BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def affine_respective_zoom_matrix(w_range=0.8, h_range=1.1): """Get affine transform matrix for zooming/scaling that height and width are changed independently. OpenCV format, x is width. Parameters ----------- w_range : float or tuple of 2 floats The zooming/scaling ratio of width, greater...
def transform_matrix_offset_center(matrix, y, x): """Convert the matrix from Cartesian coordinates (the origin in the middle of image) to Image coordinates (the origin on the top-left of image). Parameters ---------- matrix : numpy.array Transform matrix. x and y : 2 int Size of ima...
def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1): """Return transformed images by given an affine matrix in Scipy format (x is height). Parameters ---------- x : numpy.array An image with dimension of [row, col, channel] (default). transform_...
def affine_transform_cv2(x, transform_matrix, flags=None, border_mode='constant'): """Return transformed images by given an affine matrix in OpenCV format (x is width). (Powered by OpenCV2, faster than ``tl.prepro.affine_transform``) Parameters ---------- x : numpy.array An image with dimension...
def affine_transform_keypoints(coords_list, transform_matrix): """Transform keypoint coordinates according to a given affine transform matrix. OpenCV format, x is width. Note that, for pose estimation task, flipping requires maintaining the left and right body information. We should not flip the left a...
def projective_transform_by_points( x, src, dst, map_args=None, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False ): """Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applic...
def rotation( x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Rotate an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). rg : int or floa...
def crop(x, wrg, hrg, is_random=False, row_index=0, col_index=1): """Randomly or centrally crop an image. Parameters ---------- x : numpy.array An image with dimension of [row, col, channel] (default). wrg : int Size of width. hrg : int Size of height. is_random : bo...
def crop_multi(x, wrg, hrg, is_random=False, row_index=0, col_index=1): """Randomly or centrally crop multiple images. Parameters ---------- x : list of numpy.array List of images with dimension of [n_images, row, col, channel] (default). others : args See ``tl.prepro.crop``. R...
def flip_axis(x, axis=1, is_random=False): """Flip the axis of an image, such as flip left and right, up and down, randomly or non-randomly, Parameters ---------- x : numpy.array An image with dimension of [row, col, channel] (default). axis : int Which axis to flip. - 0...
def flip_axis_multi(x, axis, is_random=False): """Flip the axises of multiple images together, such as flip left and right, up and down, randomly or non-randomly, Parameters ----------- x : list of numpy.array List of images with dimension of [n_images, row, col, channel] (default). others ...
def shift( x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shift an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). w...
def shift_multi( x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shift images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Par...
def shear( x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). inte...
def shear2( x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). ...
def swirl( x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False ): """Swirl an image randomly or non-randomly, see `scikit-image swirl API <http://scikit-image.org/docs/dev/api/skimage.transform.html...
def elastic_transform(x, alpha, sigma, mode="constant", cval=0, is_random=False): """Elastic transformation for image as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`__. Parameters ----------- x : numpy.array A greyscale image. alpha : float Alpha valu...
def zoom(x, zoom_range=(0.9, 1.1), flags=None, border_mode='constant'): """Zooming/Scaling a single image that height and width are changed together. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). zoom_range : float or tuple of 2 floats ...
def respective_zoom(x, h_range=(0.9, 1.1), w_range=(0.9, 1.1), flags=None, border_mode='constant'): """Zooming/Scaling a single image that height and width are changed independently. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). h_range : f...
def zoom_multi(x, zoom_range=(0.9, 1.1), flags=None, border_mode='constant'): """Zoom in and out of images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpy.array List...
def brightness(x, gamma=1, gain=1, is_random=False): """Change the brightness of a single image, randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). gamma : float Non negative real number. Default value is 1. ...
def brightness_multi(x, gamma=1, gain=1, is_random=False): """Change the brightness of multiply images, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpyarray List of images with dimension of ...
def illumination(x, gamma=1., contrast=1., saturation=1., is_random=False): """Perform illumination augmentation for a single image, randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). gamma : float Change bright...
def rgb_to_hsv(rgb): """Input RGB image [0~255] return HSV image [0~1]. Parameters ------------ rgb : numpy.array An image with values between 0 and 255. Returns ------- numpy.array A processed image. """ # Translated from source of colorsys.rgb_to_hsv # r,g,b ...
def hsv_to_rgb(hsv): """Input HSV image [0~1] return RGB image [0~255]. Parameters ------------- hsv : numpy.array An image with values between 0.0 and 1.0 Returns ------- numpy.array A processed image. """ # Translated from source of colorsys.hsv_to_rgb # h,s s...
def adjust_hue(im, hout=0.66, is_offset=True, is_clip=True, is_random=False): """Adjust hue of an RGB image. This is a convenience method that converts an RGB image to float representation, converts it to HSV, add an offset to the hue channel, converts back to RGB and then back to the original data type. F...
def imresize(x, size=None, interp='bicubic', mode=None): """Resize an image by given output size and method. Warning, this function will rescale the value to [0, 255]. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). size : list of 2 int ...
def pixel_value_scale(im, val=0.9, clip=None, is_random=False): """Scales each value in the pixels of the image. Parameters ----------- im : numpy.array An image. val : float The scale value for changing pixel value. - If is_random=False, multiply this value with all pix...
