diff --git a/docs/finn/_build/doctrees/environment.pickle b/docs/finn/_build/doctrees/environment.pickle
index fea633144c18d76692cb8554887fae3ca177c98c..514b0d9424d1f5a1bd10354404412a83a8bba92d 100644
Binary files a/docs/finn/_build/doctrees/environment.pickle and b/docs/finn/_build/doctrees/environment.pickle differ
diff --git a/docs/finn/_build/html/end_to_end_flow.html b/docs/finn/_build/html/end_to_end_flow.html
index c9bdcf888e7eafbba036db71bc768dc6adbaecca..ae7c61e881b1466aa054842d1fc32a4d46275bd6 100644
--- a/docs/finn/_build/html/end_to_end_flow.html
+++ b/docs/finn/_build/html/end_to_end_flow.html
@@ -35,8 +35,8 @@
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
- <link rel="next" title="Getting Started" href="getting_started.html" />
- <link rel="prev" title="FINN" href="index.html" />
+ <link rel="next" title="finn package" href="source_code/finn.html" />
+ <link rel="prev" title="Getting Started" href="getting_started.html" />
</head>
<body class="wy-body-for-nav">
@@ -81,8 +81,8 @@
<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">FINN - End-to-End Flow</a></li>
<li class="toctree-l1"><a class="reference internal" href="getting_started.html">Getting Started</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">FINN - End-to-End Flow</a></li>
<li class="toctree-l1"><a class="reference internal" href="source_code/finn.html">finn package</a></li>
</ul>
@@ -161,10 +161,10 @@
<div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
- <a href="getting_started.html" class="btn btn-neutral float-right" title="Getting Started" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
+ <a href="source_code/finn.html" class="btn btn-neutral float-right" title="finn package" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
- <a href="index.html" class="btn btn-neutral float-left" title="FINN" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
+ <a href="getting_started.html" class="btn btn-neutral float-left" title="Getting Started" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
</div>
diff --git a/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py b/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
index fe07b92a19683c035db6d710c8e58b6796555935..89f0c170ae354700ff23f0c1fd529482bb38f892 100644
--- a/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
+++ b/src/finn/analysis/fpgadataflow/hls_synth_res_estimation.py
@@ -7,6 +7,7 @@ import finn.util.basic as util
def hls_synth_res_estimation(model):
"""Extracts the results from the vivado synthesis.
+
Returns {node name : resource estimation}."""
res_dict = {}
diff --git a/src/finn/analysis/fpgadataflow/res_estimation.py b/src/finn/analysis/fpgadataflow/res_estimation.py
index 27ad91b857316c533b35621cfd51a8262d61f94b..8b9e3df9472f6218edfc4ab3ef2bd6a63fbd1bae 100644
--- a/src/finn/analysis/fpgadataflow/res_estimation.py
+++ b/src/finn/analysis/fpgadataflow/res_estimation.py
@@ -4,6 +4,7 @@ import finn.util.basic as util
def res_estimation(model):
"""Estimates the resources needed for the given model.
+
Returns {node name : resource estimation}."""
res_dict = {}
diff --git a/src/finn/analysis/topology.py b/src/finn/analysis/topology.py
index 8f8d426dc4e1cbbae40c1976605c32f71599ef5f..27cb111f572030b6194f73084790b484ba23a83c 100644
--- a/src/finn/analysis/topology.py
+++ b/src/finn/analysis/topology.py
@@ -4,7 +4,9 @@ import numpy as np
def is_linear(model):
"""Checks whether the given model graph is linear. This is done by looking
at the fan-out of each tensor. All tensors have a fan-out <= 1 in a linear
- graph. Returns {"is_linear": Bool}."""
+ graph.
+
+ Returns {"is_linear": Bool}."""
per_tensor_fanouts = get_per_tensor_fanouts(model)
# check for tensors that have fanout > 1
multi_fanouts = list(filter(lambda x: x[1] > 1, per_tensor_fanouts.items()))
@@ -23,7 +25,9 @@ def get_per_tensor_fanouts(model):
def all_tensors_f32(model):
"""Checks whether all tensors have a float32 dtype, extra quantization
- annotations notwithstanding. Returns {"all_tensors_f32": Bool}."""
