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Merge pull request #10 from dx-tan/add_elements

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Add elements
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Đỗ Xuân Tân 2023-12-12 12:26:59 +07:00 committed by GitHub Enterprise
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*.sqlite3 *.sqlite3
*.log *.log
__pycache__ __pycache__
migrations/ # migrations/
test/ test/
._git/ ._git/
sdsvkvu_/ sdsvkvu_/

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# Builds
*.egg-info
__pycache__
# Checkpoint
hub

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under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.

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## Introduction
This repo serve as source code storage for the Standalone YoloX Detection packages.
Installing this package requires 2 additional packages: PyTorch and MMCV.
## Installation
```shell
conda create -n sdsvtd-env python=3.8
conda activate sdsvtd-env
conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
pip install mmcv-full
git clone https://github.com/moewiee/sdsvtd.git
cd sdsvtd
pip install -v -e .
```
## Basic Usage
```python
from sdsvtd import StandaloneYOLOXRunner
runner = StandaloneYOLOXRunner(version='yolox-s-general-text-pretrain-20221226', device='cpu')
```
The `version` parameter accepts version names declared in `sdsvtd.factory.online_model_factory` or a local path such as `$DIR\model.pth`. To check for available versions in the hub, run:
```python
import sdsvtd
print(sdsvtd.__hub_available_versions__)
```
Naturally, a `StandaloneYOLOXRunner` instance assumes the input to be an instance of `np.ndarray` or an instance of `str` (batch inferece is not supported), for examples:
```python
import numpy as np
from sdsvtd import StandaloneYOLOXRunner
runner = StandaloneYOLOXRunner(version='yolox-s-general-text-pretrain-20221226', device='cpu')
dummy_input = np.ndarray(500, 500, 3)
result = runner(dummy_input)
```
**Note:** Output of `StandaloneYOLOXRunner` instance will be in format `List[np.ndarray]` with each list element corresponds to one class. Each `np.ndarray` will be a 5-d vector `[x1 y1 x2 y2 confident]` with coordinates rescaled to fit the original image size.
**AutoRotation:**
From version 0.1.0, `sdsvtd` support *AutoRotation* feature which accept a `np.ndarray/str` as input and return an straight rotated image (only available rotation degrees are 90, 180 and 270) of type `np.ndarray` and its bounding boxes of type `List[np.ndarray]`. Usage:
```python
import numpy as np
from sdsvtd import StandaloneYOLOXRunner
runner = StandaloneYOLOXRunner(version='yolox-s-general-text-pretrain-20221226', device='cpu', auto_rotate=True)
rotated_image, result = runner(cv2.imread('path-to-image')) # or
rotated_image, result = runner(np.ndarray)
```
## Version Changelog
* **[0.0.1]**
Initial version with specified features.
* **[0.0.2]**
Update more versions in model hub.
* **[0.0.3]**
Update feature to specify running device while initialize runner. [Issue](https://github.com/moewiee/sdsvtd/issues/2)
* **[0.0.4]**
Fix a minor bugs when existing hub/local version != current hub/local version.
* **[0.0.5]**
Update model hub with handwritten text line detection version.
* **[0.1.0]**
Update new feature: Auto Rotation.
* **[0.1.1]**
Fix a bug in API inference class where return double result with auto_rotate=False.
* **[0.1.2]**
Fix a bug in rotate feature where rotator_version was ignored.

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torch
mmcv-full

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from .api import StandaloneYOLOXRunner
from .version import __version__
from .factory import __hub_available_versions__

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from .model import SingleStageDetector, AutoRotateDetector
import numpy as np
class StandaloneYOLOXRunner:
def __init__(self,
version,
device,
auto_rotate=False,
rotator_version=None):
self.model = SingleStageDetector(version,
device)
self.auto_rotate = auto_rotate
if self.auto_rotate:
if rotator_version is None:
rotator_version = version
self.rotator = AutoRotateDetector(rotator_version,
device)
self.warmup_()
def warmup_(self):
''' Call on dummpy input to warm up instance '''
x = np.ndarray((1280, 1280, 3)).astype(np.uint8)
self.model(x)
if self.auto_rotate:
self.rotator(x)
def __call__(self, img):
if self.auto_rotate:
img = self.rotator(img)
result = self.model(img)
return result if not self.auto_rotate else (img, result)

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import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from torch.nn.modules.batchnorm import _BatchNorm
class DarknetBottleneck(nn.Module):
"""The basic bottleneck block used in Darknet.
Each ResBlock consists of two ConvModules and the input is added to the
final output. Each ConvModule is composed of Conv, BN, and LeakyReLU.
The first convLayer has filter size of 1x1 and the second one has the
filter size of 3x3.
Args:
in_channels (int): The input channels of this Module.
out_channels (int): The output channels of this Module.
expansion (int): The kernel size of the convolution. Default: 0.5
add_identity (bool): Whether to add identity to the out.
Default: True
use_depthwise (bool): Whether to use depthwise separable convolution.
Default: False
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='Swish').
"""
def __init__(self,
in_channels,
out_channels,
expansion=0.5,
add_identity=True,
use_depthwise=False,
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')):
super().__init__()
hidden_channels = int(out_channels * expansion)
self.conv1 = ConvModule(
in_channels,
hidden_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.conv2 = ConvModule(
hidden_channels,
out_channels,
3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.add_identity = \
add_identity and in_channels == out_channels
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.conv2(out)
if self.add_identity:
return out + identity
else:
return out
class CSPLayer(nn.Module):
"""Cross Stage Partial Layer.
Args:
in_channels (int): The input channels of the CSP layer.
out_channels (int): The output channels of the CSP layer.
expand_ratio (float): Ratio to adjust the number of channels of the
hidden layer. Default: 0.5
num_blocks (int): Number of blocks. Default: 1
add_identity (bool): Whether to add identity in blocks.
Default: True
use_depthwise (bool): Whether to depthwise separable convolution in
blocks. Default: False
conv_cfg (dict, optional): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN')
act_cfg (dict): Config dict for activation layer.
Default: dict(type='Swish')
"""
def __init__(self,
in_channels,
out_channels,
expand_ratio=0.5,
num_blocks=1,
add_identity=True,
use_depthwise=False,
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')):
super().__init__()
mid_channels = int(out_channels * expand_ratio)
self.main_conv = ConvModule(
in_channels,
mid_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.short_conv = ConvModule(
in_channels,
mid_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.final_conv = ConvModule(
2 * mid_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.blocks = nn.Sequential(*[
DarknetBottleneck(
mid_channels,
mid_channels,
1.0,
add_identity,
use_depthwise,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg) for _ in range(num_blocks)
])
def forward(self, x):
x_short = self.short_conv(x)
x_main = self.main_conv(x)
x_main = self.blocks(x_main)
x_final = torch.cat((x_main, x_short), dim=1)
return self.final_conv(x_final)
class Focus(nn.Module):
"""Focus width and height information into channel space.
Args:
in_channels (int): The input channels of this Module.
out_channels (int): The output channels of this Module.
kernel_size (int): The kernel size of the convolution. Default: 1
stride (int): The stride of the convolution. Default: 1
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN', momentum=0.03, eps=0.001).
act_cfg (dict): Config dict for activation layer.
Default: dict(type='Swish').
"""
def __init__(self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')):
super().__init__()
self.conv = ConvModule(
in_channels * 4,
out_channels,
kernel_size,
stride,
padding=(kernel_size - 1) // 2,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
def forward(self, x):
# shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
patch_top_left = x[..., ::2, ::2]
patch_top_right = x[..., ::2, 1::2]
patch_bot_left = x[..., 1::2, ::2]
patch_bot_right = x[..., 1::2, 1::2]
x = torch.cat(
(
patch_top_left,
patch_bot_left,
patch_top_right,
patch_bot_right,
),
dim=1,
)
return self.conv(x)
class SPPBottleneck(nn.Module):
"""Spatial pyramid pooling layer used in YOLOv3-SPP.
Args:
in_channels (int): The input channels of this Module.
out_channels (int): The output channels of this Module.
kernel_sizes (tuple[int]): Sequential of kernel sizes of pooling
layers. Default: (5, 9, 13).
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN').
act_cfg (dict): Config dict for activation layer.
Default: dict(type='Swish').
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(self,
in_channels,
out_channels,
kernel_sizes=(5, 9, 13),
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')):
super().__init__()
mid_channels = in_channels // 2
self.conv1 = ConvModule(
in_channels,
mid_channels,
1,
stride=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.poolings = nn.ModuleList([
nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2)
for ks in kernel_sizes
])
conv2_channels = mid_channels * (len(kernel_sizes) + 1)
self.conv2 = ConvModule(
conv2_channels,
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
def forward(self, x):
x = self.conv1(x)
x = torch.cat([x] + [pooling(x) for pooling in self.poolings], dim=1)
x = self.conv2(x)
return x
class CSPDarknet(nn.Module):
"""CSP-Darknet backbone used in YOLOv5 and YOLOX.
Args:
arch (str): Architecture of CSP-Darknet, from {P5, P6}.
Default: P5.
deepen_factor (float): Depth multiplier, multiply number of
blocks in CSP layer by this amount. Default: 1.0.
widen_factor (float): Width multiplier, multiply number of
channels in each layer by this amount. Default: 1.0.
out_indices (Sequence[int]): Output from which stages.
Default: (2, 3, 4).
frozen_stages (int): Stages to be frozen (stop grad and set eval
mode). -1 means not freezing any parameters. Default: -1.
use_depthwise (bool): Whether to use depthwise separable convolution.
Default: False.
arch_ovewrite(list): Overwrite default arch settings. Default: None.
spp_kernal_sizes: (tuple[int]): Sequential of kernel sizes of SPP
layers. Default: (5, 9, 13).
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Dictionary to construct and config norm layer.
Default: dict(type='BN', requires_grad=True).
act_cfg (dict): Config dict for activation layer.
Default: dict(type='LeakyReLU', negative_slope=0.1).
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
"""
# From left to right:
# in_channels, out_channels, num_blocks, add_identity, use_spp
arch_settings = {
'P5': [[64, 128, 3, True, False], [128, 256, 9, True, False],
[256, 512, 9, True, False], [512, 1024, 3, False, True]],
'P6': [[64, 128, 3, True, False], [128, 256, 9, True, False],
[256, 512, 9, True, False], [512, 768, 3, True, False],
[768, 1024, 3, False, True]]
}
def __init__(self,
arch='P5',
deepen_factor=1.0,
widen_factor=1.0,
out_indices=(2, 3, 4),
frozen_stages=-1,
use_depthwise=False,
arch_ovewrite=None,
spp_kernal_sizes=(5, 9, 13),
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish'),
norm_eval=False):
super().__init__()
arch_setting = self.arch_settings[arch]
if arch_ovewrite:
arch_setting = arch_ovewrite
assert set(out_indices).issubset(
i for i in range(len(arch_setting) + 1))
if frozen_stages not in range(-1, len(arch_setting) + 1):
raise ValueError('frozen_stages must be in range(-1, '
'len(arch_setting) + 1). But received '
f'{frozen_stages}')
self.out_indices = out_indices
self.frozen_stages = frozen_stages
self.use_depthwise = use_depthwise
self.norm_eval = norm_eval
self.stem = Focus(
3,
int(arch_setting[0][0] * widen_factor),
kernel_size=3,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.layers = ['stem']
for i, (in_channels, out_channels, num_blocks, add_identity,
use_spp) in enumerate(arch_setting):
in_channels = int(in_channels * widen_factor)
out_channels = int(out_channels * widen_factor)
num_blocks = max(round(num_blocks * deepen_factor), 1)
stage = []
conv_layer = ConvModule(
in_channels,
out_channels,
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
stage.append(conv_layer)
if use_spp:
spp = SPPBottleneck(
out_channels,
out_channels,
kernel_sizes=spp_kernal_sizes,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
stage.append(spp)
csp_layer = CSPLayer(
out_channels,
out_channels,
num_blocks=num_blocks,
add_identity=add_identity,
use_depthwise=use_depthwise,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
stage.append(csp_layer)
self.add_module(f'stage{i + 1}', nn.Sequential(*stage))
self.layers.append(f'stage{i + 1}')
def _freeze_stages(self):
if self.frozen_stages >= 0:
for i in range(self.frozen_stages + 1):
m = getattr(self, self.layers[i])
m.eval()
for param in m.parameters():
param.requires_grad = False
def train(self, mode=True):
super(CSPDarknet, self).train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval()
def forward(self, x):
outs = []
for i, layer_name in enumerate(self.layers):
layer = getattr(self, layer_name)
x = layer(x)
if i in self.out_indices:
outs.append(x)
return tuple(outs)

