logo
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Readme
Files and versions

316 lines
11 KiB

# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import numpy
import os
from pathlib import Path
from typing import List, Union
import towhee
from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
from towhee import register
from towhee.types import Image
try:
from towhee import accelerate
except:
def accelerate(func):
return func
import torch
from torch import nn
from PIL import Image as PILImage
import timm
from timm.data import create_transform, resolve_data_config
from timm.models import create_model, get_pretrained_cfg
import warnings
warnings.filterwarnings('ignore')
log = logging.getLogger('timm_op')
log.setLevel(logging.ERROR)
def torch_no_grad(f):
def wrap(*args, **kwargs):
with torch.no_grad():
return f(*args, **kwargs)
return wrap
@accelerate
class Model:
def __init__(self, model_name, device, num_classes, checkpoint_path=None):
self.device = device
if checkpoint_path:
assert os.path.exists(checkpoint_path), f'File not found: {checkpoint_path}'
self.model = create_model(model_name, pretrained=False, checkpoint_path=checkpoint_path, num_classes=num_classes)
else:
self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
self.model.eval()
self.model.to(device)
def __call__(self, x: torch.Tensor):
return self.model.forward_features(x.to(self.device))
@register(output_schema=['vec'])
class TimmImage(NNOperator):
"""
Pytorch image embedding operator that uses the Pytorch Image Model (timm) collection.
Args:
model_name (`str`):
Which model to use for the embeddings.
num_classes (`int = 1000`):
Number of classes for classification.
skip_preprocess (`bool = False`):
Whether skip image transforms.
"""
def __init__(self,
model_name: str = None,
num_classes: int = 1000,
skip_preprocess: bool = False,
device: str = None,
checkpoint_path: str = None
) -> None:
super().__init__()
if not torch.cuda.is_available():
log.warning('Gpu is not available, use cpu')
self.device = 'cpu'
else:
if device is None:
self.device = 'cuda'
else:
self.device = device
self.model_name = model_name
if self.model_name:
self.model = Model(
model_name=model_name,
device=self.device,
num_classes=num_classes,
checkpoint_path=checkpoint_path
)
try:
self.tfms = create_transform(
input_size=self.config['input_size'],
interpolation=self.config['interpolation'],
mean=self.config['mean'],
std=self.config['std'],
crop_pct=self.config['crop_pct']
)
except:
self.tfms = create_transform(**resolve_data_config({}, model=self.model.model))
self.skip_tfms = skip_preprocess
else:
log.warning('The operator is initialized without specified model.')
pass
@torch_no_grad
def __call__(self, data: Union[List['towhee.types.Image'], 'towhee.types.Image']):
if not isinstance(data, list):
imgs = [data]
else:
imgs = data
img_list = []
for img in imgs:
img = self.convert_img(img) if isinstance(img, numpy.ndarray) else img.convert('RGB')
img = img if self.skip_tfms else self.tfms(img)
img_list.append(img)
inputs = torch.stack(img_list)
inputs = inputs
features = self.model(inputs)
if isinstance(features, list):
features = [self.post_proc(x) for x in features]
else:
features = self.post_proc(features)
if isinstance(data, list):
vecs = [list(x.detach().numpy()) for x in features] if isinstance(features, list) \
else list(features.detach().numpy())
else:
vecs = [x.squeeze(0).detach().numpy() for x in features] if isinstance(features, list) \
else features.squeeze(0).detach().numpy()
return vecs
@property
def _model(self):
return self.model.model
@property
def config(self):
config = get_pretrained_cfg(self.model_name)
if not isinstance(config, dict) and hasattr(config, 'to_dict'):
config = config.to_dict()
return config
@arg(1, to_image_color('RGB'))
def convert_img(self, img: 'towhee.types.Image'):
img = PILImage.fromarray(img.astype('uint8'), 'RGB')
return img
def post_proc(self, features):
features = features.to('cpu')
if features.dim() == 3:
features = features[:, 0]
if features.dim() == 4:
global_pool = nn.AdaptiveAvgPool2d(1)
features = global_pool(features)
features = features.flatten(1)
assert features.dim() == 2, f'Invalid output dim {features.dim()}'
return features
def save_model(self, format: str = 'pytorch', path: str = 'default'):
if path == 'default':
path = str(Path(__file__).parent)
path = os.path.join(path, 'saved', format)
os.makedirs(path, exist_ok=True)
name = self.model_name.replace('/', '-')
path = os.path.join(path, name)
if format in ['pytorch', 'torchscript']:
path = path + '.pt'
elif format == 'onnx':
path = path + '.onnx'
else:
raise AttributeError(f'Invalid format {format}.')
