Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-09-15 14:13:49 +08:00
commit 7e4ef6f98b
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+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+
+from mmpose.core import build_optimizers
+
+
+class ExampleModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.model1 = nn.Conv2d(3, 8, kernel_size=3)
+        self.model2 = nn.Conv2d(3, 4, kernel_size=3)
+
+    def forward(self, x):
+        return x
+
+
+def test_build_optimizers():
+    base_lr = 0.0001
+    base_wd = 0.0002
+    momentum = 0.9
+
+    # basic config with ExampleModel
+    optimizer_cfg = dict(
+        model1=dict(
+            type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum),
+        model2=dict(
+            type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum))
+    model = ExampleModel()
+    optimizers = build_optimizers(model, optimizer_cfg)
+    param_dict = dict(model.named_parameters())
+    assert isinstance(optimizers, dict)
+    for i in range(2):
+        optimizer = optimizers[f'model{i+1}']
+        param_groups = optimizer.param_groups[0]
+        assert isinstance(optimizer, torch.optim.SGD)
+        assert optimizer.defaults['lr'] == base_lr
+        assert optimizer.defaults['momentum'] == momentum
+        assert optimizer.defaults['weight_decay'] == base_wd
+        assert len(param_groups['params']) == 2
+        assert torch.equal(param_groups['params'][0],
+                           param_dict[f'model{i+1}.weight'])
+        assert torch.equal(param_groups['params'][1],
+                           param_dict[f'model{i+1}.bias'])
+
+    # basic config with Parallel model
+    model = torch.nn.DataParallel(ExampleModel())
+    optimizers = build_optimizers(model, optimizer_cfg)
+    param_dict = dict(model.named_parameters())
+    assert isinstance(optimizers, dict)
+    for i in range(2):
+        optimizer = optimizers[f'model{i+1}']
+        param_groups = optimizer.param_groups[0]
+        assert isinstance(optimizer, torch.optim.SGD)
+        assert optimizer.defaults['lr'] == base_lr
+        assert optimizer.defaults['momentum'] == momentum
+        assert optimizer.defaults['weight_decay'] == base_wd
+        assert len(param_groups['params']) == 2
+        assert torch.equal(param_groups['params'][0],
+                           param_dict[f'module.model{i+1}.weight'])
+        assert torch.equal(param_groups['params'][1],
+                           param_dict[f'module.model{i+1}.bias'])
+
+    # basic config with ExampleModel (one optimizer)
+    optimizer_cfg = dict(
+        type='SGD', lr=base_lr, weight_decay=base_wd, momentum=momentum)
+    model = ExampleModel()
+    optimizer = build_optimizers(model, optimizer_cfg)
+    param_dict = dict(model.named_parameters())
+    assert isinstance(optimizers, dict)
+    param_groups = optimizer.param_groups[0]
+    assert isinstance(optimizer, torch.optim.SGD)
+    assert optimizer.defaults['lr'] == base_lr
+    assert optimizer.defaults['momentum'] == momentum
+    assert optimizer.defaults['weight_decay'] == base_wd
+    assert len(param_groups['params']) == 4
+    assert torch.equal(param_groups['params'][0], param_dict['model1.weight'])
+    assert torch.equal(param_groups['params'][1], param_dict['model1.bias'])
+    assert torch.equal(param_groups['params'][2], param_dict['model2.weight'])
+    assert torch.equal(param_groups['params'][3], param_dict['model2.bias'])
+
+    # basic config with Parallel model (one optimizer)
+    model = torch.nn.DataParallel(ExampleModel())
+    optimizer = build_optimizers(model, optimizer_cfg)
+    param_dict = dict(model.named_parameters())
+    assert isinstance(optimizers, dict)
+    param_groups = optimizer.param_groups[0]
+    assert isinstance(optimizer, torch.optim.SGD)
+    assert optimizer.defaults['lr'] == base_lr
+    assert optimizer.defaults['momentum'] == momentum
+    assert optimizer.defaults['weight_decay'] == base_wd
+    assert len(param_groups['params']) == 4
+    assert torch.equal(param_groups['params'][0],
+                       param_dict['module.model1.weight'])
+    assert torch.equal(param_groups['params'][1],
+                       param_dict['module.model1.bias'])
+    assert torch.equal(param_groups['params'][2],
+                       param_dict['module.model2.weight'])
+    assert torch.equal(param_groups['params'][3],
+                       param_dict['module.model2.bias'])