diff --git a/demo/README.md b/demo/README.md
index dc4daee..572e51f 100644
--- a/demo/README.md
+++ b/demo/README.md
@@ -2,18 +2,9 @@
We provide a command line tool to run a simple demo of builtin models.
-You can run this command to get rank visualization results by cosine similarites between different images.
+You can run this command to get cosine similarites between different images
-```shell script
-python3 demo/visualize_result.py --config-file logs/dukemtmc/mgn_R50-ibn/config.yaml \
---parallel --vis-label --dataset-name 'DukeMTMC' --output logs/mgn_duke_vis \
---opts MODEL.WEIGHTS logs/dukemtmc/mgn_R50-ibn/model_final.pth
-```
-
-You can also run this command to extract image features.
-
-```shell script
-python3 demo/demo.py --config-file logs/dukemtmc/sbs_R50/config.yaml \
---parallel --input tools/deploy/test_data/*.jpg --output sbs_R50_feat \
---opts MODEL.WEIGHTS logs/dukemtmc/sbs_R50/model_final.pth
+```bash
+cd demo/
+sh run_demo.sh
```
\ No newline at end of file
diff --git a/demo/demo.py b/demo/demo.py
index 23405b6..26f330f 100644
--- a/demo/demo.py
+++ b/demo/demo.py
@@ -9,6 +9,7 @@ import glob
import os
import sys
+import torch.nn.functional as F
import cv2
import numpy as np
import tqdm
@@ -23,7 +24,7 @@ from fastreid.utils.file_io import PathManager
from predictor import FeatureExtractionDemo
# import some modules added in project like this below
-# sys.path.append('../projects/PartialReID')
+# sys.path.append("projects/PartialReID")
# from partialreid import *
cudnn.benchmark = True
@@ -72,6 +73,13 @@ def get_parser():
return parser
+def postprocess(features):
+ # Normalize feature to compute cosine distance
+ features = F.normalize(features)
+ features = features.cpu().data.numpy()
+ return features
+
+
if __name__ == '__main__':
args = get_parser().parse_args()
cfg = setup_cfg(args)
@@ -85,5 +93,5 @@ if __name__ == '__main__':
for path in tqdm.tqdm(args.input):
img = cv2.imread(path)
feat = demo.run_on_image(img)
- feat = feat.numpy()
+ feat = postprocess(feat)
np.save(os.path.join(args.output, os.path.basename(path).split('.')[0] + '.npy'), feat)
diff --git a/fastreid/data/transforms/build.py b/fastreid/data/transforms/build.py
index 1404143..5ca06a7 100644
--- a/fastreid/data/transforms/build.py
+++ b/fastreid/data/transforms/build.py
@@ -78,8 +78,8 @@ def build_transforms(cfg, is_train=True):
if do_cj:
res.append(T.RandomApply([T.ColorJitter(cj_brightness, cj_contrast, cj_saturation, cj_hue)], p=cj_prob))
if do_affine:
- res.append(T.RandomAffine(degrees=0, translate=None, scale=[0.9, 1.1], shear=None, resample=False,
- fillcolor=128))
+ res.append(T.RandomAffine(degrees=10, translate=None, scale=[0.9, 1.1], shear=0.1, resample=False,
+ fillcolor=0))
if do_augmix:
res.append(AugMix(prob=augmix_prob))
res.append(ToTensor())
diff --git a/fastreid/layers/__init__.py b/fastreid/layers/__init__.py
index 1f575b4..e68064c 100644
--- a/fastreid/layers/__init__.py
+++ b/fastreid/layers/__init__.py
@@ -5,11 +5,15 @@
"""
from .activation import *
-from .batch_drop import BatchDrop
from .batch_norm import *
from .context_block import ContextBlock
+from .drop import DropPath, DropBlock2d, drop_block_2d, drop_path
from .frn import FRN, TLU
+from .gather_layer import GatherLayer
+from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible
from .non_local import Non_local
