# 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=12, 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)]