I'm in the process of adapting my model to TensorFlow's estimator API.
I recently asked a question regarding early stopping based on validation data where in addition to early stopping, the best model at this point should be exported.
It seems that my understanding of what a model export is and what a checkpoint is is not complete.
Checkpoints are made automatically. From my understanding, the checkpoints are sufficient for the estimator to start "warm" - either using so per-trained weights or weights prior to an error (e.g. if you experienced a power outage).
What is nice about checkpoints is that I do not have to write any code besides what is necessary for a custom estimator (namely, input_fn and model_fn).
While, given an initialized estimator, one can just call its train method to train the model, in practice this method is rather lackluster. Often one would like to do several things:
compare the network periodically to a validation dataset to ensure you are not over-fitting
stop the training early if over-fitting occurs
save the best model whenever the network finishes (either by hitting the specified number of training steps or by the early stopping criteria).
To someone new to the "high level" estimator API, a lot of low level expertise seems to be required (e.g. for the input_fn) as how one could get the estimator to do this is not straight forward.
By some light code reworking #1 can be achieved by using tf.estimator.TrainSpec and tf.estimator.EvalSpec with tf.estimator.train_and_evaluate.
In the previous question user #GPhilo clarifies how #2 can be achieved by using a semi-unintuitive function from the tf.contrib:
tf.contrib.estimator.stop_if_no_decrease_hook(my_estimator,'my_metric_to_monitor', 10000)
(unintuitive as "the early stopping is not triggered according to the number of non-improving evaluations, but to the number of non-improving evals in a certain step range").
#GPhilo - noting that it is unrelated to #2 - also answered how to do #3 (as requested in the original post). Yet, I do not understand what an input_serving_fn is, why it is needed, or how to make it.
This is further confusing to me as no such function is needed to make checkpoints, or for the estimator to start "warm" from the checkpoint.
So my questions are:
what is the difference between a checkpoint and an exported best model?
what exactly is a serving input receiver function and how to write one? (I have spent a bit of time reading over the tensorflow docs and do not find it sufficient to understand how I should write one, and why I even have to).
how can I train my estimator, save the best model, and then later load it.
To aid in answering my question I am providing this Colab document.
This self contained notebook produces some dummy data, saves it in TF Records, has a very simple custom estimator via model_fn and trains this model with an input_fn that uses the TF Record files. Thus it should be sufficient for someone to explain to me what placeholders I need to make for the input serving receiver function and and how I can accomplish #3.
Update
#GPhilo foremost I can not understate my appreciation for you thoughtful consideration and care in aiding me (and hopefully others) understand this matter.
My “goal” (motivating me to ask this question) is to try and build a reusable framework for training networks so I can just pass a different build_fn and go (plus have the quality of life features of exported model, early stopping, etc).
An updated (based off your answers) Colab can be found here.
After several readings of your answer, I have found now some more confusion:
1.
the way you provide input to the inference model is different than the one you use for the training
Why? To my understanding the data input pipeline is not:
load raw —> process —> feed to model
But rather:
Load raw —> pre process —> store (perhaps as tf records)
# data processing has nothing to do with feeding data to the model?
Load processed —> feed to model
In other words, it is my understanding (perhaps wrongly) that the point of a tf Example / SequenceExample is to store a complete singular datum entity ready to go - no other processing needed other than reading from the TFRecord file.
Thus there can be a difference between the training / evaluation input_fn and the inference one (e.g. reading from file vs eager / interactive evaluation of in memory), but the data format is the same (except for inference you might want to feed only 1 example rather than a batch…)
I agree that the “input pipeline is not part of the model itself”. However, in my mind, and I am apparently wrong in thinking so, with the estimator I should be able to feed it a batch for training and a single example (or batch) for inference.
An aside: “When evaluating, you don't need the gradients and you need a different input function. “, the only difference (at least in my case) is the files from which you reading?
I am familiar with that TF Guide, but I have not found it useful because it is unclear to me what placeholders I need to add and what additional ops needed to be added to convert the data.
