Python mxnet.ndarray.split() Examples

The following are 30 code examples of mxnet.ndarray.split(). 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 mxnet.ndarray , or try the search function .
Example #1
Source File: test_contrib_autograd.py    From SNIPER-mxnet with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with train_section():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #2
Source File: test_autograd.py    From SNIPER-mxnet with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with record():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #3
Source File: test_autograd.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with record():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #4
Source File: test_contrib_autograd.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 6 votes vote down vote up 
def test_out_grads():
    x = nd.ones((3, 5))
    dx = nd.zeros_like(x)
    mark_variables([x], [dx])
    da = None
    db = nd.array([1,2,3,4,5])
    dc = nd.array([5,4,3,2,1])

    with train_section():
        a, b, c = nd.split(x, axis=0, num_outputs=3, squeeze_axis=True)
        backward([a, b, c], [da, db, dc])

    assert (dx.asnumpy() == np.array(
        [[1,1,1,1,1],
         [1,2,3,4,5],
         [5,4,3,2,1]])).all()
Example #5
Source File: tensor.py    From dgl with Apache License 2.0 6 votes vote down vote up 
def split(x, sizes_or_sections, dim):
    if isinstance(sizes_or_sections, list) and len(sizes_or_sections) == 1:
        assert len(x) == sizes_or_sections[0]
        return [x]

    if MX_VERSION.version[0] == 1 and MX_VERSION.version[1] >= 5:
        if isinstance(sizes_or_sections, (np.ndarray, list)):
            sizes_or_sections1 = tuple(np.cumsum(sizes_or_sections)[:-1])
        return nd.split_v2(x, sizes_or_sections1, axis=dim)

    if isinstance(sizes_or_sections, list) or isinstance(sizes_or_sections, np.ndarray):
        # Old MXNet doesn't support split with different section sizes.
        np_arr = x.asnumpy()
        indices = np.cumsum(sizes_or_sections)[:-1]
        res = np.split(np_arr, indices, axis=dim)
        return [tensor(arr, dtype=x.dtype) for arr in res]
    else:
        return nd.split(x, sizes_or_sections, axis=dim)
Example #6
Source File: irevnet.py    From imgclsmob with MIT License 6 votes vote down vote up 
def inverse(self, y):
        import mxnet.ndarray as F

        scale_sqr = self.scale * self.scale
        batch, y_channels, y_height, y_width = y.shape
        assert (y_channels % scale_sqr == 0)
        x_channels = y_channels // scale_sqr
        x_height = y_height * self.scale
        x_width = y_width * self.scale

