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torchcam_vis.py
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torchcam_vis.py
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import numpy as np
import torch
from sloter.slot_model import load_backbone
import argparse
from train import get_args_parser
from torchvision import datasets, transforms
from matplotlib.colors import LinearSegmentedColormap
import os
from PIL import Image
import torch.nn.functional as F
from sloter.utils.vis import apply_colormap_on_image
from torchcam.cams import CAM, GradCAM, GradCAMpp, SmoothGradCAMpp, ScoreCAM, SSCAM, ISSCAM
from torchcam.utils import overlay_mask
from tqdm import tqdm
from dataset.ConText import ConText, MakeList, MakeListImage
from dataset.CUB200 import CUB_200
from torchcam.IGOS import Get_blurred_img, Integrated_Mask
from torchray.attribution.rise import rise
from torchray.attribution.extremal_perturbation import extremal_perturbation
from torchcam.IBA.pytorch import IBA, tensor_to_np_img, get_imagenet_folder, imagenet_transform
from torchcam.IBA.utils import plot_saliency_map, to_unit_interval, load_monkeys
from torch.utils.data import DataLoader
def show_cam_on_image(img, masks, target_index, save_name):
final = np.uint8(255*masks)
mask_image = Image.fromarray(final, mode='L')
mask_image.save(f'sloter/vis/{save_name}_{target_index}_mask.png')
heatmap_only, heatmap_on_image = apply_colormap_on_image(img, final, 'jet')
heatmap_on_image.save(f'sloter/vis/{save_name}_{target_index}.png')
def make_grad(extractor, output, img_heat, grad_min_level, save_name):
img_heat = img_heat.resize((args.img_size, args.img_size), Image.BILINEAR)
# If None, returns the map for the highest scoring category.
# Otherwise, targets the requested index.
# target_index = None
for target_index in tqdm(range(0, args.num_classes)):
mask = extractor(target_index, output).cpu().unsqueeze(0).unsqueeze(0)
mask = F.interpolate(mask, size=(args.img_size, args.img_size), mode="bilinear")
mask = mask.squeeze(dim=0).squeeze(dim=0)
mask = mask.detach().numpy()
mask = np.maximum(mask, 0)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
mask = np.maximum(mask, grad_min_level)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
show_cam_on_image(img_heat, mask, target_index, save_name)
def for_vis(args):
transform = transforms.Compose([
transforms.Resize((args.img_size, args.img_size)),
transforms.ToTensor(),
])
# Con-text
if args.dataset == 'ConText':
train, val = MakeList(args).get_data()
dataset_val = ConText(val, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val, args.batch_size, shuffle=False, num_workers=1, pin_memory=True)
data = iter(data_loader_val).next()
image = data["image"][0]
label = data["label"][0]
image_orl = Image.fromarray((image.cpu().detach().numpy()*255).astype(np.uint8).transpose((1,2,0)), mode='RGB')
image = transform(image_orl)
transform = transforms.Compose([transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
elif args.dataset == 'ImageNet':
train, val = MakeListImage(args).get_data()
dataset_val = ConText(val, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val, args.batch_size, shuffle=False, num_workers=1, pin_memory=True)
iter_loader = iter(data_loader_val)
for i in range(0, 1):
data = iter_loader.next()
image = data["image"][0]
label = data["label"][0].item()
image_orl = Image.fromarray((image.cpu().detach().numpy()*255).astype(np.uint8).transpose((1,2,0)), mode='RGB')
image = transform(image_orl)
transform = transforms.Compose([transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
# MNIST
elif args.dataset == 'MNIST':
dataset_val = datasets.MNIST('./data/mnist', train=False, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val, args.batch_size, shuffle=False, num_workers=1, pin_memory=True)
image = iter(data_loader_val).next()[0][0]
label = ''
image_orl = Image.fromarray((image.cpu().detach().numpy()*255).astype(np.uint8)[0], mode='L')
image = transform(image_orl)
transform = transforms.Compose([transforms.Normalize((0.1307,), (0.3081,))])
# CUB
elif args.dataset == 'CUB200':
dataset_val = CUB_200(args, train=False, transform=transform)
data_loader_val = torch.utils.data.DataLoader(dataset_val, args.batch_size, shuffle=False, num_workers=1, pin_memory=True)
data = iter(data_loader_val).next()
image = data["image"][0]
label = data["label"][0]
image_orl = Image.fromarray((image.cpu().detach().numpy()*255).astype(np.uint8).transpose((1,2,0)), mode='RGB')
image = transform(image_orl)
transform = transforms.Compose([transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
image = transform(image)
image = image.unsqueeze(0)
device = torch.device(args.device)
### IGOS
model = load_backbone(args)
model = model.to(device)
model.eval()
image_orl_for_blur = np.float32(image_orl) / 255.
img, blurred_img, logitori = Get_blurred_img(image_orl_for_blur, label, model, resize_shape=(260, 260),
Gaussian_param=[51, 50],
Median_param=11, blur_type='Gaussian', use_cuda=1)
for target_index in tqdm(range(0, args.num_classes)):
mask, upsampled_mask, imgratio, curvetop, curve1, curve2, category = Integrated_Mask(img, blurred_img, model,
label,
max_iterations=15,
integ_iter=20,
tv_beta=2,
l1_coeff=0.01 * 100,
tv_coeff=0.2 * 100,
size_init=8,
use_cuda=1) #
mask = upsampled_mask.cpu().detach().numpy()[0,0]
mask = -mask + mask.max()*2.
