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predict_Gnet_c3d.py
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predict_Gnet_c3d.py
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from utils import *
import copy
import numpy as np
class Predict(object):
def build_sampler(self, layers, tparams, options, use_noise, trng):
debug_print = []
#debug_print.append( theano.printing.Print('input_p.shapa')(input_p.shape))
# context: #annotations x dim
ctx0 = T.matrix('ctx_sampler', dtype='float32')
ctx_mask = T.vector('ctx_mask', dtype='float32')
c3d0 = T.matrix('c3d_sampler', dtype='float32')
c3d_mask = T.vector('c3d_mask', dtype='float32')
ctx_ = ctx0
counts = ctx_mask.sum(-1)
ctx_mean = ctx_.sum(0)/counts
ctx_max = ctx_.max(0)
ctx_ = ctx0.dimshuffle('x',0,1)
ctx_input = ctx_mean
c3d_ = c3d0
counts_c3d = c3d_mask.sum(-1)
c3d_mean = c3d_.sum(0)/counts_c3d
c3d_max = c3d_.max(0)
c3d_ = c3d0.dimshuffle('x',0,1)
c3d_input = c3d_mean
lstm_cond_input = T.concatenate([ctx_input,c3d_input],axis=-1)
# initial state/cell
tu_init_state = [T.alloc(0., options['rnn_word_dim'])]
tu_init_memory = [T.alloc(0., options['rnn_word_dim'])]
mu_init_state = [T.alloc(0., options['rnn_cond_wv_dim'])]
mu_init_memory = [T.alloc(0., options['rnn_cond_wv_dim'])]
'''
if options['smoothing'] :
a_init_state = [T.alloc(0., options['latent_size_a'])]
#a_init_memory = [T.alloc(0., options['latent_size_a'])]
else :
a_init_state = None
'''
z_init_state = [T.alloc(0., options['latent_size_z'])]
mu_p_init = [T.alloc(0., options['latent_size_z'])]
print 'Building f_init...',
'''
f_init = theano.function([ctx0, ctx_mask], [ctx0]+tu_init_state+tu_init_memory+
mu_init_state+mu_init_memory+
a_init_state+a_init_memory+
z_init_state+
mu_p_init, name='f_init',
on_unused_input='ignore',
profile=False)
'''
f_init = theano.function([ctx0, ctx_mask, c3d0, c3d_mask], [ctx0]+tu_init_state+tu_init_memory+
mu_init_state+mu_init_memory+
z_init_state+
mu_p_init, name='f_init',
on_unused_input='ignore',
profile=False)
print 'Done'
x = T.vector('x_sampler', dtype='int64')
tu_init_state = [T.matrix('tu_init_state', dtype='float32')]
tu_init_memory = [T.matrix('tu_init_memory', dtype='float32')]
mu_init_state = [T.matrix('mu_init_state', dtype='float32')]
mu_init_memory = [T.matrix('mu_init_memory', dtype='float32')]
'''
if options['smoothing'] :
a_init_state = [T.matrix('a_init_state', dtype='float32')]
#a_init_memory = [T.matrix('a_init_memory', dtype='float32')]
'''
# if it's the first word, emb should be all zero
emb = T.switch(x[:, None] < 0,
T.alloc(0., 1, tparams['Wemb'].shape[1]), tparams['Wemb'][x])
# emb ff
emb_ff1 = layers.get_layer('ff')[1](tparams, emb,activ=options['nonlin_decoder'],
prefix="emb_ff1")
#emb_ff2 = layers.get_layer('ff')[1](tparams, emb_ff1,activ=options['nonlin_decoder'],
# prefix='emb_ff2')
emb_drop = layers.dropout_layer(emb_ff1, use_noise, trng)
tu_gru = layers.get_layer('lstm')[1](options,tparams, emb, one_step=True,
init_state=tu_init_state[0],
init_memory=tu_init_memory[0],
prefix='tu_rnn')
#debug_print.append( theano.printing.Print('mu_init_state.shapa')(mu_init_state.shape))
if options['att_fun'] == None:
mu_gru = layers.get_layer('lstm_cond')[1](options, tparams, tu_gru[0],
mask=None, context=lstm_cond_input,
one_step=True,
init_state=mu_init_state[0],
init_memory=mu_init_memory[0],
trng=trng,
use_noise=use_noise,
prefix='mu_rnn')
else :
mu_gru = layers.get_layer('lstm_att')[1](options, tparams, tu_gru[0],
mask=None, context=ctx_,
one_step=True,
init_state=mu_init_state[0],
init_memory=mu_init_memory[0],
trng=trng,
use_noise=use_noise,
