Decoupling metrics from benchmark to allow different kinds of forward process

benchmark/benchmark.py

@@ -2,61 +2,30 @@
 import argparse
 
 import yaml
-import tqdm
 import numpy as np
 import cv2 as cv
 
+# from ..models import MODELS
 from models import MODELS
+from utils import METRICS
 
 parser = argparse.ArgumentParser("Benchmarks for OpenCV Zoo.")
 parser.add_argument('--cfg', '-c', type=str,
                     help='Benchmarking on the given config.')
 args = parser.parse_args()
 
-class Timer:
-    def __init__(self, warmup=0, reduction='median'):
-        self._warmup = warmup
-        self._reduction = reduction
-        self._tm = cv.TickMeter()
-        self._time_record = []
-        self._calls = 0
-
-    def start(self):
-        self._tm.start()
-
-    def stop(self):
-        self._tm.stop()
-        self._calls += 1
-        self._time_record.append(self._tm.getTimeMilli())
-        self._tm.reset()
-
-    def reset(self):
-        self._time_record = []
-        self._calls = 0
-
-    def getResult(self):
-        if self._reduction == 'median':
-            return self._getMedian(self._time_record[self._warmup:])
-        elif self._reduction == 'gmean':
-            return self._getGMean(self._time_record[self._warmup:])
-        else:
-            raise NotImplementedError()
-
-    def _getMedian(self, records):
-        ''' Return median time
-        '''
-        l = len(records)
-        mid = int(l / 2)
-        if l % 2 == 0:
-            return (records[mid] + records[mid - 1]) / 2
-        else:
-            return records[mid]
+def build_from_cfg(cfg, registery, key='name'):
+    obj_name = cfg.pop(key)
+    obj = registery.get(obj_name)
+    return obj(**cfg)
 
-    def _getGMean(self, records, drop_largest=3):
-        ''' Return geometric mean of time
-        '''
-        time_record_sorted = sorted(records, reverse=True)
-        return sum(records[drop_largest:]) / (self._calls - drop_largest)
+def prepend_pythonpath(cfg):
+    for k, v in cfg.items():
+        if isinstance(v, dict):
+            prepend_pythonpath(v)
+        else:
+            if 'path' in k.lower():
+                cfg[k] = os.path.join(os.environ['PYTHONPATH'], v)
 
 class Data:
     def __init__(self, **kwargs):
@@ -105,64 +74,15 @@ def __getitem__(self, idx):
         else:
             return self._files[idx], image
 
-class Metric:
-    def __init__(self, **kwargs):
-        self._sizes = kwargs.pop('sizes', None)
-        self._warmup = kwargs.pop('warmup', 3)
-        self._repeat = kwargs.pop('repeat', 10)
-        assert self._warmup < self._repeat, 'The value of warmup must be smaller than the value of repeat.'
-        self._batch_size = kwargs.pop('batchSize', 1)
-        self._reduction = kwargs.pop('reduction', 'median')
-
-        self._timer = Timer(self._warmup, self._reduction)
-
-    def getReduction(self):
-        return self._reduction
-
-    def forward(self, model, *args, **kwargs):
-        img = args[0]
-        h, w, _ = img.shape
-        if not self._sizes:
-            self._sizes = [[w, h]]
-
-        results = dict()
-        self._timer.reset()
-        if len(args) == 1:
-            for size in self._sizes:
-                img_r = cv.resize(img, size)
-                try:
-                    model.setInputSize(size)
-                except:
-                    pass
-                # TODO: batched inference
-                # input_data = [img] * self._batch_size
-                input_data = img_r
-                for _ in range(self._repeat+self._warmup):
-                    self._timer.start()
-                    model.infer(input_data)
-                    self._timer.stop()
-                results[str(size)] = self._timer.getResult()
-        else:
-            # TODO: batched inference
-            # input_data = [args] * self._batch_size
-            bboxes = args[1]
-            for idx, bbox in enumerate(bboxes):
-                for _ in range(self._repeat+self._warmup):
-                    self._timer.start()
-                    model.infer(img, bbox)
-                    self._timer.stop()
-                results['bbox{}'.format(idx)] = self._timer.getResult()
-
-        return results
-
 class Benchmark:
     def __init__(self, **kwargs):
         self._data_dict = kwargs.pop('data', None)
         assert self._data_dict, 'Benchmark[\'data\'] cannot be empty and must have path and files.'
         self._data = Data(**self._data_dict)
 
         self._metric_dict = kwargs.pop('metric', None)
-        self._metric = Metric(**self._metric_dict)
+        # self._metric = Metric(**self._metric_dict)
+        self._metric = build_from_cfg(self._metric_dict, registery=METRICS, key='type')
 
         backend_id = kwargs.pop('backend', 'default')
         available_backends = dict(
@@ -206,20 +126,6 @@ def printResults(self):
                 total_latency += latency
                 print('      {}, latency ({}): {:.4f} ms'.format(key, self._metric.getReduction(), latency))
 
