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')
# data_utils.shuffle_points(TEST_DATA)
# current_data, current_label = data_utils.get_current_data(TEST_DATA, TEST_LABELS, NUM_POINT)
# current_data, current_label = data_utils.get_current_data_h5(TEST_DATA, TEST_LABELS, NUM_POINT)
if (".h5" in TEST_FILE):
current_data, current_label = data_utils.get_current_data_h5(
TEST_DATA, TEST_LABELS, NUM_POINT)
else:
current_data, current_label = data_utils.get_current_data(
TEST_DATA, TEST_LABELS, NUM_POINT)
current_label = np.squeeze(current_label)
num_batches = current_data.shape[0] // BATCH_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)
xforms_np, rotations_np = pf.get_xforms(
BATCH_SIZE,
rotation_range=rotation_range_val,
scaling_range=scaling_range_val,
order=setting.rotation_order)
# Augment batched point clouds by rotation and jittering
feed_dict = {
ops['pointclouds_pl']: rotated_data,
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training,
ops['xforms']: xforms_np,
ops['rotations']: rotations_np,
ops['jitter_range']: np.array([jitter_val])
}
loss_val, pred_val = sess.run([ops['loss'], ops['pred']],
feed_dict=feed_dict)
pred_val = np.sum(pred_val, axis=1)
# pred_val = np.argmax(pred_val, 1)
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('%s, %s\n' %
(SHAPE_NAMES[pred_val[i - start_idx]], SHAPE_NAMES[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)
#save ply
ply_filename = '%d_label_%s_pred_%s.ply' % (
error_cnt, SHAPE_NAMES[l],
SHAPE_NAMES[pred_val[i - start_idx]])
ply_filename = os.path.join(DUMP_DIR, ply_filename)
data_utils.save_ply(np.squeeze(current_data[i, :, :]),
ply_filename)
error_cnt += 1
log_string('total seen: %d' % (total_seen))
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]))
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_C)]
total_correct_class = [0 for _ in range(NUM_C)]
fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w')
# data_utils.shuffle_points(TEST_DATA)
# current_data, current_label = data_utils.get_current_data(TEST_DATA, TEST_LABELS, NUM_POINT)
# current_data, current_label = data_utils.get_current_data_h5(TEST_DATA, TEST_LABELS, NUM_POINT)
if (".h5" in TEST_FILE):
current_data, current_label = data_utils.get_current_data_h5(
TEST_DATA, TEST_LABELS, NUM_POINT)
else:
current_data, current_label = data_utils.get_current_data(
TEST_DATA, TEST_LABELS, NUM_POINT)
current_label = np.squeeze(current_label)
####################################################
print(current_data.shape)
print(current_label.shape)
filtered_data = []
filtered_label = []
for i in range(current_label.shape[0]):
if (current_label[i] in OBJECTDATASET_TO_MODELNET.keys()):
filtered_label.append(current_label[i])
filtered_data.append(current_data[i, :])
filtered_data = np.array(filtered_data)
filtered_label = np.array(filtered_label)
print(filtered_data.shape)
print(filtered_label.shape)
current_data = filtered_data
current_label = filtered_label
###################################################
num_batches = current_data.shape[0] // BATCH_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, 40)) # score for classes
batch_pred_classes = np.zeros((cur_batch_size, 40)) # 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
for i in range(start_idx, end_idx):
total_seen += 1
if (pred_val[i - start_idx]
not in MODELNET_TO_OBJECTDATASET.keys()):
continue
pred = MODELNET_TO_OBJECTDATASET[pred_val[i - start_idx]]
# if (pred_val[i-start_idx] == current_label[i]):
if (pred == current_label[i]):
total_correct += 1
for i in range(start_idx, end_idx):
l = current_label[i]
total_seen_class[l] += 1
if pred_val[i - start_idx] not in MODELNET_TO_OBJECTDATASET:
pred_label = "NA"
else:
pred = MODELNET_TO_OBJECTDATASET[pred_val[i - start_idx]]
total_correct_class[l] += (pred == l)
pred_label = SHAPE_NAMES[pred]
# groundtruth_label = SHAPE_NAMES[MODELNET_TO_OBJECTDATASET[l]]
groundtruth_label = SHAPE_NAMES[l]
fout.write('%s, %s\n' % (pred_label, groundtruth_label))
if pred_val[i -
start_idx] != l and FLAGS.visu: # ERROR CASE, DUMP!
