Merge pull request #1917 from zhiboniu/develop

add attribute support

ppcls/arch/backbone/legendary_models/resnet.py

@@ -20,9 +20,10 @@ import numpy as np
 import paddle
 from paddle import ParamAttr
 import paddle.nn as nn
-from paddle.nn import Conv2D, BatchNorm, Linear
+from paddle.nn import Conv2D, BatchNorm, Linear, BatchNorm2D
 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
 from paddle.nn.initializer import Uniform
+from paddle.regularizer import L2Decay
 import math
 
 from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
@@ -132,11 +133,12 @@ class ConvBNLayer(TheseusLayer):
             weight_attr=ParamAttr(learning_rate=lr_mult),
             bias_attr=False,
             data_format=data_format)
-        self.bn = BatchNorm(
-            num_filters,
-            param_attr=ParamAttr(learning_rate=lr_mult),
-            bias_attr=ParamAttr(learning_rate=lr_mult),
-            data_layout=data_format)
+
+        weight_attr = ParamAttr(learning_rate=lr_mult, trainable=True)
+        bias_attr = ParamAttr(learning_rate=lr_mult, trainable=True)
+
+        self.bn = BatchNorm2D(
+            num_filters, weight_attr=weight_attr, bias_attr=bias_attr)
         self.relu = nn.ReLU()
 
     def forward(self, x):
@@ -192,6 +194,7 @@ class BottleneckBlock(TheseusLayer):
                 is_vd_mode=False if if_first else True,
                 lr_mult=lr_mult,
                 data_format=data_format)
+
         self.relu = nn.ReLU()
         self.shortcut = shortcut
 
@@ -312,7 +315,7 @@ class ResNet(TheseusLayer):
             [[input_image_channel, 32, 3, 2], [32, 32, 3, 1], [32, 64, 3, 1]]
         }
 
-        self.stem = nn.Sequential(*[
+        self.stem = nn.Sequential(* [
             ConvBNLayer(
                 num_channels=in_c,
                 num_filters=out_c,

ppcls/configs/Attr/StrongBaselineAttr.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output/"
+  device: "gpu"
+  save_interval: 5
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 30
+  print_batch_step: 20
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 256, 192]
+  save_inference_dir: "./inference"
+  use_multilabel: True
+
+# model architecture
+Arch:
+  name: "ResNet50"
+  pretrained: True
+  class_num: 26
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - MultiLabelLoss:
+        weight: 1.0
+        weight_ratio: True
+        size_sum: True
+  Eval:
+    - MultiLabelLoss:
+        weight: 1.0
+        weight_ratio: True
+        size_sum: True
+
+Optimizer:
+  name: Adam
+  lr:
+    name: Piecewise
+    decay_epochs: [12, 18, 24, 28]
+    values: [0.0001, 0.00001, 0.000001, 0.0000001]
+  regularizer:
+    name: 'L2'
+    coeff: 0.0005
+  clip_norm: 10
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: MultiLabelDataset
+      image_root: "dataset/attribute/data/"
+      cls_label_path: "dataset/attribute/trainval.txt"
+      label_ratio: True
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: [192, 256]
+        - Padv2:
+            size: [212, 276]
+            pad_mode: 1
+            fill_value: 0
+        - RandomCropImage:
+            size: [192, 256]
+        - 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: 64
+      drop_last: True
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+  Eval:
+    dataset:
+      name: MultiLabelDataset
+      image_root: "dataset/attribute/data/"
+      cls_label_path: "dataset/attribute/test.txt"
+      label_ratio: True
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            size: [192, 256]
+        - 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: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+
+Metric:
+  Eval:
+    - ATTRMetric:
+
+

ppcls/data/dataloader/common_dataset.py

@@ -44,11 +44,11 @@ def create_operators(params):
 
