update ut

tests/test_models/test_selfsup/test_itpn.py

@@ -0,0 +1,57 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import platform
+
+import pytest
+import torch
+
+from mmpretrain.models import iTPN
+from mmpretrain.structures import DataSample
+
+
+@pytest.mark.skipif(platform.system() == 'Windows', reason='Windows mem limit')
+def test_itpn():
+    data_preprocessor = {
+        'mean': [0.5, 0.5, 0.5],
+        'std': [0.5, 0.5, 0.5],
+        'to_rgb': True
+    }
+    backbone = dict(
+        type='iTPNHiViT',
+        arch='base',
+        reconstruction_type='pixel',
+        mask_ratio=0.75)
+    neck = dict(
+        type='iTPNPretrainDecoder',
+        num_patches=196,
+        patch_size=16,
+        in_chans=3,
+        embed_dim=512,
+        decoder_embed_dim=512,
+        decoder_depth=6,
+        decoder_num_heads=16,
+        mlp_ratio=4.,
+        reconstruction_type='pixel',
+        #  transformer pyramid
+        fpn_dim=256,
+        fpn_depth=2,
+        num_outs=3,
+    )
+    head = dict(
+        type='MAEPretrainHead',
+        norm_pix=True,
+        patch_size=16,
+        loss=dict(type='PixelReconstructionLoss', criterion='L2'))
+
+    alg = iTPN(
+        backbone=backbone,
+        neck=neck,
+        head=head,
+        data_preprocessor=data_preprocessor)
+
+    fake_data = {
+        'inputs': torch.randn((2, 3, 224, 224)),
+        'data_samples': [DataSample() for _ in range(2)]
+    }
+    fake_inputs = alg.data_preprocessor(fake_data)
+    fake_outputs = alg(**fake_inputs, mode='loss')
+    assert isinstance(fake_outputs['loss'].item(), float)