Trying to get the CNNTransformer to work, but it is hard.

7 files changed, 140 insertions, 17 deletions

diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index 0855079..28aa52e 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -1,16 +1,19 @@
 """Model modules."""
 from .base import Model
 from .character_model import CharacterModel
-from .line_ctc_model import LineCTCModel
+from .crnn_model import CRNNModel
 from .metrics import accuracy, cer, wer
+from .transformer_encoder_model import TransformerEncoderModel
 from .vision_transformer_model import VisionTransformerModel
 
 __all__ = [
     "Model",
     "cer",
     "CharacterModel",
+    "CRNNModel",
     "CNNTransfromerModel",
-    "LineCTCModel",
     "accuracy",
+    "TransformerEncoderModel",
+    "VisionTransformerModel",
     "wer",
 ]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index cbef787..cc44c92 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -141,11 +141,12 @@ class Model(ABC):
             "transform" in self.dataset_args["args"]
             and self.dataset_args["args"]["transform"] is not None
         ):
-            transform_ = [
-                getattr(transforms_module, t["type"])()
-                for t in self.dataset_args["args"]["transform"]
-            ]
+            transform_ = []
+            for t in self.dataset_args["args"]["transform"]:
+                args = t["args"] or {}
+                transform_.append(getattr(transforms_module, t["type"])(**args))
             self.dataset_args["args"]["transform"] = Compose(transform_)
+
         if (
             "target_transform" in self.dataset_args["args"]
             and self.dataset_args["args"]["target_transform"] is not None
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index 3cf6695..f9944f3 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -47,8 +47,9 @@ class CharacterModel(Model):
             swa_args,
             device,
         )
+        self.pad_token = dataset_args["args"]["pad_token"]
         if self._mapper is None:
-            self._mapper = EmnistMapper()
+            self._mapper = EmnistMapper(pad_token=self.pad_token,)
         self.tensor_transform = ToTensor()
         self.softmax = nn.Softmax(dim=0)
 
diff --git a/src/text_recognizer/models/line_ctc_model.py b/src/text_recognizer/models/crnn_model.py
index cdc2d8b..1e01a83 100644
--- a/src/text_recognizer/models/line_ctc_model.py
+++ b/src/text_recognizer/models/crnn_model.py
@@ -1,4 +1,4 @@
-"""Defines the LineCTCModel class."""
+"""Defines the CRNNModel class."""
 from typing import Callable, Dict, Optional, Tuple, Type, Union
 
 import numpy as np
@@ -13,7 +13,7 @@ from text_recognizer.models.base import Model
 from text_recognizer.networks import greedy_decoder
 
 
-class LineCTCModel(Model):
+class CRNNModel(Model):
     """Model for predicting a sequence of characters from an image of a text line."""
 
     def __init__(
@@ -47,8 +47,10 @@ class LineCTCModel(Model):
             swa_args,
             device,
         )
+
+        self.pad_token = dataset_args["args"]["pad_token"]
         if self._mapper is None:
-            self._mapper = EmnistMapper()
+            self._mapper = EmnistMapper(pad_token=self.pad_token,)
         self.tensor_transform = ToTensor()
 
     def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
@@ -112,6 +114,6 @@ class LineCTCModel(Model):
         log_probs, _ = log_probs.max(dim=2)
 
         predicted_characters = "".join(raw_pred[0])
-        confidence_of_prediction = torch.exp(-log_probs.sum()).item()
+        confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
 
         return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
index 6a26216..42c3c6e 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/models/metrics.py
@@ -17,7 +17,10 @@ def accuracy(outputs: Tensor, labels: Tensor) -> float:
         float: The accuracy for the batch.
 
