文档检测

object_detection/core/config.py

@@ -0,0 +1,164 @@
+import os
+
+import numpy as np
+
+
+class SystemConfig(object):
+    def __init__(self):
+        self._configs = {}
+        self._configs["dataset"] = None
+        self._configs["sampling_function"] = "coco_detection"
+
+        # Training Config
+        self._configs["display"] = 5
+        self._configs["snapshot"] = 400
+        self._configs["stepsize"] = 5000
+        self._configs["learning_rate"] = 0.001
+        self._configs["decay_rate"] = 10
+        self._configs["max_iter"] = 100000
+        self._configs["val_iter"] = 20
+        self._configs["batch_size"] = 1
+        self._configs["snapshot_name"] = None
+        self._configs["prefetch_size"] = 100
+        self._configs["pretrain"] = None
+        self._configs["opt_algo"] = "adam"
+        self._configs["chunk_sizes"] = None
+
+        # Directories
+        self._configs["data_dir"] = "./data"
+        self._configs["cache_dir"] = "./cache"
+        self._configs["config_dir"] = "./config"
+        self._configs["result_dir"] = "./results"
+
+        # Split
+        self._configs["train_split"] = "training"
+        self._configs["val_split"] = "validation"
+        self._configs["test_split"] = "testdev"
+
+        # Rng
+        self._configs["data_rng"] = np.random.RandomState(123)
+        self._configs["nnet_rng"] = np.random.RandomState(317)
+
+    @property
+    def chunk_sizes(self):
+        return self._configs["chunk_sizes"]
+
+    @property
+    def train_split(self):
+        return self._configs["train_split"]
+
+    @property
+    def val_split(self):
+        return self._configs["val_split"]
+
+    @property
+    def test_split(self):
+        return self._configs["test_split"]
+
+    @property
+    def full(self):
+        return self._configs
+
+    @property
+    def sampling_function(self):
+        return self._configs["sampling_function"]
+
+    @property
+    def data_rng(self):
+        return self._configs["data_rng"]
+
+    @property
+    def nnet_rng(self):
+        return self._configs["nnet_rng"]
+
+    @property
+    def opt_algo(self):
+        return self._configs["opt_algo"]
+
+    @property
+    def prefetch_size(self):
+        return self._configs["prefetch_size"]
+
+    @property
+    def pretrain(self):
+        return self._configs["pretrain"]
+
+    @property
+    def result_dir(self):
+        result_dir = os.path.join(self._configs["result_dir"], self.snapshot_name)
+        if not os.path.exists(result_dir):
+            os.makedirs(result_dir)
+        return result_dir
+
+    @property
+    def dataset(self):
+        return self._configs["dataset"]
+
+    @property
+    def snapshot_name(self):
+        return self._configs["snapshot_name"]
+
+    @property
+    def snapshot_dir(self):
+        snapshot_dir = os.path.join(self.cache_dir, "nnet", self.snapshot_name)
+
+        if not os.path.exists(snapshot_dir):
+            os.makedirs(snapshot_dir)
+        return snapshot_dir
+
+    @property
+    def snapshot_file(self):
+        snapshot_file = os.path.join(self.snapshot_dir, self.snapshot_name + "_{}.pkl")
+        return snapshot_file
+
+    @property
+    def config_dir(self):
+        return self._configs["config_dir"]
+
+    @property
+    def batch_size(self):
+        return self._configs["batch_size"]
+
+    @property
+    def max_iter(self):
+        return self._configs["max_iter"]
+
+    @property
+    def learning_rate(self):
+        return self._configs["learning_rate"]
+
+    @property
+    def decay_rate(self):
+        return self._configs["decay_rate"]
+
+    @property
+    def stepsize(self):
+        return self._configs["stepsize"]
+
+    @property
+    def snapshot(self):
+        return self._configs["snapshot"]
+
+    @property
+    def display(self):
+        return self._configs["display"]
+
+    @property
+    def val_iter(self):
+        return self._configs["val_iter"]
+
+    @property
+    def data_dir(self):
+        return self._configs["data_dir"]
+
+    @property
+    def cache_dir(self):
+        if not os.path.exists(self._configs["cache_dir"]):
+            os.makedirs(self._configs["cache_dir"])
+        return self._configs["cache_dir"]
+
+    def update_config(self, new):
+        for key in new:
+            if key in self._configs:
+                self._configs[key] = new[key]
+        return self