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import argparse
import math
import os
import sys
from copy import deepcopy
if not os.getcwd() in sys.path:
sys.path.append(os.getcwd())
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from configs.get_config import load_config
from image_utils import overlay_mask
from logs.logger import Logger
from models import MODELS, build_model
from models.utils import load_pretrained
from natsort import natsorted
from PIL import Image
from torchcam import methods
from torchvision.transforms.functional import resize, to_pil_image
from transform import final_transform
def main():
argparser = argparse.ArgumentParser("Arguments for CAM visualization...")
argparser.add_argument("--cfg", help="Specify config to load", required=True)
argparser.add_argument(
"--target_layer",
"-t",
help="Specify layer names to visualize CAM",
required=False,
)
argparser.add_argument(
"--method", "-m", type=str, default="GradCAM", help="CAM method to use"
)
argparser.add_argument(
"--mode",
type=str,
choices=["image", "video"],
default="image",
help="Mode to visualize gradCAM",
)
argparser.add_argument(
"--image", "-i", help="Specify image to overlay CAM", required=False
)
argparser.add_argument(
"--video", "-v", help="Specify video to overlay CAM", required=False
)
argparser.add_argument(
"--savefig", type=str, default=None, help="Path to save figure"
)
argparser.add_argument(
"--rows", type=int, default=1, help="Number of rows for the layout"
)
argparser.add_argument(
"--class-idx", type=int, default=0, help="Index of the class to inspect"
)
argparser.add_argument(
"--alpha", type=float, default=0.5, help="Transparency of the heatmap"
)
argparser.add_argument("--cuda", action="store_true", help="Running CAM with cuda")
argparser.add_argument(
"--save_inverse", action="store_true", help="Saving the inverse of CAM"
)
args = argparser.parse_args()
print(args)
# Loading configs
cfg = load_config(args.cfg)
# Logger
logger = Logger(task="CAM_vis")
# Loading model based on the config
model = build_model(cfg.MODEL, MODELS).to(torch.float)
logger.info("Loading weight ... {}".format(cfg.TEST.pretrained))
model = load_pretrained(model, cfg.TEST.pretrained)
if args.cuda:
model = model.cuda()
model.eval()
# Freeze the model
for p in model.parameters():
p.requires_grad_(False)
# Loading image
img_list = []
if args.mode == "image":
assert os.path.exists(
args.image
), "Image path must be valid, please check the path again!"
img = Image.open(args.image)
H, W = img.size
img = img.crop((0, 0, W - 0, H - 0))
img_list.append(img)
elif args.mode == "video":
assert os.path.exists(
args.video
), "Video path must be valid, please check the path again!"
n_frames = cfg.DATASET.DATA.SAMPLES_PER_VIDEO.NUM_FRAMES
assert n_frames is not None, "Number of video frames can not be None!"
# Load first n_frames inside the video
img_paths = glob(f"{args.video}/*.png")
img_paths = natsorted(img_paths) # correct the order of image paths
img_paths = img_paths[:n_frames]
for img_path in img_paths:
img = Image.open(img_path)
H, W = img.size
img = img.crop((0, 0, W - 0, H - 0))
img_list.append(img)
else:
raise ValueError(
"We only support GradCAM for image or video mode at the moment!"
