Python numpy.squeeze方法代码示例

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def draw_heatmap(img, heatmap, alpha=0.5):
    """Draw a heatmap overlay over an image."""
    assert len(heatmap.shape) == 2 or \
        (len(heatmap.shape) == 3 and heatmap.shape[2] == 1)
    assert img.dtype in [np.uint8, np.int32, np.int64]
    assert heatmap.dtype in [np.float32, np.float64]

    if img.shape[0:2] != heatmap.shape[0:2]:
        heatmap_rs = np.clip(heatmap * 255, 0, 255).astype(np.uint8)
        heatmap_rs = ia.imresize_single_image(
            heatmap_rs[..., np.newaxis],
            img.shape[0:2],
            interpolation="nearest"
        )
        heatmap = np.squeeze(heatmap_rs) / 255.0

    cmap = plt.get_cmap('jet')
    heatmap_cmapped = cmap(heatmap)
    heatmap_cmapped = np.delete(heatmap_cmapped, 3, 2)
    heatmap_cmapped = heatmap_cmapped * 255
    mix = (1-alpha) * img + alpha * heatmap_cmapped
    mix = np.clip(mix, 0, 255).astype(np.uint8)
    return mix 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def cleverhans_attack_wrapper(cleverhans_attack_fn, reset=True):
    def attack(a):
        session = tf.Session()
        with session.as_default():
            model = RVBCleverhansModel(a)
            adversarial_image = cleverhans_attack_fn(model, session, a)
            adversarial_image = np.squeeze(adversarial_image, axis=0)
            if reset:
                # optionally, reset to ignore other adversarials
                # found during the search
                a._reset()
            # run predictions to make sure the returned adversarial
            # is taken into account
            min_, max_ = a.bounds()
            adversarial_image = np.clip(adversarial_image, min_, max_)
            a.predictions(adversarial_image)
    return attack 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def __getitem__(self, idx):
        if self.fasta_extractor is None:
            self.fasta_extractor = FastaExtractor(self.fasta_file)
        interval = self.bt[idx]

        if interval.stop - interval.start != self.SEQ_WIDTH:
            raise ValueError("Expected the interval to be {0} wide. Recieved stop - start = {1}".
                             format(self.SEQ_WIDTH, interval.stop - interval.start))

        # Run the fasta extractor
        seq = np.squeeze(self.fasta_extractor([interval]), axis=0)
        return {
            "inputs": {"dna": seq},
            "metadata": {
                "ranges": GenomicRanges.from_interval(interval)
            }
        } 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def apply_affine(aff, coords):
    '''
    apply_affine(affine, coords) yields the result of applying the given affine transformation to
      the given coordinate or coordinates.

    This function expects coords to be a (dims X n) matrix but if the first dimension is neither 2
    nor 3, coords.T is used; i.e.:
      apply_affine(affine3x3, coords2xN) ==> newcoords2xN
      apply_affine(affine4x4, coords3xN) ==> newcoords3xN
      apply_affine(affine3x3, coordsNx2) ==> newcoordsNx2 (for N != 2)
      apply_affine(affine4x4, coordsNx3) ==> newcoordsNx3 (for N != 3)
    '''
    if aff is None: return coords
    (coords,tr) = (np.asanyarray(coords), False)
    if len(coords.shape) == 1: return np.squeeze(apply_affine(np.reshape(coords, (-1,1)), aff))
    elif len(coords.shape) > 2: raise ValueError('cannot apply affine to ND-array for N > 2')
    if   len(coords) == 2: aff = to_affine(aff, 2)
    elif len(coords) == 3: aff = to_affine(aff, 3)
    else: (coords,aff,tr) = (coords.T, to_affine(aff, coords.shape[1]), True)
    r = np.dot(aff, np.vstack([coords, np.ones([1,coords.shape[1]])]))[:-1]
    return r.T if tr else r 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def visualize(net, preprocessed_img, orig_img, conv_layer_name):
    # Returns grad-cam heatmap, guided grad-cam, guided grad-cam saliency
    imggrad = get_image_grad(net, preprocessed_img)
    conv_out, conv_out_grad = get_conv_out_grad(net, preprocessed_img, conv_layer_name=conv_layer_name)