from .se_layer import SELayer
from .splat import SplAtConv2d, DropBlock2D
-from .gather_layer import GatherLayer
+from .weight_init import (
+ trunc_normal_, variance_scaling_, lecun_normal_, weights_init_kaiming, weights_init_classifier
+)
diff --git a/fastreid/layers/any_softmax.py b/fastreid/layers/any_softmax.py
index 9d643bd..3d8392d 100644
--- a/fastreid/layers/any_softmax.py
+++ b/fastreid/layers/any_softmax.py
@@ -23,7 +23,7 @@ class Linear(nn.Module):
self.m = margin
def forward(self, logits, targets):
- return logits
+ return logits.mul_(self.s)
def extra_repr(self):
return f"num_classes={self.num_classes}, scale={self.s}, margin={self.m}"
diff --git a/fastreid/layers/batch_drop.py b/fastreid/layers/batch_drop.py
deleted file mode 100644
index 5c25697..0000000
--- a/fastreid/layers/batch_drop.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# encoding: utf-8
-"""
-@author: liaoxingyu
-@contact: sherlockliao01@gmail.com
-"""
-
-import random
-
-from torch import nn
-
-
-class BatchDrop(nn.Module):
- """ref: https://github.com/daizuozhuo/batch-dropblock-network/blob/master/models/networks.py
- batch drop mask
- """
-
- def __init__(self, h_ratio, w_ratio):
- super(BatchDrop, self).__init__()
- self.h_ratio = h_ratio
- self.w_ratio = w_ratio
-
- def forward(self, x):
- if self.training:
- h, w = x.size()[-2:]
- rh = round(self.h_ratio * h)
- rw = round(self.w_ratio * w)
- sx = random.randint(0, h - rh)
- sy = random.randint(0, w - rw)
- mask = x.new_ones(x.size())
- mask[:, :, sx:sx + rh, sy:sy + rw] = 0
- x = x * mask
- return x
diff --git a/fastreid/layers/pooling.py b/fastreid/layers/pooling.py
index ff8bdb2..2f6c603 100644
--- a/fastreid/layers/pooling.py
+++ b/fastreid/layers/pooling.py
@@ -61,7 +61,7 @@ class GeneralizedMeanPooling(nn.Module):
be the same as that of the input.
"""
- def __init__(self, norm=3, output_size=1, eps=1e-6, *args, **kwargs):
+ def __init__(self, norm=3, output_size=(1, 1), eps=1e-6, *args, **kwargs):
super(GeneralizedMeanPooling, self).__init__()
assert norm > 0
self.p = float(norm)
@@ -82,7 +82,7 @@ class GeneralizedMeanPoolingP(GeneralizedMeanPooling):
""" Same, but norm is trainable
"""
- def __init__(self, norm=3, output_size=1, eps=1e-6, *args, **kwargs):
+ def __init__(self, norm=3, output_size=(1, 1), eps=1e-6, *args, **kwargs):
super(GeneralizedMeanPoolingP, self).__init__(norm, output_size, eps)
self.p = nn.Parameter(torch.ones(1) * norm)
diff --git a/fastreid/modeling/losses/triplet_loss.py b/fastreid/modeling/losses/triplet_loss.py
index b1cd11d..5275008 100644
--- a/fastreid/modeling/losses/triplet_loss.py
+++ b/fastreid/modeling/losses/triplet_loss.py
@@ -42,7 +42,7 @@ def hard_example_mining(dist_mat, is_pos, is_neg):
dist_ap, _ = torch.max(dist_mat * is_pos, dim=1)
# `dist_an` means distance(anchor, negative)
# both `dist_an` and `relative_n_inds` with shape [N]
- dist_an, _ = torch.min(dist_mat * is_neg + is_pos * 99999999., dim=1)
+ dist_an, _ = torch.min(dist_mat * is_neg + is_pos * 1e9, dim=1)
return dist_ap, dist_an
diff --git a/projects/FastAttr/fastattr/modeling/attr_head.py b/projects/FastAttr/fastattr/modeling/attr_head.py
index 621de8a..dc7972e 100644
--- a/projects/FastAttr/fastattr/modeling/attr_head.py
+++ b/projects/FastAttr/fastattr/modeling/attr_head.py
@@ -10,7 +10,7 @@ from torch import nn
