Merge pull request #1908 from flytocc/PeleeNet_PR

add PeleeNet

ppcls/arch/backbone/__init__.py

@@ -67,7 +67,9 @@ from ppcls.arch.backbone.model_zoo.pvt_v2 import PVT_V2_B0, PVT_V2_B1, PVT_V2_B2
 from ppcls.arch.backbone.model_zoo.mobilevit import MobileViT_XXS, MobileViT_XS, MobileViT_S
 from ppcls.arch.backbone.model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG_B1, RepVGG_B2, RepVGG_B1g2, RepVGG_B1g4, RepVGG_B2g4, RepVGG_B3g4
 from ppcls.arch.backbone.model_zoo.van import VAN_tiny
+from ppcls.arch.backbone.model_zoo.peleenet import PeleeNet
 from ppcls.arch.backbone.model_zoo.convnext import ConvNeXt_tiny
+
 from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
 from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
 from ppcls.arch.backbone.variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh

ppcls/arch/backbone/model_zoo/peleenet.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+# Code was heavily based on https://github.com/Robert-JunWang/PeleeNet
+# reference: https://arxiv.org/pdf/1804.06882.pdf
+
+import math
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn.initializer import Normal, Constant
+
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "peleenet": ""  # TODO
+}
+
+__all__ = MODEL_URLS.keys()
+
+normal_ = lambda x, mean=0, std=1: Normal(mean, std)(x)
+constant_ = lambda x, value=0: Constant(value)(x)
+zeros_ = Constant(value=0.)
+ones_ = Constant(value=1.)
+
+
+class _DenseLayer(nn.Layer):
+    def __init__(self, num_input_features, growth_rate, bottleneck_width, drop_rate):
+        super(_DenseLayer, self).__init__()
+
+        growth_rate = int(growth_rate / 2)
+        inter_channel = int(growth_rate * bottleneck_width / 4) * 4
+
+        if inter_channel > num_input_features / 2:
+            inter_channel = int(num_input_features / 8) * 4
+            print('adjust inter_channel to ', inter_channel)
+
+        self.branch1a = BasicConv2D(
+            num_input_features, inter_channel, kernel_size=1)
+        self.branch1b = BasicConv2D(
+            inter_channel, growth_rate, kernel_size=3, padding=1)
+
+        self.branch2a = BasicConv2D(
+            num_input_features, inter_channel, kernel_size=1)
+        self.branch2b = BasicConv2D(
+            inter_channel, growth_rate, kernel_size=3, padding=1)
+        self.branch2c = BasicConv2D(
+            growth_rate, growth_rate, kernel_size=3, padding=1)
+
+    def forward(self, x):
+        branch1 = self.branch1a(x)
+        branch1 = self.branch1b(branch1)
+
+        branch2 = self.branch2a(x)
+        branch2 = self.branch2b(branch2)
+        branch2 = self.branch2c(branch2)
+
+        return paddle.concat([x, branch1, branch2], 1)
+
+
+class _DenseBlock(nn.Sequential):
+    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
+        super(_DenseBlock, self).__init__()
+        for i in range(num_layers):
+            layer = _DenseLayer(num_input_features + i *
+                                growth_rate, growth_rate, bn_size, drop_rate)
+            setattr(self, 'denselayer%d' % (i + 1), layer)
+
+
+class _StemBlock(nn.Layer):
+    def __init__(self, num_input_channels, num_init_features):
+        super(_StemBlock, self).__init__()
+
+        num_stem_features = int(num_init_features/2)
+
+        self.stem1 = BasicConv2D(
+            num_input_channels, num_init_features, kernel_size=3, stride=2, padding=1)
+        self.stem2a = BasicConv2D(
+            num_init_features, num_stem_features, kernel_size=1, stride=1, padding=0)
+        self.stem2b = BasicConv2D(
+            num_stem_features, num_init_features, kernel_size=3, stride=2, padding=1)
+        self.stem3 = BasicConv2D(
+            2*num_init_features, num_init_features, kernel_size=1, stride=1, padding=0)
+        self.pool = nn.MaxPool2D(kernel_size=2, stride=2)
+
+    def forward(self, x):
+        out = self.stem1(x)
+
+        branch2 = self.stem2a(out)
+        branch2 = self.stem2b(branch2)
+        branch1 = self.pool(out)
+
+        out = paddle.concat([branch1, branch2], 1)
+        out = self.stem3(out)
+
+        return out
+
+
+class BasicConv2D(nn.Layer):
+
+    def __init__(self, in_channels, out_channels, activation=True, **kwargs):
+        super(BasicConv2D, self).__init__()
+        self.conv = nn.Conv2D(in_channels, out_channels,
+                              bias_attr=False, **kwargs)
+        self.norm = nn.BatchNorm2D(out_channels)
+        self.activation = activation
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm(x)
+        if self.activation:
+            return F.relu(x)
+        else:
+            return x
+
+
+class PeleeNetDY(nn.Layer):
+    r"""PeleeNet model class, based on
+    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf> and
+     "Pelee: A Real-Time Object Detection System on Mobile Devices" <https://arxiv.org/pdf/1804.06882.pdf>`
