text: #!/usr/bin/env python3
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  
  import itertools
  import numpy as np
  import matplotlib.pyplot as plt
  import torch
  from sklearn.metrics import confusion_matrix
  
  import corr_net.utils.logging as logging
  from corr_net.datasets.utils import pack_pathway_output, tensor_normalize
  
  logger = logging.get_logger(__name__)
  
  
  def get_confusion_matrix(preds, labels, num_classes, normalize="true"):
      """
      Calculate confusion matrix on the provided preds and labels.
      Args:
          preds (tensor or lists of tensors): predictions. Each tensor is in
              in the shape of (n_batch, num_classes). Tensor(s) must be on CPU.
          labels (tensor or lists of tensors): corresponding labels. Each tensor is
              in the shape of either (n_batch,) or (n_batch, num_classes).
          num_classes (int): number of classes. Tensor(s) must be on CPU.
          normalize (Optional[str]) : {‘true’, ‘pred’, ‘all’}, default="true"
              Normalizes confusion matrix over the true (rows), predicted (columns)
              conditions or all the population. If None, confusion matrix
              will not be normalized.
      Returns:
          cmtx (ndarray): confusion matrix of size (num_classes x num_classes)
      """
      if isinstance(preds, list):
          preds = torch.cat(preds, dim=0)
      if isinstance(labels, list):
          labels = torch.cat(labels, dim=0)
      # If labels are one-hot encoded, get their indices.
      if labels.ndim == preds.ndim:
          labels = torch.argmax(labels, dim=-1)
      # Get the predicted class indices for examples.
      preds = torch.flatten(torch.argmax(preds, dim=-1))
      labels = torch.flatten(labels)
      cmtx = confusion_matrix(
          labels, preds, labels=list(range(num_classes)), normalize=normalize
      )
      return cmtx
  
  
  def plot_confusion_matrix(cmtx, num_classes, class_names=None, figsize=None):
      """
      A function to create a colored and labeled confusion matrix matplotlib figure
      given true labels and preds.
      Args:
          cmtx (ndarray): confusion matrix.
          num_classes (int): total number of classes.
          class_names (Optional[list of strs]): a list of class names.
          figsize (Optional[float, float]): the figure size of the confusion matrix.
              If None, default to [6.4, 4.8].
  
      Returns:
          img (figure): matplotlib figure.
      """
      if class_names is None or type(class_names) != list:
          class_names = [str(i) for i in range(num_classes)]
  
      figure = plt.figure(figsize=figsize)
      plt.imshow(cmtx, interpolation="nearest", cmap=plt.cm.Blues)
      plt.title("Confusion matrix")
      plt.colorbar()
      tick_marks = np.arange(len(class_names))
      plt.xticks(tick_marks, class_names, rotation=45)
      plt.yticks(tick_marks, class_names)
  
      # Use white text if squares are dark; otherwise black.
      threshold = cmtx.max() / 2.0
      for i, j in itertools.product(range(cmtx.shape[0]), range(cmtx.shape[1])):
          color = "white" if cmtx[i, j] > threshold else "black"
          plt.text(
              j,
              i,
              format(cmtx[i, j], ".2f") if cmtx[i, j] != 0 else ".",
              horizontalalignment="center",
              color=color,
          )
  
      plt.tight_layout()
      plt.ylabel("True label")
      plt.xlabel("Predicted label")
  
      return figure
  
  
  def plot_topk_histogram(tag, array, k=10, class_names=None, figsize=None):
      """
      Plot histogram of top-k value from the given array.
      Args:
          tag (str): histogram title.
          array (tensor): a tensor to draw top k value from.
          k (int): number of top values to draw from array.
              Defaut to 10.
          class_names (list of strings, optional):
              a list of names for values in array.
          figsize (Optional[float, float]): the figure size of the confusion matrix.
              If None, default to [6.4, 4.8].
      Returns:
          fig (matplotlib figure): a matplotlib figure of the histogram.
      """
      val, ind = torch.topk(array, k)
  