prefix='mu_rnn')
tu_next_state = [tu_gru[0]]
tu_next_memory = [tu_gru[1]]
mu_next_state = [mu_gru[0]]
mu_next_memory = [mu_gru[1]]
proj_h = mu_gru[0]
d_layer = proj_h
if options['use_dropout']:
d_drop_layer = layers.dropout_layer(d_layer, use_noise, trng)
'''
input_a_layer = T.concatenate([d_drop_layer, emb_drop], axis=1)
if options['smoothing']:
a_layer = layers.get_layer('gru_cond')[1](options, tparams, input_a_layer,one_step=True,
init_state=a_init_state[0],context=ctx_input,
prefix='a_rnn')
#a_layer = layers.get_layer('lstm')[1](options, tparams, input_a_layer,one_step=True,
# init_state=a_init_state[0],init_memory=a_init_memory[0]
# prefix='a_rnn')
#a_layer = a_layer[:, ::-1]
a_next_state = [a_layer[0]]
#a_next_memory = [a_layer[1]]
input_a = a_layer[0]
else:
temp_a = layers.get_layer('ff')[1](options, tparams, input_a_layer,
prefix='a_layer_0')
for i in range(options['flat_mlp_num'] - 1):
temp_a = layers.get_layer('ff')[1](options, tparams, temp_a,
prefix='a_layer_' + str(i + 1))
a_layer = temp_a
input_a = a_layer
#debug_print.append( theano.printing.Print('a_layer.shapa')(a_layer.shape))
'''
#################
###stochastic parts####
#################
# Define shared variables for quantities to be updated across batches (truncated BPTT)
z_init = [T.matrix('z', dtype='float32')]
mu_p_init = [T.matrix('mu_p_init', dtype='float32')]
stochastic_layer = layers.stochastic_layer_onestep_noq(options,tparams,
input_p=d_drop_layer,#input_q=input_a,
z_init=z_init[0],mu_p_init=mu_p_init[0],
num_units=options['latent_size_z'],
unroll_scan=options['unroll_scan'],
use_mu_residual_q=options['use_mu_residual_q']
)
z_layer = [stochastic_layer[0]]
mean_prior_layer = [stochastic_layer[1]]
log_var_prior_layer = stochastic_layer[2]
'''
mean_q_layer = stochastic_layer[3]
log_var_q_layer = stochastic_layer[4]
'''
z_dropout_layer = layers.dropout_layer(z_layer[0], use_noise, trng)
'''
z_layer_shp = z_dropout_layer.shape
z_layer_reshaped = z_dropout_layer.reshape([z_layer_shp[0]*z_layer_shp[1],
z_layer_shp[2]])
d_layer_shp = d_drop_layer.shape
d_layer_reshaped = d_drop_layer.reshape([d_layer_shp[0]*d_layer_shp[1],
d_layer_shp[2]])
'''
input_gen_ff = T.concatenate([d_drop_layer, z_dropout_layer], axis=1)
'''
gen_word_emb_ff = layers.get_layer('ff')[1](tparams, input_gen_ff, activ=options['nonlin_decoder'],
prefix='gen_word_emb_ff')
'''
logit = layers.get_layer('ff')[1](tparams, input_gen_ff,
prefix='ff_logit_zd', activ='linear')
# logit_shp = logit.shape
next_probs = T.nnet.softmax(logit)
next_sample = trng.multinomial(pvals=next_probs).argmax(1)
# next word probability
print 'building f_next...'
'''
f_next = theano.function([x, ctx0, ctx_mask]+
tu_init_state+tu_init_memory+
mu_init_state+mu_init_memory+
a_init_state+a_init_memory
z_init+
mu_p_init,
[next_probs, next_sample]+
tu_next_state+tu_next_memory+
mu_next_state+mu_next_memory+
a_next_state+a_next_memory+
z_layer+
mean_prior_layer,
name='f_next', profile=False,
on_unused_input='ignore')
'''
f_next = theano.function([x, ctx0, ctx_mask, c3d0, c3d_mask]+
tu_init_state+tu_init_memory+
mu_init_state+mu_init_memory+
z_init+
mu_p_init,
[next_probs, next_sample]+
tu_next_state+tu_next_memory+
mu_next_state+mu_next_memory+
z_layer+
mean_prior_layer,
name='f_next', profile=False,
on_unused_input='ignore')
print 'Done'
return f_init, f_next
def gen_sample(self, tparams, f_init, f_next, ctx0, ctx_mask, c3d, c3d_mask,
trng=None, k=1, maxlen=30, stochastic=False):
'''
ctx0: (26,1024)
ctx_mask: (26,)
'''
if k > 1:
assert not stochastic, 'Beam search does not support stochastic sampling'