+ annotations notwithstanding.
+
+ Returns {"all_tensors_f32": Bool}."""
all_tensors = model.make_empty_exec_context().items()
non_f32_tensors = filter(lambda x: x[1].dtype != np.float32, all_tensors)
return {"all_tensors_f32": len(list(non_f32_tensors)) == 0}
@@ -33,8 +37,9 @@ def node_inputs_in_expected_order(model):
"""Verifies that the node inputs are ordered in the way that FINN expects
them. When a node has a mixture of static (= constant, initialized) inputs
and dynamic inputs, the dynamic input should come first, followed by the
- static one. Only verifiable for a small subset of op_types for now. Returns
- {"node_inputs_in_expected_order": Bool}."""
+ static one. Only verifiable for a small subset of op_types for now.
+
+ Returns {"node_inputs_in_expected_order": Bool}."""
op_types = ["MatMul", "Conv", "Add", "Mul"]
nodes = filter(lambda x: x.op_type in op_types, model.graph.node)
all_OK = True
diff --git a/src/finn/analysis/verify_custom_nodes.py b/src/finn/analysis/verify_custom_nodes.py
index e49a0e77495a2d2daffbfb1eac010e656d183a8f..b7a2573e5e5a66011c4a9aef5d1fea5f95e6662d 100644
--- a/src/finn/analysis/verify_custom_nodes.py
+++ b/src/finn/analysis/verify_custom_nodes.py
@@ -3,7 +3,9 @@ import finn.custom_op.registry as registry
def verify_nodes(model):
"""Checks if custom ops in graph are correctly built, with all attributes
- and inputs. Returns {node op_type : info_messages}
+ and inputs.
+
+ Returns {node op_type : info_messages}
* info_messages: is list of strings about the result of the verification."""
diff --git a/src/finn/core/datatype.py b/src/finn/core/datatype.py
index 15fb661d63262aa6eeaeb82ea63fc036febcb96f..21648caa510d10cd8c06c60315e39e773fac00c2 100644
--- a/src/finn/core/datatype.py
+++ b/src/finn/core/datatype.py
@@ -32,9 +32,16 @@ import numpy as np
class DataType(Enum):
"""Enum class that contains FINN data types to set the quantization annotation.
ONNX does not support data types smaller than 8-bit integers, whereas in FINN we are
- interested in smaller integers down to ternary and bipolar."""
- # important to maintain ordering here: unsigned to signed, fewer to more
- # bits. The get_smallest_possible() member function is dependent on this.
+ interested in smaller integers down to ternary and bipolar.
+
+ Assignment of DataTypes to indices based on following ordering:
+
+ * unsigned to signed
+
+ * fewer to more bits
+
+ Currently supported DataTypes: """
+ # important: the get_smallest_possible() member function is dependent on ordering.
BINARY = auto()
UINT2 = auto()
UINT3 = auto()
@@ -105,7 +112,7 @@ class DataType(Enum):
def allowed(self, value):
"""Check whether given value is allowed for this DataType.
- value (float32): value to be checked"""
+ * value (float32): value to be checked"""
if "FLOAT" in self.name:
return True
@@ -125,7 +132,7 @@ class DataType(Enum):
raise Exception("Unrecognized data type: %s" % self.name)
def get_num_possible_values(self):
- """Return the number of possible values this DataType can take. Only
+ """Returns the number of possible values this DataType can take. Only
implemented for integer types for now."""
assert self.is_integer()
if "INT" in self.name:
@@ -136,7 +143,7 @@ class DataType(Enum):
return 3
def get_smallest_possible(value):
- """Return smallest (fewest bits) possible DataType that can represent
+ """Returns smallest (fewest bits) possible DataType that can represent
value. Prefers unsigned integers where possible."""
if not int(value) == value:
return DataType["FLOAT32"]
@@ -146,16 +153,16 @@ class DataType(Enum):
return dt
def signed(self):
- """Return whether this DataType can represent negative numbers."""