dummy_input = torch.rand((1,) + self.config['input_size'])
if format == 'pytorch':
torch.save(self._model, path)
elif format == 'torchscript':
try:
try:
jit_model = torch.jit.script(self._model)
except Exception:
jit_model = torch.jit.trace(self._model, dummy_input, strict=False)
torch.jit.save(jit_model, path)
except Exception as e:
log.error(f'Fail to save as torchscript: {e}.')
raise RuntimeError(f'Fail to save as torchscript: {e}.')
elif format == 'onnx':
self._model.forward = self._model.forward_features
try:
torch.onnx.export(self._model.to('cpu'),
dummy_input,
path,
input_names=['input_0'],
output_names=['output_0'],
opset_version=14,
dynamic_axes={
'input_0': {0: 'batch_size'},
'output_0': {0: 'batch_size'}
},
do_constant_folding=True
)
except Exception as e:
log.error(f'Fail to save as onnx: {e}.')
raise RuntimeError(f'Fail to save as onnx: {e}.')
# todo: elif format == 'tensorrt':
else:
log.error(f'Unsupported format "{format}".')
return Path(path).resolve()
@staticmethod
def supported_model_names(format: str = None):
if timm.__version__ != '0.6.12':
log.warning('Please note that the model list is tested with timm==0.6.12, please check your timm version.')
full_list = list(set(timm.list_models(pretrained=True)) - set([
'coat_mini',
'coat_tiny',
'crossvit_9_240',
'crossvit_9_dagger_240',
'crossvit_15_240',
'crossvit_15_dagger_240',
'crossvit_15_dagger_408',
'crossvit_18_240',
'crossvit_18_dagger_240',
'crossvit_18_dagger_408',
'crossvit_base_240',
'crossvit_small_240',
'crossvit_tiny_240',
]))
full_list.sort()
if format in [None, 'pytorch']:
model_list = full_list
elif format == 'onnx':
to_remove = [
'bat_resnext26ts',
'convmixer_1024_20_ks9_p14',
'convmixer_1536_20',
'convmixer_768_32',
'eca_halonext26ts',
'efficientformer_l1',
'efficientformer_l3',
'efficientformer_l7',
'halo2botnet50ts_256',
'halonet26t',
'halonet50ts',
'haloregnetz_b',
'lamhalobotnet50ts_256',
'levit_128',
'levit_128s',
'levit_192',
'levit_256',
'levit_384',
'pvt_v2_b2_li',
'sehalonet33ts',
'tf_efficientnet_cc_b0_4e',
'tf_efficientnet_cc_b0_8e',
'tf_efficientnet_cc_b1_8e',
'tresnet_l',
'tresnet_l_448',
'tresnet_m',
'tresnet_m_448',
'tresnet_m_miil_in21k',
'tresnet_v2_l',
'tresnet_xl',
'tresnet_xl_448',
'volo_d1_224',
'volo_d1_384',
'volo_d2_224',
'volo_d2_384',
'volo_d3_224',
'volo_d3_448',
'volo_d4_224',
'volo_d4_448',
'volo_d5_224',
'volo_d5_448',
'volo_d5_512'
]
# assert set(to_remove).issubset(set(full_list))
model_list = list(set(full_list) - set(to_remove))
# todo: elif format == 'torchscript':
# todo: elif format == 'tensorrt'
else:
log.error(f'Invalid format "{format}". Currently supported formats: "pytorch".')
return model_list
@property
def supported_formats(self):
if self.model_name in self.supported_model_names(format='onnx'):
return ['onnx']
else:
return []
def input_schema(self):
return [(Image, (-1, -1, 3))]
def output_schema(self):
image = Image(numpy.random.randn(480, 480, 3), "RGB")
ret = self(image)
data_type = type(ret.reshape(-1)[0])
return [(data_type, ret.shape)]