        x = y.transpose(axes=(0, 2, 3, 1))
        x = x.reshape(batch, y_height, y_width, scale_sqr, x_channels)
        d3_split_seq = x.split(axis=3, num_outputs=(x.shape[3] // self.scale))
        d3_split_seq = [t.reshape(batch, y_height, x_width, x_channels) for t in d3_split_seq]
        x = F.stack(*d3_split_seq, axis=0)
        x = x.swapaxes(0, 1).transpose(axes=(0, 2, 1, 3, 4)).reshape(batch, x_height, x_width, x_channels)
        x = x.transpose(axes=(0, 3, 1, 2))
        return x
Example #7
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #8
Source File: lstm_crf.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def _get_lstm_features(self, sentence):
        self.hidden = self.init_hidden()
        length = sentence.shape[0]
        embeds = self.word_embeds(sentence).reshape((length, 1, -1))
        lstm_out, self.hidden = self.lstm(embeds, self.hidden)
        lstm_out = lstm_out.reshape((length, self.hidden_dim))
        lstm_feats = self.hidden2tag(lstm_out)
        return nd.split(lstm_feats, num_outputs=length, axis=0, squeeze_axis=True)
Example #9
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #10
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def tensor_save_bgrimage(tensor, filename, cuda=False):
    (b, g, r) = F.split(tensor, num_outputs=3, axis=0)
    tensor = F.concat(r, g, b, dim=0)
    tensor_save_rgbimage(tensor, filename, cuda)
Example #11
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #12
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #13
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #14
Source File: utils.py    From MXNet-Gluon-Style-Transfer with MIT License 5 votes vote down vote up 
def preprocess_batch(batch):
    batch = F.swapaxes(batch, 0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch, 0, 1)
    return batch
Example #15
Source File: model.py    From NER_BiLSTM_CRF_Chinese with Apache License 2.0 5 votes vote down vote up 
def _get_lstm_features(self, sentence):
        self.hidden = self.init_hidden()
        length = sentence.shape[0]
        embeds = self.word_embeds(sentence).reshape((length, 1, -1))
        lstm_out, self.hidden = self.lstm(embeds, self.hidden)
        lstm_out = lstm_out.reshape((length, self.hidden_dim))
        lstm_feats = self.hidden2tag(lstm_out)
        return nd.split(lstm_feats, num_outputs=length, axis=0, squeeze_axis=True)
Example #16
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def tensor_save_bgrimage(tensor, filename, cuda=False):
    (b, g, r) = F.split(tensor, num_outputs=3, axis=0)
    tensor = F.concat(r, g, b, dim=0)
    tensor_save_rgbimage(tensor, filename, cuda)
Example #17
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #18
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_preprocess_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #19
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def add_imagenet_mean_batch(batch):
    batch = F.swapaxes(batch,0, 1)
    (b, g, r) = F.split(batch, num_outputs=3, axis=0)
    r = r + 123.680
    g = g + 116.779
    b = b + 103.939
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch,0, 1)
    """
    batch = denormalizer(batch)
    """
    return batch
Example #20
Source File: utils.py    From SNIPER-mxnet with Apache License 2.0 5 votes vote down vote up 
def preprocess_batch(batch):
    batch = F.swapaxes(batch, 0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    batch = F.concat(b, g, r, dim=0)
    batch = F.swapaxes(batch, 0, 1)
    return batch
Example #21
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def subtract_imagenet_mean_batch(batch):
    """Subtract ImageNet mean pixel-wise from a BGR image."""
    batch = F.swapaxes(batch,0, 1)
    (r, g, b) = F.split(batch, num_outputs=3, axis=0)
    r = r - 123.680
    g = g - 116.779
    b = b - 103.939
    batch = F.concat(r, g, b, dim=0)
    batch = F.swapaxes(batch,0, 1)
    return batch
Example #22
Source File: utils.py    From training_results_v0.6 with Apache License 2.0 5 votes vote down vote up 
def tensor_save_bgrimage(tensor, filename, cuda=False):
    (b, g, r) = F.split(tensor, num_outputs=3, axis=0)
    tensor = F.concat(r, g, b, dim=0)
    tensor_save_rgbimage(tensor, filename, cuda)
Example #23
Source File: utils.py    From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 5 votes vote down vote up 
def tensor_save_bgrimage(tensor, filename, cuda=False):
    (b, g, r) = F.split(tensor, num_outputs=3, axis=0)
    tensor = F.concat(r, g, b, dim=0)
    tensor_save_rgbimage(tensor, filename, cuda)
Example #24
Source File: irevnet.py    From imgclsmob with MIT License 5 votes vote down vote up 
def hybrid_forward(self, F, x1, x2):
        if self.do_padding:
            x = F.concat(x1, x2, dim=1)
            x = self.pad(x)
            x1, x2 = F.split(x, axis=1, num_outputs=2)
        fx2 = self.bottleneck(x2)
        if self.do_downscale:
            x1 = self.psi(x1)
            x2 = self.psi(x2)
        y1 = fx2 + x1
        return x2, y1
Example #25
Source File: irevnet.py    From imgclsmob with MIT License 5 votes vote down vote up 
def inverse(self, x, _):
        import mxnet.ndarray as F
        x1, x2 = F.split(x, axis=1, num_outputs=2)
        return x1, x2
Example #26
Source File: irevnet.py    From imgclsmob with MIT License 5 votes vote down vote up 
def hybrid_forward(self, F, x, _):
        x1, x2 = F.split(x, axis=1, num_outputs=2)
        return x1, x2
Example #27
Source File: irevnet.py    From imgclsmob with MIT License 5 votes vote down vote up 
def hybrid_forward(self, F, x):
        batch, x_channels, x_height, x_width = x.shape
        y_channels = x_channels * self.scale * self.scale
        assert (x_height % self.scale == 0)
        y_height = x_height // self.scale