img_filename = '%d_label_%s_pred_%s.jpg' % (
error_cnt, groundtruth_label, pred_label)
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)
#save ply
ply_filename = '%d_label_%s_pred_%s.ply' % (
error_cnt, groundtruth_label, pred_label)
data_utils.save_ply(np.squeeze(current_data[i, :, :]),
ply_filename)
error_cnt += 1
log_string('total seen: %d' % (total_seen))
# log_string('eval mean loss: %f' % (loss_sum / float(total_seen)))
log_string('eval accuracy: %f' % (total_correct / float(total_seen)))
seen_class_accuracies = []
seen_correct_class = []
for i in range(len(total_seen_class)):
if total_seen_class[i] != 0:
seen_class_accuracies.append(total_seen_class[i])
seen_correct_class.append(total_correct_class[i])
log_string('eval avg class acc: %f' % (np.mean(
np.array(seen_correct_class) /
np.array(seen_class_accuracies, dtype=np.float))))
for i, name in enumerate(SHAPE_NAMES):
if (total_seen_class[i] == 0):
accuracy = -1
else:
accuracy = total_correct_class[i] / float(total_seen_class[i])
log_string('%10s:\t%0.3f' % (name, accuracy))
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
if pred_val[i-start_idx] == l and FLAGS.visu: # ERROR CASE, DUMP!
img_filename = '%d_label_%s_pred_%s.jpg' % (total_correct, SHAPE_NAMES[l],
SHAPE_NAMES[pred_val[i-start_idx]])
img_filename = os.path.join(SUCC_DIR, img_filename)
output_img = pc_util.point_cloud_three_views(np.squeeze(current_data[i, :, :]))
scipy.misc.imsave(img_filename, output_img)
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]))
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, :]
# print(current_data)
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):
point_cloud_transformed_val = None
TNet1 = None
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, point_cloud_transformed_val, TNet1 = sess.run(
[
ops['loss'], ops['pred'],
ops['point_cloud_transformed'], ops['TNet1']
],
feed_dict=feed_dict)
for i in range(start_idx, end_idx):
l = current_label[i]
img_filename = os.path.join(
DUMP_DIR,
"{label}.{fn}.{batch_idx}.{i}.{vote_idx}.rotated_data.jpg"
.format(fn=fn,
batch_idx=batch_idx,
i=i,
label=SHAPE_NAMES[l],
vote_idx=vote_idx))
# input: test point cloud
output_img = pc_util.point_cloud_three_views(
np.squeeze(rotated_data[i - start_idx, :, :]))
# using utilities include in this repo
# to draw point cloud into jpg
scipy.misc.imsave(img_filename, output_img)
img_filename = os.path.join(
DUMP_DIR,
"{label}.{fn}.{batch_idx}.{i}.{vote_idx}.point_cloud_transformed.jpg"
.format(fn=fn,
batch_idx=batch_idx,
i=i,
label=SHAPE_NAMES[l],
vote_idx=vote_idx))
# output: transformed point cloud
output_img = pc_util.point_cloud_three_views(
np.squeeze(
point_cloud_transformed_val[i - start_idx, :, :]))
# using utilities include in this repo
# to draw point cloud into jpg
scipy.misc.imsave(img_filename, output_img)
