Python numpy.amax() Examples
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Example #1
Source File: pose_dataset.py From tf-pose with Apache License 2.0 | 6 votes |
def get_heatmap(self, target_size): heatmap = np.zeros((CocoMetadata.__coco_parts, self.height, self.width), dtype=np.float32) for joints in self.joint_list: for idx, point in enumerate(joints): if point[0] < 0 or point[1] < 0: continue CocoMetadata.put_heatmap(heatmap, idx, point, self.sigma) heatmap = heatmap.transpose((1, 2, 0)) # background heatmap[:, :, -1] = np.clip(1 - np.amax(heatmap, axis=2), 0.0, 1.0) if target_size: heatmap = cv2.resize(heatmap, target_size, interpolation=cv2.INTER_AREA) return heatmap.astype(np.float16)
Example #2
Source File: np_box_list_ops.py From SlowFast-Network-pytorch with MIT License | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #3
Source File: cartpole.py From LearningX with MIT License | 6 votes |
def train(self, batch_size=32): # Train using replay experience minibatch = random.sample(self.memory, batch_size) for memory in minibatch: state, action, reward, state_next, done = memory # Build Q target: # -> Qtarget[!action] = Q[!action] # Qtarget[action] = reward + gamma * max[a'](Q_next(state_next)) Qtarget = self.model.predict(state) dQ = reward if not done: dQ += self.gamma * np.amax(self.model.predict(state_next)[0]) Qtarget[0][action] = dQ self.model.fit(state, Qtarget, epochs=1, verbose=0) # Decary exploration after training if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay
Example #4
Source File: spatial_heatmap.py From NanoPlot with GNU General Public License v3.0 | 6 votes |
def spatial_heatmap(array, path, title=None, color="Greens", figformat="png"): """Taking channel information and creating post run channel activity plots.""" logging.info("Nanoplotter: Creating heatmap of reads per channel using {} reads." .format(array.size)) activity_map = Plot( path=path + "." + figformat, title="Number of reads generated per channel") layout = make_layout(maxval=np.amax(array)) valueCounts = pd.value_counts(pd.Series(array)) for entry in valueCounts.keys(): layout.template[np.where(layout.structure == entry)] = valueCounts[entry] plt.figure() ax = sns.heatmap( data=pd.DataFrame(layout.template, index=layout.yticks, columns=layout.xticks), xticklabels="auto", yticklabels="auto", square=True, cbar_kws={"orientation": "horizontal"}, cmap=color, linewidths=0.20) ax.set_title(title or activity_map.title) activity_map.fig = ax.get_figure() activity_map.save(format=figformat) plt.close("all") return [activity_map]
Example #5
Source File: reblock.py From pyqmc with MIT License | 6 votes |
def optimally_reblocked(data): """ Find optimal reblocking of input data. Takes in pandas DataFrame of raw data to reblock, returns DataFrame of reblocked data. """ opt = opt_block(data) n_reblock = int(np.amax(opt)) rb_data = reblock_by2(data, n_reblock) serr = rb_data.sem(axis=0) d = { "mean": rb_data.mean(axis=0), "standard error": serr, "standard error error": serr / np.sqrt(2 * (len(rb_data) - 1)), "reblocks": n_reblock, } return pd.DataFrame(d)
Example #6
Source File: test_0092_ag2_ae.py From pyscf with Apache License 2.0 | 6 votes |
def test_gw(self): """ This is GW """ mol = gto.M(verbose=0, atom='''Ag 0 0 -0.3707; Ag 0 0 0.3707''', basis = 'cc-pvdz-pp',) gto_mf = scf.RHF(mol)#.density_fit() gto_mf.kernel() #print('gto_mf.mo_energy:', gto_mf.mo_energy) s = nao(mf=gto_mf, gto=mol, verbosity=0) oref = s.overlap_coo().toarray() #print('s.norbs:', s.norbs, oref.sum()) pb = prod_basis(nao=s, algorithm='fp') pab2v = pb.get_ac_vertex_array() mom0,mom1=pb.comp_moments() orec = np.einsum('p,pab->ab', mom0, pab2v) self.assertTrue(np.allclose(orec,oref, atol=1e-3), \ "{} {}".format(abs(orec-oref).sum()/oref.size, np.amax(abs(orec-oref))))
