Add new training loop with PyTorch Lightning, remove stale files

4 files changed, 3 insertions, 208 deletions

diff --git a/text_recognizer/networks/loss/__init__.py b/text_recognizer/networks/loss/__init__.py
index b489264..cb83608 100644
--- a/text_recognizer/networks/loss/__init__.py
+++ b/text_recognizer/networks/loss/__init__.py
@@ -1,2 +1,2 @@
 """Loss module."""
-from .loss import EmbeddingLoss, LabelSmoothingCrossEntropy
+from .loss import LabelSmoothingCrossEntropy
diff --git a/text_recognizer/networks/loss/loss.py b/text_recognizer/networks/loss/loss.py
index cf9fa0d..d12dc9c 100644
--- a/text_recognizer/networks/loss/loss.py
+++ b/text_recognizer/networks/loss/loss.py
@@ -1,39 +1,9 @@
 """Implementations of custom loss functions."""
-from pytorch_metric_learning import distances, losses, miners, reducers
 import torch
 from torch import nn
 from torch import Tensor
-from torch.autograd import Variable
-import torch.nn.functional as F
 
-__all__ = ["EmbeddingLoss", "LabelSmoothingCrossEntropy"]
-
-
-class EmbeddingLoss:
-    """Metric loss for training encoders to produce information-rich latent embeddings."""
-
-    def __init__(self, margin: float = 0.2, type_of_triplets: str = "semihard") -> None:
-        self.distance = distances.CosineSimilarity()
-        self.reducer = reducers.ThresholdReducer(low=0)
-        self.loss_fn = losses.TripletMarginLoss(
-            margin=margin, distance=self.distance, reducer=self.reducer
-        )
-        self.miner = miners.MultiSimilarityMiner(epsilon=margin, distance=self.distance)
-
-    def __call__(self, embeddings: Tensor, labels: Tensor) -> Tensor:
-        """Computes the metric loss for the embeddings based on their labels.
-
-        Args:
-            embeddings (Tensor): The laten vectors encoded by the network.
-            labels (Tensor): Labels of the embeddings.
-
-        Returns:
-            Tensor: The metric loss for the embeddings.
-
-        """
-        hard_pairs = self.miner(embeddings, labels)
-        loss = self.loss_fn(embeddings, labels, hard_pairs)
-        return loss
+__all__ = ["LabelSmoothingCrossEntropy"]
 
 
 class LabelSmoothingCrossEntropy(nn.Module):
diff --git a/text_recognizer/networks/util.py b/text_recognizer/networks/util.py
index 131a6b4..d292680 100644
--- a/text_recognizer/networks/util.py
+++ b/text_recognizer/networks/util.py
@@ -1,38 +1,13 @@
 """Miscellaneous neural network functionality."""
 import importlib
 from pathlib import Path
-from typing import Dict, Tuple, Type
+from typing import Dict, Type
 
-from einops import rearrange
 from loguru import logger
 import torch
 from torch import nn
 