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import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops.nms import batched_nms
from mmcv.runner import force_fp32
from functools import partial
from .priors import MlvlPointGenerator
def multi_apply(func, *args, **kwargs):
"""Apply function to a list of arguments.
Note:
This function applies the ``func`` to multiple inputs and
map the multiple outputs of the ``func`` into different
list. Each list contains the same type of outputs corresponding
to different inputs.
Args:
func (Function): A function that will be applied to a list of
arguments
Returns:
tuple(list): A tuple containing multiple list, each list contains \
a kind of returned results by the function
"""
pfunc = partial(func, **kwargs) if kwargs else func
map_results = map(pfunc, *args)
return tuple(map(list, zip(*map_results)))
class YOLOXHead(nn.Module):
"""YOLOXHead head used in `YOLOX <https://arxiv.org/abs/2107.08430>`_.
Args:
num_classes (int): Number of categories excluding the background
category.
in_channels (int): Number of channels in the input feature map.
feat_channels (int): Number of hidden channels in stacking convs.
Default: 256
stacked_convs (int): Number of stacking convs of the head.
Default: 2.
strides (tuple): Downsample factor of each feature map.
use_depthwise (bool): Whether to depthwise separable convolution in
blocks. Default: False
dcn_on_last_conv (bool): If true, use dcn in the last layer of
towers. Default: False.
conv_bias (bool | str): If specified as `auto`, it will be decided by
the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
None, otherwise False. Default: "auto".
conv_cfg (dict): Config dict for convolution layer. Default: None.
norm_cfg (dict): Config dict for normalization layer. Default: None.
act_cfg (dict): Config dict for activation layer. Default: None.
"""
def __init__(self,
num_classes,
in_channels,
feat_channels=256,
stacked_convs=2,
strides=[8, 16, 32],
use_depthwise=False,
dcn_on_last_conv=False,
conv_bias='auto',
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish'),
nms_score_thr=0.3,
nms_iou_threshold=0.1):
super().__init__()
self.nms_config = dict(score_thr=nms_score_thr, nms=dict(iou_threshold=nms_iou_threshold))
self.num_classes = num_classes
self.cls_out_channels = num_classes
self.in_channels = in_channels
self.feat_channels = feat_channels
self.stacked_convs = stacked_convs
self.strides = strides
self.use_depthwise = use_depthwise
self.dcn_on_last_conv = dcn_on_last_conv
assert conv_bias == 'auto' or isinstance(conv_bias, bool)
self.conv_bias = conv_bias
self.use_sigmoid_cls = True
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.prior_generator = MlvlPointGenerator(strides, offset=0)
self.fp16_enabled = False
self._init_layers()
def _init_layers(self):
self.multi_level_cls_convs = nn.ModuleList()
self.multi_level_reg_convs = nn.ModuleList()
self.multi_level_conv_cls = nn.ModuleList()
self.multi_level_conv_reg = nn.ModuleList()
self.multi_level_conv_obj = nn.ModuleList()
for _ in self.strides:
self.multi_level_cls_convs.append(self._build_stacked_convs())
self.multi_level_reg_convs.append(self._build_stacked_convs())
conv_cls, conv_reg, conv_obj = self._build_predictor()
self.multi_level_conv_cls.append(conv_cls)
self.multi_level_conv_reg.append(conv_reg)
self.multi_level_conv_obj.append(conv_obj)
def _build_stacked_convs(self):
"""Initialize conv layers of a single level head."""
conv = ConvModule
stacked_convs = []
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
if self.dcn_on_last_conv and i == self.stacked_convs - 1:
conv_cfg = dict(type='DCNv2')
else:
conv_cfg = self.conv_cfg
stacked_convs.append(
conv(
chn,
self.feat_channels,
3,
stride=1,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=self.norm_cfg,
act_cfg=self.act_cfg,
bias=self.conv_bias))
return nn.Sequential(*stacked_convs)
def _build_predictor(self):
"""Initialize predictor layers of a single level head."""
conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
conv_reg = nn.Conv2d(self.feat_channels, 4, 1)
conv_obj = nn.Conv2d(self.feat_channels, 1, 1)
return conv_cls, conv_reg, conv_obj
def forward_single(self, x, cls_convs, reg_convs, conv_cls, conv_reg,
conv_obj):
"""Forward feature of a single scale level."""
cls_feat = cls_convs(x)
reg_feat = reg_convs(x)
cls_score = conv_cls(cls_feat)
bbox_pred = conv_reg(reg_feat)
objectness = conv_obj(reg_feat)
return cls_score, bbox_pred, objectness
def forward(self, feats):
"""Forward features from the upstream network.
Args:
feats (tuple[Tensor]): Features from the upstream network, each is
a 4D-tensor.
Returns:
tuple[Tensor]: A tuple of multi-level predication map, each is a
4D-tensor of shape (batch_size, 5+num_classes, height, width).
"""
return multi_apply(self.forward_single, feats,
self.multi_level_cls_convs,
self.multi_level_reg_convs,
self.multi_level_conv_cls,
self.multi_level_conv_reg,
self.multi_level_conv_obj)
@force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses'))
def get_bboxes(self,
cls_scores,
bbox_preds,
objectnesses,
cfg=None):
"""Transform network outputs of a batch into bbox results.
Args:
cls_scores (list[Tensor]): Classification scores for all
scale levels, each is a 4D-tensor, has shape
(batch_size, num_priors * num_classes, H, W).
bbox_preds (list[Tensor]): Box energies / deltas for all
scale levels, each is a 4D-tensor, has shape
(batch_size, num_priors * 4, H, W).
objectnesses (list[Tensor], Optional): Score factor for
all scale level, each is a 4D-tensor, has shape
(batch_size, 1, H, W).
cfg (mmcv.Config, Optional): Test / postprocessing configuration,
if None, test_cfg would be used. Default None.
rescale (bool): If True, return boxes in original image space.
Default False.
with_nms (bool): If True, do nms before return boxes.
Default True.
Returns:
list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is an (n, 5) tensor, where the first 4 columns
are bounding box positions (tl_x, tl_y, br_x, br_y) and the
5-th column is a score between 0 and 1. The second item is a
(n,) tensor where each item is the predicted class label of
the corresponding box.
"""
assert len(cls_scores) == len(bbox_preds) == len(objectnesses)
cfg = self.nms_config if cfg is None else cfg
featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
mlvl_priors = self.prior_generator.grid_priors(
featmap_sizes,
dtype=cls_scores[0].dtype,
device=cls_scores[0].device,
with_stride=True)
# flatten cls_scores, bbox_preds and objectness
flatten_cls_scores = [
cls_score.permute(0, 2, 3, 1).reshape(1, -1,
self.cls_out_channels)
for cls_score in cls_scores
]
flatten_bbox_preds = [
bbox_pred.permute(0, 2, 3, 1).reshape(1, -1, 4)
for bbox_pred in bbox_preds
]
flatten_objectness = [
objectness.permute(0, 2, 3, 1).reshape(1, -1)
for objectness in objectnesses
]
flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid()
flatten_priors = torch.cat(mlvl_priors)
flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds)
result_list = []
for img_id in range(1):
cls_scores = flatten_cls_scores[img_id]
score_factor = flatten_objectness[img_id]
bboxes = flatten_bboxes[img_id]
result_list.append(
self._bboxes_nms(cls_scores, bboxes, score_factor, cfg))
return result_list
def _bbox_decode(self, priors, bbox_preds):
xys = (bbox_preds[..., :2] * priors[:, 2:]) + priors[:, :2]
whs = bbox_preds[..., 2:].exp() * priors[:, 2:]
tl_x = (xys[..., 0] - whs[..., 0] / 2)
tl_y = (xys[..., 1] - whs[..., 1] / 2)
br_x = (xys[..., 0] + whs[..., 0] / 2)
br_y = (xys[..., 1] + whs[..., 1] / 2)
decoded_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1)
return decoded_bboxes
def _bboxes_nms(self, cls_scores, bboxes, score_factor, cfg):
max_scores, labels = torch.max(cls_scores, 1)
valid_mask = score_factor * max_scores >= cfg['score_thr']
bboxes = bboxes[valid_mask]
scores = max_scores[valid_mask] * score_factor[valid_mask]
labels = labels[valid_mask]
if labels.numel() == 0:
return bboxes, labels
else:
dets, keep = batched_nms(bboxes.float(), scores.float(), labels, cfg['nms'])
return dets, labels[keep]
def simple_test_bboxes(self, feats):
"""Test det bboxes without test-time augmentation, can be applied in
DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
etc.
Args:
feats (tuple[torch.Tensor]): Multi-level features from the
upstream network, each is a 4D-tensor.
rescale (bool, optional): Whether to rescale the results.
Defaults to False.
Returns:
list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
The first item is ``bboxes`` with shape (n, 5),
where 5 represent (tl_x, tl_y, br_x, br_y, score).
The shape of the second tensor in the tuple is ``labels``
with shape (n,)
"""
outs = self.forward(feats)
results_list = self.get_bboxes(*outs)
return results_list

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import os
import shutil
import hashlib
import warnings
def sha256sum(filename):
h = hashlib.sha256()
b = bytearray(128*1024)
mv = memoryview(b)
with open(filename, 'rb', buffering=0) as f:
for n in iter(lambda : f.readinto(mv), 0):
h.update(mv[:n])
return h.hexdigest()
online_model_factory = {
'yolox-s-general-text-pretrain-20221226': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/62j266xm8r.pth',
'hash': '89bff792685af454d0cfea5d6d673be6914d614e4c2044e786da6eddf36f8b50'},
'yolox-s-checkbox-20220726': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/1647d7eys7.pth',
'hash': '7c1e188b7375dcf0b7b9d317675ebd92a86fdc29363558002249867249ee10f8'},
'yolox-s-idcard-5c-20221027': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/jr0egad3ix.pth',
'hash': '73a7772594c1f6d3f6d6a98b6d6e4097af5026864e3bd50531ad9e635ae795a7'},
'yolox-s-handwritten-text-line-20230228': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/rb07rtwmgi.pth',
'hash': 'a31d1bf8fc880479d2e11463dad0b4081952a13e553a02919109b634a1190ef1'}
}
__hub_available_versions__ = online_model_factory.keys()
def _get_from_hub(file_path, version, version_url):
os.system(f'wget -O {file_path} {version_url}')
assert os.path.exists(file_path), \
'wget failed while trying to retrieve from hub.'
downloaded_hash = sha256sum(file_path)
if downloaded_hash != online_model_factory[version]['hash']:
os.remove(file_path)
raise ValueError('sha256 hash doesnt match for version retrieved from hub.')
def _get(version):
use_online = version in __hub_available_versions__
if not use_online and not os.path.exists(version):
raise ValueError(f'Model version {version} not found online and not found local.')
hub_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'hub')
if not os.path.exists(hub_path):
os.makedirs(hub_path)
if use_online:
version_url = online_model_factory[version]['url']
file_path = os.path.join(hub_path, os.path.basename(version_url))
else:
file_path = os.path.join(hub_path, os.path.basename(version))
if not os.path.exists(file_path):
if use_online:
_get_from_hub(file_path, version, version_url)
else:
shutil.copy2(version, file_path)
else:
if use_online:
downloaded_hash = sha256sum(file_path)
if downloaded_hash != online_model_factory[version]['hash']:
os.remove(file_path)
warnings.warn('existing hub version sha256 hash doesnt match, now re-download from hub.')
_get_from_hub(file_path, version, version_url)
else:
if sha256sum(file_path) != sha256sum(version):
os.remove(file_path)
warnings.warn('existing local version sha256 hash doesnt match, now replace with new local version.')
shutil.copy2(version, file_path)
return file_path

