Delete model.py

model.py

@@ -1,158 +0,0 @@
-import os
-import glob
-import torch
-import torch.jit
-import torch.nn as nn
-
-
-class Model(torch.jit.ScriptModule):
-    CHECKPOINT_FILENAME_PATTERN = "model-{}.pth"
-
-    __constants__ = [
-        "_hidden1",
-        "_hidden2",
-        "_hidden3",
-        "_hidden4",
-        "_hidden5",
-        "_hidden6",
-        "_hidden7",
-        "_hidden8",
-        "_hidden9",
-        "_hidden10",
-        "_features",
-        "_classifier",
-        "_digit_length",
-        "_digit1",
-        "_digit2",
-        "_digit3",
-        "_digit4",
-        "_digit5",
-    ]
-
-    def __init__(self):
-        super(Model, self).__init__()
-
-        self._hidden1 = nn.Sequential(
-            nn.Conv2d(in_channels=3, out_channels=48, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=48),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden2 = nn.Sequential(
-            nn.Conv2d(in_channels=48, out_channels=64, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=64),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=1, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden3 = nn.Sequential(
-            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=128),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden4 = nn.Sequential(
-            nn.Conv2d(in_channels=128, out_channels=160, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=160),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=1, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden5 = nn.Sequential(
-            nn.Conv2d(in_channels=160, out_channels=192, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=192),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden6 = nn.Sequential(
-            nn.Conv2d(in_channels=192, out_channels=192, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=192),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=1, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden7 = nn.Sequential(
-            nn.Conv2d(in_channels=192, out_channels=192, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=192),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden8 = nn.Sequential(
-            nn.Conv2d(in_channels=192, out_channels=192, kernel_size=5, padding=2),
-            nn.BatchNorm2d(num_features=192),
-            nn.ReLU(),
-            nn.MaxPool2d(kernel_size=2, stride=1, padding=1),
-            nn.Dropout(0.2),
-        )
-        self._hidden9 = nn.Sequential(nn.Linear(192 * 7 * 7, 3072), nn.ReLU())
-        self._hidden10 = nn.Sequential(nn.Linear(3072, 3072), nn.ReLU())
-
-        self._digit_length = nn.Sequential(nn.Linear(3072, 7))
-        self._digit1 = nn.Sequential(nn.Linear(3072, 11))
-        self._digit2 = nn.Sequential(nn.Linear(3072, 11))
-        self._digit3 = nn.Sequential(nn.Linear(3072, 11))
-        self._digit4 = nn.Sequential(nn.Linear(3072, 11))
-        self._digit5 = nn.Sequential(nn.Linear(3072, 11))
-
-    @torch.jit.script_method
-    def forward(self, x):
-        x = self._hidden1(x)
-        x = self._hidden2(x)
-        x = self._hidden3(x)
-        x = self._hidden4(x)
-        x = self._hidden5(x)
-        x = self._hidden6(x)
-        x = self._hidden7(x)
-        x = self._hidden8(x)
-        x = x.view(x.size(0), 192 * 7 * 7)
-        x = self._hidden9(x)
-        x = self._hidden10(x)
-
-        length_logits = self._digit_length(x)
-        digit1_logits = self._digit1(x)
-        digit2_logits = self._digit2(x)
-        digit3_logits = self._digit3(x)
-        digit4_logits = self._digit4(x)
-        digit5_logits = self._digit5(x)
-
-        return (
-            length_logits,
-            digit1_logits,
-            digit2_logits,
-            digit3_logits,
-            digit4_logits,
-            digit5_logits,
-        )
-
-    def store(self, path_to_dir, step, maximum=5):
-        path_to_models = glob.glob(
-            os.path.join(path_to_dir, Model.CHECKPOINT_FILENAME_PATTERN.format("*"))
-        )
-        if len(path_to_models) == maximum:
-            min_step = min(
-                [
-                    int(path_to_model.split("\\")[-1][6:-4])
-                    for path_to_model in path_to_models
-                ]
-            )
-            path_to_min_step_model = os.path.join(
-                path_to_dir, Model.CHECKPOINT_FILENAME_PATTERN.format(min_step)
-            )
-            os.remove(path_to_min_step_model)
-
-        path_to_checkpoint_file = os.path.join(
-            path_to_dir, Model.CHECKPOINT_FILENAME_PATTERN.format(step)
-        )
-        torch.save(self.state_dict(), path_to_checkpoint_file)
-        return path_to_checkpoint_file
-
-    def restore(self, path_to_checkpoint_file):
-        self.load_state_dict(
-            torch.load(path_to_checkpoint_file, map_location=torch.device("cpu"))
-        )
-        step = int(path_to_checkpoint_file.split("model-")[-1][:-4])
-        return step