Python numpy.round() Examples
The following are 30 code examples for showing how to use numpy.round(). 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: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: visualization.py License: MIT License | 6 votes |
def draw_bounding_boxes(image, gt_boxes, im_info): num_boxes = gt_boxes.shape[0] gt_boxes_new = gt_boxes.copy() gt_boxes_new[:,:4] = np.round(gt_boxes_new[:,:4].copy() / im_info[2]) disp_image = Image.fromarray(np.uint8(image[0])) for i in range(num_boxes): this_class = int(gt_boxes_new[i, 4]) disp_image = _draw_single_box(disp_image, gt_boxes_new[i, 0], gt_boxes_new[i, 1], gt_boxes_new[i, 2], gt_boxes_new[i, 3], 'N%02d-C%02d' % (i, this_class), FONT, color=STANDARD_COLORS[this_class % NUM_COLORS]) image[0, :] = np.array(disp_image) return image
Example 2
Project: FRIDA Author: LCAV File: doa.py License: MIT License | 6 votes |
def compute_mode(self): """ Pre-compute mode vectors from candidate locations (in spherical coordinates). """ if self.num_loc is None: raise ValueError('Lookup table appears to be empty. \ Run build_lookup().') self.mode_vec = np.zeros((self.max_bin,self.M,self.num_loc), dtype='complex64') if (self.nfft % 2 == 1): raise ValueError('Signal length must be even.') f = 1.0 / self.nfft * np.linspace(0, self.nfft / 2, self.max_bin) \ * 1j * 2 * np.pi for i in range(self.num_loc): p_s = self.loc[:, i] for m in range(self.M): p_m = self.L[:, m] if (self.mode == 'near'): dist = np.linalg.norm(p_m - p_s, axis=1) if (self.mode == 'far'): dist = np.dot(p_s, p_m) # tau = np.round(self.fs*dist/self.c) # discrete - jagged tau = self.fs * dist / self.c # "continuous" - smoother self.mode_vec[:, m, i] = np.exp(f * tau)
Example 3
Project: StructEngPy Author: zhuoju36 File: test.py License: MIT License | 6 votes |
def cantilever_beam_test(): #FEModel Test model=FEModel() model.add_node(0,0,0) model.add_node(2,1,1) E=1.999e11 mu=0.3 A=4.265e-3 J=9.651e-8 I3=6.572e-5 I2=3.301e-6 rho=7849.0474 model.add_beam(0,1,E,mu,A,I2,I3,J,rho) model.set_node_force(1,(0,0,-1e6,0,0,0)) model.set_node_restraint(0,[True]*6) model.assemble_KM() model.assemble_f() model.assemble_boundary() solve_linear(model) print(np.round(model.d_,6)) print("The result of node 1 should be about [0.12879,0.06440,-0.32485,-0.09320,0.18639,0]")
Example 4
Project: kalman_filter_multi_object_tracking Author: srianant File: kalman_filter.py License: MIT License | 6 votes |
def predict(self): """Predict state vector u and variance of uncertainty P (covariance). where, u: previous state vector P: previous covariance matrix F: state transition matrix Q: process noise matrix Equations: u'_{k|k-1} = Fu'_{k-1|k-1} P_{k|k-1} = FP_{k-1|k-1} F.T + Q where, F.T is F transpose Args: None Return: vector of predicted state estimate """ # Predicted state estimate self.u = np.round(np.dot(self.F, self.u)) # Predicted estimate covariance self.P = np.dot(self.F, np.dot(self.P, self.F.T)) + self.Q self.lastResult = self.u # same last predicted result return self.u
Example 5
Project: pytorch_NER_BiLSTM_CNN_CRF Author: bamtercelboo File: Embed.py License: Apache License 2.0 | 6 votes |
def _uniform_embed(self, embed_dict, words_dict): """ :param embed_dict: :param words_dict: """ print("loading pre_train embedding by uniform for out of vocabulary.") embeddings = np.zeros((int(self.words_count), int(self.dim))) inword_list = {} for word in words_dict: if word in embed_dict: embeddings[words_dict[word]] = np.array([float(i) for i in embed_dict[word]], dtype='float32') inword_list[words_dict[word]] = 1 self.exact_count += 1 elif word.lower() in embed_dict: embeddings[words_dict[word]] = np.array([float(i) for i in embed_dict[word.lower()]], dtype='float32') inword_list[words_dict[word]] = 1 self.fuzzy_count += 1 else: self.oov_count += 1 uniform_col = np.random.uniform(-0.25, 0.25, int(self.dim)).round(6) # uniform for i in range(len(words_dict)): if i not in inword_list and i != self.padID: embeddings[i] = uniform_col final_embed = torch.from_numpy(embeddings).float() return final_embed