def samplewise_norm( x, rescale=None, samplewise_center=False, samplewise_std_normalization=False, channel_index=2, epsilon=1e-7 ): """Normalize an image by rescale, samplewise centering and samplewise centering in order. Parameters ----------- x : numpy.array An image with dimension of...
def featurewise_norm(x, mean=None, std=None, epsilon=1e-7): """Normalize every pixels by the same given mean and std, which are usually compute from all examples. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). mean : float Value ...
def get_zca_whitening_principal_components_img(X): """Return the ZCA whitening principal components matrix. Parameters ----------- x : numpy.array Batch of images with dimension of [n_example, row, col, channel] (default). Returns ------- numpy.array A processed image. ...
def zca_whitening(x, principal_components): """Apply ZCA whitening on an image by given principal components matrix. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). principal_components : matrix Matrix from ``get_zca_whitening_princip...
def channel_shift(x, intensity, is_random=False, channel_index=2): """Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`__. Parameters ----------- x : numpy.array An image with dimension of [r...
def channel_shift_multi(x, intensity, is_random=False, channel_index=2): """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`__. Usually be used for image segmentation which x=[X, Y], X ...
def drop(x, keep=0.5): """Randomly set some pixels to zero by a given keeping probability. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] or [row, col]. keep : float The keeping probability (0, 1), the lower more values will be set to zero. ...
def array_to_img(x, dim_ordering=(0, 1, 2), scale=True): """Converts a numpy array to PIL image object (uint8 format). Parameters ---------- x : numpy.array An image with dimension of 3 and channels of 1 or 3. dim_ordering : tuple of 3 int Index of row, col and channel, default (0, ...
def find_contours(x, level=0.8, fully_connected='low', positive_orientation='low'): """Find iso-valued contours in a 2D array for a given level value, returns list of (n, 2)-ndarrays see `skimage.measure.find_contours <http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.find_contours>`__. ...
def pt2map(list_points=None, size=(100, 100), val=1): """Inputs a list of points, return a 2D image. Parameters -------------- list_points : list of 2 int [[x, y], [x, y]..] for point coordinates. size : tuple of 2 int (w, h) for output size. val : float or int For the c...
def binary_dilation(x, radius=3): """Return fast binary morphological dilation of an image. see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`__. Parameters ----------- x : 2D array A binary image. r...
def dilation(x, radius=3): """Return greyscale morphological dilation of an image, see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`__. Parameters ----------- x : 2D array An greyscale image. radius : int ...
def binary_erosion(x, radius=3): """Return binary morphological erosion of an image, see `skimage.morphology.binary_erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_erosion>`__. Parameters ----------- x : 2D array A binary image. radius : i...
def erosion(x, radius=3): """Return greyscale morphological erosion of an image, see `skimage.morphology.erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.erosion>`__. Parameters ----------- x : 2D array A greyscale image. radius : int For ...
def obj_box_coords_rescale(coords=None, shape=None): """Scale down a list of coordinates from pixel unit to the ratio of image size i.e. in the range of [0, 1]. Parameters ------------ coords : list of list of 4 ints or None For coordinates of more than one images .e.g.[[x, y, w, h], [x, y, w, ...
def obj_box_coord_rescale(coord=None, shape=None): """Scale down one coordinates from pixel unit to the ratio of image size i.e. in the range of [0, 1]. It is the reverse process of ``obj_box_coord_scale_to_pixelunit``. Parameters ------------ coords : list of 4 int or None One coordinates ...
def obj_box_coord_scale_to_pixelunit(coord, shape=None): """Convert one coordinate [x, y, w (or x2), h (or y2)] in ratio format to image coordinate format. It is the reverse process of ``obj_box_coord_rescale``. Parameters ----------- coord : list of 4 float One coordinate of one image [x, ...
def obj_box_coord_centroid_to_upleft_butright(coord, to_int=False): """Convert one coordinate [x_center, y_center, w, h] to [x1, y1, x2, y2] in up-left and botton-right format. Parameters ------------ coord : list of 4 int/float One coordinate. to_int : boolean Whether to convert ou...
def obj_box_coord_upleft_butright_to_centroid(coord): """Convert one coordinate [x1, y1, x2, y2] to [x_center, y_center, w, h]. It is the reverse process of ``obj_box_coord_centroid_to_upleft_butright``. Parameters ------------ coord : list of 4 int/float One coordinate. Returns --...
def obj_box_coord_centroid_to_upleft(coord): """Convert one coordinate [x_center, y_center, w, h] to [x, y, w, h]. It is the reverse process of ``obj_box_coord_upleft_to_centroid``. Parameters ------------ coord : list of 4 int/float One coordinate. Returns ------- list of 4 nu...
def parse_darknet_ann_str_to_list(annotations): r"""Input string format of class, x, y, w, h, return list of list format. Parameters ----------- annotations : str The annotations in darkent format "class, x, y, w, h ...." seperated by "\\n". Returns ------- list of list of 4 number...
def parse_darknet_ann_list_to_cls_box(annotations): """Parse darknet annotation format into two lists for class and bounding box. Input list of [[class, x, y, w, h], ...], return two list of [class ...] and [[x, y, w, h], ...]. Parameters ------------ annotations : list of list A list of c...
def obj_box_horizontal_flip(im, coords=None, is_rescale=False, is_center=False, is_random=False): """Left-right flip the image and coordinates for object detection. Parameters ---------- im : numpy.array An image with dimension of [row, col, channel] (default). coords : list of list of 4 in...
def obj_box_imresize(im, coords=None, size=None, interp='bicubic', mode=None, is_rescale=False): """Resize an image, and compute the new bounding box coordinates. Parameters ------------- im : numpy.array An image with dimension of [row, col, channel] (default). coords : list of list of 4 i...
