small lesson about problems during train my own dataset
LongxingTan opened this issue · 23 comments
thanks zzh8829 for the code sharing, really nice writing, I like it
when i use it to try training my own dataset, i have some problems, that's how i solve them.
hope this could save some time for others.
- nan loss
- nan loss, i first change the learning rate smaller
- i found that the data input labelled by vott and labelImg is different. so make sure the input box is right (without nan and the box is smaller than the width and height), and check carefully the box format is x1,y1,x2,y2, or x,y,w,h, or x1/width,y1/height,x2/width,y2/height
- loss is unbelievable large
- the first step loss is ok. but after 2nd step, the loss is very large and can't converge any more. i change the backbone part according to other repositories of yolov3. and it solves
- hard to convergence
- remove the sigmoid operator of class_prob_loss
- add the
conf_focal=tf.pow(true_obj-pred_obj , 2)as a multiplier in confidence_loss
- Resize the image by resize or pad
- I checked the process to train VOC2012. if you use the voc2012.py to save the tf-record, there is no problem. In object detection, if you resize the image with Pad, then you have to pad the labelled box at the same time. But if you use resize function in cv or tf, and the label is relative (0,1), then no necessary to adjust it.
thanks for the update, I will add these to the readme, a lot of people are having nan loss problem,
@zzh8829 I've provided another detailed explanation here on nan lose, its found almost in every nan loss issue here. Hopefully you can add it too
I added a section in read me with @LongxingTan 's insight, @AnaRhisT94 is it possible for you to make a pull request on the readme file with your detailed explanation? I am not sure which one specifically you are referring to. I really appreciate you helping other people to solve their training problems. It would be great if we can share that knowledge to everyone else too.
@antongisli for sure that would be amazing
I compiled a full tutorial at https://github.com/zzh8829/yolov3-tf2/blob/master/docs/training_voc.md on custom training. welcome to add your learning on it
@LongxingTan @zzh8829
Thanks for your insight. I'm just wondering how you came up with the idea of conf_focal? Is there any mathematical reason behind it?
I applied this idea to the code as follows and it did converge a lot faster. And we can see obj_loss was many orders of magnitude to start with and made the total loss very imbalanced. Bringing obj_loss down would probably make its contribution to the total loss more fair.
conf_focal = tf.squeeze(tf.pow(true_obj-pred_obj, 2), -1)
obj_loss = binary_crossentropy(true_obj, pred_obj) * conf_focalCan you shed some light on it?
If I'm not mistaken, this is the focal loss for gamma=2.
I'm also thinking of using focal loss instead of cross entropy, this paper - Focal Loss for Dense Object Detectio shows some improvement compared to cross entropy.
@zzh8829 I'm just wondering why you chose sigmoid for class_prob_loss? It doesn't make much sense when there are more than 2 classes for sparse_categorical_crossentropy. From my experiment, if you pass sigmoid, the trained model actually acts like a binary classification model that can only detects two strongest classes with the most training data (others are possbily ignored due to a low objectness score). I guess it might work fine the train data is equally distributed among all classes.
@nicolefinnie
Hi nicolefinnie, good to hear that it might help you a little.
what i use in my code is similar
conf_focal=tf.pow(obj_mask-tf.squeeze(tf.sigmoid(pred_obj),-1),2)
loss_obj=tf.squeeze(tf.nn.sigmoid_cross_entropy_with_logits(true_obj,pred_obj),axis=-1)
loss_obj=conf_focal*(obj_mask*loss_obj + noobj_mask*loss_obj) # batch * grid * grid * anchors_per_scale
you are right that this is focal loss for gamma=2.
i also read this paper to know that it helps to improve in detect the difficult box, and imbalance class box. To be honest, i have no idea why it help converge faster.
i guess, the coefficient is always less than 1, so it shows that loss itself is smaller, and smaller loss let it look like converge faster, actually it's not.
If you have any idea, i am happy to know that.
@nicolefinnie
Hi nicolefinnie, good to hear that it might help you a little.
what i use in my code is similarconf_focal=tf.pow(obj_mask-tf.squeeze(tf.sigmoid(pred_obj),-1),2) loss_obj=tf.squeeze(tf.nn.sigmoid_cross_entropy_with_logits(true_obj,pred_obj),axis=-1) loss_obj=conf_focal*(obj_mask*loss_obj + noobj_mask*loss_obj) # batch * grid * grid * anchors_per_scaleyou are right that this is focal loss for gamma=2.
i also read this paper to know that it helps to improve in detect the difficult box, and imbalance class box. To be honest, i have no idea why it help converge faster.
i guess, the coefficient is always less than 1, so it shows that loss itself is smaller, and smaller loss let it look like converge faster, actually it's not.
