Merge pull request #81 from heavengate/fix_yolov3

fix yolov3

examples/yolov3/darknet.py

@@ -12,12 +12,11 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import math
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
-from paddle.fluid.dygraph.nn import Conv2D, BatchNorm, Pool2D, Linear
+from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
 
 from paddle.incubate.hapi.model import Model
 from paddle.incubate.hapi.download import get_weights_path_from_url
@@ -25,10 +24,9 @@ from paddle.incubate.hapi.download import get_weights_path_from_url
 __all__ = ['DarkNet', 'darknet53']
 
 # {num_layers: (url, md5)}
-model_urls = {
-    'darknet53':
-    ('https://paddle-hapi.bj.bcebos.com/models/darknet53.pdparams',
-     'ca506a90e2efecb9a2093f8ada808708')
+pretrain_infos = {
+        53: ('https://paddlemodels.bj.bcebos.com/hapi/darknet53.pdparams',
+            '2506357a5c31e865785112fc614a487d')
 }
 
 
@@ -68,14 +66,17 @@ class ConvBNLayer(fluid.dygraph.Layer):
     def forward(self, inputs):
         out = self.conv(inputs)
         out = self.batch_norm(out)
-
         if self.act == 'leaky':
             out = fluid.layers.leaky_relu(x=out, alpha=0.1)
         return out
 
-
 class DownSample(fluid.dygraph.Layer):
-    def __init__(self, ch_in, ch_out, filter_size=3, stride=2, padding=1):
+    def __init__(self,
+                 ch_in,
+                 ch_out,
+                 filter_size=3,
+                 stride=2,
+                 padding=1):
 
         super(DownSample, self).__init__()
 
@@ -86,45 +87,46 @@ class DownSample(fluid.dygraph.Layer):
             stride=stride,
             padding=padding)
         self.ch_out = ch_out
-
     def forward(self, inputs):
         out = self.conv_bn_layer(inputs)
         return out
 
-
 class BasicBlock(fluid.dygraph.Layer):
     def __init__(self, ch_in, ch_out):
         super(BasicBlock, self).__init__()
 
         self.conv1 = ConvBNLayer(
-            ch_in=ch_in, ch_out=ch_out, filter_size=1, stride=1, padding=0)
+            ch_in=ch_in,
+            ch_out=ch_out,
+            filter_size=1,
+            stride=1,
+            padding=0)
         self.conv2 = ConvBNLayer(
             ch_in=ch_out,
-            ch_out=ch_out * 2,
+            ch_out=ch_out*2,
             filter_size=3,
             stride=1,
             padding=1)
-
     def forward(self, inputs):
         conv1 = self.conv1(inputs)
         conv2 = self.conv2(conv1)
         out = fluid.layers.elementwise_add(x=inputs, y=conv2, act=None)
         return out
 
-
 class LayerWarp(fluid.dygraph.Layer):
     def __init__(self, ch_in, ch_out, count):
-        super(LayerWarp, self).__init__()
+        super(LayerWarp,self).__init__()
 
         self.basicblock0 = BasicBlock(ch_in, ch_out)
         self.res_out_list = []
-        for i in range(1, count):
+        for i in range(1,count):
             res_out = self.add_sublayer("basic_block_%d" % (i),
-                                        BasicBlock(ch_out * 2, ch_out))
+                BasicBlock(
+                    ch_out*2,
+                    ch_out))
             self.res_out_list.append(res_out)
         self.ch_out = ch_out
-
-    def forward(self, inputs):
+    def forward(self,inputs):
         y = self.basicblock0(inputs)
         for basic_block_i in self.res_out_list:
             y = basic_block_i(y)
@@ -140,100 +142,78 @@ class DarkNet(Model):
 
     Args:
         num_layers (int): layer number of DarkNet, only 53 supported currently, default: 53.
-        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
-                            will not be defined. Default: 1000.
-        with_pool (bool): use pool before the last fc layer or not. Default: True.
-        classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
-
+        ch_in (int): channel number of input data, default 3.
     """
 
-    def __init__(self,
-                 num_layers=53,
-                 num_classes=1000,
-                 with_pool=True,
-                 classifier_activation='softmax'):
+    def __init__(self, num_layers=53, ch_in=3):
         super(DarkNet, self).__init__()
         assert num_layers in DarkNet_cfg.keys(), \
             "only support num_layers in {} currently" \
             .format(DarkNet_cfg.keys())
         self.stages = DarkNet_cfg[num_layers]
         self.stages = self.stages[0:5]
-        self.num_classes = num_classes
-        self.with_pool = True
-        ch_in = 3
+
         self.conv0 = ConvBNLayer(
-            ch_in=ch_in, ch_out=32, filter_size=3, stride=1, padding=1)
+            ch_in=ch_in,
+            ch_out=32,
+            filter_size=3,
+            stride=1,
+            padding=1)
 
