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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Dict, Optional
import torch
from torch import nn
from torchmetrics import MetricCollection
from dinov2.data import DatasetWithEnumeratedTargets, SamplerType, make_data_loader
import dinov2.distributed as distributed
from dinov2.logging import MetricLogger
logger = logging.getLogger("dinov2")
class ModelWithNormalize(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.model = model
def forward(self, samples):
return nn.functional.normalize(self.model(samples), dim=1, p=2)
class ModelWithIntermediateLayers(nn.Module):
def __init__(self, feature_model, n_last_blocks, autocast_ctx):
super().__init__()
self.feature_model = feature_model
self.feature_model.eval()
self.n_last_blocks = n_last_blocks
self.autocast_ctx = autocast_ctx
def forward(self, images):
with torch.inference_mode():
with self.autocast_ctx():
features = self.feature_model.get_intermediate_layers(
images, self.n_last_blocks, return_class_token=True
)
return features
@torch.inference_mode()
def evaluate(
model: nn.Module,
data_loader,
postprocessors: Dict[str, nn.Module],
metrics: Dict[str, MetricCollection],
device: torch.device,
criterion: Optional[nn.Module] = None,
):
model.eval()
if criterion is not None:
criterion.eval()
for metric in metrics.values():
metric = metric.to(device)
metric_logger = MetricLogger(delimiter=" ")
header = "Test:"
for samples, targets, *_ in metric_logger.log_every(data_loader, 10, header):
outputs = model(samples.to(device))
targets = targets.to(device)
if criterion is not None:
loss = criterion(outputs, targets)
metric_logger.update(loss=loss.item())
for k, metric in metrics.items():
metric_inputs = postprocessors[k](outputs, targets)
metric.update(**metric_inputs)
metric_logger.synchronize_between_processes()
logger.info(f"Averaged stats: {metric_logger}")
stats = {k: metric.compute() for k, metric in metrics.items()}
metric_logger_stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
return metric_logger_stats, stats
def all_gather_and_flatten(tensor_rank):
tensor_all_ranks = torch.empty(
distributed.get_global_size(),
*tensor_rank.shape,
dtype=tensor_rank.dtype,
device=tensor_rank.device,
)
tensor_list = list(tensor_all_ranks.unbind(0))
torch.distributed.all_gather(tensor_list, tensor_rank.contiguous())
return tensor_all_ranks.flatten(end_dim=1)
def extract_features(model, dataset, batch_size, num_workers, gather_on_cpu=False):
dataset_with_enumerated_targets = DatasetWithEnumeratedTargets(dataset)
sample_count = len(dataset_with_enumerated_targets)
data_loader = make_data_loader(
dataset=dataset_with_enumerated_targets,
batch_size=batch_size,
num_workers=num_workers,
sampler_type=SamplerType.DISTRIBUTED,
drop_last=False,
shuffle=False,
)
return extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu)
@torch.inference_mode()
def extract_features_with_dataloader(model, data_loader, sample_count, gather_on_cpu=False):
gather_device = torch.device("cpu") if gather_on_cpu else torch.device("cuda")
metric_logger = MetricLogger(delimiter=" ")
features, all_labels = None, None
for samples, (index, labels_rank) in metric_logger.log_every(data_loader, 10):
samples = samples.cuda(non_blocking=True)
labels_rank = labels_rank.cuda(non_blocking=True)
index = index.cuda(non_blocking=True)
features_rank = model(samples).float()
# init storage feature matrix
if features is None:
features = torch.zeros(sample_count, features_rank.shape[-1], device=gather_device)
labels_shape = list(labels_rank.shape)
labels_shape[0] = sample_count
all_labels = torch.full(labels_shape, fill_value=-1, device=gather_device)
logger.info(f"Storing features into tensor of shape {features.shape}")
# share indexes, features and labels between processes
index_all = all_gather_and_flatten(index).to(gather_device)
features_all_ranks = all_gather_and_flatten(features_rank).to(gather_device)
labels_all_ranks = all_gather_and_flatten(labels_rank).to(gather_device)
# update storage feature matrix
if len(index_all) > 0:
features.index_copy_(0, index_all, features_all_ranks)
all_labels.index_copy_(0, index_all, labels_all_ranks)
logger.info(f"Features shape: {tuple(features.shape)}")
logger.info(f"Labels shape: {tuple(all_labels.shape)}")
assert torch.all(all_labels > -1)
return features, all_labels
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