bugfix

Author: biplovbhandari

chkpnt_2_h5_2_tf_model.py

@@ -12,9 +12,9 @@
 from model import model
 
 # specify directory as data io info
-BASEDIR = Path('/Users/biplovbhandari/Works/SIG/hydrafloods/output')
-MODEL_DIR = BASEDIR / 'trial_469ae9d7b6c82488deb9be9c0a0a25e7'
-CHECKPOINT_DIR = MODEL_DIR / '469ae9d7b6c82488deb9be9c0a0a25e7' / 'checkpoint'
+BASEDIR = Path('/mnt/hydrafloods/output/jrc_adjusted_LR_2020_07_13_V1/model/sentinel1-surface-water')
+MODEL_DIR = BASEDIR / 'trial_6ba0bc0ef8458bf43280b5814775bd2b'
+CHECKPOINT_DIR = MODEL_DIR / 'checkpoints' / 'epoch_0' / 'checkpoint'
 H5_MODEL = MODEL_DIR / 'tf-model-h5'
 TF_MODEL_DIR = MODEL_DIR / 'tf-model'
 

evaluate_models.py

@@ -9,14 +9,16 @@
 import os
 import tensorflow as tf
 
+from tensorflow import keras
 from pathlib import Path
 from model import dataio, model
 
 
 # specify directory as data io info
-BASEDIR = Path('/Users/biplovbhandari/Works/SIG/hydrafloods/output')
-MODEL_DIR = BASEDIR / 'trial_469ae9d7b6c82488deb9be9c0a0a25e7'
-H5_MODEL = MODEL_DIR / 'tf-model-h5'
+DATADIR = Path('/home/ubuntu/hydrafloods')
+BASEDIR = Path('/mnt/hydrafloods/output/jrc_adjusted_LR_2020_07_13_V1/model/sentinel1-surface-water')
+MODEL_DIR = BASEDIR / 'trial_6ba0bc0ef8458bf43280b5814775bd2b'
+CHECKPOINT = MODEL_DIR / 'checkpoints' / 'epoch_0'/ 'checkpoint'
 
 # specify some data structure
 FEATURES = ast.literal_eval(os.getenv('FEATURES'))
@@ -25,14 +27,28 @@
 in_shape = (None, None) + (len(FEATURES),)
 out_classes = int(os.getenv('OUT_CLASSES_NUM'))
 
-VALIDATION_DIR = BASEDIR / 'validation_patches'
+VALIDATION_DIR = DATADIR / 'validation_patches_jrc'
 validation_files = glob.glob(str(VALIDATION_DIR) + '/*')
+# eval is batched by 1
 validation = dataio.get_dataset(validation_files, FEATURES, LABELS, PATCH_SHAPE, 1)
 
 this_model = model.get_model(in_shape, out_classes)
 
 # open and save model
-this_model.load_weights(f'{str(H5_MODEL)}')
+this_model.load_weights(f'{str(CHECKPOINT)}')
+
+# compile the model
+this_model.compile(
+    optimizer='adam',
+    loss=model.bce_loss,
+    metrics=[
+        keras.metrics.categorical_accuracy,
+        keras.metrics.Precision(),
+        keras.metrics.Recall(),
+        model.dice_coef,
+        model.f1_m
+    ]
+)
 
 # check how the model trained
 score = this_model.evaluate(validation)