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import torch
import torch.nn as nn
import numpy as np
from .backbone import CSPDarknet
from .neck import YOLOXPAFPN
from .bbox_head import YOLOXHead
from .transform import DetectorDataPipeline, AutoRotateDetectorDataPipeline
from .factory import _get as get_version
def bbox2result(bboxes, labels, num_classes):
"""Convert detection results to a list of numpy arrays.
Args:
bboxes (torch.Tensor | np.ndarray): shape (n, 5)
labels (torch.Tensor | np.ndarray): shape (n, )
num_classes (int): class number, including background class
Returns:
list(ndarray): bbox results of each class
"""
if bboxes.shape[0] == 0:
return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)]
else:
if isinstance(bboxes, torch.Tensor):
bboxes = bboxes.detach().cpu().numpy()
labels = labels.detach().cpu().numpy()
return [bboxes[labels == i, :] for i in range(num_classes)]
def normalize_bbox(bboxes, scale):
for i in range(len(bboxes)):
bboxes[i][:,:4] /= scale
return bboxes
class SingleStageDetector(nn.Module):
def __init__(self,
version,
device):
super(SingleStageDetector, self).__init__()
assert 'cpu' in device or 'cuda' in device
checkpoint = get_version(version)
pt = torch.load(checkpoint, 'cpu')
self.pipeline = DetectorDataPipeline(**pt['pipeline_args'], device=device)
self.backbone = CSPDarknet(**pt['backbone_args'])
self.neck = YOLOXPAFPN(**pt['neck_args'])
self.bbox_head = YOLOXHead(**pt['bbox_head_args'])
self.load_state_dict(pt['state_dict'], strict=True)
self.eval()
for param in self.parameters():
param.requires_grad = False
self = self.to(device=device)
print(f'Text detection load from version {version}.')
def extract_feat(self, img):
"""Directly extract features from the backbone + neck."""
x = self.backbone(img)
x = self.neck(x)
return x
def forward(self, img):
"""Test function without test-time augmentation.
Args:
img (np.ndarray): Images with shape (H, W, C) or
img (str): Path to image.
Returns:
list[list[np.ndarray]]: BBox results of each image and classes.
The list corresponds to each class.
"""
img, origin_shape, new_shape = self.pipeline(img)
scale = min(new_shape / origin_shape)
feat = self.extract_feat(img)
results_list = self.bbox_head.simple_test_bboxes(feat)
bbox_results = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
for det_bboxes, det_labels in results_list
][0]
bbox_results = normalize_bbox(bbox_results, scale)
return bbox_results
class AutoRotateDetector(nn.Module):
def __init__(self,
version,
device):
super(AutoRotateDetector, self).__init__()
assert 'cpu' in device or 'cuda' in device
checkpoint = get_version(version)
pt = torch.load(checkpoint, 'cpu')
self.pipeline = AutoRotateDetectorDataPipeline(**pt['pipeline_args'], device=device)
self.backbone = CSPDarknet(**pt['backbone_args'])
self.neck = YOLOXPAFPN(**pt['neck_args'])
self.bbox_head = YOLOXHead(**pt['bbox_head_args'], nms_score_thr=0.8)
self.load_state_dict(pt['state_dict'], strict=True)
self.eval()
for param in self.parameters():
param.requires_grad = False
self = self.to(device=device)
print(f'Auto rotate detector load from version {version}.')
def extract_feat(self, img):
"""Directly extract features from the backbone + neck."""
x = self.backbone(img)
x = self.neck(x)
return x
def forward(self, img):
"""Test function without test-time augmentation.
Args:
img (np.ndarray): Images with shape (H, W, C) or
img (str): Path to image.
Returns:
np.ndarray: Straight rotated image.
"""
imgs, imgs_np = self.pipeline(img)
maxCount = -1
maxCountRot = None
for idx, img in enumerate(imgs):
currentCount = 0
feat = self.extract_feat(img)
results_list = self.bbox_head.simple_test_bboxes(feat)
bbox_results = [
bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
for det_bboxes, det_labels in results_list
][0]
for class_result in bbox_results:
currentCount += len(class_result)
if currentCount > maxCount:
maxCount = currentCount
maxCountRot = idx
return imgs_np[maxCountRot]

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import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from .backbone import CSPLayer
class YOLOXPAFPN(nn.Module):
"""Path Aggregation Network used in YOLOX.
Args:
in_channels (List[int]): Number of input channels per scale.
out_channels (int): Number of output channels (used at each scale)
num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 3
use_depthwise (bool): Whether to depthwise separable convolution in
blocks. Default: False
upsample_cfg (dict): Config dict for interpolate layer.
Default: `dict(scale_factor=2, mode='nearest')`
conv_cfg (dict, optional): Config dict for convolution layer.
Default: None, which means using conv2d.
norm_cfg (dict): Config dict for normalization layer.
Default: dict(type='BN')
act_cfg (dict): Config dict for activation layer.
Default: dict(type='Swish')
init_cfg (dict or list[dict], optional): Initialization config dict.
Default: None.
"""
def __init__(self,
in_channels,
out_channels,
num_csp_blocks=3,
use_depthwise=False,
upsample_cfg=dict(scale_factor=2, mode='nearest'),
conv_cfg=None,
norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
act_cfg=dict(type='Swish')):
super(YOLOXPAFPN, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
# build top-down blocks
self.upsample = nn.Upsample(**upsample_cfg)
self.reduce_layers = nn.ModuleList()
self.top_down_blocks = nn.ModuleList()
for idx in range(len(in_channels) - 1, 0, -1):
self.reduce_layers.append(
ConvModule(
in_channels[idx],
in_channels[idx - 1],
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.top_down_blocks.append(
CSPLayer(
in_channels[idx - 1] * 2,
in_channels[idx - 1],
num_blocks=num_csp_blocks,
add_identity=False,
use_depthwise=use_depthwise,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
# build bottom-up blocks
self.downsamples = nn.ModuleList()
self.bottom_up_blocks = nn.ModuleList()
for idx in range(len(in_channels) - 1):
self.downsamples.append(
ConvModule(
in_channels[idx],
in_channels[idx],
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.bottom_up_blocks.append(
CSPLayer(
in_channels[idx] * 2,
in_channels[idx + 1],
num_blocks=num_csp_blocks,
add_identity=False,
use_depthwise=use_depthwise,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
self.out_convs = nn.ModuleList()
for i in range(len(in_channels)):
self.out_convs.append(
ConvModule(
in_channels[i],
out_channels,
1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg))
def forward(self, inputs):
"""
Args:
inputs (tuple[Tensor]): input features.
Returns:
tuple[Tensor]: YOLOXPAFPN features.
"""
assert len(inputs) == len(self.in_channels)
# top-down path
inner_outs = [inputs[-1]]
for idx in range(len(self.in_channels) - 1, 0, -1):
feat_heigh = inner_outs[0]
feat_low = inputs[idx - 1]
feat_heigh = self.reduce_layers[len(self.in_channels) - 1 - idx](
feat_heigh)
inner_outs[0] = feat_heigh
upsample_feat = self.upsample(feat_heigh)
inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
torch.cat([upsample_feat, feat_low], 1))
inner_outs.insert(0, inner_out)
# bottom-up path
outs = [inner_outs[0]]
for idx in range(len(self.in_channels) - 1):
feat_low = outs[-1]
feat_height = inner_outs[idx + 1]
downsample_feat = self.downsamples[idx](feat_low)
out = self.bottom_up_blocks[idx](
torch.cat([downsample_feat, feat_height], 1))
outs.append(out)
# out convs
for idx, conv in enumerate(self.out_convs):
outs[idx] = conv(outs[idx])
return tuple(outs)

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import torch
import numpy as np
from torch.nn.modules.utils import _pair
class MlvlPointGenerator:
"""Standard points generator for multi-level (Mlvl) feature maps in 2D
points-based detectors.
Args:
strides (list[int] | list[tuple[int, int]]): Strides of anchors
in multiple feature levels in order (w, h).
offset (float): The offset of points, the value is normalized with
corresponding stride. Defaults to 0.5.
"""
def __init__(self, strides, offset=0.5):
self.strides = [_pair(stride) for stride in strides]
self.offset = offset
@property
def num_levels(self):
"""int: number of feature levels that the generator will be applied"""
return len(self.strides)
@property
def num_base_priors(self):
"""list[int]: The number of priors (points) at a point
on the feature grid"""
return [1 for _ in range(len(self.strides))]
def _meshgrid(self, x, y, row_major=True):
yy, xx = torch.meshgrid(y, x)
if row_major:
# warning .flatten() would cause error in ONNX exporting
# have to use reshape here
return xx.flatten(), yy.flatten()
else:
return yy.flatten(), xx.flatten()
def grid_priors(self,
featmap_sizes,
dtype=torch.float32,
device='cuda',
with_stride=False):
"""Generate grid points of multiple feature levels.
Args:
featmap_sizes (list[tuple]): List of feature map sizes in
multiple feature levels, each size arrange as
as (h, w).
dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32.
device (str): The device where the anchors will be put on.
with_stride (bool): Whether to concatenate the stride to
the last dimension of points.
Return:
list[torch.Tensor]: Points of multiple feature levels.
The sizes of each tensor should be (N, 2) when with stride is
``False``, where N = width * height, width and height
are the sizes of the corresponding feature level,
and the last dimension 2 represent (coord_x, coord_y),
otherwise the shape should be (N, 4),
and the last dimension 4 represent
(coord_x, coord_y, stride_w, stride_h).
"""
assert self.num_levels == len(featmap_sizes)
multi_level_priors = []
for i in range(self.num_levels):
priors = self.single_level_grid_priors(
featmap_sizes[i],
level_idx=i,
dtype=dtype,
device=device,
with_stride=with_stride)
multi_level_priors.append(priors)
return multi_level_priors
def single_level_grid_priors(self,
featmap_size,
level_idx,
dtype=torch.float32,
device='cuda',
with_stride=False):
"""Generate grid Points of a single level.
Note:
This function is usually called by method ``self.grid_priors``.
Args:
featmap_size (tuple[int]): Size of the feature maps, arrange as
(h, w).
level_idx (int): The index of corresponding feature map level.
dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32.
device (str, optional): The device the tensor will be put on.
Defaults to 'cuda'.
with_stride (bool): Concatenate the stride to the last dimension
of points.
Return:
Tensor: Points of single feature levels.
The shape of tensor should be (N, 2) when with stride is
``False``, where N = width * height, width and height
are the sizes of the corresponding feature level,
and the last dimension 2 represent (coord_x, coord_y),
otherwise the shape should be (N, 4),
and the last dimension 4 represent
(coord_x, coord_y, stride_w, stride_h).
"""
feat_h, feat_w = featmap_size
stride_w, stride_h = self.strides[level_idx]
shift_x = (torch.arange(0, feat_w, device=device) +
self.offset) * stride_w
# keep featmap_size as Tensor instead of int, so that we
# can convert to ONNX correctly
shift_x = shift_x.to(dtype)
shift_y = (torch.arange(0, feat_h, device=device) +
self.offset) * stride_h
# keep featmap_size as Tensor instead of int, so that we
# can convert to ONNX correctly
shift_y = shift_y.to(dtype)
shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
if not with_stride:
shifts = torch.stack([shift_xx, shift_yy], dim=-1)
else:
# use `shape[0]` instead of `len(shift_xx)` for ONNX export
stride_w = shift_xx.new_full((shift_xx.shape[0], ),
stride_w).to(dtype)
stride_h = shift_xx.new_full((shift_yy.shape[0], ),
stride_h).to(dtype)
shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h],
dim=-1)
all_points = shifts.to(device)
return all_points
def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
"""Generate valid flags of points of multiple feature levels.
Args:
featmap_sizes (list(tuple)): List of feature map sizes in
multiple feature levels, each size arrange as
as (h, w).
pad_shape (tuple(int)): The padded shape of the image,
arrange as (h, w).
device (str): The device where the anchors will be put on.
Return:
list(torch.Tensor): Valid flags of points of multiple levels.
"""
assert self.num_levels == len(featmap_sizes)
multi_level_flags = []
for i in range(self.num_levels):
point_stride = self.strides[i]
feat_h, feat_w = featmap_sizes[i]
h, w = pad_shape[:2]
valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h)
valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w)
flags = self.single_level_valid_flags((feat_h, feat_w),
(valid_feat_h, valid_feat_w),
device=device)
multi_level_flags.append(flags)
return multi_level_flags
def single_level_valid_flags(self,
featmap_size,
valid_size,
device='cuda'):
"""Generate the valid flags of points of a single feature map.
Args:
featmap_size (tuple[int]): The size of feature maps, arrange as
as (h, w).
valid_size (tuple[int]): The valid size of the feature maps.
The size arrange as as (h, w).
device (str, optional): The device where the flags will be put on.
Defaults to 'cuda'.
Returns:
torch.Tensor: The valid flags of each points in a single level \
feature map.
"""
feat_h, feat_w = featmap_size
valid_h, valid_w = valid_size
assert valid_h <= feat_h and valid_w <= feat_w
valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
valid_x[:valid_w] = 1
valid_y[:valid_h] = 1
valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
valid = valid_xx & valid_yy
return valid
def sparse_priors(self,
prior_idxs,
featmap_size,
level_idx,
dtype=torch.float32,
device='cuda'):
"""Generate sparse points according to the ``prior_idxs``.
Args:
prior_idxs (Tensor): The index of corresponding anchors
in the feature map.
featmap_size (tuple[int]): feature map size arrange as (w, h).
level_idx (int): The level index of corresponding feature
map.
dtype (obj:`torch.dtype`): Date type of points. Defaults to
``torch.float32``.
device (obj:`torch.device`): The device where the points is
located.
Returns:
Tensor: Anchor with shape (N, 2), N should be equal to
the length of ``prior_idxs``. And last dimension
2 represent (coord_x, coord_y).
"""
height, width = featmap_size
x = (prior_idxs % width + self.offset) * self.strides[level_idx][0]
y = ((prior_idxs // width) % height +
self.offset) * self.strides[level_idx][1]
prioris = torch.stack([x, y], 1).to(dtype)
prioris = prioris.to(device)
return prioris

