I have been training and fine tuning few models for detection task on a custom dataset,
I would like to plot relevant metrics such as mean Average Precision (taking into account the predicted bounding box location and the enclosed object's classification).
I'm using Pytorch and have started using Weights & Biases (Weights & Biases integrated with pytorch)
For avoiding inventing the wheel, I have used some files from here:
https://github.com/pytorch/vision/tree/main/references/detection
engine.py - holds the train_one_epoch() function
cocoeval.py - holds the summarize() function
Now I would like to log those metrics to a Weights & Biases,
so I'll we able to get more clear view and intuition about the fine-tuning phase,but I'm not sure where is the proper place to put the logger invocation.
can somebody please assist me?
wandb.watch()
Thank you
To log your training metrics, within the train_one_epoch function, there is a MetricLogger class which currently logs some metrics in a basic way. To add W&B and get more powerful metric logging and visualisation, you can add wandb.log in place of the MetricLogger.
wandb.log({'loss': losses_reduced, 'lr':optimizer.param_groups[0]["lr"]}
https://github.com/pytorch/vision/blob/528651a031a08f9f97cc75bd619a326387708219/references/detection/engine.py#L56
Above you mention wandb.watch. If you intend on using that to track gradients, you would call it once when the model is initialised.
https://github.com/pytorch/vision/blob/528651a031a08f9f97cc75bd619a326387708219/references/detection/train.py#L222
Related
I am currently implementing a CNN with a custom error function.
The problem I am trying to solve is physics-based, so I can calculate the maximal achievable precision, or to put it another way, I know the best possible (i.e. minimal) standard deviation I can achieve. Those best possible precisions are calculated during the generation of the training data using the Cramer-Rao-lower bound (CRLB).
Right now, my error function looks something like this (in Keras):
def customLoss(yTrue, yPred):
STD = yTrue[:, 10:20]
yTrue = yTrue[:, 0:10]
dev = K.mean(K.abs(K.abs(yTrue - yPred) - STD))
return dev
In this case, I have 10 parameters, so I want to estimate with 10 CRLB's. I put the CRLB's in the target vector just to be able to handle the in the error function.
To my question. This method works, but it is not what I want. The problem is that the error is calculated considering a single prediction of the network, but to be correct the network would have to predict the same dataset/batch multiple times. By doing that I would be able to see the standard deviation of the prediction and use that to calculate the error (I'm using a Bayesian CNN).
Has someone an idea how to implement such a function in Keras or Tensorflow (I would also not mind switching to PyTorch)?
Reference to https://dzone.com/articles/machine-learning-with-h2o-hands-on-guide-for-data
I was able to follow the example and plotted the ROC and AUC curve but when I did the same thing with my data, I have "SCORING HISTORY - DEVIANCE" instead of "SCORING HISTORY - LOGLOSS" and my "ROC CURVE - TRAINING METRICS" did not appear.
My dataset has 2 classes, 0, 1 instead of yes and no from the example. What determines whether the ROC curve will be portable?
Updated 10 Jul 2019 reference to Maurever's reply:
I have "convert to enum" for my response "label" and change distribution to "bernoulli" but I still can't plot ROC.
Update: For future users who has the same encounter as me. Seems like h2o cannot use response with class as "0" and "1" even if you convert to enum. My problem is solved by relabel of 0 and 1 to "Normal" and "Malicious" and the ROC curve will be plot.
My dataset has 2 classes, 0, 1 instead of yes and no from the example.
What will have happened is that H2O has decided that was a numeric column, rather than a categorical (aka factor) column. The fix is simply to use as.factor() on that column. Do this just after importing the data, but before using it to build a model.
Then it will know to build a classification model, not a regression model, and you will get the metrics you expect to see.
Here is an example: https://stackoverflow.com/a/41441578/841830 (or search for as.factor in the H2O docs, where you will find loads more)
In Flow, you first upload the file, then you click parse files, and it shows a list of the columns. The categorical/factor columns are called "Enum" there (yes, it is confusing). Your column is presumably showing as numeric, so go to the dropdown box and change it to be enum. Then start the parse. Then, after that, build your model.