If you have any idea, i am happy to know that.
Oops, @LongxingTan thanks! I overlooked your reply. Adding sigmoid() is a good idea to redistribute the loss. I think adding the focal loss does not necessarily make the loss to converge faster, but it makes sense in your case, because the 2nd term of your loss noobj_mask*loss_obj (the loss for false positives) may be many orders of magnitude greater than the 1st term (the loss for true positives) after you redistributed your loss, so you'd make both terms to be more equal. (See my reference below)
But if we add the focal loss, the first term will get more weight and get penalized more, so adding the focal loss actually changes the focus (no puns intended) and it will focus on the loss of true positives and false positives go unpunished. However, I jumped into this conclusion from my experimental results using your loss and my loss. DL is often not explainable though we try hard to do so.
Reference:
original impl. from the paper's author, both terms are equally treated.
// calculating the best IOU, skip...
// calculate the loss of false positives, ignore true positives when it crosses the ignore threshold, equivalent to `(1-obj_mask)*ignore_mask*obj_loss` in this repo
l.delta[obj_index] = 0 - l.output[obj_index];
if (best_iou > l.ignore_thresh) {
l.delta[obj_index] = 0;
}
// calculate the loss of the true positives above the threshold, equivalent to `obj_mask*obj_loss` in this repo
if (best_iou > l.truth_thresh) {
l.delta[obj_index] = 1 - l.output[obj_index];
}@LongxingTan Is ignore_thresh mask already accounted for in noobj_mask. If not is there a reason for not using it? Thanks!
The validation losses are zeros from the first to the end iteration. The detection has no boxes into the output.jpg when I use my training model for 7 epochs (yolov3_train_7.tf)
There are so many different codes and formats out there and most are just to get a PoC.
check carefully the box format is x1,y1,x2,y2, or x,y,w,h, or x1/width,y1/height,x2/width,y2/height
What does that mean ? Which one ?
Normalized YOLO format (x,y,w,h all normalized):
dw = size[1]
dh = size[0]
x = (x1 + x2)/2.0
y = (y1 + y2)/2.0
w = x2 - x1
h = y2 - y1
x = x
w = w
y = y
h = h
output = str(x/dw) + " " + str(y/dh) + " " + str(w/dw) + " " + str(h/dh)
Or as the FAQ is saying, is it what I may understand:
w = size[1]
h = size[0]
xmin = x1/w
ymax = y1/h
xmax = x2/w
ymin = y2/h
?
Got it, it's the second one
@LongxingTan Hi, I am experiencing the problem of the validation loss exploding and then not converging properly on my custom dataset. In the first few epochs, validation loss is reasonable. Then it explodes to some large number like 2000000. You mentioned that you made changes to the backbone? Can you guide me with making those changes to see if they can help my problem?
Thank you.
Edit: I would also like to add that I successfully trained the yolov3 tiny model on my custom dataset. This problem only seems to be happening with yolov3.
yeah, it looks like the same phenomenon as my situation.
I can copy my code here, could you please check the difference, to be honest, i maybe change a lot little by little, so don't remember exactly i changed where to solve this. hope it may give you some hint to solve your problem,
import tensorflow as tf
def conv_block(inputs, kernel_size, filters, strides=1, padding='same', downsample=False, activate=True, bn=True):
# basic block
if downsample:
inputs = tf.keras.layers.ZeroPadding2D(((1, 0), (1, 0)))(inputs)
padding = 'valid'
strides = 2
else:
strides = 1
padding = 'same'
conv = tf.keras.layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding,
use_bias=not bn, kernel_regularizer=tf.keras.regularizers.l2(0.0005),
kernel_initializer=tf.random_normal_initializer(stddev=0.01),
bias_initializer=tf.constant_initializer(0.))(inputs)
if bn:
conv = BatchNormalization()(conv)
if activate:
conv = tf.nn.leaky_relu(conv, alpha=0.1)
return conv
class BatchNormalization(tf.keras.layers.BatchNormalization):
"""
tf.keras.layers.BatchNormalization doesn't work very well for transfer learning,
"Frozen state" and "inference mode" are two separate concepts.
