def test():
import os
im_file = 'demo/004545.jpg'
image = cv2.imread(im_file)
detector = FasterRCNN()
network.load_net('/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5',
detector)
detector.cuda()
print('load model successfully!')
# network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
# print('save model succ')
t = Timer()
t.tic()
# image = np.zeros(shape=[600, 800, 3], dtype=np.uint8) + 255
dets, scores, classes = detector.detect(image, 0.3)
runtime = t.toc()
print('total spend: {}s'.format(runtime))
im2show = np.copy(image)
for i, det in enumerate(dets):
if scores[i] < 0.3:
continue
det = tuple(int(x) for x in det)
cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
cv2.putText(im2show,
'%s: %.3f' % (classes[i], scores[i]),
(det[0], det[1] + 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
cv2.imwrite(os.path.join('demo', 'out.jpg'), im2show)
def main():
global args
print "Loading testing set..."
# train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
print "Done."
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
net = RPN(args.use_kmeans_anchors)
network.load_net('./output/RPN/RPN_relationship_best_kmeans.h5', net)
net.cuda()
# best_recall = np.array([0.0, 0.0])
# Testing
recall = test(test_loader, net)
print(
'Recall: '
'object: {recall[0]: .3f}%'
'relationship: {recall[1]: .3f}%'.format(recall=recall * 100))
def test():
import os
im_file = 'demo/00001.jpg'
# im_file = 'data/VOCdevkit2007/VOC2007/JPEGImages/009036.jpg'
# im_file = '/media/longc/Data/data/2DMOT2015/test/ETH-Crossing/img1/000100.jpg'
image = cv2.imread(im_file)
model_file = './model/VGGnet_fast_rcnn_iter_70000.h5'
# model_file = '/media/longc/Data/models/faster_rcnn_pytorch3/faster_rcnn_100000.h5'
# model_file = '/media/longc/Data/models/faster_rcnn_pytorch2/faster_rcnn_2000.h5'
detector = FasterRCNN()
network.load_net(model_file, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
# network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
# print('save model succ')
t = Timer()
t.tic()
# image = np.zeros(shape=[600, 800, 3], dtype=np.uint8) + 255
dets, scores, classes = detector.detect(image, 0.7)
runtime = t.toc()
print('total spend: {}s'.format(runtime))
im2show = np.copy(image)
for i, det in enumerate(dets):
det = tuple(int(x) for x in det)
cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
cv2.putText(im2show, '%s: %.3f' % (classes[i], scores[i]), (det[0], det[1] + 15), cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255), thickness=1)
cv2.imwrite(os.path.join('demo', 'out.jpg'), im2show)
def main():
global args
print "Loading training set and testing set..."
# train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
print "Done."
# train_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=8, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
net = RPN(args.use_normal_anchors)
network.load_net('./output/RPN/RPN_region_best.h5', net)
# network.set_trainable(net.features, requires_grad=False)
net.cuda()
best_recall = np.array([0.0, 0.0])
# Testing
recall = test(test_loader, net)
print(
'Recall: '
'object: {recall[0]: .3f}%% (Best: {best_recall[0]: .3f}%%)'
'relationship: {recall[1]: .3f}%% (Best: {best_recall[1]: .3f}%%)'.
format(recall=recall * 100, best_recall=best_recall * 100))
def __init__(self, weights=None):
if weights is None:
if not os.path.exists('weights'):
os.mkdir('weights')
download_url = 'https://github.com/ArnholdInstitute/ColdSpots/releases/download/1.0/faster-rcnn.zip'
if not os.path.exists('weights/faster-rcnn'):
print('Downloading weights for faster-rcnn')
if not os.path.exists(os.path.join('weights/faster-rcnn.zip')):
check_output([
'wget', download_url, '-O', 'weights/faster-rcnn.zip'
])
print('Unzipping...')
check_output(
['unzip', 'weights/faster-rcnn.zip', '-d', 'weights'])
description = json.load(
open('weights/faster-rcnn/description.json'))
weights = os.path.join('weights/faster-rcnn',
description['weights'])
print('Building model...')
self.model = FasterRCNNModel(classes=['__backround__', 'building'],
debug=False)
network.load_net(weights, self.model)
self.model.cuda()
self.model.eval()
def main():
global args
print "Loading training set and testing set..."
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
print "Done."
train_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=8, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=False, num_workers=8, pin_memory=True)
net = RPN(not args.use_normal_anchors)
if args.resume_training:
print 'Resume training from: {}'.format(args.resume_model)
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
optimizer = torch.optim.SGD([
{'params': list(net.parameters())[26:]},
], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
else:
print 'Training from scratch...Initializing network...'
optimizer = torch.optim.SGD(list(net.parameters())[26:], lr=args.lr, momentum=args.momentum, weight_decay=0.0005)
network.set_trainable(net.features, requires_grad=False)
net.cuda()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
best_recall = 0.0
for epoch in range(0, args.max_epoch):
# Training
# train(train_loader, net, optimizer, epoch)
# Testing
recall, RPN_precision, RPN_recall = test(test_loader, net)
print('Epoch[{epoch:d}]: '
'Recall: '
'object: {recall: .3f}%% (Best: {best_recall: .3f}%%)'.format(
epoch = epoch, recall=recall * 100, best_recall=best_recall * 100))
print('object: {precision: .3f}%% '
'object: {recall: .3f}%% '.format(precision=RPN_precision*100, recall=RPN_recall*100))
# update learning rate
if epoch % args.step_size == 0:
args.disable_clip_gradient = True
args.lr /= 10
for param_group in optimizer.param_groups:
param_group['lr'] = args.lr
save_name = os.path.join(args.output_dir, '{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
if np.all(recall > best_recall):
best_recall = recall
save_name = os.path.join(args.output_dir, '{}_best.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
def load_model(model_file_path):
detector = FasterRCNN()
network.load_net(model_file_path, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
return detector
def build_extractor(model_file, classes=None):
if classes is None:
extractor = FasterRCNN()
else:
extractor = FasterRCNN(classes)
extractor.cuda()
extractor.eval()
network.load_net(model_file, extractor)
print('load model successfully!')
