Python provider.rotate_point_cloud方法代码示例

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def get_example(self, i):
        """Return i-th data"""
        if self.augment:
            rotated_data = provider.rotate_point_cloud(
                self.data[i:i + 1, :, :])
            jittered_data = provider.jitter_point_cloud(rotated_data)
            point_data = jittered_data[0]
        else:
            point_data = self.data[i]
        # pint_data (2048, 3): (num_point, k) --> convert to (k, num_point, 1)
        point_data = np.transpose(
            point_data.astype(np.float32), (1, 0))[:, :, None]
        assert point_data.dtype == np.float32
        assert self.label[i].dtype == np.int32
        return point_data, self.label[i] 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def _augment_batch_data(self, batch_data):
        rotated_data = provider.rotate_point_cloud(batch_data)
        rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
        jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
        jittered_data = provider.shift_point_cloud(jittered_data)
        jittered_data = provider.jitter_point_cloud(jittered_data)
        rotated_data[:,:,0:3] = jittered_data
        return provider.shuffle_points(rotated_data) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def _augment_batch_data(self, batch_data):
        if self.normal_channel:
            rotated_data = provider.rotate_point_cloud_with_normal(batch_data)
            rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data)
        else:
            rotated_data = provider.rotate_point_cloud(batch_data)
            rotated_data = provider.rotate_perturbation_point_cloud(rotated_data)
    
        jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3])
        jittered_data = provider.shift_point_cloud(jittered_data)
        jittered_data = provider.jitter_point_cloud(jittered_data)
        rotated_data[:,:,0:3] = jittered_data
        return provider.shuffle_points(rotated_data) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    current_data, current_label, current_parts = data_utils.get_current_data_parts_h5(TRAIN_DATA, TRAIN_LABELS, TRAIN_PARTS, NUM_POINT)

    current_label = np.squeeze(current_label)
    current_parts = np.squeeze(current_parts)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_seen = 0
    loss_sum = 0
    total_correct_seg = 0    

    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx+1) * BATCH_SIZE

        # Augment batched point clouds by rotation and jittering
        rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
        jittered_data = provider.jitter_point_cloud(rotated_data)
        feed_dict = {ops['pointclouds_pl']: jittered_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['parts_pl']: current_parts[start_idx:end_idx],
                     ops['is_training_pl']: is_training,}
        summary, step, _, loss_val, seg_val = sess.run([ops['merged'], ops['step'],
            ops['train_op'], ops['loss'], ops['seg_pred']], feed_dict=feed_dict)
        train_writer.add_summary(summary, step)
        
        seg_val = np.argmax(seg_val, 2)
        seg_correct = np.sum(seg_val == current_parts[start_idx:end_idx])
        total_correct_seg += seg_correct

        total_seen += BATCH_SIZE
        loss_sum += loss_val

    
    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('seg accuracy: %f' % (total_correct_seg / (float(total_seen)*NUM_POINT))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    if (".h5" in TRAIN_FILE):
        current_data, current_label = data_utils.get_current_data_h5(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)
    else:
        current_data, current_label = data_utils.get_current_data(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)


    current_label = np.squeeze(current_label)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_correct = 0
    total_seen = 0
    loss_sum = 0
       
    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx+1) * BATCH_SIZE
        
        # Augment batched point clouds by rotation and jittering
        rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
        jittered_data = provider.jitter_point_cloud(rotated_data)

        feed_dict = {ops['pointclouds_pl']: jittered_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['is_training_pl']: is_training,}
        summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
            ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
        train_writer.add_summary(summary, step)
        pred_val = np.argmax(pred_val, 1)
        correct = np.sum(pred_val == current_label[start_idx:end_idx])
        total_correct += correct
        total_seen += BATCH_SIZE
        loss_sum += loss_val
    
    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('accuracy: %f' % (total_correct / float(total_seen))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train files
    train_file_idxs = np.arange(0, len(TRAIN_FILES))
    np.random.shuffle(train_file_idxs)
    
    for fn in range(len(TRAIN_FILES)):
        log_string('----' + str(fn) + '-----')
        current_data, current_label, _ = provider.loadDataFile_with_normal(TRAIN_FILES[train_file_idxs[fn]])
        current_data = current_data[:,0:NUM_POINT,:]
        current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))           
        current_label = np.squeeze(current_label)

        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        total_correct = 0
        total_seen = 0
        loss_sum = 0
       