+ """Returns whether this DataType can represent negative numbers."""
return self.min() < 0
def is_integer(self):
- """Return whether this DataType represents integer values only."""
+ """Returns whether this DataType represents integer values only."""
# only FLOAT32 is noninteger for now
return self != DataType.FLOAT32
def get_hls_datatype_str(self):
- """Return the corresponding Vivado HLS datatype name."""
+ """Returns the corresponding Vivado HLS datatype name."""
if self.is_integer():
if self.signed():
return "ap_int<%d>" % self.bitwidth()
diff --git a/src/finn/core/modelwrapper.py b/src/finn/core/modelwrapper.py
index b01c82399adfabb289e484057d22808deac31175..cf5026ebf0fe02f3c658ea00b7e9daa5d2596ece 100644
--- a/src/finn/core/modelwrapper.py
+++ b/src/finn/core/modelwrapper.py
@@ -52,16 +52,17 @@ class ModelWrapper:
self._model_proto = value
def save(self, filename):
- """Save the wrapper ONNX ModelProto into a file with given name."""
+ """Saves the wrapper ONNX ModelProto into a file with given name."""
onnx.save(self._model_proto, filename)
def analysis(self, analysis_fxn):
- """Run given anaylsis_fxn on this model and return resulting dict."""
+ """Runs given anaylsis_fxn on this model and return resulting dict."""
return analysis_fxn(self)
def transform(self, transformation, make_deepcopy=True):
"""Applies given Transformation repeatedly until no more changes can be made
and returns a transformed ModelWrapper instance.
+
If make_deepcopy is specified, operates on a new (deep)copy of model.
"""
transformed_model = self
@@ -76,8 +77,11 @@ class ModelWrapper:
def check_compatibility(self):
"""Checks this model for FINN compatibility:
+
* no embedded subgraphs
+
* all tensor shapes are specified, including activations
+
* all constants are initializers
"""
# TODO check for no embedded subgraphs
@@ -147,7 +151,7 @@ class ModelWrapper:
return None
def set_tensor_shape(self, tensor_name, tensor_shape, dtype=TensorProto.FLOAT):
- """Assign shape in ValueInfoProto for tensor with given name."""
+ """Assigns shape in ValueInfoProto for tensor with given name."""
new_vi = oh.make_tensor_value_info(tensor_name, dtype, tensor_shape)
# find what container tis tensor's ValueInfo lives in
# if not found anywhere, we assume it's a new value_info
@@ -161,7 +165,7 @@ class ModelWrapper:
target_container.append(new_vi)
def set_initializer(self, tensor_name, tensor_value):
- """Set the initializer value for tensor with given name."""
+ """Sets the initializer value for tensor with given name."""
graph = self._model_proto.graph
# convert tensor_value (numpy array) into TensorProto w/ correct name
tensor_init_proto = np_helper.from_array(tensor_value)
@@ -181,7 +185,7 @@ class ModelWrapper:
self.set_tensor_shape(tensor_name, list(tensor_value.shape), dtype)
def rename_tensor(self, old_name, new_name):
- """Rename a tensor from old_name to new_name."""
+ """Renames a tensor from old_name to new_name."""
graph = self.graph
# sweep over inputs
if util.get_by_name(graph.input, old_name) is not None:
@@ -211,7 +215,7 @@ class ModelWrapper:
n.output[list(n.output).index(old_name)] = new_name
def get_initializer(self, tensor_name):
- """Get the initializer value for tensor with given name, if any."""
+ """Gets the initializer value for tensor with given name, if any."""
graph = self._model_proto.graph
init_names = [x.name for x in graph.initializer]
try:
@@ -221,7 +225,7 @@ class ModelWrapper:
return None
def find_producer(self, tensor_name):
- """Find and return the node that produces the tensor with given name.
