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pytorch2keras

Build Status

Pytorch to Keras model convertor. Still beta for now.

Installation

pip install pytorch2keras

Important notice

In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.

To use the converter properly, please, make changes in your ~/.keras/keras.json:

...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.

Tensorflow.js

For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.

How to build the latest PyTorch

Please, follow this guide to compile the latest version.

How to use

It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.

Firstly, we need to load (or create) pytorch model:

class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:

input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.

from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True)

That's all! If all is ok, the Keras model is stores into the k_model variable.

Supported layers

Layers:

  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d

Reshape:

  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)

Activations:

  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid

Element-wise:

  • Addition
  • Multiplication
  • Subtraction

Misc:

  • reduce sum ( .sum() method)

Unsupported parameters

  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group

Models converted with pytorch2keras

  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet

Usage

Look at the tests directory.

License

This software is covered by MIT License.

About

Pytorch to Keras model convertor

Resources

Readme
MIT license
Activity
Custom properties

Stars

1 star

Watchers

6 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
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1 star
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6 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
pytorch2keras
Build Status
Pytorch to Keras model convertor. Still beta for now.
Installation
pip install pytorch2keras 
Important notice
In that moment the only PyTorch 0.2 (deprecated) and PyTorch 0.4 (latest stable) are supported.
To use the converter properly, please, make changes in your ~/.keras/keras.json:
...
"backend": "tensorflow",
"image_data_format": "channels_first",
...

From the latest releases, multiple inputs is also supported.
Tensorflow.js
For the proper convertion to the tensorflow.js format, please use a new flag short_names=True.
How to build the latest PyTorch
Please, follow this guide to compile the latest version.
How to use
It's a convertor of pytorch graph to a Keras (Tensorflow backend) graph.
Firstly, we need to load (or create) pytorch model:
class TestConv2d(nn.Module):
"""Module for Conv2d convertion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, stride=(inp % 3 + 1), kernel_size=kernel_size, bias=True)
def forward(self, x):
x = self.conv2d(x)
return x
model = TestConv2d()
# load weights here
# model.load_state_dict(torch.load(path_to_weights.pth))

The next step - create a dummy variable with correct shapes:
input_np = np.random.uniform(0, 1, (1, 10, 32, 32))
input_var = Variable(torch.FloatTensor(input_np))

We're using dummy-variable in order to trace the model.
from converter import pytorch_to_keras
# we should specify shape of the input tensor
k_model = pytorch_to_keras(model, input_var, [(10, 32, 32,)], verbose=True) 
That's all! If all is ok, the Keras model is stores into the k_model variable.
Supported layers
Layers:
  • Linear
  • Conv2d
  • Conv3d
  • ConvTranspose2d
  • MaxPool2d
  • MaxPool3d
  • AvgPool2d
  • Global average pooling (as special case of AdaptiveAvgPool2d)
  • Embedding
  • UpsamplingNearest2d
Reshape:
  • View
  • Reshape (only with 0.4)
  • Transpose (only with 0.4)
Activations:
  • ReLU
  • LeakyReLU
  • PReLU (only with 0.2)
  • SELU (only with 0.2)
  • Tanh
  • Softmax
  • Softplus (only with 0.2)
  • Softsign (only with 0.2)
  • Sigmoid
Element-wise:
  • Addition
  • Multiplication
  • Subtraction
Misc:
  • reduce sum ( .sum() method)
Unsupported parameters
  • Pooling: count_include_pad, dilation, ceil_mode
  • Convolution: group
Models converted with pytorch2keras
  • ResNet18
  • ResNet34
  • ResNet50
  • SqueezeNet (with ceil_mode=False)
  • DenseNet
  • AlexNet
  • Inception (v4 only)
  • SeNet
Usage
Look at the tests directory.
License
This software is covered by MIT License.
About
Pytorch to Keras model convertor
Resources
Readme
MIT license
Activity
Custom properties
Stars
1 star
Watchers
6 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages