init

README.md

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+<!--
+ * @Copyright (c) tkianai All Rights Reserved.
+ * @Author         : tkianai
+ * @Github         : https://github.com/tkianai
+ * @Date           : 2020-04-26 13:58:01
+ * @FilePath       : /ImageCls.detectron2/README.md
+ * @Description    : 
+ -->
+
+
+# ImageClassification.detectron2
+
+Image classification based on detectron2.
+
+This provides a convenient way to initialize backbone in detectron2.
+
+
+## Usage
+
+- Trained with detectron2 builtin trainer
+
+1. Use default data flow in detectron2, you only need rename `forward_d2` to `forward`, while renaming `forward` to `forward_imgnet` in `imgcls/modeling/meta_arch/clsnet.py`
+
+2. Create your own model config
+
+3. Run: `python train_net_builtin.py --num-gpus <gpu number> --config-file configs/<your config file>`. For example: `sh scripts/train_net_builtin.sh`
+
+
+- Trained with pytorch formal imagenet trainer
+
+1. Read carefully with some arguments in `train_net.py`
+2. Run: `sh /scripts/train_net.sh`

configs/Base_image_cls.yaml

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+MODEL:
+  META_ARCHITECTURE: "ClsNet"
+  BACKBONE:
+    NAME: "build_mnetv1_backbone"
+    FREEZE_AT: 0
+  MNET:
+    OUT_FEATURES: ['linear']
+    WIDTH_MULT: 0.25
+  CLSNET:
+    ENABLE: True
+    NUM_CLASSES: 1000
+    INPUT_SIZE: 224
+
+DATASETS:
+  TRAIN: ("imagenet_train", )
+  TEST: ("imagenet_val", )
+DATALOADER:
+  ASPECT_RATIO_GROUPING: False
+  NUM_WORKERS: 4
+SOLVER:
+  IMS_PER_BATCH: 512
+  BASE_LR: 0.1
+  STEPS: (150000, 180000)
+  MAX_ITER: 200000
+INPUT:
+  CROP:
+    ENABLED: True
+VERSION: 2

imgcls/__init__.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 14:00:17
+@FilePath       : /ImageCls.detectron2/imgcls/__init__.py
+@Description    : 
+'''
+
+

imgcls/config/__init__.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 14:18:06
+@FilePath       : /ImageCls.detectron2/imgcls/config/__init__.py
+@Description    : 
+'''
+
+from detectron2.config import CfgNode
+
+
+__all__ = ['get_cfg']
+
+
+def get_cfg() -> CfgNode:
+    """Get a copy of the default config
+
+    Returns:
+        CfgNode -- a detectron2 CfgNode instance
+    """
+
+    from .defaults import _C
+    return _C.clone()
+
+

imgcls/config/defaults.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 14:26:03
+@FilePath       : /ImageCls.detectron2/imgcls/config/defaults.py
+@Description    : 
+'''
+
+
+from detectron2.config.defaults import _C
+from detectron2.config import CfgNode as CN
+
+
+# ---------------------------------------------------------------------------- #
+# MobileNets
+# ---------------------------------------------------------------------------- #
+_C.MODEL.MNET = CN()
+
+# Output features
+_C.MODEL.MNET.OUT_FEATURES = ['linear']
+# Width mult
+_C.MODEL.MNET.WIDTH_MULT = 1.0
+
+
+# ---------------------------------------------------------------------------- #
+# ClsNets
+# ---------------------------------------------------------------------------- #
+_C.MODEL.CLSNET = CN()
+_C.MODEL.CLSNET.ENABLE = False
+# classes number
+_C.MODEL.CLSNET.NUM_CLASSES = 1000
+# In features
+_C.MODEL.CLSNET.IN_FEATURES = ['linear']
+# Input Size
+_C.MODEL.CLSNET.INPUT_SIZE = 224

imgcls/data/__init__.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 16:40:05
+@FilePath       : /ImageCls.detectron2/imgcls/data/__init__.py
+@Description    : 
+'''
+
+
+from .imagenet import register_imagenet_instances
+from .dataset_mapper import DatasetMapper

imgcls/data/classification_utils.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 17:01:36
+@FilePath       : /ImageCls.detectron2/imgcls/data/classification_utils.py
+@Description    : 
+'''
+
+
+import detectron2.data.transforms as T
+import logging
+
+
+def build_transform_gen(cfg, is_train):
+    """
+    Create a list of :class:`TransformGen` from config.
+    Now it includes resizing and flipping.
+
+    Returns:
+        list[TransformGen]
+    """
+    input_size = cfg.MODEL.CLSNET.INPUT_SIZE
+
+    logger = logging.getLogger("detectron2.data.classification_utils")
+    tfm_gens = []
+    tfm_gens.append(T.Resize((input_size, input_size)))
+    if is_train:
+        tfm_gens.append(T.RandomContrast(0.5, 1.5))
+        tfm_gens.append(T.RandomBrightness(0.5, 1.5))
+        tfm_gens.append(T.RandomSaturation(0.5, 1.5))
+        tfm_gens.append(T.RandomFlip())
+        logger.info(
+            "TransformGens used in training[Updated]: " + str(tfm_gens))
+    return tfm_gens

