Python tqdm.trange方法代碼示例

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def validate_on_lfw(model, lfw_160_path):
    # Read the file containing the pairs used for testing
    pairs = lfw.read_pairs('validation-LFW-pairs.txt')
    # Get the paths for the corresponding images
    paths, actual_issame = lfw.get_paths(lfw_160_path, pairs)
    num_pairs = len(actual_issame)

    all_embeddings = np.zeros((num_pairs * 2, 512), dtype='float32')
    for k in tqdm.trange(num_pairs):
        img1 = cv2.imread(paths[k * 2], cv2.IMREAD_COLOR)[:, :, ::-1]
        img2 = cv2.imread(paths[k * 2 + 1], cv2.IMREAD_COLOR)[:, :, ::-1]
        batch = np.stack([img1, img2], axis=0)
        embeddings = model.eval_embeddings(batch)
        all_embeddings[k * 2: k * 2 + 2, :] = embeddings

    tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(
        all_embeddings, actual_issame, distance_metric=1, subtract_mean=True)

    print('Accuracy: %2.5f+-%2.5f' % (np.mean(accuracy), np.std(accuracy)))
    print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))

    auc = metrics.auc(fpr, tpr)
    print('Area Under Curve (AUC): %1.3f' % auc)
    eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x), 0., 1.)
    print('Equal Error Rate (EER): %1.3f' % eer) 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def create_training_file(docs, tokenizer, args, epoch_num):
    epoch_filename = args.output_dir / "epoch_{}.json".format(epoch_num)
    num_instances = 0
    with epoch_filename.open('w') as epoch_file:
        for doc_idx in trange(len(docs), desc="Document"):
            doc_instances = create_instances_from_document(
                docs, doc_idx, max_seq_length=args.max_seq_len, short_seq_prob=args.short_seq_prob,
                masked_lm_prob=args.masked_lm_prob, max_predictions_per_seq=args.max_predictions_per_seq,
                whole_word_mask=args.do_whole_word_mask, tokenizer=tokenizer,
                next_sent_prediction=args.do_next_sent_prediction)
            doc_instances = [json.dumps(instance) for instance in doc_instances]
            for instance in doc_instances:
                epoch_file.write(instance + '\n')
                num_instances += 1
    metrics_file = args.output_dir / "epoch_{}_metrics.json".format(epoch_num)
    with metrics_file.open('w') as metrics_file:
        metrics = {
            "num_training_examples": num_instances,
            "max_seq_len": args.max_seq_len
        }
        metrics_file.write(json.dumps(metrics)) 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def _preprocess(self, ids, ids_file):
        print("Preprocessing mask, this will take a while. " + \
              "But don't worry, it only run once for each split.")
        tbar = trange(len(ids))
        new_ids = []
        for i in tbar:
            img_id = ids[i]
            cocotarget = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))
            img_metadata = self.coco.loadImgs(img_id)[0]
            mask = self._gen_seg_mask(cocotarget, img_metadata['height'],
                                      img_metadata['width'])
            # more than 1k pixels
            if (mask > 0).sum() > 1000:
                new_ids.append(img_id)
            tbar.set_description('Doing: {}/{}, got {} qualified images'. \
                                 format(i, len(ids), len(new_ids)))
        print('Found number of qualified images: ', len(new_ids))
        torch.save(new_ids, ids_file)
        return new_ids 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def start(self):
        """
        Start testing with a progress bar.
        """
        if not self._reset_called:
            self.ds.reset_state()
        itr = self.ds.__iter__()
        if self.warmup:
            for _ in tqdm.trange(self.warmup, **get_tqdm_kwargs()):
                next(itr)
        # add smoothing for speed benchmark
        with get_tqdm(total=self.test_size,
                      leave=True, smoothing=0.2) as pbar:
            for idx, dp in enumerate(itr):
                pbar.update()
                if idx == self.test_size - 1:
                    break 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def test(data):
    print('Testing model...')
    model = Model(data).to(device)
    model.load_state_dict(torch.load(data.model_path))
    instances = data.ids
    pred_results = []
    model.eval()
    test_num = len(instances)
    total_batch = test_num // data.batch_size + 1
    for batch in trange(total_batch):
        start, end = slice_set(batch, data.batch_size, test_num)
        instance = instances[start:end]
        if not instance: continue
        _, mask, *model_input, char_recover = load_batch(instance, True)
        tag_seq = model(mask, *model_input)
        pred_label = seq2label(tag_seq, mask, data.label_alphabet, char_recover)
        pred_results += pred_label
    return pred_results 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def create_and_train_model(self):
        """
        Model training and scoring.
        """
        print("\nTraining started.\n")
        self.model = SignedGraphConvolutionalNetwork(self.device, self.args, self.X).to(self.device)
