From 370c3eb35f47e64eab7926a5f995947c63c6b208 Mon Sep 17 00:00:00 2001
From: Gustaf Rydholm <gustaf.rydholm@gmail.com>
Date: Mon, 5 Apr 2021 23:13:34 +0200
Subject: Configure backbone commented out, transformer init change

---
 text_recognizer/networks/transformer/__init__.py |  2 +-
 text_recognizer/networks/util.py                 | 77 ++++++++++++------------
 2 files changed, 39 insertions(+), 40 deletions(-)

(limited to 'text_recognizer')

diff --git a/text_recognizer/networks/transformer/__init__.py b/text_recognizer/networks/transformer/__init__.py
index 652e82e..627fa7b 100644
--- a/text_recognizer/networks/transformer/__init__.py
+++ b/text_recognizer/networks/transformer/__init__.py
@@ -4,4 +4,4 @@ from .positional_encoding import (
     PositionalEncoding2D,
     target_padding_mask,
 )
-from .transformer import Decoder, Encoder, EncoderLayer, Transformer
+from .transformer import Decoder, DecoderLayer, Encoder, EncoderLayer, Transformer
diff --git a/text_recognizer/networks/util.py b/text_recognizer/networks/util.py
index d292680..9c6b151 100644
--- a/text_recognizer/networks/util.py
+++ b/text_recognizer/networks/util.py
@@ -1,7 +1,7 @@
 """Miscellaneous neural network functionality."""
 import importlib
 from pathlib import Path
-from typing import Dict, Type
+from typing import Dict, NamedTuple, Union, Type
 
 from loguru import logger
 import torch
@@ -24,41 +24,40 @@ def activation_function(activation: str) -> Type[nn.Module]:
     return activation_fns[activation.lower()]
 
 
-def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]:
-    """Loads a backbone network."""
-    network_module = importlib.import_module("text_recognizer.networks")
-    backbone_ = getattr(network_module, backbone)
-
-    if "pretrained" in backbone_args:
-        logger.info("Loading pretrained backbone.")
-        checkpoint_file = Path(__file__).resolve().parents[2] / backbone_args.pop(
-            "pretrained"
-        )
-
-        # Loading state directory.
-        state_dict = torch.load(checkpoint_file)
-        network_args = state_dict["network_args"]
-        weights = state_dict["model_state"]
-
-        freeze = False
-        if "freeze" in backbone_args and backbone_args["freeze"] is True:
-            backbone_args.pop("freeze")
-            freeze = True
-        network_args = backbone_args
-
-        # Initializes the network with trained weights.
-        backbone = backbone_(**network_args)
-        backbone.load_state_dict(weights)
-        if freeze:
-            for params in backbone.parameters():
-                params.requires_grad = False
-    else:
-        backbone_ = getattr(network_module, backbone)
-        backbone = backbone_(**backbone_args)
-
-    if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
-        backbone = nn.Sequential(
-            *list(backbone.children())[:][: -backbone_args["remove_layers"]]
-        )
-
-    return backbone
+# def configure_backbone(backbone: Union[OmegaConf, NamedTuple]) -> Type[nn.Module]:
+#     """Loads a backbone network."""
+#     network_module = importlib.import_module("text_recognizer.networks")
+#     backbone_class = getattr(network_module, backbone.type)
+#
+#     if "pretrained" in backbone.args:
+#         logger.info("Loading pretrained backbone.")
+#         checkpoint_file = Path(__file__).resolve().parents[2] / backbone.args.pop(
+#             "pretrained"
+#         )
+#
+#         # Loading state directory.
+#         state_dict = torch.load(checkpoint_file)
+#         network_args = state_dict["network_args"]
+#         weights = state_dict["model_state"]
+#
+#         freeze = False
+#         if "freeze" in backbone.args and backbone.args["freeze"] is True:
+#             backbone.args.pop("freeze")
+#             freeze = True
+#
+#         # Initializes the network with trained weights.
+#         backbone_ = backbone_(**backbone.args)
+#         backbone_.load_state_dict(weights)
+#         if freeze:
+#             for params in backbone_.parameters():
+#                 params.requires_grad = False
+#     else:
+#         backbone_ = getattr(network_module, backbone.type)
+#         backbone_ = backbone_(**backbone.args)
+#
+#     if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
+#         backbone = nn.Sequential(
+#             *list(backbone.children())[:][: -backbone_args["remove_layers"]]
+#         )
+#
+#     return backbone
-- 
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