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eval_diffcut.py
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import os
import argparse
from typing import Literal
import logging
import torch
import torch.nn.functional as F
import numpy as np
import pandas as pd
from scipy.ndimage import median_filter
from diffcut.recursive_normalized_cut import DiffCut
from tools.ldm import LdmExtractor
from tools.pamr import PAMR
from tools.utils import hungarian_matching
from dataloader.iterator import DataIterator, get_fine_to_coarse, load_imdb
np.seterr(divide='ignore', invalid='ignore')
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class Benchmark_Segmentation:
def __init__(self,
model_name: Literal["SSD-1B", "SSD-vega", "SD1.4"] = "SSD-1B",
dataset_name: Literal["COCO-Stuff", "COCO-Object", "VOC20", "Cityscapes", \
"Context", "ADE20K"] = "VOC20",
step: int = 50,
img_size: int = 1024,
refinement: bool = False,
alpha: int = 10,
):
refining = "pamr" if refinement else "no_pamr"
self.root_path = f'./Evaluation/{dataset_name}/{refining}'
self.folder_path = os.path.join(self.root_path)
if not os.path.exists(self.folder_path):
os.makedirs(self.folder_path)
self.img_size = img_size
self.step = step
self.refinement = refinement
self.dataset_name = dataset_name
self.alpha = alpha
self.diffcut = DiffCut()
if dataset_name == "COCO-Stuff":
file_list = load_imdb("./dataloader/coco/val2017/Coco164kFull_Stuff_Coarse_7.txt")
root = "./datasets/coco"
dataset = DataIterator(dataset_name, root, "val", file_list, self.img_size)
fine_to_coarse_map = get_fine_to_coarse("COCO-Stuff", "./dataloader/coco/fine_to_coarse_dict.pickle")
self.N_CLASS = 27
elif dataset_name == "COCO-Object":
file_list = load_imdb("./dataloader/coco/val2017/Coco164kFull_Stuff_Coarse_7.txt")
root = "./datasets/coco"
dataset = DataIterator(dataset_name, root, "val", file_list, self.img_size)
fine_to_coarse_map = get_fine_to_coarse("COCO-Object", "./dataloader/coco/coco_object_mapping.pickle")
self.N_CLASS = 81 # 80 classes + background
elif dataset_name == "VOC20":
root = "./datasets/pascal_voc_d2"
dataset = DataIterator(dataset_name, root, "validation", None, self.img_size)
fine_to_coarse_map = get_fine_to_coarse("VOC20")
self.N_CLASS = 21 # 20 classes + background
elif dataset_name == "Context":
root = "./datasets/pascal_ctx_d2"
dataset = DataIterator(dataset_name, root, "validation", None, self.img_size)
fine_to_coarse_map = get_fine_to_coarse("Context")
self.N_CLASS = 60 # 59 classes + background
elif dataset_name == "Cityscapes":
root = "./datasets/cityscapes"
dataset = DataIterator(dataset_name, root, "val", None, self.img_size)
fine_to_coarse_map = get_fine_to_coarse("Cityscapes", "./dataloader/cityscapes/cityscapes_27_mapping.pickle")
self.N_CLASS = 27
elif dataset_name == "ADE20K":
root = "./datasets/ADEChallengeData2016"
dataset = DataIterator(dataset_name, root, "validation", None, self.img_size)
fine_to_coarse_map = get_fine_to_coarse("ADE20K")
self.N_CLASS = 150
self.dataset = dataset
self.fine_to_coarse_map = fine_to_coarse_map
self.extractor = LdmExtractor(model_name=model_name)
def get_features(self, images):
features = self.extractor(images, step=self.step, img_size=self.img_size)
return features
def pamr(self, labels, image):
masks = torch.cat([1. * (labels == label) for label in torch.unique(labels)], dim=1)
labels = PAMR(num_iter=10, dilations=[1, 2, 4, 8])(image, masks) # 1, 2, 4, 8
labels = 1. * torch.argmax(labels, dim=1)
labels = median_filter(labels.cpu().numpy(), 3).astype(int)