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import mmcv
import numpy as np
import cv2
import torch
class DetectorDataPipeline:
def __init__(self,
img_scale,
device):
self.scale = img_scale
self.device = device
def load(self, img):
if isinstance(img, str):
return cv2.imread(img)
elif isinstance(img, np.ndarray):
return img
else:
raise ValueError(f'img input must be a str/np.ndarray, got {type(img)}')
def resize(self, img):
origin_shape = img.shape[:2]
img = mmcv.imrescale(img,
self.scale,
return_scale=False,
interpolation='bilinear',
backend='cv2')
return img, origin_shape, np.array(self.scale)
def pad(self, img):
if self.scale is not None:
width = max(self.scale[1] - img.shape[1], 0)
height = max(self.scale[0] - img.shape[0], 0)
padding = (0, 0, width, height)
img = cv2.copyMakeBorder(img,
padding[1],
padding[3],
padding[0],
padding[2],
cv2.BORDER_CONSTANT,
value=(114, 114, 114,))
return img
def to_tensor(self, img):
img = torch.from_numpy(img.astype(np.float32)).permute(2,0,1).unsqueeze(0)
img = img.to(device=self.device)
return img
def __call__(self, img):
img = self.load(img)
img, origin_shape, new_shape = self.resize(img)
img = self.pad(img)
img = self.to_tensor(img)
return img, origin_shape, new_shape
class AutoRotateDetectorDataPipeline(DetectorDataPipeline):
def __call__(self, img):
img = self.load(img)
imgs = []
imgs_np = []
for flag in (None, cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_180, cv2.ROTATE_90_COUNTERCLOCKWISE):
img_t = img if flag is None else cv2.rotate(img, flag)
imgs_np.append(img_t)
img_t, _, _ = self.resize(img_t)
img_t = self.pad(img_t)
img_t = self.to_tensor(img_t)
imgs.append(img_t)
return imgs, imgs_np

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__version__ = '0.1.2'

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import os
import os.path as osp
import shutil
import sys
import warnings
from setuptools import find_packages, setup
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
version_file = 'sdsvtd/version.py'
is_windows = sys.platform == 'win32'
def add_mim_extention():
"""Add extra files that are required to support MIM into the package.
These files will be added by creating a symlink to the originals if the
package is installed in `editable` mode (e.g. pip install -e .), or by
copying from the originals otherwise.
"""
# parse installment mode
if 'develop' in sys.argv:
# installed by `pip install -e .`
mode = 'symlink'
elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
# installed by `pip install .`
# or create source distribution by `python setup.py sdist`
mode = 'copy'
else:
return
filenames = ['tools', 'configs', 'model-index.yml']
repo_path = osp.dirname(__file__)
mim_path = osp.join(repo_path, 'mmocr', '.mim')
os.makedirs(mim_path, exist_ok=True)
for filename in filenames:
if osp.exists(filename):
src_path = osp.join(repo_path, filename)
tar_path = osp.join(mim_path, filename)
if osp.isfile(tar_path) or osp.islink(tar_path):
os.remove(tar_path)
elif osp.isdir(tar_path):
shutil.rmtree(tar_path)
if mode == 'symlink':
src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
try:
os.symlink(src_relpath, tar_path)
except OSError:
# Creating a symbolic link on windows may raise an
# `OSError: [WinError 1314]` due to privilege. If
# the error happens, the src file will be copied
mode = 'copy'
warnings.warn(
f'Failed to create a symbolic link for {src_relpath}, '
f'and it will be copied to {tar_path}')
else:
continue
if mode == 'copy':
if osp.isfile(src_path):
shutil.copyfile(src_path, tar_path)
elif osp.isdir(src_path):
shutil.copytree(src_path, tar_path)
else:
warnings.warn(f'Cannot copy file {src_path}.')
else:
raise ValueError(f'Invalid mode {mode}')
def get_version():
with open(version_file, 'r') as f:
exec(compile(f.read(), version_file, 'exec'))
import sys
# return short version for sdist
if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
return locals()['short_version']
else:
return locals()['__version__']
def parse_requirements(fname='requirements.txt', with_version=True):
"""Parse the package dependencies listed in a requirements file but strip
specific version information.
Args:
fname (str): Path to requirements file.
with_version (bool, default=False): If True, include version specs.
Returns:
info (list[str]): List of requirements items.
CommandLine:
python -c "import setup; print(setup.parse_requirements())"
"""
import re
import sys
from os.path import exists
require_fpath = fname
def parse_line(line):
"""Parse information from a line in a requirements text file."""
if line.startswith('-r '):
# Allow specifying requirements in other files
target = line.split(' ')[1]
for info in parse_require_file(target):
yield info
else:
info = {'line': line}
if line.startswith('-e '):
info['package'] = line.split('#egg=')[1]
else:
# Remove versioning from the package
pat = '(' + '|'.join(['>=', '==', '>']) + ')'
parts = re.split(pat, line, maxsplit=1)
parts = [p.strip() for p in parts]
info['package'] = parts[0]
if len(parts) > 1:
op, rest = parts[1:]
if ';' in rest:
# Handle platform specific dependencies
# http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
version, platform_deps = map(str.strip,
rest.split(';'))
info['platform_deps'] = platform_deps
else:
version = rest # NOQA
info['version'] = (op, version)
yield info
def parse_require_file(fpath):
with open(fpath, 'r') as f:
for line in f.readlines():
line = line.strip()
if line and not line.startswith('#'):
for info in parse_line(line):
yield info
def gen_packages_items():
if exists(require_fpath):
for info in parse_require_file(require_fpath):
parts = [info['package']]
if with_version and 'version' in info:
parts.extend(info['version'])
if not sys.version.startswith('3.4'):
# apparently package_deps are broken in 3.4
platform_deps = info.get('platform_deps')
if platform_deps is not None:
parts.append(';' + platform_deps)
item = ''.join(parts)
yield item
packages = list(gen_packages_items())
return packages
if __name__ == '__main__':
setup(
name='sdsvtd',
version=get_version(),
description='SDSV OCR Team Text Detection',
long_description=readme(),
long_description_content_type='text/markdown',
packages=find_packages(exclude=('configs', 'tools', 'demo')),
include_package_data=True,
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
license='Apache License 2.0',
install_requires=parse_requirements('requirements.txt'),
zip_safe=False)

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# Builds
*.egg-info
__pycache__
# Checkpoint
hub

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GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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Everyone is permitted to copy and distribute verbatim copies
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Nothing in this License shall be construed as excluding or limiting
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Also add information on how to contact you by electronic and paper mail.
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This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
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The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
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## Introduction
This repo serve as source code storage for the Standalone SATRN Text Recognizer packages.
Installing this package requires 3 additional packages: PyTorch, MMCV, and colorama.
## Installation
```shell
conda create -n sdsvtr-env python=3.8
conda activate sdsvtr-env
conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
pip install -U openmim
mim install mmcv-full
pip install colorama
git clone https://github.com/moewiee/sdsvtr.git
cd sdsvtr
pip install -v -e .
```
## Basic Usage
```python
from sdsvtr import StandaloneSATRNRunner
runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, use_cuda=False)
```
The `version` parameter accepts version names declared in `sdsvtr.factory.online_model_factory` or a local path such as `$DIR\model.pth`. To check for available versions in the hub, run:
```python
import sdsvtr
print(sdsvtr.__hub_available_versions__)
```
Naturally, a `StandaloneSATRNRunner` instance assumes the input to be one of the following: an instance of `np.ndarray`, an instance of `str`, a list of `np.ndarray`, or a list of `str`, for examples:
```python
import numpy as np
from sdsvtr import StandaloneSATRNRunner
runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, use_cuda=False)
dummy_list = [np.ndarray((32,128,3)) for _ in range(100)]
result = runner(dummy_list)
```
To run with a specific batchsize, try:
```python
import numpy as np
from sdsvtr import StandaloneSATRNRunner
runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cuda:0')
dummy_list = [np.ndarray(1,3,32,128) for _ in range(100)]
bs = min(32, len(imageFiles)) # batchsize = 32
all_results = []
while len(dummy_list) > 0:
dummy_batch = dummy_list[:bs]
dummy_list = dummy_list[bs:]
all_results += runner(dummy_batch)
```
## Version Changelog
* **[0.0.1]**
Initial version with specified features.
* **[0.0.2]**
Update online model hub.
* **[0.0.3]**
Update API now able to inference with 4 types of inputs: list/instance of `np.ndarray`/`str`
Update API interface with `return_confident` parameter.
Update `wget` check and `sha256` check for model hub retrieval.
* **[0.0.4]**
Update decoder module with EarlyStopping mechanism to possibly improve inference speed on short sequences.
Update API interface with optional argument `max_seq_len_overwrite` to overwrite checkpoint's `max_seq_len` config.
* **[0.0.5]**
Allow inference on a specific device

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torch
colorama
mmcv-full

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from .api import StandaloneSATRNRunner
from .version import __version__
from .factory import __hub_available_versions__

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import torch
import torch.nn as nn
from colorama import Fore, Style
from .converter import AttnConvertor
from .backbone import ResNetABI
from .encoder import SatrnEncoder
from .decoder import NRTRDecoder
from .transform import DataPipelineSATRN
from .fp16_utils import patch_norm_fp32
from .factory import _get as get_version
class SATRN(nn.Module):
"""Standalone implementation for SATRN encode-decode recognizer."""
def __init__(self,
version,
return_confident=False,
device='cpu',
max_seq_len_overwrite=None):
super().__init__()
checkpoint = get_version(version)
pt = torch.load(checkpoint, 'cpu')
if device == 'cpu':
print(Fore.RED + 'Warning: You are using CPU inference method. Init with device=cuda:<id> to run with CUDA method.' + Style.RESET_ALL)
self.pipeline = DataPipelineSATRN(**pt['pipeline_args'], device=device)
# Convertor
self.label_convertor = AttnConvertor(**pt['label_convertor_args'], return_confident=return_confident)
# Backbone
self.backbone = ResNetABI(**pt['backbone_args'])
# Encoder module
self.encoder = SatrnEncoder(**pt['encoder_args'])
# Decoder module
decoder_max_seq_len = max_seq_len_overwrite if max_seq_len_overwrite is not None else pt['max_seq_len']
self.decoder = NRTRDecoder(
**pt['decoder_args'],
max_seq_len=decoder_max_seq_len,
num_classes=self.label_convertor.num_classes(),
start_idx=self.label_convertor.start_idx,
padding_idx=self.label_convertor.padding_idx,
return_confident=return_confident,
end_idx=self.label_convertor.end_idx
)
self.load_state_dict(pt['state_dict'], strict=True)
print(f'Text recognition from version {version}.')
if device != 'cpu':
self = self.to(device)
self = self.half()
patch_norm_fp32(self)
self.eval()
for param in self.parameters():
param.requires_grad = False
def extract_feat(self, img):
x = self.backbone(img)
return x
def forward(self, img):
"""Test function with test time augmentation.
Args:
imgs (torch.Tensor): Image input tensor.
Returns:
list[str]: Text label result of each image.
"""
img = self.pipeline(img)
feat = self.extract_feat(img)
out_enc = self.encoder(feat)
out_dec = self.decoder(out_enc).cpu().numpy()
label_strings = self.label_convertor(out_dec)
return label_strings
class StandaloneSATRNRunner:
def __init__(self,
version,
return_confident,
device='cpu',
max_seq_len_overwrite=None):
self.device = device
self.model = SATRN(version=version,
return_confident=return_confident,
device=self.device,
max_seq_len_overwrite=max_seq_len_overwrite)
def __call__(self, imgs):
results = self.model(imgs)
return results