 
 class CommonDataset(Dataset):
-    def __init__(
-            self,
-            image_root,
-            cls_label_path,
-            transform_ops=None, ):
+    def __init__(self,
+                 image_root,
+                 cls_label_path,
+                 transform_ops=None,
+                 label_ratio=False):
         self._img_root = image_root
         self._cls_path = cls_label_path
         if transform_ops:
@@ -56,7 +56,10 @@ class CommonDataset(Dataset):
 
         self.images = []
         self.labels = []
-        self._load_anno()
+        if label_ratio:
+            self.label_ratio = self._load_anno(label_ratio=label_ratio)
+        else:
+            self._load_anno()
 
     def _load_anno(self):
         pass

ppcls/data/dataloader/multilabel_dataset.py

@@ -25,7 +25,7 @@ from .common_dataset import CommonDataset
 
 
 class MultiLabelDataset(CommonDataset):
-    def _load_anno(self):
+    def _load_anno(self, label_ratio=False):
         assert os.path.exists(self._cls_path)
         assert os.path.exists(self._img_root)
         self.images = []
@@ -41,6 +41,8 @@ class MultiLabelDataset(CommonDataset):
 
                 self.labels.append(labels)
                 assert os.path.exists(self.images[-1])
+        if label_ratio:
+            return np.array(self.labels).mean(0).astype("float32")
 
     def __getitem__(self, idx):
         try:
@@ -50,7 +52,10 @@ class MultiLabelDataset(CommonDataset):
                 img = transform(img, self._transform_ops)
             img = img.transpose((2, 0, 1))
             label = np.array(self.labels[idx]).astype("float32")
-            return (img, label)
+            if self.label_ratio is not None:
+                return (img, np.array([label, self.label_ratio]))
+            else:
+                return (img, label)
 
         except Exception as ex:
             logger.error("Exception occured when parse line: {} with msg: {}".

ppcls/data/preprocess/__init__.py

@@ -33,6 +33,8 @@ from ppcls.data.preprocess.ops.operators import AugMix
 from ppcls.data.preprocess.ops.operators import Pad
 from ppcls.data.preprocess.ops.operators import ToTensor
 from ppcls.data.preprocess.ops.operators import Normalize
+from ppcls.data.preprocess.ops.operators import RandomCropImage
+from ppcls.data.preprocess.ops.operators import Padv2
 
 from ppcls.data.preprocess.batch_ops.batch_operators import MixupOperator, CutmixOperator, OpSampler, FmixOperator
 
@@ -40,6 +42,7 @@ import numpy as np
 from PIL import Image
 import random
 
+
 def transform(data, ops=[]):
     """ transform """
     for op in ops:

ppcls/data/preprocess/ops/operators.py

@@ -190,6 +190,105 @@ class CropImage(object):
         return img[h_start:h_end, w_start:w_end, :]
 
