def test(epoch):
print("================================")
print("Testing after epoch {}".format(epoch))
print("================================")
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
regr_rpn_loss= 0
class_rpn_loss =0
total_rpn_loss = 0
regr_class_loss= 0
class_class_loss =0
total_class_loss = 0
count_rpn = 0
count_class = 0
total_count = 0
for i,(image, boxes, labels , temp, num_pos) in enumerate(test_loader):
count_rpn +=1
y_is_box_label = temp[0].to(device=model_rpn_cuda)
y_rpn_regr = temp[1].to(device=model_rpn_cuda)
image = Variable(image).to(device=model_rpn_cuda)
base_x , cls_k , reg_k = model_rpn(image)
l1 = rpn_loss_regr(y_true=y_rpn_regr, y_pred=reg_k , y_is_box_label=y_is_box_label , lambda_rpn_regr=args.lambda_rpn_regr, device=model_rpn_cuda)
l2 = rpn_loss_cls_fixed_num(y_pred = cls_k , y_is_box_label= y_is_box_label , lambda_rpn_class = args.lambda_rpn_class)
regr_rpn_loss += l1.item()
class_rpn_loss += l2.item()
loss = l1 + l2
total_rpn_loss += loss.item()
base_x , cls_k , reg_k = model_rpn(image)
for b in range(image.size(0)):
img_data = {}
rpn_rois = rpn_to_roi(cls_k[b,:].cpu(), reg_k[b,:].cpu(), no_anchors=num_anchors, all_possible_anchor_boxes=all_possible_anchor_boxes_tensor.cpu().clone() )
rpn_rois.to(device=model_classifier_cuda)
img_data["boxes"] = boxes[b].to(device=model_classifier_cuda) // downscale
img_data['labels'] = labels[b]
X2, Y1, Y2, _ = calc_iou(rpn_rois, img_data, class_mapping=config.label_map )
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
X2 = X2.to(device=model_classifier_cuda)
Y1 = Y1.to(device=model_classifier_cuda)
Y2 = Y2.to(device=model_classifier_cuda)
count_class += 1
rpn_base = base_x[b].unsqueeze(0)
out_class , out_regr = model_classifier(base_x = rpn_base , rois= X2 )
l3 = class_loss_cls(y_true=Y1, y_pred=out_class , lambda_cls_class=args.lambda_cls_class)
l4 = class_loss_regr(y_true=Y2, y_pred= out_regr , lambda_cls_regr= args.lambda_cls_regr)
loss = l3 + l4
class_class_loss += l3.item()
regr_class_loss += l4.item()
total_class_loss += loss.item()
if args.save_evaluations :
total_count += 1
if total_count % 100 == 0 :
predicted_boxes = X2
predicted_boxes[:,2] = predicted_boxes[:,2] + predicted_boxes[:,0]
predicted_boxes[:,3] = predicted_boxes[:,3] + predicted_boxes[:,1]
predicted_boxes = predicted_boxes * downscale
temp_img = (denormalize['std'].cpu() * image[b].cpu()) + denormalize['mean'].cpu()
save_evaluations_image(image=temp_img, boxes=predicted_boxes, labels=Y1, count=total_count, config=config , save_dir=args.save_dir)
if count_class == 0 :
count_class = 1
total_class_loss = 0
print('[Test Accuracy] Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(0, 0 ,0 ))
else:
print('[Test Accuracy] Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(class_class_loss / count_class, regr_class_loss / count_class ,total_class_loss/ count_class ))
print('[Test Accuracy] RPN Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(class_rpn_loss / count_rpn, regr_rpn_loss / count_rpn ,total_rpn_loss/ count_rpn ))
if len(rpn_accuracy_rpn_monitor) == 0 :
print('[Test Accuracy] RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
print('[Test Accuracy] Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
return total_class_loss/ count_class + total_rpn_loss/ count_rpn
r_loss_rpn_cls = record_df['loss_rpn_cls']
r_loss_rpn_regr = record_df['loss_rpn_regr']
r_loss_class_cls = record_df['loss_class_cls']
r_loss_class_regr = record_df['loss_class_regr']
r_curr_loss = record_df['curr_loss']
r_elapsed_time = record_df['elapsed_time']
r_mAP = record_df['mAP']
print('Already train %dK batches' % (len(record_df)))
optimizer = Adam(lr=1e-5)
optimizer_classifier = Adam(lr=1e-5)
model_rpn.compile(
optimizer=optimizer,
loss=[rpn_loss_cls(num_anchors),
rpn_loss_regr(num_anchors)])
model_classifier.compile(
optimizer=optimizer_classifier,
loss=[class_loss_cls,
class_loss_regr(len(classes_count) - 1)],