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import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
def conv1x1(in_planes, out_planes):
"""1x1 convolution with padding."""
return nn.Conv2d(
in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
use_conv1x1=False):
super(BasicBlock, self).__init__()
if use_conv1x1:
self.conv1 = conv1x1(inplanes, planes)
self.conv2 = conv3x3(planes, planes * self.expansion, stride)
else:
self.conv1 = conv3x3(inplanes, planes, stride)
self.conv2 = conv3x3(planes, planes * self.expansion)
self.planes = planes
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.bn2 = nn.BatchNorm2d(planes * self.expansion)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNetABI(nn.Module):
"""Implement ResNet backbone for text recognition, modified from `ResNet.
<https://arxiv.org/pdf/1512.03385.pdf>`_ and
`<https://github.com/FangShancheng/ABINet>`_
Args:
in_channels (int): Number of channels of input image tensor.
stem_channels (int): Number of stem channels.
base_channels (int): Number of base channels.
arch_settings (list[int]): List of BasicBlock number for each stage.
strides (Sequence[int]): Strides of the first block of each stage.
out_indices (None | Sequence[int]): Indices of output stages. If not
specified, only the last stage will be returned.
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
"""
def __init__(self,
in_channels=3,
stem_channels=32,
base_channels=32,
arch_settings=[3, 4, 6, 6, 3],
strides=[2, 1, 2, 1, 1],
out_indices=None,
last_stage_pool=False):
super().__init__()
self.out_indices = out_indices
self.last_stage_pool = last_stage_pool
self.block = BasicBlock
self.inplanes = stem_channels
self._make_stem_layer(in_channels, stem_channels)
self.res_layers = []
planes = base_channels
for i, num_blocks in enumerate(arch_settings):
stride = strides[i]
res_layer = self._make_layer(
block=self.block,
inplanes=self.inplanes,
planes=planes,
blocks=num_blocks,
stride=stride)
self.inplanes = planes * self.block.expansion
planes *= 2
layer_name = f'layer{i + 1}'
self.add_module(layer_name, res_layer)
self.res_layers.append(layer_name)
def _make_layer(self, block, inplanes, planes, blocks, stride=1):
layers = []
downsample = None
if stride != 1 or inplanes != planes:
downsample = nn.Sequential(
nn.Conv2d(inplanes, planes, 1, stride, bias=False),
nn.BatchNorm2d(planes),
)
layers.append(
block(
inplanes,
planes,
use_conv1x1=True,
stride=stride,
downsample=downsample))
inplanes = planes
for _ in range(1, blocks):
layers.append(block(inplanes, planes, use_conv1x1=True))
return nn.Sequential(*layers)
def _make_stem_layer(self, in_channels, stem_channels):
self.conv1 = nn.Conv2d(
in_channels, stem_channels, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(stem_channels)
self.relu1 = nn.ReLU(inplace=True)
def forward(self, x):
"""
Args:
x (Tensor): Image tensor of shape :math:`(N, 3, H, W)`.
Returns:
Tensor or list[Tensor]: Feature tensor. Its shape depends on
ResNetABI's config. It can be a list of feature outputs at specific
layers if ``out_indices`` is specified.
"""
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
outs = []
for i, layer_name in enumerate(self.res_layers):
res_layer = getattr(self, layer_name)
x = res_layer(x)
if self.out_indices and i in self.out_indices:
outs.append(x)
return tuple(outs) if self.out_indices else x

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import warnings
import torch.nn as nn
from torch.nn.modules.instancenorm import _InstanceNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmcv.cnn import build_padding_layer, build_conv_layer, build_norm_layer, build_activation_layer
class ConvModule(nn.Module):
"""A conv block that bundles conv/norm/activation layers.
This block simplifies the usage of convolution layers, which are commonly
used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
It is based upon three build methods: `build_conv_layer()`,
`build_norm_layer()` and `build_activation_layer()`.
Besides, we add some additional features in this module.
1. Automatically set `bias` of the conv layer.
2. Spectral norm is supported.
3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only
supports zero and circular padding, and we add "reflect" padding mode.
Args:
in_channels (int): Number of channels in the input feature map.
Same as that in ``nn._ConvNd``.
out_channels (int): Number of channels produced by the convolution.
Same as that in ``nn._ConvNd``.
kernel_size (int | tuple[int]): Size of the convolving kernel.
Same as that in ``nn._ConvNd``.
stride (int | tuple[int]): Stride of the convolution.
Same as that in ``nn._ConvNd``.
padding (int | tuple[int]): Zero-padding added to both sides of
the input. Same as that in ``nn._ConvNd``.
dilation (int | tuple[int]): Spacing between kernel elements.
Same as that in ``nn._ConvNd``.
groups (int): Number of blocked connections from input channels to
output channels. Same as that in ``nn._ConvNd``.
bias (bool | str): If specified as `auto`, it will be decided by the
norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise
False. Default: "auto".
conv_cfg (dict): Config dict for convolution layer. Default: None,
which means using conv2d.
norm_cfg (dict): Config dict for normalization layer. Default: None.
act_cfg (dict): Config dict for activation layer.
Default: dict(type='ReLU').
inplace (bool): Whether to use inplace mode for activation.
Default: True.
with_spectral_norm (bool): Whether use spectral norm in conv module.
Default: False.
padding_mode (str): If the `padding_mode` has not been supported by
current `Conv2d` in PyTorch, we will use our own padding layer
instead. Currently, we support ['zeros', 'circular'] with official
implementation and ['reflect'] with our own implementation.
Default: 'zeros'.
order (tuple[str]): The order of conv/norm/activation layers. It is a
sequence of "conv", "norm" and "act". Common examples are
("conv", "norm", "act") and ("act", "conv", "norm").
Default: ('conv', 'norm', 'act').
"""
_abbr_ = 'conv_block'
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias='auto',
conv_cfg=None,
norm_cfg=None,
act_cfg=dict(type='ReLU'),
inplace=True,
with_spectral_norm=False,
padding_mode='zeros',
order=('conv', 'norm', 'act')):
super(ConvModule, self).__init__()
assert conv_cfg is None or isinstance(conv_cfg, dict)
assert norm_cfg is None or isinstance(norm_cfg, dict)
assert act_cfg is None or isinstance(act_cfg, dict)
official_padding_mode = ['zeros', 'circular']
self.conv_cfg = conv_cfg
self.norm_cfg = norm_cfg
self.act_cfg = act_cfg
self.inplace = inplace
self.with_spectral_norm = with_spectral_norm
self.with_explicit_padding = padding_mode not in official_padding_mode
self.order = order
assert isinstance(self.order, tuple) and len(self.order) == 3
assert set(order) == set(['conv', 'norm', 'act'])
self.with_norm = norm_cfg is not None
self.with_activation = act_cfg is not None
# if the conv layer is before a norm layer, bias is unnecessary.
if bias == 'auto':
bias = not self.with_norm
self.with_bias = bias
if self.with_explicit_padding:
pad_cfg = dict(type=padding_mode)
self.padding_layer = build_padding_layer(pad_cfg, padding)
# reset padding to 0 for conv module
conv_padding = 0 if self.with_explicit_padding else padding
# build convolution layer
self.conv = build_conv_layer(
conv_cfg,
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=conv_padding,
dilation=dilation,
groups=groups,
bias=bias)
# export the attributes of self.conv to a higher level for convenience
self.in_channels = self.conv.in_channels
self.out_channels = self.conv.out_channels
self.kernel_size = self.conv.kernel_size
self.stride = self.conv.stride
self.padding = padding
self.dilation = self.conv.dilation
self.transposed = self.conv.transposed
self.output_padding = self.conv.output_padding
self.groups = self.conv.groups
if self.with_spectral_norm:
self.conv = nn.utils.spectral_norm(self.conv)
# build normalization layers
if self.with_norm:
# norm layer is after conv layer
if order.index('norm') > order.index('conv'):
norm_channels = out_channels
else:
norm_channels = in_channels
self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels)
self.add_module(self.norm_name, norm)
if self.with_bias:
if isinstance(norm, (_BatchNorm, _InstanceNorm)):
warnings.warn(
'Unnecessary conv bias before batch/instance norm')
else:
self.norm_name = None
# build activation layer
if self.with_activation:
act_cfg_ = act_cfg.copy()
# nn.Tanh has no 'inplace' argument
if act_cfg_['type'] not in [
'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish'
]:
act_cfg_.setdefault('inplace', inplace)
self.activate = build_activation_layer(act_cfg_)
@property
def norm(self):
if self.norm_name:
return getattr(self, self.norm_name)
else:
return None
def forward(self, x, activate=True, norm=True):
for layer in self.order:
if layer == 'conv':
if self.with_explicit_padding:
x = self.padding_layer(x)
x = self.conv(x)
elif layer == 'norm' and norm and self.with_norm:
x = self.norm(x)
elif layer == 'act' and activate and self.with_activation:
x = self.activate(x)
return x

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import torch
import numpy as np
class BaseConvertor:
"""Convert between text, index and tensor for text recognize pipeline.
Args:
dict_type (str): Type of dict, options are 'DICT36', 'DICT37', 'DICT90'
and 'DICT91'.
dict_file (None|str): Character dict file path. If not none,
the dict_file is of higher priority than dict_type.
dict_list (None|list[str]): Character list. If not none, the list
is of higher priority than dict_type, but lower than dict_file.
"""
start_idx = end_idx = padding_idx = 0
unknown_idx = None
lower = False
DICT36 = tuple('0123456789abcdefghijklmnopqrstuvwxyz')
DICT63 = tuple('0123456789abcdefghijklmnopqrstuvwxyz'
'ABCDEFGHIJKLMNOPQRSTUVWXYZ')
DICT90 = tuple('0123456789abcdefghijklmnopqrstuvwxyz'
'ABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()'
'*+,-./:;<=>?@[\\]_`~')
DICT131 = tuple('0123456789abcdefghijklmnopqrstuvwxyz'
'!"#$%&\'()'
'*+,-./:;<=>?@[\\]_`~'
'ạảãàáâậầấẩẫăắằặẳẵóòọõỏôộổỗồốơờớợởỡéèẻẹẽêếềệểễúùụủũưựữửừứíìịỉĩýỳỷỵỹđ')
DICT224 = tuple('0123456789abcdefghijklmnopqrstuvwxyz'
'ABCDEFGHIJKLMNOPQRSTUVWXYZ!"#$%&\'()'
'*+,-./:;<=>?@[\\]_`~{}|^ ̂'
'ạảãàáâậầấẩẫăắằặẳẵóòọõỏôộổỗồốơờớợởỡéèẻẹẽêếềệểễúùụủũưựữửừứíìịỉĩýỳỷỵỹđ'
'ẠẢÃÀÁÂẬẦẤẨẪĂẮẰẶẲẴÓÒỌÕỎÔỘỔỖỒỐƠỜỚỢỞỠÉÈẺẸẼÊẾỀỆỂỄÚÙỤỦŨƯỰỮỬỪỨÍÌỊỈĨÝỲỶỴỸĐ✪')
def __init__(self, dict_type='DICT90'):
assert dict_type in ('DICT36', 'DICT63', 'DICT90', 'DICT131', 'DICT224')
self.idx2char = []
if dict_type == 'DICT36':
self.idx2char = list(self.DICT36)
elif dict_type == 'DICT63':
self.idx2char = list(self.DICT63)
elif dict_type == 'DICT90':
self.idx2char = list(self.DICT90)
elif dict_type == 'DICT131':
self.idx2char = list(self.DICT131)
elif dict_type == 'DICT224':
self.idx2char = list(self.DICT224)
else:
raise ('Dictonary not implemented')
assert len(set(self.idx2char)) == len(self.idx2char), \
'Invalid dictionary: Has duplicated characters.'
self.char2idx = {char: idx for idx, char in enumerate(self.idx2char)}
def num_classes(self):
"""Number of output classes."""
return len(self.idx2char)
class AttnConvertor(BaseConvertor):
"""Convert between text, index and tensor for encoder-decoder based
pipeline.
Args:
dict_type (str): Type of dict, should be one of {'DICT36', 'DICT90'}.
dict_file (None|str): Character dict file path. If not none,
higher priority than dict_type.
dict_list (None|list[str]): Character list. If not none, higher
priority than dict_type, but lower than dict_file.
with_unknown (bool): If True, add `UKN` token to class.
max_seq_len (int): Maximum sequence length of label.
lower (bool): If True, convert original string to lower case.
start_end_same (bool): Whether use the same index for
start and end token or not. Default: True.
"""
def __init__(self,
dict_type='DICT90',
with_unknown=True,
max_seq_len=40,
lower=False,
start_end_same=True,
return_confident=False,
**kwargs):
super().__init__(dict_type)
assert isinstance(with_unknown, bool)
assert isinstance(max_seq_len, int)
assert isinstance(lower, bool)
self.with_unknown = with_unknown
self.max_seq_len = max_seq_len
self.lower = lower
self.start_end_same = start_end_same
self.return_confident = return_confident
self.update_dict()
def update_dict(self):
start_end_token = '<BOS/EOS>'
unknown_token = '<UKN>'
padding_token = '<PAD>'
# unknown
self.unknown_idx = None
if self.with_unknown:
self.idx2char.append(unknown_token)
self.unknown_idx = len(self.idx2char) - 1
# BOS/EOS
self.idx2char.append(start_end_token)
self.start_idx = len(self.idx2char) - 1
if not self.start_end_same:
self.idx2char.append(start_end_token)
self.end_idx = len(self.idx2char) - 1
# padding
self.idx2char.append(padding_token)
self.padding_idx = len(self.idx2char) - 1
# update char2idx
self.char2idx = {}
for idx, char in enumerate(self.idx2char):
self.char2idx[char] = idx
def __call__(self, indexes):
strings = []
confidents = []
if self.return_confident:
b,sq,_ = indexes.shape
for idx in range(b):
index = indexes[idx, :, :]
chars = index.argmax(-1)
confident = index.max(-1)
i = 0
while i < sq and chars[i] != self.end_idx and chars[i] != self.padding_idx: i += 1
chars = chars[:i]
confident = confident[:i].min()
string = [self.idx2char[i] for i in chars]
strings.append(''.join(string))
confidents.append(confident)
return strings, confidents
else:
for index in indexes:
i, l = 0, len(index)
while i < l and index[i] != self.end_idx and index[i] != self.padding_idx: i += 1
index = index[:i]
string = [self.idx2char[i] for i in index]
strings.append(''.join(string))
return strings

