuda(GPU)
loss = F.binary_cross_entropy_with_logits(input=y_hat*y_mask, target=y_gt.cuda(GPU)*y_mask)
total_vloss += loss.item()
# Decision
y_prob = (torch.sigmoid(y_hat)*y_mask).detach().cpu().numpy()
y_pred = ((torch.sigmoid(y_hat)>0.5).float()*y_mask).detach().cpu().long().numpy()
# Eval, Submission
_y_gt = label_que[:,:,1].detach().cpu().numpy()
if eval_mode is True:
for b in np.arange(batch_sz):
submit.append(y_pred[b,:num_query[b]].flatten())
gt.append(_y_gt[b,:num_query[b]].flatten())
# Prepare display
sample_sup = label_sup[0,:num_support[0],1].detach().long().cpu().numpy().flatten()
sample_que = label_que[0,:num_query[0],1].long().numpy().flatten()
sample_pred = y_pred[0,:num_query[0]].flatten()
sample_prob = y_prob[0, :num_query[0]].flatten()
# Acc
total_vcorrects += np.sum((y_pred == label_que[:,:,1].long().numpy()) * y_mask.cpu().numpy())
total_vquery += np.sum(num_query)
if (val_session+1)%400 == 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/val_session, total_vcorrects/total_vquery))
# Restore GPU memory
del loss, y_hat
# Avg.Acc
aacc = evaluate(submit, gt)
tqdm.write("AACC={0:.6f}, FirstAcc={1:.6f}".format(aacc[0], aacc[1]))
hist_vloss.append(total_vloss/(val_session+1))
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) # shuffle은 True로 해야됨 나중에...
mval_loader = SpotifyDataloader(config_fpath=args.config,
mtrain_mode=True, # True, because we use part of trainset as testset
data_sel=(3000000, 5000000),#(99965071, 124950714),#(99965071, 124950714), # 20%를 테스트
batch_size=2048,
shuffle=False)
# Init neural net
#FeatEnc = MLP(input_sz=29, hidden_sz=512, output_sz=64).apply(weights_init).cuda(GPU)
FeatEnc = MLP(input_sz=29, hidden_sz=256, output_sz=64).cuda(GPU)
RN = RelationNetwork().cuda(GPU)
FeatEnc_optim = torch.optim.Adam(FeatEnc.parameters(), lr=LEARNING_RATE)
RN_optim = torch.optim.Adam(RN.parameters(), lr=LEARNING_RATE)
FeatEnc_scheduler = StepLR(FeatEnc_optim, step_size=100000, gamma=0.2)
RN_scheduler = StepLR(RN_optim, step_size=100000, gamma=0.2)
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['hist_trloss'][-1]))
FeatEnc.load_state_dict(checkpoint['FE_state'])
RN.load_state_dict(checkpoint['RN_state'])
FeatEnc_optim.load_state_dict(checkpoint['FE_opt_state'])
RN_optim.load_state_dict(checkpoint['RN_opt_state'])
FeatEnc_scheduler.load_state_dict(checkpoint['FE_sch_state'])
RN_scheduler.load_state_dict(checkpoint['RN_sch_state'])
START_EPOCH = checkpoint['ep']
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 = 0
for session in trange(len(tr_sessions_iter), desc='sessions', position=1, ascii=True):
FeatEnc.train(); RN.train();
x_sup, x_que, x_log_sup, x_log_que, label_sup, label_que, num_items, index = tr_sessions_iter.next() # FIXED 13.Dec. SEPARATE LOGS. QUERY SHOULT NOT INCLUDE LOGS
x_sup, x_que = Variable(x_sup).cuda(GPU), Variable(x_que).cuda(GPU)
x_log_sup, x_log_que = Variable(x_log_sup).cuda(GPU), Variable(x_log_que).cuda(GPU)