You have to change the type of response column to "enum",
or change distribution in the model parameters setting to "bernoulli" (binomial classification).
In the image, you share there is "Enum(3)" so the result is multinomial classification not binomial. So you should see Confusion Matrix but not the ROC curve. The ROC curve is able to display only for 2-class classification (more about ROC here: https://towardsdatascience.com/understanding-auc-roc-curve-68b2303cc9c5).
You should see something like this (if you set distribution to "multinomial"):
My problem was fixed by changing the target variable from 0s and 1s to False and True values.
I'm setting up the new Tensorflow Object Detection API to find small objects in large areas of satellite imagery. It works quite well - it finds all 10 objects I want, but I also get 50-100 false positives [things that look a little like the target object, but aren't].
I'm using the sample config from the 'pets' tutorial, to fine-tune the faster_rcnn_resnet101_coco model they offer. I've started small, with only 100 training examples of my objects (just 1 class). 50 examples in my validation set. Each example is a 200x200 pixel image with a labeled object (~40x40) in the center. I train until my precision & loss curves plateau.
I'm relatively new to using deep learning for object detection. What is the best strategy to increase my precision? e.g. Hard-negative mining? Increase my training dataset size? I've yet to try the most accurate model they offer faster_rcnn_inception_resnet_v2_atrous_coco as i'd like to maintain some speed, but will do so if needed.
Hard-negative mining seems to be a logical step. If you agree, how do I implement it w.r.t setting up the tfrecord file for my training dataset? Let's say I make 200x200 images for each of the 50-100 false positives:
Do I create 'annotation' xml files for each, with no 'object' element?
...or do I label these hard negatives as a second class?
If I then have 100 negatives to 100 positives in my training set - is that a healthy ratio? How many negatives can I include?
I've revisited this topic recently in my work and thought I'd update with my current learnings for any who visit in the future.
The topic appeared on Tensorflow's Models repo issue tracker. SSD allows you to set the ratio of how many negative:postive examples to mine (max_negatives_per_positive: 3), but you can also set a minimum number for images with no postives (min_negatives_per_image: 3). Both of these are defined in the model-ssd-loss config section.
That said, I don't see the same option in Faster-RCNN's model configuration. It's mentioned in the issue that models/research/object_detection/core/balanced_positive_negative_sampler.py contains the code used for Faster-RCNN.
One other option discussed in the issue is creating a second class specifically for lookalikes. During training, the model will attempt to learn class differences which should help serve your purpose.
Lastly, I came across this article on Filter Amplifier Networks (FAN) that may be informative for your work on aerial imagery.
===================================================================
The following paper describes hard negative mining for the same purpose you describe:
Training Region-based Object Detectors with Online Hard Example Mining
In section 3.1 they describe using a foreground and background class:
Background RoIs. A region is labeled background (bg) if its maximum
IoU with ground truth is in the interval [bg lo, 0.5). A lower
threshold of bg lo = 0.1 is used by both FRCN and SPPnet, and is
hypothesized in [14] to crudely approximate hard negative mining; the
assumption is that regions with some overlap with the ground truth are
more likely to be the confusing or hard ones. We show in Section 5.4
that although this heuristic helps convergence and detection accuracy,
it is suboptimal because it ignores some infrequent, but important,
difficult background regions. Our method removes the bg lo threshold.
In fact this paper is referenced and its ideas are used in Tensorflow's object detection losses.py code for hard mining:
class HardExampleMiner(object):
"""Hard example mining for regions in a list of images.
Implements hard example mining to select a subset of regions to be
back-propagated. For each image, selects the regions with highest losses,
subject to the condition that a newly selected region cannot have
an IOU > iou_threshold with any of the previously selected regions.
This can be achieved by re-using a greedy non-maximum suppression algorithm.
