Python nd.flip方法代碼示例

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def transform(data, target_wd, target_ht, is_train, box):
    """Crop and normnalize an image nd array."""
    if box is not None:
        x, y, w, h = box
        data = data[y:min(y+h, data.shape[0]), x:min(x+w, data.shape[1])]

    # Resize to target_wd * target_ht.
    data = mx.image.imresize(data, target_wd, target_ht)

    # Normalize in the same way as the pre-trained model.
    data = data.astype(np.float32) / 255.0
    data = (data - mx.nd.array([0.485, 0.456, 0.406])) / mx.nd.array([0.229, 0.224, 0.225])

    if is_train:
        if random.random() < 0.5:
            data = nd.flip(data, axis=1)
        data, _ = mx.image.random_crop(data, (224, 224))
    else:
        data, _ = mx.image.center_crop(data, (224, 224))

    # Transpose from (target_wd, target_ht, 3)
    # to (3, target_wd, target_ht).
    data = nd.transpose(data, (2, 0, 1))

    # If image is greyscale, repeat 3 times to get RGB image.
    if data.shape[0] == 1:
        data = nd.tile(data, (3, 1, 1))
    return data.reshape((1,) + data.shape) 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def test_layer_bidirectional():
    class RefBiLSTM(gluon.Block):
        def __init__(self, size, **kwargs):
            super(RefBiLSTM, self).__init__(**kwargs)
            with self.name_scope():
                self._lstm_fwd = gluon.rnn.LSTM(size, bidirectional=False, prefix='l0')
                self._lstm_bwd = gluon.rnn.LSTM(size, bidirectional=False, prefix='r0')

        def forward(self, inpt):
            fwd = self._lstm_fwd(inpt)
            bwd_inpt = nd.flip(inpt, 0)
            bwd = self._lstm_bwd(bwd_inpt)
            bwd = nd.flip(bwd, 0)
            return nd.concat(fwd, bwd, dim=2)

    size = 7
    in_size = 5
    weights = {}
    for d in ['l', 'r']:
        weights['lstm_{}0_i2h_weight'.format(d)] = mx.random.uniform(shape=(size*4, in_size))
        weights['lstm_{}0_h2h_weight'.format(d)] = mx.random.uniform(shape=(size*4, size))
        weights['lstm_{}0_i2h_bias'.format(d)] = mx.random.uniform(shape=(size*4,))
        weights['lstm_{}0_h2h_bias'.format(d)] = mx.random.uniform(shape=(size*4,))

    net = gluon.rnn.LSTM(size, bidirectional=True, prefix='lstm_')
    ref_net = RefBiLSTM(size, prefix='lstm_')
    net.initialize()
    ref_net.initialize()
    net_params = net.collect_params()
    ref_net_params = ref_net.collect_params()
    for k in weights:
        net_params[k].set_data(weights[k])
        ref_net_params[k.replace('l0', 'l0l0').replace('r0', 'r0l0')].set_data(weights[k])

    data = mx.random.uniform(shape=(3, 10, in_size))
    assert_allclose(net(data).asnumpy(), ref_net(data).asnumpy()) 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def fliplr(img):
    '''flip horizontal'''
    img_flip = nd.flip(img, axis=3)
    return img_flip 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def validate(val_data, val_dataset, net, ctx):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric.reset()

    from tqdm import tqdm
    for batch in tqdm(val_data):
        data, scale, center, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [flip_heatmap(o, val_dataset.joint_pairs, shift=True) for o in outputs_flip]
            outputs = [(o + o_flip)/2 for o, o_flip in zip(outputs, outputs_flipback)]

        if opt.dsnt:
            outputs = [net_dsnt(X)[0] for X in outputs]

        if len(outputs) > 1:
            outputs_stack = nd.concat(*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        if opt.dsnt:
            preds = (outputs_stack - 0.5) * scale.expand_dims(axis=1) + center.expand_dims(axis=1)
            maxvals = nd.ones(preds.shape[0:2]+(1, ))
        else:
            preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
        val_metric.update(preds, maxvals, score, imgid)

