Python numpy.moveaxis() Examples
The following are 30 code examples for showing how to use numpy.moveaxis(). These examples are extracted from open source projects. 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.
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Example 1
Project: recruit Author: Frank-qlu File: nanfunctions.py License: Apache License 2.0 | 6 votes |
def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear'): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanpercentile for parameter usage """ if axis is None or a.ndim == 1: part = a.ravel() result = _nanquantile_1d(part, q, overwrite_input, interpolation) else: result = np.apply_along_axis(_nanquantile_1d, axis, a, q, overwrite_input, interpolation) # apply_along_axis fills in collapsed axis with results. # Move that axis to the beginning to match percentile's # convention. if q.ndim != 0: result = np.moveaxis(result, axis, 0) if out is not None: out[...] = result return result
Example 2
Project: DSFD-Pytorch-Inference Author: hukkelas File: base.py License: Apache License 2.0 | 6 votes |
def _pre_process(self, image: np.ndarray, shrink: float) -> torch.Tensor: """Takes N RGB image and performs and returns a set of bounding boxes as detections Args: image (np.ndarray): shape [N, height, width, 3] Returns: torch.Tensor: shape [N, 3, height, width] """ assert image.dtype == np.uint8 height, width = image.shape[1:3] image = image.astype(np.float32) - self.mean image = np.moveaxis(image, -1, 1) image = torch.from_numpy(image) if self.max_resolution is not None: shrink_factor = self.max_resolution / max((height, width)) if shrink_factor <= shrink: shrink = shrink_factor image = torch.nn.functional.interpolate(image, scale_factor=shrink) image = image.to(self.device) return image
Example 3
Project: lambda-packs Author: ryfeus File: nanfunctions.py License: MIT License | 6 votes |
def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear'): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanpercentile for parameter usage """ if axis is None or a.ndim == 1: part = a.ravel() result = _nanquantile_1d(part, q, overwrite_input, interpolation) else: result = np.apply_along_axis(_nanquantile_1d, axis, a, q, overwrite_input, interpolation) # apply_along_axis fills in collapsed axis with results. # Move that axis to the beginning to match percentile's # convention. if q.ndim != 0: result = np.moveaxis(result, axis, 0) if out is not None: out[...] = result return result
Example 4
Project: auto-alt-text-lambda-api Author: abhisuri97 File: test_numeric.py License: MIT License | 6 votes |
def test_errors(self): x = np.random.randn(1, 2, 3) assert_raises_regex(ValueError, 'invalid axis .* `source`', np.moveaxis, x, 3, 0) assert_raises_regex(ValueError, 'invalid axis .* `source`', np.moveaxis, x, -4, 0) assert_raises_regex(ValueError, 'invalid axis .* `destination`', np.moveaxis, x, 0, 5) assert_raises_regex(ValueError, 'repeated axis in `source`', np.moveaxis, x, [0, 0], [0, 1]) assert_raises_regex(ValueError, 'repeated axis in `destination`', np.moveaxis, x, [0, 1], [1, 1]) assert_raises_regex(ValueError, 'must have the same number', np.moveaxis, x, 0, [0, 1]) assert_raises_regex(ValueError, 'must have the same number', np.moveaxis, x, [0, 1], [0])
Example 5