-        self.downsample0 = DownSample(ch_in=32, ch_out=32 * 2)
+        self.downsample0 = DownSample(
+            ch_in=32,
+            ch_out=32 * 2)
         self.darknet53_conv_block_list = []
         self.downsample_list = []
-        ch_in = [64, 128, 256, 512, 1024]
+        ch_in = [64,128,256,512,1024]
         for i, stage in enumerate(self.stages):
-            conv_block = self.add_sublayer("stage_%d" % (i),
+            conv_block = self.add_sublayer(
+                "stage_%d" % (i),
                 LayerWarp(
-                                               int(ch_in[i]), 32 * (2**i),
+                int(ch_in[i]),
+                32*(2**i),
                 stage))
             self.darknet53_conv_block_list.append(conv_block)
-
         for i in range(len(self.stages) - 1):
             downsample = self.add_sublayer(
                 "stage_%d_downsample" % i,
                 DownSample(
-                    ch_in=32 * (2**(i + 1)), ch_out=32 * (2**(i + 2))))
+                    ch_in = 32*(2**(i+1)),
+                    ch_out = 32*(2**(i+2))))
             self.downsample_list.append(downsample)
 
-        if self.with_pool:
-            self.global_pool = Pool2D(
-                pool_size=7, pool_type='avg', global_pooling=True)
-
-        if self.num_classes > 0:
-            stdv = 1.0 / math.sqrt(32 * (2**(i + 2)))
-            self.fc_input_dim = 32 * (2**(i + 2))
-
-            self.fc = Linear(
-                self.fc_input_dim,
-                num_classes,
-                act='softmax',
-                param_attr=fluid.param_attr.ParamAttr(
-                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
-
-    def forward(self, inputs):
+    def forward(self,inputs):
         
         out = self.conv0(inputs)
         out = self.downsample0(out)
-
+        blocks = []
         for i, conv_block_i in enumerate(self.darknet53_conv_block_list):
             out = conv_block_i(out)
+            blocks.append(out)
             if i < len(self.stages) - 1:
                 out = self.downsample_list[i](out)
-
-        if self.with_pool:
-            out = self.global_pool(out)
-        if self.num_classes > 0:
-            out = fluid.layers.reshape(out, shape=[-1, self.fc_input_dim])
-            out = self.fc(out)
-        return out
+        return blocks[-1:-4:-1]
 
 
-def _darknet(arch, num_layers=53, pretrained=False, **kwargs):
-    model = DarkNet(num_layers, **kwargs)
+def _darknet(num_layers=53, input_channels=3, pretrained=True):
+    model = DarkNet(num_layers, input_channels)
     if pretrained:
-        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
-            arch)
-        weight_path = get_weights_path_from_url(*(model_urls[arch]))
+        assert num_layers in pretrain_infos.keys(), \
+                "DarkNet{} do not have pretrained weights now, " \
+                "pretrained should be set as False".format(num_layers)
+        weight_path = get_weights_path_from_url(*(pretrain_infos[num_layers]))
         assert weight_path.endswith('.pdparams'), \
                 "suffix of weight must be .pdparams"
-        model.load(weight_path)
+        model.load(weight_path[:-9])
     return model
 
 
-def darknet53(pretrained=False, **kwargs):
+def darknet53(input_channels=3, pretrained=True):
     """DarkNet 53-layer model
     
     Args:
         input_channels (bool): channel number of input data, default 3. 
         pretrained (bool): If True, returns a model pre-trained on ImageNet,
             default True.
-            
     """
-    return _darknet('darknet53', 53, pretrained, **kwargs)
+    return _darknet(53, input_channels, pretrained)

examples/yolov3/main.py

@@ -215,7 +215,7 @@ if __name__ == '__main__':
         metavar='LR',
         help='initial learning rate')
     parser.add_argument(
-        "-b", "--batch_size", default=8, type=int, help="batch size")
+        "-b", "--batch_size", default=16, type=int, help="batch size")
     parser.add_argument(
         "-j",
         "--num_workers",

examples/yolov3/modeling.py

@@ -20,9 +20,9 @@ from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
 from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.regularizer import L2Decay
 
-from hapi.model import Model
-from hapi.loss import Loss
-from hapi.download import get_weights_path_from_url
+from paddle.incubate.hapi.model import Model
+from paddle.incubate.hapi.loss import Loss
+from paddle.incubate.hapi.download import get_weights_path_from_url
 from darknet import darknet53
 
 __all__ = ['YoloLoss', 'YOLOv3', 'yolov3_darknet53']
@@ -158,10 +158,7 @@ class YOLOv3(Model):
         self.nms_posk = 100
         self.draw_thresh = 0.5
 