from fastreid.modeling.heads import EmbeddingHead
from fastreid.modeling.heads.build import REID_HEADS_REGISTRY
-from fastreid.utils.weight_init import weights_init_kaiming
+from fastreid.layers.weight_init import weights_init_kaiming
@REID_HEADS_REGISTRY.register()
diff --git a/projects/FastClas/fastclas/__init__.py b/projects/FastClas/fastclas/__init__.py
index c1a8bc1..cfabb8c 100644
--- a/projects/FastClas/fastclas/__init__.py
+++ b/projects/FastClas/fastclas/__init__.py
@@ -5,4 +5,6 @@
"""
from .bee_ant import *
+from .distracted_driver import *
from .dataset import ClasDataset
+from .trainer import ClasTrainer
diff --git a/projects/FastClas/fastclas/bee_ant.py b/projects/FastClas/fastclas/bee_ant.py
index d4a9e7d..dbcd4de 100644
--- a/projects/FastClas/fastclas/bee_ant.py
+++ b/projects/FastClas/fastclas/bee_ant.py
@@ -10,6 +10,7 @@ import os
from fastreid.data.datasets import DATASET_REGISTRY
from fastreid.data.datasets.bases import ImageDataset
+
__all__ = ["Hymenoptera"]
diff --git a/projects/FastClas/fastclas/dataset.py b/projects/FastClas/fastclas/dataset.py
index e80f5bb..4681a41 100644
--- a/projects/FastClas/fastclas/dataset.py
+++ b/projects/FastClas/fastclas/dataset.py
@@ -12,18 +12,22 @@ from fastreid.data.data_utils import read_image
class ClasDataset(Dataset):
"""Image Person ReID Dataset"""
- def __init__(self, img_items, transform=None):
+ def __init__(self, img_items, transform=None, idx_to_class=None):
self.img_items = img_items
self.transform = transform
- classes = set()
- for i in img_items:
- classes.add(i[1])
+ if idx_to_class is not None:
+ self.idx_to_class = idx_to_class
+ self.class_to_idx = {clas_name: int(i) for i, clas_name in self.idx_to_class.items()}
+ self.classes = sorted(list(self.idx_to_class.values()))
+ else:
+ classes = set()
+ for i in img_items:
+ classes.add(i[1])
- self.classes = list(classes)
- self.classes.sort()
- self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}
- self.idx_to_class = {idx: clas for clas, idx in self.class_to_idx.items()}
+ self.classes = sorted(list(classes))
+ self.class_to_idx = {cls_name: i for i, cls_name in enumerate(self.classes)}
+ self.idx_to_class = {idx: clas for clas, idx in self.class_to_idx.items()}
def __len__(self):
return len(self.img_items)
diff --git a/projects/FastClas/fastclas/trainer.py b/projects/FastClas/fastclas/trainer.py
new file mode 100644
index 0000000..bf0b76c
--- /dev/null
+++ b/projects/FastClas/fastclas/trainer.py
@@ -0,0 +1,82 @@
+# encoding: utf-8
+"""
+@author: xingyu liao
+@contact: sherlockliao01@gmail.com
+"""
+
+import json
+import logging
+import os
+
+from fastreid.data.build import _root
+from fastreid.data.build import build_reid_train_loader, build_reid_test_loader
+from fastreid.data.datasets import DATASET_REGISTRY
+from fastreid.data.transforms import build_transforms
+from fastreid.engine import DefaultTrainer
+from fastreid.evaluation.clas_evaluator import ClasEvaluator
+from fastreid.utils import comm
+from fastreid.utils.checkpoint import PathManager
+from .dataset import ClasDataset
+
+
+class ClasTrainer(DefaultTrainer):
+ idx2class = None
+
+ @classmethod
+ def build_train_loader(cls, cfg):
+ """
+ Returns:
+ iterable
+ It now calls :func:`fastreid.data.build_reid_train_loader`.
+ Overwrite it if you'd like a different data loader.