Project: discopy Author: oxford-quantum-group File: tensor.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def __init__(self, *dim): """ >>> Id(1) Tensor(dom=Dim(1), cod=Dim(1), array=[1]) >>> list(Id(2).array.flatten()) [1.0, 0.0, 0.0, 1.0] >>> Id(2).array.shape (2, 2) >>> list(Id(2, 2).array.flatten())[:8] [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0] >>> list(Id(2, 2).array.flatten())[8:] [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0] """ dim = dim[0] if isinstance(dim[0], Dim) else Dim(*dim) array = functools.reduce( lambda a, x: np.tensordot(a, np.identity(x), 0) if a.shape else np.identity(x), dim, np.array(1)) array = np.moveaxis( array, [2 * i for i in range(len(dim))], list(range(len(dim)))) super().__init__(dim, dim, array)
Example 6
Project: ocelot Author: ocelot-collab File: math_op.py License: GNU General Public License v3.0 | 6 votes |
def n_moment(x, counts, c, n): x = np.squeeze(x) if x.ndim is not 1: raise ValueError("scale of x should be 1-dimensional") if x.size not in counts.shape: raise ValueError("operands could not be broadcast together with shapes %s %s" %(str(x.shape), str(counts.shape))) if np.sum(counts)==0: return 0 else: if x.ndim == 1 and counts.ndim == 1: return (np.sum((x-c)**n*counts) / np.sum(counts))**(1./n) else: if x.size in counts.shape: dim_ = [i for i, v in enumerate(counts.shape) if v == x.size] counts = np.moveaxis(counts, dim_, -1) return (np.sum((x-c)**n*counts, axis=-1) / np.sum(counts, axis=-1))**(1./n)
Example 7
Project: vnpy_crypto Author: birforce File: nanfunctions.py License: MIT License | 6 votes |
def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear'): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanpercentile for parameter usage """ if axis is None or a.ndim == 1: part = a.ravel() result = _nanpercentile1d(part, q, overwrite_input, interpolation) else: result = np.apply_along_axis(_nanpercentile1d, axis, a, q, overwrite_input, interpolation) # apply_along_axis fills in collapsed axis with results. # Move that axis to the beginning to match percentile's # convention. if q.ndim != 0: result = np.moveaxis(result, axis, 0) if out is not None: out[...] = result return result
Example 8
Project: Mastering-Elasticsearch-7.0 Author: PacktPublishing File: nanfunctions.py License: MIT License | 6 votes |
def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear'): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce See nanpercentile for parameter usage """ if axis is None or a.ndim == 1: part = a.ravel() result = _nanquantile_1d(part, q, overwrite_input, interpolation) else: result = np.apply_along_axis(_nanquantile_1d, axis, a, q, overwrite_input, interpolation) # apply_along_axis fills in collapsed axis with results. # Move that axis to the beginning to match percentile's # convention. if q.ndim != 0: result = np.moveaxis(result, axis, 0) if out is not None: out[...] = result return result
Example 9
Project: neural-pipeline Author: toodef File: img_segmentation.py License: MIT License | 5 votes |
def augmentate_and_to_pytorch(item: {}): res = augmentate(item) return {'data': torch.from_numpy(np.moveaxis(res['data'].astype(np.float32) / 255., -1, 0)), 'target': torch.from_numpy(np.expand_dims(res['target'].astype(np.float32) / 255, axis=0))}
Example 10
Project: L3C-PyTorch Author: fab-jul File: figure_plotter.py License: GNU General Public License v3.0 | 5 votes |
def _render_to_rgb(figure, close): canvas = plt_backend_agg.FigureCanvasAgg(figure) canvas.draw() data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8) w, h = figure.canvas.get_width_height() image_hwc = data.reshape([h, w, 4])[..., :3] image_chw = np.moveaxis(image_hwc, source=2, destination=0) if close: plt.close(figure) return image_chw
Example 11
Project: pytorch-a3c Author: ikostrikov File: envs.py License: MIT License | 5 votes |
