Merge pull request #2269 from zengshao0622/merge_CAE

Merge CAE

ppcls/arch/backbone/__init__.py

@@ -69,6 +69,7 @@ from .model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG
 from .model_zoo.van import VAN_tiny
 from .model_zoo.peleenet import PeleeNet
 from .model_zoo.convnext import ConvNeXt_tiny
+from .model_zoo.cae import cae_base_patch16_224, cae_large_patch16_224
 
 from .variant_models.resnet_variant import ResNet50_last_stage_stride1
 from .variant_models.vgg_variant import VGG19Sigmoid

ppcls/configs/CAE/cae_base_patch16_224_finetune.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 20
+  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: cae_base_patch16_224
+  class_num: 102
+  drop_rate: 0.0
+  drop_path_rate: 0.1
+  attn_drop_rate: 0.0
+
+  use_mean_pooling: True
+  init_scale: 0.001
+  use_rel_pos_bias: True
+  use_abs_pos_emb: False
+  init_values: 0.1
+  lin_probe: False
+
+  sin_pos_emb: True
+
+  abs_pos_emb: False
+  enable_linear_eval: False
+  model_key: model|module|state_dict
+  rel_pos_bias: True
+  model_ema:
+    enable_model_ema: False 
+    model_ema_decay: 0.9999
+    model_ema_force_cpu: False
+  pretrained: True
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - SoftTargetCrossEntropy:
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: AdamWDL
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.05
+  layerwise_decay: 0.65
+  lr:
+    name: Cosine
+    learning_rate: 0.001
+    eta_min: 1e-6
+    warmup_epoch: 10
+    warmup_start_lr: 1e-6
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/flowers102/
+      cls_label_path: ./dataset/flowers102/train_list.txt
+      batch_transform_ops:
+        - MixupCutmixHybrid:
+            mixup_alpha: 0.8
+            cutmix_alpha: 1.0
+            switch_prob: 0.5
+            num_classes: 102
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+            interpolation: bilinear
+        - RandFlipImage:
+            flip_code: 1
+        - RandAugment:
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+        - RandomErasing:
+            EPSILON: 0.5
+            sl: 0.02
+            sh: 0.3
+            r1: 0.3
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 16
+      drop_last: True
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/flowers102/
+      cls_label_path: ./dataset/flowers102/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: 16
+      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: [0.5, 0.5, 0.5]
+        std: [0.5, 0.5, 0.5]
+        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]

ppcls/data/preprocess/__init__.py

@@ -42,6 +42,7 @@ from ppcls.data.preprocess.ops.operators import RandomRotation
 from ppcls.data.preprocess.ops.operators import Padv2
 
 from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, OpSampler, FmixOperator
+from ppcls.data.preprocess.batch_ops.batch_operators import MixupCutmixHybrid
 
 import numpy as np
 from PIL import Image

ppcls/data/preprocess/batch_ops/batch_operators.py

@@ -23,6 +23,9 @@ import numpy as np
 from ppcls.utils import logger
 from ppcls.data.preprocess.ops.fmix import sample_mask
 