A constraint on the number of negatives mined per positive region can also be
enforced.
Reference papers: "Training Region-based Object Detectors with Online
Hard Example Mining" (CVPR 2016) by Srivastava et al., and
"SSD: Single Shot MultiBox Detector" (ECCV 2016) by Liu et al.
"""
Based on your model config file, the HardMinerObject is returned by losses_builder.py in this bit of code:
def build_hard_example_miner(config,
classification_weight,
localization_weight):
"""Builds hard example miner based on the config.
Args:
config: A losses_pb2.HardExampleMiner object.
classification_weight: Classification loss weight.
localization_weight: Localization loss weight.
Returns:
Hard example miner.
"""
loss_type = None
if config.loss_type == losses_pb2.HardExampleMiner.BOTH:
loss_type = 'both'
if config.loss_type == losses_pb2.HardExampleMiner.CLASSIFICATION:
loss_type = 'cls'
if config.loss_type == losses_pb2.HardExampleMiner.LOCALIZATION:
loss_type = 'loc'
max_negatives_per_positive = None
num_hard_examples = None
if config.max_negatives_per_positive > 0:
max_negatives_per_positive = config.max_negatives_per_positive
if config.num_hard_examples > 0:
num_hard_examples = config.num_hard_examples
hard_example_miner = losses.HardExampleMiner(
num_hard_examples=num_hard_examples,
iou_threshold=config.iou_threshold,
loss_type=loss_type,
cls_loss_weight=classification_weight,
loc_loss_weight=localization_weight,
max_negatives_per_positive=max_negatives_per_positive,
min_negatives_per_image=config.min_negatives_per_image)
return hard_example_miner
which is returned by model_builder.py and called by train.py. So basically, it seems to me that simply generating your true positive labels (with a tool like LabelImg or RectLabel) should be enough for the train algorithm to find hard negatives within the same images. The related question gives an excellent walkthrough.
In the event you want to feed in data that has no true positives (i.e. nothing should be classified in the image), just add the negative image to your tfrecord with no bounding boxes.
I think I was passing through the same or close scenario and it's worth it to share with you.
I managed to solve it by passing images without annotations to the trainer.
On my scenario I'm building a project to detect assembly failures from my client's products, at real time.
I successfully achieved very robust results (for production env) by using detection+classification for components that has explicity a negative pattern (e.g. a screw that has screw on/off(just the hole)) and only detection for things that doesn't has the negative pattens (e.g. a tape that can be placed anywhere).
On the system it's mandatory that the user record 2 videos, one containing the positive scenario and another containing the negative (or the n videos, containing n patterns of positive and negative so the algorithm can generalize).
After a while testing I found out that if I register to detected only tape the detector was giving very confident (0.999) false positive detections of tape. It was learning the pattern where the tape was inserted instead of the tape itself. When I had another component (like a screw on it's negative format) I was passing the negative pattern of tape without being explicitly aware of it, so the FPs didn't happen.
So I found out that, in this scenario, I had to necessarily pass the images without tape so it could differentiate between tape and no-tape.
I considered two alternatives to experiment and try to solve this behavior:
Train passing an considerable amount of images that doesn't has any annotation (10% of all my negative samples) along with all images that I have real annotations.
On the images that I don't have annotation I create a dummy annotation with a dummy label so I could force the detector to train with that image (thus learning the no-tape pattern). Later on, when get the dummy predictions, just ignore them.
Concluded that both alternatives worked perfectly on my scenario.
The training loss got a little messy but the predictions work with robustness for my very controlled scenario (the system's camera has its own box and illumination to decrease variables).
