def main(): ''' pipeline for testing and evaluation ''' # preset parameters save_path = '../data_viper/model_feat/' # load data # imL = 48 bs = 100 datafile_viper = '../data_viper/viper.pkl' viper = loadfile(datafile_viper) # load model modelfile_viper = '../data_viper/model_feat/model.pkl' model = loadfile(modelfile_viper) # evaluation and testing # test_x = viper.test_x.get_value(borrow=True) test_x = np.asarray(viper.test_feat) test_y = viper.test_y n_test = test_x.shape[0] test_ypred = model.predict(viper.test_feat) test_ypred = np.asarray(test_ypred).flatten() # test_ims = test_x.reshape((n_test, imL, imL, -1)) # assign predicted scores to images h, w = viper.imgs[0].shape[:2] mh, mw = len(np.unique(viper.yy)), len(np.unique(viper.xx)) msk0 = np.zeros(mh*mw).astype(np.uint8) msks = [msk0.copy() for im in viper.imgs] showlist = [] for i in range(n_test): imgid = viper.test_imgids[i] patid = viper.test_ctrids[i] score = test_ypred[i] msks[imgid][patid] = score*255 # resize predicted salience map to match image size msks_rs = [imresize(msk.reshape((mw, mh)).T, size=(h, w))/255. for msk in msks] # save salience map for comparison test_imids = np.asarray(np.unique(viper.test_imgids)) salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids]) salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids]) savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred]) # quantize to show different test patches # kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10) # kmeans.fit(test_ypred.reshape(n_test, 1)) # # save to result folder # for i in range(10): # idx = kmeans.labels_== i # if any(idx): # im = immontage(list(test_ims[idx])) # imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), im) print 'testing finished'
def main(): ''' pipeline for supervised salience training ''' if os.path.isdir('../data_viper/'): datapath = '../data_viper/' else: datapath = '../data/' save_path = '../data_viper/model_feat/' DATA_OPT = 'feat' # feature type TRAIN_OPT = 'SVR' # training model option TRAIN = True # wheather re-train the model # prepare training data for supervised salience training #======================================================= datafile_viper = datapath + 'viper.pkl' if not os.path.isfile(datafile_viper): viper = DataMan_viper_small() viper.make_data() savefile(datafile_viper, viper) else: viper = loadfile(datafile_viper) viper = preprocess_data(viper, DATA_OPT) # training # ============== modelfile = datapath + 'model_feat/model.pkl' if TRAIN: tic = time.clock() model = train_model(viper, TRAIN_OPT) toc = time.clock() print 'Elapsed training time: {0:.2f} min'.format((toc-tic)/60.) savefile(modelfile, model) os.system('ls -lh ' + modelfile) else: model = loadfile(modelfile) ## validation #========================================= print 'validating trained model' nValid = 5000 valididx = np.random.permutation(viper.valid_feat.shape[0])[:nValid] # valid_ypred = model.predict(viper.valid_feat[valididx]) valid_ypred = predict(model, viper.valid_feat[valididx], viper.yy[viper.valid_ctrids][valididx], viper.imH) #- quantize patches based on testing scores kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10, verbose=1) kmeans.fit(valid_ypred.reshape(nValid, 1)) #- crop patches for testing image valid_patset = np.asarray(viper.get_patchset('valid'))[valididx] #- save to result folder os.system('rm '+save_path+'*.jpg') for i in range(10): idx = kmeans.labels_== i if any(idx): pats = immontage(list(valid_patset[idx])) imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), pats) print 'patchset {} saved'.format(i) ### testing #=============== print 'testing' # test_ypred = model.predict(viper.test_feat) test_ypred = predict(model, viper.test_feat, viper.yy_test[viper.test_ctrids], viper.imH) ## assign predicted scores to images h, w = viper.imgs[0].shape[:2] mh, mw = len(np.unique(viper.yy_test)), len(np.unique(viper.xx_test)) msk0 = np.zeros(mh*mw, dtype=np.float32) msks = [msk0.copy() for im in viper.imgs] showlist = [] n_test = len(test_ypred) for i in range(n_test): imgid = viper.test_imgids[i] patid = viper.test_ctrids[i] score = test_ypred[i] msks[imgid][patid] = score # resize predicted salience map to match image size msks_rs = [mapresize(msk.reshape((mw, mh)).T, size=(h, w)) for msk in msks] # msks_rs = msks # save salience map for comparison test_imids = np.asarray(np.unique(viper.test_imgids)) salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids]) salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids]) savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred])
def main(): ''' pipeline for supervised salience training ''' if os.path.isdir('../data_viper/'): datapath = '../data_viper/' else: datapath = '../data/' # prepare training data for supervised salience training datafile_viper = datapath + 'viper.pkl' if not os.path.isfile(datafile_viper): viper = DataMan_viper_small() viper.make_data() savefile(datafile_viper, viper) viper = loadfile(datafile_viper) viper = change_label(viper) viper.train_feat = viper.get_pixeldata('train') viper.valid_feat = viper.get_pixeldata('valid') viper.test_feat = viper.get_pixeldata('test') bs = 100 imL = 10 nfilter1 = 16 filterL = 3 x = T.tensor4(name='x', dtype=theano.config.floatX) y = T.ivector(name='y') # layer0 = x.reshape((bs, 3, imL, imL)) conv1 = ConvLayer(input=x, image_shape=(bs, 3, imL, imL), filter_shape=(nfilter1, 3, filterL, filterL), flatten=True, actfun=tanh, tag='_convpool1') # outL = np.floor((imL-filterL+1.)/recfield).astype(np.int) outL = imL-filterL+1 # nfilter3 = 16 # filterL3 = 3 # conv3 = ConvLayer(input=conv2.output(), image_shape=(bs, nfilter2, outL2, outL2), # filter_shape=(nfilter3, nfilter2, filterL3, filterL3), # flatten=True, # actfun=tanh, # tag='_conv3') # # outL3 = outL2-filterL3+1 fc2 = FCLayer(input=conv1.output(), n_in=nfilter1*outL*outL, n_out=256, actfun=tanh, tag='_fc2') fc3 = FCLayer(input=fc2.output(), n_in=256, n_out=10, actfun=sigmoid, tag='_fc3') params_cmb = conv1.params + fc2.params + fc3.params # ypred = fc3.output().flatten() ypred = fc3.output() model = GeneralModel(input=x, data=viper, output=ypred, target=y, params=params_cmb, regularizers=0, cost_func=negative_log_likelihood, error_func=sqr_error, batch_size=bs) sgd = sgd_optimizer(data=viper, model=model, batch_size=bs, learning_rate=0.001, n_epochs=500) sgd.fit_viper() filepath = datapath + 'model/model.pkl' savefile(filepath, model) os.system('ls -lh ' + filepath)