sample = []
z_res = []
p_mean = []
sample_score = []
if stochastic:
sample_score = 0
live_k = 1
dead_k = 0
hyp_samples = [[]] * live_k
hyp_z_res = [[]]* live_k
hyp_p_meam= [[]]* live_k
hyp_scores = np.zeros(live_k).astype('float32')
# [(26,1024),(512,),(512,)]
rval = f_init(ctx0, ctx_mask,c3d, c3d_mask)
ctx0 = rval[0]
# next gru and stacked gru state and memory
next_states = []
next_memorys = []
n_layers_rnn = 2
n_rnn_return = 2
for lidx in xrange(n_layers_rnn):
next_states.append([])
next_memorys.append([])
next_states[lidx].append(rval[n_rnn_return*lidx+1])
next_states[lidx][-1] = next_states[lidx][-1].reshape([live_k, next_states[lidx][-1].shape[0]])
next_memorys[lidx].append(rval[n_rnn_return*lidx+2])
next_memorys[lidx][-1] = next_memorys[lidx][-1].reshape([live_k, next_memorys[lidx][-1].shape[0]])
#print "init gru state shape is ",len(next_states),',',len(next_states[0])
'''
next_a_state = []
next_a_state.append([])
next_a_state[0].append(rval[-3])
next_a_state = []
next_a_state.append([])
next_a_state[0].append(rval[-4])
next_a_memory = []
next_a_memory.append([])
next_a_memory[0].append(rval[-3])
'''
next_z = []
next_z.append([])
next_z[0].append(rval[-2])
next_mu_p = []
next_mu_p.append([])
next_mu_p[0].append(rval[-1])
#print "init next_mu_p shape is ",len(next_mu_p),',',len(next_mu_p[0]),','
next_w = -1 * np.ones((1,)).astype('int64')
# next_state: [(1,512)]
# next_memory: [(1,512)]
for ii in xrange(maxlen):
# return [(1, 50000), (1,), (1, 512), (1, 512)]
# next_w: vector
# ctx: matrix
# ctx_mask: vector
# next_state: [matrix]
# next_memory: [matrix]
#print "next_states ", len(next_states),',',len(next_states[1]),',',len(next_states[1][0]),',',len(next_states[1][0][0])
rval = f_next(*([next_w, ctx0, ctx_mask, c3d, c3d_mask] +
next_states[0] + next_memorys[0] +
next_states[1] + next_memorys[1] +
next_z +
next_mu_p))
next_p = rval[0]
next_w = rval[1] # already argmax sorted
next_states = []
next_memorys = []
for lidx in xrange(n_layers_rnn):
next_states.append([])
next_memorys.append([])
next_states[lidx].append(rval[n_rnn_return*lidx+2])
next_memorys[lidx].append(rval[n_rnn_return*lidx+3])
#print "gru state is ", len(next_states),',',len(next_states[0]),',',len(next_states[0][0])
'''
next_a_state = [rval[-3]]
next_a_state = [rval[-4]]
next_a_memory = [rval[-3]]
'''
next_z = [rval[-2]]
next_mu_p = [rval[-1]]
#print "init next_a shape is ",len(next_a),',',len(next_a[0]),','
#print "init next_mu_p shape is ",len(next_mu_p),',',len(next_mu_p[0]),','
if stochastic:
sample.append(next_w[0]) # take the most likely one
sample_score += next_p[0,next_w[0]]
z_layer.append(next_z[0])
if next_w[0] == 0:
break
else:
# the first run is (1,50000)
cand_scores = hyp_scores[:,None] - np.log(next_p)
cand_flat = cand_scores.flatten()
ranks_flat = cand_flat.argsort()[:(k-dead_k)]
voc_size = next_p.shape[1]
trans_indices = ranks_flat / voc_size # index of row
word_indices = ranks_flat % voc_size # index of col
costs = cand_flat[ranks_flat]
new_hyp_samples = []
new_hyp_scores = np.zeros(k-dead_k).astype('float32')
new_hyp_z_res = []
new_hyp_p_mean=[]
new_hyp_states = []
new_hyp_memories = []
#new_hyp_a_state = []
#new_hyp_a_state.append([])
#new_hyp_a_memory = []
#new_hyp_a_memory.append([])
new_hyp_z = []
new_hyp_z.append([])
new_hyp_mu_p = []
new_hyp_mu_p.append([])
for lidx in xrange(n_layers_rnn):
new_hyp_states.append([])
new_hyp_memories.append([])
for idx, [ti, wi] in enumerate(zip(trans_indices, word_indices)):
new_hyp_samples.append(hyp_samples[ti]+[wi])
new_hyp_z_res.append(hyp_z_res[ti]+[next_z[0][ti]])