return extractor
def test():
import os
im_file = 'demo/004545.jpg'
# im_file = 'data/VOCdevkit2007/VOC2007/JPEGImages/009036.jpg'
# im_file = '/media/longc/Data/data/2DMOT2015/test/ETH-Crossing/img1/000100.jpg'
image = cv2.imread(im_file)
# model_file = './VGGnet_fast_rcnn_iter_70000.h5'
# model_file = '/media/longc/Data/models/faster_rcnn_pytorch3/faster_rcnn_100000.h5'
# model_file = '/media/longc/Data/models/faster_rcnn_pytorch2/faster_rcnn_2000.h5'
model_file = './models/saved_model_max/faster_rcnn_100000.h5'
detector = FasterRCNN()
network.load_net(model_file, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
# network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
# print('save model succ')
t = Timer()
t.tic()
# image = np.zeros(shape=[600, 800, 3], dtype=np.uint8) + 255
dets, scores, classes = detector.detect(image, 0.7)
runtime = t.toc()
print('total spend: {}s'.format(runtime))
im2show = np.copy(image)
img = mpimg.imread(im_file)
# Create figure and axes
fig, ax = plt.subplots(1)
# Display the image
ax.imshow(img)
# Create a Rectangle patch
for i, det in enumerate(dets):
w = det[2] - det[0]
h = det[3] - det[1]
rect = patches.Rectangle(det[0:2],
w,
h,
linewidth=1,
edgecolor='r',
facecolor='none')
# text
plt.text(det[0], det[1], '%s: %.3f' % (classes[i], scores[i]))
# Add the patch to the Axes
ax.add_patch(rect)
plt.show()
print('aa')
def test(visualize=False):
import os
im_file = 'data/cervix/train/Type_2/1381.jpg'
im_name = im_file.split('/')[-1]
image = cv2.imread(im_file)
# model_file = 'models/VGGnet_fast_rcnn_iter_70000.h5'
model_file = 'models/saved_model3/faster_rcnn_100000.h5'
expm = model_file.split('/')[-1].split('.')[0]
expm_dir = os.path.join('demo', expm)
if not os.path.exists(expm_dir):
os.makedirs(expm_dir)
detector = FasterRCNN()
network.load_net(model_file, detector)
detector.cuda()
detector.eval(
) # set model in evaluation mode, has effect on Dropout and Batchnorm. Use train() to set train mode.
print('load model successfully!')
# network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
# print('save model succ')
t = Timer()
t.tic()
# image = np.zeros(shape=[600, 800, 3], dtype=np.uint8) + 255
dets, scores, classes = detector.detect(image, 0.7)
runtime = t.toc()
print('total spend: {}s'.format(runtime))
im2show = np.copy(image)
for i, det in enumerate(dets):
det = tuple(int(x) for x in det)
cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 4)
cv2.putText(im2show,
'%s: %.3f' % (classes[i], scores[i]),
(det[0], det[1] + 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
cv2.imwrite(os.path.join('demo', expm, im_name), im2show)
if visualize:
im2show = cv2.resize(im2show,
None,
None,
fx=0.15,
fy=0.15,
interpolation=cv2.INTER_LINEAR)
cv2.imshow('demo', im2show)
cv2.waitKey(0)
def test():
import os
img_file = 'demo/images.jpeg'
image = cv2.imread(img_file)
#imdb_name = 'CaltechPedestrians_train'
imdb_name = 'coco_2017_train'
#imdb_name = 'voc_2007_trainval'
imdb = get_imdb(imdb_name)
cfg_file = 'experiments/cfgs/faster_rcnn_end2end.yml'
model_dir = 'data/pretrained_model/'
#pre_model_name = 'VGGnet_fast_rcnn_iter_70000.h5'
pre_model_name = 'coco_2017_train_10_vgg16_0.7_b1.h5'
#pre_model_name = 'CaltechPedestrians_train_1_vgg16_0.7_b1.h5'
pretrained_model = model_dir + pre_model_name
cfg_from_file(cfg_file)
print(imdb.classes)
if 'vgg16' in pre_model_name.split('_'):
detector = FasterRCNN_VGG(classes=imdb.classes, debug=False)
elif 'resnet50' in pre_model_name.split('_'):
detector = FasterRCNN_RES(classes=imdb.classes, debug=False)
else:
detector = FasterRCNN_VGG(classes=imdb.classes, debug=False)
network.load_net(pretrained_model, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
blob = init_data(is_cuda=True)
t = Timer()
t.tic()
dets, scores, classes = detector.detect(image,
blob,
thr=0.7,
nms_thresh=0.3)
runtime = t.toc()
print('total spend: {}s'.format(runtime))
im2show = np.copy(image)
for i, det in enumerate(dets):
det = tuple(int(x) for x in det)
cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
cv2.putText(im2show, '%s: %.3f' % (classes[i], scores[i]), (det[0], det[1] + 15),\
cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), thickness=1)
cv2.imwrite(os.path.join('demo', 'out.jpg'), im2show)
cv2.imshow('demo', im2show)
cv2.waitKey(0)
def analysis_video(self, result_dir):
self.statusbar_stringvar.set('Analysis..Please wait..')
model_file = 'model.h5'
detector = FasterRCNN()
network.load_net(model_file, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
info_dict = {}
info_dict['pictures'] = []
for index in range(len(self.image_list)):
accuracy = 0.
pic_info = {}
pic_info['objects'] = []
dets, scores, classes = detector.detect(self.image_list[index], 0.8)
im2show = np.copy(self.image_list[index])
for i, det in enumerate(dets):
object_info = {}
det = tuple(int(x) for x in det)
cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
cv2.putText(im2show, '%s: %.3f' % (classes[i], scores[i]), (det[0], det[1] + 15), cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255), thickness=1)
accuracy += scores[i]
#object info initial
object_info['name'] = classes[i]
object_info['accuracy'] = scores[i]
object_info['bbox'] = det
pic_info['objects'].append(object_info)
# pic_info initial
pic_info['filename'] = os.path.basename(self.video_path).split('.')[0] + '_' + str(index + 1) + '.jpg'
pic_info['size'] = im2show.shape
info_dict['pictures'].append(pic_info)
cv2.imwrite(os.path.join(result_dir, pic_info['filename']), im2show)
self.view_table.update(index + 1, **{
'name': pic_info['filename'],
'accuracy': accuracy / len(classes),
'state': 'yes'
})
self.statusbar_stringvar.set('Analysis done!')
return info_dict
def main():
global args
print "Loading training set and testing set..."
# train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
object_classes = test_set.object_classes
print "Done."
# train_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=8, pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=False, num_workers=8, pin_memory=True)
net = FasterRCNN(use_kmeans_anchors=args.use_kmeans_anchors, n_classes=len(object_classes), model=args.base_model)
network.load_net('./output/detection/Faster_RCNN_small_vgg_12epoch_epoch_11.h5', net)
# network.load_net('./output/detection/RPN_object1_best.h5', net)
# network.set_trainable(net.features, requires_grad=False)
net.cuda()
# Testing
recall = test(test_loader, net)
print('Recall: '
'object: {recall: .3f}%'.format(recall=recall*100))
def test():
# Set up dataloader
data_loader = DAVIS_seq_dataloader(split='val')
model_file = './model/VGGnet_fast_rcnn_iter_70000.h5'
detector = FasterRCNN()
network.load_net(model_file, detector)
detector.cuda()
detector.eval()
print('Load Faster R-CNN model successfully!')
