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[to #42322933]fix: animal recognition and general recognition and reove rfconv

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Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9477652
This commit is contained in:
yanheng.wyh
2022-08-05 17:28:48 +08:00
committed by yingda.chen
parent 794e277270
commit 4cb1dbdaf3
10 changed files with 160 additions and 19 deletions
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3
modelscope/metainfo.py
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@@ -76,7 +76,8 @@ class Pipelines(object):
person_image_cartoon = 'unet-person-image-cartoon'
ocr_detection = 'resnet18-ocr-detection'
action_recognition = 'TAdaConv_action-recognition'
animal_recognation = 'resnet101-animal_recog'
animal_recognition = 'resnet101-animal-recognition'
general_recognition = 'resnet101-general-recognition'
cmdssl_video_embedding = 'cmdssl-r2p1d_video_embedding'
body_2d_keypoints = 'hrnetv2w32_body-2d-keypoints_image'
human_detection = 'resnet18-human-detection'

9
modelscope/models/cv/animal_recognition/resnet.py
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@@ -81,8 +81,7 @@ class Bottleneck(nn.Module):
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
elif rectified_conv:
from rfconv import RFConv2d
self.conv2 = RFConv2d(
self.conv2 = nn.Conv2d(
group_width,
group_width,
kernel_size=3,
@@ -90,8 +89,7 @@ class Bottleneck(nn.Module):
padding=dilation,
dilation=dilation,
groups=cardinality,
bias=False,
average_mode=rectify_avg)
bias=False)
self.bn2 = norm_layer(group_width)
else:
self.conv2 = nn.Conv2d(
@@ -190,8 +188,7 @@ class ResNet(nn.Module):
self.rectified_conv = rectified_conv
self.rectify_avg = rectify_avg
if rectified_conv:
from rfconv import RFConv2d
conv_layer = RFConv2d
conv_layer = nn.Conv2d
else:
conv_layer = nn.Conv2d
conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}

4
modelscope/models/cv/animal_recognition/splat.py
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@@ -39,8 +39,7 @@ class SplAtConv2d(Module):
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
from rfconv import RFConv2d
self.conv = RFConv2d(
self.conv = Conv2d(
in_channels,
channels * radix,
kernel_size,
@@ -49,7 +48,6 @@ class SplAtConv2d(Module):
dilation,
groups=groups * radix,
bias=bias,
average_mode=rectify_avg,
**kwargs)
else:
self.conv = Conv2d(

8
modelscope/pipelines/cv/__init__.py
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@@ -10,8 +10,9 @@ if TYPE_CHECKING:
from .cmdssl_video_embedding_pipeline import CMDSSLVideoEmbeddingPipeline
from .image_detection_pipeline import ImageDetectionPipeline
from .face_detection_pipeline import FaceDetectionPipeline
from .face_recognition_pipeline import FaceRecognitionPipeline
from .face_image_generation_pipeline import FaceImageGenerationPipeline
from .face_recognition_pipeline import FaceRecognitionPipeline
from .general_recognition_pipeline import GeneralRecognitionPipeline
from .image_cartoon_pipeline import ImageCartoonPipeline
from .image_classification_pipeline import GeneralImageClassificationPipeline
from .image_color_enhance_pipeline import ImageColorEnhancePipeline
@@ -23,7 +24,7 @@ if TYPE_CHECKING:
from .image_portrait_enhancement_pipeline import ImagePortraitEnhancementPipeline
from .image_style_transfer_pipeline import ImageStyleTransferPipeline
from .image_super_resolution_pipeline import ImageSuperResolutionPipeline
from .image_to_image_generate_pipeline import Image2ImageGenerationePipeline
from .image_to_image_generate_pipeline import Image2ImageGenerationPipeline
from .image_to_image_translation_pipeline import Image2ImageTranslationPipeline
from .product_retrieval_embedding_pipeline import ProductRetrievalEmbeddingPipeline
from .live_category_pipeline import LiveCategoryPipeline
@@ -41,6 +42,7 @@ else:
'face_detection_pipeline': ['FaceDetectionPipeline'],
'face_image_generation_pipeline': ['FaceImageGenerationPipeline'],
'face_recognition_pipeline': ['FaceRecognitionPipeline'],
'general_recognition_pipeline': ['GeneralRecognitionPipeline'],
'image_classification_pipeline':
['GeneralImageClassificationPipeline', 'ImageClassificationPipeline'],
'image_cartoon_pipeline': ['ImageCartoonPipeline'],
@@ -60,7 +62,7 @@ else:
['ProductRetrievalEmbeddingPipeline'],
'live_category_pipeline': ['LiveCategoryPipeline'],
'image_to_image_generation_pipeline':
['Image2ImageGenerationePipeline'],
['Image2ImageGenerationPipeline'],
'ocr_detection_pipeline': ['OCRDetectionPipeline'],
'skin_retouching_pipeline': ['SkinRetouchingPipeline'],
'video_category_pipeline': ['VideoCategoryPipeline'],

2
modelscope/pipelines/cv/animal_recognition_pipeline.py
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@@ -21,7 +21,7 @@ logger = get_logger()
@PIPELINES.register_module(
Tasks.image_classification, module_name=Pipelines.animal_recognation)
Tasks.animal_recognition, module_name=Pipelines.animal_recognition)
class AnimalRecognitionPipeline(Pipeline):
def __init__(self, model: str, **kwargs):

