增加cifar100参数yaml

ppcls/arch/__init__.py

@@ -70,10 +70,9 @@ class RecModel(TheseusLayer):
         super().__init__()
         backbone_config = config["Backbone"]
         backbone_name = backbone_config.pop("name")
-        self.decoup = False
-        if backbone_config.get('decoup', False):
-            self.decoup = backbone_config.pop('decoup')
         self.backbone = eval(backbone_name)(**backbone_config)
+        self.head_feature_from = config.get('head_feature_from', 'neck')
+
         if "BackboneStopLayer" in config:
             backbone_stop_layer = config["BackboneStopLayer"]["name"]
             self.backbone.stop_after(backbone_stop_layer)
@@ -94,19 +93,16 @@ class RecModel(TheseusLayer):
         x = self.backbone(x)
         
         out["backbone"] = x
-        if self.decoup:
-            logits_index, features_index = self.decoup['logits_index'], self.decoup['features_index']
-            logits, feat = x[logits_index], x[features_index]
-            out['logits'] = logits
-            out['features'] =feat
-            return out
 
         if self.neck is not None:
             feat = self.neck(x)
             out["neck"] = feat
         out["features"] = out['neck'] if self.neck else x
         if self.head is not None:
-            y = self.head(out['features'], label)
+            if self.head_feature_from == 'backbone':
+                y = self.head(out['backbone'], label)
+            elif self.head_feature_from == 'neck':
+                y = self.head(out['features'], label)
             out["logits"] = y
         return out
 

ppcls/arch/backbone/model_zoo/wideresnet.py

@@ -2,6 +2,7 @@ import paddle
 import paddle.nn as nn
 import paddle.nn.functional as F
 from paddle import ParamAttr
+from ..base.theseus_layer import TheseusLayer
 """
 backbone option "WideResNet"
 code in this file is adpated from
@@ -123,7 +124,7 @@ class Normalize(nn.Layer):
         return out
 
 
-class Wide_ResNet(nn.Layer):
+class Wide_ResNet(TheseusLayer):
     def __init__(self,
                  num_classes,
                  depth=28,

ppcls/arch/gears/__init__.py

@@ -20,6 +20,7 @@ from .vehicle_neck import VehicleNeck
 from paddle.nn import Tanh
 from .bnneck import BNNeck
 from .adamargin import AdaMargin
+from .frfn_neck import FRFNNeck
 
 __all__ = ['build_gear']
 
@@ -27,7 +28,7 @@ __all__ = ['build_gear']
 def build_gear(config):
     support_dict = [
         'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck', 'Tanh',
-        'BNNeck', 'AdaMargin',
+        'BNNeck', 'AdaMargin', 'FRFNNeck'
     ]
     module_name = config.pop('name')
     assert module_name in support_dict, Exception(

ppcls/arch/gears/frfn_neck.py

@@ -0,0 +1,32 @@
+import paddle.nn as nn
+
+
+class Normalize(nn.Layer):
+    """ Ln normalization copied from
+    https://github.com/salesforce/CoMatch
+    """
+
+    def __init__(self, power=2):
+        super(Normalize, self).__init__()
+        self.power = power
+
+    def forward(self, x):
+        norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
+        out = x.divide(norm)
+        return out
+
+
+class FRFNNeck(nn.Layer):
+    def __init__(self, num_features, low_dim, **kwargs):
+        super(FRFNNeck, self).__init__()
+        self.l2norm = Normalize(2)
+        self.fc1 = nn.Linear(num_features, num_features)
+        self.relu_mlp = nn.LeakyReLU(negative_slope=0.1)
+        self.fc2 = nn.Linear(num_features, low_dim)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu_mlp(x)
+        x = self.fc2(x)
+        x = self.l2norm(x)
+        return x

ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar100_10000_4gpu.yaml

@@ -26,6 +26,7 @@ Arch:
   name: RecModel
   infer_output_key: logits
   infer_add_softmax: false
+  head_feature_from: backbone 
   Backbone:
     name: WideResNet
     widen_factor: 8
@@ -33,13 +34,18 @@ Arch:
     dropout: 0 
     num_classes: 100
     low_dim: 64
-    proj: true
+    proj: false
     proj_after: false
-
+  BackboneStopLayer:
-  Decoup:
+    name: bn1
-    name: Decoup
+  Neck:
-    logits_index: 0
+    name: FRFNNeck
-    features_index: 1 
+    num_features: 512
+    low_dim: 64
+  Head:
+    name: FC
+    embedding_size: 512
+    class_num: 100
 
   use_sync_bn: true
 
@@ -66,33 +72,36 @@ Optimizer:
   use_nesterov: true
   weight_decay: 0.001
   lr:
-    name: 'cosine_schedule_with_warmup'
+    name: 'CosineFixmatch'
     learning_rate: 0.03
     num_warmup_steps: 0
-    num_training_steps: 524800
 
