add evaluation

evaluate.py

@@ -0,0 +1,170 @@
+import tensorflow as tf
+import numpy as np
+import argparse
+import socket
+import importlib
+import time
+import os
+import scipy.misc
+import sys
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(BASE_DIR)
+sys.path.append(os.path.join(BASE_DIR, 'models'))
+sys.path.append(os.path.join(BASE_DIR, 'utils'))
+import provider
+import pc_util
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]')
+parser.add_argument('--model', default='dgcnn', help='Model name: dgcnn [default: dgcnn]')
+parser.add_argument('--batch_size', type=int, default=4, help='Batch Size during training [default: 1]')
+parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]')
+parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]')
+parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]')
+parser.add_argument('--visu', action='store_true', help='Whether to dump image for error case [default: False]')
+FLAGS = parser.parse_args()
+
+
+BATCH_SIZE = FLAGS.batch_size
+NUM_POINT = FLAGS.num_point
+MODEL_PATH = FLAGS.model_path
+GPU_INDEX = FLAGS.gpu
+MODEL = importlib.import_module(FLAGS.model) # import network module
+DUMP_DIR = FLAGS.dump_dir
+if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR)
+LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w')
+LOG_FOUT.write(str(FLAGS)+'\n')
+
+NUM_CLASSES = 40
+SHAPE_NAMES = [line.rstrip() for line in \
+    open(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] 
+
+HOSTNAME = socket.gethostname()
+
+# ModelNet40 official train/test split
+TRAIN_FILES = provider.getDataFiles( \
+    os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'))
+TEST_FILES = provider.getDataFiles(\
+    os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'))
+
+def log_string(out_str):
+    LOG_FOUT.write(out_str+'\n')
+    LOG_FOUT.flush()
+    print(out_str)
+
+def evaluate(num_votes):
+    is_training = False
+
+    with tf.device('/gpu:'+str(GPU_INDEX)):
+        pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT)
+        is_training_pl = tf.placeholder(tf.bool, shape=())
+
+        # simple model
+        pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl)
+        loss = MODEL.get_loss(pred, labels_pl, end_points)
+
+        # Add ops to save and restore all the variables.
+        saver = tf.train.Saver()
+
+    # Create a session
+    config = tf.ConfigProto()
+    config.gpu_options.allow_growth = True
+    config.allow_soft_placement = True
+    config.log_device_placement = True
+    sess = tf.Session(config=config)
+
+    # Restore variables from disk.
+    saver.restore(sess, MODEL_PATH)
+    log_string("Model restored.")
+
+    ops = {'pointclouds_pl': pointclouds_pl,
+           'labels_pl': labels_pl,
+           'is_training_pl': is_training_pl,
+           'pred': pred,
+           'loss': loss}
+
+    eval_one_epoch(sess, ops, num_votes)
+
+
+def eval_one_epoch(sess, ops, num_votes=1, topk=1):
+    error_cnt = 0
+    is_training = False
+    total_correct = 0
+    total_seen = 0
+    loss_sum = 0
+    total_seen_class = [0 for _ in range(NUM_CLASSES)]
+    total_correct_class = [0 for _ in range(NUM_CLASSES)]
+    fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w')
+    for fn in range(len(TEST_FILES)):
+        log_string('----'+str(fn)+'----')
+        current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
+        current_data = current_data[:,0:NUM_POINT,:]
+        current_label = np.squeeze(current_label)
+        print(current_data.shape)
+
+        file_size = current_data.shape[0]
+        num_batches = file_size // BATCH_SIZE
+        print(file_size)
+
+        for batch_idx in range(num_batches):
+            start_idx = batch_idx * BATCH_SIZE
+            end_idx = (batch_idx+1) * BATCH_SIZE
+            cur_batch_size = end_idx - start_idx
+
+            # Aggregating BEG
+            batch_loss_sum = 0 # sum of losses for the batch
+            batch_pred_sum = np.zeros((cur_batch_size, NUM_CLASSES)) # score for classes
+            batch_pred_classes = np.zeros((cur_batch_size, NUM_CLASSES)) # 0/1 for classes
+            for vote_idx in range(num_votes):
+                rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :],
+                                                  vote_idx/float(num_votes) * np.pi * 2)
+                feed_dict = {ops['pointclouds_pl']: rotated_data,
+                             ops['labels_pl']: current_label[start_idx:end_idx],
+                             ops['is_training_pl']: is_training}
+                loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
+                                          feed_dict=feed_dict)
+                batch_pred_sum += pred_val
+                batch_pred_val = np.argmax(pred_val, 1)
+                for el_idx in range(cur_batch_size):
+                    batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
+                batch_loss_sum += (loss_val * cur_batch_size / float(num_votes))
+            # pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1]
+            # pred_val = np.argmax(batch_pred_classes, 1)
+            pred_val = np.argmax(batch_pred_sum, 1)
+            # Aggregating END
+
+            correct = np.sum(pred_val == current_label[start_idx:end_idx])
+            # correct = np.sum(pred_val_topk[:,0:topk] == label_val)
+            total_correct += correct
+            total_seen += cur_batch_size
+            loss_sum += batch_loss_sum
+
+            for i in range(start_idx, end_idx):
+                l = current_label[i]
+                total_seen_class[l] += 1
+                total_correct_class[l] += (pred_val[i-start_idx] == l)
+                fout.write('%d, %d\n' % (pred_val[i-start_idx], l))
+
+                if pred_val[i-start_idx] != l and FLAGS.visu: # ERROR CASE, DUMP!
+                    img_filename = '%d_label_%s_pred_%s.jpg' % (error_cnt, SHAPE_NAMES[l],
+                                                           SHAPE_NAMES[pred_val[i-start_idx]])
+                    img_filename = os.path.join(DUMP_DIR, img_filename)
+                    output_img = pc_util.point_cloud_three_views(np.squeeze(current_data[i, :, :]))
+                    scipy.misc.imsave(img_filename, output_img)
+                    error_cnt += 1
+
+    log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
+    log_string('eval accuracy: %f' % (total_correct / float(total_seen)))
+    log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float))))
+
+    class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)
+    for i, name in enumerate(SHAPE_NAMES):
+        log_string('%10s:\t%0.3f' % (name, class_accuracies[i]))
+
+
+
+if __name__=='__main__':
+    with tf.Graph().as_default():
+        evaluate(num_votes=12)
+    LOG_FOUT.close()