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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.vision.caption.core</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from ... import imports as I
from ...torch_base import TorchBase
class ImageCaptioner(TorchBase):
"""
interface to Image Captioner
"""
def __init__(self, model_name="ydshieh/vit-gpt2-coco-en", device=None):
"""
```
ImageCaptioner constructor
Args:
model_name(str): name of image captioning model
device(str): device to use (e.g., 'cuda', 'cpu')
```
"""
if not I.PIL_INSTALLED:
raise Exception(
"PIL is not installed. Please install with: pip install pillow>=9.0.1"
)
super().__init__(
device=device, quantize=False, min_transformers_version="4.12.3"
)
self.model_name = model_name
from transformers import (
AutoTokenizer,
VisionEncoderDecoderModel,
ViTFeatureExtractor,
)
self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name).to(
self.torch_device
)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.extractor = ViTFeatureExtractor.from_pretrained(self.model_name)
def caption(self, images):
"""
```
Performs image captioning
This method supports a single image or a list of images. If the input is an image, the return
type is a string. If text is a list, a list of strings is returned
Args:
images: image|list
Returns:
list of captions
```
"""
# Convert single element to list
values = [images] if not isinstance(images, list) else images
# Open images if file strings
values = [
I.Image.open(image) if isinstance(image, str) else image for image in values
]
# Feature extraction
pixels = self.extractor(images=values, return_tensors="pt").pixel_values
pixels = pixels.to(self.torch_device)
# Run model
import torch
with torch.no_grad():
outputs = self.model.generate(
pixels, max_length=16, num_beams=4, return_dict_in_generate=True
).sequences
# Tokenize outputs into text results
captions = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
captions = [caption.strip() for caption in captions]
# Return single element if single element passed in
return captions[0] if not isinstance(images, list) else captions</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.vision.caption.core.ImageCaptioner"><code class="flex name class">
<span>class <span class="ident">ImageCaptioner</span></span>
<span>(</span><span>model_name='ydshieh/vit-gpt2-coco-en', device=None)</span>
</code></dt>
<dd>
<div class="desc"><p>interface to Image Captioner</p>
<pre><code>ImageCaptioner constructor
Args:
model_name(str): name of image captioning model
device(str): device to use (e.g., 'cuda', 'cpu')
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ImageCaptioner(TorchBase):
"""
interface to Image Captioner
"""
def __init__(self, model_name="ydshieh/vit-gpt2-coco-en", device=None):
"""
```
ImageCaptioner constructor
Args:
model_name(str): name of image captioning model
device(str): device to use (e.g., 'cuda', 'cpu')
```
"""
if not I.PIL_INSTALLED:
raise Exception(
"PIL is not installed. Please install with: pip install pillow>=9.0.1"
)
super().__init__(
device=device, quantize=False, min_transformers_version="4.12.3"
)
self.model_name = model_name
from transformers import (
AutoTokenizer,
VisionEncoderDecoderModel,
ViTFeatureExtractor,
)
self.model = VisionEncoderDecoderModel.from_pretrained(self.model_name).to(
self.torch_device
)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
self.extractor = ViTFeatureExtractor.from_pretrained(self.model_name)
def caption(self, images):
"""
```
Performs image captioning
This method supports a single image or a list of images. If the input is an image, the return
type is a string. If text is a list, a list of strings is returned
Args:
images: image|list
Returns:
list of captions
```
"""
# Convert single element to list
values = [images] if not isinstance(images, list) else images
# Open images if file strings
values = [
I.Image.open(image) if isinstance(image, str) else image for image in values
]
# Feature extraction
pixels = self.extractor(images=values, return_tensors="pt").pixel_values
pixels = pixels.to(self.torch_device)
# Run model
import torch
with torch.no_grad():
outputs = self.model.generate(
pixels, max_length=16, num_beams=4, return_dict_in_generate=True
).sequences
# Tokenize outputs into text results
captions = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
captions = [caption.strip() for caption in captions]
# Return single element if single element passed in
return captions[0] if not isinstance(images, list) else captions</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.torch_base.TorchBase" href="../../torch_base.html#ktrain.torch_base.TorchBase">TorchBase</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.vision.caption.core.ImageCaptioner.caption"><code class="name flex">
<span>def <span class="ident">caption</span></span>(<span>self, images)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Performs image captioning
This method supports a single image or a list of images. If the input is an image, the return
type is a string. If text is a list, a list of strings is returned
Args:
images: image|list
Returns:
list of captions
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def caption(self, images):
"""
```
Performs image captioning
This method supports a single image or a list of images. If the input is an image, the return
type is a string. If text is a list, a list of strings is returned
Args:
images: image|list
Returns:
list of captions
```
"""
# Convert single element to list
values = [images] if not isinstance(images, list) else images
# Open images if file strings
values = [
I.Image.open(image) if isinstance(image, str) else image for image in values
]
# Feature extraction
pixels = self.extractor(images=values, return_tensors="pt").pixel_values
pixels = pixels.to(self.torch_device)
# Run model
import torch
with torch.no_grad():
outputs = self.model.generate(
pixels, max_length=16, num_beams=4, return_dict_in_generate=True
).sequences
# Tokenize outputs into text results
captions = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)
captions = [caption.strip() for caption in captions]
# Return single element if single element passed in
return captions[0] if not isinstance(images, list) else captions</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="ktrain.torch_base.TorchBase" href="../../torch_base.html#ktrain.torch_base.TorchBase">TorchBase</a></b></code>:
<ul class="hlist">
<li><code><a title="ktrain.torch_base.TorchBase.quantize_model" href="../../torch_base.html#ktrain.torch_base.TorchBase.quantize_model">quantize_model</a></code></li>
</ul>
</li>
</ul>
</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.vision.caption" href="index.html">ktrain.vision.caption</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.vision.caption.core.ImageCaptioner" href="#ktrain.vision.caption.core.ImageCaptioner">ImageCaptioner</a></code></h4>
<ul class="">
<li><code><a title="ktrain.vision.caption.core.ImageCaptioner.caption" href="#ktrain.vision.caption.core.ImageCaptioner.caption">caption</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
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