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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Evaluation utilities."""
import os
from functools import partial
import torch
from megatron import get_args
from megatron import print_rank_0, print_rank_last
from megatron.core import mpu
from megatron.schedules import get_forward_backward_func
from tasks.vision.finetune_utils import build_data_loader
from tasks.vision.finetune_utils import process_batch
from torchvision import datasets, transforms
def accuracy_func_provider():
"""Provide function that calculates accuracies."""
args = get_args()
data_path = args.data_path
crop_size = (args.img_h, args.img_w)
# Build dataloaders.
val_data_path = data_path[1]
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
transform_val = transforms.Compose(
[
transforms.Resize(crop_size),
transforms.CenterCrop(crop_size),
transforms.ToTensor(),
normalize,
]
)
dataset = datasets.ImageFolder(root=val_data_path, transform=transform_val)
dataloader = build_data_loader(
dataset,
args.micro_batch_size,
num_workers=args.num_workers,
drop_last=(mpu.get_data_parallel_world_size() > 1),
shuffle=False
)
def metrics_func(model, epoch):
print_rank_0("calculating metrics ...")
correct, total = calculate_correct_answers(model, dataloader, epoch)
percent = float(correct) * 100.0 / float(total)
print_rank_last(
" >> |epoch: {}| overall: correct / total = {} / {} = "
"{:.4f} %".format(epoch, correct, total, percent)
)
return metrics_func
def calculate_correct_answers(model, dataloader, epoch):
"""Calculate correct over total answers"""
forward_backward_func = get_forward_backward_func()
for m in model:
m.eval()
def loss_func(labels, output_tensor):
logits = output_tensor
loss_dict = {}
# Compute the correct answers.
predicted = torch.argmax(logits, dim=-1)
corrects = (predicted == labels).float()
# Add to the counters.
loss_dict['total'] = labels.size(0)
loss_dict['correct'] = corrects.sum().item()
return 0, loss_dict
#defined inside to capture output_predictions
def correct_answers_forward_step(batch, model):
try:
batch_ = next(batch)
except BaseException:
batch_ = batch
images, labels = process_batch(batch_)
# Forward model.
output_tensor = model(images)
return output_tensor, partial(loss_func, labels)
with torch.no_grad():
# For all the batches in the dataset.
total = 0
correct = 0
for _, batch in enumerate(dataloader):
loss_dicts = forward_backward_func(correct_answers_forward_step, batch, model,
optimizer=None, timers=None, forward_only=True)
for loss_dict in loss_dicts:
total += loss_dict['total']
correct += loss_dict['correct']
for m in model:
m.train()
# Reduce.
if mpu.is_pipeline_last_stage():
unreduced = torch.cuda.LongTensor([correct, total])
torch.distributed.all_reduce(unreduced,
group=mpu.get_data_parallel_group())
# Print on screen.
correct_ans = unreduced[0].item()
total_count = unreduced[1].item()
return correct_ans, total_count
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