Example #7
Source File: utilities.py From pytim with GNU General Public License v3.0 | 6 votes |
def guess_normal(universe, group): """ Guess the normal of a liquid slab """ universe.atoms.pack_into_box() dim = universe.coord.dimensions delta = [] for direction in range(0, 3): histo, _ = np.histogram( group.positions[:, direction], bins=5, range=(0, dim[direction]), density=True) max_val = np.amax(histo) min_val = np.amin(histo) delta.append(np.sqrt((max_val - min_val)**2)) if np.max(delta) / np.min(delta) < 5.0: print("Warning: the result of the automatic normal detection (", np.argmax(delta), ") is not reliable") return np.argmax(delta)
Example #8
Source File: coords2sort_order.py From pyscf with Apache License 2.0 | 6 votes |
def coords2sort_order(a2c): """ Delivers a list of atom indices which generates a near-diagonal overlap for a given set of atom coordinates """ na = a2c.shape[0] aa2d = squareform(pdist(a2c)) mxd = np.amax(aa2d)+1.0 a = 0 lsa = [] for ia in range(na): lsa.append(a) asrt = np.argsort(aa2d[a]) for ja in range(1,na): b = asrt[ja] if b not in lsa: break aa2d[a,b] = aa2d[b,a] = mxd a = b return np.array(lsa)
Example #9
Source File: m_ion_log.py From pyscf with Apache License 2.0 | 6 votes |
def __init__(self, ao_log, sp): self.ion = ao_log.sp2ion[sp] self.rr,self.pp,self.nr = ao_log.rr,ao_log.pp,ao_log.nr self.interp_rr = log_interp_c(self.rr) self.interp_pp = log_interp_c(self.pp) self.mu2j = ao_log.sp_mu2j[sp] self.nmult= len(self.mu2j) self.mu2s = ao_log.sp_mu2s[sp] self.norbs= self.mu2s[-1] self.mu2ff = ao_log.psi_log[sp] self.mu2ff_rl = ao_log.psi_log_rl[sp] self.mu2rcut = ao_log.sp_mu2rcut[sp] self.rcut = np.amax(self.mu2rcut) self.charge = ao_log.sp2charge[sp]
Example #10
Source File: np_box_mask_list_ops.py From SlowFast-Network-pytorch with MIT License | 6 votes |
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0): """Prunes the boxes in list1 that overlap less than thresh with list2. For each mask in box_mask_list1, we want its IOA to be more than minoverlap with at least one of the masks in box_mask_list2. If it does not, we remove it. If the masks are not full size image, we do the pruning based on boxes. Args: box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks. box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned box_mask_list with size [N', 4]. """ intersection_over_area = ioa(box_mask_list2, box_mask_list1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_box_mask_list1 = gather(box_mask_list1, keep_inds) return new_box_mask_list1
Example #11
Source File: gw_iter.py From pyscf with Apache License 2.0 | 6 votes |
def si_c_check (self, tol = 1e-5): """ This compares np.solve and LinearOpt-lgmres methods for solving linear equation (1-v\chi_{0}) * W_c = v\chi_{0}v """ import time import numpy as np ww = 1j*self.ww_ia t = time.time() si0_1 = self.si_c(ww) #method 1: numpy.linalg.solve t1 = time.time() - t print('numpy: {} sec'.format(t1)) t2 = time.time() si0_2 = self.si_c2(ww) #method 2: scipy.sparse.linalg.lgmres t3 = time.time() - t2 print('lgmres: {} sec'.format(t3)) summ = abs(si0_1 + si0_2).sum() diff = abs(si0_1 - si0_2).sum() if diff/summ < tol and diff/si0_1.size < tol: print('OK! scipy.lgmres methods and np.linalg.solve have identical results') else: print('Results (W_c) are NOT similar!') return [[diff/summ] , [np.amax(abs(diff))] ,[tol]] #@profile
Example #12
Source File: gw.py From pyscf with Apache License 2.0 | 6 votes |