return labels
def evaluate(self,
tau: int = 0.5):
#Dataloader
validation_dataloader = torch.utils.data.DataLoader(self.dataset, batch_size=5)
TP = np.zeros(self.N_CLASS)
FP = np.zeros(self.N_CLASS)
FN = np.zeros(self.N_CLASS)
ALL = 0
for i, batch in enumerate(validation_dataloader):
# Transfer to GPU
batch_size = batch["images"].shape[0]
images = batch["images"].to("cuda")
labels = self.fine_to_coarse_map(batch["labels"])
features = self.extractor(images, step=self.step, img_size=self.img_size)
for j in range(batch_size):
img_feat = features[j:j+1].to(torch.float32)
label_map = labels[j:j+1]
pred = self.diffcut.generate_masks(img_feat, tau, mask_size=(128, 128), alpha=self.alpha, img_size=self.img_size)
# Interpolate label_map on gpu
label_map = F.interpolate(torch.Tensor(label_map).to("cuda"), size=(128, 128))
label_map = label_map.cpu().numpy().astype(int)
# Many-to-one matching for background
if self.dataset_name in ["VOC20", "Context", "COCO-Object"]:
_, _, _, _, hist, col_ind = hungarian_matching(pred, label_map, self.N_CLASS-1)
# Assign a valid label to the background in ground truth labels
label_map[label_map==-1] = self.N_CLASS-1
# Assign a unique label to the background in pred maps
assigned_gt_clusters = np.where(hist.max(axis=1)>0)[0].tolist()
assigned_pred_clusters = [col_ind[i] for i in assigned_gt_clusters]
background_clusters = list(set(np.unique(pred)) - set(assigned_pred_clusters))
bg_label = pred.max() + 1
for bg_cls in background_clusters:
pred[pred==bg_cls] = bg_label
if self.refinement:
pred = torch.Tensor(pred).to("cuda")
im = F.interpolate(images[j][None], size=(128, 128), mode='bilinear')
pred = self.pamr(pred, im)[None]
tp, fp, fn, all, _, _ = hungarian_matching(pred, label_map, self.N_CLASS)
TP += tp
FP += fp
FN += fn
ALL += all
# Print accuracy and mean IoU occasionally.
if (i+1) % 10 == 0:
acc = TP.sum()/ALL
iou = TP / (TP + FP + FN)
miou = np.nanmean(iou)
logging.info("pixel accuracy: %s mIoU: %s", acc, miou)
# Print final accuracy and mean IoU.
acc = TP.sum()/ALL
iou = TP / (TP + FP + FN)
miou = np.nanmean(iou)
logging.info("pixel accuracy: %s mIoU: %s", acc, miou)
# Save results in a csv file.
new_row = [{'Acc': acc, 'mIoU': miou}]
df = pd.DataFrame(new_row, columns = ['Acc', 'mIoU'])
file_path = self.folder_path + f'/{tau}'
if not os.path.exists(file_path):
os.makedirs(file_path)
df.to_csv(os.path.join(file_path, f'eval_alpha_{self.alpha}_tau_{tau}.csv'), index=False)
def parse_args():
parser = argparse.ArgumentParser("Segmentation Benchmark Script")
parser.add_argument("--model_name", type=str, help="Model name")
parser.add_argument("--dataset_name", type=str, choices=["COCO-Stuff", "COCO-Object", "VOC20", "Cityscapes", "Context", "ADE20K"], help="dataset")
parser.add_argument("--step", type=int, default=50, help="Denoising timestep")
parser.add_argument("--img_size", type=int, default=1024, help="Size of input images")
parser.add_argument("--refinement", dest='refinement', default=False, action='store_true', help="Mask refinement with PAMR")
parser.add_argument("--tau", type=float, default=0.5, help="Threshold value for Recursive NCut")
parser.add_argument("--alpha", type=int, default=10, help="Affinity matrix exponent value")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
logging.info(args)
sem_seg = Benchmark_Segmentation(model_name=args.model_name, dataset_name=args.dataset_name,
step=args.step, img_size=args.img_size, refinement=args.refinement,
alpha=args.alpha)
sem_seg.evaluate(tau=args.tau)