Python numpy.expand_dims方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def __init__(self, input_wave_file, output_wave_file, target_phrase):
        self.pop_size = 100
        self.elite_size = 10
        self.mutation_p = 0.005
        self.noise_stdev = 40
        self.noise_threshold = 1
        self.mu = 0.9
        self.alpha = 0.001
        self.max_iters = 3000
        self.num_points_estimate = 100
        self.delta_for_gradient = 100
        self.delta_for_perturbation = 1e3
        self.input_audio = load_wav(input_wave_file).astype(np.float32)
        self.pop = np.expand_dims(self.input_audio, axis=0)
        self.pop = np.tile(self.pop, (self.pop_size, 1))
        self.output_wave_file = output_wave_file
        self.target_phrase = target_phrase
        self.funcs = self.setup_graph(self.pop, np.array([toks.index(x) for x in target_phrase])) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def predict(self, f, k=5, resize_mode='fill'):
        from keras.preprocessing import image
        from vergeml.img import resize_image

        filename = os.path.basename(f)

        if not os.path.exists(f):
            return dict(filename=filename, prediction=[])

        img = image.load_img(f)
        img = resize_image(img, self.image_size, self.image_size, 'antialias', resize_mode)

        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = self.preprocess_input(x)
        preds = self.model.predict(x)
        pred = self._decode(preds, top=k)[0]
        prediction=[dict(probability=np.asscalar(perc), label=klass) for _, klass, perc in pred]

        return dict(filename=filename, prediction=prediction) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def transform(self, sample):
        if not self.model:
            if not self.architecture.startswith("@"):
                _, self.preprocess_input, self.model = \
                    get_imagenet_architecture(self.architecture, self.variant, self.size, self.alpha, self.output_layer)
            else:
                self.model = get_custom_architecture(self.architecture, self.trainings_dir, self.output_layer)
                self.preprocess_input = generic_preprocess_input

        x = sample.x
        x = x.convert('RGB')
        x = resize_image(x, self.image_size, self.image_size, 'antialias', 'aspect-fill')
        #x = x.resize((self.image_size, self.image_size))
        x = np.asarray(x)
        x = np.expand_dims(x, axis=0)
        x = self.preprocess_input(x)
        features = self.model.predict(x)
        features = features.flatten()
        sample.x = features
        sample.y = None
        return sample 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def calculate_fdiff_stress(self, x, u, nu, side=1, dx=1e-6):
        """
        Calculate the derivative of the Von Mises stress using finite
        differences given the densities x, displacements u, and young modulus
        nu. Optionally, provide the side length (default: 1) and delta x
        (default: 1e-6).
        """
        ds = self.calculate_diff_stress(x, u, nu, side)
        dsf = numpy.zeros(x.shape)
        x = numpy.expand_dims(x, -1)
        for i in range(x.shape[0]):
            delta = scipy.sparse.coo_matrix(([dx], [[i], [0]]), shape=x.shape)
            s1 = self.calculate_stress((x + delta.A).squeeze(), u, nu, side)
            s2 = self.calculate_stress((x - delta.A).squeeze(), u, nu, side)
            dsf[i] = ((s1 - s2) / (2. * dx))[i]
        print("finite differences: {:g}".format(numpy.linalg.norm(dsf - ds)))
        return dsf 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def __call__(self, results):
        """Call function to convert image in results to :obj:`torch.Tensor` and
        transpose the channel order.

        Args:
            results (dict): Result dict contains the image data to convert.