def obj_box_crop( im, classes=None, coords=None, wrg=100, hrg=100, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12. ): """Randomly or centrally crop an image, and compute the new bounding box coordinates. Objects outside the cropped image will be removed. P...
def obj_box_shift( im, classes=None, coords=None, wrg=0.1, hrg=0.1, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12. ): """Shift an image randomly or non-randomly, and compute the new ...
def obj_box_zoom( im, classes=None, coords=None, zoom_range=(0.9, 1.1), row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12. ): """Zoom i...
def pad_sequences(sequences, maxlen=None, dtype='int32', padding='post', truncating='pre', value=0.): """Pads each sequence to the same length: the length of the longest sequence. If maxlen is provided, any sequence longer than maxlen is truncated to maxlen. Truncation happens off either the beginni...
def remove_pad_sequences(sequences, pad_id=0): """Remove padding. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. pad_id : int The pad ID. Returns ---------- list of list of int The processed sequences. Exa...
def process_sequences(sequences, end_id=0, pad_val=0, is_shorten=True, remain_end_id=False): """Set all tokens(ids) after END token to the padding value, and then shorten (option) it to the maximum sequence length in this batch. Parameters ----------- sequences : list of list of int All sequenc...
def sequences_add_start_id(sequences, start_id=0, remove_last=False): """Add special start token(id) in the beginning of each sequence. Parameters ------------ sequences : list of list of int All sequences where each row is a sequence. start_id : int The start ID. remove_last : ...
def sequences_add_end_id(sequences, end_id=888): """Add special end token(id) in the end of each sequence. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. end_id : int The end ID. Returns ---------- list of list of int ...
def sequences_add_end_id_after_pad(sequences, end_id=888, pad_id=0): """Add special end token(id) in the end of each sequence. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. end_id : int The end ID. pad_id : int The pad...
def sequences_get_mask(sequences, pad_val=0): """Return mask for sequences. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. pad_val : int The pad value. Returns ---------- list of list of int The mask. Exam...
def keypoint_random_crop(image, annos, mask=None, size=(368, 368)): """Randomly crop an image and corresponding keypoints without influence scales, given by ``keypoint_random_resize_shortestedge``. Parameters ----------- image : 3 channel image The given image for augmentation. annos : list...
def keypoint_resize_random_crop(image, annos, mask=None, size=(368, 368)): """Reszie the image to make either its width or height equals to the given sizes. Then randomly crop image without influence scales. Resize the image match with the minimum size before cropping, this API will change the zoom scale of...
def keypoint_random_rotate(image, annos, mask=None, rg=15.): """Rotate an image and corresponding keypoints. Parameters ----------- image : 3 channel image The given image for augmentation. annos : list of list of floats The keypoints annotation of people. mask : single channel ...
def keypoint_random_flip( image, annos, mask=None, prob=0.5, flip_list=(0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16, 18) ): """Flip an image and corresponding keypoints. Parameters ----------- image : 3 channel image The given image for augmentation. annos : list of...
def keypoint_random_resize(image, annos, mask=None, zoom_range=(0.8, 1.2)): """Randomly resize an image and corresponding keypoints. The height and width of image will be changed independently, so the scale will be changed. Parameters ----------- image : 3 channel image The given image for ...
def Vgg19(rgb): """ Build the VGG 19 Model Parameters ----------- rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1] """ start_time = time.time() print("build model started") rgb_scaled = rgb * 255.0 # Convert RGB to BGR red, green, blue = tf.split(rg...
def Vgg19_simple_api(rgb): """ Build the VGG 19 Model Parameters ----------- rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1] """ start_time = time.time() print("build model started") rgb_scaled = rgb * 255.0 # Convert RGB to BGR red, green, blue = ...
def prepro(I): """Prepro 210x160x3 uint8 frame into 6400 (80x80) 1D float vector.""" I = I[35:195] I = I[::2, ::2, 0] I[I == 144] = 0 I[I == 109] = 0 I[I != 0] = 1 return I.astype(np.float).ravel()
def discount_episode_rewards(rewards=None, gamma=0.99, mode=0): """Take 1D float array of rewards and compute discounted rewards for an episode. When encount a non-zero value, consider as the end a of an episode. Parameters ---------- rewards : list List of rewards gamma : float ...
def cross_entropy_reward_loss(logits, actions, rewards, name=None): """Calculate the loss for Policy Gradient Network. Parameters ---------- logits : tensor The network outputs without softmax. This function implements softmax inside. actions : tensor or placeholder The agent action...
def log_weight(probs, weights, name='log_weight'): """Log weight. Parameters ----------- probs : tensor If it is a network output, usually we should scale it to [0, 1] via softmax. weights : tensor The weights. Returns -------- Tensor The Tensor after appling th...
def choice_action_by_probs(probs=(0.5, 0.5), action_list=None): """Choice and return an an action by given the action probability distribution. Parameters ------------ probs : list of float. The probability distribution of all actions. action_list : None or a list of int or others A...
def cross_entropy(output, target, name=None): """Softmax cross-entropy operation, returns the TensorFlow expression of cross-entropy for two distributions, it implements softmax internally. See ``tf.nn.sparse_softmax_cross_entropy_with_logits``. Parameters ---------- output : Tensor A batch...
def sigmoid_cross_entropy(output, target, name=None): """Sigmoid cross-entropy operation, see ``tf.nn.sigmoid_cross_entropy_with_logits``. Parameters ---------- output : Tensor A batch of distribution with shape: [batch_size, num of classes]. target : Tensor A batch of index with sh...
def binary_cross_entropy(output, target, epsilon=1e-8, name='bce_loss'): """Binary cross entropy operation. Parameters ---------- output : Tensor Tensor with type of `float32` or `float64`. target : Tensor The target distribution, format the same with `output`. epsilon : float ...