    metric_name, metric_score = val_metric.get()
    print("Inference Completed! %s = %.4f" % (metric_name, metric_score))
    return 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def validate(val_data, val_dataset, net, ctx):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric.reset()

    from tqdm import tqdm
    for batch in tqdm(val_data):
        # data, scale, center, score, imgid = val_batch_fn(batch, ctx)
        data, scale_box, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [flip_heatmap(o, val_dataset.joint_pairs, shift=True) for o in outputs_flip]
            outputs = [(o + o_flip)/2 for o, o_flip in zip(outputs, outputs_flipback)]

        if len(outputs) > 1:
            outputs_stack = nd.concat(*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        # preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
        preds, maxvals = heatmap_to_coord_alpha_pose(outputs_stack, scale_box)
        # print(preds, maxvals, scale_box)
        # print(preds, maxvals)
        # raise
        val_metric.update(preds, maxvals, score, imgid)

    res = val_metric.get()
    return 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def validate(val_data, val_dataset, net, ctx, opt):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric = COCOKeyPointsMetric(val_dataset, 'coco_keypoints',
                                     in_vis_thresh=0)

    for batch in tqdm(val_data, dynamic_ncols=True):
        # data, scale, center, score, imgid = val_batch_fn(batch, ctx)
        data, scale_box, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [flip_heatmap(o, val_dataset.joint_pairs, shift=True) for o in outputs_flip]
            outputs = [(o + o_flip) / 2 for o, o_flip in zip(outputs, outputs_flipback)]

        if len(outputs) > 1:
            outputs_stack = nd.concat(*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        # preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
        preds, maxvals = heatmap_to_coord_alpha_pose(outputs_stack, scale_box)
        val_metric.update(preds, maxvals, score, imgid)

    nullwriter = NullWriter()
    oldstdout = sys.stdout
    sys.stdout = nullwriter
    try:
        res = val_metric.get()
    finally:
        sys.stdout = oldstdout
    return res 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def random_flip(src, px=0, py=0, copy=False):
    """Randomly flip image along horizontal and vertical with probabilities.

    Parameters
    ----------
    src : mxnet.nd.NDArray
        Input image with HWC format.
    px : float
        Horizontal flip probability [0, 1].
    py : float
        Vertical flip probability [0, 1].
    copy : bool
        If `True`, return a copy of input

    Returns
    -------
    mxnet.nd.NDArray
        Augmented image.
    tuple
        Tuple of (flip_x, flip_y), records of whether flips are applied.

    """
    flip_y = np.random.choice([False, True], p=[1-py, py])
    flip_x = np.random.choice([False, True], p=[1-px, px])
    if flip_y:
        src = nd.flip(src, axis=0)
    if flip_x:
        src = nd.flip(src, axis=1)
    if copy:
        src = src.copy()
    return src, (flip_x, flip_y) 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def validate(val_data, val_dataset, net, ctx):
    if isinstance(ctx, mx.Context):
        ctx = [ctx]

    val_metric.reset()

    from tqdm import tqdm
    for batch in tqdm(val_data):
        data, scale, center, score, imgid = val_batch_fn(batch, ctx)

        outputs = [net(X) for X in data]
        if opt.flip_test:
            data_flip = [nd.flip(X, axis=3) for X in data]
            outputs_flip = [net(X) for X in data_flip]
            outputs_flipback = [flip_heatmap(o, val_dataset.joint_pairs, shift=True) for o in outputs_flip]
            outputs = [(o + o_flip)/2 for o, o_flip in zip(outputs, outputs_flipback)]

        if len(outputs) > 1:
            outputs_stack = nd.concat(*[o.as_in_context(mx.cpu()) for o in outputs], dim=0)
        else:
            outputs_stack = outputs[0].as_in_context(mx.cpu())

        preds, maxvals = get_final_preds(outputs_stack, center.asnumpy(), scale.asnumpy())
        val_metric.update(preds, maxvals, score, imgid)

