add ssd vgg backbone support

model_zoo/official/cv/ssd/README.md

@@ -35,11 +35,12 @@ SSD discretizes the output space of bounding boxes into a set of default boxes o
 
 The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.
 
-We present three different base architecture.
+We present four different base architecture.
 
 - **ssd300**, reference from the paper. Using mobilenetv2 as backbone and the same bbox predictor as the paper present.
 - ***ssd-mobilenet-v1-fpn**, using mobilenet-v1 and FPN as feature extractor with weight-shared box predcitors.
 - ***ssd-resnet50-fpn**, using resnet50 and FPN as feature extractor with weight-shared box predcitors.
+- **ssd-vgg16**, reference from the paper. Using vgg16 as backbone and the same bbox predictor as the paper present.
 
 ## [Dataset](#contents)
 

model_zoo/official/cv/ssd/eval.py

@@ -21,7 +21,7 @@ import time
 import numpy as np
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
-from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn
+from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
 from src.dataset import create_ssd_dataset, create_mindrecord
 from src.config import config
 from src.eval_utils import metrics
@@ -34,6 +34,8 @@ def ssd_eval(dataset_path, ckpt_path, anno_json):
                             is_training=False, use_multiprocessing=False)
     if config.model == "ssd300":
         net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
+    elif config.model == "ssd_vgg16":
+        net = ssd_vgg16(config=config)
     elif config.model == "ssd_mobilenet_v1_fpn":
         net = ssd_mobilenet_v1_fpn(config=config)
     elif config.model == "ssd_resnet50_fpn":

model_zoo/official/cv/ssd/export.py

@@ -19,7 +19,7 @@ import numpy as np
 import mindspore
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
-from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn
+from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
 from src.config import config
 from src.box_utils import default_boxes
 
@@ -40,6 +40,8 @@ if args.device_target == "Ascend":
 if __name__ == '__main__':
     if config.model == "ssd300":
         net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
+    elif config.model == "ssd_vgg16":
+        net = ssd_vgg16(config=config)
     elif config.model == "ssd_mobilenet_v1_fpn":
         net = ssd_mobilenet_v1_fpn(config=config)
     elif config.model == "ssd_resnet50_fpn":

model_zoo/official/cv/ssd/src/config.py

@@ -11,18 +11,20 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-#" ============================================================================
+# ============================================================================
 
 """Config parameters for SSD models."""
 
 from .config_ssd300 import config as config_ssd300
 from .config_ssd_mobilenet_v1_fpn import config as config_ssd_mobilenet_v1_fpn
 from .config_ssd_resnet50_fpn import config as config_ssd_resnet50_fpn
+from .config_ssd_vgg16 import config as config_ssd_vgg16
 
 using_model = "ssd300"
 
 config_map = {
     "ssd300": config_ssd300,
+    "ssd_vgg16": config_ssd_vgg16,
     "ssd_mobilenet_v1_fpn": config_ssd_mobilenet_v1_fpn,
     "ssd_resnet50_fpn": config_ssd_resnet50_fpn
 }

model_zoo/official/cv/ssd/src/config_ssd_vgg16.py

@@ -0,0 +1,84 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Config parameters for SSD models."""
+
+from easydict import EasyDict as ed
+
+config = ed({
+    "model": "ssd_vgg16",
+    "img_shape": [300, 300],
+    "num_ssd_boxes": 7308,
+    "match_threshold": 0.5,
+    "nms_threshold": 0.6,
+    "min_score": 0.1,
+    "max_boxes": 100,
+    "ssd_vgg_bn": False,
+
+    # learing rate settings
+    "lr_init": 0.001,
+    "lr_end_rate": 0.001,
+    "warmup_epochs": 2,
+    "momentum": 0.9,
+    "weight_decay": 1.5e-4,
+
+    # network
+    "num_default": [3, 6, 6, 6, 6, 6],
+    "extras_in_channels": [256, 512, 1024, 512, 256, 256],
+    "extras_out_channels": [512, 1024, 512, 256, 256, 256],
+    "extras_strides": [1, 1, 2, 2, 2, 2],
+    "extras_ratio": [0.2, 0.2, 0.2, 0.25, 0.5, 0.25],
+    "feature_size": [38, 19, 10, 5, 3, 1],
+    "min_scale": 0.2,
+    "max_scale": 0.95,
+    "aspect_ratios": [(), (2, 3), (2, 3), (2, 3), (2, 3), (2, 3)],
+    "steps": (8, 16, 32, 64, 100, 300),
+    "prior_scaling": (0.1, 0.2),
+    "gamma": 2.0,
+    "alpha": 0.75,
+
+    # `mindrecord_dir` and `coco_root` are better to use absolute path.
+    "feature_extractor_base_param": "",
+    "pretrain_vgg_bn": False,
+    "checkpoint_filter_list": ['multi_loc_layers', 'multi_cls_layers'],
+    "mindrecord_dir": "/data/MindRecord_COCO",
+    "coco_root": "/data/coco2017",
+    "train_data_type": "train2017",
+    "val_data_type": "val2017",
+    "instances_set": "annotations/instances_{}.json",
+    "classes": ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+                'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
+                'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
+                'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
+                'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
+                'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
+                'kite', 'baseball bat', 'baseball glove', 'skateboard',
+                'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
+                'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+                'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
+                'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
+                'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
+                'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
+                'refrigerator', 'book', 'clock', 'vase', 'scissors',
+                'teddy bear', 'hair drier', 'toothbrush'),
+    "num_classes": 81,
+    # The annotation.json position of voc validation dataset.
+    "voc_json": "annotations/voc_instances_val.json",
+    # voc original dataset.
+    "voc_root": "/data/voc_dataset",
+    # if coco or voc used, `image_dir` and `anno_path` are useless.
+    "image_dir": "",
+    "anno_path": ""
+})

