Merge pull request #1166 from Intsigstephon/develop

add Deephash method: DLBHC

ppcls/arch/backbone/__init__.py

@@ -58,6 +58,7 @@ from ppcls.arch.backbone.model_zoo.rednet import RedNet26, RedNet38, RedNet50, R
 from ppcls.arch.backbone.model_zoo.tnt import TNT_small
 from ppcls.arch.backbone.model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
 from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
+from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
 
 
 def get_apis():

ppcls/arch/backbone/variant_models/__init__.py

 from .resnet_variant import ResNet50_last_stage_stride1
+from .vgg_variant import VGG19Sigmoid

ppcls/arch/backbone/variant_models/vgg_variant.py

+import paddle
+from paddle.nn import Sigmoid
+from ppcls.arch.backbone.legendary_models.vgg import VGG19
+ 
+__all__ = ["VGG19Sigmoid"]
+ 
+ 
+class SigmoidSuffix(paddle.nn.Layer):
+    def __init__(self, origin_layer):
+        super(SigmoidSuffix, self).__init__()
+        self.origin_layer = origin_layer
+        self.sigmoid = Sigmoid()
+ 
+    def forward(self, input, res_dict=None, **kwargs):
+        x = self.origin_layer(input)
+        x = self.sigmoid(x)
+        return x
+ 
+ 
+def VGG19Sigmoid(pretrained=False, use_ssld=False, **kwargs):
+    def replace_function(origin_layer):
+        new_layer = SigmoidSuffix(origin_layer)
+        return new_layer
+ 
+    match_re = "linear_2"
+    model = VGG19(pretrained=pretrained, use_ssld=use_ssld, **kwargs)
+    model.replace_sub(match_re, replace_function, True)
+    return model

ppcls/arch/gears/circlemargin.py

@@ -28,7 +28,7 @@ class CircleMargin(nn.Layer):
 
         weight_attr = paddle.ParamAttr(
             initializer=paddle.nn.initializer.XavierNormal())
-        self.fc0 = paddle.nn.Linear(
+        self.fc = paddle.nn.Linear(
             self.embedding_size, self.class_num, weight_attr=weight_attr)
 
     def forward(self, input, label):
@@ -36,19 +36,22 @@ class CircleMargin(nn.Layer):
             paddle.sum(paddle.square(input), axis=1, keepdim=True))
         input = paddle.divide(input, feat_norm)
 
-        weight = self.fc0.weight
+        weight = self.fc.weight
         weight_norm = paddle.sqrt(
             paddle.sum(paddle.square(weight), axis=0, keepdim=True))
         weight = paddle.divide(weight, weight_norm)
 
         logits = paddle.matmul(input, weight)
+        if not self.training or label is None:
+            return logits
 
         alpha_p = paddle.clip(-logits.detach() + 1 + self.margin, min=0.)
         alpha_n = paddle.clip(logits.detach() + self.margin, min=0.)
         delta_p = 1 - self.margin
         delta_n = self.margin
-        index = paddle.fluid.layers.where(label != -1).reshape([-1])
+        
         m_hot = F.one_hot(label.reshape([-1]), num_classes=logits.shape[1])
+        
         logits_p = alpha_p * (logits - delta_p)
         logits_n = alpha_n * (logits - delta_n)
         pre_logits = logits_p * m_hot + logits_n * (1 - m_hot)

ppcls/arch/gears/cosmargin.py

@@ -46,6 +46,9 @@ class CosMargin(paddle.nn.Layer):
         weight = paddle.divide(weight, weight_norm)
 
         cos = paddle.matmul(input, weight)
+        if not self.training or label is None:
+            return cos
+        
         cos_m = cos - self.margin
 
         one_hot = paddle.nn.functional.one_hot(label, self.class_num)

ppcls/configs/Products/MV3_Large_1x_Aliproduct_DLBHC.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output_dlbhc/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 100
+  #eval_mode: "retrieval"
+  print_batch_step: 10
+  use_visualdl: False
+
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+  #feature postprocess
+  feature_normalize: False
+  feature_binarize: "round"
+
+# model architecture
+Arch:  
+  name: "RecModel"
+  Backbone:
+    name: "MobileNetV3_large_x1_0"
+    pretrained: True
+    class_num: 512
+  Head:
+    name: "FC"
+    class_num: 50030
+    embedding_size: 512
+    
+  infer_output_key:  "features"
+  infer_add_softmax: "false"
+ 
+# loss function config for train/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.1
+    decay_epochs: [50, 150]
+    values: [0.1, 0.01, 0.001]
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/Aliproduct/
+      cls_label_path: ./dataset/Aliproduct/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 256
+        - RandCropImage:
+            size: 227
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.4914, 0.4822, 0.4465]
+            std: [0.2023, 0.1994, 0.2010]
+            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/Aliproduct/
+      cls_label_path: ./dataset/Aliproduct/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 227
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.4914, 0.4822, 0.4465]
+            std: [0.2023, 0.1994, 0.2010]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/whl/demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 227
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.4914, 0.4822, 0.4465]
+        std: [0.2023, 0.1994, 0.2010]
+        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]
+        
+# switch to metric below when eval by retrieval
+#     - Recallk:
+#         topk: [1]
+#     - mAP:
+#     - Precisionk:
+#         topk: [1]
+