-        self.backbone = darknet53(
-            pretrained=(model_mode == 'train'),
-            with_pool=False,
-            num_classes=-1)
+        self.backbone = darknet53(pretrained=(model_mode == 'train'))
         self.block_outputs = []
         self.yolo_blocks = []
         self.route_blocks = []
@@ -300,7 +297,7 @@ class YoloLoss(Loss):
                 anchors=self.anchors,
                 class_num=self.num_classes,
                 ignore_thresh=self.ignore_thresh,
-                use_label_smooth=True)
+                use_label_smooth=False)
             loss = fluid.layers.reduce_mean(loss)
             losses.append(loss)
             downsample //= 2

hapi/vision/models/__init__.py

@@ -16,19 +16,16 @@ from . import resnet
 from . import vgg
 from . import mobilenetv1
 from . import mobilenetv2
-from . import darknet
 from . import lenet
 
 from .resnet import *
 from .mobilenetv1 import *
 from .mobilenetv2 import *
 from .vgg import *
-from .darknet import *
 from .lenet import *
 
 __all__ = resnet.__all__ \
         + vgg.__all__ \
         + mobilenetv1.__all__ \
         + mobilenetv2.__all__ \
-        + darknet.__all__\
         + lenet.__all__

hapi/vision/models/darknet.py

-#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-#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.
-
-import math
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-from paddle.fluid.regularizer import L2Decay
-
-from paddle.fluid.dygraph.nn import Conv2D, BatchNorm, Pool2D, Linear
-
-from hapi.model import Model
-from hapi.download import get_weights_path_from_url
-
-__all__ = ['DarkNet', 'darknet53']
-
-# {num_layers: (url, md5)}
-model_urls = {
-    'darknet53':
-    ('https://paddle-hapi.bj.bcebos.com/models/darknet53.pdparams',
-     'ca506a90e2efecb9a2093f8ada808708')
-}
-
-
-class ConvBNLayer(fluid.dygraph.Layer):
-    def __init__(self,
-                 ch_in,
-                 ch_out,
-                 filter_size=3,
-                 stride=1,
-                 groups=1,
-                 padding=0,
-                 act="leaky"):
-        super(ConvBNLayer, self).__init__()
-
-        self.conv = Conv2D(
-            num_channels=ch_in,
-            num_filters=ch_out,
-            filter_size=filter_size,
-            stride=stride,
-            padding=padding,
-            groups=groups,
-            param_attr=ParamAttr(
-                initializer=fluid.initializer.Normal(0., 0.02)),
-            bias_attr=False,
-            act=None)
-        self.batch_norm = BatchNorm(
-            num_channels=ch_out,
-            param_attr=ParamAttr(
-                initializer=fluid.initializer.Normal(0., 0.02),
-                regularizer=L2Decay(0.)),
-            bias_attr=ParamAttr(
-                initializer=fluid.initializer.Constant(0.0),
-                regularizer=L2Decay(0.)))
-
-        self.act = act
-
-    def forward(self, inputs):
-        out = self.conv(inputs)
-        out = self.batch_norm(out)
-        # out = fluid.layers.relu(out)
-        if self.act == 'leaky':
-            out = fluid.layers.leaky_relu(x=out, alpha=0.1)
-        return out
-
-
-class DownSample(fluid.dygraph.Layer):
-    def __init__(self, ch_in, ch_out, filter_size=3, stride=2, padding=1):
-
-        super(DownSample, self).__init__()
-
-        self.conv_bn_layer = ConvBNLayer(
-            ch_in=ch_in,
-            ch_out=ch_out,
-            filter_size=filter_size,
-            stride=stride,
-            padding=padding)
-        self.ch_out = ch_out
-
-    def forward(self, inputs):
-        out = self.conv_bn_layer(inputs)
-        return out
-
-
-class BasicBlock(fluid.dygraph.Layer):
-    def __init__(self, ch_in, ch_out):
-        super(BasicBlock, self).__init__()
-
-        self.conv1 = ConvBNLayer(
-            ch_in=ch_in, ch_out=ch_out, filter_size=1, stride=1, padding=0)
-        self.conv2 = ConvBNLayer(
-            ch_in=ch_out,
-            ch_out=ch_out * 2,
-            filter_size=3,
-            stride=1,
-            padding=1)
-
-    def forward(self, inputs):
-        conv1 = self.conv1(inputs)
-        conv2 = self.conv2(conv1)
-        out = fluid.layers.elementwise_add(x=inputs, y=conv2, act=None)
-        return out
-
-
-class LayerWarp(fluid.dygraph.Layer):
-    def __init__(self, ch_in, ch_out, count):
-        super(LayerWarp, self).__init__()
-
-        self.basicblock0 = BasicBlock(ch_in, ch_out)
-        self.res_out_list = []
-        for i in range(1, count):
-            res_out = self.add_sublayer("basic_block_%d" % (i),
-                                        BasicBlock(ch_out * 2, ch_out))