-
-def build_from_cfg(cfg, registery):
-    obj_name = cfg.pop('name')
-    obj = registery.get(obj_name)
-    return obj(**cfg)
-
-def prepend_pythonpath(cfg):
-    for k, v in cfg.items():
-        if isinstance(v, dict):
-            prepend_pythonpath(v)
-        else:
-            if 'path' in k.lower():
-                cfg[k] = os.path.join(os.environ['PYTHONPATH'], v)
-
 if __name__ == '__main__':
     assert args.cfg.endswith('yaml'), 'Currently support configs of yaml format only.'
     with open(args.cfg, 'r') as f:

benchmark/config/face_detection_yunet.yaml

@@ -4,13 +4,13 @@ Benchmark:
     path: "benchmark/data/face/detection"
     files: ["group.jpg", "concerts.jpg", "dance.jpg"]
   metric:
+    type: "Detection"
     sizes: # [[w1, h1], ...], Omit to run at original scale
       - [160, 120]
       - [640, 480]
-    warmup: 3
+    warmup: 30
     repeat: 10
-    batchSize: 1
-    reduction: 'median'
+    reduction: "median"
   backend: "default"
   target: "cpu"
 

benchmark/config/face_recognition_sface.yaml

@@ -5,10 +5,10 @@ Benchmark:
     files: ["Aaron_Tippin_0001.jpg", "Alvaro_Uribe_0028.jpg", "Alvaro_Uribe_0029.jpg", "Jose_Luis_Rodriguez_Zapatero_0001.jpg"]
     useLabel: True
   metric: # 'sizes' is omitted since this model requires input of fixed size
-    warmup: 3
+    type: "Recognition"
+    warmup: 30
     repeat: 10
-    batchSize: 1
-    reduction: 'median'
+    reduction: "median"
   backend: "default"
   target: "cpu"
 

benchmark/config/human_segmentation_pphumanseg.yaml

@@ -6,10 +6,10 @@ Benchmark:
     toRGB: True
     resize: [192, 192]
   metric:
-    warmup: 3
+    type: "Base"
+    warmup: 30
     repeat: 10
-    batchSize: 1
-    reduction: 'median'
+    reduction: "median"
   backend: "default"
   target: "cpu"
 

benchmark/config/image_classification_ppresnet.yaml

@@ -7,10 +7,10 @@ Benchmark:
     resize: [256, 256]
     centerCrop: 224
   metric:
-    warmup: 3
+    type: "Base"
+    warmup: 30
     repeat: 10
-    batchSize: 1
-    reduction: 'median'
+    reduction: "median"
   backend: "default"
   target: "cpu"
 

benchmark/config/qrcode_wechatqrcode.yaml

@@ -4,18 +4,19 @@ Benchmark:
     path: "benchmark/data/qrcode"
     files: ["opencv.png", "opencv_zoo.png"]
   metric:
+    type: "Detection"
     sizes:
       - [100, 100]
       - [300, 300]
-    warmup: 3
+    warmup: 30
     repeat: 10
     reduction: "median"
   backend: "default"
   target: "cpu"
 
 Model:
   name: "WeChatQRCode"
-  detect_prototxt_path: "models/qrcode_wechatqrcode/detect_2021sep.prototxt"
-  detect_model_path: "models/qrcode_wechatqrcode/detect_2021sep.caffemodel"
-  sr_prototxt_path: "models/qrcode_wechatqrcode/sr_2021sep.prototxt"
-  sr_model_path: "models/qrcode_wechatqrcode/sr_2021sep.caffemodel"
+  detect_prototxt_path: "models/qrcode_wechatqrcode/detect_2021nov.prototxt"
+  detect_model_path: "models/qrcode_wechatqrcode/detect_2021nov.caffemodel"
+  sr_prototxt_path: "models/qrcode_wechatqrcode/sr_2021nov.prototxt"
+  sr_model_path: "models/qrcode_wechatqrcode/sr_2021nov.caffemodel"