Example 6
Project: neuropythy Author: noahbenson File: models.py License: GNU Affero General Public License v3.0 | 6 votes |
def cortex_to_angle(self, x, y): iterX = hasattr(x, '__iter__') iterY = hasattr(y, '__iter__') jarr = None if iterX and iterY: if len(x) != len(y): raise RuntimeError('Arguments x and y must be the same length!') jarr = self._java_object.cortexToAngle(to_java_doubles(x), to_java_doubles(y)) elif iterX: jarr = self._java_object.cortexToAngle(to_java_doubles(x), to_java_doubles([y for i in x])) elif iterY: jarr = self._java_object.cortexToAngle(to_java_doubles([x for i in y]), to_java_doubles(y)) else: return self._java_object.cortexToAngle(x, y) dat = np.asarray([[c for c in r] for r in jarr]) a = dat[:,2] a = np.round(np.abs(a)) a[a > 3] = 0 dat[:,2] = a return dat
Example 7
Project: padasip Author: matousc89 File: preprocess.py License: MIT License | 6 votes |
def test_lda(self): """ Linear Disciminant Analysis """ np.random.seed(100) N = 150 classes = np.array(["1", "a", 3]) cols = 4 x = np.random.random((N, cols)) # random data labels = np.random.choice(classes, size=N) # random labels # LDA components out = pa.preprocess.LDA_discriminants(x, labels) self.assertEqual(np.round(np.array(out).mean(), 5), 0.01298) # LDA analysis new_x = pa.preprocess.LDA(x, labels, n=2) self.assertEqual(np.round(np.array(new_x).mean(), 5), -0.50907) self.assertEqual(new_x.shape, (150, 2))
Example 8
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: image.py License: Apache License 2.0 | 6 votes |
def resize(im, short, max_size): """ only resize input image to target size and return scale :param im: BGR image input by opencv :param short: one dimensional size (the short side) :param max_size: one dimensional max size (the long side) :return: resized image (NDArray) and scale (float) """ im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(short) / float(im_size_min) # prevent bigger axis from being more than max_size: if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale
Example 9
Project: DOTA_models Author: ringringyi File: map_utils.py License: Apache License 2.0 | 6 votes |
def _project_to_map(map, vertex, wt=None, ignore_points_outside_map=False): """Projects points to map, returns how many points are present at each location.""" num_points = np.zeros((map.size[1], map.size[0])) vertex_ = vertex[:, :2] - map.origin vertex_ = np.round(vertex_ / map.resolution).astype(np.int) if ignore_points_outside_map: good_ind = np.all(np.array([vertex_[:,1] >= 0, vertex_[:,1] < map.size[1], vertex_[:,0] >= 0, vertex_[:,0] < map.size[0]]), axis=0) vertex_ = vertex_[good_ind, :] if wt is not None: wt = wt[good_ind, :] if wt is None: np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), 1) else: assert(wt.shape[0] == vertex.shape[0]), \ 'number of weights should be same as vertices.' np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), wt) return num_points
Example 10
Project: DOTA_models Author: ringringyi File: nav_env.py License: Apache License 2.0 | 6 votes |
def raw_valid_fn_vec(self, xyt): """Returns if the given set of nodes is valid or not.""" height = self.traversible.shape[0] width = self.traversible.shape[1] x = np.round(xyt[:,[0]]).astype(np.int32) y = np.round(xyt[:,[1]]).astype(np.int32) is_inside = np.all(np.concatenate((x >= 0, y >= 0, x < width, y < height), axis=1), axis=1) x = np.minimum(np.maximum(x, 0), width-1) y = np.minimum(np.maximum(y, 0), height-1) ind = np.ravel_multi_index((y,x), self.traversible.shape) is_traversible = self.traversible.ravel()[ind] is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible), axis=1), axis=1) return is_valid
Example 11
Project: DOTA_models Author: ringringyi File: box_list_ops_test.py License: Apache License 2.0 | 6 votes |