def mean_squared_error(output, target, is_mean=False, name="mean_squared_error"): """Return the TensorFlow expression of mean-square-error (L2) of two batch of data. Parameters ---------- output : Tensor 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, width] or [batch_siz...
def normalized_mean_square_error(output, target, name="normalized_mean_squared_error_loss"): """Return the TensorFlow expression of normalized mean-square-error of two distributions. Parameters ---------- output : Tensor 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, wid...
def absolute_difference_error(output, target, is_mean=False, name="absolute_difference_error_loss"): """Return the TensorFlow expression of absolute difference error (L1) of two batch of data. Parameters ---------- output : Tensor 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, h...
def dice_coe(output, target, loss_type='jaccard', axis=(1, 2, 3), smooth=1e-5): """Soft dice (Sørensen or Jaccard) coefficient for comparing the similarity of two batch of data, usually be used for binary image segmentation i.e. labels are binary. The coefficient between 0 to 1, 1 means totally match. ...
def dice_hard_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5): """Non-differentiable Sørensen–Dice coefficient for comparing the similarity of two batch of data, usually be used for binary image segmentation i.e. labels are binary. The coefficient between 0 to 1, 1 if totally match. Par...
def iou_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5): """Non-differentiable Intersection over Union (IoU) for comparing the similarity of two batch of data, usually be used for evaluating binary image segmentation. The coefficient between 0 to 1, and 1 means totally match. Parameters...
def cross_entropy_seq(logits, target_seqs, batch_size=None): # , batch_size=1, num_steps=None): """Returns the expression of cross-entropy of two sequences, implement softmax internally. Normally be used for fixed length RNN outputs, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/exam...
def cross_entropy_seq_with_mask(logits, target_seqs, input_mask, return_details=False, name=None): """Returns the expression of cross-entropy of two sequences, implement softmax internally. Normally be used for Dynamic RNN with Synced sequence input and output. Parameters ----------- logits : Tenso...
def cosine_similarity(v1, v2): """Cosine similarity [-1, 1]. Parameters ---------- v1, v2 : Tensor Tensor with the same shape [batch_size, n_feature]. References ---------- - `Wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`__. """ return tf.reduce_sum(tf.multiply(...
def li_regularizer(scale, scope=None): """Li regularization removes the neurons of previous layer. The `i` represents `inputs`. Returns a function that can be used to apply group li regularization to weights. The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/maste...
def maxnorm_regularizer(scale=1.0): """Max-norm regularization returns a function that can be used to apply max-norm regularization to weights. More about max-norm, see `wiki-max norm <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_. The implementation follows `TensorFlow contrib <https://github.com/...
def maxnorm_o_regularizer(scale): """Max-norm output regularization removes the neurons of current layer. Returns a function that can be used to apply max-norm regularization to each column of weight matrix. The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master...
def ramp(x, v_min=0, v_max=1, name=None): """Ramp activation function. Parameters ---------- x : Tensor input. v_min : float cap input to v_min as a lower bound. v_max : float cap input to v_max as a upper bound. name : str The function name (optional). ...
def leaky_relu(x, alpha=0.2, name="leaky_relu"): """leaky_relu can be used through its shortcut: :func:`tl.act.lrelu`. This function is a modified version of ReLU, introducing a nonzero gradient for negative input. Introduced by the paper: `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A...
def leaky_relu6(x, alpha=0.2, name="leaky_relu6"): """:func:`leaky_relu6` can be used through its shortcut: :func:`tl.act.lrelu6`. This activation function is a modified version :func:`leaky_relu` introduced by the following paper: `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maa...
def leaky_twice_relu6(x, alpha_low=0.2, alpha_high=0.2, name="leaky_relu6"): """:func:`leaky_twice_relu6` can be used through its shortcut: :func:`:func:`tl.act.ltrelu6`. This activation function is a modified version :func:`leaky_relu` introduced by the following paper: `Rectifier Nonlinearities Improve N...
def swish(x, name='swish'): """Swish function. See `Swish: a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941>`__. Parameters ---------- x : Tensor input. name: str function name (optional). Returns ------- Tensor A ``Tensor`` in the same t...
def pixel_wise_softmax(x, name='pixel_wise_softmax'): """Return the softmax outputs of images, every pixels have multiple label, the sum of a pixel is 1. Usually be used for image segmentation. Parameters ---------- x : Tensor input. - For 2d image, 4D tensor (batch_size, heigh...
def _conv_linear(args, filter_size, num_features, bias, bias_start=0.0, scope=None): """convolution: Parameters ---------- args : tensor 4D Tensor or a list of 4D, batch x n, Tensors. filter_size : tuple of int Filter height and width. num_features : int Nnumber of featu...
def advanced_indexing_op(inputs, index): """Advanced Indexing for Sequences, returns the outputs by given sequence lengths. When return the last output :class:`DynamicRNNLayer` uses it to get the last outputs with the sequence lengths. Parameters ----------- inputs : tensor for data With sh...
def retrieve_seq_length_op(data): """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features], it can be used when the features of padding (on right hand side) are all zeros. Parameters ----------- data : tensor [batch_size, n_step(max), n_features...
def retrieve_seq_length_op2(data): """An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)], it can be used when the features of padding (on right hand side) are all zeros. Parameters ----------- data : tensor [batch_size, n_step(max)] with zero padding on...
def retrieve_seq_length_op3(data, pad_val=0): # HangSheng: return tensor for sequence length, if input is tf.string """Return tensor for sequence length, if input is ``tf.string``.""" data_shape_size = data.get_shape().ndims if data_shape_size == 3: return tf.reduce_sum(tf.cast(tf.reduce_any(tf.not...

