File size: 5,059 Bytes
da716ed |
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 |
import cv2
import numpy as np
import torch
import tqdm
from pytorch_grad_cam.base_cam import BaseCAM
class AblationLayer(torch.nn.Module):
def __init__(self, layer, reshape_transform, indices):
super(AblationLayer, self).__init__()
self.layer = layer
self.reshape_transform = reshape_transform
# The channels to zero out:
self.indices = indices
def forward(self, x):
self.__call__(x)
def __call__(self, x):
output = self.layer(x)
# Hack to work with ViT,
# Since the activation channels are last and not first like in CNNs
# Probably should remove it?
if self.reshape_transform is not None:
output = output.transpose(1, 2)
for i in range(output.size(0)):
# Commonly the minimum activation will be 0,
# And then it makes sense to zero it out.
# However depending on the architecture,
# If the values can be negative, we use very negative values
# to perform the ablation, deviating from the paper.
if torch.min(output) == 0:
output[i, self.indices[i], :] = 0
else:
ABLATION_VALUE = 1e5
output[i, self.indices[i], :] = torch.min(
output) - ABLATION_VALUE
if self.reshape_transform is not None:
output = output.transpose(2, 1)
return output
def replace_layer_recursive(model, old_layer, new_layer):
for name, layer in model._modules.items():
if layer == old_layer:
model._modules[name] = new_layer
return True
elif replace_layer_recursive(layer, old_layer, new_layer):
return True
return False
class AblationCAM(BaseCAM):
def __init__(self, model, target_layers, use_cuda=False,
reshape_transform=None):
super(AblationCAM, self).__init__(model, target_layers, use_cuda,
reshape_transform)
if len(target_layers) > 1:
print(
"Warning. You are usign Ablation CAM with more than 1 layers. "
"This is supported only if all layers have the same output shape")
def set_ablation_layers(self):
self.ablation_layers = []
for target_layer in self.target_layers:
ablation_layer = AblationLayer(target_layer,
self.reshape_transform, indices=[])
self.ablation_layers.append(ablation_layer)
replace_layer_recursive(self.model, target_layer, ablation_layer)
def unset_ablation_layers(self):
# replace the model back to the original state
for ablation_layer, target_layer in zip(
self.ablation_layers, self.target_layers):
replace_layer_recursive(self.model, ablation_layer, target_layer)
def set_ablation_layer_batch_indices(self, indices):
for ablation_layer in self.ablation_layers:
ablation_layer.indices = indices
def trim_ablation_layer_batch_indices(self, keep):
for ablation_layer in self.ablation_layers:
ablation_layer.indices = ablation_layer.indices[:keep]
def get_cam_weights(self,
input_tensor,
target_category,
activations,
grads):
with torch.no_grad():
outputs = self.model(input_tensor).cpu().numpy()
original_scores = []
for i in range(input_tensor.size(0)):
original_scores.append(outputs[i, target_category[i]])
original_scores = np.float32(original_scores)
self.set_ablation_layers()
if hasattr(self, "batch_size"):
BATCH_SIZE = self.batch_size
else:
BATCH_SIZE = 32
number_of_channels = activations.shape[1]
weights = []
with torch.no_grad():
# Iterate over the input batch
for tensor, category in zip(input_tensor, target_category):
batch_tensor = tensor.repeat(BATCH_SIZE, 1, 1, 1)
for i in tqdm.tqdm(range(0, number_of_channels, BATCH_SIZE)):
self.set_ablation_layer_batch_indices(
list(range(i, i + BATCH_SIZE)))
if i + BATCH_SIZE > number_of_channels:
keep = number_of_channels - i
batch_tensor = batch_tensor[:keep]
self.trim_ablation_layer_batch_indices(self, keep)
score = self.model(batch_tensor)[:, category].cpu().numpy()
weights.extend(score)
weights = np.float32(weights)
weights = weights.reshape(activations.shape[:2])
original_scores = original_scores[:, None]
weights = (original_scores - weights) / original_scores
# replace the model back to the original state
self.unset_ablation_layers()
return weights
|