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import numpy as np
import torch
import ttach as tta
from typing import Callable, List, Tuple
from pytorch_grad_cam.activations_and_gradients import ActivationsAndGradients
from pytorch_grad_cam.utils.svd_on_activations import get_2d_projection
from pytorch_grad_cam.utils.image import scale_cam_image
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
import pandas as pd
import config as config
import utils
class BaseCAM:
def __init__(self,
model: torch.nn.Module,
target_layers: List[torch.nn.Module],
use_cuda: bool = False,
reshape_transform: Callable = None,
compute_input_gradient: bool = False,
uses_gradients: bool = True) -> None:
self.model = model.eval()
self.target_layers = target_layers
self.cuda = use_cuda
if self.cuda:
self.model = model.cuda()
self.reshape_transform = reshape_transform
self.compute_input_gradient = compute_input_gradient
self.uses_gradients = uses_gradients
self.activations_and_grads = ActivationsAndGradients(
self.model, target_layers, reshape_transform)
""" Get a vector of weights for every channel in the target layer.
Methods that return weights channels,
will typically need to only implement this function. """
def get_cam_image(self,
input_tensor: torch.Tensor,
target_layer: torch.nn.Module,
targets: List[torch.nn.Module],
activations: torch.Tensor,
grads: torch.Tensor,
eigen_smooth: bool = False) -> np.ndarray:
return get_2d_projection(activations)
def forward(self,
input_tensor: torch.Tensor,
targets: List[torch.nn.Module],
eigen_smooth: bool = False) -> np.ndarray:
if self.cuda:
input_tensor = input_tensor.cuda()
if self.compute_input_gradient:
input_tensor = torch.autograd.Variable(input_tensor,
requires_grad=True)
outputs = self.activations_and_grads(input_tensor)
if targets is None:
bboxes = [[] for _ in range(1)]
for i in range(3):
batch_size, A, S, _, _ = outputs[i].shape
anchor = config.SCALED_ANCHORS[i]
boxes_scale_i = utils.cells_to_bboxes(
outputs[i], anchor, S=S, is_preds=True
)
for idx, (box) in enumerate(boxes_scale_i):
bboxes[idx] += box
nms_boxes = utils.non_max_suppression(
bboxes[0], iou_threshold=0.5, threshold=0.4, box_format="midpoint",
)
# target_categories = np.argmax(outputs.cpu().data.numpy(), axis=-1)
target_categories = [box[0] for box in nms_boxes]
targets = [ClassifierOutputTarget(
category) for category in target_categories]
if self.uses_gradients:
self.model.zero_grad()
loss = sum([target(output)
for target, output in zip(targets, outputs)])
loss.backward(retain_graph=True)
# In most of the saliency attribution papers, the saliency is
# computed with a single target layer.
# Commonly it is the last convolutional layer.
# Here we support passing a list with multiple target layers.
# It will compute the saliency image for every image,
# and then aggregate them (with a default mean aggregation).
# This gives you more flexibility in case you just want to
# use all conv layers for example, all Batchnorm layers,
# or something else.
cam_per_layer = self.compute_cam_per_layer(input_tensor,
targets,
eigen_smooth)
return self.aggregate_multi_layers(cam_per_layer)
def get_target_width_height(self,
input_tensor: torch.Tensor) -> Tuple[int, int]:
width, height = input_tensor.size(-1), input_tensor.size(-2)
return width, height
def compute_cam_per_layer(
self,
input_tensor: torch.Tensor,
targets: List[torch.nn.Module],
eigen_smooth: bool) -> np.ndarray:
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]
target_size = self.get_target_width_height(input_tensor)
cam_per_target_layer = []
# Loop over the saliency image from every layer
for i in range(len(self.target_layers)):
target_layer = self.target_layers[i]
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_image(input_tensor,
target_layer,
targets,
layer_activations,
layer_grads,
eigen_smooth)
cam = np.maximum(cam, 0)
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: np.ndarray) -> np.ndarray:
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) |