 
+class Padv2(object):
+    def __init__(self,
+                 size=None,
+                 size_divisor=32,
+                 pad_mode=0,
+                 offsets=None,
+                 fill_value=(127.5, 127.5, 127.5)):
+        """
+        Pad image to a specified size or multiple of size_divisor.
+        Args:
+            size (int, list): image target size, if None, pad to multiple of size_divisor, default None
+            size_divisor (int): size divisor, default 32
+            pad_mode (int): pad mode, currently only supports four modes [-1, 0, 1, 2]. if -1, use specified offsets
+                if 0, only pad to right and bottom. if 1, pad according to center. if 2, only pad left and top
+            offsets (list): [offset_x, offset_y], specify offset while padding, only supported pad_mode=-1
+            fill_value (bool): rgb value of pad area, default (127.5, 127.5, 127.5)
+        """
+
+        if not isinstance(size, (int, list)):
+            raise TypeError(
+                "Type of target_size is invalid when random_size is True. \
+                            Must be List, now is {}".format(type(size)))
+
+        if isinstance(size, int):
+            size = [size, size]
+
+        assert pad_mode in [
+            -1, 0, 1, 2
+        ], 'currently only supports four modes [-1, 0, 1, 2]'
+        if pad_mode == -1:
+            assert offsets, 'if pad_mode is -1, offsets should not be None'
+
+        self.size = size
+        self.size_divisor = size_divisor
+        self.pad_mode = pad_mode
+        self.fill_value = fill_value
+        self.offsets = offsets
+
+    def apply_image(self, image, offsets, im_size, size):
+        x, y = offsets
+        im_h, im_w = im_size
+        h, w = size
+        canvas = np.ones((h, w, 3), dtype=np.float32)
+        canvas *= np.array(self.fill_value, dtype=np.float32)
+        canvas[y:y + im_h, x:x + im_w, :] = image.astype(np.float32)
+        return canvas
+
+    def __call__(self, img):
+        im_h, im_w = img.shape[:2]
+        if self.size:
+            w, h = self.size
+            assert (
+                im_h <= h and im_w <= w
+            ), '(h, w) of target size should be greater than (im_h, im_w)'
+        else:
+            h = int(np.ceil(im_h / self.size_divisor) * self.size_divisor)
+            w = int(np.ceil(im_w / self.size_divisor) * self.size_divisor)
+
+        if h == im_h and w == im_w:
+            return img.astype(np.float32)
+
+        if self.pad_mode == -1:
+            offset_x, offset_y = self.offsets
+        elif self.pad_mode == 0:
+            offset_y, offset_x = 0, 0
+        elif self.pad_mode == 1:
+            offset_y, offset_x = (h - im_h) // 2, (w - im_w) // 2
+        else:
+            offset_y, offset_x = h - im_h, w - im_w
+
+        offsets, im_size, size = [offset_x, offset_y], [im_h, im_w], [h, w]
+
+        return self.apply_image(img, offsets, im_size, size)
+
+
+class RandomCropImage(object):
+    """Random crop image only
+    """
+
+    def __init__(self, size):
+        super(RandomCropImage, self).__init__()
+        if isinstance(size, int):
+            size = [size, size]
+        self.size = size
+
+    def __call__(self, img):
+
+        h, w = img.shape[:2]
+        tw, th = self.size
+        i = random.randint(0, h - th)
+        j = random.randint(0, w - tw)
+
+        img = img[i:i + th, j:j + tw, :]
+        if img.shape[0] != 256 or img.shape[1] != 192:
+            raise ValueError('sample: ', h, w, i, j, th, tw, img.shape)
+
+        return img
+
+
 class RandCropImage(object):
     """ random crop image """
 
@@ -463,8 +562,8 @@ class Pad(object):
         # Process fill color for affine transforms
         major_found, minor_found = (int(v)
                                     for v in PILLOW_VERSION.split('.')[:2])
-        major_required, minor_required = (
-            int(v) for v in min_pil_version.split('.')[:2])
+        major_required, minor_required = (int(v) for v in
+                                          min_pil_version.split('.')[:2])
         if major_found < major_required or (major_found == major_required and
                                             minor_found < minor_required):
             if fill is None:

ppcls/engine/evaluation/classification.py

@@ -82,6 +82,7 @@ def classification_eval(engine, epoch_id=0):
         # gather Tensor when distributed
         if paddle.distributed.get_world_size() > 1:
             label_list = []
+
             paddle.distributed.all_gather(label_list, batch[1])
             labels = paddle.concat(label_list, 0)
 
@@ -123,6 +124,7 @@ def classification_eval(engine, epoch_id=0):
                     output_info[key] = AverageMeter(key, '7.5f')
                 output_info[key].update(loss_dict[key].numpy()[0],
                                         current_samples)
+
         #  calc metric
         if engine.eval_metric_func is not None:
             engine.eval_metric_func(preds, labels)
@@ -137,11 +139,14 @@ def classification_eval(engine, epoch_id=0):
             ips_msg = "ips: {:.5f} images/sec".format(
                 batch_size / time_info["batch_cost"].avg)
 
