Tingquan Gao
parent
7865207096
commit
8002ccf4b6
|
|
@ -88,15 +88,14 @@ def worker_init_fn(worker_id: int, num_workers: int, rank: int, seed: int):
|
|||
random.seed(worker_seed)
|
||||
|
||||
|
||||
def build_dataloader(config, *mode, seed=None):
|
||||
dataloader_config = config["DataLoader"]
|
||||
for m in mode:
|
||||
assert m in [
|
||||
'Train', 'Eval', 'Test', 'Gallery', 'Query', 'UnLabelTrain'
|
||||
], "Dataset mode should be Train, Eval, Test, Gallery, Query, UnLabelTrain"
|
||||
assert m in dataloader_config.keys(), "{} config not in yaml".format(m)
|
||||
dataloader_config = dataloader_config[m]
|
||||
def build_dataloader(config, mode, seed=None):
|
||||
assert mode in [
|
||||
'Train', 'Eval', 'Test', 'Gallery', 'Query', 'UnLabelTrain'
|
||||
], "Dataset mode should be Train, Eval, Test, Gallery, Query, UnLabelTrain"
|
||||
assert mode in config["DataLoader"].keys(), "{} config not in yaml".format(
|
||||
mode)
|
||||
|
||||
dataloader_config = config["DataLoader"][mode]
|
||||
class_num = config["Arch"].get("class_num", None)
|
||||
epochs = config["Global"]["epochs"]
|
||||
use_dali = config["Global"].get("use_dali", False)
|
||||
|
|
|
|||
|
|
@ -22,7 +22,6 @@ from paddle import nn
|
|||
import numpy as np
|
||||
import random
|
||||
|
||||
from ..utils.amp import AMPForwardDecorator
|
||||
from ppcls.utils import logger
|
||||
from ppcls.utils.logger import init_logger
|
||||
from ppcls.utils.config import print_config
|
||||
|
|
|
|||
|
|
@ -13,17 +13,17 @@
|
|||
# limitations under the License.
|
||||
|
||||
from .classification import ClassEval
|
||||
from .retrieval import RetrievalEval
|
||||
from .retrieval import retrieval_eval
|
||||
from .adaface import adaface_eval
|
||||
|
||||
|
||||
def build_eval_func(config, mode, model):
|
||||
if mode not in ["eval", "train"]:
|
||||
return None
|
||||
task = config["Global"].get("task", "classification")
|
||||
if task == "classification":
|
||||
eval_mode = config["Global"].get("eval_mode", None)
|
||||
if eval_mode is None:
|
||||
config["Global"]["eval_mode"] = "classification"
|
||||
return ClassEval(config, mode, model)
|
||||
elif task == "retrieval":
|
||||
return RetrievalEval(config, mode, model)
|
||||
else:
|
||||
raise Exception()
|
||||
return getattr(sys.modules[__name__], eval_mode + "_eval")(config,
|
||||
mode, model)
|
||||
|
|
|
|||
|
|
@ -21,202 +21,182 @@ import numpy as np
|
|||
import paddle
|
||||
import scipy
|
||||
|
||||
from ...utils.misc import AverageMeter
|
||||
from ...utils import all_gather, logger
|
||||
from ...data import build_dataloader
|
||||
from ...loss import build_loss
|
||||
from ...metric import build_metrics
|
||||
from ppcls.utils import all_gather, logger
|
||||
|
||||
|
||||
class RetrievalEval(object):
|
||||
def __init__(self, config, mode, model):
|
||||
self.config = config
|
||||
self.model = model
|
||||
self.print_batch_step = self.config["Global"]["print_batch_step"]
|
||||
self.use_dali = self.config["Global"].get("use_dali", False)
|
||||
self.eval_metric_func = build_metrics(self.config, "Eval")
|
||||
self.eval_loss_func = build_loss(self.config, "Eval")
|
||||
self.output_info = dict()
|
||||
def retrieval_eval(engine, epoch_id=0):
|
||||
engine.model.eval()
|
||||
# step1. prepare query and gallery features
|
||||
if engine.gallery_query_dataloader is not None:
|
||||
gallery_feat, gallery_label, gallery_camera = compute_feature(
|
||||