metrics={'dense_class_{}'.format(len(classes_count)): 'accuracy'})
model_all.compile(optimizer='sgd', loss='mae')
# Training setting
total_epochs = len(record_df)
r_epochs = len(record_df)
epoch_length = 1000
num_epochs = 10
iter_num = 0
def train(epoch):
print("\n\nTraining epoch {}\n\n".format(epoch))
rpn_accuracy_rpn_monitor = []
rpn_accuracy_for_epoch = []
regr_rpn_loss= 0
class_rpn_loss =0
total_rpn_loss = 0
regr_class_loss= 0
class_class_loss =0
total_class_loss = 0
count_rpn = 0
count_class = 0
for i,(image, boxes, labels , temp, num_pos) in enumerate(train_loader):
count_rpn +=1
y_is_box_label = temp[0].to(device=model_rpn_cuda)
y_rpn_regr = temp[1].to(device=model_rpn_cuda)
image = Variable(image).to(device=model_rpn_cuda)
boxes = boxes
base_x , cls_k , reg_k = model_rpn(image)
l1 = rpn_loss_regr(y_true=y_rpn_regr, y_pred=reg_k , y_is_box_label=y_is_box_label , lambda_rpn_regr=args.lambda_rpn_regr , device=model_rpn_cuda)
l2 = rpn_loss_cls_fixed_num(y_pred = cls_k , y_is_box_label= y_is_box_label , lambda_rpn_class = args.lambda_rpn_class)
regr_rpn_loss += l1.item()
class_rpn_loss += l2.item()
loss = l1 + l2
total_rpn_loss += loss.item()
optimizer_model_rpn.zero_grad()
loss.backward()
optimizer_model_rpn.step()
with torch.no_grad():
base_x , cls_k , reg_k = model_rpn(image)
cls_k = cls_k.to(device=model_classifier_cuda)
reg_k = reg_k.to(device=model_classifier_cuda)
base_x = base_x.to(device=model_classifier_cuda)
for b in range(args.train_batch):
img_data = {}
with torch.no_grad():
# Convert rpn layer to roi bboxes
# cls_k.shape : b, h, w, 9
# reg_k : b, h, w, 36
# model_classifier_cuda
# cls_k[b,:].shape == [13, 10, 9]
# reg_k[b,:].shape == [13, 10, 36]
# num_anchors = 9
# all_possible_anchor_boxes_tensor.shape == [4, 13, 10, 9]
rpn_rois = rpn_to_roi(cls_k[b,:].cpu(), reg_k[b,:].cpu(), no_anchors=num_anchors, all_possible_anchor_boxes=all_possible_anchor_boxes_tensor.cpu().clone() )
rpn_rois.to(device=model_classifier_cuda)
# can't concatenate batch
# no of boxes may vary across the batch
img_data["boxes"] = boxes[b].to(device=model_classifier_cuda) // downscale
img_data['labels'] = labels[b]
# rpn_rois : 300, 4
# img_data["boxes"].shape : 68,4
# len(img_data["labels"]) : 68
# X2 are qualified anchor boxes from model_rpn (converted anochors)
# Y1 are the label, Y1[-1] is the background bounding box (negative bounding box), ambigous (neutral boxes are eliminated < min overlap thresold)
# Y2 is concat of 1 , tx, ty, tw, th and 0, tx, ty, tw, th
X2, Y1, Y2, _ = calc_iou(rpn_rois, img_data, class_mapping=config.label_map )
X2 = X2.to(device=model_classifier_cuda)
Y1 = Y1.to(device=model_classifier_cuda)
Y2 = Y2.to(device=model_classifier_cuda)
# If X2 is None means there are no matching bboxes
if X2 is None:
rpn_accuracy_rpn_monitor.append(0)
rpn_accuracy_for_epoch.append(0)
continue
neg_samples = torch.where(Y1[:, -1] == 1)[0]
pos_samples = torch.where(Y1[:, -1] == 0)[0]
rpn_accuracy_rpn_monitor.append(pos_samples.size(0))
rpn_accuracy_for_epoch.append(pos_samples.size(0))
db = Dataset_roi(pos=pos_samples.cpu() , neg= neg_samples.cpu())
roi_loader = DataLoader(db, shuffle=True,
batch_size=args.n_roi // 4, num_workers=args.workers//2, pin_memory=False, drop_last=False)
# list(roi_loader)
for j,potential_roi in enumerate(roi_loader):
pos = potential_roi[0]
neg = potential_roi[1]
if type(pos) == list :
rois = X2[neg]
rpn_base = base_x[b].unsqueeze(0)
Y11 = Y1[neg]
Y22 = Y2[neg]
# out_class : args.n_roi // 2 , # no of class
elif type(neg) == list :
rois = X2[pos]
rpn_base = base_x[b].unsqueeze(0)
#out_class : args.n_roi // 2 , # no of class
Y11 = Y1[pos]
Y22 = Y2[pos]
else:
ind = torch.cat([pos,neg])
rois = X2[ind]
rpn_base = base_x[b].unsqueeze(0)
#out_class: args.n_roi , # no of class
Y11 = Y1[ind]
Y22 = Y2[ind]