def affine_respective_zoom_matrix(w_range=0.8, h_range=1.1): """Get affine transform matrix for zooming/scaling that height and width are changed independently. OpenCV format, x is width. Parameters ----------- w_range : float or tuple of 2 floats The zooming/scaling ratio of width, greater...

def transform_matrix_offset_center(matrix, y, x): """Convert the matrix from Cartesian coordinates (the origin in the middle of image) to Image coordinates (the origin on the top-left of image). Parameters ---------- matrix : numpy.array Transform matrix. x and y : 2 int Size of ima...

def affine_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0., order=1): """Return transformed images by given an affine matrix in Scipy format (x is height). Parameters ---------- x : numpy.array An image with dimension of [row, col, channel] (default). transform_...

def affine_transform_cv2(x, transform_matrix, flags=None, border_mode='constant'): """Return transformed images by given an affine matrix in OpenCV format (x is width). (Powered by OpenCV2, faster than ``tl.prepro.affine_transform``) Parameters ---------- x : numpy.array An image with dimension...

def affine_transform_keypoints(coords_list, transform_matrix): """Transform keypoint coordinates according to a given affine transform matrix. OpenCV format, x is width. Note that, for pose estimation task, flipping requires maintaining the left and right body information. We should not flip the left a...

def projective_transform_by_points( x, src, dst, map_args=None, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False ): """Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applic...

def rotation( x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Rotate an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). rg : int or floa...

def crop(x, wrg, hrg, is_random=False, row_index=0, col_index=1): """Randomly or centrally crop an image. Parameters ---------- x : numpy.array An image with dimension of [row, col, channel] (default). wrg : int Size of width. hrg : int Size of height. is_random : bo...

def crop_multi(x, wrg, hrg, is_random=False, row_index=0, col_index=1): """Randomly or centrally crop multiple images. Parameters ---------- x : list of numpy.array List of images with dimension of [n_images, row, col, channel] (default). others : args See ``tl.prepro.crop``. R...

def flip_axis(x, axis=1, is_random=False): """Flip the axis of an image, such as flip left and right, up and down, randomly or non-randomly, Parameters ---------- x : numpy.array An image with dimension of [row, col, channel] (default). axis : int Which axis to flip. - 0...

def flip_axis_multi(x, axis, is_random=False): """Flip the axises of multiple images together, such as flip left and right, up and down, randomly or non-randomly, Parameters ----------- x : list of numpy.array List of images with dimension of [n_images, row, col, channel] (default). others ...

def shift( x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shift an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). w...

def shift_multi( x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shift images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Par...

def shear( x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). inte...

def shear2( x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1 ): """Shear an image randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). ...

def swirl( x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False ): """Swirl an image randomly or non-randomly, see `scikit-image swirl API <http://scikit-image.org/docs/dev/api/skimage.transform.html...

def elastic_transform(x, alpha, sigma, mode="constant", cval=0, is_random=False): """Elastic transformation for image as described in `[Simard2003] <http://deeplearning.cs.cmu.edu/pdfs/Simard.pdf>`__. Parameters ----------- x : numpy.array A greyscale image. alpha : float Alpha valu...

def zoom(x, zoom_range=(0.9, 1.1), flags=None, border_mode='constant'): """Zooming/Scaling a single image that height and width are changed together. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). zoom_range : float or tuple of 2 floats ...

def respective_zoom(x, h_range=(0.9, 1.1), w_range=(0.9, 1.1), flags=None, border_mode='constant'): """Zooming/Scaling a single image that height and width are changed independently. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). h_range : f...

def zoom_multi(x, zoom_range=(0.9, 1.1), flags=None, border_mode='constant'): """Zoom in and out of images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpy.array List...

def brightness(x, gamma=1, gain=1, is_random=False): """Change the brightness of a single image, randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). gamma : float Non negative real number. Default value is 1. ...

def brightness_multi(x, gamma=1, gain=1, is_random=False): """Change the brightness of multiply images, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched. Parameters ----------- x : list of numpyarray List of images with dimension of ...

def illumination(x, gamma=1., contrast=1., saturation=1., is_random=False): """Perform illumination augmentation for a single image, randomly or non-randomly. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). gamma : float Change bright...

def rgb_to_hsv(rgb): """Input RGB image [0~255] return HSV image [0~1]. Parameters ------------ rgb : numpy.array An image with values between 0 and 255. Returns ------- numpy.array A processed image. """ # Translated from source of colorsys.rgb_to_hsv # r,g,b ...

def hsv_to_rgb(hsv): """Input HSV image [0~1] return RGB image [0~255]. Parameters ------------- hsv : numpy.array An image with values between 0.0 and 1.0 Returns ------- numpy.array A processed image. """ # Translated from source of colorsys.hsv_to_rgb # h,s s...

def adjust_hue(im, hout=0.66, is_offset=True, is_clip=True, is_random=False): """Adjust hue of an RGB image. This is a convenience method that converts an RGB image to float representation, converts it to HSV, add an offset to the hue channel, converts back to RGB and then back to the original data type. F...

def imresize(x, size=None, interp='bicubic', mode=None): """Resize an image by given output size and method. Warning, this function will rescale the value to [0, 255]. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). size : list of 2 int ...

def pixel_value_scale(im, val=0.9, clip=None, is_random=False): """Scales each value in the pixels of the image. Parameters ----------- im : numpy.array An image. val : float The scale value for changing pixel value. - If is_random=False, multiply this value with all pix...

def samplewise_norm( x, rescale=None, samplewise_center=False, samplewise_std_normalization=False, channel_index=2, epsilon=1e-7 ): """Normalize an image by rescale, samplewise centering and samplewise centering in order. Parameters ----------- x : numpy.array An image with dimension of...

def featurewise_norm(x, mean=None, std=None, epsilon=1e-7): """Normalize every pixels by the same given mean and std, which are usually compute from all examples. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). mean : float Value ...

def get_zca_whitening_principal_components_img(X): """Return the ZCA whitening principal components matrix. Parameters ----------- x : numpy.array Batch of images with dimension of [n_example, row, col, channel] (default). Returns ------- numpy.array A processed image. ...

def zca_whitening(x, principal_components): """Apply ZCA whitening on an image by given principal components matrix. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] (default). principal_components : matrix Matrix from ``get_zca_whitening_princip...

def channel_shift(x, intensity, is_random=False, channel_index=2): """Shift the channels of an image, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`__. Parameters ----------- x : numpy.array An image with dimension of [r...

