[FIX] Fix mistake in FASTConvLayer and tf reparameterization

doctr/models/classification/textnet/pytorch.py

@@ -22,21 +22,21 @@
         "std": (0.299, 0.296, 0.301),
         "input_shape": (3, 32, 32),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_tiny-c5970fe0.pt&src=0",
+        "url": "https://github.com/mindee/doctr/releases/download/v0.8.1/textnet_tiny-27288d12.pt",
     },
     "textnet_small": {
         "mean": (0.694, 0.695, 0.693),
         "std": (0.299, 0.296, 0.301),
         "input_shape": (3, 32, 32),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_small-6e8ab0ce.pt&src=0",
+        "url": "https://github.com/mindee/doctr/releases/download/v0.8.1/textnet_small-43166ee6.pt",
     },
     "textnet_base": {
         "mean": (0.694, 0.695, 0.693),
         "std": (0.299, 0.296, 0.301),
         "input_shape": (3, 32, 32),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_base-8295dc85.pt&src=0",
+        "url": "https://github.com/mindee/doctr/releases/download/v0.8.1/textnet_base-7f68d7e0.pt",
     },
 }
 

doctr/models/classification/textnet/tensorflow.py

@@ -22,21 +22,21 @@
         "std": (0.299, 0.296, 0.301),
         "input_shape": (32, 32, 3),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_tiny-9e605bd8.zip&src=0",
+        "url": "https://github.com/mindee/doctr/releases/download/v0.8.1/textnet_tiny-fe9cc245.zip",
     },
     "textnet_small": {
         "mean": (0.694, 0.695, 0.693),
         "std": (0.299, 0.296, 0.301),
         "input_shape": (32, 32, 3),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_small-4784b292.zip&src=0",
+        "url": "https://github.com/mindee/doctr/releases/download/v0.8.1/textnet_small-29c39c82.zip",
     },
     "textnet_base": {
         "mean": (0.694, 0.695, 0.693),
         "std": (0.299, 0.296, 0.301),
         "input_shape": (32, 32, 3),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_base-2c3f3265.zip&src=0",
+        "url": "https://github.com/mindee/doctr/releases/download/v0.8.1/textnet_base-168aa82c.zip",
     },
 }
 

doctr/models/detection/fast/base.py

@@ -153,7 +153,7 @@ class _FAST(BaseModel):
 
     min_size_box: int = 3
     assume_straight_pages: bool = True
-    shrink_ratio = 0.1
+    shrink_ratio = 0.4
 
     def build_target(
         self,

doctr/models/detection/fast/pytorch.py

@@ -26,7 +26,7 @@
         "input_shape": (3, 1024, 1024),
         "mean": (0.798, 0.785, 0.772),
         "std": (0.264, 0.2749, 0.287),
-        "url": "https://doctr-static.mindee.com/models?id=v0.8.1/fast_tiny-7bee86e2.pt&src=0",
+        "url": None,
     },
     "fast_small": {
         "input_shape": (3, 1024, 1024),
@@ -122,7 +122,7 @@ def __init__(
         bin_thresh: float = 0.3,
         box_thresh: float = 0.1,
         dropout_prob: float = 0.1,
-        pooling_size: int = 9,
+        pooling_size: int = 4,  # different from paper performs better on close text-rich images
         assume_straight_pages: bool = True,
         exportable: bool = False,
         cfg: Optional[Dict[str, Any]] = {},

doctr/models/detection/fast/tensorflow.py

@@ -125,7 +125,7 @@ def __init__(
         bin_thresh: float = 0.3,
         box_thresh: float = 0.1,
         dropout_prob: float = 0.1,
-        pooling_size: int = 9,
+        pooling_size: int = 4,  # different from paper performs better on close text-rich images
         assume_straight_pages: bool = True,
         exportable: bool = False,
         cfg: Optional[Dict[str, Any]] = {},

doctr/models/modules/layers/pytorch.py

@@ -87,7 +87,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         horizontal_outputs = (
             self.hor_bn(self.hor_conv(x)) if self.hor_bn is not None and self.hor_conv is not None else 0
         )
-        id_out = self.rbr_identity(x) if self.rbr_identity is not None and self.ver_bn is not None else 0
+        id_out = self.rbr_identity(x) if self.rbr_identity is not None else 0
 
         return self.activation(main_outputs + vertical_outputs + horizontal_outputs + id_out)
 
@@ -155,7 +155,6 @@ def reparameterize_layer(self):
         )
         self.fused_conv.weight.data = kernel
         self.fused_conv.bias.data = bias  # type: ignore[union-attr]
-        self.deploy = True
         for para in self.parameters():
             para.detach_()
         for attr in ["conv", "bn", "ver_conv", "ver_bn", "hor_conv", "hor_bn"]:

doctr/models/modules/layers/tensorflow.py

@@ -97,7 +97,7 @@ def call(self, x: tf.Tensor, **kwargs: Any) -> tf.Tensor:
             if self.hor_bn is not None and self.hor_conv is not None
             else 0
         )
-        id_out = self.rbr_identity(x, **kwargs) if self.rbr_identity is not None and self.ver_bn is not None else 0
+        id_out = self.rbr_identity(x, **kwargs) if self.rbr_identity is not None else 0
 
         return self.activation(main_outputs + vertical_outputs + horizontal_outputs + id_out)
 
