diff --git a/LICENSE b/LICENSE
index 1314424a..1a031b88 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Grigory Malivenko
+Copyright (c) 2020 TensorLeap
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index 03852067..00000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1,3 +0,0 @@
-include LICENSE
-include README.md
-include requirements.txt
\ No newline at end of file
diff --git a/Makefile b/Makefile
new file mode 100644
index 00000000..5707814d
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,31 @@
+PYTHONPATH := .
+POETRY_MODULE := poetry run python -m
+PYTEST := $(POETRY_MODULE) pytest
+
+.PHONY: run_tests
+run_tests:
+	$(PYTEST) test -v
+
+.PHONY: test_models
+test_models:
+	$(PYTEST) test/models -v
+
+.PHONY: watch
+watch:
+	$(POETRY_MODULE) pytest_watch --runner "python -m pytest -v -k $(K)"
+
+.PHONY: lint
+lint:
+	$(POETRY_MODULE) mypy --install-types --non-interactive .
+
+.PHONY: lint_strict_code
+lint_strict_code:
+	$(POETRY_MODULE) mypy --install-types --non-interactive --strict code_loader
+
+.PHONY: lint_tests
+lint_tests:
+	$(POETRY_MODULE) mypy --install-types --non-interactive tests
+
+.PHONY: test_with_coverage
+test_with_coverage:
+	$(PYTEST) --cov=code_loader --cov-branch --no-cov-on-fail --cov-report term-missing --cov-report html -v tests/
diff --git a/onnx2kerastl/activation_layers.py b/onnx2kerastl/activation_layers.py
index 55be903b..9f76850b 100644
--- a/onnx2kerastl/activation_layers.py
+++ b/onnx2kerastl/activation_layers.py
@@ -1,5 +1,7 @@
 from tensorflow import keras
 import logging
+
+from .customonnxlayer.onnxhardsigmoid import OnnxHardSigmoid
 from .utils import ensure_tf_type, ensure_numpy_type
 
 
@@ -176,3 +178,25 @@ def convert_prelu(node, params, layers, lambda_func, node_name, keras_name):
 
     prelu = keras.layers.PReLU(weights=[W], shared_axes=shared_axes, name=keras_name)
     layers[node_name] = prelu(input_0)
+
+
+def convert_hard_sigmoid(node, params, layers, lambda_func, node_name, keras_name):
+    """
+    Convert Hard Sigmoid activation layer
+    :param node: current operation node
+    :param params: operation attributes
+    :param layers: available keras layers
+    :param lambda_func: function for keras Lambda layer
+    :param node_name: internal converter name
+    :param keras_name: resulting layer name
+    :return: None
+    """
+    if len(node.input) != 1:
+        assert AttributeError('More than 1 input for an activation layer.')
+
+    input_0 = ensure_tf_type(layers[node.input[0]], name="%s_const" % keras_name)
+
+    alpha = params.get("alpha", 0.2)
+    beta = params.get("beta", 0.5)
+    onnx_hard_sigmoid = OnnxHardSigmoid(alpha=alpha, beta=beta, name=keras_name)
+    layers[node_name] = onnx_hard_sigmoid(input_0)
diff --git a/onnx2kerastl/converter.py b/onnx2kerastl/converter.py
index 593ac227..81506987 100644
--- a/onnx2kerastl/converter.py
+++ b/onnx2kerastl/converter.py
@@ -8,6 +8,7 @@
 import collections
 from onnx import numpy_helper
 
+from .customonnxlayer import onnx_custom_objects_map
 from .layers import AVAILABLE_CONVERTERS
 
 
@@ -290,7 +291,7 @@ def onnx_to_keras(onnx_model, input_names,
                 layer['config']['function'] = tuple(kerasf)
 
         keras.backend.set_image_data_format('channels_last')
-        model_tf_ordering = keras.models.Model.from_config(conf)
+        model_tf_ordering = keras.models.Model.from_config(conf, custom_objects=onnx_custom_objects_map)
 