      fig = plt.Figure(figsize=figsize, facecolor="w", edgecolor="k")
  
      ax = fig.add_subplot(1, 1, 1)
  
      if class_names is None:
          class_names = [str(i) for i in ind]
      else:
          class_names = [class_names[i] for i in ind]
  
      tick_marks = np.arange(k)
      width = 0.75
      ax.bar(
          tick_marks,
          val,
          width,
          color="orange",
          tick_label=class_names,
          edgecolor="w",
          linewidth=1,
      )
  
      ax.set_xlabel("Candidates")
      ax.set_xticks(tick_marks)
      ax.set_xticklabels(class_names, rotation=-45, ha="center")
      ax.xaxis.set_label_position("bottom")
      ax.xaxis.tick_bottom()
  
      y_tick = np.linspace(0, 1, num=10)
      ax.set_ylabel("Frequency")
      ax.set_yticks(y_tick)
      y_labels = [format(i, ".1f") for i in y_tick]
      ax.set_yticklabels(y_labels, ha="center")
  
      for i, v in enumerate(val.numpy()):
          ax.text(
              i - 0.1,
              v + 0.03,
              format(v, ".2f"),
              color="orange",
              fontweight="bold",
          )
  
      ax.set_title(tag)
  
      fig.set_tight_layout(True)
  
      return fig
  
  
  class GetWeightAndActivation:
      """
      A class used to get weights and activations from specified layers from a Pytorch model.
      """
  
      def __init__(self, model, layers):
          """
          Args:
              model (nn.Module): the model containing layers to obtain weights and activations from.
              layers (list of strings): a list of layer names to obtain weights and activations from.
                  Names are hierarchical, separated by /. For example, If a layer follow a path
                  "s1" ---> "pathway0_stem" ---> "conv", the layer path is "s1/pathway0_stem/conv".
          """
          self.model = model
          self.hooks = {}
          self.layers_names = layers
          # eval mode
          self.model.eval()
          self._register_hooks()
  
      def _get_layer(self, layer_name):
          """
          Return a layer (nn.Module Object) given a hierarchical layer name, separated by /.
          Args:
              layer_name (str): the name of the layer.
          """
          layer_ls = layer_name.split("/")
          prev_module = self.model
          for layer in layer_ls:
              prev_module = prev_module._modules[layer]
  
          return prev_module
  
      def _register_single_hook(self, layer_name):
          """
          Register hook to a layer, given layer_name, to obtain activations.
          Args:
              layer_name (str): name of the layer.
          """
  
          def hook_fn(module, input, output):
              self.hooks[layer_name] = output.clone().detach()
  
          layer = get_layer(self.model, layer_name)
          layer.register_forward_hook(hook_fn)
  
      def _register_hooks(self):
          """
          Register hooks to layers in `self.layers_names`.
          """
          for layer_name in self.layers_names:
              self._register_single_hook(layer_name)
  
      def get_activations(self, input, bboxes=None):
          """
          Obtain all activations from layers that we register hooks for.
          Args:
              input (tensors, list of tensors): the model input.
              bboxes (Optional): Bouding boxes data that might be required
                  by the model.
          Returns:
              activation_dict (Python dictionary): a dictionary of the pair
                  {layer_name: list of activations}, where activations are outputs returned
                  by the layer.
          """
          input_clone = [inp.clone() for inp in input]
          if bboxes is not None:
              preds = self.model(input_clone, bboxes)
          else:
              preds = self.model(input_clone)
  
          activation_dict = {}
          for layer_name, hook in self.hooks.items():
              # list of activations for each instance.
              activation_dict[layer_name] = hook
  
          return activation_dict, preds
  
      def get_weights(self):
          """
          Returns weights from registered layers.
          Returns:
              weights (Python dictionary): a dictionary of the pair
              {layer_name: weight}, where weight is the weight tensor.
          """
          weights = {}
          for layer in self.layers_names:
              cur_layer = get_layer(self.model, layer)
              if hasattr(cur_layer, "weight"):
                  weights[layer] = cur_layer.weight.clone().detach()
              else:
                  logger.error(
                      "Layer {} does not have weight attribute.".format(layer)
                  )
          return weights
  