def get_wmin_wmax_tmax_ia_def(self, tol): from numpy import log, exp, sqrt, where, amin, amax """ This is a default choice of the wmin and wmax parameters for a log grid along imaginary axis. The default choice is based on the eigenvalues. """ E = self.ksn2e[0,0,:] E_fermi = self.fermi_energy E_homo = amax(E[where(E<=E_fermi)]) E_gap = amin(E[where(E>E_fermi)]) - E_homo E_maxdiff = amax(E) - amin(E) d = amin(abs(E_homo-E)[where(abs(E_homo-E)>1e-4)]) wmin_def = sqrt(tol * (d**3) * (E_gap**3)/(d**2+E_gap**2)) wmax_def = (E_maxdiff**2/tol)**(0.250) tmax_def = -log(tol)/ (E_gap) tmin_def = -100*log(1.0-tol)/E_maxdiff return wmin_def, wmax_def, tmin_def,tmax_def
Example #13
Source File: np_box_list_ops.py From object_detector_app with MIT License | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #14
Source File: np_box_mask_list_ops.py From vehicle_counting_tensorflow with MIT License | 6 votes |
def prune_non_overlapping_masks(box_mask_list1, box_mask_list2, minoverlap=0.0): """Prunes the boxes in list1 that overlap less than thresh with list2. For each mask in box_mask_list1, we want its IOA to be more than minoverlap with at least one of the masks in box_mask_list2. If it does not, we remove it. If the masks are not full size image, we do the pruning based on boxes. Args: box_mask_list1: np_box_mask_list.BoxMaskList holding N boxes and masks. box_mask_list2: np_box_mask_list.BoxMaskList holding M boxes and masks. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned box_mask_list with size [N', 4]. """ intersection_over_area = ioa(box_mask_list2, box_mask_list1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_box_mask_list1 = gather(box_mask_list1, keep_inds) return new_box_mask_list1
Example #15
Source File: np_box_list_ops.py From vehicle_counting_tensorflow with MIT License | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #16
Source File: OutlierDetection.py From sparse-subspace-clustering-python with MIT License | 6 votes |
def OutlierDetection(CMat, s): n = np.amax(s) _, N = CMat.shape OutlierIndx = list() FailCnt = 0 Fail = False for i in range(0, N): c = CMat[:, i] if np.sum(np.isnan(c)) >= 1: OutlierIndx.append(i) FailCnt += 1 sc = s.astype(float) sc[OutlierIndx] = np.nan CMatC = CMat.astype(float) CMatC[OutlierIndx, :] = np.nan CMatC[:, OutlierIndx] = np.nan OutlierIndx = OutlierIndx if FailCnt > (N - n): CMatC = np.nan sc = np.nan Fail = True return CMatC, sc, OutlierIndx, Fail
Example #17
Source File: util.py From DeepFloorplan with GNU General Public License v3.0 | 6 votes |
def refine_room_region(cw_mask, rm_ind): label_rm, num_label = ndimage.label((1-cw_mask)) new_rm_ind = np.zeros(rm_ind.shape) for j in xrange(1, num_label+1): mask = (label_rm == j).astype(np.uint8) ys, xs = np.where(mask!=0) area = (np.amax(xs)-np.amin(xs))*(np.amax(ys)-np.amin(ys)) if area < 100: continue else: room_types, type_counts = np.unique(mask*rm_ind, return_counts=True) if len(room_types) > 1: room_types = room_types[1:] # ignore background type which is zero type_counts = type_counts[1:] # ignore background count new_rm_ind += mask*room_types[np.argmax(type_counts)] return new_rm_ind
Example #18
Source File: np_box_list_ops.py From DOTA_models with Apache License 2.0 | 6 votes |
def prune_non_overlapping_boxes(boxlist1, boxlist2, minoverlap=0.0): """Prunes the boxes in boxlist1 that overlap less than thresh with boxlist2. For each box in boxlist1, we want its IOA to be more than minoverlap with at least one of the boxes in boxlist2. If it does not, we remove it. Args: boxlist1: BoxList holding N boxes. boxlist2: BoxList holding M boxes. minoverlap: Minimum required overlap between boxes, to count them as overlapping. Returns: A pruned boxlist with size [N', 4]. """ intersection_over_area = ioa(boxlist2, boxlist1) # [M, N] tensor intersection_over_area = np.amax(intersection_over_area, axis=0) # [N] tensor keep_bool = np.greater_equal(intersection_over_area, np.array(minoverlap)) keep_inds = np.nonzero(keep_bool)[0] new_boxlist1 = gather(boxlist1, keep_inds) return new_boxlist1
Example #19