         for dst_layer, src_layer, conf in zip(model_tf_ordering.layers, model.layers, conf['layers']):
             W = src_layer.get_weights()
diff --git a/onnx2kerastl/customonnxlayer/__init__.py b/onnx2kerastl/customonnxlayer/__init__.py
new file mode 100644
index 00000000..be07b10d
--- /dev/null
+++ b/onnx2kerastl/customonnxlayer/__init__.py
@@ -0,0 +1,3 @@
+from onnx2kerastl.customonnxlayer.onnxhardsigmoid import OnnxHardSigmoid
+
+onnx_custom_objects_map = {"OnnxHardSigmoid": OnnxHardSigmoid}
diff --git a/onnx2kerastl/customonnxlayer/onnxhardsigmoid.py b/onnx2kerastl/customonnxlayer/onnxhardsigmoid.py
new file mode 100644
index 00000000..67440cf8
--- /dev/null
+++ b/onnx2kerastl/customonnxlayer/onnxhardsigmoid.py
@@ -0,0 +1,23 @@
+from keras.layers import Layer
+import tensorflow as tf
+
+
+class OnnxHardSigmoid(Layer):
+    def __init__(self, alpha: float = 0.2, beta: float = 0.5, **kwargs):
+        super().__init__(**kwargs)
+        self.alpha = alpha
+        self.beta = beta
+
+    def call(self, inputs, **kwargs):
+        x = tf.multiply(inputs, self.alpha)
+        x = tf.add(x, self.beta)
+        x = tf.clip_by_value(x, 0., 1.)
+        return x
+
+    def get_config(self):
+        config = super().get_config()
+        config.update({
+            "alpha": self.alpha,
+            "beta": self.beta,
+        })
+        return config
diff --git a/onnx2kerastl/layers.py b/onnx2kerastl/layers.py
index 336e8255..c9f84c4c 100644
--- a/onnx2kerastl/layers.py
+++ b/onnx2kerastl/layers.py
@@ -1,6 +1,6 @@
 from .convolution_layers import convert_conv, convert_convtranspose
 from .activation_layers import convert_relu, convert_elu, convert_lrelu, convert_selu, \
-    convert_sigmoid, convert_tanh, convert_softmax, convert_prelu
+    convert_sigmoid, convert_tanh, convert_softmax, convert_prelu, convert_hard_sigmoid
 from .operation_layers import convert_clip, convert_exp, convert_reduce_sum, convert_reduce_mean, \
     convert_log, convert_pow, convert_sqrt, convert_split, convert_cast, convert_floor, convert_identity, \
     convert_argmax, convert_reduce_l2, convert_reduce_max
@@ -22,6 +22,7 @@
     'Elu': convert_elu,
     'LeakyRelu': convert_lrelu,
     'Sigmoid': convert_sigmoid,
+    'HardSigmoid': convert_hard_sigmoid,
     'Tanh': convert_tanh,
     'Selu': convert_selu,
     'Clip': convert_clip,
diff --git a/onnx2kerastl/upsampling_layers.py b/onnx2kerastl/upsampling_layers.py
index 586e9405..f0bc2ac1 100644
--- a/onnx2kerastl/upsampling_layers.py
+++ b/onnx2kerastl/upsampling_layers.py
@@ -27,12 +27,17 @@ def convert_upsample(node, params, layers, lambda_func, node_name, keras_name):
         # Upsample since opset version 9 uses input[1] as 'scales' instead of attributes.
         scale = np.uint8(layers[node.input[1]][-2:])
 
-    if params['mode'].decode('utf-8') != 'nearest':
-        logger.error('Cannot convert non-nearest upsampling.')
-        raise AssertionError('Cannot convert non-nearest upsampling')
+    interpolation_mode = params['mode'].decode('utf-8')
+    if interpolation_mode == 'nearest':
+        interpolation = "nearest"
+    elif interpolation_mode in ['bilinear', 'linear']:
+        interpolation = "bilinear"
+    elif interpolation_mode in "cubic":
+        interpolation = "bicubic"
+    else:
+        logger.error(f'Cannot convert upsampling. interpolation mode: {interpolation_mode} is not supported')
+        raise AssertionError(f'Cannot convert upsampling. interpolation mode: {interpolation_mode} is not supported')
 