def channel_shift_multi(x, intensity, is_random=False, channel_index=2): """Shift the channels of images with the same arguments, randomly or non-randomly, see `numpy.rollaxis <https://docs.scipy.org/doc/numpy/reference/generated/numpy.rollaxis.html>`__. Usually be used for image segmentation which x=[X, Y], X ...

def drop(x, keep=0.5): """Randomly set some pixels to zero by a given keeping probability. Parameters ----------- x : numpy.array An image with dimension of [row, col, channel] or [row, col]. keep : float The keeping probability (0, 1), the lower more values will be set to zero. ...

def array_to_img(x, dim_ordering=(0, 1, 2), scale=True): """Converts a numpy array to PIL image object (uint8 format). Parameters ---------- x : numpy.array An image with dimension of 3 and channels of 1 or 3. dim_ordering : tuple of 3 int Index of row, col and channel, default (0, ...

def find_contours(x, level=0.8, fully_connected='low', positive_orientation='low'): """Find iso-valued contours in a 2D array for a given level value, returns list of (n, 2)-ndarrays see `skimage.measure.find_contours <http://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.find_contours>`__. ...

def pt2map(list_points=None, size=(100, 100), val=1): """Inputs a list of points, return a 2D image. Parameters -------------- list_points : list of 2 int [[x, y], [x, y]..] for point coordinates. size : tuple of 2 int (w, h) for output size. val : float or int For the c...

def binary_dilation(x, radius=3): """Return fast binary morphological dilation of an image. see `skimage.morphology.binary_dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_dilation>`__. Parameters ----------- x : 2D array A binary image. r...

def dilation(x, radius=3): """Return greyscale morphological dilation of an image, see `skimage.morphology.dilation <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.dilation>`__. Parameters ----------- x : 2D array An greyscale image. radius : int ...

def binary_erosion(x, radius=3): """Return binary morphological erosion of an image, see `skimage.morphology.binary_erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.binary_erosion>`__. Parameters ----------- x : 2D array A binary image. radius : i...

def erosion(x, radius=3): """Return greyscale morphological erosion of an image, see `skimage.morphology.erosion <http://scikit-image.org/docs/dev/api/skimage.morphology.html#skimage.morphology.erosion>`__. Parameters ----------- x : 2D array A greyscale image. radius : int For ...

def obj_box_coords_rescale(coords=None, shape=None): """Scale down a list of coordinates from pixel unit to the ratio of image size i.e. in the range of [0, 1]. Parameters ------------ coords : list of list of 4 ints or None For coordinates of more than one images .e.g.[[x, y, w, h], [x, y, w, ...

def obj_box_coord_rescale(coord=None, shape=None): """Scale down one coordinates from pixel unit to the ratio of image size i.e. in the range of [0, 1]. It is the reverse process of ``obj_box_coord_scale_to_pixelunit``. Parameters ------------ coords : list of 4 int or None One coordinates ...

def obj_box_coord_scale_to_pixelunit(coord, shape=None): """Convert one coordinate [x, y, w (or x2), h (or y2)] in ratio format to image coordinate format. It is the reverse process of ``obj_box_coord_rescale``. Parameters ----------- coord : list of 4 float One coordinate of one image [x, ...

def obj_box_coord_centroid_to_upleft_butright(coord, to_int=False): """Convert one coordinate [x_center, y_center, w, h] to [x1, y1, x2, y2] in up-left and botton-right format. Parameters ------------ coord : list of 4 int/float One coordinate. to_int : boolean Whether to convert ou...

def obj_box_coord_upleft_butright_to_centroid(coord): """Convert one coordinate [x1, y1, x2, y2] to [x_center, y_center, w, h]. It is the reverse process of ``obj_box_coord_centroid_to_upleft_butright``. Parameters ------------ coord : list of 4 int/float One coordinate. Returns --...

def obj_box_coord_centroid_to_upleft(coord): """Convert one coordinate [x_center, y_center, w, h] to [x, y, w, h]. It is the reverse process of ``obj_box_coord_upleft_to_centroid``. Parameters ------------ coord : list of 4 int/float One coordinate. Returns ------- list of 4 nu...

def parse_darknet_ann_str_to_list(annotations): r"""Input string format of class, x, y, w, h, return list of list format. Parameters ----------- annotations : str The annotations in darkent format "class, x, y, w, h ...." seperated by "\\n". Returns ------- list of list of 4 number...

def parse_darknet_ann_list_to_cls_box(annotations): """Parse darknet annotation format into two lists for class and bounding box. Input list of [[class, x, y, w, h], ...], return two list of [class ...] and [[x, y, w, h], ...]. Parameters ------------ annotations : list of list A list of c...

def obj_box_horizontal_flip(im, coords=None, is_rescale=False, is_center=False, is_random=False): """Left-right flip the image and coordinates for object detection. Parameters ---------- im : numpy.array An image with dimension of [row, col, channel] (default). coords : list of list of 4 in...

def obj_box_imresize(im, coords=None, size=None, interp='bicubic', mode=None, is_rescale=False): """Resize an image, and compute the new bounding box coordinates. Parameters ------------- im : numpy.array An image with dimension of [row, col, channel] (default). coords : list of list of 4 i...

def obj_box_crop( im, classes=None, coords=None, wrg=100, hrg=100, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12. ): """Randomly or centrally crop an image, and compute the new bounding box coordinates. Objects outside the cropped image will be removed. P...

def obj_box_shift( im, classes=None, coords=None, wrg=0.1, hrg=0.1, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12. ): """Shift an image randomly or non-randomly, and compute the new ...

def obj_box_zoom( im, classes=None, coords=None, zoom_range=(0.9, 1.1), row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0., order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12. ): """Zoom i...

def pad_sequences(sequences, maxlen=None, dtype='int32', padding='post', truncating='pre', value=0.): """Pads each sequence to the same length: the length of the longest sequence. If maxlen is provided, any sequence longer than maxlen is truncated to maxlen. Truncation happens off either the beginni...

