Jupyter notebook for vista 2D

Signed-off-by: Vishwesh Nath <[email protected]>

vista_2d/cell_sam_wrapper.py

@@ -0,0 +1,80 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+from segment_anything.build_sam import build_sam_vit_b
+from torch import nn
+from torch.nn import functional as F
+
+
+class CellSamWrapper(torch.nn.Module):
+    def __init__(
+        self,
+        auto_resize_inputs=True,
+        network_resize_roi=[1024, 1024],
+        checkpoint="sam_vit_b_01ec64.pth",
+        return_features=False,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+
+        print(
+            f"CellSamWrapper auto_resize_inputs {auto_resize_inputs} network_resize_roi {network_resize_roi} checkpoint {checkpoint}"
+        )
+        self.network_resize_roi = network_resize_roi
+        self.auto_resize_inputs = auto_resize_inputs
+        self.return_features = return_features
+
+        model = build_sam_vit_b(checkpoint=checkpoint)
+
+        model.prompt_encoder = None
+        model.mask_decoder = None
+
+        model.mask_decoder = nn.Sequential(
+            nn.BatchNorm2d(num_features=256),
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(
+                256,
+                128,
+                kernel_size=3,
+                stride=2,
+                padding=1,
+                output_padding=1,
+                bias=False,
+            ),
+            nn.BatchNorm2d(num_features=128),
+            nn.ReLU(inplace=True),
+            nn.ConvTranspose2d(
+                128, 3, kernel_size=3, stride=2, padding=1, output_padding=1, bias=True
+            ),
+        )
+
+        self.model = model
+
+    def forward(self, x):
+        # print("CellSamWrapper x0", x.shape)
+        sh = x.shape[2:]
+
+        if self.auto_resize_inputs:
+            x = F.interpolate(x, size=self.network_resize_roi, mode="bilinear")
+
+        # print("CellSamWrapper x1", x.shape)
+        x = self.model.image_encoder(x)  # shape: (1, 256, 64, 64)
+        # print("CellSamWrapper image_embeddings", x.shape)
+
+        if not self.return_features:
+            x = self.model.mask_decoder(x)
+            if self.auto_resize_inputs:
+                x = F.interpolate(x, size=sh, mode="bilinear")
+
+        # print("CellSamWrapper x final", x.shape)
+        return x