Source File: multi_input.py From aboleth with Apache License 2.0 | 6 votes |
def input_fn(df): """Format the downloaded data.""" # Creates a dictionary mapping from each continuous feature column name (k) # to the values of that column stored in a constant Tensor. continuous_cols = [df[k].values for k in CONTINUOUS_COLUMNS] X_con = np.stack(continuous_cols).astype(np.float32).T # Standardise X_con -= X_con.mean(axis=0) X_con /= X_con.std(axis=0) # Creates a dictionary mapping from each categorical feature column name categ_cols = [np.where(pd.get_dummies(df[k]).values)[1][:, np.newaxis] for k in CATEGORICAL_COLUMNS] n_values = [np.amax(c) + 1 for c in categ_cols] X_cat = np.concatenate(categ_cols, axis=1).astype(np.int32) # Converts the label column into a constant Tensor. label = df[LABEL_COLUMN].values[:, np.newaxis] # Returns the feature columns and the label. return X_con, X_cat, n_values, label
Example #20
Source File: loss.py From pygbm with MIT License | 5 votes |
def _logsumexp(a): """logsumexp(x) = log(sum(exp(x))) Custom logsumexp function with numerical stability, based on scipy's logsumexp which is unfortunately not supported (neither is np.logaddexp.reduce, which is equivalent). Only supports 1d arrays. """ a_max = np.amax(a) if not np.isfinite(a_max): a_max = 0 s = np.sum(np.exp(a - a_max)) return np.log(s) + a_max
Example #21
Source File: test_transformers.py From deepchem with MIT License | 5 votes |
def test_X_normalization_transformer(self): """Tests normalization transformer.""" solubility_dataset = dc.data.tests.load_solubility_data() normalization_transformer = dc.trans.NormalizationTransformer( transform_X=True, dataset=solubility_dataset) X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids) solubility_dataset = normalization_transformer.transform(solubility_dataset) X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids) # Check ids are unchanged. for id_elt, id_t_elt in zip(ids, ids_t): assert id_elt == id_t_elt # Check y is unchanged since this is a X transformer np.testing.assert_allclose(y, y_t) # Check w is unchanged since this is a y transformer np.testing.assert_allclose(w, w_t) # Check that X_t has zero mean, unit std. # np.set_printoptions(threshold='nan') mean = X_t.mean(axis=0) assert np.amax(np.abs(mean - np.zeros_like(mean))) < 1e-7 orig_std_array = X.std(axis=0) std_array = X_t.std(axis=0) # Entries with zero std are not normalized for orig_std, std in zip(orig_std_array, std_array): if not np.isclose(orig_std, 0): assert np.isclose(std, 1) # TODO(rbharath): Untransform doesn't work properly for binary feature # vectors. Need to figure out what's wrong here. (low priority) ## Check that untransform does the right thing. # np.testing.assert_allclose(normalization_transformer.untransform(X_t), X)
Example #22
Source File: snn_training.py From Training-Neural-Networks-for-Event-Based-End-to-End-Robot-Control with GNU General Public License v3.0 | 5 votes |
def __init__(self, path): # Read DQN data h5f = h5py.File(path + '/dqn_data.h5', 'r') self.states = np.array(h5f['states'], dtype=float) self.actions = np.array(h5f['actions']) h5f.close() # Delete empty states at the end of array for i in range(self.states.shape[0]): if self.states[-i].any(): break # Normalize states self.states = self.states[:-i+1]/np.amax(self.states) self.actions = self.actions[:-i+1]
Example #23
Source File: plot_state_input_sample.py From Training-Neural-Networks-for-Event-Based-End-to-End-Robot-Control with GNU General Public License v3.0 | 5 votes |
def __init__(self, path): h5f = h5py.File(path+'dqn_data.h5', 'r') self.states = np.array(h5f['states'], dtype=float) self.actions = np.array(h5f['actions']) h5f.close() for i in range(self.states.shape[0]): if self.states[-i].any(): break self.states = self.states[:-i+1]/np.amax(self.states) self.actions = self.actions[:-i+1]