+ """
+ logger = logging.getLogger("fastreid.clas_dataset")
+ logger.info("Prepare training set")
+
+ train_items = list()
+ for d in cfg.DATASETS.NAMES:
+ data = DATASET_REGISTRY.get(d)(root=_root)
+ if comm.is_main_process():
+ data.show_train()
+ train_items.extend(data.train)
+ transforms = build_transforms(cfg, is_train=True)
+ train_set = ClasDataset(train_items, transforms)
+ cls.idx2class = train_set.idx_to_class
+
+ data_loader = build_reid_train_loader(cfg, train_set=train_set)
+ return data_loader
+
+ @classmethod
+ def build_test_loader(cls, cfg, dataset_name):
+ """
+ Returns:
+ iterable
+ It now calls :func:`fastreid.data.build_reid_test_loader`.
+ Overwrite it if you'd like a different data loader.
+ """
+ data = DATASET_REGISTRY.get(dataset_name)(root=_root)
+ if comm.is_main_process():
+ data.show_test()
+ transforms = build_transforms(cfg, is_train=False)
+
+ test_set = ClasDataset(data.query, transforms, cls.idx2class)
+ data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)
+ return data_loader
+
+ @classmethod
+ def build_evaluator(cls, cfg, dataset_name, output_dir=None):
+ data_loader = cls.build_test_loader(cfg, dataset_name)
+ return data_loader, ClasEvaluator(cfg, output_dir)
+
+ @staticmethod
+ def auto_scale_hyperparams(cfg, num_classes):
+ cfg = DefaultTrainer.auto_scale_hyperparams(cfg, num_classes)
+
+ # Save index to class dictionary
+ output_dir = cfg.OUTPUT_DIR
+ if comm.is_main_process() and output_dir:
+ path = os.path.join(output_dir, "idx2class.json")
+ with PathManager.open(path, "w") as f:
+ json.dump(ClasTrainer.idx2class, f)
+
+ return cfg
diff --git a/projects/FastClas/train_net.py b/projects/FastClas/train_net.py
index 4d779da..82c1f47 100644
--- a/projects/FastClas/train_net.py
+++ b/projects/FastClas/train_net.py
@@ -14,75 +14,11 @@ sys.path.append('.')
from fastreid.config import get_cfg
from fastreid.engine import default_argument_parser, default_setup, launch
-from fastreid.data.build import build_reid_train_loader, build_reid_test_loader
-from fastreid.evaluation.clas_evaluator import ClasEvaluator
from fastreid.utils.checkpoint import Checkpointer, PathManager
-from fastreid.utils import comm
-from fastreid.engine import DefaultTrainer
-from fastreid.data.datasets import DATASET_REGISTRY
-from fastreid.data.transforms import build_transforms
-from fastreid.data.build import _root
from fastclas import *
-class ClasTrainer(DefaultTrainer):
-
- @classmethod
- def build_train_loader(cls, cfg):
- """
- Returns:
- iterable
- It now calls :func:`fastreid.data.build_reid_train_loader`.
- Overwrite it if you'd like a different data loader.
- """
- logger = logging.getLogger("fastreid.clas_dataset")
- logger.info("Prepare training set")
-
- train_items = list()
- for d in cfg.DATASETS.NAMES:
- data = DATASET_REGISTRY.get(d)(root=_root)
- if comm.is_main_process():
- data.show_train()
- train_items.extend(data.train)
-
- transforms = build_transforms(cfg, is_train=True)
- train_set = ClasDataset(train_items, transforms)
-
- data_loader = build_reid_train_loader(cfg, train_set=train_set)
-
- # Save index to class dictionary
- output_dir = cfg.OUTPUT_DIR
- if comm.is_main_process() and output_dir:
- path = os.path.join(output_dir, "idx2class.json")
- with PathManager.open(path, "w") as f:
- json.dump(train_set.idx_to_class, f)
-
- return data_loader
-
- @classmethod
- def build_test_loader(cls, cfg, dataset_name):
- """
- Returns:
- iterable
- It now calls :func:`fastreid.data.build_reid_test_loader`.