def _process_frame42(frame): frame = frame[34:34 + 160, :160] # Resize by half, then down to 42x42 (essentially mipmapping). If # we resize directly we lose pixels that, when mapped to 42x42, # aren't close enough to the pixel boundary. frame = cv2.resize(frame, (80, 80)) frame = cv2.resize(frame, (42, 42)) frame = frame.mean(2, keepdims=True) frame = frame.astype(np.float32) frame *= (1.0 / 255.0) frame = np.moveaxis(frame, -1, 0) return frame
Example 12
Project: recruit Author: Frank-qlu File: test_function_base.py License: Apache License 2.0 | 5 votes |
def test_extended_axis(self): o = np.random.normal(size=(71, 23)) x = np.dstack([o] * 10) assert_equal(np.percentile(x, 30, axis=(0, 1)), np.percentile(o, 30)) x = np.moveaxis(x, -1, 0) assert_equal(np.percentile(x, 30, axis=(-2, -1)), np.percentile(o, 30)) x = x.swapaxes(0, 1).copy() assert_equal(np.percentile(x, 30, axis=(0, -1)), np.percentile(o, 30)) x = x.swapaxes(0, 1).copy() assert_equal(np.percentile(x, [25, 60], axis=(0, 1, 2)), np.percentile(x, [25, 60], axis=None)) assert_equal(np.percentile(x, [25, 60], axis=(0,)), np.percentile(x, [25, 60], axis=0)) d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11)) np.random.shuffle(d.ravel()) assert_equal(np.percentile(d, 25, axis=(0, 1, 2))[0], np.percentile(d[:,:,:, 0].flatten(), 25)) assert_equal(np.percentile(d, [10, 90], axis=(0, 1, 3))[:, 1], np.percentile(d[:,:, 1,:].flatten(), [10, 90])) assert_equal(np.percentile(d, 25, axis=(3, 1, -4))[2], np.percentile(d[:,:, 2,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 1, 2))[2], np.percentile(d[2,:,:,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(3, 2))[2, 1], np.percentile(d[2, 1,:,:].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, -2))[2, 1], np.percentile(d[2,:,:, 1].flatten(), 25)) assert_equal(np.percentile(d, 25, axis=(1, 3))[2, 2], np.percentile(d[2,:, 2,:].flatten(), 25))
Example 13
Project: recruit Author: Frank-qlu File: test_function_base.py License: Apache License 2.0 | 5 votes |
def test_extended_axis(self): o = np.random.normal(size=(71, 23)) x = np.dstack([o] * 10) assert_equal(np.median(x, axis=(0, 1)), np.median(o)) x = np.moveaxis(x, -1, 0) assert_equal(np.median(x, axis=(-2, -1)), np.median(o)) x = x.swapaxes(0, 1).copy() assert_equal(np.median(x, axis=(0, -1)), np.median(o)) assert_equal(np.median(x, axis=(0, 1, 2)), np.median(x, axis=None)) assert_equal(np.median(x, axis=(0, )), np.median(x, axis=0)) assert_equal(np.median(x, axis=(-1, )), np.median(x, axis=-1)) d = np.arange(3 * 5 * 7 * 11).reshape((3, 5, 7, 11)) np.random.shuffle(d.ravel()) assert_equal(np.median(d, axis=(0, 1, 2))[0], np.median(d[:,:,:, 0].flatten())) assert_equal(np.median(d, axis=(0, 1, 3))[1], np.median(d[:,:, 1,:].flatten())) assert_equal(np.median(d, axis=(3, 1, -4))[2], np.median(d[:,:, 2,:].flatten())) assert_equal(np.median(d, axis=(3, 1, 2))[2], np.median(d[2,:,:,:].flatten())) assert_equal(np.median(d, axis=(3, 2))[2, 1], np.median(d[2, 1,:,:].flatten())) assert_equal(np.median(d, axis=(1, -2))[2, 1], np.median(d[2,:,:, 1].flatten())) assert_equal(np.median(d, axis=(1, 3))[2, 2], np.median(d[2,:, 2,:].flatten()))
Example 14
Project: recruit Author: Frank-qlu File: test_shape_base.py License: Apache License 2.0 | 5 votes |
def test_exceptions(self): # test axis must be in bounds for ndim in [1, 2, 3]: a = np.ones((1,)*ndim) np.concatenate((a, a), axis=0) # OK assert_raises(np.AxisError, np.concatenate, (a, a), axis=ndim) assert_raises(np.AxisError, np.concatenate, (a, a), axis=-(ndim + 1)) # Scalars cannot be concatenated assert_raises(ValueError, concatenate, (0,)) assert_raises(ValueError, concatenate, (np.array(0),)) # test shapes must match except for concatenation axis a = np.ones((1, 2, 3)) b = np.ones((2, 2, 3)) axis = list(range(3)) for i in range(3): np.concatenate((a, b), axis=axis[0]) # OK assert_raises(ValueError, np.concatenate, (a, b), axis=axis[1]) assert_raises(ValueError, np.concatenate, (a, b), axis=axis[2]) a = np.moveaxis(a, -1, 0) b = np.moveaxis(b, -1, 0) axis.append(axis.pop(0)) # No arrays to concatenate raises ValueError assert_raises(ValueError, concatenate, ())