def remove_pad_sequences(sequences, pad_id=0): """Remove padding. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. pad_id : int The pad ID. Returns ---------- list of list of int The processed sequences. Exa...

def process_sequences(sequences, end_id=0, pad_val=0, is_shorten=True, remain_end_id=False): """Set all tokens(ids) after END token to the padding value, and then shorten (option) it to the maximum sequence length in this batch. Parameters ----------- sequences : list of list of int All sequenc...

def sequences_add_start_id(sequences, start_id=0, remove_last=False): """Add special start token(id) in the beginning of each sequence. Parameters ------------ sequences : list of list of int All sequences where each row is a sequence. start_id : int The start ID. remove_last : ...

def sequences_add_end_id(sequences, end_id=888): """Add special end token(id) in the end of each sequence. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. end_id : int The end ID. Returns ---------- list of list of int ...

def sequences_add_end_id_after_pad(sequences, end_id=888, pad_id=0): """Add special end token(id) in the end of each sequence. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. end_id : int The end ID. pad_id : int The pad...

def sequences_get_mask(sequences, pad_val=0): """Return mask for sequences. Parameters ----------- sequences : list of list of int All sequences where each row is a sequence. pad_val : int The pad value. Returns ---------- list of list of int The mask. Exam...

def keypoint_random_crop(image, annos, mask=None, size=(368, 368)): """Randomly crop an image and corresponding keypoints without influence scales, given by ``keypoint_random_resize_shortestedge``. Parameters ----------- image : 3 channel image The given image for augmentation. annos : list...

def keypoint_resize_random_crop(image, annos, mask=None, size=(368, 368)): """Reszie the image to make either its width or height equals to the given sizes. Then randomly crop image without influence scales. Resize the image match with the minimum size before cropping, this API will change the zoom scale of...

def keypoint_random_rotate(image, annos, mask=None, rg=15.): """Rotate an image and corresponding keypoints. Parameters ----------- image : 3 channel image The given image for augmentation. annos : list of list of floats The keypoints annotation of people. mask : single channel ...

def keypoint_random_flip( image, annos, mask=None, prob=0.5, flip_list=(0, 1, 5, 6, 7, 2, 3, 4, 11, 12, 13, 8, 9, 10, 15, 14, 17, 16, 18) ): """Flip an image and corresponding keypoints. Parameters ----------- image : 3 channel image The given image for augmentation. annos : list of...

def keypoint_random_resize(image, annos, mask=None, zoom_range=(0.8, 1.2)): """Randomly resize an image and corresponding keypoints. The height and width of image will be changed independently, so the scale will be changed. Parameters ----------- image : 3 channel image The given image for ...

def Vgg19(rgb): """ Build the VGG 19 Model Parameters ----------- rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1] """ start_time = time.time() print("build model started") rgb_scaled = rgb * 255.0 # Convert RGB to BGR red, green, blue = tf.split(rg...

def Vgg19_simple_api(rgb): """ Build the VGG 19 Model Parameters ----------- rgb : rgb image placeholder [batch, height, width, 3] values scaled [0, 1] """ start_time = time.time() print("build model started") rgb_scaled = rgb * 255.0 # Convert RGB to BGR red, green, blue = ...

def prepro(I): """Prepro 210x160x3 uint8 frame into 6400 (80x80) 1D float vector.""" I = I[35:195] I = I[::2, ::2, 0] I[I == 144] = 0 I[I == 109] = 0 I[I != 0] = 1 return I.astype(np.float).ravel()

def discount_episode_rewards(rewards=None, gamma=0.99, mode=0): """Take 1D float array of rewards and compute discounted rewards for an episode. When encount a non-zero value, consider as the end a of an episode. Parameters ---------- rewards : list List of rewards gamma : float ...

def cross_entropy_reward_loss(logits, actions, rewards, name=None): """Calculate the loss for Policy Gradient Network. Parameters ---------- logits : tensor The network outputs without softmax. This function implements softmax inside. actions : tensor or placeholder The agent action...

def log_weight(probs, weights, name='log_weight'): """Log weight. Parameters ----------- probs : tensor If it is a network output, usually we should scale it to [0, 1] via softmax. weights : tensor The weights. Returns -------- Tensor The Tensor after appling th...

def choice_action_by_probs(probs=(0.5, 0.5), action_list=None): """Choice and return an an action by given the action probability distribution. Parameters ------------ probs : list of float. The probability distribution of all actions. action_list : None or a list of int or others A...

def cross_entropy(output, target, name=None): """Softmax cross-entropy operation, returns the TensorFlow expression of cross-entropy for two distributions, it implements softmax internally. See ``tf.nn.sparse_softmax_cross_entropy_with_logits``. Parameters ---------- output : Tensor A batch...

def sigmoid_cross_entropy(output, target, name=None): """Sigmoid cross-entropy operation, see ``tf.nn.sigmoid_cross_entropy_with_logits``. Parameters ---------- output : Tensor A batch of distribution with shape: [batch_size, num of classes]. target : Tensor A batch of index with sh...

def binary_cross_entropy(output, target, epsilon=1e-8, name='bce_loss'): """Binary cross entropy operation. Parameters ---------- output : Tensor Tensor with type of `float32` or `float64`. target : Tensor The target distribution, format the same with `output`. epsilon : float ...

def mean_squared_error(output, target, is_mean=False, name="mean_squared_error"): """Return the TensorFlow expression of mean-square-error (L2) of two batch of data. Parameters ---------- output : Tensor 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, width] or [batch_siz...

def normalized_mean_square_error(output, target, name="normalized_mean_squared_error_loss"): """Return the TensorFlow expression of normalized mean-square-error of two distributions. Parameters ---------- output : Tensor 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, height, wid...

