Merge pull request #294 from Manojkumarmuru/efficientdet

Efficientdet

examples/object_detection/debugger.py

@@ -8,8 +8,7 @@
 from paz.datasets import VOC
 from paz.abstract import Processor, SequentialProcessor
 from paz import processors as pr
-from detection import AugmentDetection
-# from paz.pipelines import AugmentDetection
+from paz.pipelines import AugmentDetection
 
 
 class ShowBoxes(Processor):

examples/object_detection/train.py

@@ -1,4 +1,5 @@
 import os
+import copy
 import argparse
 import tensorflow as tf
 gpus = tf.config.experimental.list_physical_devices('GPU')
@@ -11,7 +12,7 @@
 from tensorflow.keras.optimizers import SGD
 from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint
 from paz.optimization.callbacks import LearningRateScheduler
-from detection import AugmentDetection
+from paz.pipelines import AugmentDetection
 from paz.models import SSD300
 from paz.datasets import VOC
 from paz.optimization import MultiBoxLoss
@@ -97,19 +98,29 @@
 checkpoint = ModelCheckpoint(save_path, verbose=1, save_weights_only=True)
 schedule = LearningRateScheduler(
     args.learning_rate, args.gamma_decay, args.scheduled_epochs)
-evaluate = EvaluateMAP(
+evaluate_test = EvaluateMAP(
     evaluation_data_managers[0],
     DetectSingleShot(model, data_managers[0].class_names, 0.01, 0.45),
     args.evaluation_period,
     args.save_path,
     args.AP_IOU)
 
+data_manager = VOC(args.data_path, ['trainval', 'trainval'],
+                   name=['VOC2007', 'VOC2012'], evaluate=True)
+class_names = copy.deepcopy(data_manager.class_names)
+evaluate_trainval = EvaluateMAP(
+    data_manager,
+    DetectSingleShot(model, class_names, 0.01, 0.45),
+    args.evaluation_period,
+    args.save_path,
+    args.AP_IOU)
+
 # training
 model.fit(
     sequencers[0],
     epochs=args.num_epochs,
     verbose=1,
-    callbacks=[checkpoint, log, schedule, evaluate],
+    callbacks=[checkpoint, log, schedule, evaluate_trainval, evaluate_test],
     validation_data=sequencers[1],
     use_multiprocessing=args.multiprocessing,
     workers=args.workers)

paz/models/detection/efficientdet/efficientdet.py

@@ -76,10 +76,11 @@ def EFFICIENTDET(image, num_classes, base_weights, head_weights,
         model_filename = '-'.join([model_name, str(base_weights),
                                    str(head_weights) + '_weights.hdf5'])
 
-    weights_path = get_file(model_filename, WEIGHT_PATH + model_filename,
-                            cache_subdir='paz/models')
-    print('Loading %s model weights' % weights_path)
-    model.load_weights(weights_path)
+    if not ((base_weights is None) and (head_weights is None)):
+        weights_path = get_file(model_filename, WEIGHT_PATH + model_filename,
+                                cache_subdir='paz/models')
+        print('Loading %s model weights' % weights_path)
+        model.load_weights(weights_path)
 
     image_shape = image.shape[1:3].as_list()
     model.prior_boxes = build_anchors(

paz/pipelines/detection.py

@@ -25,7 +25,7 @@ def __init__(self, mean=pr.BGR_IMAGENET_MEAN):
         super(AugmentBoxes, self).__init__()
         self.add(pr.ToImageBoxCoordinates())
         self.add(pr.Expand(mean=mean))
-        self.add(pr.RandomSampleCrop())
+        self.add(pr.RandomSampleCrop(1.0))
         self.add(pr.RandomFlipBoxesLeftRight())
         self.add(pr.ToNormalizedBoxCoordinates())
 

paz/pipelines/image.py

@@ -11,7 +11,7 @@ def __init__(self):
         super(AugmentImage, self).__init__()
         self.add(pr.RandomContrast())
         self.add(pr.RandomBrightness())
-        self.add(pr.RandomSaturation())
+        self.add(pr.RandomSaturation(0.7))
         self.add(pr.RandomHue())