ppcls/configs/quick_start/professional/VGG19_CIFAR10_DeepHash.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  eval_mode: "retrieval"
+  epochs: 128
+  print_batch_step: 10
+  use_visualdl: False
+
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+  #feature postprocess
+  feature_normalize: False
+  feature_binarize: "round"
+
+# model architecture
+Arch:
+  name: "RecModel"
+  Backbone:
+    name: "VGG19Sigmoid"
+    pretrained: True
+    class_num: 48
+  Head:
+    name: "FC"
+    class_num: 10
+    embedding_size: 48
+    
+  infer_output_key:  "features"
+  infer_add_softmax: "false"
+
+# loss function config for train/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.01
+    decay_epochs: [200]
+    values: [0.01, 0.001]
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/cifar10/
+      cls_label_path: ./dataset/cifar10/cifar10-2/train.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: 256
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.4914, 0.4822, 0.4465]
+            std: [0.2023, 0.1994, 0.2010]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    Query:
+      dataset: 
+        name: ImageNetDataset
+        image_root: ./dataset/cifar10/
+        cls_label_path: ./dataset/cifar10/cifar10-2/test.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.4914, 0.4822, 0.4465]
+              std: [0.2023, 0.1994, 0.2010]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 512
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 4
+        use_shared_memory: True
+
+    Gallery:
+      dataset: 
+        name: ImageNetDataset
+        image_root: ./dataset/cifar10/
+        cls_label_path: ./dataset/cifar10/cifar10-2/database.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 1.0/255.0
+              mean: [0.4914, 0.4822, 0.4465]
+              std: [0.2023, 0.1994, 0.2010]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 512
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 4
+        use_shared_memory: True
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - mAP:
+    - Precisionk:
+        topk: [1, 5]
+        

ppcls/engine/evaluation/retrieval.py

@@ -125,6 +125,13 @@ def cal_feature(evaler, name='gallery'):
                 paddle.sum(paddle.square(batch_feas), axis=1, keepdim=True))
             batch_feas = paddle.divide(batch_feas, feas_norm)
             
+        # do binarize
+        if evaler.config["Global"].get("feature_binarize") == "round":
+            batch_feas = paddle.round(batch_feas).astype("float32") * 2.0 - 1.0
+
+        if evaler.config["Global"].get("feature_binarize") == "sign":
+            batch_feas = paddle.sign(batch_feas).astype("float32")
+
         if all_feas is None:
             all_feas = batch_feas
             if has_unique_id:
@@ -135,8 +142,10 @@ def cal_feature(evaler, name='gallery'):
             all_image_id = paddle.concat([all_image_id, batch[1]])
             if has_unique_id:
                 all_unique_id = paddle.concat([all_unique_id, batch[2]])
+                
     if evaler.use_dali:
         dataloader_tmp.reset()
+        
     if paddle.distributed.get_world_size() > 1:
         feat_list = []
         img_id_list = []

