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attention.py
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attention.py
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from PIL import Image
import tensorflow as tf
import tflib
import tflib.ops
import tflib.network
from tqdm import tqdm
import numpy as np
import data_loaders
import time
import os
BATCH_SIZE = 20
EMB_DIM = 80
ENC_DIM = 256
DEC_DIM = ENC_DIM*2
NUM_FEATS_START = 64
D = NUM_FEATS_START*8
V = 502
NB_EPOCHS = 50
H = 20
W = 50
# with tf.device("/cpu:0"):
# custom_runner = data_loaders.CustomRunner()
# X,seqs,mask,reset = custom_runner.get_inputs()
#
# print X,seqs
X = tf.placeholder(shape=(None,None,None,None),dtype=tf.float32)
mask = tf.placeholder(shape=(None,None),dtype=tf.int32)
seqs = tf.placeholder(shape=(None,None),dtype=tf.int32)
learn_rate = tf.placeholder(tf.float32)
input_seqs = seqs[:,:-1]
target_seqs = seqs[:,1:]
emb_seqs = tflib.ops.Embedding('Embedding',V,EMB_DIM,input_seqs)
ctx = tflib.network.im2latex_cnn(X,NUM_FEATS_START,True)
out,state = tflib.ops.FreeRunIm2LatexAttention('AttLSTM',emb_seqs,ctx,EMB_DIM,ENC_DIM,DEC_DIM,D,H,W)
logits = tflib.ops.Linear('MLP.1',out,DEC_DIM,V)
predictions = tf.argmax(tf.nn.softmax(logits[:,-1]),axis=1)
loss = tf.reshape(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=tf.reshape(logits,[-1,V]),
labels=tf.reshape(seqs[:,1:],[-1])
), [tf.shape(X)[0], -1])
mask_mult = tf.to_float(mask[:,1:])
loss = tf.reduce_sum(loss*mask_mult)/tf.reduce_sum(mask_mult)
#train_step = tf.train.AdamOptimizer(1e-2).minimize(loss)
optimizer = tf.train.GradientDescentOptimizer(learn_rate)
gvs = optimizer.compute_gradients(loss)
capped_gvs = [(tf.clip_by_norm(grad, 5.), var) for grad, var in gvs]
train_step = optimizer.apply_gradients(capped_gvs)
def predict(set='test',batch_size=1,visualize=True):
if visualize:
assert (batch_size==1), "Batch size should be 1 for visualize mode"
import random
# f = np.load('train_list_buckets.npy').tolist()
f = np.load(set+'_buckets.npy').tolist()
random_key = random.choice(f.keys())
#random_key = (160,40)
f = f[random_key]
imgs = []
print "Image shape: ",random_key
while len(imgs)!=batch_size:
start = np.random.randint(0,len(f),1)[0]
if os.path.exists('./images_processed/'+f[start][0]):
imgs.append(np.asarray(Image.open('./images_processed/'+f[start][0]).convert('YCbCr'))[:,:,0][:,:,None])
imgs = np.asarray(imgs,dtype=np.float32).transpose(0,3,1,2)
inp_seqs = np.zeros((batch_size,160)).astype('int32')
print imgs.shape
inp_seqs[:,0] = np.load('properties.npy').tolist()['char_to_idx']['#START']
tflib.ops.ctx_vector = []
l_size = random_key[0]*2
r_size = random_key[1]*2
inp_image = Image.fromarray(imgs[0][0]).resize((l_size,r_size))
l = int(np.ceil(random_key[1]/8.))
r = int(np.ceil(random_key[0]/8.))