engine, "gallery_query")
|
||||
query_feat, query_label, query_camera = gallery_feat, gallery_label, gallery_camera
|
||||
else:
|
||||
gallery_feat, gallery_label, gallery_camera = compute_feature(
|
||||
engine, "gallery")
|
||||
query_feat, query_label, query_camera = compute_feature(engine,
|
||||
"query")
|
||||
|
||||
self.gallery_query_dataloader = None
|
||||
if len(self.config["DataLoader"]["Eval"].keys()) == 1:
|
||||
self.gallery_query_dataloader = build_dataloader(self.config,
|
||||
"Eval")
|
||||
# step2. split features into feature blocks for saving memory
|
||||
num_query = len(query_feat)
|
||||
block_size = engine.config["Global"].get("sim_block_size", 64)
|
||||
sections = [block_size] * (num_query // block_size)
|
||||
if num_query % block_size > 0:
|
||||
sections.append(num_query % block_size)
|
||||
|
||||
query_feat_blocks = paddle.split(query_feat, sections)
|
||||
query_label_blocks = paddle.split(query_label, sections)
|
||||
query_camera_blocks = paddle.split(
|
||||
query_camera, sections) if query_camera is not None else None
|
||||
metric_key = None
|
||||
|
||||
# step3. compute metric
|
||||
if engine.eval_loss_func is None:
|
||||
metric_dict = {metric_key: 0.0}
|
||||
else:
|
||||
use_reranking = engine.config["Global"].get("re_ranking", False)
|
||||
logger.info(f"re_ranking={use_reranking}")
|
||||
if use_reranking:
|
||||
# compute distance matrix
|
||||
distmat = compute_re_ranking_dist(
|
||||
query_feat, gallery_feat, engine.config["Global"].get(
|
||||
"feature_normalize", True), 20, 6, 0.3)
|
||||
# exclude illegal distance
|
||||
if query_camera is not None:
|
||||
camera_mask = query_camera != gallery_camera.t()
|
||||
label_mask = query_label != gallery_label.t()
|
||||
keep_mask = label_mask | camera_mask
|
||||
distmat = keep_mask.astype(query_feat.dtype) * distmat + (
|
||||
~keep_mask).astype(query_feat.dtype) * (distmat.max() + 1)
|
||||
else:
|
||||
keep_mask = None
|
||||
# compute metric with all samples
|
||||
metric_dict = engine.eval_metric_func(-distmat, query_label,
|
||||
gallery_label, keep_mask)
|
||||
else:
|
||||
self.gallery_dataloader = build_dataloader(self.config, "Eval",
|
||||
"Gallery")
|
||||
self.query_dataloader = build_dataloader(self.config, "Eval",
|
||||
"Query")
|
||||
|
||||
def __call__(self, epoch_id=0):
|
||||
self.model.eval()
|
||||
|
||||
# step1. prepare query and gallery features
|
||||
if self.gallery_query_dataloader is not None:
|
||||
gallery_feat, gallery_label, gallery_camera = self.compute_feature(
|
||||
"gallery_query")
|
||||
query_feat, query_label, query_camera = gallery_feat, gallery_label, gallery_camera
|
||||
else:
|
||||
gallery_feat, gallery_label, gallery_camera = self.compute_feature(
|
||||
"gallery")
|
||||
query_feat, query_label, query_camera = self.compute_feature(
|
||||
"query")
|
||||
|
||||
# step2. split features into feature blocks for saving memory
|
||||
num_query = len(query_feat)
|
||||
block_size = self.config["Global"].get("sim_block_size", 64)
|
||||
sections = [block_size] * (num_query // block_size)
|
||||
if num_query % block_size > 0:
|
||||
sections.append(num_query % block_size)
|
||||
|
||||
query_feat_blocks = paddle.split(query_feat, sections)
|
||||
query_label_blocks = paddle.split(query_label, sections)
|
||||
query_camera_blocks = paddle.split(
|
||||
query_camera, sections) if query_camera is not None else None
|
||||
metric_key = None
|
||||
|
