Merge remote-tracking branch 'upstream/develop' into ConvNeXt

ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.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: 100
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: "./inference"
+
+# model architecture
+Arch:
+  name: "DistillationModel"
+  # if not null, its lengths should be same as models
+  pretrained_list:
+  # if not null, its lengths should be same as models
+  freeze_params_list:
+  - True
+  - False
+  models:
+    - Teacher:
+        name: ResNet34
+        pretrained: True
+
+    - Student:
+        name: ResNet18
+        pretrained: False
+
+  infer_model_name: "Student"
+
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - DistillationGTCELoss:
+        weight: 1.0
+        model_names: ["Student"]
+    - DistillationDKDLoss:
+        weight: 1.0
+        model_name_pairs: [["Student", "Teacher"]]
+        temperature: 1
+        alpha: 1.0
+        beta: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+        
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  weight_decay: 1e-4
+  lr:
+    name: MultiStepDecay
+    learning_rate: 0.2
+    milestones: [30, 60, 90]
+    step_each_epoch: 1
+    gamma: 0.1
+
+
+# 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: 0.00392157
+              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: 8
+        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: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+    sampler:
+        name: DistributedBatchSampler
+        batch_size: 64
+        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: DistillationPostProcess
+    func: Topk
+    topk: 5
+    class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"
+
+Metric:
+    Train:
+    - DistillationTopkAcc:
+        model_key: "Student"
+        topk: [1, 5]
+    Eval:
+    - DistillationTopkAcc:
+        model_key: "Student"
+        topk: [1, 5]

ppcls/configs/Pedestrian/strong_baseline_baseline.yaml

@@ -31,6 +31,14 @@ Arch:
     name: "FC"
     embedding_size: 2048
     class_num: 751
+    weight_attr:
+      initializer:
+        name: KaimingUniform
+        fan_in: 12288 # 6*embedding_size
+    bias_attr:
+      initializer:
+        name: KaimingUniform
+        fan_in: 12288 # 6*embedding_size
 
 # loss function config for traing/eval process
 Loss:
@@ -52,7 +60,6 @@ Optimizer:
     name: Piecewise
     decay_epochs: [40, 70]
     values: [0.00035, 0.000035, 0.0000035]
-    warmup_epoch: 10
     by_epoch: True
     last_epoch: 0
   regularizer:

ppcls/loss/__init__.py

@@ -23,6 +23,7 @@ from .distillationloss import DistillationDMLLoss
 from .distillationloss import DistillationDistanceLoss
 from .distillationloss import DistillationRKDLoss
 from .distillationloss import DistillationKLDivLoss
+from .distillationloss import DistillationDKDLoss
 from .multilabelloss import MultiLabelLoss
 from .afdloss import AFDLoss
 

ppcls/loss/distillationloss.py

@@ -21,6 +21,7 @@ from .dmlloss import DMLLoss
 from .distanceloss import DistanceLoss
 from .rkdloss import RKdAngle, RkdDistance
 from .kldivloss import KLDivLoss
+from .dkdloss import DKDLoss
 
 
 class DistillationCELoss(CELoss):
@@ -204,3 +205,33 @@ class DistillationKLDivLoss(KLDivLoss):
             for key in loss:
                 loss_dict["{}_{}_{}".format(key, pair[0], pair[1])] = loss[key]
         return loss_dict
+
+
+class DistillationDKDLoss(DKDLoss):
+    """
+    DistillationDKDLoss
+    """
+
+    def __init__(self,
+                 model_name_pairs=[],
+                 key=None,
+                 temperature=1.0,
+                 alpha=1.0,
+                 beta=1.0,
+                 name="loss_dkd"):
+        super().__init__(temperature=temperature, alpha=alpha, beta=beta)
+        self.key = key
+        self.model_name_pairs = model_name_pairs
+        self.name = name
+
+    def forward(self, predicts, batch):
+        loss_dict = dict()
+        for idx, pair in enumerate(self.model_name_pairs):
+            out1 = predicts[pair[0]]
+            out2 = predicts[pair[1]]
+            if self.key is not None:
+                out1 = out1[self.key]
+                out2 = out2[self.key]
+            loss = super().forward(out1, out2, batch)
+            loss_dict[f"{self.name}_{pair[0]}_{pair[1]}"] = loss
+        return loss_dict