`layer.trainable = False` is to freeze the layer, so the layer will use
stored moving `var` and `mean` in the "inference mode", and both `gama`
and `beta` will not be updated !
"""
def call(self, x, training=False):
if not training:
training = tf.constant(False)
training = tf.logical_and(training, self.trainable)
return super().call(x, training)
def residual_block(inputs, filter_num1, filter_num2):
shortcut = inputs
conv = conv_block(inputs, 1, filter_num1)
conv = conv_block(conv, 3, filter_num2)
residual_output = shortcut + conv
return residual_output
def upsample(inputs):
return tf.image.resize(inputs, (inputs.shape[1] * 2, inputs.shape[2] * 2), method='nearest')
import tensorflow as tf
from model.backbones.common import *
class DarkNet(object):
def __init__(self):
pass
def __call__(self,name):
x=inputs = tf.keras.layers.Input([416, 416, 3])
x=conv_block(x, 3, 32) # => batch_size * 416 * 416 * 32
x=conv_block(x, 3, 64, downsample=True) # => batch_size * 208 * 208 * 64
for _ in range(1):
x=residual_block(x,32,64) # => batch_size * 208 * 208 * 64
x=conv_block(x,3,128,downsample=True) # => batch_size * 104 * 104 * 128
for _ in range(2):
x=residual_block(x,64,128) # => batch_size * 104 * 104 * 128
x=conv_block(x,3,256,downsample=True) # => batch_size * 52 * 52 * 256
for _ in range(8):
x=residual_block(x,128,256) # => batch_size * 52 * 52 * 256
route_1=x # => batch_size * 52 *52 * 256
x=conv_block(x,3,512,downsample=True) # => batch_size * 26 * 26 * 512
for _ in range(8):
x=residual_block(x,256,512)
route_2=x # => batch_size * 26 * 26 * 512
x=conv_block(x,3,1024,downsample=True)
for _ in range(4):
x=residual_block(x,512,1024)
route_3=x # => batch_size * 13 * 13 * 1024
return tf.keras.Model(inputs,(route_1,route_2,route_3),name=name)
import numpy as np
import tensorflow as tf
from model.backbones.darknet53 import DarkNet
from model.backbones.common import conv_block,upsample
# https://github.com/HKU-ICRA/YoloV3_TF2.0/blob/master/yolo.py
class YoloPreOut(object):
def __init__(self):
pass
def __call__(self, input, skip_filters=None,ratio=1,name=None):
if isinstance(input,tuple):
model_inputs=tf.keras.layers.Input(input[0].shape[1:]),tf.keras.layers.Input(input[1].shape[1:])
x,skip=model_inputs
x=conv_block(x,1,skip_filters)
x=upsample(x)
x=tf.concat([x,skip],axis=-1)
else:
x=model_inputs=tf.keras.layers.Input(input.shape[1:])
x=conv_block(x,1,512//ratio)
x=conv_block(x,3,1024//ratio)
x=conv_block(x,1,512//ratio)
x=conv_block(x,3,1024//ratio)
x=conv_block(x,1,512//ratio)
return tf.keras.Model(model_inputs,x,name=name)(input)
class YoloOutput(object):
def __init__(self):
pass
def __call__(self,input,kernel_sizes,filters,grid_channels,name):
x=tf.keras.layers.Input(input.shape[1:])
output=conv_block(x,kernel_sizes,filters)
output=conv_block(output,1,3*grid_channels,activate=False,bn=False)
output=tf.reshape(output,(-1,output.shape[1],output.shape[2],3,grid_channels))
return tf.keras.Model(x,output,name=name)(input)
class YoloDecode(object):
def __init__(self):
pass
def __call__(self, anchors, image_input_size, num_classes,inputs_shape, name='decode'):
'''
x: batch * grid_size * grid_size * 3 * 5+num_classes Todo: format is different
'''
x=tf.keras.layers.Input(inputs_shape)
box_xy, box_wh, objectness, class_probs = tf.split(x, (2, 2, 1, num_classes), axis=-1)
box_xy = tf.sigmoid(box_xy)
objectness = tf.sigmoid(objectness)
class_probs = tf.sigmoid(class_probs)
grid_size = inputs_shape[0]
grid_xy = tf.meshgrid(tf.range(grid_size), tf.range(grid_size))