-    upsampling = keras.layers.UpSampling2D(
-        size=scale, name=keras_name
-    )
+    upsampling = keras.layers.UpSampling2D(size=scale, name=keras_name, interpolation=interpolation)
 
     layers[node_name] = upsampling(layers[node.input[0]])
diff --git a/onnx2kerastl/utils.py b/onnx2kerastl/utils.py
index bc4dca2b..6d0dcd94 100644
--- a/onnx2kerastl/utils.py
+++ b/onnx2kerastl/utils.py
@@ -1,5 +1,6 @@
 import numpy as np
 from tensorflow import keras
+from keras_data_format_converter import convert_channels_first_to_last
 
 
 def is_numpy(obj):
@@ -47,7 +48,8 @@ def target_layer(_, inp=obj, dtype=obj.dtype.name):
         return obj
 
 
-def check_torch_keras_error(model, k_model, input_np, epsilon=1e-5, change_ordering=False):
+def check_torch_keras_error(model, k_model, input_np, epsilon=1e-5, change_ordering=False,
+                            should_transform_inputs=False):
     """
     Check difference between Torch and Keras models
     :param model: torch model
@@ -55,23 +57,24 @@ def check_torch_keras_error(model, k_model, input_np, epsilon=1e-5, change_order
     :param input_np: input data as numpy array or list of numpy array
     :param epsilon: allowed difference
     :param change_ordering: change ordering for keras input
+    :param should_transform_inputs: default False, set to True for converting channel first inputs to  channel last format
     :return: actual difference
+
     """
     from torch.autograd import Variable
     import torch
 
-    initial_keras_image_format = keras.backend.image_data_format()
-
     if isinstance(input_np, np.ndarray):
         input_np = [input_np.astype(np.float32)]
 
-
     input_var = [Variable(torch.FloatTensor(i)) for i in input_np]
     pytorch_output = model(*input_var)
-    if not isinstance(pytorch_output, tuple):
-        pytorch_output = [pytorch_output.data.numpy()]
-    else:
+    if isinstance(pytorch_output, dict):
+        pytorch_output = [p.data.numpy() for p in list(pytorch_output.values())]
+    elif isinstance(pytorch_output, (tuple, list)):
         pytorch_output = [p.data.numpy() for p in pytorch_output]
+    else:
+        pytorch_output = [pytorch_output.data.numpy()]
 
     if change_ordering:
         # change image data format
@@ -101,13 +104,31 @@ def check_torch_keras_error(model, k_model, input_np, epsilon=1e-5, change_order
             _koutput.append(k)
         keras_output = _koutput
     else:
-        keras.backend.set_image_data_format("channels_first")
-        keras_output = k_model.predict(input_np)
+        inputs_to_transpose = []
+        if should_transform_inputs:
+            inputs_to_transpose = [k_input.name for k_input in k_model.inputs]
+
+            _input_np = []
+            for i in input_np:
+                axes = list(range(len(i.shape)))
+                axes = axes[0:1] + axes[2:] + axes[1:2]
+                _input_np.append(np.transpose(i, axes))
+            input_np = _input_np
+
+        k_model = convert_channels_first_to_last(k_model, inputs_to_transpose)
+        keras_output = k_model(*input_np)
         if not isinstance(keras_output, list):
             keras_output = [keras_output]
 
-    # reset to previous image_data_format
-    keras.backend.set_image_data_format(initial_keras_image_format)
+        _koutput = []
+        for i, k in enumerate(keras_output):
+            if k.shape != pytorch_output[i].shape:
+                axes = list(range(len(k.shape)))
+                axes = axes[0:1] + axes[-1:] + axes[1:-1]
+                k = np.transpose(k, axes)
+            _koutput.append(k)
+        keras_output = _koutput
+
 