def test_convert_to_normalized_and_back(self): coordinates = np.random.uniform(size=(100, 4)) coordinates = np.round(np.sort(coordinates) * 200) coordinates[:, 2:4] += 1 coordinates[99, :] = [0, 0, 201, 201] img = tf.ones((128, 202, 202, 3)) boxlist = box_list.BoxList(tf.constant(coordinates, tf.float32)) boxlist = box_list_ops.to_normalized_coordinates(boxlist, tf.shape(img)[1], tf.shape(img)[2]) boxlist = box_list_ops.to_absolute_coordinates(boxlist, tf.shape(img)[1], tf.shape(img)[2]) with self.test_session() as sess: out = sess.run(boxlist.get()) self.assertAllClose(out, coordinates)
Example 12
Project: cascade-rcnn_Pytorch Author: guoruoqian File: blob.py License: MIT License | 6 votes |
def prep_im_for_blob(im, pixel_means, pixel_stds, target_size, max_size): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) im /= 255.0 im -= pixel_means im /= pixel_stds # im = im[:, :, ::-1] im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE # if np.round(im_scale * im_size_max) > max_size: # im_scale = float(max_size) / float(im_size_max) # im = imresize(im, im_scale) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale
Example 13
Project: cascade-rcnn_Pytorch Author: guoruoqian File: net_utils.py License: MIT License | 6 votes |
def vis_det_and_mask(im, class_name, dets, masks, thresh=0.8): """Visual debugging of detections.""" num_dets = np.minimum(10, dets.shape[0]) colors_mask = random_colors(num_dets) colors_bbox = np.round(np.random.rand(num_dets, 3) * 255) # sort rois according to the coordinates, draw upper bbox first draw_mask = np.zeros(im.shape[:2], dtype=np.uint8) for i in range(1): bbox = tuple(int(np.round(x)) for x in dets[i, :4]) mask = masks[i, :, :] full_mask = unmold_mask(mask, bbox, im.shape) score = dets[i, -1] if score > thresh: word_width = len(class_name) cv2.rectangle(im, bbox[0:2], bbox[2:4], colors_bbox[i], 2) cv2.rectangle(im, bbox[0:2], (bbox[0] + 18 + word_width*8, bbox[1]+15), colors_bbox[i], thickness=cv2.FILLED) apply_mask(im, full_mask, draw_mask, colors_mask[i], 0.5) draw_mask += full_mask cv2.putText(im, '%s' % (class_name), (bbox[0]+5, bbox[1] + 12), cv2.FONT_HERSHEY_PLAIN, 1.0, (255,255,255), thickness=1) return im
Example 14
Project: Pytorch-Networks Author: HaiyangLiu1997 File: RegNet2020.py License: MIT License | 6 votes |
def __init__(self, w_in, w_out, stride, bm, gw, se_r): super(BottleneckTransform, self).__init__() w_b = int(round(w_out * bm)) g = w_b // gw self.a = nn.Conv2d(w_in, w_b, 1, stride=1, padding=0, bias=False) self.a_bn = nn.BatchNorm2d(w_b, eps=1e-5, momentum=0.1) self.a_relu = nn.ReLU(inplace=True) self.b = nn.Conv2d(w_b, w_b, 3, stride=stride, padding=1, groups=g, bias=False) self.b_bn = nn.BatchNorm2d(w_b, eps=1e-5, momentum=0.1) self.b_relu = nn.ReLU(inplace=True) if se_r: w_se = int(round(w_in * se_r)) self.se = SE(w_b, w_se) self.c = nn.Conv2d(w_b, w_out, 1, stride=1, padding=0, bias=False) self.c_bn = nn.BatchNorm2d(w_out, eps=1e-5, momentum=0.1) self.c_bn.final_bn = True
Example 15
Project: Pytorch-Networks Author: HaiyangLiu1997 File: RegNet2020.py License: MIT License | 6 votes |
def forward(self, x): for layer in self.children(): x = layer(x) return x # @staticmethod # def complexity(cx, w_in, w_out, stride, bm, gw, se_r): # w_b = int(round(w_out * bm)) # g = w_b // gw # cx = net.complexity_conv2d(cx, w_in, w_b, 1, 1, 0) # cx = net.complexity_batchnorm2d(cx, w_b) # cx = net.complexity_conv2d(cx, w_b, w_b, 3, stride, 1, g) # cx = net.complexity_batchnorm2d(cx, w_b) # if se_r: # w_se = int(round(w_in * se_r)) # cx = SE.complexity(cx, w_b, w_se) # cx = net.complexity_conv2d(cx, w_b, w_out, 1, 1, 0) # cx = net.complexity_batchnorm2d(cx, w_out) # return cx
Example 16
Project: pywr Author: pywr File: timestepper.py License: GNU General Public License v3.0 | 6 votes |
def next(self, ): self._current = current = self._next if current.index >= len(self._periods): raise StopIteration() # Increment to next timestep next_index = current.index + 1 if next_index >= len(self._periods): # The final time-step is one offset beyond the end of the model. # Here we compute its delta and create the object. final_period = current.period + self.offset delta = final_period.end_time - final_period.start_time delta = np.round(delta.total_seconds()) delta = delta / SECONDS_IN_DAY self._next = _core.Timestep(final_period, next_index, delta) else: self._next = _core.Timestep(self._periods[next_index], next_index, self._deltas[next_index]) # Return this timestep return current