imgcls/data/dataset_mapper.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 17:01:20
+@FilePath       : /ImageCls.detectron2/imgcls/data/dataset_mapper.py
+@Description    : 
+'''
+
+from . import classification_utils as c_utils
+
+
+from detectron2.data.dataset_mapper import DatasetMapper as _DatasetMapper
+
+
+
+class DatasetMapper(_DatasetMapper):
+
+    def __init__(self, cfg, is_train=True):
+        super().__init__(cfg, is_train)
+
+        self.tfm_gens = c_utils.build_transform_gen(cfg, is_train)

imgcls/data/imagenet.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 17:02:07
+@FilePath       : /ImageCls.detectron2/imgcls/data/imagenet.py
+@Description    : 
+'''
+
+
+import os
+import os.path as osp
+import json
+from detectron2.data import DatasetCatalog, MetadataCatalog
+
+
+def register_imagenet_instances(name, metadata, json_file):
+
+    assert isinstance(name, str), name
+    assert isinstance(json_file, (str, os.PathLike)), json_file
+
+    # 1. register a function which returns dicts
+    DatasetCatalog.register(name, lambda: json.load(open(json_file)))
+
+    # 2. Optionally, add metadata about this dataset,
+    # since they might be useful in evaluation, visualization or logging
+    MetadataCatalog.get(name).set(
+        json_file=json_file, evaluator_type="imagenet", **metadata
+    )
+
+
+# Update info
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+imagenet_train_annotation_file = osp.join(_root, "ImageNet2012/imagenet_detectron2_train.json")
+imagenet_val_annotation_file = osp.join(_root, "ImageNet2012/imagenet_detectron2_val.json")
+
+# Register into DatasetCatalog
+register_imagenet_instances("imagenet_train", {}, imagenet_train_annotation_file)
+register_imagenet_instances("imagenet_val", {}, imagenet_val_annotation_file)
+
+

imgcls/evaluation/__init__.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 19:41:56
+@FilePath       : /ImageCls.detectron2/imgcls/evaluation/__init__.py
+@Description    : 
+'''
+
+
+from .imagenet_evaluation import ImageNetEvaluator

imgcls/evaluation/imagenet_evaluation.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 19:42:05
+@FilePath       : /ImageCls.detectron2/imgcls/evaluation/imagenet_evaluation.py
+@Description    : 
+'''
+
+
+import itertools
+import json
+import logging
+from collections import OrderedDict
+import torch
+from fvcore.common.file_io import PathManager
+
+import detectron2.utils.comm as comm
+from detectron2.data import MetadataCatalog
+from detectron2.evaluation.evaluator import DatasetEvaluator
+
+
+class ImageNetEvaluator(DatasetEvaluator):
+
+    def __init__(self, dataset_name, cfg, distributed, output_dir=None):
+        self._distributed = distributed
+        self._output_dir = output_dir
+
+        self._cpu_device = torch.device("cpu")
+        self._logger = logging.getLogger("detectron2.evaluation.imagenet_evaluation")
+
+        self._metadata = MetadataCatalog.get(dataset_name)
+
+        json_file = PathManager.get_local_path(self._metadata.json_file)
+        self._gt = json.load(open(json_file))
+
+    def reset(self):
+        self._predictions = []
+
+    def process(self, inputs, outputs):
+        for input, output in zip(inputs, outputs):
+            prediction = {"image_id": input["image_id"]}
+            prediction["gt"] = input["label"]
+            prediction["pred"] = output["pred_classes"].to(self._cpu_device)
+            self._predictions.append(prediction)
+
+
+    def evaluate(self):
+        if self._distributed:
+            comm.synchronize()
+            predictions = comm.gather(self._predictions, dst=0)
+            predictions = list(itertools.chain(*predictions))
+
+            if not comm.is_main_process():
+                return {}
+        else:
+            predictions = self._predictions
+
+        if len(predictions) == 0:
+            self._logger.warning(
+                "[ImageNetEvaluator] Did not receive valid predictions.")
+            return {}
+
+        topk = len(self._predictions[0]['pred'])
+        target = []
+        pred = []
+        for p in self._predictions:
+            target.append(p['gt'])
+            pred.append(p['pred'])
+        pred = torch.stack(pred, dim=0)
+        target = torch.as_tensor(target, dtype=pred.dtype)
+        num_samples = target.size(0)
+
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        topk_acc = []
+        for k in range(1, topk + 1):
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            topk_acc.append(correct_k.mul_(100.0 / num_samples))
+        result = OrderedDict(
+            accuracy={"top{}".format(i + 1): acc.item() for i, acc in enumerate(topk_acc)},
+        )
+        return result

imgcls/modeling/__init__.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 14:11:12
+@FilePath       : /ImageCls.detectron2/imgcls/modeling/__init__.py
+@Description    : 
+'''
+
+
+from .backbone import *
+from .meta_arch import *
+
+
+__all__ = [k for k in globals().keys() if not k.startswith("_")]

imgcls/modeling/backbone/__init__.py

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+'''
+@Copyright (c) tkianai All Rights Reserved.
+@Author         : tkianai
+@Github         : https://github.com/tkianai
+@Date           : 2020-04-26 14:11:49
+@FilePath       : /ImageCls.detectron2/imgcls/modeling/backbone/__init__.py
+@Description    : 
+'''
+
+
+from .mobilenet import *
+
+
+__all__ = [k for k in globals().keys() if not k.startswith("_")]