# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import time import platform import paddle from ppcls.utils.misc import AverageMeter from ppcls.utils import logger def classification_eval(engine, epoch_id=0): if hasattr(engine.eval_metric_func, "reset"): engine.eval_metric_func.reset() output_info = dict() time_info = { "batch_cost": AverageMeter( "batch_cost", '.5f', postfix=" s,"), "reader_cost": AverageMeter( "reader_cost", ".5f", postfix=" s,"), } print_batch_step = engine.config["Global"]["print_batch_step"] tic = time.time() total_samples = engine.dataloader_dict["Eval"].total_samples accum_samples = 0 max_iter = engine.dataloader_dict["Eval"].max_iter for iter_id, batch in enumerate(engine.dataloader_dict["Eval"]): if iter_id >= max_iter: break if iter_id == 5: for key in time_info: time_info[key].reset() time_info["reader_cost"].update(time.time() - tic) batch_size = batch[0].shape[0] batch[0] = paddle.to_tensor(batch[0]) if not engine.config["Global"].get("use_multilabel", False): batch[1] = batch[1].reshape([-1, 1]).astype("int64") # image input if engine.amp and engine.amp_eval: with paddle.amp.auto_cast( custom_black_list={ "flatten_contiguous_range", "greater_than" }, level=engine.amp_level): out = engine.model(batch) else: out = engine.model(batch) # just for DistributedBatchSampler issue: repeat sampling current_samples = batch_size * paddle.distributed.get_world_size() accum_samples += current_samples if isinstance(out, dict) and "Student" in out: out = out["Student"] if isinstance(out, dict) and "logits" in out: out = out["logits"] # gather Tensor when distributed if paddle.distributed.get_world_size() > 1: label_list = [] device_id = paddle.distributed.ParallelEnv().device_id label = batch[1].cuda(device_id) if engine.config["Global"][ "device"] == "gpu" else batch[1] paddle.distributed.all_gather(label_list, label) labels = paddle.concat(label_list, 0) if isinstance(out, list): preds = [] for x in out: pred_list = [] paddle.distributed.all_gather(pred_list, x) pred_x = paddle.concat(pred_list, 0) preds.append(pred_x) else: pred_list = [] paddle.distributed.all_gather(pred_list, out) preds = paddle.concat(pred_list, 0) if accum_samples > total_samples and not engine.use_dali: if isinstance(preds, list): preds = [ pred[:total_samples + current_samples - accum_samples] for pred in preds ] else: preds = preds[:total_samples + current_samples - accum_samples] labels = labels[:total_samples + current_samples - accum_samples] current_samples = total_samples + current_samples - accum_samples else: labels = batch[1] preds = out # calc loss if engine.eval_loss_func is not None: if engine.amp and engine.amp_eval: with paddle.amp.auto_cast( custom_black_list={ "flatten_contiguous_range", "greater_than" }, level=engine.amp_level): loss_dict = engine.eval_loss_func(preds, labels) else: loss_dict = engine.eval_loss_func(preds, labels) for key in loss_dict: if key not in output_info: output_info[key] = AverageMeter(key, '7.5f') output_info[key].update(float(loss_dict[key]), current_samples) # calc metric if engine.eval_metric_func is not None: engine.eval_metric_func(preds, labels) time_info["batch_cost"].update(time.time() - tic) if iter_id % print_batch_step == 0: time_msg = "s, ".join([ "{}: {:.5f}".format(key, time_info[key].avg) for key in time_info ]) ips_msg = "ips: {:.5f} images/sec".format( batch_size / time_info["batch_cost"].avg) if "ATTRMetric" in engine.config["Metric"]["Eval"][0]: metric_msg = "" else: metric_msg = ", ".join([ "{}: {:.5f}".format(key, output_info[key].val) for key in output_info ]) metric_msg += ", {}".format(engine.eval_metric_func.avg_info) logger.info("[Eval][Epoch {}][Iter: {}/{}]{}, {}, {}".format( epoch_id, iter_id, max_iter, metric_msg, time_msg, ips_msg)) tic = time.time() if engine.use_dali: engine.dataloader_dict["Eval"].reset() if "ATTRMetric" in engine.config["Metric"]["Eval"][0]: metric_msg = ", ".join([ "evalres: ma: {:.5f} label_f1: {:.5f} label_pos_recall: {:.5f} label_neg_recall: {:.5f} instance_f1: {:.5f} instance_acc: {:.5f} instance_prec: {:.5f} instance_recall: {:.5f}". format(*engine.eval_metric_func.attr_res()) ]) logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg)) # do not try to save best eval.model if engine.eval_metric_func is None: return -1 # return 1st metric in the dict return engine.eval_metric_func.attr_res()[0] else: metric_msg = ", ".join([ "{}: {:.5f}".format(key, output_info[key].avg) for key in output_info ]) metric_msg += ", {}".format(engine.eval_metric_func.avg_info) logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg)) # do not try to save best eval.model if engine.eval_metric_func is None: return -1 # return 1st metric in the dict return engine.eval_metric_func.avg