/
train_tdd_hmdb.py
132 lines (106 loc) · 5.4 KB
/
train_tdd_hmdb.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
# ========= STD Libs ============
from __future__ import division
from collections import OrderedDict
import os
import shutil
import sys
import logging
#import ipdb
import cPickle
import argparse
import time
sys.setrecursionlimit(100000000)
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# ========= Theano/npy ===========
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import numpy as np
import fuel
from fuel.streams import DataStream, ServerDataStream
from fuel.schemes import SequentialScheme
# ========= Tools ==============
from main_loop import MainLoop
from extensions import Printing, FinishAfter, TrackBest, Track, Report
from extensions.timing import TimeProfile
from extensions.plot import PlotLocal, Plot
from extensions.data_monitor import DataStreamTrack
from extensions.save_model import SaveModel, SaveBestModel
import importlib
def create_config(name, max_time, feature_dim, filename, args, model, lr=0.0001):
return {'name':name, 'max_time':max_time, 'feature_dim':feature_dim, 'filename':filename, 'model_kwargs':args, 'model':model, 'lr':lr}
def run():
report = file('report-hmdb-tdd.txt', 'w')
max_time = 200
configs = []
cc = create_config
for d in ['1','2','3']:
configs.append(cc('tdd-max-pool-h-4000 '+d, max_time, 4000, 'hmdb-tdd-1.hdf5', {'method':'max', 'hidden_size':4000}, 'hidden_2_layer_model', 0.0001))
configs.append(cc('tdd-mean-pool-h-4000 '+d, max_time, 4000, 'hmdb-tdd.hdf5', {'method':'mean', 'hidden_size':4000}, 'hidden_2_layer_model', 0.0001))
configs.append(cc('tdd-sum-pool-h-4000 '+d, max_time, 4000, 'hmdb-tdd.hdf5', {'method':'sum', 'hidden_size':4000}, 'hidden_2_layer_model', 0.0005))
configs.append(cc('tdd-spyramid-1-h-1000', max_time, 4000, 'hmdb-tdd.hdf5', {'levels':1, 'hidden_size':1000}, 'temporal_pyramid_model'))
configs.append(cc('tdd-spyramid-4-h-4000 '+d, max_time, 4000, '/ssd2/hmdb/hmdb-tdd.hdf5', {'levels':4, 'hidden_size':4000}, 'temporal_pyramid_model', 0.0001))
for d in ['1','2','3']:
for model in ['temporal_learned_model']:
s = s+' split='+d
for num_f in [3]:
configs.append(cc('tdd-pyramid-1-N-'+str(num_f)+'-h-1000'+s, max_time, 4000, 'hmdb-tdd.hdf5', {'levels':1, 'hidden_size':1000, 'N':num_f}, model, 0.05))
for config in configs:
name = config['name']
epochs = 250
subdir = name + "-" + time.strftime("%Y%m%d-%H%M%S")
if not os.path.isdir(subdir):
os.mkdir(subdir)
bs = 100#int(sys.argv[1])
max_time = config['max_time']#int(sys.argv[2])
feature_dim = config['feature_dim']#int(sys.argv[3])
from uniform_dataset import UniformDataset
data_train = UniformDataset(bs=bs, filename=config['filename'], which_sets=['train'], sources=['features','time_mask','labels'])
data_test = UniformDataset(bs=bs, filename=config['filename'], which_sets=['test'], sources=['features','time_mask','labels'])
train_stream = DataStream.default_stream(data_train, iteration_scheme=SequentialScheme(data_train.num_examples, bs))
test_stream = DataStream.default_stream(data_test, iteration_scheme=SequentialScheme(data_test.num_examples, bs))
x = T.tensor3('features')
time_mask = T.wmatrix('time_mask')
y = T.imatrix('labels')
mod = importlib.import_module(config['model'])
classes = 51
model = mod.TemporalModel([x,time_mask,y], bs, max_time, classes, feature_dim, **config['model_kwargs'])
prob, pred, loss, error, acc = model.run(x,time_mask,y)
prob.name = 'prob'
acc.name = 'acc'
pred.name = 'pred'
loss.name = 'loss'
error.name = 'error'
model._outputs = [prob, pred, loss, error, acc]
params = model.params
# from solvers.sgd import SGD as solver
from solvers.RMSProp import RMSProp as solver
updates = solver(loss, params, lr=config['lr'], clipnorm=10.0)
for i,u in enumerate(updates):
if u[0].name == 'g' or u[0].name == 'sigma' or u[0].name == 'd':
updates[i] = (u[0], T.mean(u[1]).dimshuffle(['x']))
model._updates = updates
# ============= TRAIN =========
plots = [['train_loss','test_loss'],['train_acc','test_acc']]
main_loop = MainLoop(model, train_stream,
[FinishAfter(epochs),
Track(variables=['loss','error','acc'], prefix='train'),
DataStreamTrack(test_stream, ['loss','error','acc'], prefix='test', best_method=[min,min,max]),
#SaveModel(subdir, name+'.model'),
TimeProfile(),
Report(os.path.join(subdir, 'report.txt'), name=name),
Printing()])
main_loop.run()
config['best_acc'] = main_loop.log.current_row['best_test_acc']
print >> report, config['name'], 'best test acc', config['best_acc']
report.flush()
print ''.join(79 * '-')
print 'FINAL REPORT'
print ''.join(79 * '-')
for config in configs:
print config['name'], 'best test acc',config['best_acc']
if __name__ == '__main__':
#theano.config.optimizer='fast_compile'
run()