# j = i - start_idx
# f = open(
# "dump/{label}.{fn}.{batch_idx}.{i}.{vote_idx}.txt".
# format(
# fn=fn,
# batch_idx=batch_idx,
# i=i,
# label=SHAPE_NAMES[l],
# vote_idx=vote_idx), "w")
# f.write(str(TNet1[j])) # T-Net1: 3X3 martix
# f.write("\r\n")
# # ||1-AA'||
# det = np.linalg.det(
# np.eye(3) - np.dot(TNet1[j], TNet1[j].T))
# f.write(str(det))
# f.write("\r\n")
# z, y, x = mat2euler(TNet1[j])
# print(z, y, x)
# theta, vec = euler2angle_axis(z, y, x)
# print(theta, vec)
# f.write(str(theta*180/np.pi) + "\r\n" + str(vec))
# f.close()
# print(point_cloud_transformed_val.shape) # (4, 1024, 3)
# print(rotated_data.shape) # (4, 1024, 3)
# print(current_data[0, :, :].shape) # (1024, 3)
# print(np.squeeze(current_data[0, :, :]).shape) # (1024, 3)
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]))
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) #输出是（420，1024，3）
# print(current_data)#输出是一堆数字
print(current_label) #输出是正确的标签
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
print(file_size) #是420
print(BATCH_SIZE) #是4
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
print(start_idx) #输出开始位置
print(end_idx) #输出结束位置
print(cur_batch_size) #输出4
# 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 ########预测后的分数
batch_pred_classes = np.zeros(
(cur_batch_size,
NUM_CLASSES)) # 0/1 for classes ########预测后分类的正确与否（0或1）
# print(batch_loss_sum)
print(batch_pred_sum) #输出40个0
print(batch_pred_classes) #输出40个0
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 #######分数
batch_pred_val = np.argmax(pred_val, 1) ####### 预测值
# print(loss_val)
print(pred_val) #将数据输入网络，得到每个类别的分数
print(batch_pred_sum)
# print(batch_pred_val) #根据每个类别的分数来得到预测的类别和下面的pred_val是一样的
for el_idx in range(cur_batch_size):
batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1
print(batch_pred_classes) #根据得到的类别，
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)
print(pred_val) ###打印出2468个数据的类别是哪一类
# Aggregating END
correct = np.sum(pred_val == current_label[start_idx:end_idx])
print(correct)
# correct = np.sum(pred_val_topk[:,0:topk] == label_val)
total_correct += correct
total_seen += cur_batch_size
loss_sum += batch_loss_sum
print(total_correct) #总共正确的个数
print(total_seen) #总共的个数
for i in range(start_idx, end_idx):
l = current_label[i]
print(l) #输出是正确的标签
total_seen_class[l] += 1
# print(total_seen_class[l]) #输出没明白
total_correct_class[l] += (pred_val[i - start_idx] == l)
# print(total_correct_class[l] )#输出没明白
fout.write('%d, %d\n' %
(pred_val[i - start_idx], l)) #是将正确的标签和预测的标签都写入到里面
print(pred_val[i - start_idx]) #输出是预测的标签
# fout.write('%d\n'%(pred_val[i - start_idx])) #仅仅将预测的标签写入到日志文件中
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
# print(output_img)
log_string('eval mean loss: %f' %
(loss_sum / float(total_seen))) #损失的总和除以总共对象的个数
log_string('eval accuracy: %f' %
(total_correct / float(total_seen))) #总共预测正确的个数除以总共对象的个数
log_string('eval avg class acc: %f' % (np.mean(
np.array(total_correct_class) /
np.array(total_seen_class, dtype=np.float)))) #没明白
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]))
def eval_one_epoch(sess, ops, num_votes=1, topk=1, run_metadata=None):
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)]
total_time = 0.
total_batches = 0.
