semvec/pypi-packages huggingface dataset:
simpleprotocol-BenHack 1.0.0
A package for using a basic protocol socket
Requires Python: >=3.6

# python-simpleprotocol
A small socket wrapper

Used for sending code, subcode, parameters.
Code: Integer
Subcode: Integer
Params: Array of strings


Only has basic use, as shown in this example:


Server:

```

import simpleprotocol

server = simpleprotocol.SimpleServer(port=8080)

while True:
    handler = server.accept()
    handler.send(0)
    print(handler.get())
    handler.send(1)
    print(handler.get())


```

Client:

```

import simpleprotocol

socket = simpleprotocol.SimpleProtocol()

if socket.get().code:
    socket.send(1, 0, ["That sounds good!"])
else:
    socket.send(1, 1, ["Oh dear, that's not right..."])

```
proto-matcher 0.0.3
Test matcher for protobuffer
Requires Python: >=3.7

# proto-matcher

Test matchers for Protobuffer in Python.

This is based on [MessageDifferencer](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.util.message_differencer) as well as the [EqualsProto](https://github.com/google/googletest/issues/1761) googletest matcher.

## API

This packages provides the following [PyHamcrest](https://github.com/hamcrest/PyHamcrest) matchers:

### `equals_proto`

```python
equals_proto(message: Union[Message, str])
```
Test the argument equals the given protobuf message.

### `approximately`

```python
approximately(proto_matcher: Matcher[Message],
              float_margin: Optional[float] = None,
              float_fraction: Optional[float] = None)
```
Test the argument equals the given protobuf message, while comparing any float field using approximation.

### `ignoring_field_paths`

```python
ignoring_field_paths(field_paths: Set[Tuple[str]],
                     matcher: _ProtoMatcher)
```
Test the argument equals the given protobuf message, while ignoring those fields specified in the field paths.


### `ignoring_repeated_field_ordering`

```python
ignoring_repeated_field_ordering(proto_matcher: Matcher[Message])
```
Test the argument equals the given protobuf message, ignoring the ordering of any repeated field.


### `partially`

```python
partially(proto_matcher: Matcher[Message])
```
Test the argument partially equals the given protobuf message, i.e. if a field is in the argument but not in the expected message, it's ignored in the comparsion.
tensorflow-resnets 0.1.0
ResNet variations for TensorFlow.
Requires Python: >=3.6

# TensorFlow-ResNet

This repository contains TensorFlow Keras ResNet models.
Below, you will find the supported variants of ResNet and what weights are supported.   

The codebase takes inspiration from [TensorFlow ResNets](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) 
and [PyTorch ResNets](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py).  

This repository is compatible with TF 2.2.0 even though grouped convolutions are only
supported in TF Nightly.
Grouped convolutions will be imported from `convolutions.py` which is the TF Nightly version of convolution layers.
This allows the use of TPUs since they don't work with TF Nightly.  

These models **will not work without a GPU or TPU** due to the use of grouped convolutions.  

##Models

---

| Architecture      | Weights                | Top-1 Acc. | Top-5 Acc. |  
| ----------------- | :--------------------: | :--------: | :--------: |
| ResNeXt-50 32x4d  | ImageNet               | 77.6       | 93.7       |
| ResNeXt-101 32x8d | ImageNet               | 79.3       | 94.5       |
| Wide ResNet-50 2  | ImageNet               | 78.5       | 94.1       |
| Wide ResNet-101 2 | ImageNet               | 78.8       | 94.3       |
| ResNeXt-50 32x4d  | semi-supervised        | 80.3       | 95.4       |
| ResNeXt-101 32x8d | semi-supervised        | 81.7       | 96.1       |
| ResNeXt-50 32x4d  | semi-weakly supervised | 82.2       | 96.3       |
| ResNeXt-101 32x8d | semi-weakly supervised | 84.3       | 97.2       |

##Preprocessing

---

There are two different preprocessing functions which are meant to be used
according to the task at hand. The first one, located in `imagenet_preprocessing.py`,
is meant to be used for reproducing the ImageNet results. The second function
is located inside `resnet.py`. This function is meant to be used for transfer learning.
The first function includes an additional resize to 256 by 256 and a central crop to 224 by 224.
example-pkg-richard.xiao 0.1.0
ResNet variations for TensorFlow.
Requires Python: >=3.6

# TensorFlow-ResNet

This repository contains TensorFlow Keras ResNet models.
Below, you will find the supported variants of ResNet and what weights are supported.   

The codebase takes inspiration from [TensorFlow ResNets](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) 
and [PyTorch ResNets](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py).  

This repository is compatible with TF 2.2.0 even though grouped convolutions are only
supported in TF Nightly.
Grouped convolutions will be imported from `convolutions.py` which is the TF Nightly version of convolution layers.
This allows the use of TPUs since they don't work with TF Nightly.  

These models **will not work without a GPU or TPU** due to the use of grouped convolutions.  

##Models

---

| Architecture      | Weights                | Top-1 Acc. | Top-5 Acc. |  
| ----------------- | :--------------------: | :--------: | :--------: |
| ResNeXt-50 32x4d  | ImageNet               | 77.6       | 93.7       |
| ResNeXt-101 32x8d | ImageNet               | 79.3       | 94.5       |
| Wide ResNet-50 2  | ImageNet               | 78.5       | 94.1       |
| Wide ResNet-101 2 | ImageNet               | 78.8       | 94.3       |
| ResNeXt-50 32x4d  | semi-supervised        | 80.3       | 95.4       |
| ResNeXt-101 32x8d | semi-supervised        | 81.7       | 96.1       |
| ResNeXt-50 32x4d  | semi-weakly supervised | 82.2       | 96.3       |
| ResNeXt-101 32x8d | semi-weakly supervised | 84.3       | 97.2       |

##Preprocessing

---

There are two different preprocessing functions which are meant to be used
according to the task at hand. The first one, located in `imagenet_preprocessing.py`,
is meant to be used for reproducing the ImageNet results. The second function
is located inside `resnet.py`. This function is meant to be used for transfer learning.
The first function includes an additional resize to 256 by 256 and a central crop to 224 by 224.