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fix .to for MVANet
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@ -26,15 +26,14 @@ class PositionEmbeddingSine(fl.Module):
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Non-trainable position embedding, originally from https://github.com/facebookresearch/detr
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"""
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def __init__(self, num_pos_feats: int, device: Device | None = None) -> None:
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def __init__(self, num_pos_feats: int) -> None:
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super().__init__()
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self.device = device
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temperature = 10000
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self.dim_t = torch.arange(0, num_pos_feats, dtype=torch.float32, device=self.device)
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self.dim_t = torch.arange(0, num_pos_feats, dtype=torch.float32)
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self.dim_t = temperature ** (2 * (self.dim_t // 2) / num_pos_feats)
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def __call__(self, h: int, w: int) -> Tensor:
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mask = torch.ones([1, h, w, 1], dtype=torch.bool, device=self.device)
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mask = torch.ones([1, h, w, 1], dtype=torch.bool)
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y_embed = mask.cumsum(dim=1, dtype=torch.float32)
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x_embed = mask.cumsum(dim=2, dtype=torch.float32)
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@ -129,7 +128,7 @@ class MCLM(fl.Chain):
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if pool_ratios is None:
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pool_ratios = [2, 8, 16]
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positional_embedding = PositionEmbeddingSine(num_pos_feats=emb_dim // 2, device=device)
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positional_embedding = PositionEmbeddingSine(num_pos_feats=emb_dim // 2)
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# LayerNorms in MCLM share their weights.
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# We use the `proxy` trick below so they can be present only
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@ -174,6 +173,7 @@ class MCLM(fl.Chain):
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),
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),
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fl.Lambda(lambda t1, t2: (*t1, *t2)), # type: ignore
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fl.Converter(set_dtype=False),
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GlobalAttention(emb_dim, num_heads, device=device),
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ln1,
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FeedForward(emb_dim, device=device),
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@ -57,3 +57,25 @@ def test_mvanet(
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prediction: torch.Tensor = mvanet_model(in_t.to(test_device)).sigmoid()
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cactus_mask = tensor_to_image(prediction).resize(ref_cactus.size, Image.Resampling.BILINEAR)
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ensure_similar_images(cactus_mask.convert("RGB"), expected_cactus_mask.convert("RGB"))
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@no_grad()
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def test_mvanet_to(
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mvanet_weights: Path,
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ref_cactus: Image.Image,
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expected_cactus_mask: Image.Image,
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test_device: torch.device,
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):
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if test_device.type == "cpu":
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warn("not running on CPU, skipping")
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pytest.skip()
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model = MVANet(device=torch.device("cpu")).eval()
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model.load_from_safetensors(mvanet_weights)
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model.to(test_device)
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in_t = image_to_tensor(ref_cactus.resize((1024, 1024), Image.Resampling.BILINEAR)).squeeze()
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in_t = normalize(in_t, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]).unsqueeze(0)
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prediction: torch.Tensor = model(in_t.to(test_device)).sigmoid()
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cactus_mask = tensor_to_image(prediction).resize(ref_cactus.size, Image.Resampling.BILINEAR)
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ensure_similar_images(cactus_mask.convert("RGB"), expected_cactus_mask.convert("RGB"))
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