Update hyperparameters for model paths

Sharda-Tech · Nov 8, 2019 · 06ccc8e · 06ccc8e
1 parent 59978a4
commit 06ccc8e
Showing 1 changed file with 23 additions and 18 deletions.
diff --git a/examples/discovery_of_latent_keypoints/discover_latent_keypoints.py b/examples/discovery_of_latent_keypoints/discover_latent_keypoints.py
@@ -5,13 +5,14 @@
 
 import json
 import argparse
+
 from paz.models import KeypointNetShared
 from paz.models import Projector
 from paz.pipelines import KeypointInference
 from paz.pipelines import KeypointSharedAugmentation
 from paz.core.sequencer import GeneratingSequencer
-from paz.optimization.callbacks import DrawInferences
 from paz.optimization import KeypointNetLoss
+from paz.optimization.callbacks import DrawInferences
 
 from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint
@@ -23,13 +24,19 @@
 
 description = 'Training script for learning latent 3D keypoints'
 parser = argparse.ArgumentParser(description=description)
+parser.add_argument(
+    '-op', '--obj_path',
+    default=os.path.join(
+        os.path.expanduser('~'),
+        '.keras/paz/datasets/ycb/models/035_power_drill/textured.obj'),
+    type=str, help='Path for writing model weights and logs')
 parser.add_argument('-cl', '--class_name', default='035_power_drill', type=str,
-                    help='``FERPlus``, ``FER`` or ``IMDB``')
+                    help='Class name to be added to model save path')
 parser.add_argument('-nk', '--num_keypoints', default=10, type=int,
                     help='Number of keypoints to be learned')
 parser.add_argument('-f', '--filters', default=64, type=int,
                     help='Number of filters in convolutional blocks')
-parser.add_argument('-bs', '--batch_size', default=5, type=int,
+parser.add_argument('-bs', '--batch_size', default=20, type=int,
                     help='Batch size for training')
 parser.add_argument('-lr', '--learning_rate', default=0.001, type=float,
                     help='Initial learning rate for Adam')
@@ -39,12 +46,10 @@
                     help='Number of epochs before doing early stopping')
 parser.add_argument('-pp', '--plateau_patience', default=3, type=int,
                     help='Number of epochs before reducing learning rate')
-parser.add_argument('-e', '--max_num_epochs', default=2, type=int,
+parser.add_argument('-e', '--max_num_epochs', default=10000, type=int,
                     help='Maximum number of epochs before finishing')
-parser.add_argument('-st', '--steps_per_epoch', default=3, type=int,
+parser.add_argument('-st', '--steps_per_epoch', default=1000, type=int,
                     help='Steps per epoch')
-parser.add_argument('-sa', '--save_path', default='trained_models/',
-                    type=str, help='Path for writing model weights and logs')
 parser.add_argument('-sh', '--sphere', default='full',
                     choices=['full', 'half'], type=str,
                     help='Flag for full sphere or top half for rendering')
@@ -64,16 +69,17 @@
                     help='Background color')
 parser.add_argument('-ap', '--alpha', default=0.1, type=float,
                     help='Alpha leaky-relu parameter')
+parser.add_argument('-nm', '--num_mean_samples', default=1000, type=int,
+                    help='Number of samples used to calculate keypoints mean')
+parser.add_argument('-sa', '--save_path',
+                    default=os.path.join(
+                        os.path.expanduser('~'), '.keras/paz/models'),
+                    type=str, help='Path for writing model weights and logs')
 args = parser.parse_args()
 
 
-class_name = '035_power_drill'
-OBJ_filepath = '.keras/altamira/datasets/models/%s/textured.obj' % class_name
-OBJ_filepath = os.path.join(os.path.expanduser('~'), OBJ_filepath)
-save_path = os.path.join(os.path.expanduser('~'), '.keras/paz/models/')
-
 # setting scene
-scene = MultiView(OBJ_filepath, (args.image_size, args.image_size),
+scene = MultiView(args.obj_path, (args.image_size, args.image_size),
                   args.y_fov, args.depth, args.sphere, args.roll,
                   args.translation, args.shift, args.light, args.background)
 focal_length = scene.camera.get_projection_matrix()[0, 0]
@@ -101,20 +107,19 @@
 # model compilation
 optimizer = Adam(args.learning_rate, amsgrad=True)
 model.compile(optimizer, losses, metrics)
-model_name = '_'.join([model.name, str(args.num_keypoints), class_name])
+model_name = '_'.join([model.name, str(args.num_keypoints), args.class_name])
 model.summary()
 
 # making directory for saving model weights and logs
-save_path = os.path.join(save_path, model_name)
+save_path = os.path.join(args.save_path, model_name)
 if not os.path.exists(save_path):
     os.makedirs(save_path)
 
 # setting callbacks
 log = CSVLogger(os.path.join(save_path, '%s.log' % model_name))
 stop = EarlyStopping('loss', patience=args.stop_patience, verbose=1)
 plateau = ReduceLROnPlateau('loss', patience=args.plateau_patience, verbose=1)
-inferencer = KeypointInference(
-    model.get_layer('keypointnet'), args.num_keypoints, to_BGR=True)
+inferencer = KeypointInference(model.get_layer('keypointnet'))
 images = (sequencer.__getitem__(0)[0][0] * 255).astype('uint8')
 draw = DrawInferences(save_path, images, inferencer)
 model_path = os.path.join(save_path, '%s_weights.hdf5' % model_name)
@@ -143,7 +148,7 @@
 # rendering multiple scenes for forward passing
 keypoints_set, projector = [], Projector(focal_length, True)
 print('Calculating mean and variance of discovered keypoints...')
-num_forward_passes = 1000  # number of samples for calculating the mean
+num_forward_passes = args.num_mean_samples
 sequencer.batch_size = 1   # changing batch size of sequencer
 progress_bar = Progbar(num_forward_passes)
 for batch_arg in range(num_forward_passes):