    cam = get_cam(imggrad, conv_out)
    
    ggcam = get_guided_grad_cam(cam, imggrad)
    img_ggcam = grad_to_image(ggcam)
    
    img_heatmap = get_img_heatmap(orig_img, cam)
    
    ggcam_gray = to_grayscale(ggcam)
    img_ggcam_gray = np.squeeze(grad_to_image(ggcam_gray))
    
    return img_heatmap, img_ggcam, img_ggcam_gray 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def decode_topk(self, sess, latest_tokens, enc_top_states, dec_init_states):
    """Return the topK results and new decoder states."""
    feed = {
        self._enc_top_states: enc_top_states,
        self._dec_in_state:
            np.squeeze(np.array(dec_init_states)),
        self._abstracts:
            np.transpose(np.array([latest_tokens])),
        self._abstract_lens: np.ones([len(dec_init_states)], np.int32)}

    results = sess.run(
        [self._topk_ids, self._topk_log_probs, self._dec_out_state],
        feed_dict=feed)

    ids, probs, states = results[0], results[1], results[2]
    new_states = [s for s in states]
    return ids, probs, new_states 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def predict(self, batch_inputs, batch_ruitu):
        assert batch_ruitu.shape[0] == batch_inputs.shape[0], 'Shape Error'
        assert batch_inputs.shape[1] == 28 and batch_inputs.shape[2] == 10 and batch_inputs.shape[3] == 9, 'Error! Obs input shape must be (None, 28,10,9)'
        assert batch_ruitu.shape[1] == 37 and batch_ruitu.shape[2] == 10 and batch_ruitu.shape[3] == 29, 'Error! Ruitu input shape must be (None, 37,10, 29)'
        #all_pred={}
        pred_result_list = []
        for i in range(10):
            #print('Predict for station: 9000{}'.format(i+1))
            result = self.model.predict(x=[batch_inputs[:,:,i,:], batch_ruitu[:,:,i,:]])
            result = np.squeeze(result, axis=0)
            #all_pred[i] = result
            pred_result_list.append(result)
            #pass

        pred_result = np.stack(pred_result_list, axis=0)
        #return all_pred, pred_result
        print('Predict shape (10,37,3) means (stationID, timestep, features). Features include: t2m, rh2m and w10m')
        self.pred_result = pred_result
        return pred_result 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def set_recall(predictions, labels, weights_fn=common_layers.weights_nonzero):
  """Recall of set predictions.

  Args:
    predictions : A Tensor of scores of shape [batch, nlabels].
    labels: A Tensor of int32s giving true set elements,
      of shape [batch, seq_length].
    weights_fn: A function to weight the elements.

  Returns:
    hits: A Tensor of shape [batch, nlabels].
    weights: A Tensor of shape [batch, nlabels].
  """
  with tf.variable_scope("set_recall", values=[predictions, labels]):
    labels = tf.squeeze(labels, [2, 3])
    weights = weights_fn(labels)
    labels = tf.one_hot(labels, predictions.shape[-1])
    labels = tf.reduce_max(labels, axis=1)
    labels = tf.cast(labels, tf.bool)
    return tf.to_float(tf.equal(labels, predictions)), weights 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def __init__(self, x0, mu, epsmult = 4.0, noc = False):
        #determine number of planets and validate input
        nplanets = x0.size/6.
        if (nplanets - np.floor(nplanets) > 0):
            raise Exception('The length of x0 must be a multiple of 6.')
        