||||
# step3. compute metric
|
||||
if self.eval_loss_func is None:
|
||||
metric_dict = {metric_key: 0.0}
|
||||
else:
|
||||
use_reranking = self.config["Global"].get("re_ranking", False)
|
||||
logger.info(f"re_ranking={use_reranking}")
|
||||
if use_reranking:
|
||||
metric_dict = defaultdict(float)
|
||||
for block_idx, block_feat in enumerate(query_feat_blocks):
|
||||
# compute distance matrix
|
||||
distmat = compute_re_ranking_dist(
|
||||
query_feat, gallery_feat, self.config["Global"].get(
|
||||
"feature_normalize", True), 20, 6, 0.3)
|
||||
distmat = paddle.matmul(
|
||||
block_feat, gallery_feat, transpose_y=True)
|
||||
# exclude illegal distance
|
||||
if query_camera is not None:
|
||||
camera_mask = query_camera != gallery_camera.t()
|
||||
label_mask = query_label != gallery_label.t()
|
||||
camera_mask = query_camera_blocks[
|
||||
block_idx] != gallery_camera.t()
|
||||
label_mask = query_label_blocks[
|
||||
block_idx] != gallery_label.t()
|
||||
keep_mask = label_mask | camera_mask
|
||||
distmat = keep_mask.astype(query_feat.dtype) * distmat + (
|
||||
~keep_mask).astype(query_feat.dtype) * (distmat.max() +
|
||||
1)
|
||||
distmat = keep_mask.astype(query_feat.dtype) * distmat
|
||||
else:
|
||||
keep_mask = None
|
||||
# compute metric with all samples
|
||||
metric_dict = self.eval_metric_func(-distmat, query_label,
|
||||
gallery_label, keep_mask)
|
||||
else:
|
||||
metric_dict = defaultdict(float)
|
||||
for block_idx, block_feat in enumerate(query_feat_blocks):
|
||||
# compute distance matrix
|
||||
distmat = paddle.matmul(
|
||||
block_feat, gallery_feat, transpose_y=True)
|
||||
# exclude illegal distance
|
||||
if query_camera is not None:
|
||||
camera_mask = query_camera_blocks[
|
||||
block_idx] != gallery_camera.t()
|
||||
label_mask = query_label_blocks[
|
||||
block_idx] != gallery_label.t()
|
||||
keep_mask = label_mask | camera_mask
|
||||
distmat = keep_mask.astype(query_feat.dtype) * distmat
|
||||
else:
|
||||
keep_mask = None
|
||||
# compute metric by block
|
||||
metric_block = self.eval_metric_func(
|
||||
distmat, query_label_blocks[block_idx], gallery_label,
|
||||
keep_mask)
|
||||
# accumulate metric
|
||||
for key in metric_block:
|
||||
metric_dict[key] += metric_block[
|
||||
key] * block_feat.shape[0] / num_query
|
||||
# compute metric by block
|
||||
metric_block = engine.eval_metric_func(
|
||||
distmat, query_label_blocks[block_idx], gallery_label,
|
||||
keep_mask)
|
||||
# accumulate metric
|
||||
for key in metric_block:
|
||||
metric_dict[key] += metric_block[key] * block_feat.shape[
|
||||
0] / num_query
|
||||
|
||||
metric_info_list = []
|
||||
for key, value in metric_dict.items():
|
||||
metric_info_list.append(f"{key}: {value:.5f}")
|
||||
if metric_key is None:
|
||||
metric_key = key
|
||||
metric_msg = ", ".join(metric_info_list)
|
||||
logger.info(f"[Eval][Epoch {epoch_id}][Avg]{metric_msg}")
|
||||
metric_info_list = []
|
||||
for key, value in metric_dict.items():
|
||||
metric_info_list.append(f"{key}: {value:.5f}")
|
||||
if metric_key is None:
|
||||
metric_key = key
|
||||
metric_msg = ", ".join(metric_info_list)
|
||||
logger.info(f"[Eval][Epoch {epoch_id}][Avg]{metric_msg}")
|
||||
|
||||
return metric_dict[metric_key]
|
||||
return metric_dict[metric_key]
|
||||
|
||||
def compute_feature(self, name="gallery"):
|
||||
if name == "gallery":
|
||||
dataloader = self.gallery_dataloader
|
||||
