Add converted weights

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convert.py

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+#!/usr/bin/env python3
+
+"""Convert Satlas-Pretrain model checkpoints to a format accepted by TorchGeo.
+
+Reference implementation:
+
+* https://github.com/allenai/satlaspretrain_models/blob/main/satlaspretrain_models/models/backbones.py
+"""
+
+import glob
+import hashlib
+import os
+
+import timm
+import torch
+import torchvision
+
+
+for checkpoint in glob.iglob('*swin*.pth'):
+    # Skip if already converted
+    if '-' in checkpoint:
+        continue
+
+    print(checkpoint)
+
+    # Map to CPU
+    state_dict = torch.load(checkpoint, map_location=torch.device('cpu'), weights_only=True)
+
+    # Extract backbone
+    if 'backbone.backbone.resnet.conv1.weight' in state_dict:
+        state_dict = {key.replace('backbone.backbone.resnet.', ''): value for key, value in state_dict.items() if key.startswith('backbone.backbone.resnet.')}
+    elif 'backbone.resnet.conv1.weight' in state_dict:
+        state_dict = {key.replace('backbone.resnet.', ''): value for key, value in state_dict.items() if key.startswith('backbone.resnet.')}
+    elif 'backbone.backbone.backbone.features.0.0.weight' in state_dict:
+        state_dict = {key.replace('backbone.backbone.backbone.', ''): value for key, value in state_dict.items() if key.startswith('backbone.backbone.backbone.')}
+    elif 'backbone.backbone.features.0.0.weight' in state_dict:
+        state_dict = {key.replace('backbone.backbone.', ''): value for key, value in state_dict.items() if key.startswith('backbone.backbone.')}
+
+    if 'resnet' in checkpoint:
+        # Extract # channels
+        in_chans = state_dict['conv1.weight'].shape[1]
+
+        # Create model
+        model_name = checkpoint.split('_')[1]
+        model = timm.create_model(model_name, in_chans=in_chans)
+    elif 'swin' in checkpoint:
+        # Extract # channels
+        out_channels, num_channels, kernel_size_0, kernel_size_1 = state_dict['features.0.0.weight'].shape
+
+        # Create model
+        if 'swint' in checkpoint:
+            model = torchvision.models.swin_v2_t()
+        elif 'swinb' in checkpoint:
+            model = torchvision.models.swin_v2_b()
+
+        model.features[0][0] = torch.nn.Conv2d(num_channels, out_channels, kernel_size=(kernel_size_0, kernel_size_1), stride=(4, 4))
+
+    # Load weights
+    model.load_state_dict(state_dict)
+
+    # Save model
+    torch.save(model.state_dict(), f'{checkpoint}.tmp')
+
+    # Compute the checksum
+    with open(f'{checkpoint}.tmp', 'rb') as f:
+        checksum = hashlib.file_digest(f, 'sha256').hexdigest()
+
+    # Rename
+    os.rename(f'{checkpoint}.tmp', f'{checkpoint[:-4]}-{checksum[:8]}.pth')

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