@@ -110,14 +110,14 @@ def _identity_to_conv(
             return 0, 0
         if not hasattr(self, "id_tensor"):
             input_dim = self.in_channels // self.groups
-            kernel_value = np.zeros((self.in_channels, input_dim, 1, 1), dtype=np.float32)
+            kernel_value = np.zeros((1, 1, input_dim, self.in_channels), dtype=np.float32)
             for i in range(self.in_channels):
-                kernel_value[i, i % input_dim, 0, 0] = 1
+                kernel_value[0, 0, i % input_dim, i] = 1
             id_tensor = tf.constant(kernel_value, dtype=tf.float32)
             self.id_tensor = self._pad_to_mxn_tensor(id_tensor)
         kernel = self.id_tensor
         std = tf.sqrt(identity.moving_variance + identity.epsilon)
-        t = tf.reshape(identity.gamma / std, (-1, 1, 1, 1))
+        t = tf.reshape(identity.gamma / std, (1, 1, 1, -1))
         return kernel * t, identity.beta - identity.moving_mean * identity.gamma / std
 
     def _fuse_bn_tensor(self, conv: layers.Conv2D, bn: layers.BatchNormalization) -> Tuple[tf.Tensor, tf.Tensor]:
@@ -138,18 +138,16 @@ def _get_equivalent_kernel_bias(self):
         else:
             kernel_1xn, bias_1xn = 0, 0
         kernel_id, bias_id = self._identity_to_conv(self.rbr_identity)
-        if not isinstance(kernel_id, int):
-            kernel_id = tf.transpose(kernel_id, (2, 3, 0, 1))
         kernel_mxn = kernel_mxn + kernel_mx1 + kernel_1xn + kernel_id
         bias_mxn = bias_mxn + bias_mx1 + bias_1xn + bias_id
         return kernel_mxn, bias_mxn
 
     def _pad_to_mxn_tensor(self, kernel: tf.Tensor) -> tf.Tensor:
         kernel_height, kernel_width = self.converted_ks
-        height, width = kernel.shape[2:]
+        height, width = kernel.shape[:2]
         pad_left_right = tf.maximum(0, (kernel_width - width) // 2)
         pad_top_down = tf.maximum(0, (kernel_height - height) // 2)
-        return tf.pad(kernel, [[0, 0], [0, 0], [pad_top_down, pad_top_down], [pad_left_right, pad_left_right]])
+        return tf.pad(kernel, [[pad_top_down, pad_top_down], [pad_left_right, pad_left_right], [0, 0], [0, 0]])
 
     def reparameterize_layer(self):
         kernel, bias = self._get_equivalent_kernel_bias()

tests/pytorch/test_models_detection_pt.py

@@ -114,6 +114,16 @@ def test_detection_zoo(arch_name):
     assert all((seq_map >= 0).all() and (seq_map <= 1).all() for seq_map in seq_maps)
 
 
+def test_fast_reparameterization():
+    dummy_input = torch.rand((2, 3, 1024, 1024), dtype=torch.float32)
+    base_model = detection.fast_tiny(pretrained=True, exportable=True).eval()
+    base_out = base_model(dummy_input)["logits"]
+    rep_model = reparameterize(base_model)
+    rep_out = rep_model(dummy_input)["logits"]
+    diff = base_out - rep_out
+    assert diff.mean() < 5e-2 and diff.mean() < 5e-2
+
+
 def test_erode():
     x = torch.zeros((1, 1, 3, 3))
     x[..., 1, 1] = 1

tests/tensorflow/test_models_detection_tf.py

@@ -170,6 +170,16 @@ def test_detection_zoo_error():
         _ = detection.zoo.detection_predictor("my_fancy_model", pretrained=False)
 
 
+def test_fast_reparameterization():
+    dummy_input = tf.random.uniform(shape=[2, 1024, 1024, 3], minval=0, maxval=1)
+    base_model = detection.fast_tiny(pretrained=True, exportable=True)
+    base_out = base_model(dummy_input, training=False)["logits"]
+    rep_model = reparameterize(base_model)
+    rep_out = rep_model(dummy_input, training=False)["logits"]
+    diff = base_out - rep_out
+    assert tf.math.reduce_mean(diff) < 5e-2 and tf.math.reduce_std(diff) < 5e-2
+
+
 def test_erode():
     x = np.zeros((1, 3, 3, 1), dtype=np.float32)
     x[:, 1, 1] = 1