-
Notifications
You must be signed in to change notification settings - Fork 2
/
evaluate_mrr.py
287 lines (245 loc) · 13 KB
/
evaluate_mrr.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
from __future__ import print_function
import argparse
import os
import random
import sys
sys.path.append(os.getcwd())
import pdb
import time
import numpy as np
import datetime
from tensorboardX import SummaryWriter
import copy
import torch
import torch.optim as optim
from utils.tools import repackage_hidden, decode_txt, sample_batch_neg, create_joint_seq, \
create_joint_seq_gt, reverse_padding, load_my_state_dict, compute_perplexity_batch
from utils.create_dataloader import create_dataloader
import networks.model as model
from networks.encoder_QIH import _netE
from networks.netG import _netG
from networks.encoder_QBot import _netE as _netQE
from arguments import get_args
def evaluate():
n_neg = opt.negative_sample
ques_hidden1 = abots[0][0].init_hidden(opt.batch_size)
hist_hidden1 = abots[0][0].init_hidden(opt.batch_size)
hist_hiddenQ = qbots[0][0].init_hidden(opt.batch_size)
fact_hiddenQ = qbots[0][0].init_hidden(opt.batch_size)
data_iter = iter(dataloader_val)
imgs = dataset.imgs
iteration = 0
global img_input
prev_time = time.time()
mean_rank = np.zeros((len(dataloader_val)*opt.batch_size,11))
imgs_tensor = torch.FloatTensor(imgs)
imgs_tensor = imgs_tensor.cuda()
#Used to track metrics
mean_ranks = torch.zeros(10)
r1 = torch.zeros(10)
r5 = torch.zeros(10)
r10 = torch.zeros(10)
mean_rec_ranks = torch.zeros(10)
q_perplex = torch.zeros(10)
a_perplex = torch.zeros(10)
n_elts = 0
num_samples = 0
while iteration < len(dataloader_val):
embed_array = []
data = data_iter.next()
img_ids, img_dir, image_j, image, history, question, answer, answerT, answerLen, answerIdx, questionL, \
opt_answerT, opt_answerLen, opt_answerIdx, question_input, question_target, facts, cap = data # image is 100x7x7x512, cap is 100x24
batch_size = question.size(0)
img_input1.data.resize_(image_j.size()).copy_(image_j)
img_input.data.resize_(image.size()).copy_(image)
cap = cap.t()
cap_input.data.resize_(cap.size()).copy_(cap) # 24x100
arr_reward = []
save_tmp = [[] for j in range(batch_size)]
cap_sample_txt = decode_txt(itow, cap)
for j in range(batch_size):
save_tmp[j].append({'caption':cap_sample_txt[j], 'img_ids': img_ids[j], 'img_dir':img_dir[j]})
his = history[:,:1,:].clone().view(-1, his_length).t()
fact = facts[:,0,:].t()
his_input.data.resize_(his.size()).copy_(his)
fact_input.data.resize_(fact.size()).copy_(fact)
his_embQ = qbots[0][1](his_input, format = 'index')
fact_embQ = qbots[0][1](fact_input, format = 'index')
his_emb_g = abots[0][1](his_input, format = 'index')
ques_hidden1 = repackage_hidden(ques_hidden1, batch_size)
hist_hidden1 = repackage_hidden(hist_hidden1, his_emb_g.size(1))
fact_hiddenQ = repackage_hidden(fact_hiddenQ, fact_input.size(1))
hist_hiddenQ = repackage_hidden(hist_hiddenQ, his_input.size(1))
encoder_featQ,img_embedQ,fact_hiddenQ = qbots[0][0](fact_embQ,his_embQ,\
fact_hiddenQ, hist_hiddenQ, 1)
embed_array.append(img_embedQ)
num_samples += batch_size
n_elts += 1
for rnd in range(0,10):
sample_opt = {'beam_size':1, 'seq_length':17, 'sample_max':0}
_,fact_hiddenQ = qbots[0][2](encoder_featQ.view(1,-1,opt.ninp), fact_hiddenQ)
UNK_ques_input.data.resize_((1, batch_size)).fill_(vocab_size) # 1x100
Q,logprobQ = qbots[0][2].sample_differentiable(qbots[0][1],UNK_ques_input,fact_hiddenQ, sample_opt) # Q is 100x16, same logprobQ
sample_opt_per = {'beam_size':1, 'seq_length':17, 'sample_max':1}
Q_per,logprobQ_per = qbots[0][2].sample_differentiable(qbots[0][1],UNK_ques_input,fact_hiddenQ, sample_opt_per) # Q is 100x16, same logprobQ
q_perplex[rnd] += compute_perplexity_batch(logprobQ_per.cpu(), is_log=True).item()
Q = Q.data.t()
Q = Q[:-1]
Q_idx_search = (Q==vocab_size)
_, Q_idx_search = torch.max(Q_idx_search,dim=0)
Q_rev = reverse_padding(Q.t(), Q_idx_search.squeeze()).t()
ques_emb_g = abots[0][1](Q_rev, format = 'index') # 16x100x300
featG,ques_hidden1 = abots[0][0](ques_emb_g,his_emb_g,img_input,\
ques_hidden1, hist_hidden1, rnd+1)