        self.optimizer = torch.optim.Adam(self.model.parameters(),
                                          lr=self.args.learning_rate,
                                          weight_decay=self.args.weight_decay)
        self.model.train()
        self.epochs = trange(self.args.epochs, desc="Loss")
        for epoch in self.epochs:
            start_time = time.time()
            self.optimizer.zero_grad()
            loss, _ = self.model(self.positive_edges, self.negative_edges, self.y)
            loss.backward()
            self.epochs.set_description("SGCN (Loss=%g)" % round(loss.item(), 4))
            self.optimizer.step()
            self.logs["training_time"].append([epoch+1, time.time()-start_time])
            if self.args.test_size > 0:
                self.score_model(epoch) 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def _preprocess(self, ids, ids_file):
        print("Preprocessing mask, this will take a while." + \
              "But don't worry, it only run once for each split.")
        tbar = trange(len(ids))
        new_ids = []
        for i in tbar:
            img_id = ids[i]
            cocotarget = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))
            img_metadata = self.coco.loadImgs(img_id)[0]
            mask = self._gen_seg_mask(cocotarget, img_metadata['height'], img_metadata['width'])
            # more than 1k pixels
            if (mask > 0).sum() > 1000:
                new_ids.append(img_id)
            tbar.set_description('Doing: {}/{}, got {} qualified images'. \
                                 format(i, len(ids), len(new_ids)))
        print('Found number of qualified images: ', len(new_ids))
        with open(ids_file, 'wb') as f:
            pickle.dump(new_ids, f)
        return new_ids 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def create_dataset(seqs: List[List[str]],
                   tags: List[List[str]],
                   word_to_ix: Mapping[str, int],
                   max_seq_len: int,
                   pad_ix: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Convert List[str] -> torch.Tensor.
    Returns:
        seqs_tensor: shape=[num_seqs, max_seq_len].
        seqs_mask: shape=[num_seqs, max_seq_len].
        tags_tesnor: shape=[num_seqs, max_seq_len].
    """
    assert len(seqs) == len(tags)
    num_seqs = len(seqs)
    seqs_tensor = torch.ones(num_seqs, max_seq_len) * pad_ix
    seqs_mask = torch.zeros(num_seqs, max_seq_len)
    tags_tesnor = torch.ones(num_seqs, max_seq_len) * pad_ix
    for i in trange(num_seqs):
        seqs_mask[i, : len(seqs[i])] = 1
        for j, word in enumerate(seqs[i]):
            seqs_tensor[i, j] = word_to_ix.get(word, word_to_ix['[UNK]'])
        for j, tag in enumerate(tags[i]):
            tags_tesnor[i, j] = word_to_ix.get(tag, word_to_ix['[UNK]'])
    return seqs_tensor.long(), seqs_mask, tags_tesnor.long() 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def train(unet, batch_size, epochs, epoch_lapse, threshold, learning_rate, criterion, optimizer, x_train, y_train, x_val, y_val, width_out, height_out):
    epoch_iter = np.ceil(x_train.shape[0] / batch_size).astype(int)
    t = trange(epochs, leave=True)
    for _ in t:
        total_loss = 0
        for i in range(epoch_iter):
            batch_train_x = torch.from_numpy(x_train[i * batch_size : (i + 1) * batch_size]).float()
            batch_train_y = torch.from_numpy(y_train[i * batch_size : (i + 1) * batch_size]).long()
            if use_gpu:
                batch_train_x = batch_train_x.cuda()
                batch_train_y = batch_train_y.cuda()
            batch_loss = train_step(batch_train_x , batch_train_y, optimizer, criterion, unet, width_out, height_out)
            total_loss += batch_loss
        if (_+1) % epoch_lapse == 0:
            val_loss = get_val_loss(x_val, y_val, width_out, height_out, unet)
            print("Total loss in epoch %f : %f and validation loss : %f" %(_+1, total_loss, val_loss))
    gc.collect() 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def count_seqs_with_words(seqs, halflength, ming, maxg, alpha, revcomp, desc):
    if alpha == 'protein':
        ambiguous_character = 'X'
    else:
        ambiguous_character = 'N'
    gapped_kmer_dict = {}  # each key is the gapped k-mer word
    for g in trange(ming, maxg + 1, 1, desc=desc):
        w = g+2*halflength # length of the word
        gap = g * ambiguous_character
        for seq in seqs:
            slen = len(seq)
            for i in range(0, slen-w+1):
                word = seq[i : i+w]
                # skip word if it contains an ambiguous character
                if ambiguous_character in word:
                    continue
                # convert word to a gapped word. Only the first and last half-length letters are preserved
                word = word[0:halflength] + gap + word[-halflength:]
                update_gapped_kmer_dict(gapped_kmer_dict, word, revcomp)
    return gapped_kmer_dict 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def image_copy_to_dir(mode, x_paths, y_paths):
    target_path = '/run/media/tkwoo/myWorkspace/workspace/01.dataset/03.Mask_data/cityscape'
    target_path = os.path.join(target_path, mode)