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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from .encoder import MultiHeadAttention
class PositionwiseFeedForward(nn.Module):
"""Two-layer feed-forward module.
Args:
d_in (int): The dimension of the input for feedforward
network model.
d_hid (int): The dimension of the feedforward
network model.
dropout (float): Dropout layer on feedforward output.
act_cfg (dict): Activation cfg for feedforward module.
"""
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Linear(d_in, d_hid)
self.w_2 = nn.Linear(d_hid, d_in)
self.act = nn.GELU()
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.w_1(x)
x = self.act(x)
x = self.w_2(x)
x = self.dropout(x)
return x
class PositionalEncoding(nn.Module):
"""Fixed positional encoding with sine and cosine functions."""
def __init__(self, d_hid=512, n_position=200):
super().__init__()
# Not a parameter
# Position table of shape (1, n_position, d_hid)
self.register_buffer(
'position_table',
self._get_sinusoid_encoding_table(n_position, d_hid))
def _get_sinusoid_encoding_table(self, n_position, d_hid):
"""Sinusoid position encoding table."""
denominator = torch.Tensor([
1.0 / np.power(10000, 2 * (hid_j // 2) / d_hid)
for hid_j in range(d_hid)
])
denominator = denominator.view(1, -1)
pos_tensor = torch.arange(n_position).unsqueeze(-1).float()
sinusoid_table = pos_tensor * denominator
sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2])
sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2])
return sinusoid_table.unsqueeze(0)
def forward(self, x):
"""
Args:
x (Tensor): Tensor of shape (batch_size, pos_len, d_hid, ...)
"""
self.device = x.device
x = x + self.position_table[:, :x.size(1)].clone().detach()
return x
class TFDecoderLayer(nn.Module):
"""Transformer Decoder Layer.
Args:
d_model (int): The number of expected features
in the decoder inputs (default=512).
d_inner (int): The dimension of the feedforward
network model (default=256).
n_head (int): The number of heads in the
multiheadattention models (default=8).
d_k (int): Total number of features in key.
d_v (int): Total number of features in value.
dropout (float): Dropout layer on attn_output_weights.
qkv_bias (bool): Add bias in projection layer. Default: False.
act_cfg (dict): Activation cfg for feedforward module.
operation_order (tuple[str]): The execution order of operation
in transformer. Such as ('self_attn', 'norm', 'enc_dec_attn',
'norm', 'ffn', 'norm') or ('norm', 'self_attn', 'norm',
'enc_dec_attn', 'norm', 'ffn').
Default:None.
"""
def __init__(self,
d_model=512,
d_inner=256,
n_head=8,
d_k=64,
d_v=64,
dropout=0.1,
qkv_bias=False):
super().__init__()
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
self.self_attn = MultiHeadAttention(
n_head, d_model, d_k, d_v, dropout=dropout, qkv_bias=qkv_bias)
self.enc_attn = MultiHeadAttention(
n_head, d_model, d_k, d_v, dropout=dropout, qkv_bias=qkv_bias)
self.mlp = PositionwiseFeedForward(
d_model, d_inner, dropout=dropout)
def forward(self,
dec_input,
enc_output,
self_attn_mask=None,
dec_enc_attn_mask=None):
dec_input_norm = self.norm1(dec_input)
dec_attn_out = self.self_attn(dec_input_norm, dec_input_norm,
dec_input_norm, self_attn_mask)
dec_attn_out += dec_input
enc_dec_attn_in = self.norm2(dec_attn_out)
enc_dec_attn_out = self.enc_attn(enc_dec_attn_in, enc_output,
enc_output, dec_enc_attn_mask)
enc_dec_attn_out += dec_attn_out
mlp_out = self.mlp(self.norm3(enc_dec_attn_out))
mlp_out += enc_dec_attn_out
return mlp_out
class NRTRDecoder(nn.Module):
"""Transformer Decoder block with self attention mechanism.
Args:
n_layers (int): Number of attention layers.
d_embedding (int): Language embedding dimension.
n_head (int): Number of parallel attention heads.
d_k (int): Dimension of the key vector.
d_v (int): Dimension of the value vector.
d_model (int): Dimension :math:`D_m` of the input from previous model.
d_inner (int): Hidden dimension of feedforward layers.
n_position (int): Length of the positional encoding vector. Must be
greater than ``max_seq_len``.
dropout (float): Dropout rate.
num_classes (int): Number of output classes :math:`C`.
max_seq_len (int): Maximum output sequence length :math:`T`.
start_idx (int): The index of `<SOS>`.
padding_idx (int): The index of `<PAD>`.
init_cfg (dict or list[dict], optional): Initialization configs.
Warning:
This decoder will not predict the final class which is assumed to be
`<PAD>`. Therefore, its output size is always :math:`C - 1`. `<PAD>`
is also ignored by loss as specified in
:obj:`mmocr.models.textrecog.recognizer.EncodeDecodeRecognizer`.
"""
def __init__(self,
n_layers=6,
d_embedding=512,
n_head=8,
d_k=64,
d_v=64,
d_model=512,
d_inner=256,
n_position=200,
dropout=0.1,
num_classes=93,
max_seq_len=40,
start_idx=1,
padding_idx=92,
return_confident=False,
end_idx=None,
**kwargs):
super().__init__()
self.padding_idx = padding_idx
self.start_idx = start_idx
self.max_seq_len = max_seq_len
self.trg_word_emb = nn.Embedding(
num_classes, d_embedding, padding_idx=padding_idx)
self.position_enc = PositionalEncoding(
d_embedding, n_position=n_position)
self.layer_stack = nn.ModuleList([
TFDecoderLayer(
d_model, d_inner, n_head, d_k, d_v, dropout=dropout, **kwargs)
for _ in range(n_layers)
])
self.layer_norm = nn.LayerNorm(d_model, eps=1e-6)
pred_num_class = num_classes - 1 # ignore padding_idx
self.classifier = nn.Linear(d_model, pred_num_class)
self.return_confident = return_confident
self.end_idx = end_idx
@staticmethod
def get_pad_mask(seq, pad_idx):
return (seq != pad_idx).unsqueeze(-2)
@staticmethod
def get_subsequent_mask(seq):
"""For masking out the subsequent info."""
len_s = seq.size(1)
subsequent_mask = 1 - torch.triu(
torch.ones((len_s, len_s), device=seq.device), diagonal=1)
subsequent_mask = subsequent_mask.unsqueeze(0).bool()
return subsequent_mask
def _attention(self, trg_seq, src, src_mask=None):
trg_embedding = self.trg_word_emb(trg_seq)
trg_pos_encoded = self.position_enc(trg_embedding)
trg_mask = self.get_pad_mask(
trg_seq,
pad_idx=self.padding_idx) & self.get_subsequent_mask(trg_seq)
output = trg_pos_encoded
for dec_layer in self.layer_stack:
output = dec_layer(
output,
src,
self_attn_mask=trg_mask,
dec_enc_attn_mask=src_mask)
output = self.layer_norm(output)
return output
def _get_mask(self, logit):
N, T, _ = logit.size()
mask = logit.new_ones((N, T))
return mask
def forward(self, out_enc):
src_mask = self._get_mask(out_enc)
N = out_enc.size(0)
init_target_seq = torch.full((N, self.max_seq_len + 1),
self.padding_idx,
device=out_enc.device,
dtype=torch.long)
# bsz * seq_len
init_target_seq[:, 0] = self.start_idx
outputs = []
for step in range(0, self.max_seq_len):
decoder_output = self._attention(
trg_seq=init_target_seq,
src=out_enc,
src_mask=src_mask)
if self.return_confident:
step_result = torch.softmax(self.classifier(decoder_output[:, step, :]), -1)
next_step_init = step_result.argmax(-1)
init_target_seq[:, step + 1] = next_step_init
if next_step_init.min() >= self.end_idx:
break
else:
step_result = self.classifier(decoder_output[:, step, :]).argmax(-1)
init_target_seq[:, step + 1] = step_result
if step_result.min() >= self.end_idx:
break
outputs.append(step_result)
outputs = torch.stack(outputs, dim=1)
return outputs

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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from .conv import ConvModule
class LocalityAwareFeedforward(nn.Module):
"""Locality-aware feedforward layer in SATRN, see `SATRN.
<https://arxiv.org/abs/1910.04396>`_
"""
def __init__(self,
d_in,
d_hid,
dropout=0.1):
super().__init__()
self.conv1 = ConvModule(
d_in,
d_hid,
kernel_size=1,
padding=0,
bias=False,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'))
self.depthwise_conv = ConvModule(
d_hid,
d_hid,
kernel_size=3,
padding=1,
bias=False,
groups=d_hid,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'))
self.conv2 = ConvModule(
d_hid,
d_in,
kernel_size=1,
padding=0,
bias=False,
norm_cfg=dict(type='BN'),
act_cfg=dict(type='ReLU'))
def forward(self, x):
x = self.conv1(x)
x = self.depthwise_conv(x)
x = self.conv2(x)
return x
class ScaledDotProductAttention(nn.Module):
"""Scaled Dot-Product Attention Module. This code is adopted from
https://github.com/jadore801120/attention-is-all-you-need-pytorch.
Args:
temperature (float): The scale factor for softmax input.
attn_dropout (float): Dropout layer on attn_output_weights.
"""
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q, k, v, mask=None):
attn = torch.matmul(q / self.temperature, k.transpose(2, 3))
if mask is not None:
attn = attn.masked_fill(mask == 0, float('-inf'))
attn = self.dropout(F.softmax(attn, dim=-1))
output = torch.matmul(attn, v)
return output, attn
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention module.
Args:
n_head (int): The number of heads in the
multiheadattention models (default=8).
d_model (int): The number of expected features
in the decoder inputs (default=512).
d_k (int): Total number of features in key.
d_v (int): Total number of features in value.
dropout (float): Dropout layer on attn_output_weights.
qkv_bias (bool): Add bias in projection layer. Default: False.
"""
def __init__(self,
n_head=8,
d_model=512,
d_k=64,
d_v=64,
dropout=0.1,
qkv_bias=False):
super().__init__()
self.n_head = n_head
self.d_k = d_k
self.d_v = d_v
self.dim_k = n_head * d_k
self.dim_v = n_head * d_v
self.linear_q = nn.Linear(self.dim_k, self.dim_k, bias=qkv_bias)
self.linear_k = nn.Linear(self.dim_k, self.dim_k, bias=qkv_bias)
self.linear_v = nn.Linear(self.dim_v, self.dim_v, bias=qkv_bias)
self.attention = ScaledDotProductAttention(d_k**0.5, dropout)
self.fc = nn.Linear(self.dim_v, d_model, bias=qkv_bias)
self.proj_drop = nn.Dropout(dropout)
def forward(self, q, k, v, mask=None):
batch_size, len_q, _ = q.size()
_, len_k, _ = k.size()
q = self.linear_q(q).view(batch_size, len_q, self.n_head, self.d_k)
k = self.linear_k(k).view(batch_size, len_k, self.n_head, self.d_k)
v = self.linear_v(v).view(batch_size, len_k, self.n_head, self.d_v)
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
if mask is not None:
if mask.dim() == 3:
mask = mask.unsqueeze(1)
elif mask.dim() == 2:
mask = mask.unsqueeze(1).unsqueeze(1)
attn_out, _ = self.attention(q, k, v, mask=mask)
attn_out = attn_out.transpose(1, 2).contiguous().view(
batch_size, len_q, self.dim_v)
attn_out = self.fc(attn_out)
attn_out = self.proj_drop(attn_out)
return attn_out
class SatrnEncoderLayer(nn.Module):
""""""
def __init__(self,
d_model=512,
d_inner=512,
n_head=8,
d_k=64,
d_v=64,
dropout=0.1,
qkv_bias=False):
super().__init__()
self.norm1 = nn.LayerNorm(d_model)
self.attn = MultiHeadAttention(
n_head, d_model, d_k, d_v, qkv_bias=qkv_bias, dropout=dropout)
self.norm2 = nn.LayerNorm(d_model)
self.feed_forward = LocalityAwareFeedforward(
d_model, d_inner, dropout=dropout)
def forward(self, x, h, w, mask=None):
n, hw, c = x.size()
residual = x
x = self.norm1(x)
x = residual + self.attn(x, x, x, mask)
residual = x
x = self.norm2(x)
x = x.transpose(1, 2).contiguous().view(n, c, h, w)
x = self.feed_forward(x)
x = x.view(n, c, hw).transpose(1, 2)
x = residual + x
return x
class Adaptive2DPositionalEncoding(nn.Module):
"""Implement Adaptive 2D positional encoder for SATRN, see
`SATRN <https://arxiv.org/abs/1910.04396>`_
Modified from https://github.com/Media-Smart/vedastr
Licensed under the Apache License, Version 2.0 (the "License");
Args:
d_hid (int): Dimensions of hidden layer.
n_height (int): Max height of the 2D feature output.
n_width (int): Max width of the 2D feature output.
dropout (int): Size of hidden layers of the model.
"""
def __init__(self,
d_hid=512,
n_height=100,
n_width=100,
dropout=0.1):
super().__init__()
h_position_encoder = self._get_sinusoid_encoding_table(n_height, d_hid)
h_position_encoder = h_position_encoder.transpose(0, 1)
h_position_encoder = h_position_encoder.view(1, d_hid, n_height, 1)
w_position_encoder = self._get_sinusoid_encoding_table(n_width, d_hid)
w_position_encoder = w_position_encoder.transpose(0, 1)
w_position_encoder = w_position_encoder.view(1, d_hid, 1, n_width)
self.register_buffer('h_position_encoder', h_position_encoder)
self.register_buffer('w_position_encoder', w_position_encoder)
self.h_scale = self.scale_factor_generate(d_hid)
self.w_scale = self.scale_factor_generate(d_hid)
self.pool = nn.AdaptiveAvgPool2d(1)
self.dropout = nn.Dropout(p=dropout)
def _get_sinusoid_encoding_table(self, n_position, d_hid):
"""Sinusoid position encoding table."""
denominator = torch.Tensor([
1.0 / np.power(10000, 2 * (hid_j // 2) / d_hid)
for hid_j in range(d_hid)
])
denominator = denominator.view(1, -1)
pos_tensor = torch.arange(n_position).unsqueeze(-1).float()
sinusoid_table = pos_tensor * denominator
sinusoid_table[:, 0::2] = torch.sin(sinusoid_table[:, 0::2])
sinusoid_table[:, 1::2] = torch.cos(sinusoid_table[:, 1::2])
return sinusoid_table
def scale_factor_generate(self, d_hid):
scale_factor = nn.Sequential(
nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.ReLU(inplace=True),
nn.Conv2d(d_hid, d_hid, kernel_size=1), nn.Sigmoid())
return scale_factor
def forward(self, x):
b, c, h, w = x.size()
avg_pool = self.pool(x)
h_pos_encoding = \
self.h_scale(avg_pool) * self.h_position_encoder[:, :, :h, :]
w_pos_encoding = \
self.w_scale(avg_pool) * self.w_position_encoder[:, :, :, :w]
out = x + h_pos_encoding + w_pos_encoding
out = self.dropout(out)
return out
class SatrnEncoder(nn.Module):
"""Implement encoder for SATRN, see `SATRN.
<https://arxiv.org/abs/1910.04396>`_.
Args:
n_layers (int): Number of attention layers.
n_head (int): Number of parallel attention heads.
d_k (int): Dimension of the key vector.
d_v (int): Dimension of the value vector.
d_model (int): Dimension :math:`D_m` of the input from previous model.
n_position (int): Length of the positional encoding vector. Must be
greater than ``max_seq_len``.
d_inner (int): Hidden dimension of feedforward layers.
dropout (float): Dropout rate.
init_cfg (dict or list[dict], optional): Initialization configs.
"""
def __init__(self,
n_layers=12,
n_head=8,
d_k=64,
d_v=64,
d_model=512,
n_position=100,
d_inner=256,
dropout=0.1,
**kwargs):
super().__init__()
self.d_model = d_model
self.position_enc = Adaptive2DPositionalEncoding(
d_hid=d_model,
n_height=n_position,
n_width=n_position,
dropout=dropout)
self.layer_stack = nn.ModuleList([
SatrnEncoderLayer(
d_model, d_inner, n_head, d_k, d_v, dropout=dropout)
for _ in range(n_layers)
])
self.layer_norm = nn.LayerNorm(d_model)
def forward(self, feat):
"""
Args:
feat (Tensor): Feature tensor of shape :math:`(N, D_m, H, W)`.
Returns:
Tensor: A tensor of shape :math:`(N, T, D_m)`.
"""
feat = feat + self.position_enc(feat)
n, c, h, w = feat.size()
mask = feat.new_zeros((n, h, w))
mask[:,:,:w] = 1
mask = mask.view(n, h * w)
feat = feat.view(n, c, h * w)
output = feat.permute(0, 2, 1).contiguous()
for enc_layer in self.layer_stack:
output = enc_layer(output, h, w, mask)
output = self.layer_norm(output)
return output