I had to make two little modifications for the first alternative to work:
All images that didn't had any annotation I passed a dummy annotation (class=None, xmin/ymin/xmax/ymax=-1)
When generating the tfrecord files I use this information (xmin == -1, in this case) to add an empty list for the sample:
def create_tf_example(group, path, label_map):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
if not pd.isnull(row.xmin):
if not row.xmin == -1:
xmins.append(row['xmin'] / width)
xmaxs.append(row['xmax'] / width)
ymins.append(row['ymin'] / height)
ymaxs.append(row['ymax'] / height)
classes_text.append(row['class'].encode('utf8'))
classes.append(label_map[row['class']])
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
Part of the traning progress:
Currently I'm using tensorflow object detection along with tensorflow==1.15, using faster_rcnn_resnet101_coco.config.
Hope it will solve someone's problem as I didn't found any solution on the internet. I read a lot of people telling that faster_rcnn is not adapted for negative training for FPs reduction but my tests proved the opposite.
I've been working a bit with neural networks and I'm interested on implementing a spiking neuron model.
I've read a fair amount of tutorials but most of them seem to be about generating pulses and I haven't found any application of it on a given input train.
Say for example I got input train:
Input[0] = [0,0,0,1,0,0,1,1]
It enters the Izhikevich neuron, does the input multiply a weight or only makes use of the parameters a, b, c and d?
Izhikevich equations are:
v[n+1] = 0.04*v[n]^2 + 5*v[n] + 140 - u[n] + I
u[n+1] = a*(b*v[n] - u[n])
where v[n] is input voltage and u[n] is a general recovery variable.
Are there any texts on implementations of Izhikevich or similar spiking neuron models on a practical problem? I'm trying to understand how information is encoded on this models but it looks different from what's done with standard second generation neurons. The only tutorial I've found where it deals with a spiking train and a set of weights is [1] but I haven't seen the same with Izhikevich.
[1] https://msdn.microsoft.com/en-us/magazine/mt422587.aspx
The plain Izhikevich model by itself, does not include weights.
The two equations you mentioned, model the membrane potential (v[]) over time of a point neuron. To use weights, you could connect two or more of such cells with synapses.
Each synapse could include some sort spike detection mechanism on the source cell (pre-synaptic), and a synaptic current mechanism in the target (post-synaptic) cell side. That synaptic current could then be multiplied by a weight term, and then become part of the I term (in the 1st equation above) for the target cell.
As a very simple example of a two cell network, at every time step, you could check if pre- cell v is above (say) 0 mV. If so, inject (say) 0.01 pA * weightPrePost into the post- cell. weightPrePost would range from 0 to 1, and could be modified in response to things like firing rate, or Hebbian-like spike synchrony like in STDP.
With multiple synaptic currents going into a cell, you could devise various schemes how to sum them. The simplest one would be just a simple sum, more complicated ones could include things like distance and dendrite diameters (e.g. simulated neural morphology).
This chapter is a nice introduction to other ways to model synapses: Modelling
Synaptic Transmission
I use function predict in opencv to classify my gestures.
svm.load("train.xml");
float ret = svm.predict(mat);//mat is my feature vector
I defined 5 labels (1.0,2.0,3.0,4.0,5.0), but in fact the value of ret are (0.521220207,-0.247173533,-0.127723947······)
So I am confused about it. As Opencv official document, the function returns a class label (classification) in my case.
update: I don't still know why to appear this result. But I choose new features to train models and the return value of predict function is what I defined during train phase (e.g. 1 or 2 or 3 or etc).
During the training of an SVM you assign a label to each class of training data.
When you classify a sample the returned result will match up with one of these labels telling you which class the sample is predicted to fall into.
There's some more documentation here which might help:
http://docs.opencv.org/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html
With Support Vector Machines (SVM) you have a training function and a prediction one. The training function is to train your data and save those informations on an xml file (it facilitates the prediction process in case you use a huge number of training data and you must do the prediction function in another project).
Example : 20 images per class in your case : 20*5=100 training images,each image is associated with a label of its appropriate class and all these informations are stocked in train.xml)
For the prediction function , it tells you what's label to assign to your test image according to your training DATA (the hole work you did in training process). Your prediction results might be good and might be bad , it's all about your training data I think.
If you want try to calculate the error rate for your classifier to see how much it can give good results or bad ones.