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE
            
            # Augment batched point clouds by rotation and jittering
            rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
            jittered_data = provider.jitter_point_cloud(rotated_data)

            feed_dict = {ops['pointclouds_pl']: jittered_data,
                         ops['labels_pl']: current_label[start_idx:end_idx],
                         ops['is_training_pl']: is_training,}
            summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
                ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
            train_writer.add_summary(summary, step)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += loss_val
        
        log_string('mean loss: %f' % (loss_sum / float(num_batches)))
        log_string('accuracy: %f' % (total_correct / float(total_seen))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train files
    train_file_idxs = np.arange(0, len(TRAIN_FILES))
    np.random.shuffle(train_file_idxs)
    
    for fn in range(len(TRAIN_FILES)):
        log_string('----' + str(fn) + '-----')
        current_data, current_label, normal_data = provider.loadDataFile_with_normal(TRAIN_FILES[train_file_idxs[fn]])
        normal_data = normal_data[:,0:NUM_POINT,:]
        current_data = current_data[:,0:NUM_POINT,:]
        current_data, current_label, shuffle_idx = provider.shuffle_data(current_data, np.squeeze(current_label))           
        current_label = np.squeeze(current_label)
        normal_data = normal_data[shuffle_idx, ...]

        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        total_correct = 0
        total_seen = 0
        loss_sum = 0
       
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE
            
            # Augment batched point clouds by rotation and jittering
            rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
            jittered_data = provider.jitter_point_cloud(rotated_data)
            input_data = np.concatenate((jittered_data, normal_data[start_idx:end_idx, :, :]), 2)
            #random point dropout
            input_data = provider.random_point_dropout(input_data)


            feed_dict = {ops['pointclouds_pl']: input_data,
                         ops['labels_pl']: current_label[start_idx:end_idx],
                         ops['is_training_pl']: is_training,}
            summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
                ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
            train_writer.add_summary(summary, step)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += loss_val
        
        log_string('mean loss: %f' % (loss_sum / float(num_batches)))
        log_string('accuracy: %f' % (total_correct / float(total_seen))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    current_data, current_label, current_mask = data_utils.get_current_data_withmask_h5(TRAIN_DATA, TRAIN_LABELS, TRAIN_MASKS, NUM_POINT)

    current_label = np.squeeze(current_label)
    current_mask = np.squeeze(current_mask)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_correct = 0
    total_seen = 0
    loss_sum = 0
    total_correct_seg = 0    
    classify_loss_sum = 0
    seg_loss_sum = 0
    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx+1) * BATCH_SIZE

        # Augment batched point clouds by rotation and jittering
        rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
        jittered_data = provider.jitter_point_cloud(rotated_data)
        feed_dict = {ops['pointclouds_pl']: jittered_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['masks_pl']: current_mask[start_idx:end_idx],
                     ops['is_training_pl']: is_training,}
        summary, step, _, loss_val, pred_val, seg_val, classify_loss, seg_loss = sess.run([ops['merged'], ops['step'],
            ops['train_op'], ops['loss'], ops['pred'], ops['seg_pred'], ops['classify_loss'], ops['seg_loss']], feed_dict=feed_dict)
        train_writer.add_summary(summary, step)
        pred_val = np.argmax(pred_val, 1)
        correct = np.sum(pred_val == current_label[start_idx:end_idx])
        
        seg_val = np.argmax(seg_val, 2)
        seg_correct = np.sum(seg_val == current_mask[start_idx:end_idx])
        total_correct_seg += seg_correct

        total_correct += correct
        total_seen += BATCH_SIZE
        loss_sum += loss_val
        classify_loss_sum += classify_loss
        seg_loss_sum += seg_loss
    
    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('classify mean loss: %f' % (classify_loss_sum / float(num_batches)))
    log_string('seg mean loss: %f' % (seg_loss_sum / float(num_batches)))
    log_string('accuracy: %f' % (total_correct / float(total_seen)))
    log_string('seg accuracy: %f' % (total_correct_seg / (float(total_seen)*NUM_POINT))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, gmm, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True

    if (".h5" in TRAIN_FILE):
        current_data, current_label = data_utils.get_current_data_h5(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)
    else:
        current_data, current_label = data_utils.get_current_data(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)


    current_label = np.squeeze(current_label)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_correct = 0
    total_seen = 0
    loss_sum = 0

    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx + 1) * BATCH_SIZE

        # Augment batched point clouds by rotation and jittering

        augmented_data = current_data[start_idx:end_idx, :, :]
        if augment_scale:
            augmented_data = provider.scale_point_cloud(augmented_data, smin=0.66, smax=1.5)
        if augment_rotation:
            augmented_data = provider.rotate_point_cloud(augmented_data)
        if augment_translation:
            augmented_data = provider.translate_point_cloud(augmented_data, tval = 0.2)
        if augment_jitter:
            augmented_data = provider.jitter_point_cloud(augmented_data, sigma=0.01,
                                                    clip=0.05)  # default sigma=0.01, clip=0.05
        if augment_outlier:
            augmented_data = provider.insert_outliers_to_point_cloud(augmented_data, outlier_ratio=0.02)

        feed_dict = {ops['points_pl']: augmented_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['w_pl']: gmm.weights_,
                     ops['mu_pl']: gmm.means_,
                     ops['sigma_pl']: np.sqrt(gmm.covariances_),
                     ops['is_training_pl']: is_training, }
        summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
                                                         ops['train_op'], ops['loss'], ops['pred']],
                                                        feed_dict=feed_dict)

        train_writer.add_summary(summary, step)
        pred_val = np.argmax(pred_val, 1)
        correct = np.sum(pred_val == current_label[start_idx:end_idx])
        total_correct += correct
        total_seen += BATCH_SIZE
        loss_sum += loss_val