    res = val_metric.get()
    return 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def transform_test_flip(data, isf=False):
    flip_data = nd.flip(data, axis=1)
    if isf:
        data = nd.transpose(data, (2, 0, 1)).astype('float32')
        flip_data = nd.transpose(flip_data, (2, 0, 1)).astype('float32')
        return data, flip_data
    return transform_test(data), transform_test(flip_data) 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def validate(nfolds=10):
    metric = FaceVerification(nfolds)
    metric_flip = FaceVerification(nfolds)
    for loader, name in zip(val_datas, targets.split(",")):
        metric.reset()
        for i, batch in enumerate(loader):
            data0s = gluon.utils.split_and_load(batch[0][0][0], ctx, even_split=False)
            data1s = gluon.utils.split_and_load(batch[0][1][0], ctx, even_split=False)
            data0s_flip = gluon.utils.split_and_load(batch[0][0][1], ctx, even_split=False)
            data1s_flip = gluon.utils.split_and_load(batch[0][1][1], ctx, even_split=False)
            issame_list = gluon.utils.split_and_load(batch[1], ctx, even_split=False)

            embedding0s = [test_net(X) for X in data0s]
            embedding1s = [test_net(X) for X in data1s]
            embedding0s_flip = [test_net(X) for X in data0s_flip]
            embedding1s_flip = [test_net(X) for X in data1s_flip]

            emb0s = [nd.L2Normalization(e, mode='instance') for e in embedding0s]
            emb1s = [nd.L2Normalization(e, mode='instance') for e in embedding1s]
            for embedding0, embedding1, issame in zip(emb0s, emb1s, issame_list):
                metric.update(issame, embedding0, embedding1)

            emb0s_flip = [nd.L2Normalization(nd.concatenate([e, ef], 1), mode='instance')
                          for e, ef in zip(embedding0s, embedding0s_flip)]
            emb1s_flip = [nd.L2Normalization(nd.concatenate([e, ef], 1), mode='instance')
                          for e, ef in zip(embedding1s, embedding1s_flip)]
            for embedding0, embedding1, issame in zip(emb0s_flip, emb1s_flip, issame_list):
                metric_flip.update(issame, embedding0, embedding1)

        tpr, fpr, accuracy, val, val_std, far, accuracy_std = metric.get()
        print("{}: \t{:.6f}+-{:.6f}".format(name, accuracy, accuracy_std))
        _, _, accuracy, _, _, _, accuracy_std = metric_flip.get()
        print("{}-flip: {:.6f}+-{:.6f}".format(name, accuracy, accuracy_std)) 

# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import flip [as 別名]
def ten_crop(src, size):
    """Crop 10 regions from an array.
    This is performed same as:
    http://chainercv.readthedocs.io/en/stable/reference/transforms.html#ten-crop

    This method crops 10 regions. All regions will be in shape
    :obj`size`. These regions consist of 1 center crop and 4 corner
    crops and horizontal flips of them.
    The crops are ordered in this order.
    * center crop
    * top-left crop
    * bottom-left crop
    * top-right crop
    * bottom-right crop
    * center crop (flipped horizontally)
    * top-left crop (flipped horizontally)
    * bottom-left crop (flipped horizontally)
    * top-right crop (flipped horizontally)
    * bottom-right crop (flipped horizontally)

    Parameters
    ----------
    src : mxnet.nd.NDArray
        Input image.
    size : tuple
        Tuple of length 2, as (width, height) of the cropped areas.

    Returns
    -------
    mxnet.nd.NDArray
        The cropped images with shape (10, size[1], size[0], C)

    """
    h, w, _ = src.shape
    ow, oh = size

    if h < oh or w < ow:
        raise ValueError(
            "Cannot crop area {} from image with size ({}, {})".format(str(size), h, w))

    center = src[(h - oh) // 2:(h + oh) // 2, (w - ow) // 2:(w + ow) // 2, :]
    tl = src[0:oh, 0:ow, :]
    bl = src[h - oh:h, 0:ow, :]
    tr = src[0:oh, w - ow:w, :]
    br = src[h - oh:h, w - ow:w, :]
    crops = nd.stack(*[center, tl, bl, tr, br], axis=0)
    crops = nd.concat(*[crops, nd.flip(crops, axis=2)], dim=0)
    return crops