add aligndet

config/aligndet_r101v1_fpn_fp16_1x.py

@@ -0,0 +1,292 @@
+from symbol.builder import FasterRcnn as Detector
+from models.retinanet.builder import MSRAResNet101V1FPN as Backbone
+from models.retinanet.builder import RetinaNetNeck as Neck
+from models.aligndet.builder import AlignRetinaNetHead as RpnHead
+from models.aligndet.builder import AlignRoiExtractor as RoiExtractor
+from models.aligndet.builder import AlignHead as BboxHead
+from mxnext.complicate import normalizer_factory
+
+
+def get_config(is_train):
+    class General:
+        log_frequency = 10
+        name = __name__.rsplit("/")[-1].rsplit(".")[-1]
+        batch_image = 2 if is_train else 1
+        fp16 = True
+
+
+    class KvstoreParam:
+        kvstore     = "local"
+        batch_image = General.batch_image
+        gpus        = [0, 1, 2, 3, 4, 5, 6, 7]
+        fp16        = General.fp16
+
+
+    class NormalizeParam:
+        normalizer = normalizer_factory(type="fixbn")
+
+
+    class BackboneParam:
+        fp16 = General.fp16
+        normalizer = NormalizeParam.normalizer
+
+
+    class NeckParam:
+        fp16 = General.fp16
+        normalizer = NormalizeParam.normalizer
+
+
+    class RpnParam:
+        fp16 = General.fp16
+        normalizer = NormalizeParam.normalizer
+        batch_image = General.batch_image
+        num_class   = 1 + 80
+        loss_weight = 1.0
+
+        class anchor_generate:
+            scale = (4 * 2 ** (1.0 / 3.0),)
+            ratio = (1.0,)
+            stride = (8, 16, 32, 64, 128)
+            short = (100, 50, 25, 13, 7)
+            long = (167, 84, 42, 21, 11)
+            image_anchor = None
+
+        class head:
+            conv_channel = 256
+            mean = (0.0, 0.0, 0.0, 0.0)
+            std = (1.0, 1.0, 1.0, 1.0)
+
+        class focal_loss:
+            alpha = 0.25
+            gamma = 2.0
+
+        class proposal:
+            pre_nms_top_n = 1000
+            min_bbox_side = 0
+            min_det_score = 0.05
+
+        class subsample_proposal:
+            pass
+
+        class bbox_target:
+            class_agnostic = False
+            mean = (0.0, 0.0, 0.0, 0.0)
+            std = (1.0, 1.0, 1.0, 1.0)
+            allowed_border = 9999
+            pos_thr = 0.7
+            neg_thr = 0.7
+            min_pos_thr = 0.0
+
+
+    class BboxParam:
+        fp16        = General.fp16
+        normalizer  = NormalizeParam.normalizer
+        num_class   = 1 + 80
+        loss_weight = 1.0
+
+        class anchor_generate:
+            scale = (4 * 2 ** (1.0 / 3.0),)
+            ratio = (1.0,)
+            stride = (8, 16, 32, 64, 128)
+
+        class head:
+            merge_score = False
+            num_conv = 2
+            use_1x1 = True
+            conv_channel = 1024
+            mean = (0.0, 0.0, 0.0, 0.0)
+            std = (1.0, 1.0, 1.0, 1.0)
+
+        class focal_loss:
+            alpha = 0.25
+            gamma = 2.0
+
+        class proposal:
+            pre_nms_top_n = 1000
+            min_bbox_side = 0
+            min_det_score = 0.05
+
+
+    class RoiParam:
+        fp16 = General.fp16
+        normalizer = NormalizeParam.normalizer
+        sample_bins = 7
+        im2col = True
+        stride = (8, 16, 32, 64, 128)
+        conv_channel = 256 * 7 * 7
+        scale = (4 * 2 ** (1.0 / 3.0),)
+        ratio = (1.0,)
+
+
+    class DatasetParam:
+        if is_train:
+            image_set = ("coco_train2014", "coco_valminusminival2014")
+        else:
+            image_set = ("coco_minival2014", )
+
+    backbone = Backbone(BackboneParam)
+    neck = Neck(NeckParam)
+    rpn_head = RpnHead(RpnParam)
+    roi_extractor = RoiExtractor(RoiParam)
+    bbox_head = BboxHead(BboxParam)
+    detector = Detector()
+    if is_train:
+        train_sym = detector.get_train_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head)
+        rpn_test_sym = None
+        test_sym = None
+    else:
+        train_sym = None
+        rpn_test_sym = None
+        test_sym = detector.get_test_symbol(backbone, neck, rpn_head, roi_extractor, bbox_head)
+
+
+    class ModelParam:
+        train_symbol = train_sym
+        test_symbol = test_sym
+        rpn_test_symbol = rpn_test_sym
+
+        from_scratch = False