 DataLoader:
   mean: [0.5071, 0.4867, 0.4408]
   std: [0.2675, 0.2565, 0.2761]
   Train:
     dataset:
-      name: CIFAR100SSL
+      name: Cifar100
       data_file: null
       mode: 'train'
       download: true
+      backend: 'pil'
       sample_per_label: 100
       expand_labels: 1
       transform_ops:
-        - RandomHorizontalFlip:
+        - RandFlipImage:
-            prob: 0.5
+            flip_code: 1
-        - RandomCrop:
+        - Pad_paddle_vision:
-            size: 32
             padding: 4
-            padding_mode: "reflect"
+            padding_mode: reflect
-        - ToTensor:
+        - RandCropImageV2:
+            size: [32, 32]
+        - NormalizeImage:
         - Normalize:
+            scale: 1.0/255.0
             mean: [0.5071, 0.4867, 0.4408]
             std: [0.2675, 0.2565, 0.2761]
+            order: hwc
     
     sampler:
       name: DistributedBatchSampler
@@ -105,46 +114,51 @@ DataLoader:
   
   UnLabelTrain:
     dataset:
-      name: CIFAR100SSL
+      name: Cifar100
       data_file: null
       mode: 'train'
+      backend: 'pil'
       download: true
 
-      transform_w:
+      transform_ops_weak:
-        - RandomHorizontalFlip:
+        - RandFlipImage:
-            prob: 0.5
+            flip_code: 1
-        - RandomCrop:
+        - Pad_paddle_vision:
-            size: 32
             padding: 4
-            padding_mode: 'reflect'
+            padding_mode: reflect
-        - ToTensor:
+        - RandCropImageV2:
-        - Normalize:
+            size: [32, 32]
+        - NormalizeImage:
+            scale: 1.0/255.0
             mean: [0.5071, 0.4867, 0.4408]
             std: [0.2675, 0.2565, 0.2761]
+            order: hwc
 
-      transform_s1:
+      transform_ops_strong:
-        - RandomHorizontalFlip:
+        - RandFlipImage:
-                prob: 0.5
+            flip_code: 1
-        - RandomCrop:
+        - Pad_paddle_vision:
-            size: 32
             padding: 4
-            padding_mode: 'reflect'
+            padding_mode: reflect
-        - RandAugmentMC:
+        - RandCropImageV2:
-            n: 2
+            size: [32, 32]
-            m: 10
+        - RandAugment:
-        - ToTensor:
+            num_layers: 2
-        - Normalize:
+            magnitude: 10
+        - NormalizeImage:
+            scale: 1.0/255.0
             mean: [0.5071, 0.4867, 0.4408]
             std: [0.2675, 0.2565, 0.2761]
+            order: hwc
       
-      transform_s2:
+      transform_ops_strong2:
-        - RandomResizedCrop:
+        - RandCropImageV2:
-            size: 32
+            size: [32, 32]
-        - RandomHorizontalFlip:
+        - RandFlipImage:
-            prob: 0.5
+            flip_code: 1
         - RandomApply:
             transforms:
-              - ColorJitter:
+              - RawColorJitter:
                   brightness: 0.4
                   contrast: 0.4
                   saturation: 0.4
@@ -152,7 +166,12 @@ DataLoader:
             p: 0.8
         - RandomGrayscale:
             p: 0.2
-        - ToTensor:
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.5071, 0.4867, 0.4408]
+            std: [0.2675, 0.2565, 0.2761]
+            order: hwc
+
 
     sampler:
       name: DistributedBatchSampler
@@ -165,15 +184,17 @@ DataLoader:
 