+ """Finds and returns the node that produces the tensor with given name.
Currently only works for linear graphs."""
all_outputs = [x.output[0] for x in self._model_proto.graph.node]
try:
@@ -231,7 +235,7 @@ class ModelWrapper:
return None
def find_consumer(self, tensor_name):
- """Find and return the node that consumes the tensor with given name.
+ """Finds and returns the node that consumes the tensor with given name.
Currently only works for linear graphs."""
all_inputs = [x.input[0] for x in self._model_proto.graph.node]
try:
@@ -241,7 +245,7 @@ class ModelWrapper:
return None
def get_all_tensor_names(self):
- """Return a list of all (input, output and value_info) tensor names
+ """Returns a list of all (input, output and value_info) tensor names
in the graph."""
graph = self.graph
names = [x.name for x in graph.value_info]
@@ -259,6 +263,7 @@ class ModelWrapper:
def make_empty_exec_context(self):
"""Creates an empty execution context for this model.
+
The execution context is a dictionary of all tensors used for the
inference computation. Any initializer values will be taken into
account, all other tensors will be zero."""
@@ -294,7 +299,7 @@ class ModelWrapper:
return ret
def get_tensor_fanout(self, tensor_name):
- """Return the number of nodes for which the tensor with given name is
+ """Returns the number of nodes for which the tensor with given name is
as input."""
graph = self.graph
fanout = 0
diff --git a/src/finn/core/onnx_exec.py b/src/finn/core/onnx_exec.py
index 5ed45339cc2fe5f77339e33c4e7a8f6c556b704f..ebb5ee6d4470757127972297aec00f2a92fe4023 100644
--- a/src/finn/core/onnx_exec.py
+++ b/src/finn/core/onnx_exec.py
@@ -38,9 +38,10 @@ from finn.custom_op.registry import getCustomOp
def execute_node(node, context, graph):
- """Execute a single node by using onnxruntime, with custom function or
+ """Executes a single node by using onnxruntime, with custom function or
if dataflow partition by using remote execution or rtlsim.
- Input/output provided via context."""
+
+ * Input/output provided via context."""
if node.op_type == "StreamingDataflowPartition":
sdp_node = getCustomOp(node)
@@ -94,10 +95,12 @@ def execute_node(node, context, graph):
def execute_onnx(model, input_dict, return_full_exec_context=False):
- """Execute given ONNX ModelWrapper with given named inputs.
- If return_full_exec_context is False, a dict of named outputs is returned
+ """Executes given ONNX ModelWrapper with given named inputs.
+
+ * If return_full_exec_context is False, a dict of named outputs is returned
as indicated by the model.graph.output.
- If return return_full_exec_context is True, the full set of tensors used by
+
+ * If return return_full_exec_context is True, the full set of tensors used by
the execution (including inputs, weights, activations and final outputs)
will be returned as a dict."""
@@ -164,7 +167,7 @@ def execute_onnx(model, input_dict, return_full_exec_context=False):
def execute_onnx_and_make_model(model, input_dict):
- """Execute given ONNX ModelWrapper with given named inputs and return a new
+ """Executes given ONNX ModelWrapper with given named inputs and return a new
ModelWrapper where an initializer is provided for each tensor as taken from
the execution. This new model is useful for debugging, since it contains
all the intermediate activation values."""
@@ -187,8 +190,9 @@ def compare_execution(
input_dict,
compare_fxn=lambda x, y: np.isclose(x, y, atol=1e-3).all(),
):
- """Execute two ONNX models and compare their outputs using given function.
- compare_fxn should take in two tensors and return a Boolean"""
+ """Executes two ONNX models and compare their outputs using given function.
+
+ * compare_fxn should take in two tensors and return a Boolean"""
# compare values from first output tensors produced
res_a = list(execute_onnx(model_a, input_dict).items())[0][1]
res_b = list(execute_onnx(model_b, input_dict).items())[0][1]