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# Copyright 2022 Big Vision Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Compute PSNR, currently used for colorization and superresolution."""
import functools
import big_vision.evaluators.proj.uvim.common as common
import big_vision.pp.builder as pp_builder
import jax
import jax.numpy as jnp
import numpy as np
import tensorflow as tf
class Evaluator:
"""PSNR evaluator.
`predict_fn` accepts arbitrary dictionaries of parameters and data, where
the data dictionary is produced by the `pp_fn` op. It is expected to output a
single-key dict containing an RGB image with intensities in [-1,1].
"""
def __init__(self,
predict_fn,
pp_fn,
batch_size,
dataset="imagenet2012",
split="validation",
predict_kwargs=None):
def predict(params, batch):
def _f(x):
y = predict_fn(params, x, **(predict_kwargs or {}))
# Assume image intensities are in [-1,1].
# Evaluator expects a dict with a single item.
pred, = y.values()
return _psnr(pred, x["labels"], 2.)
return jax.lax.all_gather({
"mask": batch["mask"],
"psnr": _f(batch["input"]),
}, axis_name="data", axis=0)
self.predict_fn = jax.pmap(predict, axis_name="data")
# Prepare data for each process and pad with zeros so all processes have the
# same number of batches.
def preprocess(example):
return {
"mask": tf.constant(1),
"input": pp_builder.get_preprocess_fn(pp_fn)(example),
}
self.data = common.get_jax_process_dataset(
dataset,
split,
global_batch_size=batch_size,
add_tfds_id=True,
pp_fn=preprocess)
def run(self, params):
"""Run eval."""
psnrs = []
for batch in self.data.as_numpy_iterator():
# Outputs is a dict with values shaped (gather/same, devices, batch, ...)
out = self.predict_fn(params, batch)
if jax.process_index(): # Host0 gets all preds and does eval.
continue
# First, we remove the "gather" dim and transfer the result to host,
# leading to numpy arrays of (devices, device_batch, ...)
out = jax.tree_map(lambda x: jax.device_get(x[0]), out)
mask = out["mask"]
batch_psnrs = out["psnr"][mask != 0]
psnrs.extend(batch_psnrs)
if jax.process_index(): # Host0 gets all preds and does eval.
return
yield "PSNR", np.mean(psnrs)
@functools.partial(jax.vmap, in_axes=[0, 0, None])
def _psnr(img0, img1, dynamic_range):
mse = jnp.mean(jnp.power(img0 - img1, 2))
return 20. * jnp.log10(dynamic_range) - 10. * jnp.log10(mse)
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