new_hyp_p_mean.append(hyp_p_meam[ti]+[next_mu_p[0][ti]])
new_hyp_scores[idx] = copy.copy(costs[idx])
for lidx in np.arange(n_layers_rnn):
new_hyp_states[lidx].append(copy.copy(next_states[lidx][0][ti]))
new_hyp_memories[lidx].append(copy.copy(next_memorys[lidx][0][ti]))
#new_hyp_a_state[0].append( copy.copy(next_a_state[0][ti]))
#new_hyp_a_memory[0].append( copy.copy(next_a_memory[0][ti]))
new_hyp_z[0].append(copy.copy(next_z[0][ti]))
new_hyp_mu_p[0].append(copy.copy(next_mu_p[0][ti]))
#print "init new_hyp_states shape is ",len(new_hyp_states),',',len(new_hyp_states[0]),','
#print "init new_hyp_mu_p shape is ",len(new_hyp_mu_p),',',len(new_hyp_mu_p[0]),','
# check the finished samples
new_live_k = 0
hyp_samples = []
hyp_z_res = []
hyp_p_meam = []
hyp_scores = []
hyp_states = []
hyp_a_state = []
hyp_a_state.append([])
hyp_a_memory = []
hyp_a_memory.append([])
hyp_z = []
hyp_z.append([])
hyp_mu_p = []
hyp_mu_p.append([])
hyp_memories = []
for lidx in xrange(n_layers_rnn):
hyp_states.append([])
hyp_memories.append([])
for idx in xrange(len(new_hyp_samples)):
if new_hyp_samples[idx][-1] == 0:
sample.append(new_hyp_samples[idx])
sample_score.append(new_hyp_scores[idx])
z_res.append(new_hyp_z_res[idx])
p_mean.append(new_hyp_p_mean[idx])
dead_k += 1
else:
new_live_k += 1
hyp_samples.append(new_hyp_samples[idx])
hyp_z_res.append(new_hyp_z_res[idx])
hyp_p_meam.append(new_hyp_p_mean[idx])
hyp_scores.append(new_hyp_scores[idx])
for lidx in xrange(n_layers_rnn):
hyp_states[lidx].append(new_hyp_states[lidx][idx])
hyp_memories[lidx].append(new_hyp_memories[lidx][idx])
#hyp_a_state[0].append(new_hyp_a_state[0][idx])
#hyp_a_memory[0].append(new_hyp_a_memory[0][idx])
hyp_z[0].append(new_hyp_z[0][idx])
hyp_mu_p[0].append(new_hyp_mu_p[0][idx])
#print "init hyp_states shape is ",len(hyp_states),',',len(hyp_states[0]),','
#print "init hyp_mu_p shape is ",len(hyp_mu_p),',',len(hyp_mu_p[0]),','
hyp_scores = np.array(hyp_scores)
live_k = new_live_k
if new_live_k < 1:
break
if dead_k >= k:
break
next_w = np.array([w[-1] for w in hyp_samples])
next_states = []
next_memorys = []
for lidx in xrange(n_layers_rnn):
next_states.append([])
next_memorys.append([])
next_states[lidx].append(np.array(hyp_states[lidx]))
next_memorys[lidx].append(np.array(hyp_memories[lidx]))
#next_a_state=hyp_a_state
#next_a_memory=hyp_a_memory
next_z = hyp_z
#z_layer.append(next_z)
next_mu_p = hyp_mu_p
#print "init next_states shape is ",len(next_states),',',len(next_states[0]),',',len(next_states[0][0])
#print "init next_mu_p shape is ",len(next_mu_p),',',len(next_mu_p[0]),','
if not stochastic:
# dump every remaining one
if live_k > 0:
for idx in xrange(live_k):
sample.append(hyp_samples[idx])
sample_score.append(hyp_scores[idx])
z_res.append([hyp_z[0][idx]])
p_mean.append([hyp_mu_p[0][idx]])
return sample, sample_score, next_states, z_res,p_mean
def sample_execute(self, engine, options, tparams, f_init, f_next, x, ctx, mask_ctx, c3d, c3d_mask, trng):
stochastic = False
for jj in xrange(np.minimum(10, x.shape[1])):
sample, score, _,_,_ = self.gen_sample(tparams, f_init, f_next, ctx[jj], mask_ctx[jj], c3d[jj], c3d_mask[jj],
trng=trng, k=5, maxlen=30, stochastic=stochastic)
if not stochastic:
best_one = np.argmin(score)
sample = sample[best_one]
else:
sample = sample
print 'Truth ', jj, ': ',
for vv in x[:, jj]:
if vv == 0:
break
if vv in engine.ix_word:
print engine.ix_word[vv],
else:
print 'UNK',
print
for kk, ss in enumerate([sample]):
print 'Sample (', jj, ') ', ': ',
for vv in ss:
if vv == 0:
break
if vv in engine.ix_word:
print engine.ix_word[vv],
else:
print 'UNK',
print