mask = np.maximum(mask, 0)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
mask = np.maximum(mask, args.grad_min_level)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
# mask = Image.fromarray(mask*255, mode='L').resize((args.img_size, args.img_size), Image.BILINEAR)
# mask = np.uint8(mask)
image_orl = image_orl.resize((args.img_size, args.img_size), Image.BILINEAR)
# heatmap = np.array(heatmap)
show_cam_on_image(image_orl, mask, target_index, 'IGOS')
del model
### torchray (RISE)
model = load_backbone(args)
model = model.to(device)
model.eval()
for target_index in tqdm(range(0, args.num_classes)):
mask = rise(model, image.to(device), target_index)
mask = mask.cpu().numpy()[0,0]
mask = np.maximum(mask, 0)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
mask = np.maximum(mask, args.grad_min_level)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
image_orl = image_orl.resize((args.img_size, args.img_size), Image.BILINEAR)
# heatmap = np.array(heatmap)
show_cam_on_image(image_orl, mask, target_index, 'RISE')
del model
### torchray (Extremal)
model = load_backbone(args)
model = model.to(device)
model.eval()
for target_index in tqdm(range(0, args.num_classes)):
mask, _ = extremal_perturbation(model, image.to(device), target_index)
mask = mask.cpu().numpy()[0,0]
mask = np.maximum(mask, 0)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
mask = np.maximum(mask, args.grad_min_level)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
image_orl = image_orl.resize((args.img_size, args.img_size), Image.BILINEAR)
# heatmap = np.array(heatmap)
show_cam_on_image(image_orl, mask, target_index, 'Extremal')
del model
### IBA
model = load_backbone(args)
model = model.to(device)
model.eval()
imagenet_dir = '../../data/imagenet/ILSVRC/Data/CLS-LOC/validation'
# Add a Per-Sample Bottleneck at layer conv4_1
iba = IBA(model.layer4)
# Estimate the mean and variance of the feature map at this layer.
val_set = get_imagenet_folder(imagenet_dir)
val_loader = DataLoader(val_set, batch_size=64, shuffle=True, num_workers=4)
iba.estimate(model, val_loader, n_samples=5000, progbar=True)
for target_index in tqdm(range(0, args.num_classes)):
# Closure that returns the loss for one batch
model_loss_closure = lambda x: -torch.log_softmax(model(x.to(device)), dim=1)[:, target_index].mean()
# Explain class target for the given image
saliency_map = iba.analyze(image, model_loss_closure, beta=10)
# display result
model_loss_closure = lambda x: -torch.log_softmax(model(x.to(device)), 1)[:, target_index].mean()
heatmap = iba.analyze(image, model_loss_closure )
mask = heatmap
mask = np.maximum(mask, 0)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
mask = np.maximum(mask, args.grad_min_level)
mask = mask - np.min(mask)
mask = mask / np.max(mask)
image_orl = image_orl.resize((args.img_size, args.img_size), Image.BILINEAR)
# heatmap = np.array(heatmap)
show_cam_on_image(image_orl, mask, target_index, 'IBA')
# plot_saliency_map(heatmap, tensor_to_np_img(image[0]))
RESNET_CONFIG = dict(input_layer='conv1', conv_layer='layer4', fc_layer='fc')
MODEL_CONFIG = {**RESNET_CONFIG}
conv_layer = MODEL_CONFIG['conv_layer']
input_layer = MODEL_CONFIG['input_layer']
fc_layer = MODEL_CONFIG['fc_layer']
### torchcam
del model
model = load_backbone(args)
model = model.to(device)
model.eval()
# Hook the corresponding layer in the model
cam_extractors = [CAM(model, conv_layer, fc_layer), GradCAM(model, conv_layer),
GradCAMpp(model, conv_layer), SmoothGradCAMpp(model, conv_layer, input_layer),
ScoreCAM(model, conv_layer, input_layer),
SSCAM(model, conv_layer, input_layer),
# ISSCAM(model, conv_layer, input_layer),
]
cam_extractors_names = ['CAM', 'GradCAM',
'GradCAMpp', 'SmoothGradCAMpp',
'ScoreCAM',
'SSCAM',
# 'ISSCAM',
]
for idx, extractor in enumerate(cam_extractors):
model.zero_grad()
output1 = model(image.to(device))
output = F.softmax(output1, dim=1)
prediction_score, pred_label_idx = torch.topk(output, 1)
pred_label_idx.squeeze_()
predicted_label = str(pred_label_idx.item())
print('Predicted:', predicted_label, '(', prediction_score.squeeze().item(), ')')
make_grad(extractor, output1, image_orl, args.grad_min_level, cam_extractors_names[idx])
extractor.clear_hooks()
if __name__ == '__main__':
parser = argparse.ArgumentParser('model training and evaluation script', parents=[get_args_parser()])
args = parser.parse_args()
args_dict = vars(args)
args_for_evaluation = ['num_classes', 'lambda_value', 'power', 'slots_per_class']
args_type = [int, float, int, int]
for arg_id, arg in enumerate(args_for_evaluation):
args_dict[arg] = args_type[arg_id](args_dict[arg])
os.makedirs('sloter/vis', exist_ok=True)
for_vis(args)