What if I train my model with records and want to inference with just the dense tensors?
Tangentially, I find the example in the linked guide subpar, given the tf record interface requires the user to define multiple times how to write to / extract features from a tf record file in different contexts. Further, given that the TF team has explicitly stated they have little interest in documenting tf records, any documentation built on top of it, to me, is therefore equally unenlightening.
Regarding tf.estimator.export.build_raw_serving_input_receiver_fn.
What is the placeholder called? Input? Could you perhaps show the analog of tf.estimator.export.build_raw_serving_input_receiver_fn by writing the equivalent serving_input_receiver_fn
Regarding your example serving_input_receiver_fn with the input images. How do you know to call features ‘images’ and the receiver tensor ‘input_data’ ? Is that (the latter) standard?
How to name an export with signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY.
What is the difference between a checkpoint and an exported best model?
A checkpoint is, at its minimum, a file containing the values of all the variables of a specific graph taken at a specific time point.
By specific graph I mean that when loading back your checkpoint, what TensorFlow does is loop through all the variables defined in your graph (the one in the session you're running) and search for a variable in the checkpoint file that has the same name as the one in the graph. For resuming training, this is ideal because your graph will always look the same between restarts.
An exported model serves a different purpose. The idea of an exported model is that, once you're done training, you want to get something you can use for inference that doesn't contain all the (heavy) parts that are specific to training (some examples: gradient computation, global step variable, input pipeline, ...).
Moreover, and his is the key point, typically the way you provide input to the inference model is different than the one you use for the training. For training, you have an input pipeline that loads, preprocess and feeds data to your network. This input pipeline is not part of the model itself and may have to be altered for inference. This is a key point when operating with Estimators.
Why do I need a serving input receiver function?
To answer this I'll take first a step back. Why do we need input functions at all ad what are they? TF's Estimators, while perhaps not as intuitive as other ways to model networks, have a great advantage: they clearly separate between model logic and input processing logic by means of input functions and model functions.
A model lives in 3 different phases: Training, Evaluation and Inference. For the most common use-cases (or at least, all I can think of at the moment), the graph running in TF will be different in all these phases. The graph is the combination of input preprocessing, model and all the machinery necessary to run the model in the current phase.
A few examples to hopefully clarify further: When training, you need gradients to update the weights, an optimizer that runs the training step, metrics of all kinds to monitor how things are going, an input pipeline that grabs data from the training set, etc. When evaluating, you don't need the gradients and you need a different input function. When you are inferencing, all you need is the forward part of the model and again the input function will be different (no tf.data.* stuff but typically just a placeholder).
Each of these phases in Estimators has its own input function. You're familiar with the training and evaluation ones, the inference one is simply your serving input receiver function. In TF lingo, "serving" is the process of packing a trained model and using it for inference (there's a whole TensorFlow serving system for large-scale operation but that's beyond this question and you most likely won't need it anyhow).
Time to quote a TF guide on the topic:
During training, an input_fn() ingests data and prepares it for use by
the model. At serving time, similarly, a serving_input_receiver_fn()
accepts inference requests and prepares them for the model. This
function has the following purposes:
To add placeholders to the graph that the serving system will feed
with inference requests.
To add any additional ops needed to convert
data from the input format into the feature Tensors expected by the
model.
Now, the serving input function specification depends on how you plan of sending input to your graph.
If you're going to pack the data in a (serialized) tf.Example (which is similar to one of the records in your TFRecord files), your serving input function will have a string placeholder (that's for the serialized bytes for the example) and will need a specification of how to interpret the example in order to extract its data. If this is the way you want to go I invite you to have a look at the example in the linked guide above, it essentially shows how you setup the specification of how to interpret the example and parse it to obtain the input data.
If, instead, you're planning on directly feeding input to the first layer of your network you still need to define a serving input function, but this time it will only contain a placeholder that will be plugged directly into the network. TF offers a function that does just that: tf.estimator.export.build_raw_serving_input_receiver_fn.
