def visual_results_external_image(self, images, FLAG_MAX_VOTE = False): #train_dir = "./saved_models/segnet_vgg_bayes/segnet_vgg_bayes_30000/model.ckpt-30000" #train_dir = "./saved_models/segnet_scratch/segnet_scratch_30000/model.ckpt-30000" i_width = 64 i_height =64 images = [misc.imresize(image, (i_height, i_width)) for image in images] image_w = self.config["INPUT_WIDTH"] image_h = self.config["INPUT_HEIGHT"] image_c = self.config["INPUT_CHANNELS"] train_dir = self.config["SAVE_MODEL_DIR"] FLAG_BAYES = self.config["BAYES"] with self.sess as sess: # Restore saved session saver = tf.train.Saver() saver.restore(sess, train_dir) _, _, prediction = cal_loss(logits=self.logits, labels=self.labels_pl) prob = tf.nn.softmax(self.logits,dim = -1) num_sample_generate = 30 pred_tot = [] var_tot = [] labels = [] for i in range(len(images)): labels.append(np.array([[1 for x in range(64)] for y in range(64)])) inference_time = [] start_time = time.time() for image_batch, label_batch in zip(images,labels): #for image_batch in zip(images): image_batch = np.reshape(image_batch,[1,image_h,image_w,image_c]) label_batch = np.reshape(label_batch,[1,image_h,image_w,1]) if FLAG_BAYES is False: fetches = [prediction] feed_dict = {self.inputs_pl: image_batch, self.labels_pl: label_batch, self.is_training_pl: False, self.keep_prob_pl: 0.5, self.batch_size_pl: 1} pred = sess.run(fetches = fetches, feed_dict = feed_dict) pred = np.reshape(pred,[image_h,image_w]) var_one = [] else: feed_dict = {self.inputs_pl: image_batch, self.labels_pl: label_batch, self.is_training_pl: False, self.keep_prob_pl: 0.5, self.with_dropout_pl: True, self.batch_size_pl: 1} prob_iter_tot = [] pred_iter_tot = [] for iter_step in range(num_sample_generate): prob_iter_step = sess.run(fetches = [prob], feed_dict = feed_dict) prob_iter_tot.append(prob_iter_step) pred_iter_tot.append(np.reshape(np.argmax(prob_iter_step,axis = -1),[-1])) if FLAG_MAX_VOTE is True: prob_variance,pred = MAX_VOTE(pred_iter_tot,prob_iter_tot,self.config["NUM_CLASSES"]) #acc_per = np.mean(np.equal(pred,np.reshape(label_batch,[-1]))) var_one = var_calculate(pred,prob_variance) pred = np.reshape(pred,[image_h,image_w]) else: prob_mean = np.nanmean(prob_iter_tot,axis = 0) prob_variance = np.var(prob_iter_tot, axis = 0) pred = np.reshape(np.argmax(prob_mean,axis = -1),[-1]) #pred is the predicted label with the mean of generated samples #THIS TIME I DIDN'T INCLUDE TAU var_one = var_calculate(pred,prob_variance) pred = np.reshape(pred,[image_h,image_w]) pred_tot.append(pred) var_tot.append(var_one) inference_time.append(time.time() - start_time) start_time = time.time() try: draw_plots_bayes_external(images, pred_tot, var_tot) return pred_tot, var_tot, inference_time except: return pred_tot, var_tot, inference_time
def visual_results_external_image(self, images, model_file): images = [ misc.imresize(image, (self.input_h, self.input_w)) for image in images ] with tf.Session() as sess: # Restore saved session saver = tf.train.Saver() if model_file is None: saver.restore(sess, tf.train.latest_checkpoint(FLAGS.runtime_dir)) else: saver.restore(sess, os.path.join(FLAGS.runtime_dir, model_file)) _, _, prediction = cal_loss(logits=self.logits, labels=self.labels_pl, n_classes=self.n_classes) prob = tf.nn.softmax(self.logits, dim=-1) pred_tot = [] var_tot = [] labels = [] for i in range(len(images)): labels.append( np.array([[1 for x in range(self.input_w)] for y in range(self.input_h)])) inference_time = [] start_time = time.time() for image_batch, label_batch in zip(images, labels): image_batch = np.reshape( image_batch, [1, self.input_h, self.input_w, self.input_c]) label_batch = np.reshape(label_batch, [1, self.input_h, self.input_w, 1]) fetches = [prediction] feed_dict = { self.inputs_pl: image_batch, self.labels_pl: label_batch, self.is_training_pl: False, self.keep_prob_pl: 0.5, self.batch_size_pl: 1 } pred = sess.run(fetches=fetches, feed_dict=feed_dict) pred = np.reshape(pred, [self.input_h, self.input_w]) pred_tot.append(pred) inference_time.append(time.time() - start_time) start_time = time.time() try: draw_plots_bayes_external(images, pred_tot) return pred_tot, var_tot, inference_time except: return pred_tot, var_tot, inference_time