Python provider.random_scale_point_cloud() Examples

The following are 7 code examples of provider.random_scale_point_cloud(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module provider , or try the search function .
Example #1
Source File: train_cls.py    From dgl with Apache License 2.0 5 votes vote down vote up 
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()
Example #2
Source File: train.py    From dfc2019 with MIT License 5 votes vote down vote up 
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train samples
    train_idxs = np.arange(0, len(TRAIN_DATASET))
    np.random.shuffle(train_idxs)
    num_batches = len(TRAIN_DATASET)/BATCH_SIZE
    
    log_string(str(datetime.now()))

    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
        batch_data, batch_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx)
        # Augment batched point clouds by rotation and jittering
        #aug_data = batch_data
        #aug_data = provider.random_scale_point_cloud(batch_data)
        batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3])
        feed_dict = {ops['pointclouds_pl']: batch_data,
                     ops['labels_pl']: batch_label,
                     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, 2)
        correct = np.sum(pred_val == batch_label)
        total_correct += correct
        total_seen += (BATCH_SIZE*NUM_POINT)
        loss_sum += loss_val

        if (batch_idx+1)%10 == 0:
            log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
            log_string('mean loss: %f' % (loss_sum / 10))
            log_string('accuracy: %f' % (total_correct / float(total_seen)))
            total_correct = 0
            total_seen = 0
            loss_sum = 0
Example #3
Source File: modelnet_h5_dataset.py    From dfc2019 with MIT License 5 votes vote down vote up 
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)
Example #4
Source File: modelnet_dataset.py    From dfc2019 with MIT License 5 votes vote down vote up 
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)
Example #5
Source File: train.py    From SpiderCNN with MIT License 4 votes vote down vote up 
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train samples
    train_idxs = np.arange(0, len(TRAIN_DATASET))
    np.random.shuffle(train_idxs)
    num_batches = len(TRAIN_DATASET)/BATCH_SIZE
    
    log_string(str(datetime.now()))

    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
        batch_data, batch_label, batch_cls_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx)
        # Augment batched point clouds by rotation and jittering
        #aug_data = batch_data
        #aug_data = provider.random_scale_point_cloud(batch_data)
        batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3])
        feed_dict = {ops['pointclouds_pl']: batch_data,
                     ops['labels_pl']: batch_label,
                     ops['cls_labels_pl']: batch_cls_label,
                     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, 2)
        correct = np.sum(pred_val == batch_label)
        total_correct += correct
        total_seen += (BATCH_SIZE*NUM_POINT)
        loss_sum += loss_val

        if (batch_idx+1)%10 == 0:
            log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
            log_string('mean loss: %f' % (loss_sum / 10))
            log_string('accuracy: %f' % (total_correct / float(total_seen)))
            total_correct = 0
            total_seen = 0
            loss_sum = 0
Example #6
Source File: train_one_hot.py    From dfc2019 with MIT License 4 votes vote down vote up 
def train_one_epoch(sess, ops, train_writer):
    """ ops: dict mapping from string to tf ops """
    is_training = True
    
    # Shuffle train samples
    train_idxs = np.arange(0, len(TRAIN_DATASET))
    np.random.shuffle(train_idxs)
    num_batches = len(TRAIN_DATASET)/BATCH_SIZE
    
    log_string(str(datetime.now()))

    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
        batch_data, batch_label, batch_cls_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx)
        # Augment batched point clouds by rotation and jittering
        #aug_data = batch_data
        #aug_data = provider.random_scale_point_cloud(batch_data)
        batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3])
        feed_dict = {ops['pointclouds_pl']: batch_data,
                     ops['labels_pl']: batch_label,
                     ops['cls_labels_pl']: batch_cls_label,
                     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, 2)
        correct = np.sum(pred_val == batch_label)
        total_correct += correct
        total_seen += (BATCH_SIZE*NUM_POINT)
        loss_sum += loss_val

        if (batch_idx+1)%10 == 0:
            log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches))
            log_string('mean loss: %f' % (loss_sum / 10))
            log_string('accuracy: %f' % (total_correct / float(total_seen)))
            total_correct = 0
            total_seen = 0
            loss_sum = 0
Example #7
Source File: train.py    From ldgcnn with MIT License 4 votes vote down vote up 
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)))