# unet_model = './model/vgg_unet_1e-4_500.h5'
# unet = UNet()
# network.load_net(unet_model, unet)
# unet.cuda()
# network.weights_normal_init(unet, dev=0.01)
# unet.load_from_faster_rcnn_h5(h5py.File(model_file))
criterion_bce = torch.nn.BCELoss().cuda()
weight_decay = 5e-5
# optimizer = torch.optim.SGD(list(unet.parameters())[26:], lr=1e-4, weight_decay=weight_decay)
# print('Load U-Net model successfully!')
crop_set = []
# Iterate
for i in range(data_loader.num_seq):
# Get the first frame info
seq = data_loader.seq_list[data_loader.out_pointer]
seq_len = data_loader.seq_len[seq]
img_blobs, seg_blobs = data_loader.get_next_minibatch()
img = img_blobs[0,:,:,:]
im_data, im_scales = detector.get_image_blob(img)
im_info = np.array([[im_data.shape[1], im_data.shape[2], im_scales[0]]], dtype=np.float32)
# Get the category of the object in the first frame
rmin, rmax, cmin, cmax = bbox(seg_blobs[0,:,:,0])
features, rois = detector(im_data, im_info, rpn_only=True)
new_rois_np = np.array([[0, cmin, rmin, cmax, rmax]], dtype=np.float32)
new_rois_t = torch.from_numpy(new_rois_np).cuda()
new_rois = Variable(new_rois_t, requires_grad=False)
pooled_features = detector.roi_pool(features, new_rois)
x = pooled_features.view(pooled_features.size()[0], -1)
x = detector.fc6(x)
x = detector.fc7(x)
cls_score = detector.score_fc(x)
cls_prob = F.softmax(cls_score)
bbox_pred = detector.bbox_fc(x)
cls_prob_np = cls_prob.cpu().data.numpy()
bbox_pred_np = bbox_pred.cpu().data.numpy()
cls_idx = cls_prob_np.argmax()
cls_conf = cls_prob_np.max()
# Overfit U-Net with the first frame
# for i in range(100):
# unet.train()
# img_t = torch.from_numpy(img_blobs).permute(0,3,1,2).float().cuda()
# img_v = Variable(img_t, requires_grad=False)
# seg_t = torch.from_numpy(seg_blobs).permute(0,3,1,2).float().cuda()
# seg_v = Variable(seg_t, requires_grad=False)
# pred = unet(img_v)
# loss = criterion_bce(pred, seg_v)
# pred_view = pred.view(-1, 1)
# seg_view = seg_v.view(-1, 1)
# EPS = 1e-6
# loss = 0.6 * seg_view.mul(torch.log(pred_view+EPS)) + 0.4 * seg_view.mul(-1).add(1).mul(torch.log(1-pred+EPS))
# loss = -torch.mean(loss)
# loss_val = loss.data[0]
# optimizer.zero_grad()
# loss.backward()
# optimizer.step()
# print('{}/100: {}'.format(i, loss_val))
# unet.eval()
# Merge region proposals overlapping with last frame proposal
for j in range(1, seq_len):
img_blobs, _ = data_loader.get_next_minibatch()
img = img_blobs[0,:,:,:]
im_data, im_scales = detector.get_image_blob(img)
# 300 x 5, the first elements are useless here
features, rois = detector(im_data, im_info, rpn_only=True)
x1, y1, x2, y2 = merge_rois((rmin, rmax, cmin, cmax), rois.cpu().data.numpy(), thres=0.75)
# Have overlapping proposals
if x1 is not None:
# Send to following layers to refine the bbox
new_rois_np = np.array([[0, x1, y1, x2, y2]], dtype=np.float32)
new_rois_t = torch.from_numpy(new_rois_np).cuda()
new_rois = Variable(new_rois_t, requires_grad=False)
pooled_features = detector.roi_pool(features, new_rois)
x = pooled_features.view(pooled_features.size()[0], -1)
x = detector.fc6(x)
x = detector.fc7(x)
cls_score = detector.score_fc(x)
cls_prob = F.softmax(cls_score)
bbox_pred = detector.bbox_fc(x)
cls_prob_np = cls_prob.cpu().data.numpy()
bbox_pred_np = bbox_pred.cpu().data.numpy()
# Only regress bbox when confidence is greater than 0.8
if cls_prob_np.max() > 0.8 and cls_prob_np.argmax() != 0:
keep = cls_prob_np.argmax()
pred_boxes, scores, classes = detector.interpret_faster_rcnn(cls_prob, bbox_pred, new_rois, im_info, im_data.shape, 0.8)
cx = (x1 + x2) / 2
cy = (y1 + y2) / 2
width = x2 - x1 + 1
height = y2 - y1 + 1
dx = bbox_pred_np[0,keep*4+0]
dy = bbox_pred_np[0,keep*4+1]
dw = bbox_pred_np[0,keep*4+2]
dh = bbox_pred_np[0,keep*4+3]
pred_x = dx * width + cx
pred_y = dy * height + cy
pred_w = np.exp(dw) * width
pred_h = np.exp(dh) * height
x1 = pred_x - pred_w / 2
x2 = pred_x + pred_w / 2
y1 = pred_y - pred_h / 2
y2 = pred_y + pred_h / 2
# No overlapping proposals
if x1 is None:
# Using Faster R-CNN again to find potential objects
dets, scores, classes = detector.detect(img, 0.6)
# Cannot find any salient object
if dets.shape[0] == 0:
x1, y1, x2, y2 = cmin, rmin, cmax, rmax
else:
x1 = dets[:,0]
y1 = dets[:,1]
x2 = dets[:,2]
y2 = dets[:,3]
pred_area = (x2 - x1 + 1) * (y2 - y1 + 1)
init_area = (cmax - cmin + 1) * (rmax - rmin + 1)
xx1 = np.maximum(x1, cmin)
xx2 = np.minimum(x2, cmax)
yy1 = np.maximum(y1, rmin)
yy2 = np.minimum(y2, rmax)
inter = (xx2 - xx1 + 1) * (yy2 - yy1 + 1)
ovr = inter / (pred_area + init_area - inter)
# If there is overlapping, choose the largest IoU bbox
try:
ovr = ovr[ovr > 0.3]
ovr_idx = np.argsort(ovr)[-1]
x1 = dets[ovr_idx,0]
y1 = dets[ovr_idx,1]
x2 = dets[ovr_idx,2]
y2 = dets[ovr_idx,3]
# Else, choose the highest objectness score one
except:
if cls_idx == 0:
temp_idx = scores.argmax()
x1 = dets[temp_idx,0]
y1 = dets[temp_idx,1]
x2 = dets[temp_idx,2]
y2 = dets[temp_idx,3]
else:
cx = (x1 + x2) / 2
cy = (y1 + y2) / 2
cc = (cmin + cmax) / 2
cr = (rmin + rmax) / 2
dist = np.sqrt(np.square(cx-cc) + np.square(cy-cr))
dist_idx = np.argsort(dist)
for di in dist_idx:
if classes[di] == _CLASSES[cls_idx]:
x1 = dets[di,0]
y1 = dets[di,1]
x2 = dets[di,2]
y2 = dets[di,3]
# Crop the region and send it to U-Net
try:
x1 = int(max(x1, 0))
x2 = int(min(x2, im_data.shape[2]))