Example #24
Source File: show_video_transforms.py From deep-smoke-machine with BSD 3-Clause "New" or "Revised" License | 5 votes |
def main(argv): if len(argv) < 2: print("Usage: python show_video_transforms.py [video_file_path]") return # Read frames op = OpticalFlow(rgb_vid_in_p=argv[1]) rgb_4d = op.vid_to_frames().astype(np.uint8) # ColorJitter need uint8 tl = TestLearner() T = tl.get_transform("rgb", phase="train") rgb_4d = T(rgb_4d).numpy().transpose(1, 2, 3, 0) print(np.amin(rgb_4d), np.amax(rgb_4d)) print(rgb_4d.shape) rgb_4d = cv2.normalize(rgb_4d, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F) op.frames_to_vid(rgb_4d, "../data/transformed.mp4")
Example #25
Source File: utils.py From sato with Apache License 2.0 | 5 votes |
def logDot(a, b): # numeric stable way of calculating log (e^a, e^b) max_a = np.amax(a) max_b = np.amax(b) C = np.dot(np.exp(a - max_a), np.exp(b - max_b)) np.log(C, out=C) # else: # np.log(C + 1e-300, out=C) C += max_a + max_b return C
Example #26
Source File: inference.py From PoseWarper with Apache License 2.0 | 5 votes |
def get_max_preds(batch_heatmaps): ''' get predictions from score maps heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) ''' assert isinstance(batch_heatmaps, np.ndarray), \ 'batch_heatmaps should be numpy.ndarray' assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim' batch_size = batch_heatmaps.shape[0] num_joints = batch_heatmaps.shape[1] width = batch_heatmaps.shape[3] heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) idx = np.argmax(heatmaps_reshaped, 2) maxvals = np.amax(heatmaps_reshaped, 2) maxvals = maxvals.reshape((batch_size, num_joints, 1)) idx = idx.reshape((batch_size, num_joints, 1)) preds = np.tile(idx, (1, 1, 2)).astype(np.float32) preds[:, :, 0] = (preds[:, :, 0]) % width preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) pred_mask = pred_mask.astype(np.float32) preds *= pred_mask return preds, maxvals
Example #27
Source File: misc.py From graph_distillation with Apache License 2.0 | 5 votes |
def softmax(w, t=1.0, axis=None): w = np.array(w) / t e = np.exp(w - np.amax(w, axis=axis, keepdims=True)) dist = e / np.sum(e, axis=axis, keepdims=True) return dist
Example #28
Source File: visualize.py From graph_distillation with 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 #29
Source File: np_box_list_ops.py From vehicle_counting_tensorflow with MIT License | 5 votes |
def gather(boxlist, indices, fields=None): """Gather boxes from BoxList according to indices and return new BoxList. By default, gather returns boxes corresponding to the input index list, as well as all additional fields stored in the boxlist (indexing into the first dimension). However one can optionally only gather from a subset of fields. Args: boxlist: BoxList holding N boxes indices: a 1-d numpy array of type int_ fields: (optional) list of fields to also gather from. If None (default), all fields are gathered from. Pass an empty fields list to only gather the box coordinates. Returns: subboxlist: a BoxList corresponding to the subset of the input BoxList specified by indices Raises: ValueError: if specified field is not contained in boxlist or if the indices are not of type int_ """ if indices.size: if np.amax(indices) >= boxlist.num_boxes() or np.amin(indices) < 0: raise ValueError('indices are out of valid range.') subboxlist = np_box_list.BoxList(boxlist.get()[indices, :]) if fields is None: fields = boxlist.get_extra_fields() for field in fields: extra_field_data = boxlist.get_field(field) subboxlist.add_field(field, extra_field_data[indices, ...]) return subboxlist
Example #30
Source File: lattice_test.py From lattice with Apache License 2.0 | 5 votes |
def _Max(self, x): return np.amax(x)