elif name == "query":
|
||||
dataloader = self.query_dataloader
|
||||
elif name == "gallery_query":
|
||||
dataloader = self.gallery_query_dataloader
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Only support gallery or query or gallery_query dataset, but got {name}"
|
||||
|
||||
def compute_feature(engine, name="gallery"):
|
||||
if name == "gallery":
|
||||
dataloader = engine.gallery_dataloader
|
||||
elif name == "query":
|
||||
dataloader = engine.query_dataloader
|
||||
elif name == "gallery_query":
|
||||
dataloader = engine.gallery_query_dataloader
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Only support gallery or query or gallery_query dataset, but got {name}"
|
||||
)
|
||||
|
||||
all_feat = []
|
||||
all_label = []
|
||||
all_camera = []
|
||||
has_camera = False
|
||||
for idx, batch in enumerate(dataloader): # load is very time-consuming
|
||||
if idx % engine.config["Global"]["print_batch_step"] == 0:
|
||||
logger.info(
|
||||
f"{name} feature calculation process: [{idx}/{len(dataloader)}]"
|
||||
)
|
||||
|
||||
all_feat = []
|
||||
all_label = []
|
||||
all_camera = []
|
||||
has_camera = False
|
||||
for idx, batch in enumerate(dataloader): # load is very time-consuming
|
||||
if idx % self.print_batch_step == 0:
|
||||
logger.info(
|
||||
f"{name} feature calculation process: [{idx}/{len(dataloader)}]"
|
||||
)
|
||||
|
||||
batch = [paddle.to_tensor(x) for x in batch]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
if len(batch) >= 3:
|
||||
has_camera = True
|
||||
batch[2] = batch[2].reshape([-1, 1]).astype("int64")
|
||||
|
||||
out = self.model(batch)
|
||||
|
||||
if "Student" in out:
|
||||
out = out["Student"]
|
||||
|
||||
# get features
|
||||
if self.config["Global"].get("retrieval_feature_from",
|
||||
"features") == "features":
|
||||
# use output from neck as feature
|
||||
batch_feat = out["features"]
|
||||
else:
|
||||
# use output from backbone as feature
|
||||
batch_feat = out["backbone"]
|
||||
|
||||
# do norm(optional)
|
||||
if self.config["Global"].get("feature_normalize", True):
|
||||
batch_feat = paddle.nn.functional.normalize(batch_feat, p=2)
|
||||
|
||||
# do binarize(optional)
|
||||
if self.config["Global"].get("feature_binarize") == "round":
|
||||
batch_feat = paddle.round(batch_feat).astype(
|
||||
"float32") * 2.0 - 1.0
|
||||
elif self.config["Global"].get("feature_binarize") == "sign":
|
||||
batch_feat = paddle.sign(batch_feat).astype("float32")
|
||||
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
all_feat.append(all_gather(batch_feat))
|
||||
all_label.append(all_gather(batch[1]))
|
||||
if has_camera:
|
||||
all_camera.append(all_gather(batch[2]))
|
||||
else:
|
||||
all_feat.append(batch_feat)
|
||||
all_label.append(batch[1])
|
||||
if has_camera:
|
||||
all_camera.append(batch[2])
|
||||
|
||||
if self.use_dali:
|
||||
dataloader.reset()
|
||||
|
||||
all_feat = paddle.concat(all_feat)
|
||||
all_label = paddle.concat(all_label)
|
||||
if has_camera:
|
||||
all_camera = paddle.concat(all_camera)
|
||||
batch = [paddle.to_tensor(x) for x in batch]
|
||||
batch[1] = batch[1].reshape([-1, 1]).astype("int64")
|
||||
if len(batch) >= 3:
|
||||
has_camera = True
|
||||
batch[2] = batch[2].reshape([-1, 1]).astype("int64")
|
||||
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[0])
|
||||
else:
|
||||
all_camera = None
|
||||
# discard redundant padding sample(s) at the end
|
||||