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import os
import shutil
import colorama
import hashlib
def sha256sum(filename):
h = hashlib.sha256()
b = bytearray(128*1024)
mv = memoryview(b)
with open(filename, 'rb', buffering=0) as f:
for n in iter(lambda : f.readinto(mv), 0):
h.update(mv[:n])
return h.hexdigest()
online_model_factory = {
'satrn-lite-general-pretrain-20230106': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/jxqhbem4to.pth',
'hash': 'b0069a72bf6fc080ad5d431d5ce650c3bfbab535141adef1631fce331cb1471c'},
'satrn-lite-captcha-finetune-20230108': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/l27vitogmc.pth',
'hash': 'efcbcf2955b6b21125b073f83828d2719e908c7303b0d9901e94be5a8efba916'},
'satrn-lite-handwritten-finetune-20230108': {
'url': 'https://github.com/moewiee/satrn-model-factory/raw/main/lj9gkwelns.pth',
'hash': 'bccd8e985b131fcd4701114af5ceaef098f2eac50654bbb1d828e7f829e711dd'},
}
__hub_available_versions__ = online_model_factory.keys()
def _get(version):
use_online = version in __hub_available_versions__
if not use_online and not os.path.exists(version):
raise ValueError(f'Model version {version} not found online and not found local.')
hub_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'hub')
if not os.path.exists(hub_path):
os.makedirs(hub_path)
if use_online:
version_url = online_model_factory[version]['url']
file_path = os.path.join(hub_path, os.path.basename(version_url))
else:
file_path = os.path.join(hub_path, os.path.basename(version))
if not os.path.exists(file_path):
if use_online:
os.system(f'wget -O {file_path} {version_url}')
assert os.path.exists(file_path), \
colorama.Fore.RED + 'wget failed while trying to retrieve from hub.' + colorama.Style.RESET_ALL
downloaded_hash = sha256sum(file_path)
if downloaded_hash != online_model_factory[version]['hash']:
os.remove(file_path)
raise ValueError(colorama.Fore.RED + 'sha256 hash doesnt match for version retrieved from hub.' + colorama.Style.RESET_ALL)
else:
shutil.copy2(version, file_path)
return file_path

78
cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/fp16_utils.py vendored Normal file
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import torch
import torch.nn as nn
import numpy as np
from collections import abc
def cast_tensor_type(inputs, src_type, dst_type):
"""Recursively convert Tensor in inputs from src_type to dst_type.
Args:
inputs: Inputs that to be casted.
src_type (torch.dtype): Source type..
dst_type (torch.dtype): Destination type.
Returns:
The same type with inputs, but all contained Tensors have been cast.
"""
if isinstance(inputs, nn.Module):
return inputs
elif isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isinstance(inputs, abc.Mapping):
return type(inputs)({
k: cast_tensor_type(v, src_type, dst_type)
for k, v in inputs.items()
})
elif isinstance(inputs, abc.Iterable):
return type(inputs)(
cast_tensor_type(item, src_type, dst_type) for item in inputs)
else:
return inputs
def patch_forward_method(func, src_type, dst_type, convert_output=True):
"""Patch the forward method of a module.
Args:
func (callable): The original forward method.
src_type (torch.dtype): Type of input arguments to be converted from.
dst_type (torch.dtype): Type of input arguments to be converted to.
convert_output (bool): Whether to convert the output back to src_type.
Returns:
callable: The patched forward method.
"""
def new_forward(*args, **kwargs):
output = func(*cast_tensor_type(args, src_type, dst_type),
**cast_tensor_type(kwargs, src_type, dst_type))
if convert_output:
output = cast_tensor_type(output, dst_type, src_type)
return output
return new_forward
def patch_norm_fp32(module):
"""Recursively convert normalization layers from FP16 to FP32.
Args:
module (nn.Module): The modules to be converted in FP16.
Returns:
nn.Module: The converted module, the normalization layers have been
converted to FP32.
"""
if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
module.float()
if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3':
module.forward = patch_forward_method(module.forward, torch.half,
torch.float)
for child in module.children():
patch_norm_fp32(child)
return module

33
cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/transform.py vendored Normal file
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import torchvision.transforms.functional as TF
import cv2
import torch
class DataPipelineSATRN:
def __init__(self,
resize_height,
resize_width,
norm_mean,
norm_std,
device='cpu'):
self.resize_height = resize_height
self.resize_width = resize_width
self.norm_mean = norm_mean
self.norm_std = norm_std
self.device = device
def __call__(self, imgs):
if not isinstance(imgs, list):
imgs = [imgs]
datas = []
for img in imgs:
if isinstance(img, str):
img = cv2.imread(img)
data = torch.from_numpy(cv2.resize(img, (self.resize_width, self.resize_height), interpolation=cv2.INTER_LINEAR))
datas.append(data)
data = torch.stack(datas, dim=0)
data = data.to(self.device)
data = data.float().div_(255.).permute((0,3,1,2))
TF.normalize(data, mean=self.norm_mean, std=self.norm_std, inplace=True)
return data.half() if self.device != 'cpu' else data

1
cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/sdsvtr/version.py vendored Normal file
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__version__ = '0.0.5'

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import os
import os.path as osp
import shutil
import sys
import warnings
from setuptools import find_packages, setup
def readme():
with open('README.md', encoding='utf-8') as f:
content = f.read()
return content
version_file = 'sdsvtr/version.py'
is_windows = sys.platform == 'win32'
def add_mim_extention():
"""Add extra files that are required to support MIM into the package.
These files will be added by creating a symlink to the originals if the
package is installed in `editable` mode (e.g. pip install -e .), or by
copying from the originals otherwise.
"""
# parse installment mode
if 'develop' in sys.argv:
# installed by `pip install -e .`
mode = 'symlink'
elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
# installed by `pip install .`
# or create source distribution by `python setup.py sdist`
mode = 'copy'
else:
return
filenames = ['tools', 'configs', 'model-index.yml']
repo_path = osp.dirname(__file__)
mim_path = osp.join(repo_path, 'mmocr', '.mim')
os.makedirs(mim_path, exist_ok=True)
for filename in filenames:
if osp.exists(filename):
src_path = osp.join(repo_path, filename)
tar_path = osp.join(mim_path, filename)
if osp.isfile(tar_path) or osp.islink(tar_path):
os.remove(tar_path)
elif osp.isdir(tar_path):
shutil.rmtree(tar_path)
if mode == 'symlink':
src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
try:
os.symlink(src_relpath, tar_path)
except OSError:
# Creating a symbolic link on windows may raise an
# `OSError: [WinError 1314]` due to privilege. If
# the error happens, the src file will be copied
mode = 'copy'
warnings.warn(
f'Failed to create a symbolic link for {src_relpath}, '
f'and it will be copied to {tar_path}')
else:
continue
if mode == 'copy':
if osp.isfile(src_path):
shutil.copyfile(src_path, tar_path)
elif osp.isdir(src_path):
shutil.copytree(src_path, tar_path)
else:
warnings.warn(f'Cannot copy file {src_path}.')
else:
raise ValueError(f'Invalid mode {mode}')
def get_version():
with open(version_file, 'r') as f:
exec(compile(f.read(), version_file, 'exec'))
import sys
# return short version for sdist
if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
return locals()['short_version']
else:
return locals()['__version__']
def parse_requirements(fname='requirements.txt', with_version=True):
"""Parse the package dependencies listed in a requirements file but strip
specific version information.
Args:
fname (str): Path to requirements file.
with_version (bool, default=False): If True, include version specs.
Returns:
info (list[str]): List of requirements items.
CommandLine:
python -c "import setup; print(setup.parse_requirements())"
"""
import re
import sys
from os.path import exists
require_fpath = fname
def parse_line(line):
"""Parse information from a line in a requirements text file."""
if line.startswith('-r '):
# Allow specifying requirements in other files
target = line.split(' ')[1]
for info in parse_require_file(target):
yield info
else:
info = {'line': line}
if line.startswith('-e '):
info['package'] = line.split('#egg=')[1]
else:
# Remove versioning from the package
pat = '(' + '|'.join(['>=', '==', '>']) + ')'
parts = re.split(pat, line, maxsplit=1)
parts = [p.strip() for p in parts]
info['package'] = parts[0]
if len(parts) > 1:
op, rest = parts[1:]
if ';' in rest:
# Handle platform specific dependencies
# http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
version, platform_deps = map(str.strip,
rest.split(';'))
info['platform_deps'] = platform_deps
else:
version = rest # NOQA
info['version'] = (op, version)
yield info
def parse_require_file(fpath):
with open(fpath, 'r') as f:
for line in f.readlines():
line = line.strip()
if line and not line.startswith('#'):
for info in parse_line(line):
yield info
def gen_packages_items():
if exists(require_fpath):
for info in parse_require_file(require_fpath):
parts = [info['package']]
if with_version and 'version' in info:
parts.extend(info['version'])
if not sys.version.startswith('3.4'):
# apparently package_deps are broken in 3.4
platform_deps = info.get('platform_deps')
if platform_deps is not None:
parts.append(';' + platform_deps)
item = ''.join(parts)
yield item
packages = list(gen_packages_items())
return packages
if __name__ == '__main__':
setup(
name='sdsvtr',
version=get_version(),
description='SDSV OCR Team Text Recognizer',
long_description=readme(),
long_description_content_type='text/markdown',
packages=find_packages(exclude=('configs', 'tools', 'demo')),
include_package_data=True,
url='https://github.com/open-mmlab/mmocr',
classifiers=[
'Development Status :: 4 - Beta',
'License :: OSI Approved :: Apache Software License',
'Operating System :: OS Independent',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
],
license='Apache License 2.0',
install_requires=parse_requirements('requirements.txt'),
zip_safe=False)