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
}
# Time measurement
start = timer()
loss_val, pred_val = sess.run(
[ops['loss'], ops['pred']], feed_dict=feed_dict
) #, options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE), run_metadata=run_metadata)
end = timer()
if batch_idx != 0:
total_time += (end - start)
total_batches += 1
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
if VERBOSE:
flops = tf.profiler.profile(
sess.graph,
options=tf.profiler.ProfileOptionBuilder.float_operation(),
run_meta=run_metadata)
log_string('flops: %f' % (flops.total_float_ops))
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))))
log_string('mean evaluation time of one batch: %f' %
(total_time / total_batches))
parameter_num = np.sum(
[np.prod(v.shape.as_list()) for v in tf.trainable_variables()])
log_string('number of trainable parameters: %f' % (parameter_num))
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]))
else:
x_original, target, x_adv, faces = res
for eps_idx in range(len(args.eps)):
class_idx = np.random.choice(len(class_names),
size=len(class_names),
replace=False)
for i in class_idx:
idx = np.random.choice(len(x_original[eps_idx][i]),
size=min(3, len(x_original[eps_idx][i])),
replace=False)
for j in idx:
img_file = "%d_%s_original.jpg" % (
j, class_names[target[eps_idx][i][j]])
img_file = os.path.join(args.output, img_file)
img = pc_util.point_cloud_three_views(
x_original[eps_idx][i][j])
scipy.misc.imsave(img_file, img)
eps_str = str(args.eps[eps_idx]).replace(".", "_")
img_file = "%d_%s_adv_target_%s_eps_%s.jpg" % (j, class_names[
target[eps_idx][i][j]], class_names[i], eps_str)
img_file = os.path.join(args.output, img_file)
img = pc_util.point_cloud_three_views(x_adv[eps_idx][i][j])
scipy.misc.imsave(img_file, img)
else:
res = adversarial_utils.untargeted_attack(
args.checkpoint,
args.output,
x_pl,
t_pl,
model_loss_fn,
def eval_one_epoch(sess, ops, num_votes=1, topk=1):
error_cnt = 0
want_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)
log_string(current_data.shape.__str__())
file_size = current_data.shape[0]
num_batches = file_size // BATCH_SIZE
log_string(file_size.__str__())
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):
nnnnn = 1
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}
# if (fn==0 and batch_idx == nnnnn):
loss_val, pred_val, point_cloud_transformed,PC_after_transformed1,PC_after_transformed2,after_maxpool,rotateTransform \
= sess.run([ops['loss'], ops['pred'], ops['point_cloud_transformed'],ops['PC_after_transformed1'],ops['PC_after_transformed2'],ops['after_maxpool'],ops['rotateTransform']],
feed_dict=feed_dict)
# else:
# loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict)
if (fn==0 and batch_idx == nnnnn):
log_string ("---point_cloud_transformed---")
log_string (point_cloud_transformed.shape.__str__())
log_string(point_cloud_transformed[0, :, :][0].__str__())
log_string ("---PC_after_transformed1---")
log_string (PC_after_transformed1.shape.__str__())
log_string ("---PC_after_transformed2---")
log_string (PC_after_transformed2.shape.__str__())
log_string ("---after_maxpool---")
log_string (after_maxpool.shape.__str__())
log_string("---rotateTransform---")
log_string(rotateTransform.shape.__str__())
log_string(rotateTransform[0].__str__())
log_string ("---pred_val---")
log_string (pred_val.shape.__str__())
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))\
THS_DIR = os.path.join(FIN_DIR, SHAPE_NAMES[l])
if not os.path.exists(THS_DIR): os.mkdir(THS_DIR)
##log rotate
THS_FOUT = open(os.path.join(ROTMAT_DIR, '%s_evaluate.txt'%(SHAPE_NAMES[l])), 'a')
