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test.py
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import os
import numpy as np
import torch
import torch.nn.functional as F
import time
from config import device
from utils import box_cxcywh_to_xyxy, detect
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from utils import coco_label_map as label_map
from utils import coco_color_array
from evaluator import Evaluator
from utils import coco_label_idx_91 as label_idx_91
coco_color_array = np.random.randint(256, size=(91, 3)) / 255 # In plt, rgb color space's range from 0 to 1
label_array = list(label_map.keys()) # dict
label_dict = label_idx_91
def visualize_results(images, results):
'''
:param images:
:param results: [{'scores': s, 'labels': l, 'boxes': b}]
:param label_map:
:param color_array:
:return:
'''
color_array = coco_color_array
# 0. permute
images = images.cpu()
images = images.squeeze(0).permute(1, 2, 0) # B, C, H, W --> H, W, C
h, w = images.size(0), images.size(1)
# 1. un normalization
images *= torch.Tensor([0.229, 0.224, 0.225])
images += torch.Tensor([0.485, 0.456, 0.406])
# 2. RGB to BGR
image_np = images.numpy()
# 3. box scaling
bbox = results[0].cpu()
cls = results[1].cpu()
scores = results[2].cpu()
####################################
# set threshold for visualization
####################################
keep = scores > 0.1
bbox = bbox[keep]
cls = cls[keep]
scores = scores[keep]
####################################
plt.figure('result')
plt.imshow(image_np)
for i in range(len(bbox)):
x1 = bbox[i][0].item() * w
y1 = bbox[i][1].item() * h
x2 = bbox[i][2].item() * w
y2 = bbox[i][3].item() * h
# class and score
plt.text(x=x1 - 5,
y=y1 - 5,
s=label_array[label_dict[int(cls[i])]] + ' {:.2f}'.format(scores[i]),
fontsize=10,
bbox=dict(facecolor=color_array[int(cls[i])],
alpha=0.5))
# bounding box
plt.gca().add_patch(Rectangle(xy=(x1, y1),
width=x2 - x1,
height=y2 - y1,
linewidth=1,
edgecolor=color_array[int(cls[i])],
facecolor='none'))
plt.show()
def test(epoch, vis, test_loader, model, criterion, opts, visualize=False):
print('Testing of epoch [{}]'.format(epoch))
model.eval()
if opts.dist_mode == 'ddp':
test_device = opts.dist_gpu_id
else:
test_device = device
check_point = torch.load(os.path.join(opts.save_path, opts.save_file_name) + '.{}.pth.tar'.format(epoch),
map_location=test_device)
state_dict = check_point['model_state_dict']
model.load_state_dict(state_dict)
tic = time.time()
sum_loss = 0
is_coco = hasattr(test_loader.dataset, 'coco') # if True the set is COCO else VOC
if is_coco:
print('COCO dataset evaluation...')
else:
print('VOC dataset evaluation...')
evaluator = Evaluator(data_type=opts.data_type)
with torch.no_grad():
for idx, data in enumerate(test_loader):
## Get Loss!
images = data[0]
targets = data[1]
images = images.to(test_device)
outputs = model(images)
targets = [{k: v.to(test_device) for k, v in t.items()} for t in targets]
loss = criterion(outputs, targets)
sum_loss += loss.item()
## Evaluate!
pred_boxes, pred_labels, pred_scores = detect(pred=outputs)
if opts.data_type == 'coco':
img_id = test_loader.dataset.ids[idx]
img_info = test_loader.dataset.coco.loadImgs(ids=img_id)[0]
coco_ids = test_loader.dataset.coco_ids
info = (pred_boxes, pred_labels, pred_scores, img_id, img_info, coco_ids)
else:
print('not yet..')
exit()
evaluator.get_info(info)
toc = time.time()
# ---------- print ----------
if opts.rank == 0:
if idx % 1000 == 0 or idx == len(test_loader) - 1:
print('Epoch: [{0}]\t'
'Step: [{1}/{2}]\t'
'Loss: {loss:.4f}\t'
'Time : {time:.4f}\t'
.format(epoch,
idx, len(test_loader),
loss=loss,
time=toc - tic))
## Visualize!
if visualize:
results = detect(outputs)
visualize_results(images, results)
if opts.rank == 0:
mAP = evaluator.evaluate(test_loader.dataset)
print('mAP for Epoch {} : {}'.format(epoch, mAP))
print("Eval Time : {:.4f}".format(time.time() - tic))
mean_loss = sum_loss / len(test_loader)
if vis is not None:
# loss plot
vis.line(X=torch.ones((1, 2)).cpu() * epoch, # step
Y=torch.Tensor([mean_loss, mAP]).unsqueeze(0).cpu(),
win='test_loss',
update='append',
opts=dict(xlabel='step',
ylabel='test',
title='test loss',
legend=['test Loss', 'mAP']))
# @@@ VISDOM @@@
if vis is not None:
# loss plot
vis.line(X=torch.ones((1, 2)).cpu() * epoch, # step
Y=torch.Tensor([mean_loss, mAP]).unsqueeze(0).cpu(),
win='test_loss',
update='append',
opts=dict(xlabel='step',
ylabel='test',
title='test loss',
legend=['test Loss', 'mAP']))
if __name__ == "__main__":
import os
import sys
import visdom
from config import parse
# import torchvision.transforms as T
import dataset.transforms as T
from dataset.coco_dataset import COCO_Dataset
from losses.hungarian_loss import HungarianLoss
from losses.matcher import HungarianMatcher
from models.detr import DETR
# 1. configuration
opts = parse(sys.argv[1:])
# 2. visdom
vis = None
# if opts.data_root == "D:/data/coco":
# # for window
# vis = visdom.Visdom(port='8097')
# 3. dataset
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
transforms_val = T.Compose([
T.RandomResize([600], max_size=600),
normalize,
])
test_set = COCO_Dataset(root=opts.data_root,
split='val',
download=True,
transforms=transforms_val,
visualization=False)
# 4. test loader
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
collate_fn=test_set.collate_fn,
shuffle=False,
num_workers=0,
pin_memory=True)
# 5. model (opts.num_classes = 91)
model = DETR(num_classes=opts.num_classes, num_queries=100).to(device)
if opts.distributed:
model = torch.nn.DataParallel(model)
# 6. criterion
matcher = HungarianMatcher()
criterion = HungarianLoss(num_classes=opts.num_classes, matcher=matcher).to(device)
# 7. resume
if opts.start_epoch != 0:
checkpoint = torch.load(os.path.join(opts.save_path, opts.save_file_name) + '.{}.pth.tar'
.format(opts.start_epoch - 1),
map_location=torch.device('cuda:{}'.format(0)))
model.load_state_dict(checkpoint['model_state_dict']) # load model state dict
print('\nLoaded checkpoint from epoch %d.\n' % (int(opts.start_epoch) - 1))
else:
print('\nNo check point to resume.. train from scratch.\n')
# 11. test
test(epoch=40,
vis=vis,
test_loader=test_loader,
model=model,
criterion=criterion,
opts=opts,
visualize=True,
)