model_zoo/official/cv/ssd/src/ssd.py

@@ -27,6 +27,7 @@ from mindspore.ops import functional as F
 from mindspore.ops import composite as C
 
 from .fpn import mobilenet_v1_fpn, resnet50_fpn
+from .vgg16 import vgg16
 
 
 def _make_divisible(v, divisor, min_value=None):
@@ -641,3 +642,78 @@ def ssd_resnet50_fpn(**kwargs):
 
 def ssd_mobilenet_v2(**kwargs):
     return SSDWithMobileNetV2(**kwargs)
+
+
+class SSD300VGG16(nn.Cell):
+    def __init__(self, config):
+        super(SSD300VGG16, self).__init__()
+
+        # VGG16 backbone: block1~5
+        self.backbone = vgg16()
+
+        # SSD blocks: block6~7
+        self.b6_1 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6, pad_mode='pad')
+        self.b6_2 = nn.Dropout(0.5)
+
+        self.b7_1 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1)
+        self.b7_2 = nn.Dropout(0.5)
+
+        # Extra Feature Layers: block8~11
+        self.b8_1 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1, padding=1, pad_mode='pad')
+        self.b8_2 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, pad_mode='valid')
+
+        self.b9_1 = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=1, padding=1, pad_mode='pad')
+        self.b9_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, pad_mode='valid')
+
+        self.b10_1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1)
+        self.b10_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, pad_mode='valid')
+
+        self.b11_1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1)
+        self.b11_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, pad_mode='valid')
+
+        # boxes
+        self.multi_box = MultiBox(config)
+        if not self.training:
+            self.activation = P.Sigmoid()
+
+    def construct(self, x):
+        # VGG16 backbone: block1~5
+        block4, x = self.backbone(x)
+
+        # SSD blocks: block6~7
+        x = self.b6_1(x)  # 1024
+        x = self.b6_2(x)
+
+        x = self.b7_1(x)  # 1024
+        x = self.b7_2(x)
+        block7 = x
+
+        # Extra Feature Layers: block8~11
+        x = self.b8_1(x)  # 256
+        x = self.b8_2(x)  # 512
+        block8 = x
+
+        x = self.b9_1(x)  # 128
+        x = self.b9_2(x)  # 256
+        block9 = x
+
+        x = self.b10_1(x)  # 128
+        x = self.b10_2(x)  # 256
+        block10 = x
+
+        x = self.b11_1(x)  # 128
+        x = self.b11_2(x)  # 256
+        block11 = x
+
+        # boxes
+        multi_feature = (block4, block7, block8, block9, block10, block11)
+        pred_loc, pred_label = self.multi_box(multi_feature)
+        if not self.training:
+            pred_label = self.activation(pred_label)
+        pred_loc = F.cast(pred_loc, mstype.float32)
+        pred_label = F.cast(pred_label, mstype.float32)
+        return pred_loc, pred_label
+
+
+def ssd_vgg16(**kwargs):
+    return SSD300VGG16(**kwargs)