        y = x.transpose(axes=(0, 2, 3, 1))
        d2_split_seq = y.split(axis=2, num_outputs=(y.shape[2] // self.scale))
        d2_split_seq = [t.reshape(batch, y_height, y_channels) for t in d2_split_seq]
        y = F.stack(*d2_split_seq, axis=1)
        y = y.transpose(axes=(0, 3, 2, 1))
        return y
Example #28
Source File: score_fun.py    From dgl with Apache License 2.0 5 votes vote down vote up 
def edge_func(self, edges):
        real_head, img_head = nd.split(edges.src['emb'], num_outputs=2, axis=-1)
        real_tail, img_tail = nd.split(edges.dst['emb'], num_outputs=2, axis=-1)

        phase_rel = edges.data['emb'] / (self.emb_init / np.pi)
        re_rel, im_rel = nd.cos(phase_rel), nd.sin(phase_rel)
        real_score = real_head * re_rel - img_head * im_rel
        img_score = real_head * im_rel + img_head * re_rel
        real_score = real_score - real_tail
        img_score = img_score - img_tail
        #sqrt((x*x).sum() + eps)
        score = mx.nd.sqrt(real_score * real_score + img_score * img_score + self.eps).sum(-1)
        return {'score': self.gamma - score}
Example #29
Source File: score_fun.py    From dgl with Apache License 2.0 5 votes vote down vote up 
def create_neg(self, neg_head):
        if neg_head:
            def fn(heads, relations, tails, num_chunks, chunk_size, neg_sample_size):
                hidden_dim = heads.shape[1]
                emb_real, emb_img = nd.split(tails, num_outputs=2, axis=-1)
                rel_real, rel_img = nd.split(relations, num_outputs=2, axis=-1)
                real = emb_real * rel_real + emb_img * rel_img
                img = -emb_real * rel_img + emb_img * rel_real
                emb_complex = nd.concat(real, img, dim=-1)
                tmp = emb_complex.reshape(num_chunks, chunk_size, hidden_dim)
                heads = heads.reshape(num_chunks, neg_sample_size, hidden_dim)
                heads = nd.transpose(heads, axes=(0, 2, 1))
                return nd.linalg_gemm2(tmp, heads)
            return fn
        else:
            def fn(heads, relations, tails, num_chunks, chunk_size, neg_sample_size):
                hidden_dim = heads.shape[1]
                emb_real, emb_img = nd.split(heads, num_outputs=2, axis=-1)
                rel_real, rel_img = nd.split(relations, num_outputs=2, axis=-1)
                real = emb_real * rel_real - emb_img * rel_img
                img = emb_real * rel_img + emb_img * rel_real
                emb_complex = nd.concat(real, img, dim=-1)
                tmp = emb_complex.reshape(num_chunks, chunk_size, hidden_dim)

                tails = tails.reshape(num_chunks, neg_sample_size, hidden_dim)
                tails = nd.transpose(tails, axes=(0, 2, 1))
                return nd.linalg_gemm2(tmp, tails)
            return fn
Example #30
Source File: score_fun.py    From dgl with Apache License 2.0 5 votes vote down vote up 
def edge_func(self, edges):
        real_head, img_head = nd.split(edges.src['emb'], num_outputs=2, axis=-1)
        real_tail, img_tail = nd.split(edges.dst['emb'], num_outputs=2, axis=-1)
        real_rel, img_rel = nd.split(edges.data['emb'], num_outputs=2, axis=-1)

        score = real_head * real_tail * real_rel \
                + img_head * img_tail * real_rel \
                + real_head * img_tail * img_rel \
                - img_head * real_tail * img_rel
        # TODO: check if there exists minus sign and if gamma should be used here(jin)
        return {'score': nd.sum(score, -1)}