Example 15
Project: recruit Author: Frank-qlu File: test_numeric.py License: Apache License 2.0 | 5 votes |
def test_move_to_end(self): x = np.random.randn(5, 6, 7) for source, expected in [(0, (6, 7, 5)), (1, (5, 7, 6)), (2, (5, 6, 7)), (-1, (5, 6, 7))]: actual = np.moveaxis(x, source, -1).shape assert_(actual, expected)
Example 16
Project: recruit Author: Frank-qlu File: test_numeric.py License: Apache License 2.0 | 5 votes |
def test_move_new_position(self): x = np.random.randn(1, 2, 3, 4) for source, destination, expected in [ (0, 1, (2, 1, 3, 4)), (1, 2, (1, 3, 2, 4)), (1, -1, (1, 3, 4, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected)
Example 17
Project: recruit Author: Frank-qlu File: test_numeric.py License: Apache License 2.0 | 5 votes |
def test_preserve_order(self): x = np.zeros((1, 2, 3, 4)) for source, destination in [ (0, 0), (3, -1), (-1, 3), ([0, -1], [0, -1]), ([2, 0], [2, 0]), (range(4), range(4)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, (1, 2, 3, 4))
Example 18
Project: recruit Author: Frank-qlu File: test_numeric.py License: Apache License 2.0 | 5 votes |
def test_move_multiples(self): x = np.zeros((0, 1, 2, 3)) for source, destination, expected in [ ([0, 1], [2, 3], (2, 3, 0, 1)), ([2, 3], [0, 1], (2, 3, 0, 1)), ([0, 1, 2], [2, 3, 0], (2, 3, 0, 1)), ([3, 0], [1, 0], (0, 3, 1, 2)), ([0, 3], [0, 1], (0, 3, 1, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected)
Example 19
Project: recruit Author: Frank-qlu File: test_numeric.py License: Apache License 2.0 | 5 votes |
def test_array_likes(self): x = np.ma.zeros((1, 2, 3)) result = np.moveaxis(x, 0, 0) assert_(x.shape, result.shape) assert_(isinstance(result, np.ma.MaskedArray)) x = [1, 2, 3] result = np.moveaxis(x, 0, 0) assert_(x, list(result)) assert_(isinstance(result, np.ndarray))
Example 20
Project: buzzard Author: airware File: _dataset_back_conversions.py License: Apache License 2.0 | 5 votes |
def _make_transfo(osr_transfo): """Wrap osr coordinate transformation input/output""" def _f(*args): nargs = len(args) if nargs == 1: # When coordinates in last dimension arr = np.asarray(args[0]) assert arr.ndim >= 2 ncoord = arr.shape[-1] assert 2 <= ncoord <= 3 outshape = arr.shape arr = arr.reshape(int(arr.size / ncoord), ncoord) arr = osr_transfo(arr) arr = np.asarray(arr) arr = arr[:, 0:ncoord] arr = arr.reshape(outshape) return arr elif 2 <= nargs <= 3: # When coordinates in first dimension arr = np.asarray(args) assert arr.ndim == 2 ncoord = nargs arr = np.moveaxis(arr, 0, 1) arr = osr_transfo(arr) arr = np.asarray(arr) arr = arr[:, 0:ncoord] arr = np.moveaxis(arr, 0, 1) return tuple(arr) else: assert False # pragma: no cover return _f
Example 21
Project: graph_distillation Author: google File: visualize.py License: Apache License 2.0 | 5 votes |
def visualize_rgb(images): """Visualize RGB modality.""" images = utils.to_numpy(images) mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) images = np.moveaxis(images, -3, -1) images = images*std+mean images = np.clip(images*255, 0, 255) images = images[..., ::-1].astype(np.uint8) images = images[0, 0] # subsample imgproc.save_avi('/home/luoa/research/rgb.avi', images)
Example 22
