Spaces:
Running
Running
File size: 5,394 Bytes
278c80b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 |
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
|