    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('accuracy: %f' % (total_correct / float(total_seen))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True

    current_data, current_label, current_mask = data_utils.get_current_data_withmask_h5(TRAIN_DATA, TRAIN_LABELS, TRAIN_MASKS, NUM_POINT)

    current_label = np.squeeze(current_label)
    current_mask = np.squeeze(current_mask)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_correct = 0
    total_seen = 0
    loss_sum = 0
    total_correct_seg = 0    
    classify_loss_sum = 0
    seg_loss_sum = 0  
    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx+1) * BATCH_SIZE

        # Augment batched point clouds by rotation and jittering
        rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
        jittered_data = provider.jitter_point_cloud(rotated_data)
        feed_dict = {ops['pointclouds_pl']: jittered_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['masks_pl']: current_mask[start_idx:end_idx],
                     ops['is_training_pl']: is_training,}
        summary, step, _, loss_val, pred_val, seg_val, classify_loss, seg_loss = sess.run([ops['merged'], ops['step'],
            ops['train_op'], ops['loss'], ops['pred'], ops['seg_pred'], ops['classify_loss'], ops['seg_loss']], feed_dict=feed_dict)
        train_writer.add_summary(summary, step)
        pred_val = np.argmax(pred_val, 1)
        correct = np.sum(pred_val == current_label[start_idx:end_idx])

        seg_val = np.argmax(seg_val, 2)
        seg_correct = np.sum(seg_val == current_mask[start_idx:end_idx])
        total_correct_seg += seg_correct

        total_correct += correct
        total_seen += BATCH_SIZE
        loss_sum += loss_val
        classify_loss_sum += classify_loss
        seg_loss_sum += seg_loss          
    
    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('classify mean loss: %f' % (classify_loss_sum / float(num_batches)))
    log_string('seg mean loss: %f' % (seg_loss_sum / float(num_batches)))
    log_string('accuracy: %f' % (total_correct / float(total_seen)))
    log_string('seg accuracy: %f' % (total_correct_seg / (float(total_seen)*NUM_POINT))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    log_string(str(datetime.now()))

    current_data, current_label, current_mask = data_utils.get_current_data_withmask_h5(TRAIN_DATA, TRAIN_LABELS, TRAIN_MASKS, NUM_POINT)

    current_label = np.squeeze(current_label)
    current_mask = np.squeeze(current_mask)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_correct = 0
    total_seen = 0
    loss_sum = 0
    total_correct_seg = 0    
    classify_loss_sum = 0
    seg_loss_sum = 0    
    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx+1) * BATCH_SIZE

        # Augment batched point clouds by rotation and jittering
        rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
        jittered_data = provider.jitter_point_cloud(rotated_data)
        feed_dict = {ops['pointclouds_pl']: jittered_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['masks_pl']: current_mask[start_idx:end_idx],
                     ops['is_training_pl']: is_training,}
        summary, step, _, loss_val, pred_val, seg_val, classify_loss, seg_loss = sess.run([ops['merged'], ops['step'],
            ops['train_op'], ops['loss'], ops['pred'], ops['seg_pred'], ops['classify_loss'], ops['seg_loss']], feed_dict=feed_dict)
        train_writer.add_summary(summary, step)
        pred_val = np.argmax(pred_val, 1)
        correct = np.sum(pred_val == current_label[start_idx:end_idx])

        seg_val = np.argmax(seg_val, 2)
        seg_correct = np.sum(seg_val == current_mask[start_idx:end_idx])
        total_correct_seg += seg_correct

        total_correct += correct
        total_seen += BATCH_SIZE
        loss_sum += loss_val
        classify_loss_sum += classify_loss
        seg_loss_sum += seg_loss        
    
    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('classify mean loss: %f' % (classify_loss_sum / float(num_batches)))
    log_string('seg mean loss: %f' % (seg_loss_sum / float(num_batches)))
    log_string('accuracy: %f' % (total_correct / float(total_seen)))
    log_string('seg accuracy: %f' % (total_correct_seg / (float(total_seen)*NUM_POINT))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    log_string(str(datetime.now()))