+import paddle
+import paddle.nn.functional as F
+
 
 class BatchOperator(object):
     """ BatchOperator """
@@ -229,3 +232,270 @@ class OpSampler(object):
             list(self.ops.keys()), weights=list(self.ops.values()), k=1)[0]
         # return batch directly when None Op
         return op(batch) if op else batch
+
+
+class MixupCutmixHybrid(object):
+    """ Mixup/Cutmix that applies different params to each element or whole batch
+
+    Args:
+        mixup_alpha (float): mixup alpha value, mixup is active if > 0.
+        cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
+        cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
+        prob (float): probability of applying mixup or cutmix per batch or element
+        switch_prob (float): probability of switching to cutmix instead of mixup when both are active
+        mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
+        correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
+        label_smoothing (float): apply label smoothing to the mixed target tensor
+        num_classes (int): number of classes for target
+    """
+
+    def __init__(self,
+                 mixup_alpha=1.,
+                 cutmix_alpha=0.,
+                 cutmix_minmax=None,
+                 prob=1.0,
+                 switch_prob=0.5,
+                 mode='batch',
+                 correct_lam=True,
+                 label_smoothing=0.1,
+                 num_classes=4):
+        self.mixup_alpha = mixup_alpha
+        self.cutmix_alpha = cutmix_alpha
+        self.cutmix_minmax = cutmix_minmax
+        if self.cutmix_minmax is not None:
+            assert len(self.cutmix_minmax) == 2
+            # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
+            self.cutmix_alpha = 1.0
+        self.mix_prob = prob
+        self.switch_prob = switch_prob
+        self.label_smoothing = label_smoothing
+        self.num_classes = num_classes
+        self.mode = mode
+        self.correct_lam = correct_lam  # correct lambda based on clipped area for cutmix
+        self.mixup_enabled = True  # set to false to disable mixing (intended tp be set by train loop)
+
+    def _one_hot(self, x, num_classes, on_value=1., off_value=0.):
+        x = paddle.cast(x, dtype='int64')
+        on_value = paddle.full([x.shape[0], num_classes], on_value)
+        off_value = paddle.full([x.shape[0], num_classes], off_value)
+        return paddle.where(
+            F.one_hot(x, num_classes) == 1, on_value, off_value)
+
+    def _mixup_target(self, target, num_classes, lam=1., smoothing=0.0):
+        off_value = smoothing / num_classes
+        on_value = 1. - smoothing + off_value
+        y1 = self._one_hot(
+            target,
+            num_classes,
+            on_value=on_value,
+            off_value=off_value, )
+        y2 = self._one_hot(
+            target.flip(0),
+            num_classes,
+            on_value=on_value,
+            off_value=off_value)
+        return y1 * lam + y2 * (1. - lam)
+
+    def _rand_bbox(self, img_shape, lam, margin=0., count=None):
+        """ Standard CutMix bounding-box
+        Generates a random square bbox based on lambda value. This impl includes
+        support for enforcing a border margin as percent of bbox dimensions.
+
+        Args:
+            img_shape (tuple): Image shape as tuple
+            lam (float): Cutmix lambda value
+            margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
+            count (int): Number of bbox to generate
+        """
+        ratio = np.sqrt(1 - lam)
+        img_h, img_w = img_shape[-2:]
+        cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
+        margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
+        cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
+        cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
+        yl = np.clip(cy - cut_h // 2, 0, img_h)
+        yh = np.clip(cy + cut_h // 2, 0, img_h)