properties = np.load('properties.npy').tolist()
idx_to_chars = lambda Y: ' '.join(map(lambda x: properties['idx_to_char'][x],Y))
for i in xrange(1,160):
inp_seqs[:,i] = sess.run(predictions,feed_dict={X:imgs,input_seqs:inp_seqs[:,:i]})
#print i,inp_seqs[:,i]
if visualize==True:
att = sorted(list(enumerate(tflib.ops.ctx_vector[-1].flatten())),key=lambda tup:tup[1],reverse=True)
idxs,att = zip(*att)
j=1
while sum(att[:j])<0.9:
j+=1
positions = idxs[:j]
print "Attention weights: ",att[:j]
positions = [(pos/r,pos%r) for pos in positions]
outarray = np.ones((l,r))*255.
for loc in positions:
outarray[loc] = 0.
out_image = Image.fromarray(outarray).resize((l_size,r_size),Image.NEAREST)
print "Latex sequence: ",idx_to_chars(inp_seqs[0,:i])
outp = Image.blend(inp_image.convert('RGBA'),out_image.convert('RGBA'),0.5)
outp.show(title=properties['idx_to_char'][inp_seqs[0,i]])
# raw_input()
time.sleep(3)
os.system('pkill display')
np.save('pred_imgs',imgs)
np.save('pred_latex',inp_seqs)
print "Saved npy files! Use Predict.ipynb to view results"
return inp_seqs
def score(set='valid',batch_size=32):
score_itr = data_loaders.data_iterator(set,batch_size)
losses = []
start = time.time()
for score_imgs,score_seqs,score_mask in score_itr:
_loss = sess.run(loss,feed_dict={X:score_imgs,seqs:score_seqs,mask:score_mask})
losses.append(_loss)
print _loss
set_loss = np.mean(losses)
perp = np.mean(map(lambda x: np.power(np.e,x), losses))
print "\tMean %s Loss: ", set_loss
print "\tTotal %s Time: ", time.time()-start
print "\tMean %s Perplexity: ", perp
return set_loss, perp
sess = tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=8))
init = tf.global_variables_initializer()
# init = tf.initialize_all_variables()
sess.run(init)
saver = tf.train.Saver()
# saver.restore(sess,'./weights_best.ckpt')
## start the tensorflow QueueRunner's
# tf.train.start_queue_runners(sess=sess)
## start our custom queue runner's threads
# custom_runner.start_threads(sess)
losses = []
times = []
print "Compiled Train function!"
## Test is train func runs
# train_fn(np.random.randn(32,1,128,256).astype('float32'),np.random.randint(0,107,(32,50)).astype('int32'),np.random.randint(0,2,(32,50)).astype('int32'), np.zeros((32,1024)).astype('float32'))
i=0
lr = 0.1
best_perp = np.finfo(np.float32).max
for i in xrange(i,NB_EPOCHS):
iter=0
costs=[]
times=[]
itr = data_loaders.data_iterator('train', BATCH_SIZE)
for train_img,train_seq,train_mask in itr:
iter += 1
start = time.time()
_ , _loss = sess.run([train_step,loss],feed_dict={X:train_img,seqs:train_seq,mask:train_mask,learn_rate:lr})
# _ , _loss = sess.run([train_step,loss],feed_dict={X:train_img,seqs:train_seq,mask:train_mask})
times.append(time.time()-start)
costs.append(_loss)
if iter%100==0:
print "Iter: %d (Epoch %d)"%(iter,i+1)
print "\tMean cost: ",np.mean(costs)
print "\tMean time: ",np.mean(times)
print "\n\nEpoch %d Completed!"%(i+1)
print "\tMean train cost: ",np.mean(costs)
print "\tMean train perplexity: ",np.mean(map(lambda x: np.power(np.e,x), costs))
print "\tMean time: ",np.mean(times)
val_loss, val_perp = score('valid',BATCH_SIZE)
if val_perp < best_perp:
best_perp = val_perp
saver.save(sess,"weights_best.ckpt")
print "\tBest Perplexity Till Now! Saving state!"
else:
lr = lr * 0.5
print "\n\n"
#sess.run([train_step,loss],feed_dict={X:np.random.randn(32,1,256,512),seqs:np.random.randint(0,107,(32,40)),mask:np.random.randint(0,2,(32,40))})