So, do you actually need to write your own input function? IF al you need is a placeholder, no. Just use build_raw_serving_input_receiver_fn with the appropriate parameters. IF you need fancier preprocessing, then yes, you might need to write your own. In that case, it would look something like this:
def serving_input_receiver_fn():
"""For the sake of the example, let's assume your input to the network will be a 28x28 grayscale image that you'll then preprocess as needed"""
input_images = tf.placeholder(dtype=tf.uint8,
shape=[None, 28, 28, 1],
name='input_images')
# here you do all the operations you need on the images before they can be fed to the net (e.g., normalizing, reshaping, etc). Let's assume "images" is the resulting tensor.
features = {'input_data' : images} # this is the dict that is then passed as "features" parameter to your model_fn
receiver_tensors = {'input_data': input_images} # As far as I understand this is needed to map the input to a name you can retrieve later
return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)
How can I train my estimator, save the best model, and then later load it?
Your model_fn takes the mode parameter in order for you to build conditionally the model. In your colab, you always have a optimizer, for example. This is wrong ,as it should only be there for mode == tf.estimator.ModeKeys.TRAIN.
Secondly, your build_fn has an "outputs" parameter that is meaningless. This function should represent your inference graph, take as input only the tensors you'll fed to it in the inference and return the logits/predictions.
I'll thus assume the outputs parameters is not there as the build_fn signature should be def build_fn(inputs, params).
Moreover, you define your model_fn to take features as a tensor. While this can be done, it both limits you to having exactly one input and complicates things for the serving_fn (you can't use the canned build_raw_... but need to write your own and return a TensorServingInputReceiver instead). I'll choose the more generic solution and assume your model_fn is as follows (I omit the variable scope for brevity, add it as necessary):
def model_fn(features, labels, mode, params):
my_input = features["input_data"]
my_input.set_shape(I_SHAPE(params['batch_size']))
# output of the network
onet = build_fn(features, params)
predicted_labels = tf.nn.sigmoid(onet)
predictions = {'labels': predicted_labels, 'logits': onet}
export_outputs = { # see EstimatorSpec's docs to understand what this is and why it's necessary.
'labels': tf.estimator.export.PredictOutput(predicted_labels),
'logits': tf.estimator.export.PredictOutput(onet)
}
# NOTE: export_outputs can also be used to save models as "SavedModel"s during evaluation.
# HERE is where the common part of the graph between training, inference and evaluation stops.
if mode == tf.estimator.ModeKeys.PREDICT:
# return early and avoid adding the rest of the graph that has nothing to do with inference.
return tf.estimator.EstimatorSpec(mode=mode,
predictions=predictions,
export_outputs=export_outputs)
labels.set_shape(O_SHAPE(params['batch_size']))
# calculate loss
loss = loss_fn(onet, labels)
# add optimizer only if we're training
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.AdagradOptimizer(learning_rate=params['learning_rate'])
# some metrics used both in training and eval
mae = tf.metrics.mean_absolute_error(labels=labels, predictions=predicted_labels, name='mea_op')
mse = tf.metrics.mean_squared_error(labels=labels, predictions=predicted_labels, name='mse_op')
metrics = {'mae': mae, 'mse': mse}
tf.summary.scalar('mae', mae[1])
tf.summary.scalar('mse', mse[1])
if mode == tf.estimator.ModeKeys.EVAL:
return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics, predictions=predictions, export_outputs=export_outputs)
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, eval_metric_ops=metrics, predictions=predictions, export_outputs=export_outputs)
Now, to set up the exporting part, after your call to train_and_evaluate finished:
1) Define your serving input function:
serving_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(
{'input_data':tf.placeholder(tf.float32, [None,#YOUR_INPUT_SHAPE_HERE (without batch size)#])})
2) Export the model to some folder
est.export_savedmodel('my_directory_for_saved_models', serving_fn)
This will save the current state of the estimator to wherever you specified. If you want a specifc checkpoint, load it before calling export_savedmodel.
This will save in "my_directory_for_saved_models" a prediction graph with the trained parameters that the estimator had when you called the export function.