# IF YOU ARE NOT SEEING THESE SHAPES THEN SOMETHING IS WRONG
# Y11.shape = 20,8
# Y22.shape = 20,56
# out_class.shape = 20,8
# out_regr.shape = 20,56
# rois.shape = 20, 4
# rpn_base.shape = torch.Size([1, 2048, 50, 38])
count_class += 1
rois = Variable(rois).to(device=model_classifier_cuda)
out_class , out_regr = model_classifier(base_x = rpn_base , rois= rois )
# torch.Size([5, 2048, 7, 7]) torch.Size([5, 4])
l3 = class_loss_cls(y_true=Y11, y_pred=out_class , lambda_cls_class=args.lambda_cls_class)
l4 = class_loss_regr(y_true=Y22, y_pred= out_regr , lambda_cls_regr= args.lambda_cls_regr)
regr_class_loss += l4.item()
class_class_loss += l3.item()
loss = l3 + l4
total_class_loss += loss.item()
optimizer_classifier.zero_grad()
loss.backward()
optimizer_classifier.step()
if count_class % args.display_class == 0 :
if count_class == 0 :
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {}'.format(i,b,j,0,0,0))
else:
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i,b,j, class_class_loss / count_class, regr_class_loss / count_class ,total_class_loss/ count_class ))
if i % args.display_rpn == 0 :
if len(rpn_accuracy_rpn_monitor) == 0 :
print('[RPN] RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
print('[RPN] Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('[RPN] RPN Ex: {}-th RPN Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i ,class_rpn_loss / count_rpn, regr_rpn_loss / count_rpn ,total_rpn_loss/ count_rpn ))
print("-- END OF EPOCH -- {}".format(epoch))
print("------------------------------" )
print('[RPN] RPN Ex: {}-th RPN Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i ,class_rpn_loss / count_rpn, regr_rpn_loss / count_rpn ,total_rpn_loss/ count_rpn ))
if count_class == 0 :
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {}'.format(i,b,j,0,0,0))
else:
print('[Classifier] RPN Ex: {}-th ,Batch : {}, Anchor Box: {}-th, Classifier Model Classification loss: {} Regression loss: {} Total Loss: {} '.format(i,b,j, class_class_loss / count_class, regr_class_loss / count_class ,total_class_loss/ count_class ))
if len(rpn_accuracy_rpn_monitor) == 0 :
print('[RPN] RPN is not producing bounding boxes that overlap the ground truth boxes. Check RPN settings or keep training.')
else:
mean_overlapping_bboxes = float(sum(rpn_accuracy_rpn_monitor))/len(rpn_accuracy_rpn_monitor)
print('[RPN] Mean number of bounding boxes from RPN overlapping ground truth boxes: {}'.format(mean_overlapping_bboxes))
print('Total Loss {}'.format( total_class_loss/ count_class + total_rpn_loss/ count_rpn ))
print("------------------------------" )
########################################################################################################################################################################################################################
# Loss
########################################################################################################################################################################################################################
from loss import rpn_loss_regr, rpn_loss_cls_fixed_num
import torch
y_rpn_regr = torch.rand(1, 10, 20, 36)
pred = torch.rand(1, 10, 20, 36)
y_is_box_label = torch.rand(1, 10, 20, 9)
y_is_box_label = (y_is_box_label > 0.66).float() * 1 + (y_is_box_label <
0.33).float() * -1
l1 = rpn_loss_regr(y_true=y_rpn_regr,
y_pred=pred,
y_is_box_label=y_is_box_label)
pred = torch.rand(1, 10, 20, 9)
l2 = rpn_loss_cls_fixed_num(y_pred=pred, y_is_box_label=y_is_box_label)
########################################################################################################################################################################################################################
# default_anchors
########################################################################################################################################################################################################################
import math
from tools import default_anchors
import torch
height = 800
width = 600