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BestWJH commited on Jan 30

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models/backbones/__pycache__/__init__.cpython-310.pyc +0 -0
models/backbones/__pycache__/vit.cpython-310.pyc +0 -0
models/modules/ehm/__pycache__/EHM.cpython-310.pyc +0 -0
models/modules/ehm/__pycache__/EHM_v2.cpython-310.pyc +0 -0
models/modules/ehm/__pycache__/__init__.cpython-310.pyc +0 -0
models/modules/flame/__pycache__/FLAME.cpython-310.pyc +0 -0
models/modules/flame/__pycache__/__init__.cpython-310.pyc +0 -0
models/modules/flame/__pycache__/lbs.cpython-310.pyc +0 -0
models/modules/mano/__pycache__/MANO.cpython-310.pyc +0 -0
models/modules/mano/__pycache__/__init__.cpython-310.pyc +0 -0
models/modules/renderer/__pycache__/base_renderer.cpython-310.pyc +0 -0
models/modules/renderer/__pycache__/body_renderer.cpython-310.pyc +0 -0
models/modules/renderer/__pycache__/util.cpython-310.pyc +0 -0
models/modules/smplx/__pycache__/SMPLX.cpython-310.pyc +0 -0
models/modules/smplx/__pycache__/__init__.cpython-310.pyc +0 -0
models/modules/smplx/__pycache__/lbs.cpython-310.pyc +0 -0
models/pipeline/__pycache__/ehm_pipeline.cpython-310.pyc +0 -0
models/pipeline/ehm_pipeline.py +5 -1
models/smplx/__pycache__/SMPLXV2.cpython-310.pyc +0 -0
models/smplx/__pycache__/lbs.cpython-310.pyc +0 -0
models/smplx/__pycache__/pose_transformer.cpython-310.pyc +0 -0
models/smplx/__pycache__/smplx_head.cpython-310.pyc +0 -0
models/smplx/__pycache__/smplx_layer.cpython-310.pyc +0 -0
models/smplx/__pycache__/t_cond_mlp.cpython-310.pyc +0 -0
utils/__pycache__/__init__.cpython-310.pyc +0 -0
utils/__pycache__/general_utils.cpython-310.pyc +0 -0
utils/__pycache__/graphics.cpython-310.pyc +0 -0
utils/__pycache__/graphics_utils.cpython-310.pyc +0 -0
utils/__pycache__/helper.cpython-310.pyc +0 -0
utils/__pycache__/pipeline_utils.cpython-310.pyc +0 -0
utils/__pycache__/rotation_converter.cpython-310.pyc +0 -0

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@@ -45,7 +45,11 @@ class Ehm_Pipeline(L.LightningModule):
         # save_image(x[:, :, :, 32:-32],"input.jpg")
         x = self.normalize(x)
         B = len(x)
         # 1. Extract features from image.
         #  The input size is 256*256, but ViT needs 256*192. TODO: make this more elegant.
         feats = self.backbone(x[:, :, :, 32:-32])

         # save_image(x[:, :, :, 32:-32],"input.jpg")
         x = self.normalize(x)
         B = len(x)
+        assert x.numel() > 0
+        assert x.shape[0] > 0
+        W = x.shape[-1]
+        if W <= 64:
+            raise ValueError(f"Input width too small: {W}")
         # 1. Extract features from image.
         #  The input size is 256*256, but ViT needs 256*192. TODO: make this more elegant.
         feats = self.backbone(x[:, :, :, 32:-32])

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