total_samples = dataloader.size if self.use_dali else len(
|
||||
dataloader.dataset)
|
||||
all_feat = all_feat[:total_samples]
|
||||
all_label = all_label[:total_samples]
|
||||
if has_camera:
|
||||
all_camera = all_camera[:total_samples]
|
||||
out = engine.model(batch[0])
|
||||
if "Student" in out:
|
||||
out = out["Student"]
|
||||
|
||||
logger.info(f"Build {name} done, all feat shape: {all_feat.shape}")
|
||||
return all_feat, all_label, all_camera
|
||||
# get features
|
||||
if engine.config["Global"].get("retrieval_feature_from",
|
||||
"features") == "features":
|
||||
# use output from neck as feature
|
||||
batch_feat = out["features"]
|
||||
else:
|
||||
# use output from backbone as feature
|
||||
batch_feat = out["backbone"]
|
||||
|
||||
# do norm(optional)
|
||||
if engine.config["Global"].get("feature_normalize", True):
|
||||
batch_feat = paddle.nn.functional.normalize(batch_feat, p=2)
|
||||
|
||||
# do binarize(optional)
|
||||
if engine.config["Global"].get("feature_binarize") == "round":
|
||||
batch_feat = paddle.round(batch_feat).astype("float32") * 2.0 - 1.0
|
||||
elif engine.config["Global"].get("feature_binarize") == "sign":
|
||||
batch_feat = paddle.sign(batch_feat).astype("float32")
|
||||
|
||||
if paddle.distributed.get_world_size() > 1:
|
||||
all_feat.append(all_gather(batch_feat))
|
||||
all_label.append(all_gather(batch[1]))
|
||||
if has_camera:
|
||||
all_camera.append(all_gather(batch[2]))
|
||||
else:
|
||||
all_feat.append(batch_feat)
|
||||
all_label.append(batch[1])
|
||||
if has_camera:
|
||||
all_camera.append(batch[2])
|
||||
|
||||
if engine.use_dali:
|
||||
dataloader.reset()
|
||||
|
||||
all_feat = paddle.concat(all_feat)
|
||||
all_label = paddle.concat(all_label)
|
||||
if has_camera:
|
||||
all_camera = paddle.concat(all_camera)
|
||||
else:
|
||||
all_camera = None
|
||||
# discard redundant padding sample(s) at the end
|
||||
total_samples = dataloader.size if engine.use_dali else len(
|
||||
dataloader.dataset)
|
||||
all_feat = all_feat[:total_samples]
|
||||
all_label = all_label[:total_samples]
|
||||
if has_camera:
|
||||
all_camera = all_camera[:total_samples]
|
||||
|
||||
logger.info(f"Build {name} done, all feat shape: {all_feat.shape}")
|
||||
return all_feat, all_label, all_camera
|
||||
|
||||
|
||||
def k_reciprocal_neighbor(rank: np.ndarray, p: int, k: int) -> np.ndarray:
|
||||
|
|
|
|||
|
|
@ -22,8 +22,9 @@ from .train_progressive import train_epoch_progressive
|
|||
def build_train_func(config, mode, model, eval_func):
|
||||
if mode != "train":
|
||||
return None
|
||||
task = config["Global"].get("task", "classification")
|
||||
if task == "classification" or task == "retrieval":
|
||||
train_mode = config["Global"].get("task", None)
|
||||
if train_mode is None:
|
||||
config["Global"]["task"] = "classification"
|
||||
return ClassTrainer(config, model, eval_func)
|
||||
else:
|
||||
return getattr(sys.modules[__name__], "train_epoch_" + train_mode)(
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ from __future__ import absolute_import, division, print_function
|
|||
|
||||
from ppcls.data import build_dataloader
|
||||
from ppcls.utils import logger, type_name
|
||||
from .classification import ClassTrainer
|
||||
from .regular_train_epoch import regular_train_epoch
|
||||
|
||||
|
||||
def train_epoch_progressive(engine, epoch_id, print_batch_step):
|
||||
|
|
|
|||
Loading…
Reference in New Issue