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Configuration error
Configuration error
| import torch | |
| import torch.nn as nn | |
| class PatchCNN(nn.Module): | |
| def __init__(self, num_classes=7): | |
| super(PatchCNN, self).__init__() | |
| # ── Feature Extractor ── | |
| self.features = nn.Sequential( | |
| # Block 1 — detects edges and basic textures | |
| nn.Conv2d(3, 32, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(32), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2, 2), # 32x32 → 16x16 | |
| # Block 2 — detects patterns from edges | |
| nn.Conv2d(32, 64, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(64), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2, 2), # 16x16 → 8x8 | |
| # Block 3 — detects complex textures | |
| nn.Conv2d(64, 128, kernel_size=3, padding=1), | |
| nn.BatchNorm2d(128), | |
| nn.ReLU(), | |
| nn.MaxPool2d(2, 2), # 8x8 → 4x4 | |
| ) | |
| # ── Classifier ── | |
| self.classifier = nn.Sequential( | |
| nn.Flatten(), # (128, 4, 4) → 2048 | |
| nn.Linear(128 * 4 * 4, 256), | |
| nn.ReLU(), | |
| nn.Dropout(0.3), | |
| nn.Linear(256, num_classes) # 256 → 7 | |
| ) | |
| def forward(self, x): | |
| x = self.features(x) | |
| x = self.classifier(x) | |
| return x |