From e5b3f462ac8defee22002a3581e0294bc49038cc Mon Sep 17 00:00:00 2001
From: Olivier Dulcy <106678676+odulcy-mindee@users.noreply.github.com>
Date: Mon, 8 Jan 2024 17:29:52 +0100
Subject: [PATCH] feat: :sparkles: new TF Linknet Resnet checkpoints (#1424)

---
 doctr/models/detection/linknet/tensorflow.py | 82 ++++++++++----------
 1 file changed, 43 insertions(+), 39 deletions(-)

diff --git a/doctr/models/detection/linknet/tensorflow.py b/doctr/models/detection/linknet/tensorflow.py
index 6950e07220..cfb15b3108 100644
--- a/doctr/models/detection/linknet/tensorflow.py
+++ b/doctr/models/detection/linknet/tensorflow.py
@@ -27,39 +27,41 @@
         "mean": (0.798, 0.785, 0.772),
         "std": (0.264, 0.2749, 0.287),
         "input_shape": (1024, 1024, 3),
-        "url": "https://doctr-static.mindee.com/models?id=v0.5.0/linknet_resnet18-a48e6ed3.zip&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/linknet_resnet18-b9ee56e6.zip&src=0",
     },
     "linknet_resnet34": {
         "mean": (0.798, 0.785, 0.772),
         "std": (0.264, 0.2749, 0.287),
         "input_shape": (1024, 1024, 3),
-        "url": "https://doctr-static.mindee.com/models?id=v0.6.0/linknet_resnet34-bf30afb1.zip&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/linknet_resnet34-51909c56.zip&src=0",
     },
     "linknet_resnet50": {
         "mean": (0.798, 0.785, 0.772),
         "std": (0.264, 0.2749, 0.287),
         "input_shape": (1024, 1024, 3),
-        "url": "https://doctr-static.mindee.com/models?id=v0.6.0/linknet_resnet50-cd299262.zip&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/linknet_resnet50-ac9f3829.zip&src=0",
     },
 }
 
 
 def decoder_block(in_chan: int, out_chan: int, stride: int, **kwargs: Any) -> Sequential:
     """Creates a LinkNet decoder block"""
-    return Sequential([
-        *conv_sequence(in_chan // 4, "relu", True, kernel_size=1, **kwargs),
-        layers.Conv2DTranspose(
-            filters=in_chan // 4,
-            kernel_size=3,
-            strides=stride,
-            padding="same",
-            use_bias=False,
-            kernel_initializer="he_normal",
-        ),
-        layers.BatchNormalization(),
-        layers.Activation("relu"),
-        *conv_sequence(out_chan, "relu", True, kernel_size=1),
-    ])
+    return Sequential(
+        [
+            *conv_sequence(in_chan // 4, "relu", True, kernel_size=1, **kwargs),
+            layers.Conv2DTranspose(
+                filters=in_chan // 4,
+                kernel_size=3,
+                strides=stride,
+                padding="same",
+                use_bias=False,
+                kernel_initializer="he_normal",
+            ),
+            layers.BatchNormalization(),
+            layers.Activation("relu"),
+            *conv_sequence(out_chan, "relu", True, kernel_size=1),
+        ]
+    )
 
 
 class LinkNetFPN(Model, NestedObject):
@@ -129,28 +131,30 @@ def __init__(
         self.fpn = LinkNetFPN(fpn_channels, [_shape[1:] for _shape in self.feat_extractor.output_shape])
         self.fpn.build(self.feat_extractor.output_shape)
 
-        self.classifier = Sequential([
-            layers.Conv2DTranspose(
-                filters=32,
-                kernel_size=3,
-                strides=2,
-                padding="same",
-                use_bias=False,
-                kernel_initializer="he_normal",
-                input_shape=self.fpn.decoders[-1].output_shape[1:],
-            ),
-            layers.BatchNormalization(),
-            layers.Activation("relu"),
-            *conv_sequence(32, "relu", True, kernel_size=3, strides=1),
-            layers.Conv2DTranspose(
-                filters=num_classes,
-                kernel_size=2,
-                strides=2,
-                padding="same",
-                use_bias=True,
-                kernel_initializer="he_normal",
-            ),
-        ])
+        self.classifier = Sequential(
+            [
+                layers.Conv2DTranspose(
+                    filters=32,
+                    kernel_size=3,
+                    strides=2,
+                    padding="same",
+                    use_bias=False,
+                    kernel_initializer="he_normal",
+                    input_shape=self.fpn.decoders[-1].output_shape[1:],
+                ),
+                layers.BatchNormalization(),
+                layers.Activation("relu"),
+                *conv_sequence(32, "relu", True, kernel_size=3, strides=1),
+                layers.Conv2DTranspose(
+                    filters=num_classes,
+                    kernel_size=2,
+                    strides=2,
+                    padding="same",
+                    use_bias=True,
+                    kernel_initializer="he_normal",
+                ),
+            ]
+        )
 
         self.postprocessor = LinkNetPostProcessor(assume_straight_pages=assume_straight_pages, bin_thresh=bin_thresh)