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1327f34 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | # Copyright 2025 The Scenic 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.
"""Base class for all regression models."""
import functools
from typing import Dict, Optional, Tuple, Union
from immutabledict import immutabledict
import jax.numpy as jnp
from scenic.model_lib.base_models import base_model
from scenic.model_lib.base_models import model_utils
_REGRESSION_METRICS = immutabledict({
'mean_squared_error':
(model_utils.weighted_squared_error, model_utils.num_examples)
})
def regression_metrics_function(
predictions: jnp.ndarray,
batch: base_model.Batch,
metrics: base_model.MetricNormalizerFnDict = _REGRESSION_METRICS,
axis_name: Union[str, Tuple[str, ...]] = 'batch',
) -> Dict[str, Tuple[float, int]]:
"""Calculate metrics for the regression task.
Currently we assume each metric_fn has the API:
```metric_fn(predictions, targets, weights)```
and returns an array of shape [batch,]. We also assume that to compute
the aggregate metric, one should sum across all batches, then divide by the
total samples seen. In this way we currently only support metrics of the 1/N
sum f(inputs, targets). Note, the caller is responsible for dividing by
the normalizer when computing the mean of each metric.
Args:
predictions: Output of model in shape [batch, length].
batch: Batch (dict) with keys 'targets' and optionally 'batch_mask'.
metrics: The regression metrics to evaluate. The key is the
name of the metric, and the value is the metrics function.
axis_name: List of axes on which we run the pmsum.
Returns:
A dict of metrics, in which keys are metrics name and values are tuples of
(metric, normalizer).
"""
targets = batch['targets']
weights = batch.get('batch_mask')
evaluated_metrics = {}
for key, val in metrics.items():
evaluated_metrics[key] = model_utils.psum_metric_normalizer(
(val[0](predictions, targets, weights), val[1](predictions, targets, # pytype: disable=wrong-arg-types # jax-ndarray
weights)),
axis_name=axis_name)
return evaluated_metrics # pytype: disable=bad-return-type # jax-ndarray
class RegressionModel(base_model.BaseModel):
"""Defines commonalities between all regression models.
A model is class with three members: get_metrics_fn, loss_fn, and a
flax_model.
`get_metrics_fn` returns a callable function, metric_fn, that calculates the
metrics and returns a dictionary. The metric function computes f(x_i, y_i) on
a minibatch, it has API:
```metric_fn(predictions, targets, weights).```
The trainer will then aggregate and compute the mean across all samples
evaluated. By default, both the metric and loss are the mean squared error.
loss_fn is a function of API
loss = loss_fn(predictions, batch, model_params=None).
flax_model is returned from the build_flax_model function. A typical
usage pattern will be:
```
model_cls = model_lib.models.get_model_cls(
'my_regression_model')
model = model_cls(config, dataset.meta_data)
flax_model = model.build_flax_model
dummy_input = jnp.zeros(input_shape, model_input_dtype)
model_state, params = flax_model.init(
rng, dummy_input, train=False).pop('params')
```
The model can then be applied by:
variables = {'params': params, **model_state}
predictions, new_model_state = flax_model.apply(variables, inputs, ...)
```
"""
def get_metrics_fn(self, split: Optional[str] = None) -> base_model.MetricFn:
"""Returns a callable metric function for the model.
By default, we return the same metric for each split.
Args:
split: The split for which we calculate the metrics. It should be one of
the ['train', 'validation', 'test'].
Returns: A metric function with the following API:
```metrics_fn(predictions, batch)```
"""
del split # Same function for all splits.
return functools.partial(
regression_metrics_function, metrics=_REGRESSION_METRICS)
def loss_function(self,
predictions: jnp.ndarray,
batch: base_model.Batch,
model_params: Optional[jnp.ndarray] = None) -> float:
"""Returns the (weighted) mean squared error.
Args:
predictions: Output of model in shape [batch, length].
batch: Batch (dict) with keys 'targets' and optionally 'batch_mask'.
model_params: Parameters of the model, for optionally applying
regularization.
Returns:
The (weighted) mean squared error.
"""
weights = batch.get('batch_mask')
targets = batch['targets']
total_loss = model_utils.weighted_mean_squared_error(
predictions, targets, weights)
if self.config.get('l2_decay_factor'):
l2_loss = model_utils.l2_regularization(model_params)
total_loss += 0.5 * self.config.l2_decay_factor * l2_loss
return total_loss # pytype: disable=bad-return-type # jax-ndarray
def build_flax_model(self):
raise NotImplementedError('Subclasses must implement build_flax_model().')
def default_flax_model_config(self):
"""Default config for the flax model that is built in `build_flax_model`.
This function in particular serves the testing functions and supposed to
provide config tha are passed to the flax_model when it's build in
`build_flax_model` function, e.g., `model_dtype_str`.
"""
raise NotImplementedError(
'Subclasses must implement default_flax_model_config().')
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