THS_FOUT.write('%d_TnetR_label_%s_pred_%s\n' % (want_cnt, SHAPE_NAMES[l],SHAPE_NAMES[pred_val[i-start_idx]]))
THS_FOUT.write(rotateTransform[i-start_idx].__str__()+'\n\n')
THS_FOUT.flush()
Nx3Tnet[l]['%d'%(want_cnt)] = rotateTransform
if FLAGS.visu:
# if(fn==0 and batch_idx== nnnnn):
RAimg_filename = '%d_beforeR_label_%s_pred_%s.jpg' % (want_cnt, SHAPE_NAMES[l],SHAPE_NAMES[pred_val[i-start_idx]])
RAimg_filename = os.path.join(THS_DIR, RAimg_filename)
RAoutput_img = pc_util.point_cloud_three_views(np.squeeze(rotated_data[i-start_idx, :, :]))
# RAoutput_img = pc_util.draw_point_cloud(np.squeeze(rotated_data[0, :, :]))
# print(rotated_data[0, :, :][0])
scipy.misc.imsave(RAimg_filename, RAoutput_img)
# print("---")
# print(point_cloud_transformed[0, :, :][0])
Nimg_filename = '%d_TnetR_label_%s_pred_%s.jpg' % (want_cnt, SHAPE_NAMES[l],SHAPE_NAMES[pred_val[i-start_idx]])
Nimg_filename = os.path.join(THS_DIR, Nimg_filename)
Noutput_img = pc_util.point_cloud_three_views(np.squeeze(point_cloud_transformed[i-start_idx, :, :]))
# Noutput_img = pc_util.draw_point_cloud(np.squeeze(point_cloud_transformed[0, :, :]))
scipy.misc.imsave(Nimg_filename, Noutput_img)
if pred_val[i-start_idx] != l: # 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
# else:
# img_filename = '%d_label_%s_pred_%s.jpg' % (want_cnt, SHAPE_NAMES[l],
# SHAPE_NAMES[pred_val[i-start_idx]])
# img_filename = os.path.join(WANT_DIR, img_filename)
# output_img = pc_util.point_cloud_three_views(np.squeeze(current_data[i, :, :]))
# scipy.misc.imsave(img_filename, output_img)
# want_cnt +=
want_cnt += 1
#save to npz
print (len(SHAPE_NAMES))
for i in range(0, len(SHAPE_NAMES)):
npzFile = os.path.join(ROTMAT_DIR, '%s.npz'%(SHAPE_NAMES[i]))
np.savez(npzFile, **(Nx3Tnet[i]))
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]))
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)
file_size = current_data.shape[0]
print(file_size)
# set by wind:
# my code is based on batch_size = 1
# set batch_size = 1 for this file
for batch_idx in range(file_size):
start_idx = batch_idx
end_idx = batch_idx + 1
cur_batch_size = 1
#-------------------------------------------------------------------
# get critical points
#-------------------------------------------------------------------
no_influence_position = current_data[start_idx, 0, :].copy()
global_feature_list = []
orgin_data = current_data[start_idx, :, :].copy()
for change_point in range(NUM_POINT):
current_data[start_idx,
change_point, :] = no_influence_position.copy()
for change_point in range(NUM_POINT):
current_data[start_idx, change_point, :] = orgin_data[
change_point, :].copy()
# Aggregating BEG
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
}
global_feature_val = sess.run(ops['global_feature'],
feed_dict=feed_dict)
global_feature_list.append(global_feature_val)
critical_points = []
max_feature = np.zeros(global_feature_list[0].size) - 10
feature_points = np.zeros(global_feature_list[0].size)
for index in range(len(global_feature_list)):
#distance = math.sqrt(((global_feature_list[index] - global_feature_list[-1]) ** 2).sum())
#distance_list.append(distance)
top = global_feature_list[index]
feature_points = np.where(top > max_feature, index,
feature_points)
max_feature = np.where(top > max_feature, top, max_feature)
for index in feature_points[0]:
critical_points.append(orgin_data[int(index), :])
critical_points = list(set([tuple(t) for t in critical_points]))
print(len(critical_points))
img_filename = './test/%d_critical_points.jpg' % (start_idx)
output_img = pc_util.point_cloud_three_views(
np.squeeze(critical_points))
scipy.misc.imsave(img_filename, output_img)
img_filename = './test/%d_orgin_points.jpg' % (start_idx)
