Spaces:
Sleeping
Sleeping
File size: 6,003 Bytes
bc679dd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 |
# Author: Alexander Riedel
# License: Unlicensed
# Link: https://github.com/alexriedel1/detectron2-GradCAM
import cv2
import numpy as np
class GradCAM():
"""
Class to implement the GradCam function with it's necessary Pytorch hooks.
Attributes
----------
model : detectron2 GeneralizedRCNN Model
A model using the detectron2 API for inferencing
layer_name : str
name of the convolutional layer to perform GradCAM with
"""
def __init__(self, model, target_layer_name):
self.model = model
self.target_layer_name = target_layer_name
self.activations = None
self.gradient = None
self.model.eval()
self.activations_grads = []
self._register_hook()
def _get_activations_hook(self, module, input, output):
self.activations = output
def _get_grads_hook(self, module, input_grad, output_grad):
self.gradient = output_grad[0]
def _register_hook(self):
for (name, module) in self.model.named_modules():
if name == self.target_layer_name:
self.activations_grads.append(module.register_forward_hook(self._get_activations_hook))
self.activations_grads.append(module.register_backward_hook(self._get_grads_hook))
return True
print(f"Layer {self.target_layer_name} not found in Model!")
def _release_activations_grads(self):
for handle in self.activations_grads:
handle.remove()
def _postprocess_cam(self, raw_cam, img_width, img_height):
cam_orig = np.sum(raw_cam, axis=0) # [H,W]
cam_orig = np.maximum(cam_orig, 0) # ReLU
cam_orig -= np.min(cam_orig)
cam_orig /= np.max(cam_orig)
cam = cv2.resize(cam_orig, (img_width, img_height))
return cam, cam_orig
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_tb):
self._release_activations_grads()
def __call__(self, inputs, target_category):
"""
Calls the GradCAM++ instance
Parameters
----------
inputs : dict
The input in the standard detectron2 model input format
https://detectron2.readthedocs.io/en/latest/tutorials/models.html#model-input-format
target_category : int, optional
The target category index. If `None` the highest scoring class will be selected
Returns
-------
cam : np.array()
Gradient weighted class activation map
output : list
list of Instance objects representing the detectron2 model output
"""
self.model.zero_grad()
output = self.model.forward([inputs])
if target_category == None:
target_category = np.argmax(output[0]['instances'].scores.cpu().data.numpy(), axis=-1)
score = output[0]['instances'].scores[target_category]
#box0 = output[0]['instances'].pred_boxes[0].tensor[0][target_category]
#print(box0)
#box0.backward()
score.backward()
gradient = self.gradient[0].cpu().data.numpy() # [C,H,W]
activations = self.activations[0].cpu().data.numpy() # [C,H,W]
weight = np.mean(gradient, axis=(1, 2)) # [C]
cam = activations * weight[:, np.newaxis, np.newaxis] # [C,H,W]
cam, cam_orig = self._postprocess_cam(cam, inputs["width"], inputs["height"])
return cam, cam_orig, output
class GradCamPlusPlus(GradCAM):
"""
Subclass to implement the GradCam++ function with it's necessary PyTorch hooks.
...
Attributes
----------
model : detectron2 GeneralizedRCNN Model
A model using the detectron2 API for inferencing
target_layer_name : str
name of the convolutional layer to perform GradCAM++ with
"""
def __init__(self, model, target_layer_name):
super(GradCamPlusPlus, self).__init__(model, target_layer_name)
def __call__(self, inputs, target_category):
"""
Calls the GradCAM++ instance
Parameters
----------
inputs : dict
The input in the standard detectron2 model input format
https://detectron2.readthedocs.io/en/latest/tutorials/models.html#model-input-format
target_category : int, optional
The target category index. If `None` the highest scoring class will be selected
Returns
-------
cam : np.array()
Gradient weighted class activation map
output : list
list of Instance objects representing the detectron2 model output
"""
self.model.zero_grad()
output = self.model.forward([inputs])
if target_category == None:
target_category = np.argmax(output[0]['instances'].scores.cpu().data.numpy(), axis=-1)
score = output[0]['instances'].scores[target_category]
score.backward()
gradient = self.gradient[0].cpu().data.numpy() # [C,H,W]
activations = self.activations[0].cpu().data.numpy() # [C,H,W]
#from https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/grad_cam_plusplus.py
grads_power_2 = gradient**2
grads_power_3 = grads_power_2 * gradient
# Equation 19 in https://arxiv.org/abs/1710.11063
sum_activations = np.sum(activations, axis=(1, 2))
eps = 0.000001
aij = grads_power_2 / (2 * grads_power_2 +
sum_activations[:, None, None] * grads_power_3 + eps)
# Now bring back the ReLU from eq.7 in the paper,
# And zero out aijs where the activations are 0
aij = np.where(gradient != 0, aij, 0)
weights = np.maximum(gradient, 0) * aij
weight = np.sum(weights, axis=(1, 2))
cam = activations * weight[:, np.newaxis, np.newaxis] # [C,H,W]
cam, cam_orig = self._postprocess_cam(cam, inputs["width"], inputs["height"])
return cam, cam_orig, output
|