)
# Preprocess image
transform = final_transform(cfg.DATASET)
image_size = (cfg.DATASET.IMAGE_SIZE[0], cfg.DATASET.IMAGE_SIZE[1])
# Transform images
transformed_imgs = torch.tensor([])
for _i in img_list:
img_resize = _i.resize(image_size)
img_resize = np.array(img_resize) / 255
img_tensor = transform(img_resize).to(torch.float)
if args.cuda:
img_tensor = img_tensor.cuda()
img_tensor.requires_grad_(True)
transformed_imgs = torch.cat((transformed_imgs, img_tensor.unsqueeze(0)), 0)
# Hook the corresponding layer in the model
if isinstance(args.method, str):
cam_methods = args.method.split(",")
else:
cam_methods = [
"CAM",
"GradCAM",
"GradCAMpp",
"SmoothGradCAMpp",
"ScoreCAM",
"SSCAM",
"ISCAM",
"XGradCAM",
"LayerCAM",
]
cam_extractors = [
methods.__dict__[name](
model, target_layer=args.target_layer, enable_hooks=False
)
for name in cam_methods
]
if args.mode == "image":
num_rows = args.rows
num_cols = math.ceil((len(cam_extractors)) / num_rows) + 1
else:
num_cols = n_frames
num_rows = len(cam_extractors) + 1
_, axes = plt.subplots(num_rows, num_cols, figsize=(6, 4))
# Display input
for idx, _i in enumerate(img_list):
ax = axes[0][idx] if num_rows > 1 else axes[0] if num_cols > 1 else axes
ax.imshow(_i)
ax.set_title("Input", size=8)
for idx, extractor in zip(range(1, len(cam_extractors) + 1), cam_extractors):
extractor._hooks_enabled = True
model.zero_grad()
if args.mode == "image":
scores = model(transformed_imgs)[0]["cls"].sigmoid()
else:
transformed_imgs = transformed_imgs.transpose(0, 1).unsqueeze(0)
scores = (
model(transformed_imgs)[0]["hm"]
.sigmoid()
.view(1, -1)
.max(1, keepdim=True)
.values
)
# output, attn = model(transformed_imgs) # For visualizing the attn scores, will comeback later
# scores = output[0]['temp_loc'].sigmoid()
print("Classification Score -- {}".format(scores))
# Select the class index
class_idx = (
scores.squeeze(0).argmax().item()
if args.class_idx is None
else args.class_idx
)
# class_idx = img_idx
# Use the hooked data to compute activation map
activation_map = (
extractor(class_idx, scores)[0].to(torch.float).squeeze(0).cpu()
)
# activation_map = torch.cat((activation_map, torch.zeros(4)), 0)
# activation_map = F.adaptive_avg_pool1d(activation_map.unsqueeze(0), 196).squeeze(0)
# activation_map = activation_map[class_idx, 1:].reshape((14, 14))
# Clean data
extractor.remove_hooks()
extractor._hooks_enabled = False
for img_idx, i_ in enumerate(img_list):
# Convert it to PL image
# The indexing below means first image in batch
heatmap = to_pil_image(activation_map[img_idx].unsqueeze(0), mode="F")
# activation_map = attn[img_idx].mean(0)[0, 1:]
# activation_map = activation_map.reshape((14, 14)).detach()
# activation_map = (activation_map - activation_map.min()) / (activation_map.max() - activation_map.min())
# heatmap = to_pil_image(activation_map.unsqueeze(0), mode='F')
# Plot the result
result = overlay_mask(deepcopy(i_), heatmap, alpha=args.alpha)
ax = (
axes[idx][img_idx]
if num_rows > 1
else axes[idx] if num_cols > 1 else axes
)
ax.imshow(result)
ax.set_title(extractor.__class__.__name__, size=8)
# Compute the inverse heatmap
if args.save_inverse:
inverse_activation_map = torch.sub(
1, activation_map[img_idx].unsqueeze(0)
)
inverse_heatmap = to_pil_image(inverse_activation_map, mode="F")
result_inverse = overlay_mask(
deepcopy(img), inverse_heatmap, alpha=args.alpha
)
ax = (
axes[idx][img_idx]
if args.rows > 1
else axes[idx] if num_cols > 1 else axes
)
ax.imshow(result_inverse)
ax.set_title(f"{extractor.__class__.__name__}_inverse", size=8)
# Clear axes
if num_cols > 1:
for _axes in axes:
if num_rows > 1:
for ax in _axes:
ax.axis("off")
else:
_axes.axis("off")
else:
axes.axis("off")
plt.tight_layout()
if args.savefig:
plt.savefig(
args.savefig, dpi=200, transparent=True, bbox_inches="tight", pad_inches=0
)
if __name__ == "__main__":
main()
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