     max_error = 0
     for p, k in zip(pytorch_output, keras_output):
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 00000000..c1150b1f
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1233 @@
+[[package]]
+name = "absl-py"
+version = "1.0.0"
+description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+description = "An AST unparser for Python"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = ">=1.6.1,<2.0"
+
+[[package]]
+name = "atomicwrites"
+version = "1.4.0"
+description = "Atomic file writes."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "attrs"
+version = "21.4.0"
+description = "Classes Without Boilerplate"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[package.extras]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"]
+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
+
+[[package]]
+name = "cachetools"
+version = "5.1.0"
+description = "Extensible memoizing collections and decorators"
+category = "main"
+optional = false
+python-versions = "~=3.7"
+
+[[package]]
+name = "certifi"
+version = "2022.5.18"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "charset-normalizer"
+version = "2.0.12"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+category = "main"
+optional = false
+python-versions = ">=3.5.0"
+
+[package.extras]
+unicode_backport = ["unicodedata2"]
+
+[[package]]
+name = "colorama"
+version = "0.4.4"
+description = "Cross-platform colored terminal text."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "flatbuffers"
+version = "1.12"
+description = "The FlatBuffers serialization format for Python"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "gast"
+version = "0.4.0"
+description = "Python AST that abstracts the underlying Python version"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "google-auth"
+version = "2.6.6"
+description = "Google Authentication Library"
+category = "main"
+optional = false
+python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*"
+
+[package.dependencies]
+cachetools = ">=2.0.0,<6.0"
+pyasn1-modules = ">=0.2.1"
+rsa = {version = ">=3.1.4,<5", markers = "python_version >= \"3.6\""}
+six = ">=1.9.0"
+
+[package.extras]
+aiohttp = ["requests (>=2.20.0,<3.0.0dev)", "aiohttp (>=3.6.2,<4.0.0dev)"]
+pyopenssl = ["pyopenssl (>=20.0.0)"]
+reauth = ["pyu2f (>=0.1.5)"]
+
+[[package]]
+name = "google-auth-oauthlib"
+version = "0.4.6"
+description = "Google Authentication Library"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+google-auth = ">=1.0.0"
+requests-oauthlib = ">=0.7.0"
+
+[package.extras]
+tool = ["click (>=6.0.0)"]
+
+[[package]]
+name = "google-pasta"
+version = "0.2.0"
+description = "pasta is an AST-based Python refactoring library"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+name = "grpcio"
+version = "1.46.1"
+description = "HTTP/2-based RPC framework"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+six = ">=1.5.2"
+
+[package.extras]
+protobuf = ["grpcio-tools (>=1.46.1)"]
+
+[[package]]
+name = "h5py"
+version = "3.6.0"
+description = "Read and write HDF5 files from Python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = ">=1.14.5"
+
+[[package]]
+name = "idna"
+version = "3.3"
+description = "Internationalized Domain Names in Applications (IDNA)"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "importlib-metadata"
+version = "4.11.3"
+description = "Read metadata from Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
+perf = ["ipython"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
+
+[[package]]
+name = "iniconfig"
+version = "1.1.1"
+description = "iniconfig: brain-dead simple config-ini parsing"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "keras"
+version = "2.9.0"
+description = "Deep learning for humans."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "keras-data-format-converter"
+version = "0.0.8"
+description = "Generates equal keras models with the desired data format"
+category = "main"
+optional = false
+python-versions = ">=3.6.8,<4.0.0"
+
+[package.dependencies]
+tensorflow = ">=2.6,<3.0"
+tensorflow-addons = ">=0.16.1,<0.17.0"
+
+[[package]]
+name = "keras-preprocessing"
+version = "1.1.2"
+description = "Easy data preprocessing and data augmentation for deep learning models"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numpy = ">=1.9.1"
+six = ">=1.9.0"
+
+[package.extras]
+image = ["scipy (>=0.14)", "Pillow (>=5.2.0)"]
+pep8 = ["flake8"]
+tests = ["pandas", "pillow", "tensorflow", "keras", "pytest", "pytest-xdist", "pytest-cov"]
+
+[[package]]
+name = "libclang"