y1 = int(max(y1, 0))
y2 = int(min(y2, im_data.shape[1]))
except:
x1 = int(max(x1[0], 0))
x2 = int(min(x2[0], im_data.shape[2]))
y1 = int(max(y1[0], 0))
y2 = int(min(y2[0], im_data.shape[1]))
# MEAN_PIXEL = np.array([103.939, 116.779, 123.68])
# crop = img_blobs[:, y1:y2+1, x1:x2+1, :] - MEAN_PIXEL
# crop = img_blobs[:,:,:,:] - MEAN_PIXEL
# crop_v = Variable(torch.from_numpy(crop).permute(0, 3, 1, 2).cuda(), requires_grad=False)
# pred = unet(crop_v)
# pred_np = pred.cpu().data.numpy()[0,0,:,:]
# pred_np[pred_np < 0.5] = 0
# pred_np[pred_np >= 0.5] = 1
# pred_np = pred_np * 255
# res = pred_np.astype(int)
# cv2.imwrite('test.png', res)
if y2 - y1 <= 1 or x2 - x1 <= 1:
ipdb.set_trace()
cv2.imwrite(os.path.join('demo', 'crop_{}_{}.png'.format(i, j)), img[y1:y2+1,x1:x2+1,:])
rmin = y1
rmax = y2
cmin = x1
cmax = x2
im2show = np.copy(img)
cv2.rectangle(im2show, (int(x1),int(y1)), (int(x2),int(y2)), (0, 255, 0), 2)
cv2.imwrite(os.path.join('demo', '{}_{}.jpg'.format(i, j)), im2show)
temp = [i, j, x1, y1, x2, y2]
crop_set.append(temp)
# Save
crop_set = np.array(crop_set)
np.save('crop', crop_set)
nms_time = _t['misc'].toc(average=False)
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, detect_time, nms_time)
df = pandas.DataFrame(all_boxes)
df.columns = ['x1', 'y1', 'x2', 'y2', 'score', 'image_id']
df.to_csv('predictions.csv', index=False)
print('Total time: %.4f, per image: %.4f' %
(total_time, total_time / num_images))
if __name__ == '__main__':
# load net
net = FasterRCNN(classes=['__backround__', 'building'], debug=False)
network.load_net(args.weights, net)
print('load model successfully!')
net.cuda()
net.eval()
val_data = json.load(open(args.test_boxes))
# evaluation
test_net(net,
val_data,
max_per_image,
thresh=thresh,
vis=vis,
data_dir='../data')
def test():
import os
im_file = 'demo/004545.jpg'
# im_file = 'data/VOCdevkit2007/VOC2007/JPEGImages/009036.jpg'
# im_file = '/disk2/data/ILSVRC2015/DET/Data/DET/val/ILSVRC2013_val_00004599.JPEG'
image = cv2.imread(im_file)
model_file = '/disk2/data/pytorch_models/trained_models/resnet152_imgsize1000/saved_model3/faster_rcnn_200000.h5'
# model_file = '/media/longc/Data/models/faster_rcnn_pytorch3/faster_rcnn_100000.h5'
# model_file = '/media/longc/Data/models/faster_rcnn_pytorch2/faster_rcnn_2000.h5'
classes = np.array(['__background__',\
'n02672831', 'n02691156', 'n02219486', 'n02419796', 'n07739125', 'n02454379',\
'n07718747', 'n02764044', 'n02766320', 'n02769748', 'n07693725', 'n02777292',\
'n07753592', 'n02786058', 'n02787622', 'n02799071', 'n02802426', 'n02807133',\
'n02815834', 'n02131653', 'n02206856', 'n07720875', 'n02828884', 'n02834778',\
'n02840245', 'n01503061', 'n02870880', 'n02879718', 'n02883205', 'n02880940',\
'n02892767', 'n07880968', 'n02924116', 'n02274259', 'n02437136', 'n02951585',
'n02958343', 'n02970849', 'n02402425', 'n02992211', 'n01784675', 'n03000684',\
'n03001627', 'n03017168', 'n03062245', 'n03063338', 'n03085013', 'n03793489',\
'n03109150', 'n03128519', 'n03134739', 'n03141823', 'n07718472', 'n03797390',\
'n03188531', 'n03196217', 'n03207941', 'n02084071', 'n02121808', 'n02268443',\
'n03249569', 'n03255030', 'n03271574', 'n02503517', 'n03314780', 'n07753113',\
'n03337140', 'n03991062', 'n03372029', 'n02118333', 'n03394916', 'n01639765',\
'n03400231', 'n02510455', 'n01443537', 'n03445777', 'n03445924', 'n07583066',\
'n03467517', 'n03483316', 'n03476991', 'n07697100', 'n03481172', 'n02342885',\
'n03494278', 'n03495258', 'n03124170', 'n07714571', 'n03513137', 'n02398521',\
'n03535780', 'n02374451', 'n07697537', 'n03584254', 'n01990800', 'n01910747',\
'n01882714', 'n03633091', 'n02165456', 'n03636649', 'n03642806', 'n07749582',\
'n02129165', 'n03676483', 'n01674464', 'n01982650', 'n03710721', 'n03720891',\
'n03759954', 'n03761084', 'n03764736', 'n03770439', 'n02484322', 'n03790512',\
'n07734744', 'n03804744', 'n03814639', 'n03838899', 'n07747607', 'n02444819',\
'n03908618', 'n03908714', 'n03916031', 'n00007846', 'n03928116', 'n07753275',\
'n03942813', 'n03950228', 'n07873807', 'n03958227', 'n03961711', 'n07768694',\
'n07615774', 'n02346627', 'n03995372', 'n07695742', 'n04004767', 'n04019541',\
'n04023962', 'n04026417', 'n02324045', 'n04039381', 'n01495701', 'n02509815',\
'n04070727', 'n04074963', 'n04116512', 'n04118538', 'n04118776', 'n04131690',\
'n04141076', 'n01770393', 'n04154565', 'n02076196', 'n02411705', 'n04228054',\
'n02445715', 'n01944390', 'n01726692', 'n04252077', 'n04252225', 'n04254120',\
'n04254680', 'n04256520', 'n04270147', 'n02355227', 'n02317335', 'n04317175',\
'n04330267', 'n04332243', 'n07745940', 'n04336792', 'n04356056', 'n04371430',\
'n02395003', 'n04376876', 'n04379243', 'n04392985', 'n04409515', 'n01776313',\
'n04591157', 'n02129604', 'n04442312', 'n06874185', 'n04468005', 'n04487394',\
'n03110669', 'n01662784', 'n03211117', 'n04509417', 'n04517823', 'n04536866',\
'n04540053', 'n04542943', 'n04554684', 'n04557648', 'n04530566', 'n02062744',\
'n04591713', 'n02391049'])
detector = FasterRCNN(classes)
network.load_net(model_file, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
# network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
# print('save model succ')
t = Timer()
t.tic()
# image = np.zeros(shape=[600, 800, 3], dtype=np.uint8) + 255
dets, scores, classes = detector.detect(image, 0.)