grid_xy = tf.cast(tf.expand_dims(tf.stack(grid_xy, axis=-1), axis=2), tf.float32)
strides = tf.cast(image_input_size / grid_size, tf.float32)
box_xy = (box_xy + grid_xy) * strides /image_input_size
box_wh = tf.exp(box_wh) * anchors / image_input_size
box_x1y1 = box_xy - box_wh / 2
box_x2y2 = box_xy + box_wh / 2
bbox = tf.concat([box_x1y1, box_x2y2], axis=-1)
outputs=tf.concat([bbox, objectness, class_probs], axis=-1)
return tf.keras.Model(x,outputs,name=name)
def yolo_nms(outputs,num_classes,iou_threshold,score_threshold,nms_max_bbox):
# boxes, conf, type
b, c, t = [], [], []
for o in outputs:
b.append(tf.reshape(o[...,0:4], [tf.shape(o)[0], -1, 4]))
c.append(tf.reshape(o[...,4:5], (tf.shape(o)[0], -1, 1)))
t.append(tf.reshape(o[...,5:], (tf.shape(o)[0], -1, num_classes)))
bbox = tf.concat(b, axis=1)
confidence = tf.concat(c, axis=1)
class_probs = tf.concat(t, axis=1)
if num_classes>1:
scores = confidence * class_probs
else:
scores = confidence
boxes, scores, classes, valid_detections = tf.image.combined_non_max_suppression(
boxes=tf.reshape(bbox, (tf.shape(bbox)[0], -1, 1, 4)),
scores=tf.reshape(scores, (tf.shape(scores)[0], -1, tf.shape(scores)[-1])),
max_output_size_per_class=nms_max_bbox,
max_total_size=nms_max_bbox,
iou_threshold=iou_threshold,
score_threshold=score_threshold
)
return boxes, scores, classes, valid_detections
class YoloNet(object):
def __init__(self,params):
self.params=params
self.darknet=DarkNet()
self.yolo_preout=YoloPreOut()
self.yolo_output=YoloOutput()
self.decode=YoloDecode()
self.anchors = np.array(params['anchors'], dtype=np.float32).reshape(9, 2)
self.anchors_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
def __call__(self,training=False):
x = tf.keras.layers.Input([416, 416, 3])
x_52, x_26, x_13 = self.darknet(name='yolo_darknet')(x)
grid_channels = self.params['num_classes'] + 5
out_13 = self.yolo_preout(input=x_13, name='preout_13') # => batch_size * 13 * 13 * 512
output_13 = self.yolo_output(input=out_13,
kernel_sizes=3,
filters=1024,
grid_channels=grid_channels,
name='output_13')
# => batch_size * 26 * 26 *256
out_26 = self.yolo_preout(input=(out_13, x_26), skip_filters=256, ratio=2, name='preout_26')
output_26 = self.yolo_output(input=out_26,
kernel_sizes=3,
filters=512,
grid_channels=grid_channels,
name='output_26')
# => batch * 52 * 52 * 128
out_52 = self.yolo_preout(input=(out_26, x_52), skip_filters=128, ratio=4, name='preout_52')
output_52 = self.yolo_output(input=out_52,
kernel_sizes=3,
filters=256,
grid_channels=grid_channels,
name='output_52')
output_tensors = (output_13, output_26, output_52)
if training:
return tf.keras.Model(x,output_tensors,name='yolo_net')
bbox_tensors = []
for i, feature_map in enumerate(output_tensors):
bbox_tensor = self.decode(self.anchors[self.anchors_mask[i]],
self.params['image_input_size'],
self.params['num_classes'],
feature_map.get_shape().as_list()[1:],
name="decode_{}".format(i))(feature_map)
bbox_tensors.append(bbox_tensor)
output = yolo_nms(bbox_tensors,
num_classes=self.params['num_classes'],
iou_threshold=self.params['iou_threshold'],
score_threshold=self.params['score_threshold'],
nms_max_bbox=self.params['nms_max_bbox'])
return tf.keras.Model(inputs=x, outputs=output,name='yolo_predict')
@LongxingTan Thank you so much for the code. I will look into it and try to locate the error. Also, if you have time, can you check the new issue I created #206 . It discusses the problem I am experiencing and gives a log of my training output for further inspection.
