Merge pull request #207 from Aakanksha-Rana/3D_LSTM_Transfomers

Create unet_lstm.py

nobrainer/layers/max_pool4d.py

@@ -0,0 +1,80 @@
+# import tensorflow as tf
+# from tensorflow.python.ops import gen_nn_ops
+
+# def _get_sequence(value, n, channel_index, name):
+#   """Formats a value input for gen_nn_ops."""
+#   # Performance is fast-pathed for common cases:
+#   # `None`, `list`, `tuple` and `int`.
+#   if value is None:
+#     return [1] * (n + 2)
+
+#   # Always convert `value` to a `list`.
+#   if isinstance(value, list):
+#     pass
+#   elif isinstance(value, tuple):
+#     value = list(value)
+#   elif isinstance(value, int):
+#     value = [value]
+#   elif not isinstance(value, collections_abc.Sized):
+#     value = [value]
+#   else:
+#     value = list(value)  # Try casting to a list.
+
+#   len_value = len(value)
+
+#   # Fully specified, including batch and channel dims.
+#   if len_value == n + 2:
+#     return value
+
+#   # Apply value to spatial dims only.
+#   if len_value == 1:
+#     value = value * n  # Broadcast to spatial dimensions.
+#   elif len_value != n:
+#     raise ValueError(f"{name} should be of length 1, {n} or {n + 2}. "
+#                      f"Received: {name}={value} of length {len_value}")
+
+#   # Add batch and channel dims (always 1).
+#   if channel_index == 1:
+#     return [1, 1] + value
+#   else:
+#     return [1] + value + [1]
+
+# @tf_export("nn.max_pool4d")
+# @dispatch.add_dispatch_support
+# def max_pool4d(input, ksize, strides, padding, data_format="NVDHWC", name=None):
+#     """Performs the max pooling on the input.
+#     Args:
+#         input: A 6-D `Tensor` of the format specified by `data_format`.
+#         ksize: An int or list of `ints` that has length `1`, `3` or `5`. The size of
+#         the window for each dimension of the input tensor.
+#         strides: An int or list of `ints` that has length `1`, `3` or `5`. The
+#           stride of the sliding window for each dimension of the input tensor.
+#         padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See
+#           [here](https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2)
+#           for more information.
+#         data_format: An optional string from: "NVDHWC", "NCVDHW". Defaults to "NVDHWC".
+#           The data format of the input and output data. With the default format
+#           "NVDHWC", the data is stored in the order of: [batch, in_depth, in_height,
+#             in_width, in_channels]. Alternatively, the format could be "NCVDHW", the
+#           data storage order is: [batch, in_channels, in_volumes, in_depth, in_height,
+#             in_width].
+#         name: A name for the operation (optional).
+#     Returns:
+#         A `Tensor` of format specified by `data_format`.
+#         The max pooled output tensor.
+#   """
+#   with ops.name_scope(name, "MaxPool4D", [input]) as name:
+#     if data_format is None:
+#       data_format = "NVDHWC"
+#     channel_index = 1 if data_format.startswith("NC") else 5
+
+#     ksize = _get_sequence(ksize, 3, channel_index, "ksize")
+#     strides = _get_sequence(strides, 3, channel_index, "strides")
+
+#     return gen_nn_ops.max_pool4d(
+#         input,
+#         ksize=ksize,
+#         strides=strides,
+#         padding=padding,
+#         data_format=data_format,
+#         name=name)

nobrainer/models/tests/models_test.py

@@ -13,6 +13,7 @@
 from ..meshnet import meshnet
 from ..progressivegan import progressivegan
 from ..unet import unet
+from ..unet_lstm import unet_lstm
 from ..vnet import vnet
 from ..vox2vox import Vox_ensembler, vox_gan
 
@@ -208,6 +209,16 @@ def test_bayesian_vnet():
     )
 
 
+def test_unet_lstm():
+    input_shape = (1, 32, 32, 32, 32)
+    n_classes = 1
+    x = 10 * np.random.random(input_shape)
+    y = 10 * np.random.random(input_shape)
+    model = unet_lstm(input_shape=(32, 32, 32, 32, 1), n_classes=1)
+    actual_output = model.predict(x)
+    assert actual_output.shape == y.shape[:-1] + (n_classes,)
+
+
 def test_vox2vox():
     input_shape = (1, 32, 32, 32, 1)
     n_classes = 1