label_sup = Variable(label_sup).cuda(GPU)
# 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_sup = x_sup.unsqueeze(2) # 1x7*29 --> 1x7x1*29
x_que = x_que.unsqueeze(2) # 1x8*29 --> 1x8x1*29
# - feature encoder
x_feat_sup = FeatEnc(x_sup) # 1x7x1*64
x_feat_que = FeatEnc(x_que) # 1x8x1*64
# - relation network
y_hat = RN(x_feat_sup, x_feat_que, x_log_sup, x_log_que, label_sup) # bx8
# Prepare ground-truth simlarity score and mask
y_gt = label_que[:,:,1]
y_mask = np.zeros((batch_sz,10), dtype=np.float32)
for b in np.arange(batch_sz):
y_mask[b,:num_query[b]] = 1
y_mask = torch.FloatTensor(y_mask).cuda(GPU)
# Calcultate BCE loss
loss = F.binary_cross_entropy_with_logits(input=y_hat*y_mask, target=y_gt.cuda(GPU)*y_mask)
total_trloss += loss.item()
# Update Nets
FeatEnc.zero_grad()
RN.zero_grad()
loss.backward()
#torch.nn.utils.clip_grad_norm_(FeatEnc.parameters(), 0.5)
#torch.nn.utils.clip_grad_norm_(RN.parameters(), 0.5)
FeatEnc_optim.step()
RN_optim.step()
# Decision
y_prob = (torch.sigmoid(y_hat)*y_mask).detach().cpu().numpy()
y_pred = ((torch.sigmoid(y_hat)>0.5).float()*y_mask).detach().cpu().long().numpy()
# Prepare display
sample_sup = label_sup[0,:num_support[0],1].detach().long().cpu().numpy().flatten()
sample_que = label_que[0,:num_query[0],1].long().numpy().flatten()
sample_pred = y_pred[0,:num_query[0]].flatten()
sample_prob = y_prob[0, :num_query[0]].flatten()
# Acc
total_corrects += np.sum((y_pred == label_que[:,:,1].long().numpy()) * y_mask.cpu().numpy())
total_query += np.sum(num_query)
# Restore GPU memory
del loss, x_feat_sup, x_feat_que, y_hat
if (session+1)%900 == 0:
hist_trloss.append(total_trloss/900)
hist_tracc.append(total_corrects/total_query)
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:{1:.6f} tr_acc:{2:.4f}".format(session, hist_trloss[-1], hist_tracc[-1]))
total_corrects = 0
total_query = 0
total_trloss = 0
if (session+1)%4000 == 0:
# Validation
validate(mval_loader, FeatEnc, RN, eval_mode=True)
# Save
torch.save({'ep': epoch, 'sess':session, 'FE_state': FeatEnc.state_dict(), 'RN_state': RN.state_dict(), 'loss': hist_trloss[-1], 'hist_vacc': hist_vacc,
'hist_vloss': hist_vloss, 'hist_trloss': hist_trloss, 'FE_opt_state': FeatEnc_optim.state_dict(), 'RN_opt_state': RN_optim.state_dict(),
'FE_sch_state': FeatEnc_scheduler.state_dict(), 'RN_sch_state': RN_scheduler.state_dict()}, MODEL_SAVE_PATH + "check_{0:}_{1:}.pth".format(epoch, session))
# Validation
validate(mval_loader, FeatEnc, RN, eval_mode=True)
# Save
torch.save({'ep': epoch, 'sess':session, 'FE_state': FeatEnc.state_dict(), 'RN_state': RN.state_dict(), 'loss': hist_trloss[-1], 'hist_vacc': hist_vacc,
'hist_vloss': hist_vloss, 'hist_trloss': hist_trloss, 'FE_opt_state': FeatEnc_optim.state_dict(), 'RN_opt_state': RN_optim.state_dict(),
'FE_sch_state': FeatEnc_scheduler.state_dict(), 'RN_sch_state': RN_scheduler.state_dict()}, MODEL_SAVE_PATH + "check_{0:}_{1:}.pth".format(epoch, session))
if __name__ == '__main__':
main()