Project: graph_distillation Author: google File: visualize.py License: Apache License 2.0 | 5 votes |
def visualize_oflow(images): """Visualize optical flow modality.""" images = utils.to_numpy(images) images = np.moveaxis(images, -3, -1) images = images[0, 0] # subsample images = imgproc.proc_oflow(images) imgproc.save_avi('/home/luoa/research/oflow.avi', images)
Example 23
Project: graph_distillation Author: google File: visualize.py License: Apache License 2.0 | 5 votes |
def visualize_warp(rgb, oflow): """TODO: add info.""" rgb = utils.to_numpy(rgb) oflow = utils.to_numpy(oflow) mean = np.array([0.485, 0.456, 0.406]) std = np.array([0.229, 0.224, 0.225]) rgb = np.moveaxis(rgb, -3, -1) rgb = rgb*std+mean rgb = np.clip(rgb*255, 0, 255) bgr = rgb[..., ::-1].astype(np.uint8) bgr = bgr[0, 0] # subsample print(bgr.shape, np.amin(bgr), np.amax(bgr), np.mean(bgr), np.mean(np.absolute(bgr))) oflow = np.moveaxis(oflow, -3, -1) oflow = oflow[0, 0] # subsample print(oflow.shape, np.amin(oflow), np.amax(oflow), np.mean(oflow), np.mean(np.absolute(oflow))) warp = imgproc.warp(bgr[4], bgr[5], oflow[4]) root = '/home/luoa/research' cv2.imwrite(os.path.join(root, 'bgr1.jpg'), bgr[4]) cv2.imwrite(os.path.join(root, 'bgr2.jpg'), bgr[5]) cv2.imwrite(os.path.join(root, 'warp.jpg'), warp)
Example 24
Project: argus-tgs-salt Author: lRomul File: transforms.py License: MIT License | 5 votes |
def __call__(self, image): image = np.moveaxis(image, -1, 0) image = image.astype(np.float32) / 255.0 image = torch.from_numpy(image) if self.coord_channels: image = self.add_coord_channels(image) return image
Example 25
Project: mars Author: mars-project File: test_core_execute.py License: Apache License 2.0 | 5 votes |
def testViewDataOnMoveaxis(self): data = np.random.rand(10, 20) a = tensor(data, chunk_size=6) b = moveaxis(a, 1, 0) a[0][1] = 10 npa = data.copy() npb = np.moveaxis(npa, 1, 0) npa[0][1] = 10 np.testing.assert_array_equal(b.execute(), npb) np.testing.assert_array_equal(a.execute(), npa)
Example 26
Project: Qualia2.0 Author: Kashu7100 File: fimlp.py License: MIT License | 5 votes |
def _load_data(self, path): if gpu: import numpy data = np.asarray(numpy.moveaxis(numpy.load(path+'/face_images.npz', 'r')['face_images'], -1, 0)) else: data = np.moveaxis(np.load(path+'/face_images.npz', 'r')['face_images'], -1, 0) data = data[self.data_mask] return data[:self.divide].reshape(-1,1,96,96), data[self.divide:].reshape(-1,1,96,96)
Example 27
Project: lambda-packs Author: ryfeus File: numeric.py License: MIT License | 5 votes |
def _move_axis_to_0(a, axis): return moveaxis(a, axis, 0)
Example 28
Project: auto-alt-text-lambda-api Author: abhisuri97 File: test_numeric.py License: MIT License | 5 votes |
def test_move_to_end(self): x = np.random.randn(5, 6, 7) for source, expected in [(0, (6, 7, 5)), (1, (5, 7, 6)), (2, (5, 6, 7)), (-1, (5, 6, 7))]: actual = np.moveaxis(x, source, -1).shape assert_(actual, expected)
Example 29
Project: auto-alt-text-lambda-api Author: abhisuri97 File: test_numeric.py License: MIT License | 5 votes |
def test_move_new_position(self): x = np.random.randn(1, 2, 3, 4) for source, destination, expected in [ (0, 1, (2, 1, 3, 4)), (1, 2, (1, 3, 2, 4)), (1, -1, (1, 3, 4, 2)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, expected)
Example 30
Project: auto-alt-text-lambda-api Author: abhisuri97 File: test_numeric.py License: MIT License | 5 votes |
def test_preserve_order(self): x = np.zeros((1, 2, 3, 4)) for source, destination in [ (0, 0), (3, -1), (-1, 3), ([0, -1], [0, -1]), ([2, 0], [2, 0]), (range(4), range(4)), ]: actual = np.moveaxis(x, source, destination).shape assert_(actual, (1, 2, 3, 4))