+        xl = np.clip(cx - cut_w // 2, 0, img_w)
+        xh = np.clip(cx + cut_w // 2, 0, img_w)
+        return yl, yh, xl, xh
+
+    def _rand_bbox_minmax(self, img_shape, minmax, count=None):
+        """ Min-Max CutMix bounding-box
+        Inspired by Darknet cutmix impl, generates a random rectangular bbox
+        based on min/max percent values applied to each dimension of the input image.
+
+        Typical defaults for minmax are usually in the  .2-.3 for min and .8-.9 range for max.
+
+        Args:
+            img_shape (tuple): Image shape as tuple
+            minmax (tuple or list): Min and max bbox ratios (as percent of image size)
+            count (int): Number of bbox to generate
+        """
+        assert len(minmax) == 2
+        img_h, img_w = img_shape[-2:]
+        cut_h = np.random.randint(
+            int(img_h * minmax[0]), int(img_h * minmax[1]), size=count)
+        cut_w = np.random.randint(
+            int(img_w * minmax[0]), int(img_w * minmax[1]), size=count)
+        yl = np.random.randint(0, img_h - cut_h, size=count)
+        xl = np.random.randint(0, img_w - cut_w, size=count)
+        yu = yl + cut_h
+        xu = xl + cut_w
+        return yl, yu, xl, xu
+
+    def _cutmix_bbox_and_lam(self,
+                             img_shape,
+                             lam,
+                             ratio_minmax=None,
+                             correct_lam=True,
+                             count=None):
+        """ Generate bbox and apply lambda correction.
+        """
+        if ratio_minmax is not None:
+            yl, yu, xl, xu = self._rand_bbox_minmax(
+                img_shape, ratio_minmax, count=count)
+        else:
+            yl, yu, xl, xu = self._rand_bbox(img_shape, lam, count=count)
+        if correct_lam or ratio_minmax is not None:
+            bbox_area = (yu - yl) * (xu - xl)
+            lam = 1. - bbox_area / float(img_shape[-2] * img_shape[-1])
+        return (yl, yu, xl, xu), lam
+
+    def _params_per_elem(self, batch_size):
+        lam = np.ones(batch_size, dtype=np.float32)
+        use_cutmix = np.zeros(batch_size, dtype=np.bool)
+        if self.mixup_enabled:
+            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
+                use_cutmix = np.random.rand(batch_size) < self.switch_prob
+                lam_mix = np.where(
+                    use_cutmix,
+                    np.random.beta(
+                        self.cutmix_alpha, self.cutmix_alpha, size=batch_size),
+                    np.random.beta(
+                        self.mixup_alpha, self.mixup_alpha, size=batch_size))
+            elif self.mixup_alpha > 0.:
+                lam_mix = np.random.beta(
+                    self.mixup_alpha, self.mixup_alpha, size=batch_size)
+            elif self.cutmix_alpha > 0.:
+                use_cutmix = np.ones(batch_size, dtype=np.bool)
+                lam_mix = np.random.beta(
+                    self.cutmix_alpha, self.cutmix_alpha, size=batch_size)
+            else:
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
+            lam = np.where(
+                np.random.rand(batch_size) < self.mix_prob,
+                lam_mix.astype(np.float32), lam)
+        return lam, use_cutmix
+
+    def _params_per_batch(self):
+        lam = 1.
+        use_cutmix = False
+        if self.mixup_enabled and np.random.rand() < self.mix_prob:
+            if self.mixup_alpha > 0. and self.cutmix_alpha > 0.:
+                use_cutmix = np.random.rand() < self.switch_prob
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha) if use_cutmix else \
+                    np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            elif self.mixup_alpha > 0.:
+                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
+            elif self.cutmix_alpha > 0.:
+                use_cutmix = True
+                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