model_zoo/official/cv/ssd/src/vgg16.py

@@ -0,0 +1,99 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""VGG16 backbone for SSD"""
+
+from mindspore import nn
+from .config_ssd_vgg16 import config
+
+pretrain_vgg_bn = config.pretrain_vgg_bn
+ssd_vgg_bn = config.ssd_vgg_bn
+
+
+def _get_key_mapper():
+    vgg_key_num = [1, 1, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5]
+    size = len(vgg_key_num)
+
+    pretrain_vgg_bn_false = [0, 2, 5, 7, 10, 12, 14, 17, 19, 21, 24, 26, 28]
+    pretrain_vgg_bn_true = [0, 3, 7, 10, 14, 17, 20, 24, 27, 30, 34, 37, 40]
+    ssd_vgg_bn_false = [0, 2, 0, 2, 0, 2, 4, 0, 2, 4, 0, 2, 4]
+    ssd_vgg_bn_true = [0, 3, 0, 3, 0, 3, 6, 0, 3, 6, 0, 3, 6]
+
+    pretrain_vgg_keys = pretrain_vgg_bn_true if pretrain_vgg_bn else pretrain_vgg_bn_false
+    ssd_vgg_keys = ssd_vgg_bn_true if ssd_vgg_bn else ssd_vgg_bn_false
+
+    pretrain_vgg_keys = ['layers.' + str(pretrain_vgg_keys[i]) for i in range(size)]
+    ssd_vgg_keys = ['b' + str(vgg_key_num[i]) + '.' + str(ssd_vgg_keys[i]) for i in range(size)]
+
+    return {pretrain_vgg_keys[i]: ssd_vgg_keys[i] for i in range(size)}
+
+
+ssd_vgg_key_mapper = _get_key_mapper()
+
+
+def _make_layer(channels):
+    in_channels = channels[0]
+    layers = []
+    for out_channels in channels[1:]:
+        layers.append(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3))
+        if ssd_vgg_bn:
+            layers.append(nn.BatchNorm2d(out_channels))
+        layers.append(nn.ReLU())
+        in_channels = out_channels
+    return nn.SequentialCell(layers)
+
+
+class VGG16(nn.Cell):
+    def __init__(self):
+        super(VGG16, self).__init__()
+        self.b1 = _make_layer([3, 64, 64])
+        self.b2 = _make_layer([64, 128, 128])
+        self.b3 = _make_layer([128, 256, 256, 256])
+        self.b4 = _make_layer([256, 512, 512, 512])
+        self.b5 = _make_layer([512, 512, 512, 512])
+
+        self.m1 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
+        self.m2 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
+        self.m3 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
+        self.m4 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
+        self.m5 = nn.MaxPool2d(kernel_size=3, stride=1, pad_mode='SAME')
+
+    def construct(self, x):
+        # block1
+        x = self.b1(x)
+        x = self.m1(x)
+
+        # block2
+        x = self.b2(x)
+        x = self.m2(x)
+
+        # block3
+        x = self.b3(x)
+        x = self.m3(x)
+
+        # block4
+        x = self.b4(x)
+        block4 = x
+        x = self.m4(x)
+
+        # block5
+        x = self.b5(x)
+        x = self.m5(x)
+
+        return block4, x
+
+
+def vgg16():
+    return VGG16()

model_zoo/official/cv/ssd/train.py

@@ -25,7 +25,7 @@ from mindspore.train import Model
 from mindspore.context import ParallelMode
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.common import set_seed, dtype
-from src.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn
+from src.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
 from src.config import config
 from src.dataset import create_ssd_dataset, create_mindrecord
 from src.lr_schedule import get_lr
@@ -86,6 +86,17 @@ def ssd_model_build(args_opt):
                 param_dict["network.feature_extractor.resnet." + x] = param_dict[x]
                 del param_dict[x]
             load_param_into_net(ssd.feature_extractor.resnet, param_dict)
+    elif config.model == "ssd_vgg16":
+        ssd = ssd_vgg16(config=config)
+        init_net_param(ssd)
+        if config.feature_extractor_base_param != "":
+            param_dict = load_checkpoint(config.feature_extractor_base_param)
+            from src.vgg16 import ssd_vgg_key_mapper
+            for k in ssd_vgg_key_mapper:
+                v = ssd_vgg_key_mapper[k]
+                param_dict["network.backbone." + v + ".weight"] = param_dict[k + ".weight"]
+                del param_dict[k + ".weight"]
+            load_param_into_net(ssd.backbone, param_dict)
     else:
         raise ValueError(f'config.model: {config.model} is not supported')
     return ssd
@@ -106,6 +117,8 @@ def main():
             init()
             if config.model == "ssd_resnet50_fpn":
                 context.set_auto_parallel_context(all_reduce_fusion_config=[90, 183, 279])
+            if config.model == "ssd_vgg16":
+                context.set_auto_parallel_context(all_reduce_fusion_config=[20, 41, 62])
             else:
                 context.set_auto_parallel_context(all_reduce_fusion_config=[29, 58, 89])
             rank = get_rank()