+version = "14.0.1"
+description = "Clang Python Bindings, mirrored from the official LLVM repo: https://github.com/llvm/llvm-project/tree/main/clang/bindings/python, to make the installation process easier."
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "markdown"
+version = "3.3.7"
+description = "Python implementation of Markdown."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
+
+[package.extras]
+testing = ["coverage", "pyyaml"]
+
+[[package]]
+name = "numpy"
+version = "1.22.3"
+description = "NumPy is the fundamental package for array computing with Python."
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[[package]]
+name = "oauthlib"
+version = "3.2.0"
+description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+rsa = ["cryptography (>=3.0.0)"]
+signals = ["blinker (>=1.4.0)"]
+signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
+
+[[package]]
+name = "onnx"
+version = "1.11.0"
+description = "Open Neural Network Exchange"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+numpy = ">=1.16.6"
+protobuf = ">=3.12.2"
+typing-extensions = ">=3.6.2.1"
+
+[package.extras]
+mypy = ["mypy (==0.782)", "types-protobuf (==3.18.4)"]
+
+[[package]]
+name = "opt-einsum"
+version = "3.3.0"
+description = "Optimizing numpys einsum function"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+numpy = ">=1.7"
+
+[package.extras]
+docs = ["sphinx (==1.2.3)", "sphinxcontrib-napoleon", "sphinx-rtd-theme", "numpydoc"]
+tests = ["pytest", "pytest-cov", "pytest-pep8"]
+
+[[package]]
+name = "packaging"
+version = "21.3"
+description = "Core utilities for Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
+
+[[package]]
+name = "pillow"
+version = "9.1.1"
+description = "Python Imaging Library (Fork)"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinx-rtd-theme (>=1.0)", "sphinxext-opengraph"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "pluggy"
+version = "1.0.0"
+description = "plugin and hook calling mechanisms for python"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
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+    {file = "wrapt-1.14.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:6a9a25751acb379b466ff6be78a315e2b439d4c94c1e99cb7266d40a537995d3"},
+    {file = "wrapt-1.14.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:34aa51c45f28ba7f12accd624225e2b1e5a3a45206aa191f6f9aac931d9d56fe"},
+    {file = "wrapt-1.14.1-cp39-cp39-win32.whl", hash = "sha256:dee0ce50c6a2dd9056c20db781e9c1cfd33e77d2d569f5d1d9321c641bb903d5"},
+    {file = "wrapt-1.14.1-cp39-cp39-win_amd64.whl", hash = "sha256:dee60e1de1898bde3b238f18340eec6148986da0455d8ba7848d50470a7a32fb"},
+    {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"},
+]
+zipp = [
+    {file = "zipp-3.8.0-py3-none-any.whl", hash = "sha256:c4f6e5bbf48e74f7a38e7cc5b0480ff42b0ae5178957d564d18932525d5cf099"},
+    {file = "zipp-3.8.0.tar.gz", hash = "sha256:56bf8aadb83c24db6c4b577e13de374ccfb67da2078beba1d037c17980bf43ad"},
+]
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..38eb1714
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,22 @@
+[tool.poetry]
+name = "onnx2kerastl"
+version = "0.0.33"
+description = ""
+authors = ["dorhar <doron.harnoy@tensorleap.ai>"]
+license = "MIT"
+
+[tool.poetry.dependencies]
+python = ">=3.8, <3.11"
+tensorflow = "^2.8"
+tensorflow-addons = "^0.16.1"
+onnx = "^1.11.0"
+keras-data-format-converter = "^0.0.8"
+
+[tool.poetry.dev-dependencies]
+pytest = "^7.1.2"
+torch = "^1.11.0"
+torchvision = "^0.12.0"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index fdd09dae..00000000
--- a/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-tensorflow
-tensorflow-addons
-numpy 
-onnx
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 9d5f7979..00000000
--- a/setup.cfg
+++ /dev/null
@@ -1,3 +0,0 @@
-# Inside of setup.cfg
-[metadata]
-description-file = README.md
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index fd5d817a..00000000
--- a/setup.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from setuptools import setup, find_packages
-
-
-def parse_requirements(filename):
-    """ load requirements from a pip requirements file """
-    lineiter = (line.strip() for line in open(filename))
-    return [line for line in lineiter if line and not line.startswith("#")]
-
-
-reqs = parse_requirements('requirements.txt')
-
-with open('README.md') as f:
-    long_description = f.read()
-
-setup(name='onnx2kerastl',
-      version='0.0.32',
-      description='The deep learning models converter',