Edit: Quick question, did you get the backbone code from a single repository or did you combine them from multiple?
Thank you for your help!
hard to convergence
remove the sigmoid operator of class_prob_loss
add the conf_focal=tf.pow(true_obj-pred_obj , 2) as a multiplier in confidence_loss
@LongxingTan where exactly is class_prob_loss located and how to remove the sigmoid operator?
Where I can find confidence_loss to change the multiplier into this:
> conf_focal=tf.pow(obj_mask-tf.squeeze(tf.sigmoid(pred_obj),-1),2)
> loss_obj=tf.squeeze(tf.nn.sigmoid_cross_entropy_with_logits(true_obj,pred_obj),axis=-1)
> loss_obj=conf_focal*(obj_mask*loss_obj + noobj_mask*loss_obj) # batch * grid * grid * anchors_per_scale
- hard to convergence
remove the sigmoid operator of class_prob_loss
add the conf_focal=tf.pow(true_obj-pred_obj , 2) as a multiplier in confidence_loss@LongxingTan where exactly is class_prob_loss located and how to remove the sigmoid operator?
Where I can find confidence_loss to change the multiplier into this:> conf_focal=tf.pow(obj_mask-tf.squeeze(tf.sigmoid(pred_obj),-1),2) > loss_obj=tf.squeeze(tf.nn.sigmoid_cross_entropy_with_logits(true_obj,pred_obj),axis=-1) > loss_obj=conf_focal*(obj_mask*loss_obj + noobj_mask*loss_obj) # batch * grid * grid * anchors_per_scale
maybe like this, when parse/decode the network output to calculate the loss, we have to parse the value by anchor to the value by real coordinates. So in the decode function, there is sigmoid to change the scale of value.
But it depends if it really helps.
in this repo, you can find yolo_boxes function in models.py
box_xy = tf.sigmoid(box_xy)
objectness = tf.sigmoid(objectness)
class_probs = tf.sigmoid(class_probs)
pred_box = tf.concat((box_xy, box_wh), axis=-1) # original xywh for loss
Yololoss function in models.py
obj_loss = binary_crossentropy(true_obj, pred_obj)
obj_loss = obj_mask * obj_loss + \
(1 - obj_mask) * ignore_mask * obj_loss
# TODO: use binary_crossentropy instead
class_loss = obj_mask * sparse_categorical_crossentropy(
true_class_idx, pred_class)
the last item class_loss is the class_prob_loss.
Hi @LongxingTan, may I know what hyperparameters did you used to train the model?
I am training the model using the VOC2012 dataset.
I'm using:
learning rate: 1e-3
batch size: 8
epochs: 50
In my implementation, I have to lower down the confidence threshold to 0.1.
I'm not sure if the batch size has to be bigger, or should I train the model longer.
Many thanks in advance!
Actually i have try these code to deal the class unbalanced problem, and it work well.
first get the negative obj num and positive obj num:
obj_mask = tf.squeeze(true_obj, -1)
positive_num = tf.cast(tf.reduce_sum(obj_mask), tf.int32) + 1
negative_num = 10 * positive_num
then update the ignore_mask using the negative num:
ignore_mask = tf.cast(best_iou < ignore_thresh, tf.float32)
ignore_num = tf.cast(tf.reduce_sum(ignore_mask),tf.int32)
if ignore_num > negative_num:
neg_inds = tf.random.shuffle(tf.where(ignore_mask))[:negative_num]
neg_inds = tf.expand_dims(neg_inds, axis=1)
ones = tf.ones(tf.shape(neg_inds)[0], tf.float32)
ones = tf.expand_dims(ones, axis=1)
ignore_mask = tf.zeros_like(ignore_mask, tf.float32)
ignore_mask = tf.tensor_scatter_nd_add(ignore_mask, neg_inds, ones)
final obj_loss is:
conf_focal = tf.pow(obj_mask - tf.squeeze(pred_obj, -1), 2)
obj_loss = tf.keras.losses.binary_crossentropy(true_obj, pred_obj)
obj_loss = conf_focal * (obj_mask * obj_loss + (1 - obj_mask) * ignore_mask * obj_loss)
Hi @yyccR
I tried out your code, it helps in dealing with class imbalance issues!
May I know what is this code for? Is it a form of focal loss?
@yjwong1999
conf_focal it is the form of focal loss, and i also add the ignore_mask to balanced the positive nums and negative nums.