update config of example and README

lmnet/README.md

@@ -213,7 +213,7 @@ Currently, this feature is implemented for quantized classification of cifar10 a
 If you don't want to use this feature, set TRAIN_VALIDATION_SAVING_SIZE to zero.  
 
 The KEEP_CHECKPOINT_MAX is equivalent to 'max_to_keep' of tensorflow train.Saver parameter which indicates the maximum number of recent checkpoint files to keep. As new files are created, older files are deleted. 
-If None or 0, no checkpoints are deleted from the filesystem but only the last one is kept in the checkpoint file. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
+If None or 0, no checkpoints but only the last one is kept in the checkpoint file. Defaults to 5 (that is, the 5 most recent checkpoint files are kept.)
 
 To apply this feature to another dataset, the dataset file should define another available subset called train_validation_saving, which is split from the original train dataset in the dataset file. Also a dataset parameter TRAIN_VALIDATION_SAVING_SIZE should be included in the config file.
 
@@ -225,7 +225,7 @@ Exporting a trained model to proto buffer files and meta config yaml.
 In the case with `images` option, create each layer output value npy files in `export/{restore_path}/{image_size}/{image_name}/**.npy` for debug.
 
 * Load config file from saved experiment dir.
-* Export config file to yaml. See also [Config specification](docs/specification/config.md).
+* Export config file to yaml. See also [Config specification](../docs/specification/config.md).
   * `config.yaml` can be used for training and evaluation in python. i.e. [classification.yaml](configs/example/classification.yaml) is exported from [classification.py](configs/example/classification.py)
   * `meta.yaml` include only few parameter for application such as demo. i.e. [classification_meta.yaml](configs/example/classification_meta.yaml) is exported from [classification.py](configs/example/classification.py)
 * Save the model protocol buffer files (tf) for DLK converter.
@@ -328,7 +328,7 @@ Save the predictions npy, json, images results to output dir.
 * json: `{output_dir}/json/{batch number}.json`
 * images: `{output_dir}/images/{some type}/{input image file name}`
 
-The output predictions Tensor(npy) and json format depends on task type. Plsease see [Output Data Specification](docs/specification/output_data.md).
+The output predictions Tensor(npy) and json format depends on task type. Plsease see [Output Data Specification](../docs/specification/output_data.md).
 
 ```
 python3 executor/predict.py -h

lmnet/configs/example/classification.py

@@ -9,79 +9,77 @@
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# WITH WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # =============================================================================
 from easydict import EasyDict
 import tensorflow as tf
 
 from lmnet.common import Tasks
+from lmnet.networks.classification.lmnet_v1 import LmnetV1Quantize
 from lmnet.datasets.cifar10 import Cifar10
-from lmnet.networks.classification.lmnet_v0 import LmnetV0Quantize
 from lmnet.data_processor import Sequence
 from lmnet.pre_processor import (
     Resize,
-    PerImageStandardization,
+    DivideBy255,
 )
 from lmnet.data_augmentor import (
-    Brightness,
-    Color,
-    Contrast,
+    Crop,
     FlipLeftRight,
-    Hue,
+    Pad,
 )
 from lmnet.quantizations import (
     binary_mean_scaling_quantizer,
     linear_mid_tread_half_quantizer,
 )
 
-IS_DEBUG = True
+IS_DEBUG = False
 
-NETWORK_CLASS = LmnetV0Quantize
+NETWORK_CLASS = LmnetV1Quantize
 DATASET_CLASS = Cifar10
 
-IMAGE_SIZE = [28, 28]
-BATCH_SIZE = 32
+IMAGE_SIZE = [32, 32]
+BATCH_SIZE = 100
 DATA_FORMAT = "NHWC"
 TASK = Tasks.CLASSIFICATION
 CLASSES = DATASET_CLASS.classes
 
+MAX_STEPS = 100000
+SAVE_CHECKPOINT_STEPS = 1000
 KEEP_CHECKPOINT_MAX = 5
-MAX_EPOCHS = 1  # MAX_STEPS = 1561
-SAVE_CHECKPOINT_STEPS = 100
-TEST_STEPS = 100
-SUMMARISE_STEPS = 10
-
-
+TEST_STEPS = 1000
+SUMMARISE_STEPS = 100
 # pretrain
 IS_PRETRAIN = False
-PRETRAIN_VARS = [
-    "conv1/kernel:",
-    "conv1/bias:",
-    "conv2/kernel:",
-    "conv2/bias:",
-    "conv3/kernel:",
-    "conv3/bias:",
-    "conv4/kernel:",
-    "conv4/bias:",
-    "conv5/kernel:",
-    "conv5/bias:",
-    "conv6/kernel:",
-    "conv6/bias:",
-]
-PRETRAIN_DIR = "saved/lmnet_0.01_caltech101/checkpoints"
-PRETRAIN_FILE = "save.ckpt-99001"
+PRETRAIN_VARS = []
+PRETRAIN_DIR = ""
+PRETRAIN_FILE = ""
+
+
+# for debug
+# MAX_STEPS = 10
+# BATCH_SIZE = 31
+# SAVE_CHECKPOINT_STEPS = 2
+# TEST_STEPS = 10
+# SUMMARISE_STEPS = 2
+# IS_DEBUG = True
 
 PRE_PROCESSOR = Sequence([
     Resize(size=IMAGE_SIZE),
-    PerImageStandardization()
+    DivideBy255()
 ])
 POST_PROCESSOR = None
 
 NETWORK = EasyDict()
-NETWORK.OPTIMIZER_CLASS = tf.train.AdamOptimizer
-NETWORK.OPTIMIZER_KWARGS = {"learning_rate": 0.001}
+NETWORK.OPTIMIZER_CLASS = tf.train.MomentumOptimizer
+NETWORK.OPTIMIZER_KWARGS = {"momentum": 0.9}
+NETWORK.LEARNING_RATE_FUNC = tf.train.piecewise_constant
+step_per_epoch = int(50000 / BATCH_SIZE)
+NETWORK.LEARNING_RATE_KWARGS = {
+    "values": [0.01, 0.001, 0.0001, 0.00001],
+    "boundaries": [step_per_epoch * 50, step_per_epoch * 100, step_per_epoch * 150],
+}
 NETWORK.IMAGE_SIZE = IMAGE_SIZE
 NETWORK.BATCH_SIZE = BATCH_SIZE
 NETWORK.DATA_FORMAT = DATA_FORMAT
@@ -100,9 +98,8 @@
 DATASET.DATA_FORMAT = DATA_FORMAT
 DATASET.PRE_PROCESSOR = PRE_PROCESSOR
 DATASET.AUGMENTOR = Sequence([
+    Pad(2),
+    Crop(size=IMAGE_SIZE),
     FlipLeftRight(),
-    Brightness((0.75, 1.25)),
-    Color((0.75, 1.25)),
-    Contrast((0.75, 1.25)),
-    Hue((-10, 10)),
 ])
+DATASET.TRAIN_VALIDATION_SAVING_SIZE = 5000