def absolute_difference_error(output, target, is_mean=False, name="absolute_difference_error_loss"): """Return the TensorFlow expression of absolute difference error (L1) of two batch of data. Parameters ---------- output : Tensor 2D, 3D or 4D tensor i.e. [batch_size, n_feature], [batch_size, h...

def dice_coe(output, target, loss_type='jaccard', axis=(1, 2, 3), smooth=1e-5): """Soft dice (Sørensen or Jaccard) coefficient for comparing the similarity of two batch of data, usually be used for binary image segmentation i.e. labels are binary. The coefficient between 0 to 1, 1 means totally match. ...

def dice_hard_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5): """Non-differentiable Sørensen–Dice coefficient for comparing the similarity of two batch of data, usually be used for binary image segmentation i.e. labels are binary. The coefficient between 0 to 1, 1 if totally match. Par...

def iou_coe(output, target, threshold=0.5, axis=(1, 2, 3), smooth=1e-5): """Non-differentiable Intersection over Union (IoU) for comparing the similarity of two batch of data, usually be used for evaluating binary image segmentation. The coefficient between 0 to 1, and 1 means totally match. Parameters...

def cross_entropy_seq(logits, target_seqs, batch_size=None): # , batch_size=1, num_steps=None): """Returns the expression of cross-entropy of two sequences, implement softmax internally. Normally be used for fixed length RNN outputs, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/exam...

def cross_entropy_seq_with_mask(logits, target_seqs, input_mask, return_details=False, name=None): """Returns the expression of cross-entropy of two sequences, implement softmax internally. Normally be used for Dynamic RNN with Synced sequence input and output. Parameters ----------- logits : Tenso...

def cosine_similarity(v1, v2): """Cosine similarity [-1, 1]. Parameters ---------- v1, v2 : Tensor Tensor with the same shape [batch_size, n_feature]. References ---------- - `Wiki <https://en.wikipedia.org/wiki/Cosine_similarity>`__. """ return tf.reduce_sum(tf.multiply(...

def li_regularizer(scale, scope=None): """Li regularization removes the neurons of previous layer. The `i` represents `inputs`. Returns a function that can be used to apply group li regularization to weights. The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/maste...

def maxnorm_regularizer(scale=1.0): """Max-norm regularization returns a function that can be used to apply max-norm regularization to weights. More about max-norm, see `wiki-max norm <https://en.wikipedia.org/wiki/Matrix_norm#Max_norm>`_. The implementation follows `TensorFlow contrib <https://github.com/...

def maxnorm_o_regularizer(scale): """Max-norm output regularization removes the neurons of current layer. Returns a function that can be used to apply max-norm regularization to each column of weight matrix. The implementation follows `TensorFlow contrib <https://github.com/tensorflow/tensorflow/blob/master...

def ramp(x, v_min=0, v_max=1, name=None): """Ramp activation function. Parameters ---------- x : Tensor input. v_min : float cap input to v_min as a lower bound. v_max : float cap input to v_max as a upper bound. name : str The function name (optional). ...

def leaky_relu(x, alpha=0.2, name="leaky_relu"): """leaky_relu can be used through its shortcut: :func:`tl.act.lrelu`. This function is a modified version of ReLU, introducing a nonzero gradient for negative input. Introduced by the paper: `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A...

def leaky_relu6(x, alpha=0.2, name="leaky_relu6"): """:func:`leaky_relu6` can be used through its shortcut: :func:`tl.act.lrelu6`. This activation function is a modified version :func:`leaky_relu` introduced by the following paper: `Rectifier Nonlinearities Improve Neural Network Acoustic Models [A. L. Maa...

def leaky_twice_relu6(x, alpha_low=0.2, alpha_high=0.2, name="leaky_relu6"): """:func:`leaky_twice_relu6` can be used through its shortcut: :func:`:func:`tl.act.ltrelu6`. This activation function is a modified version :func:`leaky_relu` introduced by the following paper: `Rectifier Nonlinearities Improve N...

def swish(x, name='swish'): """Swish function. See `Swish: a Self-Gated Activation Function <https://arxiv.org/abs/1710.05941>`__. Parameters ---------- x : Tensor input. name: str function name (optional). Returns ------- Tensor A ``Tensor`` in the same t...

def pixel_wise_softmax(x, name='pixel_wise_softmax'): """Return the softmax outputs of images, every pixels have multiple label, the sum of a pixel is 1. Usually be used for image segmentation. Parameters ---------- x : Tensor input. - For 2d image, 4D tensor (batch_size, heigh...

def _conv_linear(args, filter_size, num_features, bias, bias_start=0.0, scope=None): """convolution: Parameters ---------- args : tensor 4D Tensor or a list of 4D, batch x n, Tensors. filter_size : tuple of int Filter height and width. num_features : int Nnumber of featu...

def advanced_indexing_op(inputs, index): """Advanced Indexing for Sequences, returns the outputs by given sequence lengths. When return the last output :class:`DynamicRNNLayer` uses it to get the last outputs with the sequence lengths. Parameters ----------- inputs : tensor for data With sh...

def retrieve_seq_length_op(data): """An op to compute the length of a sequence from input shape of [batch_size, n_step(max), n_features], it can be used when the features of padding (on right hand side) are all zeros. Parameters ----------- data : tensor [batch_size, n_step(max), n_features...

def retrieve_seq_length_op2(data): """An op to compute the length of a sequence, from input shape of [batch_size, n_step(max)], it can be used when the features of padding (on right hand side) are all zeros. Parameters ----------- data : tensor [batch_size, n_step(max)] with zero padding on...

def retrieve_seq_length_op3(data, pad_val=0): # HangSheng: return tensor for sequence length, if input is tf.string """Return tensor for sequence length, if input is ``tf.string``.""" data_shape_size = data.get_shape().ndims if data_shape_size == 3: return tf.reduce_sum(tf.cast(tf.reduce_any(tf.not...