-            metric_msg = ", ".join([
-                "{}: {:.5f}".format(key, output_info[key].val)
-                for key in output_info
-            ])
-            metric_msg += ", {}".format(engine.eval_metric_func.avg_info)
+            if "ATTRMetric" in engine.config["Metric"]["Eval"][0]:
+                metric_msg = ""
+            else:
+                metric_msg = ", ".join([
+                    "{}: {:.5f}".format(key, output_info[key].val)
+                    for key in output_info
+                ])
+                metric_msg += ", {}".format(engine.eval_metric_func.avg_info)
             logger.info("[Eval][Epoch {}][Iter: {}/{}]{}, {}, {}".format(
                 epoch_id, iter_id,
                 len(engine.eval_dataloader), metric_msg, time_msg, ips_msg))
@@ -149,14 +154,29 @@ def classification_eval(engine, epoch_id=0):
         tic = time.time()
     if engine.use_dali:
         engine.eval_dataloader.reset()
-    metric_msg = ", ".join([
-        "{}: {:.5f}".format(key, output_info[key].avg) for key in output_info
-    ])
-    metric_msg += ", {}".format(engine.eval_metric_func.avg_info)
-    logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
-
-    # do not try to save best eval.model
-    if engine.eval_metric_func is None:
-        return -1
-    # return 1st metric in the dict
-    return engine.eval_metric_func.avg
+
+    if "ATTRMetric" in engine.config["Metric"]["Eval"][0]:
+        metric_msg = ", ".join([
+            "evalres: ma: {:.5f} label_f1: {:.5f} label_pos_recall: {:.5f} label_neg_recall: {:.5f} instance_f1: {:.5f} instance_acc: {:.5f} instance_prec: {:.5f} instance_recall: {:.5f}".
+            format(*engine.eval_metric_func.attr_res())
+        ])
+        logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
+
+        # do not try to save best eval.model
+        if engine.eval_metric_func is None:
+            return -1
+        # return 1st metric in the dict
+        return engine.eval_metric_func.attr_res()[0]
+    else:
+        metric_msg = ", ".join([
+            "{}: {:.5f}".format(key, output_info[key].avg)
+            for key in output_info
+        ])
+        metric_msg += ", {}".format(engine.eval_metric_func.avg_info)
+        logger.info("[Eval][Epoch {}][Avg]{}".format(epoch_id, metric_msg))
+
+        # do not try to save best eval.model
+        if engine.eval_metric_func is None:
+            return -1
+        # return 1st metric in the dict
+        return engine.eval_metric_func.avg

ppcls/loss/multilabelloss.py

@@ -3,16 +3,29 @@ import paddle.nn as nn
 import paddle.nn.functional as F
 
 
+def ratio2weight(targets, ratio):
+    pos_weights = targets * (1. - ratio)
+    neg_weights = (1. - targets) * ratio
+    weights = paddle.exp(neg_weights + pos_weights)
+
+    # for RAP dataloader, targets element may be 2, with or without smooth, some element must great than 1
+    weights = weights - weights * (targets > 1)
+
+    return weights
+
+
 class MultiLabelLoss(nn.Layer):
     """
     Multi-label loss
     """
 
-    def __init__(self, epsilon=None):
+    def __init__(self, epsilon=None, size_sum=False, weight_ratio=False):
         super().__init__()
         if epsilon is not None and (epsilon <= 0 or epsilon >= 1):
             epsilon = None
         self.epsilon = epsilon
+        self.weight_ratio = weight_ratio
+        self.size_sum = size_sum
 
     def _labelsmoothing(self, target, class_num):
         if target.ndim == 1 or target.shape[-1] != class_num:
@@ -24,13 +37,21 @@ class MultiLabelLoss(nn.Layer):
         return soft_target
 