121
modelscope/pipelines/cv/general_recognition_pipeline.py Normal file
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@@ -0,0 +1,121 @@
import os.path as osp
from typing import Any, Dict
import cv2
import numpy as np
import torch
from PIL import Image
from torchvision import transforms
from modelscope.hub.snapshot_download import snapshot_download
from modelscope.metainfo import Pipelines
from modelscope.models.cv.animal_recognition import resnet
from modelscope.outputs import OutputKeys
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import LoadImage, load_image
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger
logger = get_logger()
@PIPELINES.register_module(
Tasks.general_recognition, module_name=Pipelines.general_recognition)
class GeneralRecognitionPipeline(Pipeline):
def __init__(self, model: str, device: str):
"""
use `model` to create a general recognition pipeline for prediction
Args:
model: model id on modelscope hub.
"""
super().__init__(model=model)
import torch
def resnest101(**kwargs):
model = resnet.ResNet(
resnet.Bottleneck, [3, 4, 23, 3],
radix=2,
groups=1,
bottleneck_width=64,
deep_stem=True,
stem_width=64,
avg_down=True,
avd=True,
avd_first=False,
**kwargs)
return model
def filter_param(src_params, own_state):
copied_keys = []
for name, param in src_params.items():
if 'module.' == name[0:7]:
name = name[7:]
if '.module.' not in list(own_state.keys())[0]:
name = name.replace('.module.', '.')
if (name in own_state) and (own_state[name].shape
== param.shape):
own_state[name].copy_(param)
copied_keys.append(name)
def load_pretrained(model, src_params):
if 'state_dict' in src_params:
src_params = src_params['state_dict']
own_state = model.state_dict()
filter_param(src_params, own_state)
model.load_state_dict(own_state)
self.model = resnest101(num_classes=54092)
local_model_dir = model
device = 'cpu'
if osp.exists(model):
local_model_dir = model
else:
local_model_dir = snapshot_download(model)
self.local_path = local_model_dir
src_params = torch.load(
osp.join(local_model_dir, ModelFile.TORCH_MODEL_FILE), device)
load_pretrained(self.model, src_params)
logger.info('load model done')
def preprocess(self, input: Input) -> Dict[str, Any]:
img = LoadImage.convert_to_img(input)
normalize = transforms.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(), normalize
])
img = transform(img)
result = {'img': img}
return result
def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
def set_phase(model, is_train):
if is_train:
model.train()
else:
model.eval()
is_train = False
set_phase(self.model, is_train)
img = input['img']
input_img = torch.unsqueeze(img, 0)
outputs = self.model(input_img)
return {'outputs': outputs}
def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
label_mapping_path = osp.join(self.local_path, 'meta_info.txt')
with open(label_mapping_path, 'r') as f:
label_mapping = f.readlines()
score = torch.max(inputs['outputs'])
inputs = {
OutputKeys.SCORES:
score.item(),
OutputKeys.LABELS:
label_mapping[inputs['outputs'].argmax()].split('\t')[1]
}
return inputs

2
modelscope/pipelines/cv/image_to_image_generate_pipeline.py
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@@ -32,7 +32,7 @@ logger = get_logger()
@PIPELINES.register_module(
Tasks.image_to_image_generation,
module_name=Pipelines.image_to_image_generation)
class Image2ImageGenerationePipeline(Pipeline):
class Image2ImageGenerationPipeline(Pipeline):
def __init__(self, model: str, **kwargs):
"""

2
modelscope/utils/constant.py
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@@ -17,12 +17,14 @@ class CVTasks(object):
ocr_recognition = 'ocr-recognition'
# human face body related
animal_recognition = 'animal-recognition'
face_detection = 'face-detection'
face_recognition = 'face-recognition'
human_detection = 'human-detection'
human_object_interaction = 'human-object-interaction'
face_image_generation = 'face-image-generation'
body_2d_keypoints = 'body-2d-keypoints'
general_recognition = 'general-recognition'
image_classification = 'image-classification'
image_multilabel_classification = 'image-multilabel-classification'

8
tests/pipelines/test_animal_recognation.py → tests/pipelines/test_animal_recognition.py
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@@ -5,14 +5,14 @@ from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
class MultiModalFeatureTest(unittest.TestCase):
class AnimalRecognitionTest(unittest.TestCase):
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def test_run(self):
animal_recog = pipeline(
Tasks.image_classification,
animal_recognition = pipeline(
Tasks.animal_recognition,
model='damo/cv_resnest101_animal_recognition')
result = animal_recog('data/test/images/dogs.jpg')
result = animal_recognition('data/test/images/dogs.jpg')
print(result)

20
tests/pipelines/test_general_recognition.py Normal file
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@@ -0,0 +1,20 @@
import unittest
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.test_utils import test_level
class GeneralRecognitionTest(unittest.TestCase):
@unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
def test_run(self):
general_recognition = pipeline(
Tasks.general_recognition,
model='damo/cv_resnest101_general_recognition')
result = general_recognition('data/test/images/dogs.jpg')
print(result)
if __name__ == '__main__':
unittest.main()