benchmark/config/text_detection_db.yaml

@@ -4,12 +4,12 @@ Benchmark:
     path: "benchmark/data/text"
     files: ["1.jpg", "2.jpg", "3.jpg"]
   metric:
+    type: "Detection"
     sizes: # [[w1, h1], ...], Omit to run at original scale
       - [640, 480]
-    warmup: 3
+    warmup: 30
     repeat: 10
-    batchSize: 1
-    reduction: 'median'
+    reduction: "median"
   backend: "default"
   target: "cpu"
 

benchmark/config/text_recognition_crnn.yaml

@@ -5,10 +5,10 @@ Benchmark:
     files: ["1.jpg", "2.jpg", "3.jpg"]
     useLabel: True
   metric: # 'sizes' is omitted since this model requires input of fixed size
-    warmup: 3
+    type: "Recognition"
+    warmup: 30
     repeat: 10
-    batchSize: 1
-    reduction: 'median'
+    reduction: "median"
   backend: "default"
   target: "cpu"
 

benchmark/requirements.txt

@@ -1,5 +1,4 @@
-numpy==1.21.2
+numpy
 opencv-python==4.5.4.58
-tqdm
 pyyaml
 requests

benchmark/utils/__init__.py

@@ -0,0 +1,4 @@
+from .factory import (METRICS, DATALOADERS)
+from .metrics import *
+
+__all__ = ['METRICS', 'DATALOADERS']

benchmark/utils/factory.py

@@ -0,0 +1,13 @@
+class Registery:
+    def __init__(self, name):
+        self._name = name
+        self._dict = dict()
+
+    def get(self, key):
+        return self._dict[key]
+
+    def register(self, item):
+        self._dict[item.__name__] = item
+
+METRICS = Registery('Metrics')
+DATALOADERS = Registery('DataLoaders')

benchmark/utils/metrics/__init__.py

@@ -0,0 +1,5 @@
+from .base import Base
+from .detection import Detection
+from .recognition import Recognition
+
+__all__ = ['Base', 'Detection', 'Recognition']

benchmark/utils/metrics/base.py

@@ -0,0 +1,29 @@
+import cv2 as cv
+
+from .base_metric import BaseMetric
+from ..factory import METRICS
+
+@METRICS.register
+class Base(BaseMetric):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def forward(self, model, *args, **kwargs):
+        img = args[0]
+        if not self._sizes:
+            h, w, _ = img.shape
+            self._sizes.append([w, h])
+
+        results = dict()
+        self._timer.reset()
+        for size in self._sizes:
+            input_data = cv.resize(img, size)
+            for _ in range(self._warmup):
+                model.infer(input_data)
+            for _ in range(self._repeat):
+                self._timer.start()
+                model.infer(input_data)
+                self._timer.stop()
+            results[str(size)] = self._getResult()
+
+        return results

benchmark/utils/metrics/base_metric.py

@@ -0,0 +1,48 @@
+import cv2 as cv
+
+from ..timer import Timer
+
+class BaseMetric:
+    def __init__(self, **kwargs):
+        self._sizes = kwargs.pop('sizes', None)
+        if self._sizes is None:
+            self._sizes = []
+        self._warmup = kwargs.pop('warmup', 3)
+        self._repeat = kwargs.pop('repeat', 10)
+        self._reduction = kwargs.pop('reduction', 'median')
+
+        self._timer = Timer()
+
+    def _calcMedian(self, records):
+        ''' Return the median of records
+        '''
+        l = len(records)
+        mid = int(l / 2)
+        if l % 2 == 0:
+            return (records[mid] + records[mid - 1]) / 2
+        else:
+            return records[mid]
+
+    def _calcGMean(self, records, drop_largest=3):
+        ''' Return the geometric mean of records after drop the first drop_largest
+        '''
+        l = len(records)
+        if l <= drop_largest:
+            print('len(records)({}) <= drop_largest({}), stop dropping.'.format(l, drop_largest))
+        records_sorted = sorted(records, reverse=True)
+        return sum(records_sorted[drop_largest:]) / (l - drop_largest)
+
+    def _getResult(self):
+        records = self._timer.getRecords()
+        if self._reduction == 'median':
+            return self._calcMedian(records)
+        elif self._reduction == 'gmean':
+            return self._calcGMean(records)
+        else:
+            raise NotImplementedError('Reduction {} is not supported'.format(self._reduction))
+
+    def getReduction(self):
+        return self._reduction
+
+    def forward(self, model, *args, **kwargs):
+        raise NotImplementedError('Not implemented')