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<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.torch_base</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import warnings
class TorchBase:
"""
Utility methods for working pretrained Torch models
"""
def __init__(self, device, quantize=False, min_transformers_version=None):
if min_transformers_version is not None:
import transformers
from packaging import version
if version.parse(transformers.__version__) < version.parse(
min_transformers_version
):
raise Exception(
f"This feature requires transformers>={min_transformers_version}. "
+ "It is usually safe for you to manually upgrade transformers even if ktrain installed a lower version."
)
try:
import torch
except (ImportError, OSError):
raise Exception(
"This capability in ktrain requires PyTorch to be installed. Please install for your environment: "
+ "https://pytorch.org/get-started/locally/"
)
self.quantize = quantize
self.torch_device = device
if self.torch_device is None:
self.torch_device = "cuda" if torch.cuda.is_available() else "cpu"
def quantize_model(self, model):
"""
quantize a model
"""
import torch
if self.torch_device == "cpu":
return torch.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8
)
elif self.torch_device != "cpu":
return model.half()
def device_to_id(self, device_str=None):
device_str = self.torch_device if device_str is None else device_str
if device_str.lower() == "cpu":
return -1
elif device_str.lower() == "cuda":
return 0
elif device_str.lower().startswith("cuda:"):
_, device_id = device_str.split(":")[1]
device_id = int(device_id)
return device_id
else:
warnings.warn("Could not determine device ID - defaulting to -1")
return -1</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.torch_base.TorchBase"><code class="flex name class">
<span>class <span class="ident">TorchBase</span></span>
<span>(</span><span>device, quantize=False, min_transformers_version=None)</span>
</code></dt>
<dd>
<div class="desc"><p>Utility methods for working pretrained Torch models</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TorchBase:
"""
Utility methods for working pretrained Torch models
"""
def __init__(self, device, quantize=False, min_transformers_version=None):
if min_transformers_version is not None:
import transformers
from packaging import version
if version.parse(transformers.__version__) < version.parse(
min_transformers_version
):
raise Exception(
f"This feature requires transformers>={min_transformers_version}. "
+ "It is usually safe for you to manually upgrade transformers even if ktrain installed a lower version."
)
try:
import torch
except (ImportError, OSError):
raise Exception(
"This capability in ktrain requires PyTorch to be installed. Please install for your environment: "
+ "https://pytorch.org/get-started/locally/"
)
self.quantize = quantize
self.torch_device = device
if self.torch_device is None:
self.torch_device = "cuda" if torch.cuda.is_available() else "cpu"
def quantize_model(self, model):
"""
quantize a model
"""
import torch
if self.torch_device == "cpu":
return torch.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8
)
elif self.torch_device != "cpu":
return model.half()
def device_to_id(self, device_str=None):
device_str = self.torch_device if device_str is None else device_str
if device_str.lower() == "cpu":
return -1
elif device_str.lower() == "cuda":
return 0
elif device_str.lower().startswith("cuda:"):
_, device_id = device_str.split(":")[1]
device_id = int(device_id)
return device_id
else:
warnings.warn("Could not determine device ID - defaulting to -1")
return -1</code></pre>
</details>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="ktrain.text.qa.core.QA" href="text/qa/core.html#ktrain.text.qa.core.QA">QA</a></li>
<li><a title="ktrain.text.speech.core.Transcriber" href="text/speech/core.html#ktrain.text.speech.core.Transcriber">Transcriber</a></li>
<li><a title="ktrain.text.summarization.core.TransformerSummarizer" href="text/summarization/core.html#ktrain.text.summarization.core.TransformerSummarizer">TransformerSummarizer</a></li>
<li><a title="ktrain.text.translation.core.Translator" href="text/translation/core.html#ktrain.text.translation.core.Translator">Translator</a></li>
<li><a title="ktrain.text.zsl.core.ZeroShotClassifier" href="text/zsl/core.html#ktrain.text.zsl.core.ZeroShotClassifier">ZeroShotClassifier</a></li>
<li><a title="ktrain.vision.caption.core.ImageCaptioner" href="vision/caption/core.html#ktrain.vision.caption.core.ImageCaptioner">ImageCaptioner</a></li>
<li><a title="ktrain.vision.object_detection.core.ObjectDetector" href="vision/object_detection/core.html#ktrain.vision.object_detection.core.ObjectDetector">ObjectDetector</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.torch_base.TorchBase.device_to_id"><code class="name flex">
<span>def <span class="ident">device_to_id</span></span>(<span>self, device_str=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def device_to_id(self, device_str=None):
device_str = self.torch_device if device_str is None else device_str
if device_str.lower() == "cpu":
return -1
elif device_str.lower() == "cuda":
return 0
elif device_str.lower().startswith("cuda:"):
_, device_id = device_str.split(":")[1]
device_id = int(device_id)
return device_id
else:
warnings.warn("Could not determine device ID - defaulting to -1")
return -1</code></pre>
</details>
</dd>
<dt id="ktrain.torch_base.TorchBase.quantize_model"><code class="name flex">
<span>def <span class="ident">quantize_model</span></span>(<span>self, model)</span>
</code></dt>
<dd>
<div class="desc"><p>quantize a model</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def quantize_model(self, model):
"""
quantize a model
"""
import torch
if self.torch_device == "cpu":
return torch.quantization.quantize_dynamic(
model, {torch.nn.Linear}, dtype=torch.qint8
)
elif self.torch_device != "cpu":
return model.half()</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain" href="index.html">ktrain</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.torch_base.TorchBase" href="#ktrain.torch_base.TorchBase">TorchBase</a></code></h4>
<ul class="">
<li><code><a title="ktrain.torch_base.TorchBase.device_to_id" href="#ktrain.torch_base.TorchBase.device_to_id">device_to_id</a></code></li>
<li><code><a title="ktrain.torch_base.TorchBase.quantize_model" href="#ktrain.torch_base.TorchBase.quantize_model">quantize_model</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
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