ppcls/loss/deephashloss.py

+#copyright (c) 2021 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 paddle
+import paddle.nn as nn
+
+class DSHSDLoss(nn.Layer):
+    """
+    # DSHSD(IEEE ACCESS 2019)
+    # paper [Deep Supervised Hashing Based on Stable Distribution](https://ieeexplore.ieee.org/document/8648432/)
+    # [DSHSD] epoch:70,  bit:48,  dataset:cifar10-1,  MAP:0.809, Best MAP: 0.809
+    # [DSHSD] epoch:250, bit:48,  dataset:nuswide_21, MAP:0.809, Best MAP: 0.815
+    # [DSHSD] epoch:135, bit:48,  dataset:imagenet,   MAP:0.647, Best MAP: 0.647
+    """
+    def __init__(self, n_class, bit, alpha, multi_label=False):
+        super(DSHSDLoss, self).__init__()
+        self.m = 2 * bit     
+        self.alpha = alpha
+        self.multi_label = multi_label
+        self.n_class = n_class
+        self.fc = paddle.nn.Linear(bit, n_class, bias_attr=False)
+
+    def forward(self, input, label):        
+        feature = input["features"]
+        feature = feature.tanh().astype("float32")
+
+        dist = paddle.sum(
+                    paddle.square((paddle.unsqueeze(feature, 1) - paddle.unsqueeze(feature, 0))), 
+                    axis=2)
+        
+        # label to ont-hot
+        label = paddle.flatten(label)
+        label = paddle.nn.functional.one_hot(label,  self.n_class).astype("float32")
+
+        s = (paddle.matmul(label, label, transpose_y=True) == 0).astype("float32")
+        Ld = (1 - s) / 2 * dist + s / 2 * (self.m - dist).clip(min=0)
+        Ld = Ld.mean()
+        
+        logits = self.fc(feature)
+        if self.multi_label:
+            # multiple labels classification loss
+            Lc = (logits - label * logits + ((1 + (-logits).exp()).log())).sum(axis=1).mean()
+        else:
+            # single labels classification loss
+            Lc = (-paddle.nn.functional.softmax(logits).log() * label).sum(axis=1).mean()
+
+        return {"dshsdloss": Lc + Ld * self.alpha}
+
+class LCDSHLoss(nn.Layer):
+    """
+    # paper [Locality-Constrained Deep Supervised Hashing for Image Retrieval](https://www.ijcai.org/Proceedings/2017/0499.pdf)
+    # [LCDSH] epoch:145, bit:48, dataset:cifar10-1,  MAP:0.798, Best MAP: 0.798
+    # [LCDSH] epoch:183, bit:48, dataset:nuswide_21, MAP:0.833, Best MAP: 0.834
+    """
+    def __init__(self, n_class, _lambda):
+        super(LCDSHLoss, self).__init__()
+        self._lambda = _lambda
+        self.n_class = n_class
+
+    def forward(self, input, label):
+        feature = input["features"]
+
+        # label to ont-hot
+        label = paddle.flatten(label)
+        label = paddle.nn.functional.one_hot(label,  self.n_class).astype("float32")
+        
+        s = 2 * (paddle.matmul(label, label, transpose_y=True) > 0).astype("float32") - 1
+        inner_product = paddle.matmul(feature, feature, transpose_y=True) * 0.5
+
+        inner_product = inner_product.clip(min=-50, max=50)
+        L1 = paddle.log(1 + paddle.exp(-s * inner_product)).mean()
+
+        b = feature.sign()
+        inner_product_ = paddle.matmul(b, b, transpose_y=True) * 0.5
+        sigmoid = paddle.nn.Sigmoid()
+        L2 = (sigmoid(inner_product) - sigmoid(inner_product_)).pow(2).mean()
+
+        return {"lcdshloss": L1 + self._lambda * L2}
+

ppcls/metric/__init__.py

@@ -16,7 +16,7 @@ from paddle import nn
 import copy
 from collections import OrderedDict
 
-from .metrics import TopkAcc, mAP, mINP, Recallk
+from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
 from .metrics import DistillationTopkAcc
 from .metrics import GoogLeNetTopkAcc
 

ppcls/metric/metrics.py

@@ -168,6 +168,47 @@ class Recallk(nn.Layer):
         return metric_dict
 
 
+class Precisionk(nn.Layer):
+    def __init__(self, topk=(1, 5)):
+        super().__init__()
+        assert isinstance(topk, (int, list, tuple))
+        if isinstance(topk, int):
+            topk = [topk]
+        self.topk = topk
+
+    def forward(self, similarities_matrix, query_img_id, gallery_img_id,
+                keep_mask):
+        metric_dict = dict()
+
+        #get cmc
+        choosen_indices = paddle.argsort(
+            similarities_matrix, axis=1, descending=True)
+        gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
+        gallery_labels_transpose = paddle.broadcast_to(
+            gallery_labels_transpose,
+            shape=[
+                choosen_indices.shape[0], gallery_labels_transpose.shape[1]
+            ])
+        choosen_label = paddle.index_sample(gallery_labels_transpose,
+                                            choosen_indices)
+        equal_flag = paddle.equal(choosen_label, query_img_id)
+        if keep_mask is not None:
+            keep_mask = paddle.index_sample(
+                keep_mask.astype('float32'), choosen_indices)
+            equal_flag = paddle.logical_and(equal_flag,
+                                            keep_mask.astype('bool'))
+        equal_flag = paddle.cast(equal_flag, 'float32')
+        
+        Ns = paddle.arange(gallery_img_id.shape[0]) + 1
+        equal_flag_cumsum = paddle.cumsum(equal_flag, axis=1)
+        Precision_at_k = (paddle.mean(equal_flag_cumsum, axis=0) / Ns).numpy()
+
+        for k in self.topk:
+            metric_dict["precision@{}".format(k)] = Precision_at_k[k - 1]
+
+        return metric_dict
+
+
 class DistillationTopkAcc(TopkAcc):
     def __init__(self, model_key, feature_key=None, topk=(1, 5)):
         super().__init__(topk=topk)