print "classes:{},scores:{}".format(classes, scores)
runtime = t.toc()
print('total spend: {}s'.format(runtime))
im2show = np.copy(image)
for i, det in enumerate(dets):
det = tuple(int(x) for x in det)
cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
cv2.putText(im2show,
'%s: %.3f' % (classes[i], scores[i]),
(det[0], det[1] + 15),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 255),
thickness=1)
cv2.imwrite(os.path.join('demo', 'out.jpg'), im2show)
def test():
import os
imdb_name = 'CaltechPedestrians_test'
imdb = get_imdb(imdb_name)
cfg_file = 'experiments/cfgs/faster_rcnn_end2end.yml'
model_dir = 'data/pretrained_model/'
pre_model_name = 'CaltechPedestrians_train_10_vgg16_0.7_b3.h5'
pretrained_model = model_dir + pre_model_name
cfg_from_file(cfg_file)
if 'vgg16' in pre_model_name.split('_'):
detector = FasterRCNN_VGG(classes=imdb.classes, debug=False)
elif 'res' in pre_model_name.split('_'):
detector = FasterRCNN_RES(classes=imdb.classes, debug=False)
else:
detector = FasterRCNN_VGG(classes=imdb.classes, debug=False)
network.load_net(pretrained_model, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
blob = init_data(is_cuda=True)
t = Timer()
t.tic()
cap = cv2.VideoCapture(video_file)
init = True
while (cap.isOpened()):
ret, frame = cap.read()
if ret:
p = Timer()
p.tic()
if init:
cnt = 1
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(output_file, fourcc, fps,
(frame.shape[1], frame.shape[0]))
init = False
try:
dets, scores, classes = detector.detect(frame,
blob,
thr=0.7,
nms_thresh=0.3)
frame = np.copy(frame)
for i, det in enumerate(dets):
det = tuple(int(x) for x in det)
cv2.rectangle(frame, det[0:2], det[2:4], (255, 205, 51), 2)
# cv2.putText(frame, '%s: %.3f' % (classes[i], scores[i]), (det[0], det[1] + 15), \
# cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), thickness=1)
cv2.imshow('demo', frame)
cv2.waitKey(1000)
cv2.destroyAllWindows()
except IndexError as e:
pass
finally:
print(cnt, '-frame : {:.3f}s'.format(p.toc()))
cnt += 1
out.write(frame)
else:
break
runtime = t.toc()
print('{} frames / total spend: {}s / {:2.1f} fps'.format(
cnt, int(runtime), cnt / runtime))
cap.release()
out.release()
def main():
global args, optimizer_select
# To set the model name automatically
print args
lr = args.lr
args = get_model_name(args)
print 'Model name: {}'.format(args.model_name)
# To set the random seed
random.seed(args.seed)
torch.manual_seed(args.seed + 1)
torch.cuda.manual_seed(args.seed + 2)
# print("Loading training set and testing set..."),
# train_set = visual_genome(args.dataset_option, 'train')
# test_set = visual_genome('small', 'test')
# print("Done.")
# train_loader = torch.utils.data.DataLoader(train_set, batch_size=1, shuffle=True, num_workers=8, pin_memory=True)
# test_loader = torch.utils.data.DataLoader(test_set, batch_size=1, shuffle=False, num_workers=8, pin_memory=True)
image_set = prepare_image(datapath=args.total_image_path)
image_loader = torch.utils.data.DataLoader(image_set,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
# Model declaration
net = Hierarchical_Descriptive_Model(
nhidden=args.mps_feature_len,
n_object_cats=5,
n_predicate_cats=5,
n_vocab=5,
voc_sign=5,
max_word_length=5,
MPS_iter=args.MPS_iter,
use_language_loss=not args.disable_language_model,
object_loss_weight=5,
predicate_loss_weight=5,
dropout=args.dropout,
use_kmeans_anchors=not args.use_normal_anchors,
gate_width=args.gate_width,
nhidden_caption=args.nhidden_caption,
nembedding=args.nembedding,
rnn_type=args.rnn_type,
rnn_droptout=args.caption_use_dropout,
rnn_bias=args.caption_use_bias,
use_region_reg=args.region_bbox_reg,
use_kernel=args.use_kernel_function)
params = list(net.parameters())
for param in params:
print param.size()
print net
# To group up the features
vgg_features_fix, vgg_features_var, rpn_features, hdn_features, language_features = group_features(
net)
# Setting the state of the training model
net.cuda()
net.train()
logger_path = "log/logger/{}".format(args.model_name)
if os.path.exists(logger_path):
shutil.rmtree(logger_path)
configure(logger_path, flush_secs=5) # setting up the logger
network.set_trainable(net, False)
# network.weights_normal_init(net, dev=0.01)
if args.finetune_language_model:
print 'Only finetuning the language model from: {}'.format(
args.resume_model)
args.train_all = False
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
optimizer_select = 3
elif args.load_RPN:
print 'Loading pretrained RPN: {}'.format(args.saved_model_path)
args.train_all = False
network.load_net(args.saved_model_path, net.rpn)
net.reinitialize_fc_layers()
optimizer_select = 1
elif args.resume_training:
print 'Resume training from: {}'.format(args.resume_model)
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
args.train_all = True
optimizer_select = 2
else:
print 'Training from scratch.'
net.rpn.initialize_parameters()
net.reinitialize_fc_layers()
optimizer_select = 0
args.train_all = True
optimizer = network.get_optimizer(lr, optimizer_select, args,
vgg_features_var, rpn_features,
hdn_features, language_features)
target_net = net
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
top_Ns = [50, 100]
best_recall = np.zeros(len(top_Ns))
extract_features(image_loader, net)
rdl_roidb.prepare_roidb(imdb)
roidb = imdb.roidb
data_layer = RoIDataLayer(roidb, imdb.num_classes)
# load net
print "Creating net..."
net = FasterRCNN(classes=imdb.classes, debug=_DEBUG)
network.weights_normal_init(net, dev=0.01)
print "Loading weight..."