-            self.res_out_list.append(res_out)
-        self.ch_out = ch_out
-
-    def forward(self, inputs):
-        y = self.basicblock0(inputs)
-        for basic_block_i in self.res_out_list:
-            y = basic_block_i(y)
-        return y
-
-
-DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
-
-
-class DarkNet(Model):
-    """DarkNet model from
-    `"YOLOv3: An Incremental Improvement" <https://arxiv.org/abs/1804.02767>`_
-
-    Args:
-        num_layers (int): layer number of DarkNet, only 53 supported currently, default: 53.
-        num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer 
-                            will not be defined. Default: 1000.
-        with_pool (bool): use pool before the last fc layer or not. Default: True.
-        classifier_activation (str): activation for the last fc layer. Default: 'softmax'.
-
-    Examples:
-        .. code-block:: python
-
-            from hapi.vision.models import DarkNet
-
-            model = DarkNet()
-    """
-
-    def __init__(self,
-                 num_layers=53,
-                 num_classes=1000,
-                 with_pool=True,
-                 classifier_activation='softmax'):
-        super(DarkNet, self).__init__()
-        assert num_layers in DarkNet_cfg.keys(), \
-            "only support num_layers in {} currently" \
-            .format(DarkNet_cfg.keys())
-        self.stages = DarkNet_cfg[num_layers]
-        self.stages = self.stages[0:5]
-        self.num_classes = num_classes
-        self.with_pool = True
-        ch_in = 3
-        self.conv0 = ConvBNLayer(
-            ch_in=ch_in, ch_out=32, filter_size=3, stride=1, padding=1)
-
-        self.downsample0 = DownSample(ch_in=32, ch_out=32 * 2)
-        self.darknet53_conv_block_list = []
-        self.downsample_list = []
-        ch_in = [64, 128, 256, 512, 1024]
-        for i, stage in enumerate(self.stages):
-            conv_block = self.add_sublayer("stage_%d" % (i),
-                                           LayerWarp(
-                                               int(ch_in[i]), 32 * (2**i),
-                                               stage))
-            self.darknet53_conv_block_list.append(conv_block)
-
-        for i in range(len(self.stages) - 1):
-            downsample = self.add_sublayer(
-                "stage_%d_downsample" % i,
-                DownSample(
-                    ch_in=32 * (2**(i + 1)), ch_out=32 * (2**(i + 2))))
-            self.downsample_list.append(downsample)
-
-        if self.with_pool:
-            self.global_pool = Pool2D(
-                pool_size=7, pool_type='avg', global_pooling=True)
-
-        if self.num_classes > 0:
-            stdv = 1.0 / math.sqrt(32 * (2**(i + 2)))
-            self.fc_input_dim = 32 * (2**(i + 2))
-
-            self.fc = Linear(
-                self.fc_input_dim,
-                num_classes,
-                act='softmax',
-                param_attr=fluid.param_attr.ParamAttr(
-                    initializer=fluid.initializer.Uniform(-stdv, stdv)))
-
-    def forward(self, inputs):
-
-        out = self.conv0(inputs)
-        out = self.downsample0(out)
-
-        for i, conv_block_i in enumerate(self.darknet53_conv_block_list):
-            out = conv_block_i(out)
-            if i < len(self.stages) - 1:
-                out = self.downsample_list[i](out)
-
-        if self.with_pool:
-            out = self.global_pool(out)
-        if self.num_classes > 0:
-            out = fluid.layers.reshape(out, shape=[-1, self.fc_input_dim])
-            out = self.fc(out)
-        return out
-
-
-def _darknet(arch, num_layers=53, pretrained=False, **kwargs):
-    model = DarkNet(num_layers, **kwargs)
-    if pretrained:
-        assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
-            arch)
-        weight_path = get_weights_path_from_url(*(model_urls[arch]))
-        assert weight_path.endswith('.pdparams'), \
-                "suffix of weight must be .pdparams"
-        model.load(weight_path)
-    return model
-
-
-def darknet53(pretrained=False, **kwargs):
-    """DarkNet 53-layer model
-    
-    Args:
-        input_channels (bool): channel number of input data, default 3. 
-        pretrained (bool): If True, returns a model pre-trained on ImageNet,
-            default True.
-
-    Examples:
-        .. code-block:: python
-
-            from hapi.vision.models import darknet53
-
-            # build model
-            model = darknet53()
-
-            #build model and load imagenet pretrained weight
-            model = darknet53(pretrained=True)
-            
-    """
-    return _darknet('darknet53', 53, pretrained, **kwargs)