+            else:
+                assert False, "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
+            lam = float(lam_mix)
+        return lam, use_cutmix
+
+    def _mix_elem(self, x):
+        batch_size = len(x)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size)
+        x_orig = x.clone(
+        )  # need to keep an unmodified original for mixing source
+        for i in range(batch_size):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            if lam != 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(
+                        x[i].shape,
+                        lam,
+                        ratio_minmax=self.cutmix_minmax,
+                        correct_lam=self.correct_lam)
+                    if yl < yh and xl < xh:
+                        x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
+                    lam_batch[i] = lam
+                else:
+                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
+        return paddle.to_tensor(lam_batch, dtype=x.dtype).unsqueeze(1)
+
+    def _mix_pair(self, x):
+        batch_size = len(x)
+        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
+        x_orig = x.clone(
+        )  # need to keep an unmodified original for mixing source
+        for i in range(batch_size // 2):
+            j = batch_size - i - 1
+            lam = lam_batch[i]
+            if lam != 1.:
+                if use_cutmix[i]:
+                    (yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(
+                        x[i].shape,
+                        lam,
+                        ratio_minmax=self.cutmix_minmax,
+                        correct_lam=self.correct_lam)
+                    if yl < yh and xl < xh:
+                        x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
+                        x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
+                    lam_batch[i] = lam
+                else:
+                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
+                    x[j] = x[j] * lam + x_orig[i] * (1 - lam)
+        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
+        return paddle.to_tensor(lam_batch, dtype=x.dtype).unsqueeze(1)
+
+    def _mix_batch(self, x):
+        lam, use_cutmix = self._params_per_batch()
+        if lam == 1.:
+            return 1.
+        if use_cutmix:
+            (yl, yh, xl, xh), lam = self._cutmix_bbox_and_lam(
+                x.shape,
+                lam,
+                ratio_minmax=self.cutmix_minmax,
+                correct_lam=self.correct_lam)
+            if yl < yh and xl < xh:
+                x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
+
+        else:
+            x_flipped = x.flip(0) * (1. - lam)
+            x[:] = x * lam + x_flipped
+        return lam
+
+    def _unpack(self, batch):
+        """ _unpack """
+        assert isinstance(batch, list), \
+                'batch should be a list filled with tuples (img, label)'
+        bs = len(batch)
+        assert bs > 0, 'size of the batch data should > 0'
+        #imgs, labels = list(zip(*batch))
+        imgs = []
+        labels = []
+        for item in batch:
+            imgs.append(item[0])
+            labels.append(item[1])
+        return np.array(imgs), np.array(labels), bs
+
+    def __call__(self, batch):
+        x, target, bs = self._unpack(batch)
+        x = paddle.to_tensor(x)
+        target = paddle.to_tensor(target)
+        assert len(x) % 2 == 0, 'Batch size should be even when using this'
+        if self.mode == 'elem':
+            lam = self._mix_elem(x)
+        elif self.mode == 'pair':
+            lam = self._mix_pair(x)
+        else:
+            lam = self._mix_batch(x)
+        target = self._mixup_target(target, self.num_classes, lam,
+                                    self.label_smoothing)
+
+        return list(zip(x.numpy(), target.numpy()))

