| import mlx.core as mx |
| import mlx.nn as nn |
|
|
|
|
| class ChessPieceCNN(nn.Module): |
| def __init__(self, num_classes=13): |
| super().__init__() |
| self.conv1 = nn.Conv2d(3, 32, 3, padding=1) |
| self.bn1 = nn.BatchNorm(32) |
| self.conv2 = nn.Conv2d(32, 64, 3, padding=1) |
| self.bn2 = nn.BatchNorm(64) |
| self.conv3 = nn.Conv2d(64, 128, 3, padding=1) |
| self.bn3 = nn.BatchNorm(128) |
| self.pool = nn.MaxPool2d(2, 2) |
| |
| self.fc1 = nn.Linear(128 * 4 * 4, 256) |
| self.fc2 = nn.Linear(256, num_classes) |
|
|
| def __call__(self, x): |
| |
| x = nn.relu(self.bn1(self.conv1(x))) |
| x = self.pool(x) |
| x = nn.relu(self.bn2(self.conv2(x))) |
| x = self.pool(x) |
| x = nn.relu(self.bn3(self.conv3(x))) |
| x = self.pool(x) |
| |
| x = mx.flatten(x, start_axis=1) |
| x = nn.relu(self.fc1(x)) |
| x = self.fc2(x) |
| return x |
|
|
|
|
| class ChessPieceCNN64(nn.Module): |
| """Stronger per-square classifier for 64x64 crops. |
| |
| Still classifies each square independently into the same 13 labels, but keeps |
| 4x more input pixels than the original 32x32 model and uses two convs per |
| stage so piece color/outline detail survives much better. |
| """ |
|
|
| def __init__(self, num_classes=13): |
| super().__init__() |
| self.conv1a = nn.Conv2d(3, 32, 3, padding=1) |
| self.bn1a = nn.BatchNorm(32) |
| self.conv1b = nn.Conv2d(32, 32, 3, padding=1) |
| self.bn1b = nn.BatchNorm(32) |
|
|
| self.conv2a = nn.Conv2d(32, 64, 3, padding=1) |
| self.bn2a = nn.BatchNorm(64) |
| self.conv2b = nn.Conv2d(64, 64, 3, padding=1) |
| self.bn2b = nn.BatchNorm(64) |
|
|
| self.conv3a = nn.Conv2d(64, 128, 3, padding=1) |
| self.bn3a = nn.BatchNorm(128) |
| self.conv3b = nn.Conv2d(128, 128, 3, padding=1) |
| self.bn3b = nn.BatchNorm(128) |
|
|
| self.conv4a = nn.Conv2d(128, 256, 3, padding=1) |
| self.bn4a = nn.BatchNorm(256) |
| self.conv4b = nn.Conv2d(256, 256, 3, padding=1) |
| self.bn4b = nn.BatchNorm(256) |
|
|
| self.pool = nn.MaxPool2d(2, 2) |
| self.fc1 = nn.Linear(256 * 4 * 4, 512) |
| self.fc2 = nn.Linear(512, num_classes) |
|
|
| def __call__(self, x): |
| |
| x = nn.relu(self.bn1a(self.conv1a(x))) |
| x = nn.relu(self.bn1b(self.conv1b(x))) |
| x = self.pool(x) |
|
|
| x = nn.relu(self.bn2a(self.conv2a(x))) |
| x = nn.relu(self.bn2b(self.conv2b(x))) |
| x = self.pool(x) |
|
|
| x = nn.relu(self.bn3a(self.conv3a(x))) |
| x = nn.relu(self.bn3b(self.conv3b(x))) |
| x = self.pool(x) |
|
|
| x = nn.relu(self.bn4a(self.conv4a(x))) |
| x = nn.relu(self.bn4b(self.conv4b(x))) |
| x = self.pool(x) |
|
|
| x = mx.flatten(x, start_axis=1) |
| x = nn.relu(self.fc1(x)) |
| x = self.fc2(x) |
| return x |
|
|
