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pytorch_to_coreml.py
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pytorch_to_coreml.py
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# -*- coding: utf-8 -*-
"""
This script converts PyTorch implemented Chinese-CLIP (text or vision) model to CoreML format for deployment in Apple's ecosystem.
"""
import os
import argparse
from PIL import Image
import torch
from torch import nn
import coremltools as ct
import cn_clip.clip as clip
from cn_clip.clip.utils import _MODELS, _MODEL_INFO, _download, available_models, create_model, image_transform
class ImageEncoder(nn.Module):
def __init__(self, clip_model):
super().__init__()
self.clip_model = clip_model
def forward(self, image):
return self.clip_model.encode_image(image)
class TextEncoder(nn.Module):
def __init__(self, clip_model):
super().__init__()
self.clip_model = clip_model
def forward(self, text):
return self.clip_model.encode_text(text)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-arch",
required=True,
choices=["ViT-B-16", "ViT-L-14", "ViT-L-14-336", "ViT-H-14", "RN50"],
help="Specify the architecture (model scale) of Chinese-CLIP model to be converted."
)
parser.add_argument(
"--pytorch-ckpt-path",
default=None,
type=str,
help="Path of the input PyTorch Chinese-CLIP checkpoint."
)
parser.add_argument(
"--download-root",
default=None,
type=str,
help="If --pytorch-ckpt-path is None, official pretrained ckpt will be downloaded under --download-root directory and converted."
)
parser.add_argument(
"--save-coreml-path",
required=True,
type=str,
help="Path (prefix) of the output converted CoreML Chinese-CLIP text or vision model."
)
parser.add_argument(
"--convert-text",
action="store_true",
help="Whether to convert the text encoder (text feature extractor) into CoreML."
)
parser.add_argument(
"--convert-vision",
action="store_true",
help="Whether to convert the vision encoder (vision feature extractor) into CoreML."
)
parser.add_argument(
"--precision",
default="fp16",
choices=["fp16", "fp32"],
help="Specify the architecture (model scale) of Chinese-CLIP model to be converted."
)
parser.add_argument(
"--context-length", type=int, default=52, help="The padded length of input text (include [CLS] & [SEP] tokens)."
)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
# Log params.
print("Params:")
for name in sorted(vars(args)):
val = getattr(args, name)
print(f" {name}: {val}")
# prepare the PyTorch model weights
if os.path.isfile(args.pytorch_ckpt_path):
input_ckpt_path = args.pytorch_ckpt_path
elif args.model_arch in _MODELS:
input_ckpt_path = _download(
_MODELS[args.model_arch], args.download_root or os.path.expanduser("./cache/clip"))
else:
raise RuntimeError(
f"Model {args.model_arch} not found; available models = {available_models()}")
with open(input_ckpt_path, 'rb') as opened_file:
checkpoint = torch.load(opened_file, map_location="cpu")
# prepare the PyTorch implemented model and restore weights
model = create_model(
_MODEL_INFO[args.model_arch]['struct'], checkpoint).float().eval()
# prepare empty image and text as input placeholders for CoreML
resolution = _MODEL_INFO[args.model_arch]['input_resolution']
preprocess = image_transform(resolution)
if args.precision == "fp16":
precision = ct.precision.FLOAT16
elif args.precision == "fp32":
precision = ct.precision.FLOAT32
image = preprocess(Image.new('RGB', (resolution, resolution))).unsqueeze(0)
text = clip.tokenize([""], context_length=args.context_length)
# perform conversions, CoreML text and vision encoders will be saved into separated files
if args.convert_text:
# Prepare the model for conversion
text_model = TextEncoder(model)
text_model.eval()
# Prepare text input
text = clip.tokenize([""], context_length=args.context_length).int()
# Trace the model for text input
traced_text_model = torch.jit.trace(text_model, text)
# Convert traced model to CoreML
text_outputs = [ct.TensorType(
name="text_features")]
text_coreml_model = ct.convert(
traced_text_model,
inputs=[ct.TensorType(name="text", shape=text.shape)],
outputs=text_outputs,
convert_to="mlprogram",
compute_precision=precision,
minimum_deployment_target=ct.target.iOS15
)
# Save the CoreML model
text_coreml_model_path = f"{args.save_coreml_path}.text.mlpackage"
text_coreml_model.save(text_coreml_model_path)
print(
f"Text model converted to CoreML and saved at: {text_coreml_model_path}")
if args.convert_vision:
# Prepare the model for conversion
image_model = ImageEncoder(model)
image_model.eval()
# Prepare a dummy image input
image_width = 336 if args.model_arch == "ViT-L-14-336" else 224
dummy_image_input = torch.rand(1, 3, image_width, image_width)
# Trace the model for image input
traced_image_model = torch.jit.trace(image_model, dummy_image_input)
# Convert traced model to CoreML
image_outputs = [ct.TensorType(name="image_features")]
image_coreml_model = ct.convert(
traced_image_model,
inputs=[ct.TensorType(
name="image", shape=dummy_image_input.shape)],
outputs=image_outputs,
convert_to="mlprogram",
compute_precision=precision,
minimum_deployment_target=ct.target.iOS15
)
# Save the CoreML model
image_coreml_model_path = f"{args.save_coreml_path}.image.mlpackage"
image_coreml_model.save(image_coreml_model_path)
print(
f"Image model converted to CoreML and saved at: {image_coreml_model_path}")