 
-def sliding_window(
-    images: torch.Tensor, patch_size: Tuple[int, int], stride: Tuple[int, int]
-) -> torch.Tensor:
-    """Creates patches of an image.
-
-    Args:
-        images (torch.Tensor): A Torch tensor of a 4D image(s), i.e. (batch, channel, height, width).
-        patch_size (Tuple[int, int]): The size of the patches to generate, e.g. 28x28 for EMNIST.
-        stride (Tuple[int, int]): The stride of the sliding window.
-
-    Returns:
-        torch.Tensor: A tensor with the shape (batch, patches, height, width).
-
-    """
-    unfold = nn.Unfold(kernel_size=patch_size, stride=stride)
-    # Preform the sliding window, unsqueeze as the channel dimesion is lost.
-    c = images.shape[1]
-    patches = unfold(images)
-    patches = rearrange(
-        patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1],
-    )
-    return patches
-
-
 def activation_function(activation: str) -> Type[nn.Module]:
     """Returns the callable activation function."""
     activation_fns = nn.ModuleDict(
diff --git a/text_recognizer/networks/vq_transformer.py b/text_recognizer/networks/vq_transformer.py
deleted file mode 100644
index c673d96..0000000
--- a/text_recognizer/networks/vq_transformer.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""A VQ-Transformer for image to text recognition."""
-from typing import Dict, Optional, Tuple
-
-from einops import rearrange, repeat
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.transformer import PositionalEncoding, Transformer
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.util import configure_backbone
-from text_recognizer.networks.vqvae.encoder import _ResidualBlock
-
-
-class VQTransformer(nn.Module):
-    """VQ+Transfomer for image to character sequence prediction."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        vocab_size: int,
-        num_heads: int,
-        adaptive_pool_dim: Tuple,
-        expansion_dim: int,
-        dropout_rate: float,
-        trg_pad_index: int,
-        max_len: int,
-        backbone: str,
-        backbone_args: Optional[Dict] = None,
-        activation: str = "gelu",
-    ) -> None:
-        super().__init__()
-
-        # Configure vector quantized backbone.
-        self.backbone = configure_backbone(backbone, backbone_args)
-        self.conv = nn.Sequential(
-            nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=2),
-            nn.ReLU(inplace=True),
-        )
-
-        # Configure embeddings for Transformer network.
-        self.trg_pad_index = trg_pad_index
-        self.vocab_size = vocab_size
-        self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim)
-        self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
-        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
-        nn.init.normal_(self.character_embedding.weight, std=0.02)
-
-        self.adaptive_pool = (
-            nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
-        )
-
-        self.transformer = Transformer(
-            num_encoder_layers,
-            num_decoder_layers,
-            hidden_dim,
-            num_heads,
-            expansion_dim,
-            dropout_rate,
-            activation,
-        )
-
-        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
-
-    def _create_trg_mask(self, trg: Tensor) -> Tensor:
-        # Move this outside the transformer.
-        trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
-        trg_len = trg.shape[1]
-        trg_sub_mask = torch.tril(
-            torch.ones((trg_len, trg_len), device=trg.device)
-        ).bool()
-        trg_mask = trg_pad_mask & trg_sub_mask
-        return trg_mask
-
-    def encoder(self, src: Tensor) -> Tensor:
-        """Forward pass with the encoder of the transformer."""
-        return self.transformer.encoder(src)
-
-    def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
-        """Forward pass with the decoder of the transformer + classification head."""
-        return self.head(
-            self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
-        )
-
-    def extract_image_features(self, src: Tensor) -> Tuple[Tensor, Tensor]:
-        """Extracts image features with a backbone neural network.
-
-        It seem like the winning idea was to swap channels and width dimension and collapse
-        the height dimension. The transformer is learning like a baby with this implementation!!! :D
-        Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
-
-        Args:
-            src (Tensor): Input tensor.
-
-        Returns:
-            Tensor: The input src to the transformer and the vq loss.
-
-        """
-        # If batch dimension is missing, it needs to be added.
-        if len(src.shape) < 4:
-            src = src[(None,) * (4 - len(src.shape))]
-        src, vq_loss = self.backbone.encode(src)
-        # src = self.backbone.decoder.res_block(src)
-        src = self.conv(src)
-
-        if self.adaptive_pool is not None:
-            src = rearrange(src, "b c h w -> b w c h")
-            src = self.adaptive_pool(src)
-            src = src.squeeze(3)
-        else:
-            src = rearrange(src, "b c h w -> b (w h) c")
-
-        b, t, _ = src.shape
-
-        src += self.src_position_embedding[:, :t]
-
-        return src, vq_loss
-
-    def target_embedding(self, trg: Tensor) -> Tensor:
-        """Encodes target tensor with embedding and postion.
-
-        Args:
-            trg (Tensor): Target tensor.
-
-        Returns:
-            Tensor: Encoded target tensor.
-
-        """
-        trg = self.character_embedding(trg.long())
-        trg = self.trg_position_encoding(trg)
-        return trg
-
-    def decode_image_features(
-        self, image_features: Tensor, trg: Optional[Tensor] = None
-    ) -> Tensor:
-        """Takes images features from the backbone and decodes them with the transformer."""
-        trg_mask = self._create_trg_mask(trg)
-        trg = self.target_embedding(trg)
-        out = self.transformer(image_features, trg, trg_mask=trg_mask)
-
-        logits = self.head(out)
-        return logits
-
-    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Forward pass with CNN transfomer."""
-        image_features, vq_loss = self.extract_image_features(x)
-        logits = self.decode_image_features(image_features, trg)
-        return logits, vq_loss