Is loading in eager TensorFlow broken right now? - python-3.x

Question

Weights in classes inheriting from tf.keras.Model seem unable to load at the moment. I am unable to load the weights from Example() outside of the class using checkpointing, so I tried to do it within, which by all accounts should work. Its able to save the weights, as it can when just saving Example(), however it still can't load them. This is my model code:
class Example(tf.keras.Model):
def __init__(self, cfg):
super(Example, self).__init__()
self.model = tf.keras.Sequential([
........layers.......
])
# Create saver
self.save_path = cfg.save_dir + cfg.extension
self.ckpt_prefix = self.save_path + '/ckpt'
self.saver = tf.train.Checkpoint(model=self.model)
def call(self, x_in):
x_out = self.model(x_in)
return x_out
def save(self):
self.saver.save(file_prefix=self.ckpt_prefix)
def load(self):
self.saver.restore(tf.train.latest_checkpoint(self.save_path))
And this is what I use to check if it loads:
example = Example()
if Path(self.example.save_path).is_dir():
print(self.example.weights)
print(self.example.model.weights)
self.example.load()
print(self.example.weights)
print(self.example.model.weights)
Output:
[]
[]
[]
[]
This was tested on both tensorflow 1.3 and 2.0, and I can confirm that the weights are not empty after the first batch, as well as that it is checkpointing/saving.

Answer 1

As it turns out, there are three different ways TensorFlow does checkpointing, depending on what is being checkpointed.
The checkpointed object is just a variable. This is restored immediately upon calling checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path)).
The checkpointed object is a model with input shape defined. This is also restored immediately.
The checkpointed object is a model without input shape defined. This is where the behaviour changes, as TensorFlow does a "delayed" restore, and will NOT restore the model weights until input is passed to the model.
Here is an example:
import os
import tensorflow as tf
import numpy as np
# Disable logging
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
tf.logging.set_verbosity(tf.logging.ERROR)
tf.enable_eager_execution()
# Create model
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(256, 3, padding="same"),
tf.keras.layers.Conv2D(3, 3, padding="same")
])
print("Are weights empty before training?", model.weights == [])
# Create optim, checkpoint
optimizer = tf.train.AdamOptimizer(0.001)
checkpoint = tf.train.Checkpoint(model=model)
# Make fake data
img = np.random.uniform(0, 255, (1, 32, 32, 3)).astype(np.float32)
truth = np.random.uniform(0, 255, (1, 32, 32, 3)).astype(np.float32)
# Train
with tf.GradientTape() as tape:
logits = model(img)
loss = tf.losses.mean_squared_error(truth, logits)
# Compute/apply gradients
grads = tape.gradient(loss, model.trainable_weights)
grads_and_vars = zip(grads, model.trainable_weights)
optimizer.apply_gradients(grads_and_vars)
# Save model
checkpoint_path = './ckpt/'
checkpoint.save('./ckpt/')
# Check if weights update
print("Are weights empty after training?", model.weights == [])
# Reset model
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(256, 3, padding="same"),
tf.keras.layers.Conv2D(3, 3, padding="same")
])
print("Are weights empty when resetting model?", model.weights == [])
# Update checkpoint pointer
checkpoint = tf.train.Checkpoint(model=model)
# Restore values from the checkpoint
status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_path))
# This next line is REQUIRED to restore
#model(img)
print("Are weights empty after restoring from checkpoint?", model.weights == [])
print(status)
status.assert_existing_objects_matched()
status.assert_consumed()
With output:
Are weights empty before training? True
Are weights empty after training? False
Are weights empty when resetting model? True
Are weights empty after restoring from checkpoint? True
<tensorflow.python.training.checkpointable.util.CheckpointLoadStatus object at 0x7f6256b4ddd8>
Traceback (most recent call last):
File "test.py", line 58, in <module>
status.assert_consumed()
File "/home/jpatts/.local/lib/python3.6/site-packages/tensorflow/python/training/checkpointable/util.py", line 1013, in assert_consumed
raise AssertionError("Unresolved object in checkpoint: %s" % (node,))
AssertionError: Unresolved object in checkpoint: attributes {
name: "VARIABLE_VALUE"
full_name: "sequential/conv2d/kernel"
checkpoint_key: "model/layer-0/kernel/.ATTRIBUTES/VARIABLE_VALUE"
}
However, uncommenting the line model(img) will produce the following output:
Are weights empty before training? True
Are weights empty after training? False
Are weights empty when resetting model? True
Are weights empty after restoring from checkpoint? False
<tensorflow.python.training.checkpointable.util.CheckpointLoadStatus object at 0x7ff62320fe48>
So input data needs to be passed to properly restore a shape invariant model.
References:
https://www.tensorflow.org/alpha/guide/checkpoints#delayed_restorations
https://github.com/tensorflow/tensorflow/issues/27937