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

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

third_party/hamer/third-party/ViTPose/tests/test_necks/test_gap_neck.py

@@ -0,0 +1,43 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmpose.models.necks import GlobalAveragePooling
+
+
+def test_gap():
+    """Test GlobalAveragePooling neck."""
+    gap = GlobalAveragePooling()
+
+    with pytest.raises(TypeError):
+        gap(1)
+
+    x0_shape = (32, 1024, 4, 4)
+    x1_shape = (32, 2048, 2, 2)
+    x0 = _demo_inputs(x0_shape)
+    x1 = _demo_inputs(x1_shape)
+
+    y = gap(x0)
+    assert y.shape == torch.Size([32, 1024])
+
+    y = gap([x0, x1])
+    assert y[0].shape == torch.Size([32, 1024])
+    assert y[1].shape == torch.Size([32, 2048])
+
+    y = gap((x0, x1))
+    assert y[0].shape == torch.Size([32, 1024])
+    assert y[1].shape == torch.Size([32, 2048])
+
+
+def _demo_inputs(input_shape=(1, 3, 64, 64)):
+    """Create a superset of inputs needed to run backbone.
+
+    Args:
+        input_shape (tuple): input batch dimensions.
+            Default: (1, 3, 64, 64).
+    """
+    imgs = np.random.random(input_shape)
+    imgs = torch.FloatTensor(imgs)
+
+    return imgs

third_party/hamer/third-party/ViTPose/tests/test_necks/test_posewarper_neck.py

@@ -0,0 +1,143 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmpose.models.necks import PoseWarperNeck
+
+
+def test_posewarper_neck():
+    """Test PoseWarperNeck."""
+    with pytest.raises(AssertionError):
+        # test value of trans_conv_kernel
+        _ = PoseWarperNeck(
+            out_channels=3,
+            in_channels=512,
+            inner_channels=128,
+            trans_conv_kernel=2)
+
+    with pytest.raises(TypeError):
+        # test type of res_blocks_cfg
+        _ = PoseWarperNeck(
+            out_channels=3,
+            in_channels=512,
+            inner_channels=128,
+            res_blocks_cfg=2)
+
+    with pytest.raises(AssertionError):
+        # test value of dilations
+        neck = PoseWarperNeck(
+            out_channels=3, in_channels=512, inner_channels=128, dilations=[])
+
+    in_channels = 48
+    out_channels = 17
+    inner_channels = 128
+
+    neck = PoseWarperNeck(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        inner_channels=inner_channels)
+
+    with pytest.raises(TypeError):
+        # the forward require two arguments: inputs and frame_weight
+        _ = neck(1)
+
+    with pytest.raises(AssertionError):
+        # the inputs to PoseWarperNeck must be list or tuple
+        _ = neck(1, [0.1])
+
+    # test the case when num_frames * batch_size if larger than
+    # the default value of 'im2col_step' but can not be divided
+    # by it in mmcv.ops.deform_conv
+    b_0 = 8  # batch_size
+    b_1 = 16
+    h_0 = 4  # image height
+    h_1 = 2
+
+    num_frame_0 = 2
+    num_frame_1 = 5
+
+    # test input format
+    # B, C, H, W
+    x0_shape = (b_0, in_channels, h_0, h_0)
+    x1_shape = (b_1, in_channels, h_1, h_1)
+
+    # test concat_tensors case
+    # at the same time, features output from backbone like ResNet is Tensors
+    x0_shape = (b_0 * num_frame_0, in_channels, h_0, h_0)
+    x0 = _demo_inputs(x0_shape, length=1)
+    frame_weight_0 = np.random.uniform(0, 1, num_frame_0)
+
+    # test forward
+    y = neck(x0, frame_weight_0)
+    assert y.shape == torch.Size([b_0, out_channels, h_0, h_0])
+
+    # test concat_tensors case
+    # this time, features output from backbone like HRNet
+    # is list of Tensors rather than Tensors
+    x0_shape = (b_0 * num_frame_0, in_channels, h_0, h_0)
+    x0 = _demo_inputs(x0_shape, length=2)
+    x0 = [x0]
+    frame_weight_0 = np.random.uniform(0, 1, num_frame_0)
+
+    # test forward
+    y = neck(x0, frame_weight_0)
+    assert y.shape == torch.Size([b_0, out_channels, h_0, h_0])
+
+    # test not concat_tensors case
+    # at the same time, features output from backbone like ResNet is Tensors
+    x1_shape = (b_1, in_channels, h_1, h_1)
+    x1 = _demo_inputs(x1_shape, length=num_frame_1)
+    frame_weight_1 = np.random.uniform(0, 1, num_frame_1)
+
+    # test forward
+    y = neck(x1, frame_weight_1)
+    assert y.shape == torch.Size([b_1, out_channels, h_1, h_1])
+
+    # test not concat_tensors case
+    # this time, features output from backbone like HRNet
+    # is list of Tensors rather than Tensors
+    x1_shape = (b_1, in_channels, h_1, h_1)
+    x1 = _demo_inputs(x1_shape, length=2)
+    x1 = [x1 for _ in range(num_frame_1)]
+    frame_weight_1 = np.random.uniform(0, 1, num_frame_1)
+
+    # test forward
+    y = neck(x1, frame_weight_1)
+    assert y.shape == torch.Size([b_1, out_channels, h_1, h_1])
+
+    # test special case that when in concat_tensors case,
+    # batch_size * num_frames is larger than the default value
+    # 'im2col_step' in mmcv.ops.deform_conv, but can not be divided by it
+    # see https://github.com/open-mmlab/mmcv/issues/1440
+    x1_shape = (b_1 * num_frame_1, in_channels, h_1, h_1)
+    x1 = _demo_inputs(x1_shape, length=2)
+    x1 = [x1]
+    frame_weight_0 = np.random.uniform(0, 1, num_frame_1)
+
+    y = neck(x1, frame_weight_1)
+    assert y.shape == torch.Size([b_1, out_channels, h_1, h_1])
+
+    # test the inappropriate value of `im2col_step`
+    neck = PoseWarperNeck(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        inner_channels=inner_channels,
+        im2col_step=32)
+    with pytest.raises(AssertionError):
+        _ = neck(x1, frame_weight_1)
+
+
+def _demo_inputs(input_shape=(80, 48, 4, 4), length=1):
+    """Create a superset of inputs needed to run backbone.
+
+    Args:
+        input_shape (tuple): input batch dimensions.
+            Default: (1, 3, 64, 64).
+        length (int): the length of output list
+        nested (bool): whether the output Tensor is double-nested list.
+    """
+    imgs = [
+        torch.FloatTensor(np.random.random(input_shape)) for _ in range(length)
+    ]
+    return imgs