        if (mu.size != nplanets):
            raise Exception('The length of mu must be the length of x0 divided by 6')
        
        self.nplanets = int(nplanets)
        self.mu = np.squeeze(mu)
        if (self.mu.size == 1):
            self.mu = np.array(mu)
        
        self.epsmult = epsmult
        
        if not(noc) and ('EXOSIMS.util.KeplerSTM_C.CyKeplerSTM' in sys.modules):
            self.havec = True
            self.x0 = np.squeeze(x0)
        else:
            self.havec = False
            self.updateState(np.squeeze(x0)) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def _heatmap_to_rects(self, grid_pred, bb_img):
        """Convert a heatmap to rectangles / bounding box candidates."""
        grid_pred = np.squeeze(grid_pred) # (1, H, W) => (H, W)

        # remove low activations
        grid_thresh = grid_pred >= self.heatmap_activation_threshold

        # find connected components
        grid_labeled, num_labels = morphology.label(
            grid_thresh, background=0, connectivity=1, return_num=True
        )

        # for each connected components,
        # - draw a bounding box around it,
        # - shrink the bounding box to optimal size
        # - estimate a score/confidence value
        bbs = []
        for label in range(1, num_labels+1):
            (yy, xx) = np.nonzero(grid_labeled == label)
            min_y, max_y = np.min(yy), np.max(yy)
            min_x, max_x = np.min(xx), np.max(xx)
            rect = RectangleOnImage(x1=min_x, x2=max_x+1, y1=min_y, y2=max_y+1, shape=grid_labeled)
            activation = self._rect_to_score(rect, grid_pred)
            rect_shrunk, activation_shrunk = self._shrink(grid_pred, rect)
            rect_rs_shrunk = rect_shrunk.on(bb_img)
            bbs.append((rect_rs_shrunk, activation_shrunk))

        return bbs 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def generate_video_image(batch_idx, examples, model):
    """Generate frames for a video of the training progress.
    Each frame contains N examples shown in a grid. Each example shows
    the input image and the main heatmap predicted by the model."""
    start_time = time.time()
    #print("A", time.time() - start_time)
    model.eval()

    # fw through network
    inputs, outputs_gt = examples_to_batch(examples, iaa.Noop())
    inputs_torch = torch.from_numpy(inputs)
    inputs_torch = Variable(inputs_torch, volatile=True)
    if GPU >= 0:
        inputs_torch = inputs_torch.cuda(GPU)
    outputs_pred_torch = model(inputs_torch)
    #print("B", time.time() - start_time)

    outputs_pred = outputs_pred_torch.cpu().data.numpy()
    inputs = (inputs * 255).astype(np.uint8).transpose(0, 2, 3, 1)
    #print("C", time.time() - start_time)
    heatmaps = []
    for i in range(inputs.shape[0]):
        hm_drawn = draw_heatmap(inputs[i], np.squeeze(outputs_pred[i][0]), alpha=0.5)
        heatmaps.append(hm_drawn)
    #print("D", time.time() - start_time)
    grid = ia.draw_grid(heatmaps, cols=11, rows=6).astype(np.uint8)
    #grid_rs = misc.imresize(grid, (720-32, 1280-32))
    # pad by 42 for the text and to get the image to 720p aspect ratio
    grid_pad = np.pad(grid, ((0, 42), (0, 0), (0, 0)), mode="constant")
    grid_pad_text = ia.draw_text(
        grid_pad,
        x=grid_pad.shape[1]-220,
        y=grid_pad.shape[0]-35,
        text="Batch %05d" % (batch_idx,),
        color=[255, 255, 255]
    )
    #print("E", time.time() - start_time)
    return grid_pad_text 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def Rmtx_ri(coef_ri, K, D, L):
    coef_ri = np.squeeze(coef_ri)
    coef_r = coef_ri[:K + 1]
    coef_i = coef_ri[K + 1:]
    R_r = linalg.toeplitz(np.concatenate((np.array([coef_r[-1]]),
                                          np.zeros(L - K - 1))),
                          np.concatenate((coef_r[::-1],
                                          np.zeros(L - K - 1)))
                          )
    R_i = linalg.toeplitz(np.concatenate((np.array([coef_i[-1]]),
                                          np.zeros(L - K - 1))),
                          np.concatenate((coef_i[::-1],
                                          np.zeros(L - K - 1)))
                          )
    return np.dot(np.vstack((np.hstack((R_r, -R_i)), np.hstack((R_i, R_r)))), D) 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def _logits_op(self, x, name=None):
        with tf.name_scope(name, "logits", [x]) as name:

            num_classes = self.adversarial.num_classes()

            def _backward_py(gradient_y, x):
                x = np.squeeze(x, axis=0)
                gradient_y = np.squeeze(gradient_y, axis=0)
                gradient_x = self.adversarial.backward(gradient_y, x)
                gradient_x = gradient_x.astype(np.float32)
                return gradient_x[np.newaxis]

            def _backward_tf(op, grad):
                images = op.inputs[0]
                gradient_x = tf.py_func(
                    _backward_py, [grad, images], tf.float32)
                gradient_x.set_shape(images.shape)
                return gradient_x

            def _forward_py(x):
                predictions = self.adversarial.batch_predictions(
                    x, strict=False)[0]
                predictions = predictions.astype(np.float32)
                return predictions

            op = py_func_grad(
                _forward_py,
                [x],
                [tf.float32],
                name=name,
                grad=_backward_tf)

            logits = op[0]
            logits.set_shape((x.shape[0], num_classes))

        return logits 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def pair_visual(original, adversarial, figure=None):
    """
    This function displays two images: the original and the adversarial sample
    :param original: the original input
    :param adversarial: the input after perterbations have been applied
    :param figure: if we've already displayed images, use the same plot
    :return: the matplot figure to reuse for future samples
    """
    import matplotlib.pyplot as plt

    # Squeeze the image to remove single-dimensional entries from array shape
    original = np.squeeze(original)
    adversarial = np.squeeze(adversarial)

    # Ensure our inputs are of proper shape
    assert(len(original.shape) == 2 or len(original.shape) == 3)

    # To avoid creating figures per input sample, reuse the sample plot
    if figure is None:
        plt.ion()
        figure = plt.figure()
        figure.canvas.set_window_title('Cleverhans: Pair Visualization')

    # Add the images to the plot
    perterbations = adversarial - original
    for index, image in enumerate((original, perterbations, adversarial)):
        figure.add_subplot(1, 3, index + 1)
        plt.axis('off')

        # If the image is 2D, then we have 1 color channel
        if len(image.shape) == 2:
            plt.imshow(image, cmap='gray')
        else:
            plt.imshow(image)

        # Give the plot some time to update
        plt.pause(0.01)

    # Draw the plot and return
    plt.show()
    return figure 

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import squeeze [as 别名]
def __getitem__(self, idx):
        if self.seq_extractor is None:
            self.seq_extractor = FastaExtractor(self.fasta_file)
            self.dist_extractor = DistToClosestLandmarkExtractor(gtf_file=self.gtf,
                                                                 landmarks=ALL_LANDMARKS)

        interval = self.bt[idx]

        if interval.stop - interval.start != self.SEQ_WIDTH:
            raise ValueError("Expected the interval to be {0} wide. Recieved stop - start = {1}".
                             format(self.SEQ_WIDTH, interval.stop - interval.start))
        out = {}
        out['inputs'] = {}
        # input - sequence
        out['inputs']['seq'] = np.squeeze(self.seq_extractor([interval]), axis=0)

        # input - distance
        dist_dict = self.dist_transformer.transform(self.dist_extractor([interval]))
        dist_dict = {k: np.squeeze(v, axis=0) for k, v in dist_dict.items()}  # squeeze the batch axis
        out['inputs'] = {**out['inputs'], **dist_dict}

        # targets
        if self.target_dataset is not None:
            out["targets"] = np.array([self.target_dataset[idx]])

        # metadata
        out['metadata'] = {}
        out['metadata']['ranges'] = GenomicRanges.from_interval(interval)

        return out