Example 17
Project: EXOSIMS Author: dsavransky File: FakeCatalog.py License: BSD 3-Clause "New" or "Revised" License | 6 votes |
def inverse_method(self,N,d): t = np.linspace(1e-3,0.999,N) f = np.log( t / (1 - t) ) f = f/f[0] psi= np.pi*f cosPsi = np.cos(psi) sinTheta = ( np.abs(cosPsi) + (1-np.abs(cosPsi))*np.random.rand(len(cosPsi))) theta = np.arcsin(sinTheta) theta = np.pi-theta + (2*theta - np.pi)*np.round(np.random.rand(len(t))) cosPhi = cosPsi/sinTheta phi = np.arccos(cosPhi)*(-1)**np.round(np.random.rand(len(t))) coords = SkyCoord(phi*u.rad,(np.pi/2-theta)*u.rad,d*np.ones(len(phi))*u.pc) return coords
Example 18
Project: Black-Box-Audio Author: rtaori File: run_audio_attack.py License: MIT License | 5 votes |
def save_wav(audio, output_wav_file): wav.write(output_wav_file, 16000, np.array(np.clip(np.round(audio), -2**15, 2**15-1), dtype=np.int16)) print('output dB', db(audio))
Example 19
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: blob.py License: MIT License | 5 votes |
def prep_im_for_blob(im, pixel_means, target_size, max_size): """Mean subtract and scale an image for use in a blob.""" im = im.astype(np.float32, copy=False) im -= pixel_means im_shape = im.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > max_size: im_scale = float(max_size) / float(im_size_max) im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) return im, im_scale
Example 20
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: test.py License: MIT License | 5 votes |
def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors)
Example 21
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: test_train.py License: MIT License | 5 votes |
def _get_image_blob(im): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] for target_size in cfg.TEST.SCALES: im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors)
Example 22
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: roi_pool_py.py License: MIT License | 5 votes |
def forward(self, features, rois): batch_size, num_channels, data_height, data_width = features.size() num_rois = rois.size()[0] outputs = Variable(torch.zeros(num_rois, num_channels, self.pooled_height, self.pooled_width)).cuda() for roi_ind, roi in enumerate(rois): batch_ind = int(roi[0].data[0]) roi_start_w, roi_start_h, roi_end_w, roi_end_h = np.round( roi[1:].data.cpu().numpy() * self.spatial_scale).astype(int) roi_width = max(roi_end_w - roi_start_w + 1, 1) roi_height = max(roi_end_h - roi_start_h + 1, 1) bin_size_w = float(roi_width) / float(self.pooled_width) bin_size_h = float(roi_height) / float(self.pooled_height) for ph in range(self.pooled_height): hstart = int(np.floor(ph * bin_size_h)) hend = int(np.ceil((ph + 1) * bin_size_h)) hstart = min(data_height, max(0, hstart + roi_start_h)) hend = min(data_height, max(0, hend + roi_start_h)) for pw in range(self.pooled_width): wstart = int(np.floor(pw * bin_size_w)) wend = int(np.ceil((pw + 1) * bin_size_w)) wstart = min(data_width, max(0, wstart + roi_start_w)) wend = min(data_width, max(0, wend + roi_start_w)) is_empty = (hend <= hstart) or(wend <= wstart) if is_empty: outputs[roi_ind, :, ph, pw] = 0 else: data = features[batch_ind] outputs[roi_ind, :, ph, pw] = torch.max( torch.max(data[:, hstart:hend, wstart:wend], 1)[0], 2)[0].view(-1) return outputs
Example 23
Project: Collaborative-Learning-for-Weakly-Supervised-Object-Detection Author: Sunarker File: generate_anchors.py License: MIT License | 5 votes |
def _ratio_enum(anchor, ratios): """ Enumerate a set of anchors for each aspect ratio wrt an anchor. """ w, h, x_ctr, y_ctr = _whctrs(anchor) size = w * h size_ratios = size / ratios ws = np.round(np.sqrt(size_ratios)) hs = np.round(ws * ratios) anchors = _mkanchors(ws, hs, x_ctr, y_ctr) return anchors