# pretrained_model = pytorchpath+'data/pretrained_model/VGG_imagenet.npy'
# network.load_pretrained_npy(net, pretrained_model)
# model_file = '/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5'
# model_file = 'models/saved_model3/faster_rcnn_60000.h5'
model_file = '/home/jguerry/workspace/jg_dl/jg_pyt/models/sunrgbd_train_rgb_i_100_8bits/faster_rcnn_200000.h5'
network.load_net(model_file, net)
print "Configuring parameters..."
# exp_name = 'vgg16_02-19_13-24'
start_step = 200000
end_step = 400000
lr_decay_steps = {220000, 240000, 260000, 280000, 300000, 350000}
lr_decay = 1. / 10
rand_seed = 1024
lr = 0.0001
# network.weights_normal_init([net.bbox_fc, net.score_fc, net.fc6, net.fc7], dev=0.01)
disp_interval = 1000
save_interval = 20000
if rand_seed is not None:
thickness=1)
im_name = os.path.basename(image_file)
print(os.path.join('demo/det_results', im_name))
cv2.imwrite(os.path.join('demo/det_results', im_name), im2show)
#cv2.imshow('demo', im2show)
#cv2.waitKey(0)
def folder_test(net, folder):
txt_file = folder + 'JPEGImages/file_name.txt'
with open(txt_file) as f:
for line in f:
img_path = folder + 'JPEGImages/' + line.strip('\n') + '.JPG'
anno_path = folder + 'Annotations/' + line.strip('\n') + '.xml'
image_test(net, img_path, anno_path)
if __name__ == '__main__':
model_file = 'models/saved_model3/faster_rcnn_100000.h5'
detector = FasterRCNN()
network.load_net(model_file, detector)
detector.cuda()
detector.eval()
print('load model successfully!')
#image_file = 'demo/000001.JPG'
#image_test(detector, image_file, None)
folder = '/data/jmtian/PlateData/PVW_WRM_CUT/'
folder_test(detector, folder)
# create VGG model for state featurization
print("Loading image embedding model...")
if args.image_embedding_model_type == "resnet":
im_emb_model = ResNet50()
elif args.image_embedding_model_type == "vgg":
im_emb_model = VGG16()
else:
print("--image_embedding_model_type must be either resnet or vgg")
sys.exit(0)
print("Done!")
# create Faster-RCNN model for state featurization
print("Loading Fast-RCNN...")
model_file = 'VGGnet_fast_rcnn_iter_70000.h5'
model_frcnn = FasterRCNN()
network.load_net(model_file, model_frcnn)
model_frcnn.cuda()
model_frcnn.eval()
print("Done!")
# create DQN's for the next object, predicates, and attributes
print("Creating DQN models...")
DQN_next_object_main = DQN_MLP(2048*3+9600 + parameters["maximum_num_entities_per_image"], 1)
DQN_next_object_target = DQN_MLP(2048*3+9600 + parameters["maximum_num_entities_per_image"], 1)
DQN_predicate_main = DQN_MLP(2048*3+9600 + len(semantic_action_graph.predicate_nodes), 1)
DQN_predicate_target = DQN_MLP(2048*3+9600 + len(semantic_action_graph.predicate_nodes), 1)
DQN_attribute_main = DQN_MLP(2048*3+9600 + len(semantic_action_graph.attribute_nodes), 1)
DQN_attribute_target = DQN_MLP(2048*3+9600 + len(semantic_action_graph.attribute_nodes), 1)
print("Done!")
# create shared optimizer
def main():
global args, optimizer_select
# To set the model name automatically
print args
lr = args.lr
args = get_model_name(args)
print 'Model name: {}'.format(args.model_name)
# To set the random seed
random.seed(args.seed)
torch.manual_seed(args.seed + 1)
torch.cuda.manual_seed(args.seed + 2)
print("Loading training set and testing set..."),
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome('small', 'test')
print("Done.")
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=False,
num_workers=8,
pin_memory=True)
# Model declaration
net = Hierarchical_Descriptive_Model(
nhidden=args.mps_feature_len,
n_object_cats=train_set.num_object_classes,
n_predicate_cats=train_set.num_predicate_classes,
n_vocab=train_set.voc_size,
voc_sign=train_set.voc_sign,
max_word_length=train_set.max_size,
MPS_iter=args.MPS_iter,
use_language_loss=not args.disable_language_model,
object_loss_weight=train_set.inverse_weight_object,
predicate_loss_weight=train_set.inverse_weight_predicate,
dropout=args.dropout,
use_kmeans_anchors=not args.use_normal_anchors,
gate_width=args.gate_width,
nhidden_caption=args.nhidden_caption,
nembedding=args.nembedding,
rnn_type=args.rnn_type,
rnn_droptout=args.caption_use_dropout,
rnn_bias=args.caption_use_bias,
use_region_reg=args.region_bbox_reg,
use_kernel=args.use_kernel_function)
params = list(net.parameters())
for param in params:
print param.size()
print net
# To group up the features
vgg_features_fix, vgg_features_var, rpn_features, hdn_features, language_features = group_features(
net)
# Setting the state of the training model
net.cuda()
net.train()
logger_path = "log/logger/{}".format(args.model_name)
if os.path.exists(logger_path):
shutil.rmtree(logger_path)
configure(logger_path, flush_secs=5) # setting up the logger
network.set_trainable(net, False)
# network.weights_normal_init(net, dev=0.01)
if args.finetune_language_model:
print 'Only finetuning the language model from: {}'.format(
args.resume_model)
args.train_all = False
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
optimizer_select = 3
elif args.load_RPN:
print 'Loading pretrained RPN: {}'.format(args.saved_model_path)
args.train_all = False
network.load_net(args.saved_model_path, net.rpn)
net.reinitialize_fc_layers()
optimizer_select = 1
elif args.resume_training:
print 'Resume training from: {}'.format(args.resume_model)
if len(args.resume_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.resume_model, net)
args.train_all = True
optimizer_select = 2
else:
print 'Training from scratch.'