- Overwrite it if you'd like a different data loader.
- """
-
- data = DATASET_REGISTRY.get(dataset_name)(root=_root)
- if comm.is_main_process():
- data.show_test()
- transforms = build_transforms(cfg, is_train=False)
- test_set = ClasDataset(data.query, transforms)
- data_loader, _ = build_reid_test_loader(cfg, test_set=test_set)
- return data_loader
-
- @classmethod
- def build_evaluator(cls, cfg, dataset_name, output_dir=None):
- data_loader = cls.build_test_loader(cfg, dataset_name)
- return data_loader, ClasEvaluator(cfg, output_dir)
-
-
def setup(args):
"""
Create configs and perform basic setups.
@@ -105,6 +41,16 @@ def main(args):
Checkpointer(model).load(cfg.MODEL.WEIGHTS) # load trained model
+ try:
+ output_dir = os.path.dirname(cfg.MODEL.WEIGHTS)
+ path = os.path.join(output_dir, "idx2class.json")
+ with PathManager.open(path, 'r') as f:
+ idx2class = json.load(f)
+ ClasTrainer.idx2class = idx2class
+ except:
+ logger = logging.getLogger("fastreid.fastclas")
+ logger.info(f"Cannot find idx2class dict in {os.path.dirname(cfg.MODEL.WEIGHTS)}")
+
res = ClasTrainer.test(cfg, model)
return res
diff --git a/projects/FastTune/configs/search_trial.yml b/projects/FastTune/configs/search_trial.yml
index fe67d5c..b960e0a 100644
--- a/projects/FastTune/configs/search_trial.yml
+++ b/projects/FastTune/configs/search_trial.yml
@@ -55,9 +55,6 @@ INPUT:
PADDING:
ENABLED: True
- FLIP:
- ENABLED: True
-
DATALOADER:
SAMPLER_TRAIN: NaiveIdentitySampler
NUM_INSTANCE: 16
diff --git a/projects/PartialReID/configs/partial_market.yml b/projects/PartialReID/configs/partial_market.yml
index a561311..51f8c74 100644
--- a/projects/PartialReID/configs/partial_market.yml
+++ b/projects/PartialReID/configs/partial_market.yml
@@ -26,7 +26,7 @@ MODEL:
TRI:
MARGIN: 0.3
SCALE: 1.0
- HARD_MINING: True
+ HARD_MINING: False
DATASETS:
NAMES: ("Market1501",)
@@ -44,7 +44,6 @@ DATALOADER:
NUM_INSTANCE: 4
NUM_WORKERS: 8
-
SOLVER:
AMP:
ENABLED: False
@@ -71,4 +70,4 @@ TEST:
CUDNN_BENCHMARK: True
-OUTPUT_DIR: "projects/PartialReID/logs/test_partial"
\ No newline at end of file
+OUTPUT_DIR: projects/PartialReID/logs/test_partial
\ No newline at end of file
diff --git a/projects/PartialReID/partialreid/dsr_head.py b/projects/PartialReID/partialreid/dsr_head.py
index 57b03ce..784bb30 100644
--- a/projects/PartialReID/partialreid/dsr_head.py
+++ b/projects/PartialReID/partialreid/dsr_head.py
@@ -11,7 +11,7 @@ from torch import nn
from fastreid.layers import *
from fastreid.modeling.heads import EmbeddingHead
from fastreid.modeling.heads.build import REID_HEADS_REGISTRY
-from fastreid.utils.weight_init import weights_init_kaiming
+from fastreid.layers.weight_init import weights_init_kaiming
class OcclusionUnit(nn.Module):
diff --git a/tools/deploy/onnx_export.py b/tools/deploy/onnx_export.py
index 108c2de..de5d0f7 100644
--- a/tools/deploy/onnx_export.py
+++ b/tools/deploy/onnx_export.py
@@ -28,7 +28,6 @@ from fastreid.utils.logger import setup_logger
# sys.path.append("projects/FastDistill")
# from fastdistill import *
-
setup_logger(name="fastreid")
logger = logging.getLogger("fastreid.onnx_export")