Finally, you might want t freeze the graph (look up freeze_graph.py) and optimize it for inference (look up optimize_for_inference.py and/or transform_graph) obtaining a frozen *.pb file you can then load and use for inference as you wish.
Edit: Adding answers to the new questions in the update
Sidenote:
My “goal” (motivating me to ask this question) is to try and build a
reusable framework for training networks so I can just pass a
different build_fn and go (plus have the quality of life features of
exported model, early stopping, etc).
By all means, if you manage, please post it on GitHub somewhere and link it to me. I've been trying to get just the same thing up and running for a while now and the results are not quite as good as I'd like them to be.
Question 1:
In other words, it is my understanding (perhaps wrongly) that the
point of a tf Example / SequenceExample is to store a complete
singular datum entity ready to go - no other processing needed other
than reading from the TFRecord file.
Actually, this is typically not the case (although, your way is in theory perfectly fine too).
You can see TFRecords as a (awfully documented) way to store a dataset in a compact way. For image datasets for example, a record typically contains the compressed image data (as in, the bytes composing a jpeg/png file), its label and some meta information. Then the input pipeline reads a record, decodes it, preprocesses it as needed and feeds it to the network. Of course, you can move the decoding and preprocessing before the generation of the TFRecord dataset and store in the examples the ready-to-feed data, but the size blowup of your dataset will be huge.
The specific preprocessing pipeline is one example what changes between phases (for example, you might have data augmentation in the training pipeline, but not in the others). Of course, there are cases in which these pipelines are the same, but in general this is not true.
About the aside:
“When evaluating, you don't need the gradients and you need a
different input function. “, the only difference (at least in my case)
is the files from which you reading?
In your case that may be. But again, assume you're using data augmentation: You need to disable it (or, better, don't have it at all) during eval and this alters your pipeline.
Question 2: What if I train my model with records and want to inference with just the dense tensors?
This is precisely why you separate the pipeline from the model.
The model takes as input a tensor and operates on it. Whether that tensor is a placeholder or is the output of a subgraph that converts it from an Example to a tensor, that's a detail that belongs to the framework, not to the model itself.
The splitting point is the model input. The model expects a tensor (or, in the more generic case, a dict of name:tensor items) as input and uses that to build its computation graph. Where that input comes from is decided by the input functions, but as long as the output of all input functions has the same interface, one can swap inputs as needed and the model will simply take whatever it gets and use it.
So, to recap, assuming you train/eval with Examples and predict with dense tensors, your train and eval input functions will set up a pipeline that reads examples from somewhere, decodes them into tensors and returns those to the model to use as inputs. Your predict input function, on the other hand, just sets up one placeholder per input of your model and returns them to the model, because it assumes you'll put in the placeholders the data ready to be fed to the network.
Question 3:
You pass the placeholder as a parameter of build_raw_serving_input_receiver_fn, so you choose its name:
tf.estimator.export.build_raw_serving_input_receiver_fn(
{'images':tf.placeholder(tf.float32, [None,28,28,1], name='input_images')})
Question 4:
There was a mistake in the code (I had mixed up two lines), the dict's key should have been input_data (I amended the code above).
The key in the dict has to be the key you use to retrieve the tensor from features in your model_fn. In model_fn the first line is:
my_input = features["input_data"]
hence the key is 'input_data'.
As per the key in receiver_tensor, I'm still not quite sure what role that one has, so my suggestion is try setting a different name than the key in features and check where the name shows up.
Question 5:
I'm not sure I understand, I'll edit this after some clarification
I have a standard pipeline that evaluates the model after training an epoch. I need resnet50 to be finetunable while training, so I instantiate like so:
resnet50_module = hub.Module("https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/1",
trainable=True, name="resnet50_finetunable", tags={"train"})
However, I read here that I should unset the tags when evaluating.
I realize that I can save the model, close the session, reset the graph, rebuild the model with the tags=None and load the weights from a checkpoint to do the eval. This seems very wasteful specially since the size of the model is huge due to resnet50, and I need to do hundreds of epochs to get good results. Is there a way to alternate between tags without this?
