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from utils import *
from datasets import PascalVOCDataset
from tqdm import tqdm
from pprint import PrettyPrinter
# Good formatting when printing the APs for each class and mAP
pp = PrettyPrinter()
# Parameters
data_folder = './'
keep_difficult = True # difficult ground truth objects must always be considered in mAP calculation, because these objects DO exist!
batch_size = 64
workers = 4
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = './checkpoint_ssd300.pth.tar'
# Load model checkpoint that is to be evaluated
checkpoint = torch.load(checkpoint)
model = checkpoint['model']
model = model.to(device)
# Switch to eval mode
model.eval()
# Load test data
test_dataset = PascalVOCDataset(data_folder,
split='test',
keep_difficult=keep_difficult)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False,
collate_fn=test_dataset.collate_fn, num_workers=workers, pin_memory=True)
def evaluate(test_loader, model):
"""
Evaluate.
:param test_loader: DataLoader for test data
:param model: model
"""
# Make sure it's in eval mode
model.eval()
# Lists to store detected and true boxes, labels, scores
det_boxes = list()
det_labels = list()
det_scores = list()
true_boxes = list()
true_labels = list()
true_difficulties = list() # it is necessary to know which objects are 'difficult', see 'calculate_mAP' in utils.py
with torch.no_grad():
# Batches
for i, (images, boxes, labels, difficulties) in enumerate(tqdm(test_loader, desc='Evaluating')):
images = images.to(device) # (N, 3, 300, 300)
# Forward prop.
predicted_locs, predicted_scores = model(images)
# Detect objects in SSD output
det_boxes_batch, det_labels_batch, det_scores_batch = model.detect_objects(predicted_locs, predicted_scores,
min_score=0.01, max_overlap=0.45,
top_k=200)
# Evaluation MUST be at min_score=0.01, max_overlap=0.45, top_k=200 for fair comparision with the paper's results and other repos
# Store this batch's results for mAP calculation
boxes = [b.to(device) for b in boxes]
labels = [l.to(device) for l in labels]
difficulties = [d.to(device) for d in difficulties]
det_boxes.extend(det_boxes_batch)
det_labels.extend(det_labels_batch)
det_scores.extend(det_scores_batch)
true_boxes.extend(boxes)
true_labels.extend(labels)
true_difficulties.extend(difficulties)
# Calculate mAP
APs, mAP = calculate_mAP(det_boxes, det_labels, det_scores, true_boxes, true_labels, true_difficulties)
# Print AP for each class
pp.pprint(APs)
print('\nMean Average Precision (mAP): %.3f' % mAP)
if __name__ == '__main__':
evaluate(test_loader, model)
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