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import PIL
from PIL import Image
import numpy as np
from matplotlib import pylab as P
import cv2
import torch
from torch.utils.data import TensorDataset
from torchvision import transforms
# dirpath_to_modules = './Visual-Explanation-Methods-PyTorch'
# sys.path.append(dirpath_to_modules)
from torchvex.base import ExplanationMethod
from torchvex.utils.normalization import clamp_quantile
def ShowImage(im, title='', ax=None):
image = np.array(im)
return image
def ShowGrayscaleImage(im, title='', ax=None):
if ax is None:
P.figure()
P.axis('off')
P.imshow(im, cmap=P.cm.gray, vmin=0, vmax=1)
P.title(title)
return P
def ShowHeatMap(im, title='', ax=None):
im = im - im.min()
im = im / im.max()
im = im.clip(0,1)
im = np.uint8(im * 255)
im = cv2.resize(im, (224,224))
image = cv2.resize(im, (224, 224))
# Apply JET colormap
color_heatmap = cv2.applyColorMap(image, cv2.COLORMAP_HOT)
# P.imshow(im, cmap='inferno')
# P.title(title)
return color_heatmap
def ShowMaskedImage(saliency_map, image, title='', ax=None):
"""
Save saliency map on image.
Args:
image: Tensor of size (H,W,3)
saliency_map: Tensor of size (H,W,1)
"""
# if ax is None:
# P.figure()
# P.axis('off')
saliency_map = saliency_map - saliency_map.min()
saliency_map = saliency_map / saliency_map.max()
saliency_map = saliency_map.clip(0,1)
saliency_map = np.uint8(saliency_map * 255)
saliency_map = cv2.resize(saliency_map, (224,224))
image = cv2.resize(image, (224, 224))
# Apply JET colormap
color_heatmap = cv2.applyColorMap(saliency_map, cv2.COLORMAP_HOT)
# Blend image with heatmap
img_with_heatmap = cv2.addWeighted(image, 0.4, color_heatmap, 0.6, 0)
# P.imshow(img_with_heatmap)
# P.title(title)
return img_with_heatmap
def LoadImage(file_path):
im = PIL.Image.open(file_path)
im = im.resize((224, 224))
im = np.asarray(im)
return im
def visualize_image_grayscale(image_3d, percentile=99):
r"""Returns a 3D tensor as a grayscale 2D tensor.
This method sums a 3D tensor across the absolute value of axis=2, and then
clips values at a given percentile.
"""
image_2d = np.sum(np.abs(image_3d), axis=2)
vmax = np.percentile(image_2d, percentile)
vmin = np.min(image_2d)
return np.clip((image_2d - vmin) / (vmax - vmin), 0, 1)
def visualize_image_diverging(image_3d, percentile=99):
r"""Returns a 3D tensor as a 2D tensor with positive and negative values.
"""
image_2d = np.sum(image_3d, axis=2)
span = abs(np.percentile(image_2d, percentile))
vmin = -span
vmax = span
return np.clip((image_2d - vmin) / (vmax - vmin), -1, 1)
class SimpleGradient(ExplanationMethod):
def __init__(self, model, create_graph=False,
preprocess=None, postprocess=None):
super().__init__(model, preprocess, postprocess)
self.create_graph = create_graph
def predict(self, x):
return self.model(x)
@torch.enable_grad()
def process(self, inputs, target):
self.model.zero_grad()
inputs.requires_grad_(True)
out = self.model(inputs)
out = out if type(out) == torch.Tensor else out.logits
num_classes = out.size(-1)
onehot = torch.zeros(inputs.size(0), num_classes, *target.shape[1:])
onehot = onehot.to(dtype=inputs.dtype, device=inputs.device)
onehot.scatter_(1, target.unsqueeze(1), 1)
grad, = torch.autograd.grad(
(out*onehot).sum(), inputs, create_graph=self.create_graph
)
return grad
class SmoothGradient(ExplanationMethod):
def __init__(self, model, stdev_spread=0.15, num_samples=25,
magnitude=True, batch_size=-1,
create_graph=False, preprocess=None, postprocess=None):
super().__init__(model, preprocess, postprocess)
self.stdev_spread = stdev_spread
self.nsample = num_samples
self.create_graph = create_graph
self.magnitude = magnitude
self.batch_size = batch_size
if self.batch_size == -1:
self.batch_size = self.nsample
self._simgrad = SimpleGradient(model, create_graph)
def process(self, inputs, target):
self.model.zero_grad()
maxima = inputs.flatten(1).max(-1)[0]
minima = inputs.flatten(1).min(-1)[0]
stdev = self.stdev_spread * (maxima - minima).cpu()
stdev = stdev.view(inputs.size(0), 1, 1, 1).expand_as(inputs)
stdev = stdev.unsqueeze(0).expand(self.nsample, *[-1]*4)
noise = torch.normal(0, stdev)
target_expanded = target.unsqueeze(0).cpu()
target_expanded = target_expanded.expand(noise.size(0), -1)
noiseloader = torch.utils.data.DataLoader(
TensorDataset(noise, target_expanded), batch_size=self.batch_size
)
total_gradients = torch.zeros_like(inputs)
for noise, t_exp in noiseloader:
inputs_w_noise = inputs.unsqueeze(0) + noise.to(inputs.device)
inputs_w_noise = inputs_w_noise.view(-1, *inputs.shape[1:])
gradients = self._simgrad(inputs_w_noise, t_exp.view(-1))
gradients = gradients.view(self.batch_size, *inputs.shape)
if self.magnitude:
gradients = gradients.pow(2)
total_gradients = total_gradients + gradients.sum(0)
smoothed_gradient = total_gradients / self.nsample
return smoothed_gradient
def feed_forward(model_name, image, model=None, feature_extractor=None):
if model_name in ['ConvNeXt', 'ResNet']:
inputs = feature_extractor(image, return_tensors="pt")
logits = model(**inputs).logits
prediction_class = logits.argmax(-1).item()
else:
transform_images = transforms.Compose([
transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
input_tensor = transform_images(image)
inputs = input_tensor.unsqueeze(0)
output = model(inputs)
prediction_class = output.argmax(-1).item()
#prediction_label = model.config.id2label[prediction_class]
return inputs, prediction_class
def clip_gradient(gradient):
gradient = gradient.abs().sum(1, keepdim=True)
return clamp_quantile(gradient, q=0.99)
def fig2img(fig):
"""Convert a Matplotlib figure to a PIL Image and return it"""
import io
buf = io.BytesIO()
fig.savefig(buf)
buf.seek(0)
img = Image.open(buf)
return img
def generate_smoothgrad_mask(image, model_name, model=None, feature_extractor=None, num_samples=25, return_mask=False):
inputs, prediction_class = feed_forward(model_name, image, model, feature_extractor)
smoothgrad_gen = SmoothGradient(
model, num_samples=num_samples, stdev_spread=0.1,
magnitude=False, postprocess=clip_gradient)
if type(inputs) != torch.Tensor:
inputs = inputs['pixel_values']
smoothgrad_mask = smoothgrad_gen(inputs, prediction_class)
smoothgrad_mask = smoothgrad_mask[0].numpy()
smoothgrad_mask = np.transpose(smoothgrad_mask, (1, 2, 0))
image = np.asarray(image)
# ori_image = ShowImage(image)
heat_map_image = ShowHeatMap(smoothgrad_mask)
masked_image = ShowMaskedImage(smoothgrad_mask, image)
if return_mask:
return heat_map_image, masked_image, smoothgrad_mask
else:
return heat_map_image, masked_image
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