net.rpn.initialize_parameters()
net.reinitialize_fc_layers()
optimizer_select = 0
args.train_all = True
optimizer = network.get_optimizer(lr, optimizer_select, args,
vgg_features_var, rpn_features,
hdn_features, language_features)
target_net = net
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
top_Ns = [50, 100]
best_recall = np.zeros(len(top_Ns))
if args.evaluate:
recall = test(test_loader, net, top_Ns)
print('======= Testing Result =======')
for idx, top_N in enumerate(top_Ns):
print(
'[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100))
print('==============================')
else:
for epoch in range(0, args.max_epoch):
# Training
train(train_loader, target_net, optimizer, epoch)
# snapshot the state
save_name = os.path.join(
args.output_dir,
'{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
# Testing
# network.set_trainable(net, False) # Without backward(), requires_grad takes no effect
recall = test(test_loader, net, top_Ns)
if np.all(recall > best_recall):
best_recall = recall
save_name = os.path.join(args.output_dir,
'{}_best.h5'.format(args.model_name))
network.save_net(save_name, net)
print('\nsave model: {}'.format(save_name))
print('Epoch[{epoch:d}]:'.format(epoch=epoch)),
for idx, top_N in enumerate(top_Ns):
print(
'\t[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100)),
# updating learning policy
if epoch % args.step_size == 0 and epoch > 0:
lr /= 10
args.lr = lr
print '[learning rate: {}]'.format(lr)
args.enable_clip_gradient = False
if not args.finetune_language_model:
args.train_all = True
optimizer_select = 2
# update optimizer and correponding requires_grad state
optimizer = network.get_optimizer(lr, optimizer_select, args,
vgg_features_var,
rpn_features, hdn_features,
language_features)
print ('im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, detect_time, nms_time))
if vis:
cv2.imshow('test', im2show)
cv2.waitKey(1)
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('=====Evaluating detections=====') #评估检测结果，计算MAP。
imdb.evaluate_detections(all_boxes, output_dir)
if __name__ == '__main__':
# load data
imdb = get_imdb(imdb_name) #返回一个pascal_voc对象
imdb.competition_mode(on=True)
print("load data finished!")
# load net
net = FasterRCNN(classes=imdb.classes, debug=False)
network.load_net(trained_model, net) #加载训练好的模型。
print('load model successfully!')
# net.cuda()
net.eval()
# evaluation
test_net(save_name, net, imdb, max_per_image, thresh=thresh, vis=vis)
def main():
global args, optimizer_select
# To set the model name automatically
print args
lr = args.lr
args = get_model_name(args)
print 'Model name: {}'.format(args.model_name)
# To set the random seed
random.seed(args.seed)
torch.manual_seed(args.seed + 1)
torch.cuda.manual_seed(args.seed + 2)
print("Loading training set and testing set...")
train_set = visual_genome(args.dataset_option, 'train')
test_set = visual_genome(args.dataset_option, 'test')
print("Done.")
train_loader = torch.utils.data.DataLoader(train_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
shuffle=True,
num_workers=8,
pin_memory=True)
net = Hierarchical_Descriptive_Model(
nhidden=args.mps_feature_len,
n_object_cats=train_set.num_object_classes,
n_predicate_cats=train_set.num_predicate_classes,
MPS_iter=args.MPS_iter,
object_loss_weight=train_set.inverse_weight_object,
predicate_loss_weight=train_set.inverse_weight_predicate,
dropout=args.dropout,
use_kmeans_anchors=args.use_kmeans_anchors,
base_model=args.base_model) #True
# params = list(net.parameters())
# for param in params:
# print param.size()
print net
# Setting the state of the training model
net.cuda()
net.train()
network.set_trainable(net, False)
# network.weights_normal_init(net, dev=0.01)
if args.resume_model:
print 'Resume training from: {}'.format(args.HDN_model)
if len(args.HDN_model) == 0:
raise Exception('[resume_model] not specified')
network.load_net(args.HDN_model, net)
# network.load_net(args.RPN_model, net.rpn)
args.train_all = True
optimizer_select = 3
elif args.load_RCNN:
print 'Loading pretrained RCNN: {}'.format(args.RCNN_model)
args.train_all = False
network.load_net(args.RCNN_model, net.rcnn)
optimizer_select = 2
elif args.load_RPN:
print 'Loading pretrained RPN: {}'.format(args.RPN_model)
args.train_all = False
network.load_net(args.RPN_model, net.rpn)
net.reinitialize_fc_layers()
optimizer_select = 1
else:
print 'Training from scratch.'
net.rpn.initialize_parameters()
net.reinitialize_fc_layers()
optimizer_select = 0
args.train_all = True
# To group up the features
# vgg_features_fix, vgg_features_var, rpn_features, hdn_features = group_features(net)
basenet_features, rpn_features, rcnn_feature, hdn_features = group_features(
net)
optimizer = network.get_optimizer(lr, optimizer_select, args,
basenet_features, rpn_features,
rcnn_feature, hdn_features)
target_net = net
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
top_Ns = [50, 100]
best_recall = np.zeros(len(top_Ns))
if args.evaluate:
recall = test(test_loader, target_net, top_Ns,
train_set.object_classes)
print('======= Testing Result =======')
for idx, top_N in enumerate(top_Ns):
print(
'[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100))
print('==============================')
else:
for epoch in range(0, args.max_epoch):
# Training
train(train_loader, target_net, optimizer, epoch)
# snapshot the state
save_name = os.path.join(
args.output_dir,
'{}_epoch_{}.h5'.format(args.model_name, epoch))
network.save_net(save_name, net)
print('save model: {}'.format(save_name))
recall = test(test_loader, target_net, top_Ns,
train_set.object_classes)
if np.all(recall > best_recall):
best_recall = recall
save_name = os.path.join(args.output_dir,
'{}_best.h5'.format(args.model_name))
network.save_net(save_name, net)
print('\nsave model: {}'.format(save_name))
print('Epoch[{epoch:d}]:'.format(epoch=epoch)),
for idx, top_N in enumerate(top_Ns):
print(
'\t[Recall@{top_N:d}] {recall:2.3f}%% (best: {best_recall:2.3f}%%)'
.format(top_N=top_N,
recall=recall[idx] * 100,
best_recall=best_recall[idx] * 100))
# updating learning policy
if (epoch + 1) % args.step_size == 0 or (epoch + 1) % (
args.step_size + 2) == 0:
lr /= 10
args.lr = lr
print '[learning rate: {}]'.format(lr)
args.enable_clip_gradient = False
args.train_all = False
optimizer_select = 2
# update optimizer and correponding requires_grad state
optimizer = network.get_optimizer(lr, optimizer_select, args,
basenet_features,
rpn_features, rcnn_feature,
hdn_features)
# load data
imdb = get_imdb(imdb_name)
rdl_roidb.prepare_roidb(imdb)
roidb = imdb.roidb
data_layer = RoIDataLayer(roidb, imdb.num_classes)
# load net
net = RFCN(classes=imdb.classes, debug=_DEBUG)
#init_modules = [net.rpn.conv1, net.rpn.score_conv, net.rpn.bbox_conv, net.fc6, net.fc7, net.score_fc, net.bbox_fc]
#network.weights_normal_init(init_modules, dev=0.01)
network.weights_normal_init(net, dev=0.01)
network.load_pretrained_npy(net, pretrained_model)
if resume:
pretrained_model_file = 'models/saved_model3/faster_rcnn_resnet101_20000.h5'
network.load_net(pretrained_model_file, net)
start_step = 20000
print 'Resume training...'