-      long_description=long_description,
-      long_description_content_type='text/markdown',
-      url='https://github.com/tensorleap/onnx2keras',
-      author='Grigory Malivenko, Doron Har Noy',
-      author_email='nerox8664@gmail.com, doron.harnoy@tensorleap.ai',
-      classifiers=[
-          'Development Status :: 3 - Alpha',
-          'Intended Audience :: Science/Research',
-          'License :: OSI Approved :: MIT License',
-          'Operating System :: OS Independent',
-          'Programming Language :: Python',
-          'Topic :: Scientific/Engineering :: Image Recognition',
-      ],
-      keywords='machine-learning deep-learning pytorch keras neuralnetwork vgg resnet '
-               'densenet drn dpn darknet squeezenet mobilenet onnx tensorleap',
-      license='MIT',
-      packages=find_packages(),
-      install_requires=reqs,
-      zip_safe=False)
diff --git a/test/layers/activations/test_hard_sigmoid.py b/test/layers/activations/test_hard_sigmoid.py
new file mode 100644
index 00000000..2a04891f
--- /dev/null
+++ b/test/layers/activations/test_hard_sigmoid.py
@@ -0,0 +1,46 @@
+import torch.nn as nn
+import numpy as np
+import pytest
+
+from test.utils import convert_and_test
+
+
+class LayerHardSigmoid(nn.Module):
+    """
+    Test for nn.layers based types
+    """
+    def __init__(self):
+        super(LayerHardSigmoid, self).__init__()
+        self.hard_sig = nn.Hardsigmoid()
+
+    def forward(self, x):
+        x = self.hard_sig(x)
+        return x
+
+
+class FHardSigmoid(nn.Module):
+    """
+    Test for nn.functional types
+    """
+    def __init__(self):
+        super(FHardSigmoid, self).__init__()
+
+    def forward(self, x):
+        from torch.nn import functional as F
+        return F.hardsigmoid(x)
+
+
+@pytest.mark.parametrize('change_ordering', [True, False])
+def test_layer_sigmoid(change_ordering):
+    model = LayerHardSigmoid()
+    model.eval()
+    input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
+    error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
+
+
+@pytest.mark.parametrize('change_ordering', [True, False])
+def test_f_hard_sigmoid(change_ordering):
+    model = FHardSigmoid()
+    model.eval()
+    input_np = np.random.uniform(0, 1, (1, 3, 224, 224))
+    error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering)
\ No newline at end of file
diff --git a/test/models/test_deeplab.py b/test/models/test_deeplab.py
new file mode 100644
index 00000000..b95ac5eb
--- /dev/null
+++ b/test/models/test_deeplab.py
@@ -0,0 +1,17 @@
+import numpy as np
+import pytest
+from torchvision.models.segmentation import deeplabv3_resnet50, deeplabv3_resnet101, deeplabv3_mobilenet_v3_large
+
+from test.utils import convert_and_test
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize('change_ordering', [False])
+@pytest.mark.parametrize('model_class', [deeplabv3_resnet50, deeplabv3_resnet101, deeplabv3_mobilenet_v3_large])
+def test_deeplab(change_ordering, model_class):
+    model = model_class()
+    model.eval()
+
+    input_np = np.random.uniform(0, 1, (1, 3, 256, 256))
+    error = convert_and_test(model, input_np, verbose=False, change_ordering=change_ordering,
+                             should_transform_inputs=True)
diff --git a/test/utils.py b/test/utils.py
index f52a54e9..33f62977 100644
--- a/test/utils.py
+++ b/test/utils.py
@@ -1,6 +1,10 @@
 import io
-import torch
+import warnings
+
 import onnx
+import torch
+from keras.layers import Lambda
+from keras.models import Model
 
 from onnx2kerastl import onnx_to_keras, check_torch_keras_error
 
@@ -26,7 +30,15 @@ def convert_and_test(model: torch.nn.Module,
                      input_variable,
                      verbose=True,
                      change_ordering=False,
-                     epsilon=1e-5):
+                     epsilon=1e-5,
+                     should_transform_inputs=False):
     k_model = torch2keras(model, input_variable, verbose=verbose, change_ordering=change_ordering)
-    error = check_torch_keras_error(model, k_model, input_variable, change_ordering=change_ordering, epsilon=epsilon)
+    error = check_torch_keras_error(model, k_model, input_variable, change_ordering=change_ordering, epsilon=epsilon,
+                                    should_transform_inputs=should_transform_inputs)
+    if is_lambda_layers_exist(k_model):
+        warnings.warn("Found Lambda layers")
     return error
+
+
+def is_lambda_layers_exist(model: Model):
+    return any(isinstance(layer, Lambda) for layer in model.layers)