modeling/model/cam.py

# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Get CAM activation."""

import cv2
import numpy as np
import torch


_EPSILON = 1e-15


def scale_cam_image(cam, target_size=None):
  """Normalize and rescale cam image."""
  result = []
  for img in cam:
    img = img - np.min(img)
    img = img / (_EPSILON + np.max(img))
    if target_size is not None:
      img = cv2.resize(img, target_size)
    result.append(img)
  result = np.float32(result)

  return result


class ActivationsAndGradients:
  """Class for extracting activations and registering gradients from targetted intermediate layers."""

  def __init__(self, model, target_layers, reshape_transform, stride=16):
    self.model = model
    self.gradients = []
    self.activations = []
    self.reshape_transform = reshape_transform
    self.handles = []
    self.stride = stride
    for target_layer in target_layers:
      self.handles.append(
          target_layer.register_forward_hook(self.save_activation)
      )
      # Because of https://github.com/pytorch/pytorch/issues/61519,
      # we don't use backward hook to record gradients.
      self.handles.append(
          target_layer.register_forward_hook(self.save_gradient)
      )

  # pylint: disable=unused-argument
  # pylint: disable=redefined-builtin
  def save_activation(self, module, input, output):
    """Saves activations from targetted layer."""
    activation = output

    if self.reshape_transform is not None:
      activation = self.reshape_transform(activation, self.height, self.width)
    self.activations.append(activation.cpu().detach())

  def save_gradient(self, module, input, output):
    if not hasattr(output, "requires_grad") or not output.requires_grad:
      # You can only register hooks on tensor requires grad.
      return

    # Gradients are computed in reverse order
    def _store_grad(grad):
      if self.reshape_transform is not None:
        grad = self.reshape_transform(grad, self.height, self.width)
      self.gradients = [grad.cpu().detach()] + self.gradients

    output.register_hook(_store_grad)

  # pylint: enable=unused-argument
  # pylint: enable=redefined-builtin

  def __call__(self, x, h, w):
    self.height = h // self.stride
    self.width = w // self.stride
    self.gradients = []
    self.activations = []
    if isinstance(x, tuple) or isinstance(x, list):
      return self.model.forward_last_layer(x[0], x[1])
    else:
      return self.model(x)

  def release(self):
    for handle in self.handles:
      handle.remove()


# pylint: disable=g-bare-generic
class CAM:
  """CAM module."""

  def __init__(
      self,
      model,
      target_layers,
      use_cuda=False,
      reshape_transform=None,
      compute_input_gradient=False,
      stride=16,
  ):
    self.model = model.eval()
    self.target_layers = target_layers
    self.cuda = use_cuda
    self.model = model.cuda() if self.cuda else self.model
    self.reshape_transform = reshape_transform
    self.compute_input_gradient = compute_input_gradient
    self.activations_and_grads = ActivationsAndGradients(
        self.model, target_layers, reshape_transform, stride=stride
    )

  def get_cam(self, activations, grads):
    weights = np.mean(grads, axis=(2, 3))
    weighted_activations = weights[:, :, None, None] * activations
    cam = weighted_activations.sum(axis=1)
    return cam

  def forward(
      self,
      input_tensor,
      targets,
      target_size,
  ):
    """CAM forward pass."""
    if self.compute_input_gradient:
      input_tensor = torch.autograd.Variable(input_tensor, requires_grad=True)

    w, h = self.get_target_width_height(input_tensor)
    outputs = self.activations_and_grads(input_tensor, h, w)

    self.model.zero_grad()
    if isinstance(input_tensor, (tuple, list)):
      loss = sum(
          [target(output[0]) for target, output in zip(targets, outputs)]
      )
    else:
      loss = sum([target(output) for target, output in zip(targets, outputs)])
    loss.backward(retain_graph=True)
    cam_per_layer = self.compute_cam_per_layer(target_size)
    if isinstance(input_tensor, (tuple, list)):
      return (
          self.aggregate_multi_layers(cam_per_layer),
          outputs[0],
          outputs[1],
      )
    else:
      return self.aggregate_multi_layers(cam_per_layer), outputs

  def get_target_width_height(self, input_tensor):
    width = None
    height = None
    if isinstance(input_tensor, (tuple, list)):
      width, height = input_tensor[-1], input_tensor[-2]
    return width, height

  def compute_cam_per_layer(self, target_size):
    """Computes cam per target layer."""
    activations_list = [
        a.cpu().data.numpy() for a in self.activations_and_grads.activations
    ]
    grads_list = [
        g.cpu().data.numpy() for g in self.activations_and_grads.gradients
    ]

    cam_per_target_layer = []
    # Loop over the saliency image from every layer
    for i in range(len(self.target_layers)):
      layer_activations = None
      layer_grads = None
      if i < len(activations_list):
        layer_activations = activations_list[i]
      if i < len(grads_list):
        layer_grads = grads_list[i]

      cam = self.get_cam(layer_activations, layer_grads)
      cam = np.maximum(cam, 0).astype(np.float32)  # float16->32
      scaled = scale_cam_image(cam, target_size)
      cam_per_target_layer.append(scaled[:, None, :])

    return cam_per_target_layer

  def aggregate_multi_layers(self, cam_per_target_layer):
    cam_per_target_layer = np.concatenate(cam_per_target_layer, axis=1)
    cam_per_target_layer = np.maximum(cam_per_target_layer, 0)
    result = np.mean(cam_per_target_layer, axis=1)
    return scale_cam_image(result)

  def __call__(
      self,
      input_tensor,
      targets=None,
      target_size=None,
  ):
    return self.forward(input_tensor, targets, target_size)

  def __del__(self):
    self.activations_and_grads.release()

  def __enter__(self):
    return self

  def __exit__(self, exc_type, exc_value, exc_tb):
    self.activations_and_grads.release()
    if isinstance(exc_value, IndexError):
      # Handle IndexError here...
      print(
          f"An exception occurred in CAM with block: {exc_type}. "
          f"Message: {exc_value}"
      )
      return True