   Eval:
     dataset:
-      name: CIFAR100SSL
+      name: Cifar100
       mode: 'test'
+      backend: 'pil'
       download: true
       data_file: null
       transform_ops:
-        - ToTensor:
+        - NormalizeImage:
-        - Normalize:
+            scale: 1.0/255.0
             mean: [0.5071, 0.4867, 0.4408]
             std: [0.2675, 0.2565, 0.2761]
+            order: hwc
     sampler:
       name: DistributedBatchSampler
       batch_size: 16

ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml

@@ -26,18 +26,26 @@ Arch:
   name: RecModel
   infer_output_key: logits
   infer_add_softmax: false
+  head_feature_from: backbone 
   Backbone:
     name: WideResNet
-    decoup:
-      logits_index: 0
-      features_index: 1
     widen_factor: 2
     depth: 28
     dropout: 0
     num_classes: 10
     low_dim: 64
-    proj: true
+    proj: false
     proj_after: false
+  BackboneStopLayer:
+    name: bn1
+  Neck:
+    name: FRFNNeck
+    num_features: 128
+    low_dim: 64
+  Head:
+    name: FC
+    embedding_size: 128
+    class_num: 10
 
   use_sync_bn: true
 
@@ -103,8 +111,6 @@ DataLoader:
       num_workers: 4
       use_shared_memory: true
   
