Change properties.

_.py

@@ -0,0 +1,14 @@
+import os
+
+start_image = 12000
+
+from tensorflow.keras.models import load_model
+model = load_model("saved_model/1")
+
+from data_utils import nload, ims, DataSetFromFolder
+data = nload("/home/jkwiatkowski/all/dataset/arr/val.npy")
+indexes = nload("/home/jkwiatkowski/all/dataset/arr/val_target.npy")
+
+folders = DataSetFromFolder("/home/jkwiatkowski/all/dataset/arr/RAVEN-10000-release/RAVEN-10000", file_type="dir")
+properties = DataSetFromFolder(folders[:], file_type="xml", extension="val")
+

app.py

@@ -6,13 +6,13 @@ demo = gr.Blocks()
 import models
 
 with demo:
-    headline = gr.Markdown("## Raven resolver ")
-    markdown = gr.Markdown("Below we show all 9 images from raven matrix. "
-                           "Model gets 8 images and predicts the properties of last one. "
-                           "Based on this properties the answer image is render in the  right panel. <br />"
-                           "Note that angle rotation is only used as a noise. "
-                           "There are not rules applied to angle property, so angle rotation of final output do not need to be the same as in example. "
-                           "Additionally there are cases that other properties could be used as noise.")
+    # headline = gr.Markdown("## Raven resolver ")
+    # markdown = gr.Markdown("Below we show all 9 images from raven matrix. "
+    #                        "Model gets 8 images and predicts the properties of last one. "
+    #                        "Based on this properties the answer image is render in the  right panel. <br />"
+    #                        "Note that angle rotation is only used as a noise. "
+    #                        "There are not rules applied to angle property, so angle rotation of final output do not need to be the same as in example. "
+    #                        "Additionally there are cases that other properties could be used as noise.")
     with gr.Row():
         with gr.Column():
             with gr.Row():
@@ -25,7 +25,8 @@ with demo:
                 # button = gr.Button("Run")
             with gr.Row():
                 image = gr.Image(value=load_example(models.START_IMAGE)[0], label="Raven matrix")
-                desc = gr.Markdown(value=load_example(models.START_IMAGE)[1])
+                # desc = gr.Markdown(value=load_example(models.START_IMAGE)[1])
+                desc = gr.Text(value=load_example(models.START_IMAGE)[1])
 
         with gr.Column():
             with gr.Row():

models.py

@@ -1,14 +1,29 @@
-import os
+import tensorflow as tf
+from config_utils import tf_gpu
+from data_utils import DataSetFromFolder
 
-START_IMAGE = 12000
+tf_gpu()
+tf.experimental.numpy.experimental_enable_numpy_behavior(prefer_float32=True)
+
+from huggingface_hub import from_pretrained_keras
+from datasets import load_dataset
+
+repo = "jkwiatkowski/raven"
 
-from tensorflow.keras.models import load_model
-model = load_model("saved_model/1")
+data = load_dataset(repo, split="val")
+model = from_pretrained_keras(repo)
+
+properties =  load_dataset(repo + "_properties", split="val")
+START_IMAGE = 12000
 
-from data_utils import nload, ims, DataSetFromFolder
-data = nload("/home/jkwiatkowski/all/dataset/arr/val.npy")
-indexes = nload("/home/jkwiatkowski/all/dataset/arr/val_target.npy")
 
-folders = DataSetFromFolder("/home/jkwiatkowski/all/dataset/arr/RAVEN-10000-release/RAVEN-10000", file_type="dir")
-properties = DataSetFromFolder(folders[:], file_type="xml", extension="val")
+# def convert(data):
+#     return {
+#         'inputs': tf.cast(data['inputs'], dtype="uint8"),
+#         'index': tf.cast(data['index'], dtype="uint8")[..., None],
+#         'target': tf.cast(data['target'], dtype="int8"),
+#     }
+#
+# model(convert(data[0:1]))
 
+print("xD")

models_2.py

@@ -1,22 +0,0 @@
-import tensorflow as tf
-from config_utils import tf_gpu
-tf_gpu()
-tf.experimental.numpy.experimental_enable_numpy_behavior(prefer_float32=True)
-
-from huggingface_hub import from_pretrained_keras
-from datasets import load_dataset
-
-data = load_dataset("jkwiatkowski/raven", split="val")
-model = from_pretrained_keras("jkwiatkowski/raven")
-
-def convert(data):
-    return {
-        'inputs': tf.cast(data['inputs'], dtype="uint8"),
-        'index': tf.cast(data['index'], dtype="uint8"),
-        'target': tf.cast(data['target'], dtype="int8"),
-    }
-
-print("xD")
-
-
-

raven_utils/draw.py

@@ -5,6 +5,7 @@ from funcy import identity
 from ml_utils import none, filter_keys, lu
 from models_utils import is_model
 from models_utils import ops as K
+from tensorflow.python.saved_model.load import Loader
 
 from raven_utils.constant import PROPERTY, TARGET, INPUTS
 from raven_utils.decode import decode_target, target_mask
@@ -65,7 +66,7 @@ def val_sample(generator, no=1, indexes=None):
 
 def render_from_model(data,predict,pre_fn=identity):
     data = filter_keys(data, PROPERTY, reverse=True)
-    if is_model(predict):
+    if is_model(predict) or str(type(predict)) == "<class 'tensorflow.python.saved_model.load.Loader._recreate_base_user_object.<locals>._UserObject'>":
         predict = predict(data)
     pro = np.array(target_mask(predict['predict_mask'].numpy()) * predict["predict"].numpy(), dtype=np.int8)
     return pre_fn(render_panels(pro, target=False)[None])[0]

utils.py

@@ -20,10 +20,12 @@ def load_example(index=0):
             index = 0
         index = int(index)
 
-        desc = rv.draw.extract_rules(models.properties[index])
-        desc = "<br /><br />".join(["<br />".join(d) for d in desc])
+        desc = models.properties[index]['Description']
 
-        example = get_matrix(models.data[index:index + 1], models.indexes[index:index + 1, None] + 8)
+        example = get_matrix(
+            np.array(models.data[index:index + 1]['inputs'], dtype="uint8"),
+            np.array(models.data[index:index + 1]['index'], dtype="uint8")[..., None]
+        )
     result = np.tile(draw_images(example[:9], row=3), reps=(1, 1, 3))
     return result, desc
 
@@ -46,15 +48,13 @@ def run_nn(index=0):
         if not index:
             index = models.START_IMAGE
         index = int(index)
-        data = models.data[index:index + 1]
+        data = models.data[index:index + 1]['inputs']
 
     # model = load_model("/home/jkwiatkowski/all/best/rav/full_trans/6e8e6bad403e4171ad10daa1a518ba09")
     data = {
         'inputs': data,
         'index': np.zeros(shape=(1, 1), dtype="uint8"),
-        'labels': np.zeros(shape=(1, 16, 113), dtype="int8"),
         'target': np.zeros(shape=(1, 16, 113), dtype="int8"),
-        # 'features': np.zeros(shape=(1, 16, 64), dtype="float32")
     }
     res = np.tile(render_from_model(data, models.model)[0, ..., None], reps=(1, 1, 3))