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evaluate.py
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import os
import pdb
import random
import matplotlib
import numpy as np
import torch
from tqdm import tqdm
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from wsol_model.vitol import vitol
from config import get_configs
from data_loaders import get_data_loader
from inference import CAMComputer
def set_random_seed(seed=123):
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
class PerformanceMeter(object):
def __init__(self, split, higher_is_better=True):
self.best_function = max if higher_is_better else min
self.current_value = None
self.best_value = None
self.best_epoch = None
self.value_per_epoch = [] \
if split == 'val' else [-np.inf if higher_is_better else np.inf]
def update(self, new_value):
self.value_per_epoch.append(new_value)
self.current_value = self.value_per_epoch[-1]
self.best_value = self.best_function(self.value_per_epoch)
self.best_epoch = self.value_per_epoch.index(self.best_value)
class ViTOLInference():
_NUM_CLASSES_MAPPING = {
"CUB": 200,
"ILSVRC": 1000,
}
_SPLITS = ('train', 'val', 'test')
_CHECKPOINT_NAME_TEMPLATE = '{}_checkpoint.pth.tar'
_EVAL_METRICS = ['loss', 'classification', 'localization', 'top1_loc_acc']
_BEST_CRITERION_METRIC = 'top1_loc_acc'
def __init__(self):
self.args = get_configs()
set_random_seed(self.args.seed)
print("---------------------------------------------------------------------------------------------------")
print(f'Dataset: {self.args.dataset_name}, Experiment: {self.args.experiment_name}, vit type: {self.args.vit_type}, maxboxv2: {self.args.box_v2_metric}')
print(f'Adl_layer: {self.args.adl_layer}, adl_drop_rate: {self.args.adl_drop_rate}, adl_threshold: {self.args.adl_threshold}')
print(f'Architecture: {self.args.architecture}, architecture_type: {self.args.architecture_type}, wsol_method: {self.args.wsol_method}')
print(f'BS: {self.args.batch_size}, Epochs: {self.args.epochs}')
print(f'Eval mode: {self.args.evaluate_mode}, Eval method: {self.args.eval_method}, eval ckpt type: {self.args.eval_checkpoint_type}, base model: {self.args.base_model_dir}')
print("---------------------------------------------------------------------------------------------------")
self.performance_meters = self._set_performance_meters()
self.model = self._set_model()
self.loaders = get_data_loader(
data_roots=self.args.data_paths,
metadata_root=self.args.metadata_root,
batch_size=self.args.batch_size,
workers=self.args.workers,
resize_size=self.args.resize_size,
crop_size=self.args.crop_size,
proxy_training_set=self.args.proxy_training_set,
num_val_sample_per_class=self.args.num_val_sample_per_class,
)
def _set_performance_meters(self):
self._EVAL_METRICS += ['localization_IOU_{}'.format(threshold)
for threshold in self.args.iou_threshold_list]
eval_dict = {
split: {
metric: PerformanceMeter(split,
higher_is_better=False
if metric == 'loss' else True)
for metric in self._EVAL_METRICS
}
for split in self._SPLITS
}
return eval_dict
def _set_model(self):
num_classes = self._NUM_CLASSES_MAPPING[self.args.dataset_name]
print("Loading model {}".format(self.args.architecture))
model = vitol(
dataset_name=self.args.dataset_name,
architecture_type=self.args.architecture_type,
pretrained=self.args.pretrained,
num_classes=num_classes,
large_feature_map=self.args.large_feature_map,
pretrained_path=self.args.pretrained_path,
adl_drop_rate=self.args.adl_drop_rate,
adl_drop_threshold=self.args.adl_threshold,
adl_layer = self.args.adl_layer,
vit_type=self.args.vit_type,
)
model = model.cuda()
print(model._modules['blocks']._modules['0'])
return model
def _compute_accuracy(self, loader):
num_correct = 0
num_images = 0
for i, (images, targets, image_ids) in enumerate(tqdm(loader)):
images = images.cuda()
targets = targets.cuda()
output_dict = self.model(images)
if self.args.architecture_type =='vitol':
pred = output_dict.argmax(dim=1)
else:
pred = output_dict['logits'].argmax(dim=1)
num_correct += (pred == targets).sum().item()
num_images += images.size(0)
classification_acc = num_correct / float(num_images) * 100
return classification_acc
def evaluate(self, split):
print("Evaluating model on dataset {}".format(self.args.dataset_name))
self.model.eval()
accuracy = self._compute_accuracy(loader=self.loaders[split])
print("Accuracy", accuracy)
self.performance_meters[split]['classification'].update(accuracy)
cam_computer = CAMComputer(
model=self.model,
loader=self.loaders[split],
metadata_root=os.path.join(self.args.metadata_root, split),
mask_root=self.args.mask_root,
iou_threshold_list=self.args.iou_threshold_list,
dataset_name=self.args.dataset_name,
split=split,
args = self.args,
cam_curve_interval=self.args.cam_curve_interval,
multi_contour_eval=self.args.multi_contour_eval,
log_folder=self.args.log_folder,
)
cam_performance, top1_loc_acc = cam_computer.compute_and_evaluate_cams()
if self.args.multi_iou_eval:
loc_score = np.average(cam_performance)
else:
loc_score = cam_performance[self.args.iou_threshold_list.index(50)]
print(loc_score, top1_loc_acc)
self.performance_meters[split]['localization'].update(loc_score)
self.performance_meters[split]['top1_loc_acc'].update(top1_loc_acc)
if self.args.dataset_name in ('CUB', 'ILSVRC'):
for idx, IOU_THRESHOLD in enumerate(self.args.iou_threshold_list):
self.performance_meters[split][
'localization_IOU_{}'.format(IOU_THRESHOLD)].update(
cam_performance[idx])
def print_performances(self, split):
for metric in self._EVAL_METRICS:
current_performance = \
self.performance_meters[split][metric].current_value
if current_performance is not None:
print("Split {}, metric {}, current value: {}".format(
split, metric, current_performance))
if split != 'test':
print("Split {}, metric {}, best value: {}".format(
split, metric,
self.performance_meters[split][metric].best_value))
print("Split {}, metric {}, best epoch: {}".format(
split, metric,
self.performance_meters[split][metric].best_epoch))
def load_checkpoint_eval(self, checkpoint_type):
checkpoint_path = os.path.join(
self.args.base_model_dir, self.args.ckpt_name)
if os.path.isfile(checkpoint_path):
checkpoint = torch.load(checkpoint_path)
self.model.load_state_dict(checkpoint['state_dict'], strict=True)
# self.model.load_state_dict(checkpoint, strict=True)
# self.model.load_state_dict(checkpoint['model'], strict=True)
print("Check {} loaded.".format(checkpoint_path))
else:
raise IOError("No checkpoint {}.".format(checkpoint_path))
infer = ViTOLInference()
infer.load_checkpoint_eval(checkpoint_type=infer.args.eval_checkpoint_type)
infer.evaluate(split='test')
infer.print_performances('test')