Python provider.shift_point_cloud方法代码示例

# 需要导入模块: import provider [as 别名]
# 或者: from provider import shift_point_cloud [as 别名]
def train(net, opt, scheduler, train_loader, dev):

    net.train()

    total_loss = 0
    num_batches = 0
    total_correct = 0
    count = 0
    loss_f = nn.CrossEntropyLoss()
    with tqdm.tqdm(train_loader, ascii=True) as tq:
        for data, label in tq:
            data = data.data.numpy()
            data = provider.random_point_dropout(data)
            data[:, :, 0:3] = provider.random_scale_point_cloud(data[:, :, 0:3])
            data[:, :, 0:3] = provider.jitter_point_cloud(data[:, :, 0:3])
            data[:, :, 0:3] = provider.shift_point_cloud(data[:, :, 0:3])
            data = torch.tensor(data)
            label = label[:, 0]

            num_examples = label.shape[0]
            data, label = data.to(dev), label.to(dev).squeeze().long()
            opt.zero_grad()
            logits = net(data)
            loss = loss_f(logits, label)
            loss.backward()
            opt.step()

            _, preds = logits.max(1)

            num_batches += 1
            count += num_examples
            loss = loss.item()
            correct = (preds == label).sum().item()
            total_loss += loss
            total_correct += correct

            tq.set_postfix({
                'AvgLoss': '%.5f' % (total_loss / num_batches),
                'AvgAcc': '%.5f' % (total_correct / count)})
    scheduler.step() 

# 需要导入模块: import provider [as 别名]
# 或者: from provider import shift_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 shift_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 shift_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)))