From 73ae250d7993fa48eccff4042ecd6bf768650bf3 Mon Sep 17 00:00:00 2001
From: aktersnurra <grydholm@kth.se>
Date: Wed, 18 Nov 2020 23:35:35 +0100
Subject: UNet implemented.

---
 src/text_recognizer/networks/sparse_mlp.py | 78 ------------------------------
 1 file changed, 78 deletions(-)
 delete mode 100644 src/text_recognizer/networks/sparse_mlp.py

(limited to 'src/text_recognizer/networks/sparse_mlp.py')

diff --git a/src/text_recognizer/networks/sparse_mlp.py b/src/text_recognizer/networks/sparse_mlp.py
deleted file mode 100644
index 53cf166..0000000
--- a/src/text_recognizer/networks/sparse_mlp.py
+++ /dev/null
@@ -1,78 +0,0 @@
-"""Defines the Sparse MLP network."""
-from typing import Callable, Dict, List, Optional, Union
-import warnings
-
-from einops.layers.torch import Rearrange
-from pytorch_block_sparse import BlockSparseLinear
-import torch
-from torch import nn
-
-from text_recognizer.networks.util import activation_function
-
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-
-
-class SparseMLP(nn.Module):
-    """Sparse multi layered perceptron network."""
-
-    def __init__(
-        self,
-        input_size: int = 784,
-        num_classes: int = 10,
-        hidden_size: Union[int, List] = 128,
-        num_layers: int = 3,
-        density: float = 0.1,
-        activation_fn: str = "relu",
-    ) -> None:
-        """Initialization of the MLP network.
-
-        Args:
-            input_size (int): The input shape of the network. Defaults to 784.
-            num_classes (int): Number of classes in the dataset. Defaults to 10.
-            hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
-            num_layers (int): The number of hidden layers. Defaults to 3.
-            density (float): The density of activation at each layer. Default to 0.1.
-            activation_fn (str): Name of the activation function in the hidden layers. Defaults to
-                relu.
-
-        """
-        super().__init__()
-
-        activation_fn = activation_function(activation_fn)
-
-        if isinstance(hidden_size, int):
-            hidden_size = [hidden_size] * num_layers
-
-        self.layers = [
-            Rearrange("b c h w -> b (c h w)"),
-            nn.Linear(in_features=input_size, out_features=hidden_size[0]),
-            activation_fn,
-        ]
-
-        for i in range(num_layers - 1):
-            self.layers += [
-                BlockSparseLinear(
-                    in_features=hidden_size[i],
-                    out_features=hidden_size[i + 1],
-                    density=density,
-                ),
-                activation_fn,
-            ]
-
-        self.layers.append(
-            nn.Linear(in_features=hidden_size[-1], out_features=num_classes)
-        )
-
-        self.layers = nn.Sequential(*self.layers)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """The feedforward pass."""
-        # If batch dimenstion is missing, it needs to be added.
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        return self.layers(x)
-
-    @property
-    def __name__(self) -> str:
-        """Returns the name of the network."""
-        return "mlp"
-- 
cgit v1.2.3-70-g09d2