     """
-    _, predicted = torch.max(outputs.data, dim=1)
+    # eos_index = torch.nonzero(labels == eos, as_tuple=False)
+    # eos_index = eos_index[0].item() if eos_index.nelement() else -1
+
+    _, predicted = torch.max(outputs, dim=-1)
     acc = (predicted == labels).sum().float() / labels.shape[0]
     acc = acc.item()
     return acc
diff --git a/src/text_recognizer/models/transformer_encoder_model.py b/src/text_recognizer/models/transformer_encoder_model.py
new file mode 100644
index 0000000..e35e298
--- /dev/null
+++ b/src/text_recognizer/models/transformer_encoder_model.py
@@ -0,0 +1,111 @@
+"""Defines the CNN-Transformer class."""
+from typing import Callable, Dict, List, Optional, Tuple, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+
+
+class TransformerEncoderModel(Model):
+    """A class for only using the encoder part in the sequence modelling."""
+
+    def __init__(
+        self,
+        network_fn: Type[nn.Module],
+        dataset: Type[Dataset],
+        network_args: Optional[Dict] = None,
+        dataset_args: Optional[Dict] = None,
+        metrics: Optional[Dict] = None,
+        criterion: Optional[Callable] = None,
+        criterion_args: Optional[Dict] = None,
+        optimizer: Optional[Callable] = None,
+        optimizer_args: Optional[Dict] = None,
+        lr_scheduler: Optional[Callable] = None,
+        lr_scheduler_args: Optional[Dict] = None,
+        swa_args: Optional[Dict] = None,
+        device: Optional[str] = None,
+    ) -> None:
+        super().__init__(
+            network_fn,
+            dataset,
+            network_args,
+            dataset_args,
+            metrics,
+            criterion,
+            criterion_args,
+            optimizer,
+            optimizer_args,
+            lr_scheduler,
+            lr_scheduler_args,
+            swa_args,
+            device,
+        )
+        # self.init_token = dataset_args["args"]["init_token"]
+        self.pad_token = dataset_args["args"]["pad_token"]
+        self.eos_token = dataset_args["args"]["eos_token"]
+        if network_args is not None:
+            self.max_len = network_args["max_len"]
+        else:
+            self.max_len = 128
+
+        if self._mapper is None:
+            self._mapper = EmnistMapper(
+                # init_token=self.init_token,
+                pad_token=self.pad_token,
+                eos_token=self.eos_token,
+            )
+        self.tensor_transform = ToTensor()
+
+        self.softmax = nn.Softmax(dim=2)
+
+    @torch.no_grad()
+    def _generate_sentence(self, image: Tensor) -> Tuple[List, float]:
+        logits = self.network(image)
+        # Convert logits to probabilities.
+        probs = self.softmax(logits).squeeze(0)
+
+        confidence, pred_tokens = probs.max(1)
+        pred_tokens = pred_tokens
+
+        eos_index = torch.nonzero(
+            pred_tokens == self._mapper(self.eos_token), as_tuple=False,
+        )
+
+        eos_index = eos_index[0].item() if eos_index.nelement() else -1
+
+        predicted_characters = "".join(
+            [self.mapper(x) for x in pred_tokens[:eos_index].tolist()]
+        )
+
+        confidence = np.min(confidence.tolist())
+
+        return predicted_characters, confidence
+
+    @torch.no_grad()
+    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
+        """Predict on a single input."""
+        self.eval()
+
+        if image.dtype == np.uint8:
+            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
+            image = self.tensor_transform(image)
+
+        # Rescale image between 0 and 1.
+        if image.dtype == torch.uint8:
+            # If the image is an unscaled tensor.
+            image = image.type("torch.FloatTensor") / 255
+
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+
+        (predicted_characters, confidence_of_prediction,) = self._generate_sentence(
+            image
+        )
+
+        return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/vision_transformer_model.py b/src/text_recognizer/models/vision_transformer_model.py
index 20bd4ca..3d36437 100644
--- a/src/text_recognizer/models/vision_transformer_model.py
+++ b/src/text_recognizer/models/vision_transformer_model.py
@@ -53,7 +53,7 @@ class VisionTransformerModel(Model):
         if network_args is not None:
             self.max_len = network_args["max_len"]
         else:
-            self.max_len = 128
+            self.max_len = 120
 
         if self._mapper is None:
             self._mapper = EmnistMapper(
@@ -73,10 +73,10 @@ class VisionTransformerModel(Model):
         confidence_of_predictions = []
         trg_indices = [self.mapper(self.init_token)]
 
-        for _ in range(self.max_len):
+        for _ in range(self.max_len - 1):
             trg = torch.tensor(trg_indices, device=self.device)[None, :].long()
-            trg, trg_mask = self.network.preprocess_target(trg)
-            logits = self.network.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
+            trg = self.network.preprocess_target(trg)
+            logits = self.network.decoder(trg=trg, memory=memory, trg_mask=None)
 
             # Convert logits to probabilities.
             probs = self.softmax(logits)
@@ -112,6 +112,8 @@ class VisionTransformerModel(Model):
         # Put the image tensor on the device the model weights are on.
         image = image.to(self.device)
 
-        predicted_characters, confidence_of_prediction = self._generate_sentence(image)
+        (predicted_characters, confidence_of_prediction,) = self._generate_sentence(
+            image
+        )
 
         return predicted_characters, confidence_of_prediction