net.cuda()
net.train()
params = list(net.parameters())
# optimizer = torch.optim.Adam(params[-8:], lr=lr)
optimizer = torch.optim.SGD(params[8:],
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if sav:
cv2.imwrite(output_dir_detections + str(i) + '.png', im2show)
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb_0.evaluate_detections(all_boxes, output_dir)
if __name__ == '__main__':
imdb_0 = get_imdb(imdb_test_name_0)
imdb_0.competition_mode(on=True)
net_0 = FasterRCNN(classes=imdb_0.classes, debug=False)
network.load_net(trained_model_0, net_0)
print('load model 0 successfully!')
net_0.cuda()
net_0.eval()
imdb_1 = get_imdb(imdb_test_name_1)
imdb_1.competition_mode(on=True)
net_1 = FasterRCNN(classes=imdb_1.classes, debug=False)
network.load_net(trained_model_1, net_1)
print('load model 1 successfully!')
net_1.cuda()
net_1.eval()
net_x = FasterRCNN_x(classes=imdb_0.classes, debug=False)
net_x.frcnn_0 = net_0
net_x.frcnn_1 = net_1
# for dataset in whole_data:
# for element in dataset:
# data_comp.append(element)
if args.encoding_file is None:
encoder = imSituVerbRoleLocalNounEncoder(train_set)
torch.save(encoder, args.output_dir + "/encoder")
else:
encoder = torch.load(args.encoding_file)
model = baseline_crf(encoder, cnn_type = args.cnn_type)
if args.weights_file is not None:
if args.cnn_type == 'faster_rcnn':
network.load_net(args.weights_file, model)
else:
model.load_state_dict(torch.load(args.weights_file))
dataset_train = imSituSituation(args.image_dir, train_set, encoder, model.train_preprocess())
dataset_dev = imSituSituation(args.image_dir, dev_set, encoder, model.dev_preprocess())
ngpus = 1
device_array = [i for i in range(0,ngpus)]
#batch_size = args.batch_size*ngpus
batch_size = 1
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size = batch_size, shuffle = True, num_workers = 1)
dev_loader = torch.utils.data.DataLoader(dataset_dev, batch_size = batch_size, shuffle = True, num_workers = 1)
models = os.listdir(model_dir)
pretrained_model = [
os.path.join(model_dir, model) for model in models
if db_only in model.split('_')
]
pretrained_model.sort()
imdb = get_imdb(imdb_name)
prepare_roidb(imdb)
roidb = imdb.roidb
f = open(os.path.join(model_dir, 'performance.txt'), 'a')
for model in pretrained_model:
is_resnet = True if 'res' in model.split('/') else False
if model.endswith('txt'):
continue
if not is_resnet:
detector = FasterRCNN_VGG(classes=imdb.classes, debug=False)
else:
detector = FasterRCNN_RES(classes=imdb.classes, debug=False)
network.load_net(model, detector)
match = id_match_test(model, detector, imdb,
roidb) if cfg.TRIPLET.IS_TRUE else 0.
prec, rec = test(model, detector, imdb, roidb)
# pos, neg, bg = score_analysis(model, detector, imdb, roidb)
del detector
# f.write(model+' -----pos: {:.4f} neg: {:.4f} bg: {:.4f}\n'.format(pos, neg, bg))
f.write(model +
' ----[prec: {:.2f}%, rec: {:.2f}%] / {:.2f}%\n'.format(
prec, rec, match))
f.close()
cfg_from_file(cfg_file)
lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY
disp_interval = cfg.TRAIN.DISPLAY
log_interval = cfg.TRAIN.LOG_IMAGE_ITERS
# load data
imdb = VisualGenome(split=0, num_im=50)
roidb = imdb.roidb
data_layer = RoIDataLayer(roidb, imdb.num_classes)
# load net
net = FasterRCNN(classes=imdb.classes, debug=_DEBUG)
network.weights_normal_init(net, dev=0.01)
network.load_net(pretrained_model, net)
# network.load_pretrained_npy(net, 'checkpoints/VGG_imagenet.npy')
net.cuda()
net.train()
params = list(net.parameters())
print("Params are {}".format(
'\n'.join(['{}: {}'.format(n, p.size()) for n,p in net.named_parameters()]))
)
# optimizer = torch.optim.Adam(params, lr=0.001, eps=1e-4, weight_decay=weight_decay)
optimizer = torch.optim.SGD(params[8:], lr=lr, momentum=momentum, weight_decay=weight_decay)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
cfg_file = 'experiments/cfgs/faster_rcnn_end2end.yml'
model_dir = 'data/pretrained_model/'
output_dir = 'models/saved_model3'
pre_model_name = 'CaltechPedestrians_train_triplet_1_vgg16_cls_0.7_b3.h5'
pretrained_model = model_dir + pre_model_name
_DEBUG = False
BG_SHOW = True
id_limit = 20 if BG_SHOW else 50
# load config
cfg_from_file(cfg_file)
# load data
imdb, roidb, ratio_list, ratio_index = extract_roidb(imdb_name)
detector = FasterRCNN_VGG(classes=imdb.classes, debug=_DEBUG)
network.load_net(pretrained_model, detector)
blob = init_data(is_cuda=True)
detector.cuda()
detector.eval()
name_blocks = pre_model_name.split('_')
batch_size = imdb.num_triplet_test_images
test_num = len(roidb)
blob = init_data(is_cuda=True)
features = []
bg_features = []
ids = []
print('Extracting features...')
t = Timer()
t.tic()