# featG is float 100x300, ques_hidden1 is tuple of 2 1x100x512 floats
_,ques_hidden1 = abots[0][2](featG.view(1,-1,opt.ninp),ques_hidden1)
UNK_ans_input.data.resize_((1, batch_size)).fill_(vocab_size)
sample_opt = {'beam_size':1, 'seq_length':9, 'sample_max':0}
A, logprobsA = abots[0][2].sample_differentiable(abots[0][1], UNK_ans_input, ques_hidden1, sample_opt) # 100x9,100x9
sample_opt_per = {'beam_size':1, 'seq_length':9, 'sample_max':1}
A_per, logprobsA_per = abots[0][2].sample_differentiable(abots[0][1], UNK_ans_input, ques_hidden1, sample_opt_per) # 100x9,100x9
a_perplex[rnd] += compute_perplexity_batch(logprobsA_per.cpu(), is_log=True).item()
mrank, m_rec, batch_r1, batch_r5, batch_r10 = abots[0][2].sample_opt_eval(abots[0][1], UNK_ans_input, ques_hidden1, answerT, opt_answerT, rnd, sample_opt) # 100x9,100x9
mean_ranks[rnd] += mrank
mean_rec_ranks[rnd] += m_rec
r1[rnd] += batch_r1.float()
r5[rnd] += batch_r5.float()
r10[rnd] += batch_r10.float()
A = A.data.t()
A = A[:-1]
A_idx_search = (A==vocab_size)
_, A_idx_search = torch.max(A_idx_search,dim=0)
QA = create_joint_seq(Q.t(), A.t(), Q_idx_search.squeeze() , A_idx_search.squeeze()).t()
fact_embA = abots[0][1](QA,format='index') # float 25x100x300
fact_embQ = qbots[0][1](QA,format='index') # float 25x100x300
# do this concatenation properly by having all non-UNK tokens first followed by UNKs
his_embQ = torch.cat((his_embQ.view(his_length, batch_size, -1, opt.ninp),fact_embQ.view(his_length, batch_size, 1, opt.ninp)),dim=2).view(his_length,-1,opt.ninp) # float <N>x100x300
his_emb_g = torch.cat((his_emb_g.view(his_length, batch_size, -1, opt.ninp),fact_embA.view(his_length, batch_size, 1, opt.ninp)),dim=2).view(his_length,-1,opt.ninp) # float <N2>x100x300
ques_hidden1 = repackage_hidden(ques_hidden1, batch_size)
hist_hidden1 = repackage_hidden(hist_hidden1, his_emb_g.size(1))
fact_hiddenQ = repackage_hidden(fact_hiddenQ, fact_embQ.size(1))
hist_hiddenQ = repackage_hidden(hist_hiddenQ, his_emb_g.size(1))
encoder_featQ,img_embedQ,fact_hiddenQ = qbots[0][0](fact_embQ,his_embQ,\
fact_hiddenQ, hist_hiddenQ, rnd+2)
embed_array.append(img_embedQ)
ans_sample_txt = decode_txt(itow, A)
ques_sample_txt = decode_txt(itow, Q)
iteration+=1
if iteration%2==0:
print("Done with Batch # {} | Av. Time Per Batch: {:.3f}s".format(iteration,(time.time()-prev_time)/20))
prev_time = time.time()
mean_rec_rank_final = mean_rec_ranks/ float(n_elts)
mean_rank_final = mean_ranks/ float(n_elts)
print("MRR: ",mean_rec_rank_final,torch.mean(mean_rec_rank_final))
print("mean_rank",mean_rank_final,torch.mean(mean_rank_final))
mean_rank_final = mean_ranks/ float(n_elts)
mean_rec_rank_final = mean_rec_ranks/ float(n_elts)
r1_final = 100*r1/float(num_samples)
r5_final = 100*r5/float(num_samples)
r10_final = 100*r10/float(num_samples)
np.save('mean_rank'+str(opt.num_qbots)+str(opt.num_abots),mean_rank_final.cpu().numpy())
np.save('MRR'+str(opt.num_qbots)+str(opt.num_abots),mean_rec_rank_final.cpu().numpy())
np.save('r1'+str(opt.num_qbots)+str(opt.num_abots),r1_final.cpu().numpy())
np.save('r5'+str(opt.num_qbots)+str(opt.num_abots),r5_final.cpu().numpy())
np.save('r10'+str(opt.num_qbots)+str(opt.num_abots),r10_final.cpu().numpy())
return
##############################
# Main Code Execution Starts Here
##############################
opt = get_args()
opt.manualSeed = random.randint(1, 10000)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
np.random.seed(opt.manualSeed)
if opt.cuda:
torch.cuda.manual_seed_all(opt.manualSeed)
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
t = datetime.datetime.now()
cur_time = '%s-%s-%s' %(t.day, t.month, t.hour)
save_path = os.path.join(opt.outf, cur_time)
try:
os.makedirs(save_path)
except OSError:
pass
dataset,dataset_val,dataloader,dataloader_val = create_dataloader(opt)
writer = SummaryWriter(save_path)
vocab_size = dataset.vocab_size
ques_length = dataset.ques_length
ans_length = dataset.ans_length + 1
his_length = dataset.ans_length + dataset.ques_length
cap_length = dataset.cap_length
itow = dataset.itow
qbots = []
abots = []
print('Initializing A-Bot and Q-Bot...')