    #Shuffle data
    # data_utils.shuffle_points(TRAIN_DATA)

    #get current data, shuffle and set to numpy array with desired num_point
    # current_data, current_label = data_utils.get_current_data(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)
    if (".h5" in TRAIN_FILE):
        current_data, current_label = data_utils.get_current_data_h5(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)
    else:
        current_data, current_label = data_utils.get_current_data(TRAIN_DATA, TRAIN_LABELS, NUM_POINT)

    current_label = np.squeeze(current_label)

    num_batches = current_data.shape[0]//BATCH_SIZE

    total_correct = 0
    total_seen = 0
    loss_sum = 0
    for batch_idx in range(num_batches):
        start_idx = batch_idx * BATCH_SIZE
        end_idx = (batch_idx+1) * BATCH_SIZE

        # Augment batched point clouds by rotation and jittering
        rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
        jittered_data = provider.jitter_point_cloud(rotated_data)
        feed_dict = {ops['pointclouds_pl']: jittered_data,
                     ops['labels_pl']: current_label[start_idx:end_idx],
                     ops['is_training_pl']: is_training,}
        summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
            ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
        train_writer.add_summary(summary, step)
        pred_val = np.argmax(pred_val, 1)
        correct = np.sum(pred_val == current_label[start_idx:end_idx])
        total_correct += correct
        total_seen += BATCH_SIZE
        loss_sum += loss_val
    
    log_string('mean loss: %f' % (loss_sum / float(num_batches)))
    log_string('accuracy: %f' % (total_correct / float(total_seen))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train files
    train_file_idxs = np.arange(0, len(TRAIN_FILES))
    np.random.shuffle(train_file_idxs)
    
    for fn in range(len(TRAIN_FILES)):
        log_string('----' + str(fn) + '-----')
        current_data, current_label = provider.loadDataFile(TRAIN_FILES[train_file_idxs[fn]])
        current_data = current_data[:,0:NUM_POINT,:]
        current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))            
        current_label = np.squeeze(current_label)
        
        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        total_correct = 0
        total_seen = 0
        loss_sum = 0
       
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE
            
            # Augment batched point clouds by rotation and jittering
            rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
            jittered_data = provider.jitter_point_cloud(rotated_data)
            feed_dict = {ops['pointclouds_pl']: jittered_data,
                         ops['labels_pl']: current_label[start_idx:end_idx],
                         ops['is_training_pl']: is_training,}
            summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
                ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
            train_writer.add_summary(summary, step)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += loss_val
        
        log_string('mean loss: %f' % (loss_sum / float(num_batches)))
        log_string('accuracy: %f' % (total_correct / float(total_seen))) 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import rotate_point_cloud [as 别名]
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train files
    train_file_idxs = np.arange(0, len(TRAIN_FILES))
    np.random.shuffle(train_file_idxs)
    
    for fn in range(len(TRAIN_FILES)):
        log_string('----' + str(fn) + '-----')
        # Load data and labels from the files.
        current_data, current_label = provider.loadDataFile(TRAIN_FILES[train_file_idxs[fn]])
        current_data = current_data[:,0:NUM_POINT,:]
        # Shuffle the data in the training set.
        current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label))            
        current_label = np.squeeze(current_label)
        
        file_size = current_data.shape[0]
        num_batches = file_size // BATCH_SIZE
        
        total_correct = 0
        total_seen = 0
        loss_sum = 0
       
        for batch_idx in range(num_batches):
            start_idx = batch_idx * BATCH_SIZE
            end_idx = (batch_idx+1) * BATCH_SIZE
            
            # Augment batched point clouds by rotating, jittering, shifting, 
            # and scaling.
            rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :])
            jittered_data = provider.jitter_point_cloud(rotated_data)
            jittered_data = provider.random_scale_point_cloud(jittered_data)
            jittered_data = provider.rotate_perturbation_point_cloud(jittered_data)
            jittered_data = provider.shift_point_cloud(jittered_data)
            
            # Input the augmented point cloud and labels to the graph.
            feed_dict = {ops['pointclouds_pl']: jittered_data,
                         ops['labels_pl']: current_label[start_idx:end_idx],
                         ops['is_training_pl']: is_training,}
            
            # Calculate the loss and accuracy of the input batch data.            
            summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'],
                ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict)
            
            train_writer.add_summary(summary, step)
            pred_val = np.argmax(pred_val, 1)
            correct = np.sum(pred_val == current_label[start_idx:end_idx])
            total_correct += correct
            total_seen += BATCH_SIZE
            loss_sum += loss_val
        
        log_string('mean loss: %f' % (loss_sum / float(num_batches)))
        log_string('accuracy: %f' % (total_correct / float(total_seen)))