+        random = True
+        memonger = False
+        memonger_until = "stage3_unit21_plus"
+
+        class pretrain:
+            prefix = "pretrain_model/resnet-v1-101"
+            epoch = 0
+            fixed_param = ["conv0", "stage1", "gamma", "beta"]
+
+
+    class OptimizeParam:
+        class optimizer:
+            type = "sgd"
+            lr = 0.005 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image
+            momentum = 0.9
+            wd = 0.0001
+            clip_gradient = None
+
+        class schedule:
+            begin_epoch = 0
+            end_epoch = 6
+            lr_iter = [60000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image),
+                       80000 * 16 // (len(KvstoreParam.gpus) * KvstoreParam.batch_image)]
+
+        class warmup:
+            type = "gradual"
+            lr = 0.005 / 8 * len(KvstoreParam.gpus) * KvstoreParam.batch_image / 3
+            iter = 500
+
+
+    class TestParam:
+        min_det_score = 0 # filter appended boxes
+        max_det_per_image = 100
+
+        process_roidb = lambda x: x
+        process_output = lambda x, y: x
+
+        class model:
+            prefix = "experiments/{}/checkpoint".format(General.name)
+            epoch = OptimizeParam.schedule.end_epoch
+
+        class nms:
+            type = "nms"
+            thr = 0.5
+
+        class coco:
+            annotation = "data/coco/annotations/instances_minival2014.json"
+
+    # data processing
+    class NormParam:
+        mean = (122.7717, 115.9465, 102.9801) # RGB order
+        std = (1.0, 1.0, 1.0)
+
+
+    class ResizeParam:
+        short = 800
+        long = 1333
+
+
+    class PadParam:
+        short = 800
+        long = 1333
+        max_num_gt = 100
+
+
+    class AnchorTarget2DParam:
+        def __init__(self):
+            self.generate = self._generate()
+            self.mean = (0.0, 0.0, 0.0, 0.0)
+            self.std = (1.0, 1.0, 1.0, 1.0)
+            self.class_agnostic = False
+
+        class _generate:
+            def __init__(self):
+                self.short = (100, 50, 25, 13, 7)
+                self.long = (167, 84, 42, 21, 11)
+                self.stride = (8, 16, 32, 64, 128)
+
+            scales = (4 * 2 ** (1.0 / 3.0),)
+            aspects = (1.0,)
+
+        class assign:
+            allowed_border = 9999
+            pos_thr = 0.4
+            neg_thr = 0.3
+            min_pos_thr = 0.0
+
+        class sample:
+            image_anchor = None
+            pos_fraction = None
+
+
+    class RenameParam:
+        mapping = dict(image="data")
+
+
+    from core.detection_input import ReadRoiRecord, Resize2DImageBbox, \
+        ConvertImageFromHwcToChw, Flip2DImageBbox, Pad2DImageBbox, \
+        RenameRecord
+    from models.retinanet.input import PyramidAnchorTarget2D, Norm2DImage
+
+    if is_train:
+        transform = [
+            ReadRoiRecord(None),
+            Norm2DImage(NormParam),
+            Resize2DImageBbox(ResizeParam),
+            Flip2DImageBbox(),
+            Pad2DImageBbox(PadParam),
+            ConvertImageFromHwcToChw(),
+            PyramidAnchorTarget2D(AnchorTarget2DParam()),
+            RenameRecord(RenameParam.mapping)
+        ]
+        data_name = ["data", "im_info", "gt_bbox"]
+        label_name = ["rpn_cls_label", "rpn_reg_target", "rpn_reg_weight"]
+    else:
+        transform = [
+            ReadRoiRecord(None),
+            Norm2DImage(NormParam),
+            Resize2DImageBbox(ResizeParam),
+            ConvertImageFromHwcToChw(),
+            RenameRecord(RenameParam.mapping)
+        ]
+        data_name = ["data", "im_info", "im_id", "rec_id"]
+        label_name = []
+
+    from models.retinanet import metric
+
+    rpn_acc_metric = metric.FGAccMetric(
+        "FGAcc",
+        ["cls_loss_output"],
+        ["rpn_cls_label"]
+    )
+
+    bbox_acc_metric = metric.FGAccMetric(
+        "BboxFGAcc",
+        ["align_cls_loss_output", "align_label_blockgrad_output"],
+        []
+    )
+
+    metric_list = [rpn_acc_metric, bbox_acc_metric]
+
+    return General, KvstoreParam, RpnParam, RoiParam, BboxParam, DatasetParam, \
+           ModelParam, OptimizeParam, TestParam, \
+           transform, data_name, label_name, metric_list