def pred_loop():
evalu_model, x_ = eval_model.build(cpu)
global pred_file_name
while True:
vid, cur_fr, n, fileName = q.get()
# pred_file_name = fileName.split('/')
# # pred_file_name = pred_file_name[-1].replace(".zip","_prediction.csv")
# pred_file_name = pred_file_name[-1].replace(".zip","")[-4:]
x_.set_value(vid.astype("float32"), borrow=True)
pred = evalu_model()[0][0]
pred_p = pred.max()
pred_idx = pred.argmax() + 1
fr_start = cur_fr + step - n_f
fr_end = cur_fr + step
predict(pred_idx, pred_p, fr_start)
if cur_fr + 2 * step >= n:
reinit()
def pred_loop():
evalu_model, x_ = eval_model.build(cpu)
global pred_file_name
while True:
vid, cur_fr, n, fileName = q.get()
# pred_file_name = fileName.split('/')
# # pred_file_name = pred_file_name[-1].replace(".zip","_prediction.csv")
# pred_file_name = pred_file_name[-1].replace(".zip","")[-4:]
x_.set_value(vid.astype("float32"), borrow=True)
pred = evalu_model()[0][0]
pred_p = pred.max()
pred_idx = pred.argmax() + 1
fr_start = cur_fr + step - n_f
fr_end = cur_fr + step
predict(pred_idx, pred_p, fr_start)
if cur_fr + 2 * step >= n:
reinit()
def pred_loop():
evalu_model, x_ = eval_model.build(cpu)
global pred_file_name
# evalu_model, x_ = eval_model.build()
while True:
# print "waiting"
# s_time = time.time()
# print "pred_loop waiting"
vid, cur_fr, n, fileName = q.get()
# print "waited",(time.time()-s_time)/1000.,"ms"
pred_file_name = fileName.split('/')
pred_file_name = pred_file_name[-1].replace(".zip", "_prediction.csv")
# print "get"
# print vid.shape, cur_fr
x_.set_value(vid.astype("float32"), borrow=True)
pred = evalu_model()[0][0]
pred_p = pred.max()
pred_idx = pred.argmax() + 1
# fps = int(1./((time.time()-time_start)/step))
fr_start = cur_fr + step - n_f
fr_end = cur_fr + step
# print pred_idx,"\t", "\t",pred_p,"\t",fr_start, "-",fr_end,"\t",fps,'fps'
predict(pred_idx, pred_p, fr_start)
# print v_new.shape
# for i in xrange(v_new.shape[0]):
# for j in xrange(v_new.shape[1]):
# for k in xrange(v_new.shape[2]):
# play_vid(v_new[i,j,k],wait=0)
# cur_fr += step
# print cur_fr, int(1./((time.time()-time_start)/step)),'fps'
if cur_fr + 2 * step >= n:
reinit()
def pred_loop():
evalu_model, x_ = eval_model.build(cpu)
global pred_file_name
# evalu_model, x_ = eval_model.build()
while True:
# print "waiting"
# s_time = time.time()
# print "pred_loop waiting"
vid, cur_fr,n,fileName = q.get()
# print "waited",(time.time()-s_time)/1000.,"ms"
pred_file_name = fileName.split('/')
pred_file_name = pred_file_name[-1].replace(".zip","_prediction.csv")
# print "get"
# print vid.shape, cur_fr
x_.set_value(vid.astype("float32"),borrow=True)
pred = evalu_model()[0][0]
pred_p = pred.max()
pred_idx = pred.argmax()+1
# fps = int(1./((time.time()-time_start)/step))
fr_start = cur_fr+step-n_f
fr_end = cur_fr+step
# print pred_idx,"\t", "\t",pred_p,"\t",fr_start, "-",fr_end,"\t",fps,'fps'
predict(pred_idx,pred_p,fr_start)
# print v_new.shape
# for i in xrange(v_new.shape[0]):
# for j in xrange(v_new.shape[1]):
# for k in xrange(v_new.shape[2]):
# play_vid(v_new[i,j,k],wait=0)
# cur_fr += step
# print cur_fr, int(1./((time.time()-time_start)/step)),'fps'
if cur_fr+2*step>=n:
reinit()
predictions.append(pred)
cur_fr += step
predictions = array(predictions,float32)
pred_file_name = fileName.split('/')
pred_file_name = pred_file_name[-1].replace(".zip","_prediction.zip")
file = GzipFile(dst+"/"+pred_file_name, 'wb')
dump(predictions, file, -1)
file.close()
# print cur_fr, int(1./((time.time()-time_start)/step)),'fps'
#reinit()
# @profile
evalu_model, x_ = eval_model.build(cpu)
def pred_loop(vid, cur_fr,n,fileName):
global pred_file_name
# evalu_model, x_ = eval_model.build()
# while True:
# print "waiting"
# s_time = time.time()
# print "pred_loop waiting"
# vid, cur_fr,n,fileName = q.get()
# print "waited",(time.time()-s_time)/1000.,"ms"
pred_file_name = fileName.split('/')
pred_file_name = pred_file_name[-1].replace(".zip","_prediction.csv")
cur_fr += step
predictions = array(predictions, float32)
pred_file_name = fileName.split('/')
pred_file_name = pred_file_name[-1].replace(".zip", "_prediction.zip")
file = GzipFile(dst + "/" + pred_file_name, 'wb')
dump(predictions, file, -1)
file.close()
# print cur_fr, int(1./((time.time()-time_start)/step)),'fps'
#reinit()
# @profile
evalu_model, x_ = eval_model.build(cpu)
def pred_loop(vid, cur_fr, n, fileName):
global pred_file_name
# evalu_model, x_ = eval_model.build()
# while True:
# print "waiting"
# s_time = time.time()
# print "pred_loop waiting"
# vid, cur_fr,n,fileName = q.get()
# print "waited",(time.time()-s_time)/1000.,"ms"