     def _binary_crossentropy(self, input, target, class_num):
+        if self.weight_ratio:
+            target, label_ratio = target[:, 0, :], target[:, 1, :]
         if self.epsilon is not None:
             target = self._labelsmoothing(target, class_num)
-            cost = F.binary_cross_entropy_with_logits(
-                logit=input, label=target)
-        else:
-            cost = F.binary_cross_entropy_with_logits(
-                logit=input, label=target)
+        cost = F.binary_cross_entropy_with_logits(
+            logit=input, label=target, reduction='none')
+
+        if self.weight_ratio:
+            targets_mask = paddle.cast(target > 0.5, 'float32')
+            weight = ratio2weight(targets_mask, paddle.to_tensor(label_ratio))
+            weight = weight * (target > -1)
+            cost = cost * weight
+
+        if self.size_sum:
+            cost = cost.sum(1).mean() if self.size_sum else cost.mean()
 
         return cost
 

ppcls/metric/__init__.py

@@ -20,6 +20,7 @@ from .metrics import TopkAcc, mAP, mINP, Recallk, Precisionk
 from .metrics import DistillationTopkAcc
 from .metrics import GoogLeNetTopkAcc
 from .metrics import HammingDistance, AccuracyScore
+from .metrics import ATTRMetric
 from .metrics import TprAtFpr
 
 
@@ -55,12 +56,15 @@ class CombinedMetrics(AvgMetrics):
     def avg(self):
         return self.metric_func_list[0].avg
 
+    def attr_res(self):
+        return self.metric_func_list[0].attrmeter.res()
+
     def reset(self):
         for metric in self.metric_func_list:
             if hasattr(metric, "reset"):
                 metric.reset()
 
+
 def build_metrics(config):
     metrics_list = CombinedMetrics(copy.deepcopy(config))
     return metrics_list
-

ppcls/metric/metrics.py

@@ -22,8 +22,10 @@ from sklearn.metrics import accuracy_score as accuracy_metric
 from sklearn.metrics import multilabel_confusion_matrix
 from sklearn.preprocessing import binarize
 
+from easydict import EasyDict
+
 from ppcls.metric.avg_metrics import AvgMetrics
-from ppcls.utils.misc import AverageMeter
+from ppcls.utils.misc import AverageMeter, AttrMeter
 
 
 class TopkAcc(AvgMetrics):
@@ -36,7 +38,10 @@ class TopkAcc(AvgMetrics):
         self.reset()
 
     def reset(self):
-        self.avg_meters = {"top{}".format(k): AverageMeter("top{}".format(k)) for k in self.topk}
+        self.avg_meters = {
+            "top{}".format(k): AverageMeter("top{}".format(k))
+            for k in self.topk
+        }
 
     def forward(self, x, label):
         if isinstance(x, dict):
@@ -46,7 +51,8 @@ class TopkAcc(AvgMetrics):
         for k in self.topk:
             metric_dict["top{}".format(k)] = paddle.metric.accuracy(
                 x, label, k=k)
-            self.avg_meters["top{}".format(k)].update(metric_dict["top{}".format(k)].numpy()[0], x.shape[0])
+            self.avg_meters["top{}".format(k)].update(
+                metric_dict["top{}".format(k)].numpy()[0], x.shape[0])
         return metric_dict
 
 
@@ -116,7 +122,7 @@ class mINP(nn.Layer):
                                             choosen_indices)
         equal_flag = paddle.equal(choosen_label, query_img_id)
         if keep_mask is not None:
-            keep_mask = paddle.index_sample(
+            keep_mask = paddle.indechmx_sample(
                 keep_mask.astype('float32'), choosen_indices)
             equal_flag = paddle.logical_and(equal_flag,
                                             keep_mask.astype('bool'))
@@ -140,7 +146,7 @@ class mINP(nn.Layer):
 
