3D support, Deconvolution layer and installation instruction

.gitignore

@@ -0,0 +1,4 @@
+*~
+*.swp
+.venv
+__pycache__

README.md

@@ -16,12 +16,20 @@ The layer support isn't as complete as in https://github.com/marvis/pytorch-caff
 * softmax (axis)
 * local response norm (local_size, alpha, beta)
 
-Dependencies: protobuf with Python bindings, including `protoc` binary in `PATH`.
 
 PRs to enable other layers or layer params are very welcome (see the definition of the `modules` dictionary in the code)!
 
 License is MIT.
 
+## Installation Instruction
+### Python Environment
+This converter is currently working on python3.8 in Ubuntu. To install, create a virtual environment using ```python3 -m vev .venv```. Activate the environment using ```source .venv/bin/activate```. Lastly install the requirements using ```pip install -r requirements.txt```.
+
+### Protobuf Compiler
+Dependencies: protobuf with Python bindings, including `protoc` binary in `PATH`.
+
+To install protobuf in ubuntu run: ```sudo apt install protobuf-compiler```.
+
 ## Dump weights to PT or HDF5
 ```shell
 # prototxt and caffemodel from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md

blob.py

@@ -0,0 +1,56 @@
+from functools import reduce
+
+
+class Blob(object):
+    AssignmentAdapter = type(
+        "",
+        (object,),
+        dict(
+            shape=property(lambda self: self.contents.shape),
+            __setitem__=lambda self, indices, values: setattr(self, "contents", values),
+        ),
+    )
+
+    def __init__(self, data=None, diff=None, numpy=False):
+        self.data_ = data if data is not None else Blob.AssignmentAdapter()
+        self.diff_ = diff if diff is not None else Blob.AssignmentAdapter()
+        self.shape_ = None
+        self.numpy = numpy
+
+    def reshape(self, *args):
+        self.shape_ = args
+
+    def count(self, *axis):
+        return reduce(lambda x, y: x * y, self.shape_[slice(*(axis + [-1])[:2])])
+
+    @property
+    def data(self):
+        if self.numpy and torch.is_tensor(self.data_):
+            self.data_ = self.data_.detach().cpu().numpy()
+        return self.data_
+
+    @property
+    def diff(self):
+        if self.numpy and torch.is_tensor(self.diff_):
+            self.diff_ = self.diff_.detach().cpu().numpy()
+        return self.diff_
+
+    @property
+    def shape(self):
+        return self.shape_ if self.shape_ is not None else self.data_.shape
+
+    @property
+    def num(self):
+        return self.shape[0]
+
+    @property
+    def channels(self):
+        return self.shape[1]
+
+    @property
+    def height(self):
+        return self.shape[2]
+
+    @property
+    def width(self):
+        return self.shape[3]