-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathseqskip_train_seq1H_halfv2.py
348 lines (301 loc) · 15 KB
/
seqskip_train_seq1H_halfv2.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 11 00:45:08 2018
seq1H: seqeunce learning model
- q(x), l(x)
- non-autoregressive (not feeding predicted labels)
- instance Norm.
- G: GLU version
- H: Highway-net version
@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_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_seq1H_halfv2/")
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= 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("-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()
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],
h_k_szs=[2,2,5,1,1],
h_dils=[1,2,4,1,1],
# 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(SeqFeatEnc, self).__init__()
h_io_chs[:] = [n * e_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],
# h_k_szs=[2,3,2,2,1,1],
# h_dils=[1,2,5,10,1,1],
h_io_chs=[1,1,1,1,1],
h_k_szs=[2,2,5,1,1],
h_dils=[1,2,4,1,1],
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,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)
def forward(self, x):
x = self.enc(x)
return self.clf(x)
#%%
def validate(mval_loader, SM, 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[:,10:,:41] = 0 # DELETE METALOG QUE
if USE_SUPLOG is False:
x = x[:,:,41:] # bx20*70(29)
labels_shift = torch.zeros(batch_sz,20,1)
labels_shift[:,1:,0] = labels[:,:-1].float()
labels_shift[:,11:,0] = 0 # REMOVE QUERY LABELS!
sq_state = torch.zeros(batch_sz,20,1)
sq_state[:,:11,0] = 1
# x: bx72(31)*20
x = torch.cat((x, labels_shift, sq_state), dim=2).permute(0,2,1).cuda(GPU)
if USE_PRED_LABEL is True:
# Predict
li = 70 if USE_SUPLOG is True else 29 # the label's dimension indice
_x = x[:,:,:11] # bx72*11
for q in range(11,20):
y_hat = 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[:,-1])
y_hat = SM(Variable(_x, requires_grad=False)) # y_hat(final): bx20
del _x
else:
y_hat = SM(x)
# Calcultate BCE loss
loss = F.binary_cross_entropy_with_logits(input=y_hat[:,10:]*y_mask[:,10:].cuda(GPU),
target=labels[:,10:].cuda(GPU)*y_mask[:,10:].cuda(GPU))
total_vloss += loss.item()
# Decision
y_prob = torch.sigmoid(y_hat*y_mask.cuda(GPU)).detach().cpu().numpy() # bx20
y_pred = (y_prob[:,10:]>=0.5).astype(np.int) # bx10
y_numpy = labels[:,10:].numpy() # bx10
# Acc
y_query_mask = y_mask[:,10:].numpy()
total_vcorrects += np.sum((y_pred==y_numpy)*y_query_mask)
total_vquery += np.sum(num_query)
# 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,:num_support[0]].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,loss.item(), total_vcorrects/total_vquery))
del loss, y_hat, x # Restore GPU memory
# Avg.Acc
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/val_session)
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, 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=0.7)
# 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 = 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: the first 10 items out of 20 are support items left-padded with zeros. The last 10 are queries right-padded.
if USE_SUPLOG is False:
x = x[:,:,:30] # bx20*70(30)
x[:,10:,:41] = 0 # DELETE METALOG QUE
# labels_shift: (model can only observe past labels)
labels_shift = torch.zeros(batch_sz,20,1)
labels_shift[:,1:,0] = labels[:,:-1].float()
#!!! NOLABEL for previous QUERY
labels_shift[:,11:,0] = 0
# support/query state labels
sq_state = torch.zeros(batch_sz,20,1)
sq_state[:,:11,0] = 1
# Pack x: bx72*20 (or bx32*20 if not using sup_logs)
x = Variable(torch.cat((x, labels_shift, sq_state), 2).permute(0,2,1)).cuda(GPU) # x: bx72*20
# Forward & update
y_hat = SM(x) # y_hat: b*20
# Calcultate BCE loss
# HALF
loss = F.binary_cross_entropy_with_logits(input=y_hat[:,10:]*y_mask[:,10:].cuda(GPU),
target=labels[:,10:].cuda(GPU)*y_mask[:,10:].cuda(GPU))
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*y_mask.cuda(GPU)).detach().cpu().numpy() # bx20
y_pred = (y_prob[:,10:]>=0.5).astype(np.int) # bx10
y_numpy = labels[:,10:].numpy() # bx10
# Acc
y_query_mask = y_mask[:,10:].numpy()
total_corrects += np.sum((y_pred==y_numpy)*y_query_mask)
total_query += np.sum(num_query)
# Restore GPU memory
del loss, y_hat
if (session+1)%500 == 0:
hist_trloss.append(total_trloss/900)
hist_tracc.append(total_corrects/total_query)
# Prepare display
sample_sup = labels[0,:num_support[0]].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:{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)%20000 == 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_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_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()