for j in range(opt.num_abots):
abots.append((_netE(opt.model, opt.ninp, opt.nhid, opt.nlayers, opt.dropout, opt.img_feat_size),
model._netW(vocab_size, opt.ninp, opt.dropout),
_netG(opt.model, vocab_size, opt.ninp, opt.nhid, opt.nlayers, opt.dropout)))
for j in range(opt.num_qbots):
qbots.append((_netQE(opt.model, opt.ninp, opt.nhid, opt.nlayers, opt.dropout),
model._netW(vocab_size, opt.ninp, opt.dropout),
_netG(opt.model, vocab_size, opt.ninp, opt.nhid, opt.nlayers, opt.dropout)))
critLM = model.LMCriterion()
critImg = torch.nn.MSELoss()
if opt.model_path != '':
print("=> loading checkpoint '{}'".format(opt.model_path))
checkpoint = torch.load(opt.model_path, map_location=lambda storage, loc: storage)
if opt.scratch:
pass
elif opt.curr:
print('Loading A and Q-Bots from SL')
for j in range(opt.num_abots):
load_my_state_dict(abots[j][1],checkpoint['netWA'])
load_my_state_dict(abots[j][0],checkpoint['netEA'])
load_my_state_dict(abots[j][2],checkpoint['netGA'])
for j in range(opt.num_qbots):
load_my_state_dict(qbots[j][1],checkpoint['netWQ'])
load_my_state_dict(qbots[j][0],checkpoint['netEQ'])
load_my_state_dict(qbots[j][2],checkpoint['netGQ'])
else:
print('Loading A and Q-Bots from RL')
for j in range(opt.num_abots):
load_my_state_dict(abots[j][1],checkpoint['netWA'+str(j)])
load_my_state_dict(abots[j][0],checkpoint['netEA'+str(j)])
load_my_state_dict(abots[j][2],checkpoint['netGA'+str(j)])
for j in range(opt.num_qbots):
load_my_state_dict(qbots[j][1],checkpoint['netWQ'+str(j)])
load_my_state_dict(qbots[j][0],checkpoint['netEQ'+str(j)])
load_my_state_dict(qbots[j][2],checkpoint['netGQ'+str(j)])
else:
assert not opt.eval and opt.scratch, "Must specify model files if not starting evaluating or training from scratch"
if opt.cuda: # ship to cuda, if has GPU
for k in range(opt.num_abots):
abots[k][0].cuda(),abots[k][1].cuda(),abots[k][2].cuda()
for k in range(opt.num_qbots):
qbots[k][0].cuda(),qbots[k][1].cuda(),qbots[k][2].cuda()
critLM.cuda(), critImg.cuda()
################
img_input = torch.FloatTensor(opt.batch_size).requires_grad_()
img_input1 = torch.FloatTensor(opt.batch_size).requires_grad_()
cap_input = torch.LongTensor(opt.batch_size).requires_grad_()
ques = torch.LongTensor(ques_length, opt.batch_size).requires_grad_()
ques_input = torch.LongTensor(ques_length, opt.batch_size).requires_grad_()
his_input = torch.LongTensor(his_length, opt.batch_size).requires_grad_()
fact_input = torch.LongTensor(ques_length+ans_length,opt.batch_size).requires_grad_()
ans_input = torch.LongTensor(ans_length, opt.batch_size).requires_grad_()
ans_target = torch.LongTensor(ans_length, opt.batch_size).requires_grad_()
wrong_ans_input = torch.LongTensor(ans_length, opt.batch_size).requires_grad_()
UNK_ans_input = torch.LongTensor(1, opt.batch_size).requires_grad_()
UNK_ques_input = torch.LongTensor(1, opt.batch_size).requires_grad_()
ques_target = torch.LongTensor(ques_length,opt.batch_size).requires_grad_()
if opt.cuda:
ques, ques_input, his_input, img_input, img_input1, cap_input = ques.cuda(), ques_input.cuda(), his_input.cuda(), img_input.cuda(), img_input.cuda(), cap_input.cuda()
ans_input, ans_target = ans_input.cuda(), ans_target.cuda()
ques_target = ques_target.cuda()
UNK_ans_input = UNK_ans_input.cuda()
UNK_ques_input = UNK_ques_input.cuda()
fact_input = fact_input.cuda()
##################
for k in range(opt.num_abots):
abots[k][0].eval(),abots[k][1].eval(),abots[k][2].eval()
for k in range(opt.num_qbots):
qbots[k][0].eval(),qbots[k][1].eval(),qbots[k][2].eval()
evaluate()