+
+    Args:
+        growth_rate (int or list of 4 ints) - how many filters to add each layer (`k` in paper)
+        block_config (list of 4 ints) - how many layers in each pooling block
+        num_init_features (int) - the number of filters to learn in the first convolution layer
+        bottleneck_width (int or list of 4 ints) - multiplicative factor for number of bottle neck layers
+          (i.e. bn_size * k features in the bottleneck layer)
+        drop_rate (float) - dropout rate after each dense layer
+        class_num (int) - number of classification classes
+    """
+
+    def __init__(self, growth_rate=32, block_config=[3, 4, 8, 6],
+                 num_init_features=32, bottleneck_width=[1, 2, 4, 4],
+                 drop_rate=0.05, class_num=1000):
+
+        super(PeleeNetDY, self).__init__()
+
+        self.features = nn.Sequential(*[
+            ('stemblock', _StemBlock(3, num_init_features)),
+        ])
+
+        if type(growth_rate) is list:
+            growth_rates = growth_rate
+            assert len(growth_rates) == 4, \
+                'The growth rate must be the list and the size must be 4'
+        else:
+            growth_rates = [growth_rate] * 4
+
+        if type(bottleneck_width) is list:
+            bottleneck_widths = bottleneck_width
+            assert len(bottleneck_widths) == 4, \
+                'The bottleneck width must be the list and the size must be 4'
+        else:
+            bottleneck_widths = [bottleneck_width] * 4
+
+        # Each denseblock
+        num_features = num_init_features
+        for i, num_layers in enumerate(block_config):
+            block = _DenseBlock(num_layers=num_layers,
+                                num_input_features=num_features,
+                                bn_size=bottleneck_widths[i],
+                                growth_rate=growth_rates[i],
+                                drop_rate=drop_rate)
+            setattr(self.features, 'denseblock%d' % (i + 1), block)
+            num_features = num_features + num_layers * growth_rates[i]
+
+            setattr(self.features, 'transition%d' % (i + 1), BasicConv2D(
+                num_features, num_features, kernel_size=1, stride=1, padding=0))
+
+            if i != len(block_config) - 1:
+                setattr(self.features, 'transition%d_pool' %
+                        (i + 1), nn.AvgPool2D(kernel_size=2, stride=2))
+                num_features = num_features
+
+        # Linear layer
+        self.classifier = nn.Linear(num_features, class_num)
+        self.drop_rate = drop_rate
+
+        self.apply(self._initialize_weights)
+
+    def forward(self, x):
+        features = self.features(x)
+        out = F.avg_pool2d(features, kernel_size=features.shape[2:4]).flatten(1)
+        if self.drop_rate > 0:
+            out = F.dropout(out, p=self.drop_rate, training=self.training)
+        out = self.classifier(out)
+        return out
+
+    def _initialize_weights(self, m):
+        if isinstance(m, nn.Conv2D):
+            n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+            normal_(m.weight, std=math.sqrt(2. / n))
+            if m.bias is not None:
+                zeros_(m.bias)
+        elif isinstance(m, nn.BatchNorm2D):
+            ones_(m.weight)
+            zeros_(m.bias)
+        elif isinstance(m, nn.Linear):
+            normal_(m.weight, std=0.01)
+            zeros_(m.bias)
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld):
+    if pretrained is False:
+        pass
+    elif pretrained is True:
+        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
+    elif isinstance(pretrained, str):
+        load_dygraph_pretrain(model, pretrained)
+    else:
+        raise RuntimeError(
+            "pretrained type is not available. Please use `string` or `boolean` type."
+        )
+
+
+def PeleeNet(pretrained=False, use_ssld=False, **kwargs):
+    model = PeleeNetDY(**kwargs)
+    _load_pretrained(pretrained, model, MODEL_URLS["peleenet"], use_ssld)
+    return model

ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 120
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
+
+# model architecture
+Arch:
+  name: PeleeNet
+  class_num: 1000
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.18  # for total batch size 512
+  regularizer:
+    name: 'L2'
+    coeff: 0.0001
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256  # for 2 cards
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

test_tipc/config/PeleeNet/PeleeNet_train_infer_python.txt

+===========================train_params===========================
+model_name:PeleeNet
+python:python3
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PeleeNet/PeleeNet.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+inference_dir:null
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]