ppcls/loss/__init__.py

@@ -17,6 +17,7 @@ from .supconloss import SupConLoss
 from .pairwisecosface import PairwiseCosface
 from .dmlloss import DMLLoss
 from .distanceloss import DistanceLoss
+from .softtargetceloss import SoftTargetCrossEntropy
 
 from .distillationloss import DistillationCELoss
 from .distillationloss import DistillationGTCELoss

ppcls/loss/softtargetceloss.py

+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class SoftTargetCrossEntropy(nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, target):
+        loss = paddle.sum(-target * F.log_softmax(x, axis=-1), axis=-1)
+        loss = loss.mean()
+        return {"SoftTargetCELoss": loss}
+
+    def __str__(self, ):
+        return type(self).__name__

ppcls/optimizer/optimizer.py

@@ -272,3 +272,145 @@ class AdamW(object):
 
     def _apply_decay_param_fun(self, name):
         return name not in self.no_weight_decay_param_name_list
+
+
+class AdamWDL(object):
+    """
+    The AdamWDL optimizer is implemented based on the AdamW Optimization with dynamic lr setting.
+    Generally it's used for transformer model.
+    """
+
+    def __init__(self,
+                 learning_rate=0.001,
+                 beta1=0.9,
+                 beta2=0.999,
+                 epsilon=1e-8,
+                 weight_decay=None,
+                 multi_precision=False,
+                 grad_clip=None,
+                 layerwise_decay=None,
+                 filter_bias_and_bn=True,
+                 **args):
+        self.learning_rate = learning_rate
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.epsilon = epsilon
+        self.grad_clip = grad_clip
+        self.weight_decay = weight_decay
+        self.multi_precision = multi_precision
+        self.layerwise_decay = layerwise_decay
+        self.filter_bias_and_bn = filter_bias_and_bn
+
+    class AdamWDLImpl(optim.AdamW):
+        def __init__(self,
+                     learning_rate=0.001,
+                     beta1=0.9,
+                     beta2=0.999,
+                     epsilon=1e-8,
+                     parameters=None,
+                     weight_decay=0.01,
+                     apply_decay_param_fun=None,
+                     grad_clip=None,
+                     lazy_mode=False,
+                     multi_precision=False,
+                     layerwise_decay=1.0,
+                     n_layers=12,
+                     name_dict=None,
+                     name=None):
+            if not isinstance(layerwise_decay, float) and \
+                    not isinstance(layerwise_decay, fluid.framework.Variable):
+                raise TypeError("coeff should be float or Tensor.")
+            self.layerwise_decay = layerwise_decay
+            self.name_dict = name_dict
+            self.n_layers = n_layers
+            self.set_param_lr_fun = self._layerwise_lr_decay
+            super().__init__(
+                learning_rate=learning_rate,
+                parameters=parameters,
+                beta1=beta1,
+                beta2=beta2,
+                epsilon=epsilon,
+                grad_clip=grad_clip,
+                name=name,
+                apply_decay_param_fun=apply_decay_param_fun,
+                weight_decay=weight_decay,
+                lazy_mode=lazy_mode,
+                multi_precision=multi_precision)
+
+        def _append_optimize_op(self, block, param_and_grad):
+            if self.set_param_lr_fun is None:
+                return super(AdamLW, self)._append_optimize_op(block,
+                                                               param_and_grad)
+
+            self._append_decoupled_weight_decay(block, param_and_grad)
+            prev_lr = param_and_grad[0].optimize_attr["learning_rate"]
+            self.set_param_lr_fun(self.layerwise_decay, self.name_dict,
+                                  self.n_layers, param_and_grad[0])
+            # excute Adam op
+            res = super(optim.AdamW, self)._append_optimize_op(block,
+                                                               param_and_grad)
+            param_and_grad[0].optimize_attr["learning_rate"] = prev_lr
+            return res
+
+        # Layerwise decay
+        def _layerwise_lr_decay(self, decay_rate, name_dict, n_layers, param):
+            """
+            Args:
+                decay_rate (float): 
+                    The layer-wise decay ratio.
+                name_dict (dict): 
+                    The keys of name_dict is dynamic name of model while the value
+                    of name_dict is static name.
+                    Use model.named_parameters() to get name_dict.
+                n_layers (int):
+                    Total number of layers in the transformer encoder.
+            """
+            ratio = 1.0
+            static_name = name_dict[param.name]
+            if "blocks" in static_name:
+                idx = static_name.find("blocks.")
+                layer = int(static_name[idx:].split(".")[1])
+                ratio = decay_rate**(n_layers - layer)
+            elif "embed" in static_name:
+                ratio = decay_rate**(n_layers + 1)
+            param.optimize_attr["learning_rate"] *= ratio
+
+    def __call__(self, model_list):
+        model = model_list[0]
+        if self.weight_decay and self.filter_bias_and_bn:
+            skip = {}
+            if hasattr(model, 'no_weight_decay'):
+                skip = model.no_weight_decay()
+            decay_dict = {
+                param.name: not (len(param.shape) == 1 or
+                                 name.endswith(".bias") or name in skip)
+                for name, param in model.named_parameters()
+                if not 'teacher' in name
+            }
+            parameters = [
+                param for param in model.parameters()
+                if 'teacher' not in param.name
+            ]
+            weight_decay = 0.
+        else:
+            parameters = model.parameters()
+
+        opt_args = dict(
+            learning_rate=self.learning_rate, weight_decay=self.weight_decay)
+        opt_args['parameters'] = parameters
+        if decay_dict is not None:
+            opt_args['apply_decay_param_fun'] = lambda n: decay_dict[n]
+        opt_args['epsilon'] = self.epsilon
+        opt_args['beta1'] = self.beta1
+        opt_args['beta2'] = self.beta2
+        if self.layerwise_decay and self.layerwise_decay < 1.0:
+            opt_args['layerwise_decay'] = self.layerwise_decay
+            name_dict = dict()
+            for n, p in model.named_parameters():
+                name_dict[p.name] = n
+            opt_args['name_dict'] = name_dict
+            opt_args['n_layers'] = model.get_num_layers()
+
+        optimizer = self.AdamWDLImpl(**opt_args)
+
+        return optimizer