| from typing import Any, Mapping | |
| from .configuration_arabichar import ArabiCharModelConfig | |
| from transformers import PreTrainedModel | |
| import torch | |
| import torch.nn as nn | |
| class ArabiCharModel(nn.Module): | |
| def __init__(self, config): | |
| super(ArabiCharModel, self).__init__() | |
| self.conv1 = nn.Conv2d(1, config.conv1_channels, kernel_size=5, padding=4) | |
| self.conv2 = nn.Conv2d(config.conv1_channels, config.conv1_channels, kernel_size=5) | |
| self.conv3 = nn.Conv2d(config.conv1_channels, config.conv1_channels, kernel_size=5) | |
| self.pool1 = nn.MaxPool2d(2) | |
| self.bn1 = nn.BatchNorm2d(config.conv1_channels) | |
| self.conv4 = nn.Conv2d(config.conv1_channels, config.conv2_channels, kernel_size=5, padding=4) | |
| self.conv5 = nn.Conv2d(config.conv2_channels, config.conv2_channels, kernel_size=5) | |
| self.conv6 = nn.Conv2d(config.conv2_channels, config.conv2_channels, kernel_size=5) | |
| self.pool2 = nn.MaxPool2d(2) | |
| self.bn2 = nn.BatchNorm2d(config.conv2_channels) | |
| self.fc1 = nn.Linear(config.conv2_channels * 5 * 5, config.fc1_units) | |
| self.fc2 = nn.Linear(config.fc1_units, config.fc1_units) | |
| self.dropout = nn.Dropout(config.dropout_prob) | |
| self.fc3 = nn.Linear(config.fc1_units, config.num_classes) | |
| def forward(self, x): | |
| x = torch.relu(self.conv1(x)) | |
| x = torch.relu(self.conv2(x)) | |
| x = torch.relu(self.conv3(x)) | |
| x = self.pool1(x) | |
| x = self.bn1(x) | |
| x = torch.relu(self.conv4(x)) | |
| x = torch.relu(self.conv5(x)) | |
| x = torch.relu(self.conv6(x)) | |
| x = self.pool2(x) | |
| x = self.bn2(x) | |
| x = x.view(x.size(0), -1) | |
| x = torch.relu(self.fc1(x)) | |
| x = torch.relu(self.fc2(x)) | |
| x = self.dropout(x) | |
| return torch.softmax(self.fc3(x), dim=1) | |
| class ArabiCharModelForImageClassification(PreTrainedModel): | |
| config_class = ArabiCharModelConfig | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.model = ArabiCharModel(config) | |
| def forward(self, tensor, labels=None): | |
| logits = self.model(tensor) | |
| if labels is not None: | |
| loss = torch.nn.cross_entropy(logits, labels) | |
| return {"loss": loss, "logits": logits} | |
| return {"logits": logits} | |
| def load_state_dict(self, model_name): | |
| self.model.load_state_dict(torch.load(model_name)) | |