-
-
   UnLabelTrain:
     dataset:
       name: Cifar10

ppcls/data/preprocess/ops/randaugmentmc.py

@@ -1,227 +0,0 @@
-import PIL
-import PIL.ImageDraw
-import random
-from paddle.vision.transforms import transforms as T
-from paddle.vision.transforms.transforms import ColorJitter
-import numpy as np
-
-
-PARAMETER_MAX = 10
-
-
-
-def AutoContrast(img, **kwarg):
-    return PIL.ImageOps.autocontrast(img)
-
-
-def Brightness(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    return PIL.ImageEnhance.Brightness(img).enhance(v)
-
-
-def Color(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    return PIL.ImageEnhance.Color(img).enhance(v)
-
-def Contrast(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    return PIL.ImageEnhance.Contrast(img).enhance(v)
-
-
-def Cutout(img, v, max_v, bias=0):
-    if v == 0:
-        return img
-    v = _float_parameter(v, max_v) + bias
-    v = int(v * min(img.size))
-    return CutoutAbs(img, v)
-
-
-def CutoutAbs(img, v, **kwarg):
-    w, h = img.size
-    x0 = np.random.uniform(0, w)
-    y0 = np.random.uniform(0, h)
-    x0 = int(max(0, x0 - v / 2.))
-    y0 = int(max(0, y0 - v / 2.))
-    x1 = int(min(w, x0 + v))
-    y1 = int(min(h, y0 + v))
-    xy = (x0, y0, x1, y1)
-    # gray
-    color = (127, 127, 127)
-    img = img.copy()
-    PIL.ImageDraw.Draw(img).rectangle(xy, color)
-    return img
-
-
-def Equalize(img, **kwarg):
-    return PIL.ImageOps.equalize(img)
-
-
-def Identity(img, **kwarg):
-    return img
-
-
-def Invert(img, **kwarg):
-    return PIL.ImageOps.invert(img)
-
-
-def Posterize(img, v, max_v, bias=0):
-    v = _int_parameter(v, max_v) + bias
-    return PIL.ImageOps.posterize(img, v)
-
-
-def Rotate(img, v, max_v, bias=0):
-    v = _int_parameter(v, max_v) + bias
-    if random.random() < 0.5:
-        v = -v
-    return img.rotate(v)
-
-
-def Sharpness(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    return PIL.ImageEnhance.Sharpness(img).enhance(v)
-
-
-def ShearX(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    if random.random() < 0.5:
-        v = -v
-    return img.transform(img.size, PIL.Image.Transform.AFFINE, (1, v, 0, 0, 1, 0))
-
-
-def ShearY(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    if random.random() < 0.5:
-        v = -v
-    return img.transform(img.size, PIL.Image.Transform.AFFINE, (1, 0, 0, v, 1, 0))
-
-
-def Solarize(img, v, max_v, bias=0):
-    v = _int_parameter(v, max_v) + bias
-    return PIL.ImageOps.solarize(img, 256 - v)
-
-
-def SolarizeAdd(img, v, max_v, bias=0, threshold=128):
-    v = _int_parameter(v, max_v) + bias
-    if random.random() < 0.5:
-        v = -v
-    img_np = np.array(img).astype(np.int)
-    img_np = img_np + v
-    img_np = np.clip(img_np, 0, 255)
-    img_np = img_np.astype(np.uint8)
-    img = Image.fromarray(img_np)
-    return PIL.ImageOps.solarize(img, threshold)
-
-
-def TranslateX(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    if random.random() < 0.5:
-        v = -v
-    v = int(v * img.size[0])
-    return img.transform(img.size, PIL.Image.Transform.AFFINE, (1, 0, v, 0, 1, 0))
-
-
-def TranslateY(img, v, max_v, bias=0):
-    v = _float_parameter(v, max_v) + bias
-    if random.random() < 0.5:
-        v = -v
-    v = int(v * img.size[1])
-    return img.transform(img.size, PIL.Image.Transform.AFFINE, (1, 0, 0, 0, 1, v))
-
-
-def _float_parameter(v, max_v):
-    return float(v) * max_v / PARAMETER_MAX
-
-
-def _int_parameter(v, max_v):
-    return int(v * max_v / PARAMETER_MAX)
-
-
-def fixmatch_augment_pool():
-    # FixMatch paper
-    augs = [(AutoContrast, None, None),
-            (Brightness, 0.9, 0.05),
-            (Color, 0.9, 0.05),
-            (Contrast, 0.9, 0.05),
-            (Equalize, None, None),
-            (Identity, None, None),
-            (Posterize, 4, 4),
-            (Rotate, 30, 0),
-            (Sharpness, 0.9, 0.05),
-            (ShearX, 0.3, 0),
-            (ShearY, 0.3, 0),
-            (Solarize, 256, 0),
-            (TranslateX, 0.3, 0),
-            (TranslateY, 0.3, 0)]
-    return augs
-
-
-def my_augment_pool():
-    # Test
-    augs = [(AutoContrast, None, None),
-            (Brightness, 1.8, 0.1),
-            (Color, 1.8, 0.1),
-            (Contrast, 1.8, 0.1),
-            (Cutout, 0.2, 0),
-            (Equalize, None, None),
-            (Invert, None, None),
-            (Posterize, 4, 4),
-            (Rotate, 30, 0),
-            (Sharpness, 1.8, 0.1),
-            (ShearX, 0.3, 0),
-            (ShearY, 0.3, 0),
-            (Solarize, 256, 0),
-            (SolarizeAdd, 110, 0),
-            (TranslateX, 0.45, 0),
-            (TranslateY, 0.45, 0)]
-    return augs
-
-
-class RandAugmentPC(object):
-    def __init__(self, n, m):
-        assert n >= 1
-        assert 1 <= m <= 10
-        self.n = n
-        self.m = m
-        self.augment_pool = my_augment_pool()
-
-    def __call__(self, img):
-        ops = random.choices(self.augment_pool, k=self.n)
-        for op, max_v, bias in ops:
-            prob = np.random.uniform(0.2, 0.8)
-            if random.random() + prob >= 1:
-                img = op(img, v=self.m, max_v=max_v, bias=bias)
-        img = CutoutAbs(img, int(32*0.5))
-        return img
-
-
-class RandAugmentMC(object):
-    def __init__(self, n, m):
-        assert n >= 1
-        assert 1 <= m <= 10
-        self.n = n
-        self.m = m
-        self.augment_pool = fixmatch_augment_pool()
-
-    def __call__(self, img):
-        ops = random.choices(self.augment_pool, k=self.n)
-        for op, max_v, bias in ops:
-            v = np.random.randint(1, self.m)
-            if random.random() < 0.5:
-                img = op(img, v=v, max_v=max_v, bias=bias)
-        img = CutoutAbs(img, int(32 * 0.5))
-        return img
-
-
-class RandomApply:
-    def __init__(self, p, transforms):
-        self.p = p
-        ts = []
-        for t in transforms:
-            for key in t.keys():
-                ts.append(eval(key)(**t[key]))
-
-        self.trans = T.Compose(ts)
-
-    def __call__(self, img):
-        timg = self.trans(img)
-        return timg

ppcls/engine/train/train_fixmatch_ccssl.py

@@ -11,8 +11,6 @@ import paddle
 
 
 def train_epoch_fixmatch_ccssl(engine, epoch_id, print_batch_step):
-    print(engine.model.state_dict().keys())
-    assert 1==0
     tic = time.time()
     if not hasattr(engine, 'train_dataloader_iter'):
         engine.train_dataloader_iter = iter(engine.train_dataloader)