 
 class TprAtFpr(nn.Layer):
-    def __init__(self, max_fpr=1/1000.):
+    def __init__(self, max_fpr=1 / 1000.):
         super().__init__()
         self.gt_pos_score_list = []
         self.gt_neg_score_list = []
@@ -178,14 +184,18 @@ class TprAtFpr(nn.Layer):
             threshold = i / 10000.
             if len(gt_pos_score_list) == 0:
                 continue
-            tpr = np.sum(gt_pos_score_list > threshold) / len(gt_pos_score_list)
+            tpr = np.sum(
+                gt_pos_score_list > threshold) / len(gt_pos_score_list)
             if len(gt_neg_score_list) == 0 and tpr > max_tpr:
                 max_tpr = tpr
-                result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(threshold, fpr, tpr)
-            fpr = np.sum(gt_neg_score_list > threshold) / len(gt_neg_score_list)
+                result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(
+                    threshold, fpr, tpr)
+            fpr = np.sum(
+                gt_neg_score_list > threshold) / len(gt_neg_score_list)
             if fpr <= self.max_fpr and tpr > max_tpr:
                 max_tpr = tpr
-                result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(threshold, fpr, tpr)
+                result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(
+                    threshold, fpr, tpr)
         self.max_tpr = max_tpr
         return result
 
@@ -333,7 +343,8 @@ class HammingDistance(MultiLabelMetric):
         metric_dict = dict()
         metric_dict["HammingDistance"] = paddle.to_tensor(
             hamming_loss(target, preds))
-        self.avg_meters["HammingDistance"].update(metric_dict["HammingDistance"].numpy()[0], output.shape[0])
+        self.avg_meters["HammingDistance"].update(
+            metric_dict["HammingDistance"].numpy()[0], output.shape[0])
         return metric_dict
 
 
@@ -372,5 +383,66 @@ class AccuracyScore(MultiLabelMetric):
             accuracy = (sum(tps) + sum(tns)) / (
                 sum(tps) + sum(tns) + sum(fns) + sum(fps))
         metric_dict["AccuracyScore"] = paddle.to_tensor(accuracy)
-        self.avg_meters["AccuracyScore"].update(metric_dict["AccuracyScore"].numpy()[0], output.shape[0])
+        self.avg_meters["AccuracyScore"].update(
+            metric_dict["AccuracyScore"].numpy()[0], output.shape[0])
+        return metric_dict
+
+
+def get_attr_metrics(gt_label, preds_probs, threshold):
+    """
+    index: evaluated label index
+    """
+    pred_label = (preds_probs > threshold).astype(int)
+
+    eps = 1e-20
+    result = EasyDict()
+
+    has_fuyi = gt_label == -1
+    pred_label[has_fuyi] = -1
+
+    ###############################
+    # label metrics
+    # TP + FN
+    result.gt_pos = np.sum((gt_label == 1), axis=0).astype(float)
+    # TN + FP
+    result.gt_neg = np.sum((gt_label == 0), axis=0).astype(float)
+    # TP
+    result.true_pos = np.sum((gt_label == 1) * (pred_label == 1),
+                             axis=0).astype(float)
+    # TN
+    result.true_neg = np.sum((gt_label == 0) * (pred_label == 0),
+                             axis=0).astype(float)
+    # FP
+    result.false_pos = np.sum(((gt_label == 0) * (pred_label == 1)),
+                              axis=0).astype(float)
+    # FN
+    result.false_neg = np.sum(((gt_label == 1) * (pred_label == 0)),
+                              axis=0).astype(float)
+
+    ################
+    # instance metrics
+    result.gt_pos_ins = np.sum((gt_label == 1), axis=1).astype(float)
+    result.true_pos_ins = np.sum((pred_label == 1), axis=1).astype(float)
+    # true positive
+    result.intersect_pos = np.sum((gt_label == 1) * (pred_label == 1),
+                                  axis=1).astype(float)
+    # IOU
+    result.union_pos = np.sum(((gt_label == 1) + (pred_label == 1)),
+                              axis=1).astype(float)
+
+    return result
+
+
+class ATTRMetric(nn.Layer):
+    def __init__(self, threshold=0.5):
+        super().__init__()
+        self.threshold = threshold
+
+    def reset(self):
+        self.attrmeter = AttrMeter(threshold=0.5)
+
+    def forward(self, output, target):
+        metric_dict = get_attr_metrics(target[:, 0, :].numpy(),
+                                       output.numpy(), self.threshold)
+        self.attrmeter.update(metric_dict)
         return metric_dict