        Returns:
            dict: The result dict contains the image converted
                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
        """
        for key in self.keys:
            img = results[key]
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            results[key] = to_tensor(img.transpose(2, 0, 1))
        return results 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def _prepro_cpg(self, states, dists):
        """Preprocess the state and distance of neighboring CpG sites."""
        prepro_states = []
        prepro_dists = []
        for state, dist in zip(states, dists):
            nan = state == dat.CPG_NAN
            if np.any(nan):
                state[nan] = np.random.binomial(1, state[~nan].mean(),
                                                nan.sum())
                dist[nan] = self.cpg_max_dist
            dist = np.minimum(dist, self.cpg_max_dist) / self.cpg_max_dist
            prepro_states.append(np.expand_dims(state, 1))
            prepro_dists.append(np.expand_dims(dist, 1))
        prepro_states = np.concatenate(prepro_states, axis=1)
        prepro_dists = np.concatenate(prepro_dists, axis=1)
        if self.cpg_wlen:
            center = prepro_states.shape[2] // 2
            delta = self.cpg_wlen // 2
            tmp = slice(center - delta, center + delta)
            prepro_states = prepro_states[:, :, tmp]
            prepro_dists = prepro_dists[:, :, tmp]
        return (prepro_states, prepro_dists) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def predict_on_batch(self, inputs):
        # write test fasta file
        temp_input = tempfile.NamedTemporaryFile(suffix = ".txt")
        test_fname = temp_input.name
        encode_sequence_into_fasta_file(ofname = test_fname, seq = inputs.tolist())
        # test gkmsvm
        temp_ofp = tempfile.NamedTemporaryFile(suffix = ".txt")
        threads_option = '-T %s' % (str(self.threads))
        verbosity_option = '-v 0'
        command = ' '.join(['gkmpredict',
                            test_fname,
                            self.model_file,
                            temp_ofp.name,
                            threads_option,
                            verbosity_option])
        #process = subprocess.Popen(command, shell=True)
        #process.wait()  # wait for it to finish
        exit_code = os.system(command)
        temp_input.close()
        assert exit_code == 0
        # get classification results
        temp_ofp.seek(0)
        y = np.array([line.split()[-1] for line in temp_ofp], dtype=float)
        temp_ofp.close()
        return np.expand_dims(y, 1) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def reward(tsptw_sequence,speed):
    # Convert sequence to tour (end=start)
    tour = np.concatenate((tsptw_sequence,np.expand_dims(tsptw_sequence[0],0)))
    # Compute tour length
    inter_city_distances = np.sqrt(np.sum(np.square(tour[:-1,:2]-tour[1:,:2]),axis=1))
    distance = np.sum(inter_city_distances)
    # Compute develiry times at each city and count late cities
    elapsed_time = -10
    late_cities = 0
    for i in range(tsptw_sequence.shape[0]-1):
        travel_time = inter_city_distances[i]/speed
        tw_open = tour[i+1,2]
        tw_close = tour[i+1,3]
        elapsed_time += travel_time
        if elapsed_time <= tw_open:
            elapsed_time = tw_open
        elif elapsed_time > tw_close:
            late_cities += 1
    # Reward
    return distance + 100000000*late_cities

# Swap city[i] with city[j] in sequence 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def iterate_minibatches(dataset,batch_len): 
    start = 0
    for i in batch_len:
        tokens = []
        caseing = []
        char = []
        labels = []
        data = dataset[start:i]
        start = i
        for dt in data:
            t,c,ch,l = dt
            l = np.expand_dims(l,-1)
            tokens.append(t)
            caseing.append(c)
            char.append(ch)
            labels.append(l)
        yield np.asarray(labels),np.asarray(tokens),np.asarray(caseing),np.asarray(char) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def get_data(img_path):
    """get the (1, 3, h, w) np.array data for the supplied image
                Args:
                    img_path (string): the input image path