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cope2n-ai-fi/modules/_sdsvkvu/sdsvkvu/externals/basic_ocr/externals/sdsvtr/test.py vendored Normal file
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import numpy as np
from sdsvtr import StandaloneSATRNRunner
runner = StandaloneSATRNRunner(version='satrn-lite-general-pretrain-20230106', return_confident=False, device='cuda:1')
dummy_list = [np.ndarray((32,128,3)) for _ in range(100)]
bs = 32
all_results = []
while len(dummy_list) > 0:
dummy_batch = dummy_list[:bs]
dummy_list = dummy_list[bs:]
all_results += runner(dummy_batch)

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cope2n-api/fwd_api/migrations/0001_initial.py Executable file
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# Generated by Django 4.1.3 on 2023-04-05 07:19
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
import fwd_api.models.fields.EncryptedCharField
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='OcrTemplate',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('name', models.CharField(max_length=300)),
('status', models.IntegerField()),
('file_path', fwd_api.models.fields.EncryptedCharField.EncryptedCharField(max_length=500, null=True)),
('file_name', fwd_api.models.fields.EncryptedCharField.EncryptedCharField(max_length=500, null=True)),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
migrations.CreateModel(
name='PricingPlan',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('code', models.CharField(max_length=300)),
('token_limitations', models.IntegerField(default=0)),
('duration', models.IntegerField(default=0)),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
migrations.CreateModel(
name='Subscription',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('current_token', models.IntegerField(default=0)),
('limit_token', models.IntegerField(default=0)),
('status', models.IntegerField(default=0)),
('start_at', models.DateTimeField(default=django.utils.timezone.now)),
('expired_at', models.DateTimeField(default=django.utils.timezone.now)),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
('pricing_plan', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='fwd_api.pricingplan')),
],
),
migrations.CreateModel(
name='SubscriptionRequest',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('pages', models.IntegerField()),
('doc_type', models.CharField(max_length=100)),
('request_id', models.CharField(max_length=200)),
('process_type', models.CharField(max_length=200)),
('provider_code', models.CharField(default='Guest', max_length=200)),
('predict_result', models.JSONField(null=True)),
('status', models.IntegerField()),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
('subscription', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='fwd_api.subscription')),
],
),
migrations.CreateModel(
name='UserProfile',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('full_name', models.CharField(max_length=100)),
('sync_id', models.CharField(max_length=100)),
('provider_id', models.CharField(default='Ctel', max_length=100)),
('current_total_pages', models.IntegerField(default=0)),
('limit_total_pages', models.IntegerField(default=0)),
('status', models.IntegerField(default=0)),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
],
),
migrations.CreateModel(
name='SubscriptionRequestFile',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('file_name', models.CharField(default=None, max_length=300)),
('file_path', fwd_api.models.fields.EncryptedCharField.EncryptedCharField(default=None, max_length=500)),
('file_category', models.CharField(default='Origin', max_length=200)),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
('request', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='files', to='fwd_api.subscriptionrequest')),
],
),
migrations.AddField(
model_name='subscription',
name='user',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='fwd_api.userprofile'),
),
migrations.CreateModel(
name='OcrTemplateBox',
fields=[
('id', models.AutoField(primary_key=True, serialize=False)),
('name', models.CharField(max_length=300, null=True)),
('type', models.CharField(max_length=100)),
('coordinates', models.CharField(max_length=200)),
('created_at', models.DateTimeField(default=django.utils.timezone.now)),
('updated_at', models.DateTimeField(auto_now=True)),
('template', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='fwd_api.ocrtemplate')),
],
),
migrations.AddField(
model_name='ocrtemplate',
name='subscription',
field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='fwd_api.subscription'),
),
]

28
cope2n-api/fwd_api/migrations/0002_subscriptionrequestfile_code_userprofile_email_and_more.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 08:10
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL6ad3c98077bf4f9d80514d73219f24d1', max_length=300),
),
migrations.AddField(
model_name='userprofile',
name='email',
field=models.CharField(max_length=200, null=True),
),
migrations.AlterField(
model_name='userprofile',
name='full_name',
field=models.CharField(max_length=200),
),
]

18
cope2n-api/fwd_api/migrations/0003_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:16
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0002_subscriptionrequestfile_code_userprofile_email_and_more'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL6cdfa631c89d41adb8263d8520732ea6', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0004_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:17
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0003_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa095ae5ffa3f490bab474c4f2e66a1ba', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0005_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0004_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL80569683e66148cd8aaa53a5ff622615', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0006_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:27
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0005_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL4a84b7636cfe4db39cd10fbd5a77c085', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0007_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:28
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0006_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL45b8e408ff1c4e9e8794b77e78437699', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0008_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0007_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL7a628e80e45e4c3ea09c5b90054045aa', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0009_alter_subscriptionrequestfile_code.py Executable file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0008_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa7745c2091c84eb29b57e5344bf6cb31', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0010_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 08:44
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0009_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILaff0fce70fb04779825ba49b86a65ed3', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0011_alter_subscriptionrequestfile_code.py Executable file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 08:49
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0010_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL945a1575172d45f0a680ddc178798575', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0012_alter_subscriptionrequestfile_code.py Executable file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-13 09:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0011_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILf7d18096cb3746dda5e943f06130591a', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0013_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 09:05
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0012_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa1b36d470eb74a0cb49abc006b7a45d4', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0014_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 09:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0013_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILd7e2686ad2294812b8e39028dbad95d0', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0015_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 09:54
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0014_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILc8d66f9a9820478dbeff39a7f1bdaae7', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0016_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 10:12
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0015_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILfe1b5576bb7f4b1ebbc9eff4444048f3', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0017_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-13 12:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0016_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILca899ac19c814b5e8abb6fd739950b48', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0018_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-16 06:01
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0017_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL679f7b306fa041518d362290db1109ad', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0019_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-16 07:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0018_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL9125f3f02a994758b5819d635cf354ed', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0020_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-16 07:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0019_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL0ce5f73923cc4eab9a66adadf874354c', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0021_alter_subscriptionrequestfile_code.py Executable file
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# Generated by Django 4.1.3 on 2023-10-16 07:55
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0020_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILcb3b1cfd6a174caea4a45395435e8264', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0022_alter_subscriptionrequestfile_code.py Executable file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-16 08:48
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0021_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL4c38a62d96994bf09e5a1730ecfaf10e', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0023_alter_subscriptionrequestfile_code.py Executable file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-18 06:23
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0022_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL8d7d194cce344b7da23466f277c68184', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0024_alter_subscriptionrequestfile_code.py Executable file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-10-27 03:22
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0023_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILbdffc84dde6b497c9c450c78b0640a0f', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0025_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 10:59
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0024_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL5b6b2a5176e749edae3be7cac245ea3e', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0026_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 11:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0025_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL7d56336dcec347c79da7feec7d802687', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0027_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:22
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0026_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILc2a2a162611142f9888fe60df3930fb4', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0028_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0027_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL7a6dc0368a9d42cc872dc86a115ce956', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0029_alter_subscriptionrequestfile_code.py Normal file
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# Generated by Django 4.1.3 on 2023-11-27 12:27
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0028_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL48b3c5102d7e4d109963edd70293524b', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0030_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:29
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0029_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILbf88633ff774438cb6c75663b578851f', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0031_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0030_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL31b8cc93916948b589117c19fa2c62f1', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0032_alter_subscriptionrequestfile_code.py Normal file
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# Generated by Django 4.1.3 on 2023-11-27 12:36
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0031_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa8037c1a968f4922ae5ddd5904989745', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0033_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:41
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0032_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL99554e8c9ac04e4cb710b0d3f5ca7962', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0034_alter_subscriptionrequestfile_code.py Normal file
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# Generated by Django 4.1.3 on 2023-11-27 12:45
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0033_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa03caec1116644b8ac3363c4ff86ae84', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0035_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0034_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL95116a6c7072422f8b85d6e627bb25b6', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0036_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:47
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0035_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL3cd55c695d634d8c8b0f9b174c5aa5c0', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0037_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:48
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0036_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL48af5750e75c44aba1c37b7db81c301d', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0038_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 12:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0037_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILb91182363733468fb097a90e858d8fd4', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0039_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 13:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0038_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILed00caacc0594739a7303d0017940d3f', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0040_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 13:10
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0039_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa4a846ce46c744bb9a20a35d98cc98c7', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0041_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-27 13:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0040_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL614361b87041476aa7983dded361341a', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0042_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 03:49
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0041_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILd6755b95d03c499792041b55a64d5a9f', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0043_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 08:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0042_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL2ff8a87566434d23b50baa82d0993483', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0044_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:24
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0043_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL457d126686c34c4bb200cb576c5fd31c', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0045_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:35
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0044_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL990106372b0b456e9d936e3bc170cfb4', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0046_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:37
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0045_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL8df31dbe1bfd402eb976a67919244f51', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0047_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0046_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL6bb95d8fb2f14757a4365058e7aab84c', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0048_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:42
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0047_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL6910cd1cff1849ee91109298cfee2fb0', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0049_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:42
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0048_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILd6b0d80a5bf94f87aeea803cf77a1d81', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0050_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 09:53
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0049_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL359fb138f4434fa1860f6927badb4a3c', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0051_alter_subscriptionrequestfile_code.py Normal file
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# Generated by Django 4.1.3 on 2023-11-28 09:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0050_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILee6ab5a3296345ca8d42bde2f523e852', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0052_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 10:02
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0051_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL622353d9d8d1492d8bd77f150e02aabf', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0053_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 10:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0052_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL20bddf9dd2694e56adb2f4160d9254ee', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0054_alter_subscriptionrequestfile_code.py Normal file
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# Generated by Django 4.1.3 on 2023-11-28 10:03
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0053_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILbba8d19152024d01bb26063ca26f57ef', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0055_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 10:05
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0054_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL619b04fdcd9a44ed987a72a282cc4ca3', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0056_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 10:08
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0055_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa86df077a1734c289e04b2c56afa33fe', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0057_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:21
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0056_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL996c470fedc64ad4825798642d687092', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0058_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:28
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0057_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL3eb614edc9284d7892c890ee5ebffce5', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0059_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:29
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0058_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL51bbc6937ddf4dea93a8ab57d3e04411', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0060_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:30
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0059_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILe8ad9caeca4a4f86a23673cc8d00ee65', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0061_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:33
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0060_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL63c7091d586d4acea449985045a5bba9', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0062_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:34
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0061_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL95df4ac68d194c86ba7aed53940f083a', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0063_alter_subscriptionrequestfile_code.py Normal file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:35
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0062_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILf41f2b04385845258b40ba4296aebafc', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0064_alter_subscriptionrequestfile_code.py Normal file
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@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-28 12:37
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0063_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL8e126a8613ab4346acb968121ba10465', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0065_alter_subscriptionrequestfile_code.py Normal file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-29 07:11
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0064_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL14de2e7b97194afa97a7825143af8aee', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0066_alter_subscriptionrequestfile_code.py Normal file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-29 07:56
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0065_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FILa34ea0946b9f45ac81c9a27baeeb0dec', max_length=300),
),
]

18
cope2n-api/fwd_api/migrations/0067_alter_subscriptionrequestfile_code.py Normal file
View File

@ -0,0 +1,18 @@
# Generated by Django 4.1.3 on 2023-11-29 07:57
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fwd_api', '0066_alter_subscriptionrequestfile_code'),
]
operations = [
migrations.AlterField(
model_name='subscriptionrequestfile',
name='code',
field=models.CharField(default='FIL2ced0a615954429ebcaeae17911523ae', max_length=300),
),
]

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