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

240 lines
9.0 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 sys
from pathlib import Path
import torch
from torch import nn
from torchvision import transforms
from towhee.types.image_utils import to_pil
from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
from towhee import register
from transformers import CLIPTokenizer, CLIPTextModel ,CLIPModel,CLIPProcessor
#from towhee.dc2 import accelerate
def create_model(model_name, modality, checkpoint_path, device):
hf_clip_model = CLIPModel.from_pretrained(model_name)
if checkpoint_path:
try:
state_dict = torch.load(checkpoint_path, map_location=device)
hf_clip_model.load_state_dict(state_dict)
except Exception as e:
log.error(f"Fail to load state dict from {checkpoint_path}: {e}")
hf_clip_model.to(device)
hf_clip_model.eval()
if modality == 'image':
clip = CLIPModelVision(hf_clip_model)
elif modality == 'text':
clip = CLIPModelText(hf_clip_model)
else:
raise ValueError("modality[{}] not implemented.".format(modality))
model = Model(clip)
return model
class CLIPModelVision(nn.Module):
def __init__(self, model):
super().__init__()
self.backbone = model
def forward(self, pixel_values):
image_embeds = self.backbone.get_image_features(pixel_values)
return image_embeds
class CLIPModelText(nn.Module):
def __init__(self, model):
super().__init__()
self.backbone = model
def forward(self, input_ids, attention_mask):
text_embeds = self.backbone.get_text_features(input_ids, attention_mask)
return text_embeds
#@accelerate
class Model:
def __init__(self, model):
self.model = model
def __call__(self, *args, **kwargs):
outs = self.model(*args, **kwargs)
return outs
@register(output_schema=['vec'])
class Clip(NNOperator):
"""
CLIP multi-modal embedding operator
"""
def __init__(self, model_name: str, modality: str, device: str = 'cpu', checkpoint_path: str = None):
self.model_name = model_name
self.modality = modality
self.device = device
self.checkpoint_path = checkpoint_path
cfg = self._configs()[model_name]
self.model = create_model(cfg, modality, checkpoint_path, device)
self.tokenizer = CLIPTokenizer.from_pretrained(cfg)
self.processor = CLIPProcessor.from_pretrained(cfg)
def inference_single_data(self, data):
if self.modality == 'image':
vec = self._inference_from_image(data)
elif self.modality == 'text':
vec = self._inference_from_text(data)
else:
raise ValueError("modality[{}] not implemented.".format(self.modality))
return vec.detach().cpu().numpy().flatten()
def __call__(self, data):
if not isinstance(data, list):
data = [data]
else:
data = data
results = []
for single_data in data:
result = self.inference_single_data(single_data)
results.append(result)
if len(data) == 1:
return results[0]
else:
return results
def _inference_from_text(self, text):
tokens = self.tokenizer([text], padding=True, return_tensors="pt")
text_features = self.model(tokens['input_ids'].to(self.device), tokens['attention_mask'].to(self.device))
return text_features
@arg(1, to_image_color('RGB'))
def _inference_from_image(self, img):
img = to_pil(img)
inputs = self.processor(images=img, return_tensors="pt")
image_features = self.model(inputs['pixel_values'].to(self.device))
return image_features
def train(self, **kwargs):
import sys
import pathlib
path = str(pathlib.Path(__file__).parent)
print(path)
sys.path.append(path)
from train_clip_with_hf_trainer import train_with_hf_trainer
data_args = kwargs.pop('data_args', None)
training_args = kwargs.pop('training_args', None)
train_with_hf_trainer(self._model.backbone, self.tokenizer, data_args, training_args)
def _configs(self):
config = {}
config['clip_vit_base_patch16'] = 'openai/clip-vit-base-patch16'
config['clip_vit_base_patch32'] = 'openai/clip-vit-base-patch32'
config['clip_vit_large_patch14'] = 'openai/clip-vit-large-patch14'
config['clip_vit_large_patch14_336'] ='openai/clip-vit-large-patch14-336'
return config
@property
def supported_formats(self):
onnxes = self.supported_model_names(format='onnx')
if self.model_name in onnxes:
return ['onnx']
else:
return ['pytorch']
@staticmethod
def supported_model_names(format: str = None):
if format == 'pytorch' or format == 'torchscript' or format == 'onnx':
model_list = [
'clip_vit_base_patch16',
'clip_vit_base_patch32',
'clip_vit_large_patch14',
'clip_vit_large_patch14_336'
]
else:
log.error(f'Invalid format "{format}". Currently supported formats: "pytorch", "torchscript".')
return model_list
@property
def _model(self):
return self.model.model
def save_model(self, model_type: str = 'pytorch', output_file: str = 'default'):
import os
from PIL import Image
from torch.onnx import export as onnx_export
if output_file == 'default':
output_file = str(Path(__file__).parent)
output_file = os.path.join(output_file, 'saved', model_type)
os.makedirs(output_file, exist_ok=True)
name = self.model_name.replace('/', '-')
output_file = os.path.join(output_file, name)
if model_type in ['pytorch', 'torchscript']:
output_file = output_file + '.pt'
elif model_type == 'onnx':
output_file = output_file + '.onnx'
else:
raise AttributeError('Unsupported model_type.')
if self.modality == 'image':
sz = self.processor.feature_extractor.crop_size
if isinstance(sz, int):
h = sz
w = sz
elif isinstance(sz, dict):
h = sz['height']
w = sz['width']
dummy_input = Image.new('RGB', (w, h), color = 'red')
inputs = self.processor(images=dummy_input, return_tensors='pt').to(self.device) # a dictionary
elif self.modality == 'text':
dummy_input = 'dummy'
inputs = self.tokenizer(dummy_input, padding=True, truncation=True, return_tensors='pt').to(self.device) # a dictionary
else:
raise ValueError("modality[{}] not implemented.".format(self.modality))
if model_type == 'pytorch':
torch.save(self._model, output_file)
elif model_type == 'torchscript':
inputs = list(inputs.values())
try:
try:
jit_model = torch.jit.script(self._model)
except Exception:
jit_model = torch.jit.trace(self._model, inputs, strict=False)
torch.jit.save(jit_model, output_file)
except Exception as e:
log.error(f'Fail to save as torchscript: {e}.')
raise RuntimeError(f'Fail to save as torchscript: {e}.')
elif model_type == 'onnx':
if self.modality == 'image':
input_names= ['pixel_values']
output_names=['image_embeds']
dynamic_axes={'pixel_values': {0: 'batch'}, 'image_embeds': {0: 'batch'}}
elif self.modality == 'text':
input_names= ['input_ids', 'attention_mask']
output_names=['text_embeds']
dynamic_axes={'input_ids': {0: 'batch', 1: 'sequence'}, 'attention_mask': {0: 'batch', 1: 'sequence'}, 'text_embeds': {0: 'batch'}}
else:
raise ValueError("modality[{}] not implemented.".format(self.modality))
onnx_export(self._model,
(dict(inputs),),
f=Path(output_file),
input_names= input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
do_constant_folding=True,
opset_version=14,
)
else:
raise NotImplementedError