Thanks!
I'm afraid there is no good way to do this without going through a checkpoint.
Variables are created when hub.Module() is called, so they are tied to a particular graph version (tags={"train"} for training or the empty tag set for inference). What you describe could be read as a feature request to set that separately for each application of the module, but that doesn't exist yet (and has some ramifications).
Is checkpointing to local disk really that expensive compared to the eval you want to run? Wouldn't you want to checkpoint at times anyways, to allow resuming after a crash?
I am working on building an object detection model which I would like to create with 22 new classes (most of them are not in COCO or PETS datasets)
What I've already done is:
Prepared images with multiple labels using LabelIMG.
Decrease image size in 2 for images that are bigger than 500k
Convert XML to CSV file
Convert CSV and images to TFRecord
Using the Tensorflow sample config files I've trained with several pretrained checkpoints.
Results: SSD_Mobilenet and SSD_Inception resulted in no classes
found(loss ~10.0) while faster RCNN Inception did succeed to detect some of the
objects(loss ~0.7).
My questions are:
What is the difference between train.py from Object detection, which I used in the above, to retrain.py from image_retraining to train_image_classifier.py from Slim?
Which is better for my task? Or should I do it in a different way?
While running the train.py on FRCNN inception I found that the loss was around 0.7 and not going lower even after 100k steps. Is there any goal in terms of loss to achieve?
How do you suggest to change the config file to improve this?
I found other models for instance Inception V4 etc... which doesn't have sample config files - TF slim. Should I try them and if so how can I use them?
I am pretty new in this field and I need some support in understanding the terms and actions.
BTW: I am using GTX 1060 (GPU) for training but eval is not working in parallel so I can't get the mAP for validation. I tried to force eval for CPU but with no success.
Thanks.
1) What is the difference between train.py from Object detection, which I used in the above, to retrain.py from image_retraining to train_image_classifier.py from Slim
Ans : To what i know, none. Because train.py imports trainer.py which imports slim.learning.train(the same class which is used in train_image_classifier.py) to train.
2) Which is better for my task? Or should I do it in a different way?
Ans: The above answer answers this question too.
3) While running the train.py on FRCNN inception I found that the loss was around 0.7 and not going lower even after 100k steps. Is there any goal in terms of loss to achieve?
Ans: If you use tensorboard to visualize your results, you will find that when your classification loss graph is not changing a lot(has converged), your model is trained. Regarding the loss of 0.7, thats high after training for so many steps. Just check your pipeline config file parameters.
4) How you suggest to change the config file to improve this?
Ans: Learning rate value can be a good start
5) I found other models for instance Inception V4 etc... which doesn't have sample config files - TF slim ? Should I try them and if som how can I use them?
Ans: currently, I dont have an answer for this. but will get back to you.
(Not a complete answer, but I hope it helps in some way!)
Are your annotated objects small relative to the image size?
I had the same problems with no or few detections with SSD and found that model is very sensitive to the setting which determines the size of the box proposals (anchor generator). Here is a link with some details
Further, having an active eval job running is very important when debugging and tuning a model. TotalLoss or any of the parameters returned from the train job does not inform you of the performance of the actual model, only whether it is converging. The eval job gives you e.g. mAP which is a real measure of performance.
A simple way to force an eval job on cpu is by doing the following:
a) install a virtual environment dedicated for the eval job, instructions here
b) activate the virtual environment and install tensorflow cpu in the virtual environment (yes, you should install tensorflow again, and without gpu support)
c) start the train job as usual on your tensorflow-gpu (in whatever way you have installed it)
d) run the eval job in the virtual environment (this will force it to run on the cpu and works great! I also run tensorboard from this installation to minimise risk of interference with the train job)
Retrain is used to add a level in top of pretrained model... You can win time like this.. Useful for thousand of picture, useless for million labelised picture... Less efficient than train from skratch. There is template for config file. If thereis not config file create your own.. Look at tensorflow github explainations...