output_img = pc_util.point_cloud_three_views(
np.squeeze(orgin_data))
scipy.misc.imsave(img_filename, output_img)
#-------------------------------------------------------------------
# get upper-bound points
#-------------------------------------------------------------------
upper_bound_points = np.empty_like(orgin_data.shape)
upper_bound_points = orgin_data.copy()
current_data[start_idx, :, :] = orgin_data.copy()
search_step = 0.02
stand_feature = np.empty_like(global_feature_list[-1].shape)
max_position = [-1, -1, -1]
min_position = [1, 1, 1]
for point_index in range(NUM_POINT):
max_position = np.maximum(
max_position, current_data[start_idx, point_index, :])
min_position = np.minimum(
min_position, current_data[start_idx, point_index, :])
print(max_position)
print(min_position)
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
}
global_feature_val = sess.run(ops['global_feature'],
feed_dict=feed_dict)
stand_feature = global_feature_val.copy()
change_point = 0
current_data[start_idx, :, :] = orgin_data.copy()
for point_index in range(NUM_POINT):
if not (point_index in feature_points[0]):
change_point = point_index
break
for x in np.linspace(
min_position[0], max_position[0],
(max_position[0] - min_position[0]) // search_step + 1):
for y in np.linspace(
min_position[1], max_position[1],
(max_position[1] - min_position[1]) // search_step + 1):
for z in np.linspace(
min_position[2], max_position[2],
(max_position[2] - min_position[2]) // search_step +
1):
current_data[start_idx,
change_point, :] = (x, y, z
) #+ orgin_position
# Aggregating BEG
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
}
global_feature_val = sess.run(
ops['global_feature'], feed_dict=feed_dict)
if (global_feature_val <= stand_feature).all():
upper_bound_points = np.append(
upper_bound_points,
np.array([[x, y, z]]),
axis=0)
print(x)
img_filename = './test/%d_upper_bound_points.jpg' % (start_idx)
output_img = pc_util.point_cloud_three_views(
np.squeeze(upper_bound_points))
scipy.misc.imsave(img_filename, output_img)
current_data[start_idx, :, :] = orgin_data.copy()
for fn in range(len(TEST_FILES)):
print('----' + str(fn) + '----')
current_data, current_label = provider.loadDataFile(TEST_FILES[fn])
current_data = current_data[:, 0:NUM_POINT, :]
current_label = np.squeeze(current_label)
file_size = current_data.shape[0]
for file_idx in range(0, file_size, BATCH_SIZE):
if file_idx + BATCH_SIZE > file_size:
break
data = current_data[file_idx:file_idx + BATCH_SIZE, :, :]
for idx in range(BATCH_SIZE):
img_filename = f"images/test_{count}_idx_{idx}_front.jpg"
output_img = pc_util.point_cloud_three_views(np.squeeze(data[idx]))
skimage.io.imsave(img_filename, output_img)
if ROTATE_POINT_CLOUD:
for k in range(6):
rotated_data = provider.rotate_point_by_label(
data, [k] * BATCH_SIZE)
for idx in range(BATCH_SIZE):
img_filename = f"images/test_{count}_idx_{idx}_rot_{k}.jpg"
output_img = pc_util.point_cloud_three_views(
np.squeeze(rotated_data[idx]))
skimage.io.imsave(img_filename, output_img)
if ROTATE_TENSOR:
w = tf.constant(data)
for k in range(6):
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')
total_correct_seg = 0
# data_utils.shuffle_points(TEST_DATA)
# current_data, current_label = data_utils.get_current_data(TEST_DATA, TEST_LABELS, NUM_POINT)
# current_data, current_label = data_utils.get_current_data_h5(TEST_DATA, TEST_LABELS, NUM_POINT)
current_data, current_label, current_mask = data_utils.get_current_data_withmask_h5(TEST_DATA, TEST_LABELS, TEST_MASKS, NUM_POINT, shuffle=False)
current_label = np.squeeze(current_label)
current_mask = np.squeeze(current_mask)
num_batches = current_data.shape[0]//BATCH_SIZE