Example 24
Project: dustmaps Author: gregreen File: bh.py License: GNU General Public License v2.0 | 5 votes |
def _lb2RN_northcap(self, l, b): R = 100. + (90. - b) * np.sin(np.radians(l)) / 0.3 N = 100. + (90. - b) * np.cos(np.radians(l)) / 0.3 return np.round(R).astype('i4'), np.round(N).astype('i4')
Example 25
Project: dustmaps Author: gregreen File: bh.py License: GNU General Public License v2.0 | 5 votes |
def _lb2RN_southcap(self, l, b): R = 100. + (90. + b) * np.sin(np.radians(l)) / 0.3 N = 100. + (90. + b) * np.cos(np.radians(l)) / 0.3 return np.round(R).astype('i4'), np.round(N).astype('i4')
Example 26
Project: dustmaps Author: gregreen File: bh.py License: GNU General Public License v2.0 | 5 votes |
def _lb2RN_mid(self, l, b): R = (np.abs(b) - 10.) / 0.6 N = (np.mod(l, 360.) + 0.15) / 0.3 - 1 return np.round(R).astype('i4'), np.round(N).astype('i4')
Example 27
Project: StructEngPy Author: zhuoju36 File: test.py License: MIT License | 5 votes |
def simply_supported_beam_test(): #FEModel Test model=FEModel() E=1.999e11 mu=0.3 A=4.265e-3 J=9.651e-8 I3=6.572e-5 I2=3.301e-6 rho=7849.0474 model.add_node(0,0,0) model.add_node(0.5,1,0.5) model.add_node(1,2,1) model.add_beam(0,1,E,mu,A,I2,I3,J,rho) model.add_beam(1,2,E,mu,A,I2,I3,J,rho) model.set_node_force(1,(0,0,-1e6,0,0,0)) model.set_node_restraint(2,[False,False,True]+[False]*3) model.set_node_restraint(0,[True]*3+[False]*3) model.assemble_KM() model.assemble_f() model.assemble_boundary() solve_linear(model) print(np.round(model.d_,6)) print("The result of node 1 should be about [0.00796,0.00715,-0.02296,-0.01553,-0.03106,-0.01903]")
Example 28
Project: StructEngPy Author: zhuoju36 File: test.py License: MIT License | 5 votes |
def simply_released_beam_test(): #FEModel Test model=FEModel() E=1.999e11 mu=0.3 A=4.265e-3 J=9.651e-8 I3=6.572e-5 I2=3.301e-6 rho=7849.0474 model.add_node(0,0,0) model.add_node(0.5,1,0.5) model.add_node(1,2,1) model.add_beam(0,1,E,mu,A,I2,I3,J,rho) model.add_beam(1,2,E,mu,A,I2,I3,J,rho) model.set_node_force(1,(0,0,-1e6,0,0,0)) model.set_node_restraint(2,[True]*6) model.set_node_restraint(0,[True]*6) model.set_beam_releases(0,[True]*6,[False]*6) model.set_beam_releases(1,[False]*6,[True]*6) model.assemble_KM() model.assemble_f() model.assemble_boundary() solve_linear(model) print(np.round(model.d_,6)) print("The result of node 1 should be about [0.00445,0.00890,-0.02296,-0.01930,-0.03860,-0.01930]")
Example 29
Project: mmdetection Author: open-mmlab File: regnet.py License: Apache License 2.0 | 5 votes |
def generate_regnet(self, initial_width, width_slope, width_parameter, depth, divisor=8): """Generates per block width from RegNet parameters. Args: initial_width ([int]): Initial width of the backbone width_slope ([float]): Slope of the quantized linear function width_parameter ([int]): Parameter used to quantize the width. depth ([int]): Depth of the backbone. divisor (int, optional): The divisor of channels. Defaults to 8. Returns: list, int: return a list of widths of each stage and the number of stages """ assert width_slope >= 0 assert initial_width > 0 assert width_parameter > 1 assert initial_width % divisor == 0 widths_cont = np.arange(depth) * width_slope + initial_width ks = np.round( np.log(widths_cont / initial_width) / np.log(width_parameter)) widths = initial_width * np.power(width_parameter, ks) widths = np.round(np.divide(widths, divisor)) * divisor num_stages = len(np.unique(widths)) widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist() return widths, num_stages
Example 30
Project: mmdetection Author: open-mmlab File: regnet.py License: Apache License 2.0 | 5 votes |
def quantize_float(number, divisor): """Converts a float to closest non-zero int divisible by divior. Args: number (int): Original number to be quantized. divisor (int): Divisor used to quantize the number. Returns: int: quantized number that is divisible by devisor. """ return int(round(number / divisor) * divisor)