ppcls/loss/dkdloss.py

+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class DKDLoss(nn.Layer):
+    """
+    DKDLoss
+    Reference: https://arxiv.org/abs/2203.08679
+    Code was heavily based on https://github.com/megvii-research/mdistiller
+    """
+
+    def __init__(self, temperature=1.0, alpha=1.0, beta=1.0):
+        super().__init__()
+        self.temperature = temperature
+        self.alpha = alpha
+        self.beta = beta
+
+    def forward(self, logits_student, logits_teacher, target):
+        gt_mask = _get_gt_mask(logits_student, target)
+        other_mask = 1 - gt_mask
+        pred_student = F.softmax(logits_student / self.temperature, axis=1)
+        pred_teacher = F.softmax(logits_teacher / self.temperature, axis=1)
+        pred_student = cat_mask(pred_student, gt_mask, other_mask)
+        pred_teacher = cat_mask(pred_teacher, gt_mask, other_mask)
+        log_pred_student = paddle.log(pred_student)
+        tckd_loss = (F.kl_div(
+            log_pred_student, pred_teacher,
+            reduction='sum') * (self.temperature**2) / target.shape[0])
+        pred_teacher_part2 = F.softmax(
+            logits_teacher / self.temperature - 1000.0 * gt_mask, axis=1)
+        log_pred_student_part2 = F.log_softmax(
+            logits_student / self.temperature - 1000.0 * gt_mask, axis=1)
+        nckd_loss = (F.kl_div(
+            log_pred_student_part2, pred_teacher_part2,
+            reduction='sum') * (self.temperature**2) / target.shape[0])
+        return self.alpha * tckd_loss + self.beta * nckd_loss
+
+
+def _get_gt_mask(logits, target):
+    target = target.reshape([-1]).unsqueeze(1)
+    updates = paddle.ones_like(target)
+    mask = scatter(
+        paddle.zeros_like(logits), target, updates.astype('float32'))
+    return mask
+
+
+def cat_mask(t, mask1, mask2):
+    t1 = (t * mask1).sum(axis=1, keepdim=True)
+    t2 = (t * mask2).sum(axis=1, keepdim=True)
+    rt = paddle.concat([t1, t2], axis=1)
+    return rt
+
+
+def scatter(x, index, updates):
+    i, j = index.shape
+    grid_x, grid_y = paddle.meshgrid(paddle.arange(i), paddle.arange(j))
+    index = paddle.stack([grid_x.flatten(), index.flatten()], axis=1)
+    updates_index = paddle.stack([grid_x.flatten(), grid_y.flatten()], axis=1)
+    updates = paddle.gather_nd(updates, index=updates_index)
+    return paddle.scatter_nd_add(x, index, updates)

ppcls/utils/save_load.py

@@ -125,9 +125,8 @@ def init_model(config,
             load_distillation_model(net, pretrained_model)
         else:  # common load
             load_dygraph_pretrain(net, path=pretrained_model)
-            logger.info(
-                logger.coloring("Finish load pretrained model from {}".format(
-                    pretrained_model), "HEADER"))
+            logger.info("Finish load pretrained model from {}".format(
+                    pretrained_model))
 
 
 def save_model(net,

test_tipc/config/Distillation/resnet34_distill_resnet18_dkd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DistillationModel
+python:python3.7
+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=100
+-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:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.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 -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.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/Distillation/resnet34_distill_resnet18_dkd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams
+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]}]

test_tipc/config/Distillation/resnet34_distill_resnet18_dkd_train_infer_python.txt

+===========================train_params===========================
+model_name:DistillationModel
+python:python3.7
+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=100
+-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/Distillation/resnet34_distill_resnet18_dkd.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/Distillation/resnet34_distill_resnet18_dkd.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/Distillation/resnet34_distill_resnet18_dkd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams
+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]}]