    for idx in trange(len(x_paths)):
        image = cv2.imread(x_paths[idx], 1)
        mask = cv2.imread(y_paths[idx], 0)

        image = cv2.resize(image, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_LINEAR)
        mask = cv2.resize(mask, None, fx=0.25, fy=0.25, interpolation=cv2.INTER_NEAREST)

        cv2.imwrite(os.path.join(target_path, 'image', os.path.basename(x_paths[idx])), image)
        cv2.imwrite(os.path.join(target_path, 'mask', os.path.basename(y_paths[idx])), mask)

        # show = image.copy()
        # mask = (mask.astype(np.float32)*255/33).astype(np.uint8)
        # mask_color = cv2.applyColorMap(mask, cv2.COLORMAP_JET)
        # show = cv2.addWeighted(show, 0.5, mask_color, 0.5, 0.0)
        # cv2.imshow('show', show)
        # key = cv2.waitKey(1)
        # if key == 27:
        #     return 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def _make_progress_bar(self, iterations):
        """
        Creates a progress bar using :class:`tqdm`.

        Parameters
        ----------
        iterations: `int`
            Number of iterations to be performed.

        Returns
        -------
        progress_bar: :class:`tqdm.std.tqdm`
            An iterator object.
        """

        progress_bar = tqdm.trange(
            iterations,
            unit_scale=(self._chunksize // 1024),
            unit="KiB",
            dynamic_ncols=True,
            bar_format='{desc}: {percentage:3.0f}%|{bar}| {n_fmt}/{total_fmt}KiB '
                '[{elapsed}<{remaining}, {rate_fmt}{postfix}]',
        )
        return progress_bar 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def _preprocess(self, ids, ids_file):
        print("Preprocessing mask, this will take a while." + \
              "But don't worry, it only run once for each split.")
        tbar = trange(len(ids))
        new_ids = []
        for i in tbar:
            img_id = ids[i]
            cocotarget = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))
            img_metadata = self.coco.loadImgs(img_id)[0]
            mask = self._gen_seg_mask(cocotarget, img_metadata['height'],
                                      img_metadata['width'])
            # more than 1k pixels
            if (mask > 0).sum() > 1000:
                new_ids.append(img_id)
            tbar.set_description('Doing: {}/{}, got {} qualified images'.\
                format(i, len(ids), len(new_ids)))
        print('Found number of qualified images: ', len(new_ids))
        with open(ids_file, 'wb') as f:
            pickle.dump(new_ids, f)
        return new_ids 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def _preprocess(self, ids, ids_file):
        print("Preprocessing mask, this will take a while." + \
            "But don't worry, it only run once for each split.")
        tbar = trange(len(ids))
        new_ids = []
        for i in tbar:
            img_id = ids[i]
            cocotarget = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))
            img_metadata = self.coco.loadImgs(img_id)[0]
            mask = self._gen_seg_mask(cocotarget, img_metadata['height'], 
                                      img_metadata['width'])
            # more than 1k pixels
            if (mask > 0).sum() > 1000:
                new_ids.append(img_id)
            tbar.set_description('Doing: {}/{}, got {} qualified images'.\
                format(i, len(ids), len(new_ids)))
        print('Found number of qualified images: ', len(new_ids))
        torch.save(new_ids, ids_file)
        return new_ids 

# 需要導入模塊: import tqdm [as 別名]
# 或者: from tqdm import trange [as 別名]
def _filter_idx(self,
                    idx,
                    idx_file,
                    pixels_thr=1000):
        logging.info("Filtering mask index")
        tbar = trange(len(idx))
        filtered_idx = []
        for i in tbar:
            img_id = idx[i]
            coco_target = self.coco.loadAnns(self.coco.getAnnIds(imgIds=img_id))
            img_metadata = self.coco.loadImgs(img_id)[0]
            mask = self._gen_seg_mask(
                coco_target,
                img_metadata["height"],
                img_metadata["width"])
            if (mask > 0).sum() > pixels_thr:
                filtered_idx.append(img_id)
            tbar.set_description("Doing: {}/{}, got {} qualified images".format(i, len(idx), len(filtered_idx)))
        logging.info("Found number of qualified images: {}".format(len(filtered_idx)))
        np.save(idx_file, np.array(filtered_idx, np.int32))
        return filtered_idx