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import numpy as np |
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import torch |
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from mmpose.models import build_posenet |
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def test_interhand3d_forward(): |
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model_cfg = dict( |
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type='Interhand3D', |
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pretrained='torchvision://resnet50', |
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backbone=dict(type='ResNet', depth=50), |
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keypoint_head=dict( |
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type='Interhand3DHead', |
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keypoint_head_cfg=dict( |
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in_channels=2048, |
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out_channels=21 * 64, |
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depth_size=64, |
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num_deconv_layers=3, |
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num_deconv_filters=(256, 256, 256), |
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num_deconv_kernels=(4, 4, 4), |
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), |
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root_head_cfg=dict( |
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in_channels=2048, |
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heatmap_size=64, |
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hidden_dims=(512, ), |
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), |
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hand_type_head_cfg=dict( |
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in_channels=2048, |
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num_labels=2, |
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hidden_dims=(512, ), |
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), |
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loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True), |
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loss_root_depth=dict(type='L1Loss'), |
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loss_hand_type=dict(type='BCELoss', use_target_weight=True), |
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), |
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train_cfg={}, |
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test_cfg=dict(flip_test=True, shift_heatmap=True)) |
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detector = build_posenet(model_cfg) |
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detector.init_weights() |
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input_shape = (2, 3, 256, 256) |
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mm_inputs = _demo_mm_inputs(input_shape) |
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imgs = mm_inputs.pop('imgs') |
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target = mm_inputs.pop('target') |
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target_weight = mm_inputs.pop('target_weight') |
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img_metas = mm_inputs.pop('img_metas') |
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losses = detector.forward( |
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imgs, target, target_weight, img_metas, return_loss=True) |
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assert isinstance(losses, dict) |
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with torch.no_grad(): |
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_ = detector.forward(imgs, img_metas=img_metas, return_loss=False) |
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_ = detector.forward_dummy(imgs) |
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def _demo_mm_inputs(input_shape=(1, 3, 256, 256), num_outputs=None): |
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"""Create a superset of inputs needed to run test or train batches. |
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Args: |
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input_shape (tuple): |
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input batch dimensions |
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""" |
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(N, C, H, W) = input_shape |
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rng = np.random.RandomState(0) |
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imgs = rng.rand(*input_shape) |
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imgs = torch.FloatTensor(imgs) |
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target = [ |
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imgs.new_zeros(N, 42, 64, H // 4, W // 4), |
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imgs.new_zeros(N, 1), |
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imgs.new_zeros(N, 2), |
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] |
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target_weight = [ |
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imgs.new_ones(N, 42, 1), |
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imgs.new_ones(N, 1), |
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imgs.new_ones(N), |
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] |
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img_metas = [{ |
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'img_shape': (H, W, C), |
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'center': np.array([W / 2, H / 2]), |
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'scale': np.array([0.5, 0.5]), |
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'bbox_score': 1.0, |
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'bbox_id': 0, |
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'flip_pairs': [], |
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'inference_channel': np.arange(42), |
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'image_file': '<demo>.png', |
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'heatmap3d_depth_bound': 400.0, |
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'root_depth_bound': 400.0, |
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} for _ in range(N)] |
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mm_inputs = { |
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'imgs': imgs.requires_grad_(True), |
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'target': target, |
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'target_weight': target_weight, |
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'img_metas': img_metas |
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} |
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return mm_inputs |
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