  
  def get_indexing(string):
      """
      Parse numpy-like fancy indexing from a string.
      Args:
          string (str): string represent the indices to take
              a subset of from array. Indices for each dimension
              are separated by `,`; indices for different dimensions
              are separated by `;`.
              e.g.: For a numpy array `arr` of shape (3,3,3), the string "1,2;1,2"
              means taking the sub-array `arr[[1,2], [1,2]]
      Returns:
          final_indexing (tuple): the parsed indexing.
      """
      index_ls = string.strip().split(";")
      final_indexing = []
      for index in index_ls:
          index_single_dim = index.split(",")
          index_single_dim = [int(i) for i in index_single_dim]
          final_indexing.append(index_single_dim)
  
      return tuple(final_indexing)
  
  
  def process_layer_index_data(layer_ls, layer_name_prefix=""):
      """
      Extract layer names and numpy-like fancy indexing from a string.
      Args:
          layer_ls (list of strs): list of strings containing data about layer names
              and their indexing. For each string, layer name and indexing is separated by whitespaces.
              e.g.: [layer1 1,2;2, layer2, layer3 150;3,4]
          layer_name_prefix (Optional[str]): prefix to be added to each layer name.
      Returns:
          layer_name (list of strings): a list of layer names.
          indexing_dict (Python dict): a dictionary of the pair
              {one_layer_name: indexing_for_that_layer}
      """
  
      layer_name, indexing_dict = [], {}
      for layer in layer_ls:
          ls = layer.split()
          name = layer_name_prefix + ls[0]
          layer_name.append(name)
          if len(ls) == 2:
              indexing_dict[name] = get_indexing(ls[1])
          else:
              indexing_dict[name] = ()
      return layer_name, indexing_dict
  
  
  def process_cv2_inputs(frames, cfg):
      """
      Normalize and prepare inputs as a list of tensors. Each tensor
      correspond to a unique pathway.
      Args:
          frames (list of array): list of input images (correspond to one clip) in range [0, 255].
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
      """
      inputs = torch.from_numpy(np.array(frames)).float() / 255
      inputs = tensor_normalize(inputs, cfg.DATA.MEAN, cfg.DATA.STD)
      # T H W C -> C T H W.
      inputs = inputs.permute(3, 0, 1, 2)
      # Sample frames for num_frames specified.
      index = torch.linspace(0, inputs.shape[1] - 1, cfg.DATA.NUM_FRAMES).long()
      inputs = torch.index_select(inputs, 1, index)
      inputs = pack_pathway_output(cfg, inputs)
      inputs = [inp.unsqueeze(0) for inp in inputs]
      return inputs
  
  
  def get_layer(model, layer_name):
      """
      Return the targeted layer (nn.Module Object) given a hierarchical layer name,
      separated by /.
      Args:
          model (model): model to get layers from.
          layer_name (str): name of the layer.
      Returns:
          prev_module (nn.Module): the layer from the model with `layer_name` name.
      """
      layer_ls = layer_name.split("/")
      prev_module = model
      for layer in layer_ls:
          prev_module = prev_module._modules[layer]
  
      return prev_module
  
  
  class TaskInfo:
      def __init__(self):
          self.frames = None
          self.id = -1
          self.bboxes = None
          self.action_preds = None
          self.num_buffer_frames = 0
          self.img_height = -1
          self.img_width = -1
          self.crop_size = -1
          self.clip_vis_size = -1
  
      def add_frames(self, idx, frames):
          """
          Add the clip and corresponding id.
          Args:
              idx (int): the current index of the clip.
              frames (list[ndarray]): list of images in "BGR" format.
          """
          self.frames = frames
          self.id = idx
  
      def add_bboxes(self, bboxes):
          """
          Add correspondding bounding boxes.
          """
          self.bboxes = bboxes
  
      def add_action_preds(self, preds):
          """
          Add the corresponding action predictions.
          """
          self.action_preds = preds
  