current_pred = []
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_seg_sum = np.zeros((cur_batch_size, NUM_POINT, 2)) # 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['masks_pl']: current_mask[start_idx:end_idx],
ops['labels_pl']: current_label[start_idx:end_idx],
ops['is_training_pl']: is_training}
loss_val, pred_val, seg_val = sess.run([ops['loss'], ops['pred'],ops['seg_pred']],
feed_dict=feed_dict)
batch_pred_sum += pred_val
batch_seg_sum += seg_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
seg_val = np.argmax(batch_seg_sum, 2)
seg_correct = np.sum(seg_val == current_mask[start_idx:end_idx])
total_correct_seg += seg_correct
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)
current_pred.append(pred_val[i-start_idx])
fout.write('%s, %s\n' % (SHAPE_NAMES[pred_val[i-start_idx]], SHAPE_NAMES[l]))
gt_mask = current_mask[i]
pred_mask = seg_val[i-start_idx]
pred_mask_idx = np.where(pred_mask==1)[0]
gt_mask_idx = np.where(gt_mask==1)[0]
correct_obj_mask = np.where((pred_mask==gt_mask) & (pred_mask==1))[0]
if (len(correct_obj_mask)==1):
continue
if (FLAGS.visu_mask and pred_val[i-start_idx] == l):
fname = str(start_idx)+'_gt'
fname = os.path.join(DUMP_DIR, fname)
save_binfiles(current_data[start_idx,:,:], gt_mask,fname)
fname = str(start_idx)+'_pred'
fname = os.path.join(DUMP_DIR, fname)
save_binfiles(current_data[start_idx,:,:], pred_mask,fname)
fname = str(start_idx)+'_pred.obj'
fname = os.path.join(DUMP_DIR, fname)
output_color_point_cloud(current_data[start_idx,:,:], pred_mask,fname)
fname = str(start_idx)+'_gt.obj'
fname = os.path.join(DUMP_DIR, fname)
output_color_point_cloud(current_data[start_idx,:,:], gt_mask,fname)
###1)
img_filename = '%d_label_%s_pred_%s_gtmask.jpg' % (i, 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, gt_mask_idx, :]))
scipy.misc.imsave(img_filename, output_img)
# #save ply
# ply_filename = '%d_label_%s_pred_%s_gtmask.ply' % (i, SHAPE_NAMES[l],
# SHAPE_NAMES[pred_val[i-start_idx]])
# ply_filename = os.path.join(DUMP_DIR, ply_filename)
# data_utils.save_ply(np.squeeze(current_data[i, gt_mask_idx, :]),ply_filename)
###2)
img_filename = '%d_label_%s_pred_%s_predmask.jpg' % (i, 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, pred_mask_idx, :]))
scipy.misc.imsave(img_filename, output_img)
# #save ply
# ply_filename = '%d_label_%s_pred_%s_predmask.ply' % (i, SHAPE_NAMES[l],
# SHAPE_NAMES[pred_val[i-start_idx]])
# ply_filename = os.path.join(DUMP_DIR, ply_filename)
# data_utils.save_ply(np.squeeze(current_data[i, pred_mask_idx, :]),ply_filename)
###3)
img_filename = '%d_label_%s_pred_%s_correctpredmask.jpg' % (i, 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, correct_obj_mask, :]))
scipy.misc.imsave(img_filename, output_img)
# #save ply
# ply_filename = '%d_label_%s_pred_%s_correctpredmask.ply' % (i, SHAPE_NAMES[l],
# SHAPE_NAMES[pred_val[i-start_idx]])
# ply_filename = os.path.join(DUMP_DIR, ply_filename)
# data_utils.save_ply(np.squeeze(current_data[i, correct_obj_mask, :]),ply_filename)
# 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)
# #save ply
# ply_filename = '%d_label_%s_pred_%s.ply' % (error_cnt, SHAPE_NAMES[l],
# SHAPE_NAMES[pred_val[i-start_idx]])
# ply_filename = os.path.join(DUMP_DIR, ply_filename)
# data_utils.save_ply(np.squeeze(current_data[i, :, :]),ply_filename)
# error_cnt += 1
log_string('total seen: %d' % (total_seen))
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))))
log_string('seg accuracy: %f' % (total_correct_seg / (float(total_seen)*NUM_POINT)))
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]))
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]))