                Returns:
                    np.array: image data in a (1, 3, h, w) shape

    """
    mean = np.array([123.68, 116.779, 103.939])  # (R,G,B)
    img = Image.open(img_path)
    img = np.array(img, dtype=np.float32)
    reshaped_mean = mean.reshape(1, 1, 3)
    img = img - reshaped_mean
    img = np.swapaxes(img, 0, 2)
    img = np.swapaxes(img, 1, 2)
    img = np.expand_dims(img, axis=0)
    return img 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def preprocess(self, img):
        """
        Preprocess a 210x160x3 uint8 frame into a 6400 (80x80) (1 x input_size)
        float vector.
        """
        # Crop, down-sample, erase background and set foreground to 1.
        # See https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5
        img = img[35:195]
        img = img[::2, ::2, 0]
        img[img == 144] = 0
        img[img == 109] = 0
        img[img != 0] = 1
        curr = np.expand_dims(img.astype(np.float).ravel(), axis=0)
        # Subtract the last preprocessed image.
        diff = (curr - self.prev if self.prev is not None
                else np.zeros((1, curr.shape[1])))
        self.prev = curr
        return diff 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def get_point_cloud_from_z(Y, camera_matrix):
  """Projects the depth image Y into a 3D point cloud.
  Inputs:
    Y is ...xHxW
    camera_matrix
  Outputs:
    X is positive going right
    Y is positive into the image
    Z is positive up in the image
    XYZ is ...xHxWx3
  """
  x, z = np.meshgrid(np.arange(Y.shape[-1]),
                     np.arange(Y.shape[-2]-1, -1, -1))
  for i in range(Y.ndim-2):
    x = np.expand_dims(x, axis=0)
    z = np.expand_dims(z, axis=0)
  X = (x-camera_matrix.xc) * Y / camera_matrix.f
  Z = (z-camera_matrix.zc) * Y / camera_matrix.f
  XYZ = np.concatenate((X[...,np.newaxis], Y[...,np.newaxis],
                        Z[...,np.newaxis]), axis=X.ndim)
  return XYZ 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def iou(boxes1, boxes2):
  """Computes pairwise intersection-over-union between box collections.

  Args:
    boxes1: a numpy array with shape [N, 4] holding N boxes.
    boxes2: a numpy array with shape [M, 4] holding N boxes.

  Returns:
    a numpy array with shape [N, M] representing pairwise iou scores.
  """
  intersect = intersection(boxes1, boxes2)
  area1 = area(boxes1)
  area2 = area(boxes2)
  union = np.expand_dims(area1, axis=1) + np.expand_dims(
      area2, axis=0) - intersect
  return intersect / union 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def ioa(boxes1, boxes2):
  """Computes pairwise intersection-over-area between box collections.

  Intersection-over-area (ioa) between two boxes box1 and box2 is defined as
  their intersection area over box2's area. Note that ioa is not symmetric,
  that is, IOA(box1, box2) != IOA(box2, box1).

  Args:
    boxes1: a numpy array with shape [N, 4] holding N boxes.
    boxes2: a numpy array with shape [M, 4] holding N boxes.

  Returns:
    a numpy array with shape [N, M] representing pairwise ioa scores.
  """
  intersect = intersection(boxes1, boxes2)
  areas = np.expand_dims(area(boxes2), axis=0)
  return intersect / areas 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import expand_dims [as 别名]
def optimize(self, sess, feed_dict):
    reg_input, reg_weight, old_values, targets = sess.run(
        [self.inputs, self.regression_weight, self.values, self.targets],
        feed_dict=feed_dict)

    intended_values = targets * self.mix_frac + old_values * (1 - self.mix_frac)

    # taken from rllab
    reg_coeff = 1e-5
    for _ in range(5):
      best_fit_weight = np.linalg.lstsq(
          reg_input.T.dot(reg_input) +
          reg_coeff * np.identity(reg_input.shape[1]),
          reg_input.T.dot(intended_values))[0]
      if not np.any(np.isnan(best_fit_weight)):
        break
      reg_coeff *= 10

    if len(best_fit_weight.shape) == 1:
      best_fit_weight = np.expand_dims(best_fit_weight, -1)

    sess.run(self.update_regression_weight,
             feed_dict={self.new_regression_weight: best_fit_weight})