165 lines
5.8 KiB
Python
165 lines
5.8 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import dllogger
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import horovod.tensorflow as hvd
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import tensorflow as tf
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from data.outbrain.features import DISPLAY_ID_COLUMN
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from horovod.tensorflow.mpi_ops import Sum, Average
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class Evaluator:
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def __init__(
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self,
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model,
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throughput_calculator,
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eval_dataset,
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compiled_loss,
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steps,
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args,
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):
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self.model = model
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self.steps = steps
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self.args = args
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self.throughput_calculator = throughput_calculator
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self.compiled_loss = compiled_loss
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self.eval_loss = tf.keras.metrics.Mean()
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self.metrics = []
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self.eval_dataset = eval_dataset
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with tf.device("/CPU:0"):
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self.current_step_var = tf.Variable(0, trainable=False, dtype=tf.int64)
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self.display_id_counter = tf.Variable(
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0.0, trainable=False, dtype=tf.float64
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)
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self.streaming_map = tf.Variable(
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0.0, name="STREAMING_MAP", trainable=False, dtype=tf.float64
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)
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def _reset_states(self):
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for metric in self.metrics:
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metric.reset_states()
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self.eval_loss.reset_states()
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self.display_id_counter.assign(1)
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self.current_step_var.assign(1)
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self.streaming_map.assign(1)
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@tf.function
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def _calculate_map(self, x, y, predictions):
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predictions = tf.reshape(predictions, [-1])
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predictions = tf.cast(predictions, tf.float64)
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display_ids = x[DISPLAY_ID_COLUMN]
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display_ids = tf.reshape(display_ids, [-1])
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labels = tf.reshape(y, [-1])
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sorted_ids = tf.argsort(display_ids)
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display_ids = tf.gather(display_ids, indices=sorted_ids)
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predictions = tf.gather(predictions, indices=sorted_ids)
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labels = tf.gather(labels, indices=sorted_ids)
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_, display_ids_idx, display_ids_ads_count = tf.unique_with_counts(
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display_ids, out_idx=tf.int64
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)
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pad_length = 30 - tf.reduce_max(display_ids_ads_count)
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preds = tf.RaggedTensor.from_value_rowids(
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predictions, display_ids_idx
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).to_tensor()
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labels = tf.RaggedTensor.from_value_rowids(labels, display_ids_idx).to_tensor()
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labels_mask = tf.math.reduce_max(labels, 1)
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preds_masked = tf.boolean_mask(preds, labels_mask)
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labels_masked = tf.boolean_mask(labels, labels_mask)
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labels_masked = tf.argmax(labels_masked, axis=1, output_type=tf.int32)
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labels_masked = tf.reshape(labels_masked, [-1, 1])
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preds_masked = tf.pad(preds_masked, [(0, 0), (0, pad_length)])
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_, predictions_idx = tf.math.top_k(preds_masked, 12)
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indices = tf.math.equal(predictions_idx, labels_masked)
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indices_mask = tf.math.reduce_any(indices, 1)
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masked_indices = tf.boolean_mask(indices, indices_mask)
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res = tf.argmax(masked_indices, axis=1)
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ap_matrix = tf.divide(1, tf.add(res, 1))
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ap_sum = tf.reduce_sum(ap_matrix)
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shape = tf.cast(tf.shape(indices)[0], tf.float64)
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self.display_id_counter.assign_add(shape)
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self.streaming_map.assign_add(ap_sum)
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@tf.function
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def _execute_step_calculations(self, x, y):
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predictions = self.model(x, training=False)
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with tf.device("/CPU:0"):
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loss = self.compiled_loss(y, predictions)
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for metric in self.metrics:
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metric.update_state(y, predictions)
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self.eval_loss.update_state(loss)
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self._calculate_map(x, y, predictions)
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return loss
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@tf.function
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def _reduce_results(self):
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if not self.args.cpu:
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all_streaming_map = hvd.allreduce(self.streaming_map, op=Sum)
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all_display_id_counter = hvd.allreduce(self.display_id_counter, op=Sum)
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eval_loss = hvd.allreduce(
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self.eval_loss.result(), op=Average
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)
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else:
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all_streaming_map = self.streaming_map
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all_display_id_counter = self.display_id_counter
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eval_loss = self.eval_loss.result()
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map_metric = tf.divide(all_streaming_map, all_display_id_counter)
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eval_loss = eval_loss
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return map_metric, eval_loss
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@staticmethod
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def log(eval_data, step, steps):
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dllogger.log(data=eval_data, step=(step, steps))
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def eval_step(self, x, y):
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self._execute_step_calculations(x, y)
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if self.args.benchmark:
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self.throughput_calculator(y.shape[0], eval_benchmark=True)
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def eval(self, step):
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eval_data = {}
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self._reset_states()
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range_val = 1 if not self.args.benchmark else 100
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# Graph mode part
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for _ in range(range_val):
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for x, y in self.eval_dataset:
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self.eval_step(x, y)
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map_metric, eval_loss = self._reduce_results()
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if self.args.cpu or hvd.rank() == 0:
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with tf.device("/CPU:0"):
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# Eager mode part
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current_step = int(step.numpy())
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eval_data = {
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"loss_val": f"{eval_loss.numpy():.4f}",
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"streaming_map_val": f"{map_metric.numpy():.4f}",
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}
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self.log(eval_data, current_step, self.steps)
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return eval_data
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