ppcls/utils/misc.py

@@ -65,3 +65,87 @@ class AverageMeter(object):
     def value(self):
         return '{self.name}: {self.val:{self.fmt}}{self.postfix}'.format(
             self=self)
+
+
+class AttrMeter(object):
+    """
+    Computes and stores the average and current value
+    Code was based on https://github.com/pytorch/examples/blob/master/imagenet/main.py
+    """
+
+    def __init__(self, threshold=0.5):
+        self.threshold = threshold
+        self.reset()
+
+    def reset(self):
+        self.gt_pos = 0
+        self.gt_neg = 0
+        self.true_pos = 0
+        self.true_neg = 0
+        self.false_pos = 0
+        self.false_neg = 0
+
+        self.gt_pos_ins = []
+        self.true_pos_ins = []
+        self.intersect_pos = []
+        self.union_pos = []
+
+    def update(self, metric_dict):
+        self.gt_pos += metric_dict['gt_pos']
+        self.gt_neg += metric_dict['gt_neg']
+        self.true_pos += metric_dict['true_pos']
+        self.true_neg += metric_dict['true_neg']
+        self.false_pos += metric_dict['false_pos']
+        self.false_neg += metric_dict['false_neg']
+
+        self.gt_pos_ins += metric_dict['gt_pos_ins'].tolist()
+        self.true_pos_ins += metric_dict['true_pos_ins'].tolist()
+        self.intersect_pos += metric_dict['intersect_pos'].tolist()
+        self.union_pos += metric_dict['union_pos'].tolist()
+
+    def res(self):
+        import numpy as np
+        eps = 1e-20
+        label_pos_recall = 1.0 * self.true_pos / (
+            self.gt_pos + eps)  # true positive
+        label_neg_recall = 1.0 * self.true_neg / (
+            self.gt_neg + eps)  # true negative
+        # mean accuracy
+        label_ma = (label_pos_recall + label_neg_recall) / 2
+
+        label_pos_recall = np.mean(label_pos_recall)
+        label_neg_recall = np.mean(label_neg_recall)
+        label_prec = (self.true_pos / (self.true_pos + self.false_pos + eps))
+        label_acc = (self.true_pos /
+                     (self.true_pos + self.false_pos + self.false_neg + eps))
+        label_f1 = np.mean(2 * label_prec * label_pos_recall /
+                           (label_prec + label_pos_recall + eps))
+
+        ma = (np.mean(label_ma))
+
+        self.gt_pos_ins = np.array(self.gt_pos_ins)
+        self.true_pos_ins = np.array(self.true_pos_ins)
+        self.intersect_pos = np.array(self.intersect_pos)
+        self.union_pos = np.array(self.union_pos)
+        instance_acc = self.intersect_pos / (self.union_pos + eps)
+        instance_prec = self.intersect_pos / (self.true_pos_ins + eps)
+        instance_recall = self.intersect_pos / (self.gt_pos_ins + eps)
+        instance_f1 = 2 * instance_prec * instance_recall / (
+            instance_prec + instance_recall + eps)
+
+        instance_acc = np.mean(instance_acc)
+        instance_prec = np.mean(instance_prec)
+        instance_recall = np.mean(instance_recall)
+        instance_f1 = 2 * instance_prec * instance_recall / (
+            instance_prec + instance_recall + eps)
+
+        instance_acc = np.mean(instance_acc)
+        instance_prec = np.mean(instance_prec)
+        instance_recall = np.mean(instance_recall)
+        instance_f1 = np.mean(instance_f1)
+
+        res = [
+            ma, label_f1, label_pos_recall, label_neg_recall, instance_f1,
+            instance_acc, instance_prec, instance_recall
+        ]
+        return res

requirements.txt

@@ -9,3 +9,4 @@ scipy
 scikit-learn==0.23.2
 gast==0.3.3
 faiss-cpu==1.7.1.post2
+easydict