/
convert_params_compute_diff.py
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/
convert_params_compute_diff.py
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# -*- coding: utf-8 -*-
# @Time : 2023/8/26 11:34
# @Author : zhoujun
import os
import cv2
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
import yaml
import numpy as np
import paddle
import torch
from torchocr.modeling.architectures import build_model
from ppocr.modeling.architectures import build_model as build_model_paddle
from ppocr.postprocess import build_post_process
def load_config(file_path):
_, ext = os.path.splitext(file_path)
assert ext in [".yml", ".yaml"], "only support yaml files for now"
config = yaml.load(open(file_path, "rb"), Loader=yaml.Loader)
return config
def init_head(config):
global_config = config["Global"]
post_process_class = build_post_process(config["PostProcess"], global_config)
# build model
# for rec algorithm
if hasattr(post_process_class, "character"):
char_num = len(getattr(post_process_class, "character"))
if config["Architecture"]["algorithm"] in [
"Distillation",
]: # distillation model
for key in config["Architecture"]["Models"]:
if (
config["Architecture"]["Models"][key]["Head"]["name"] == "MultiHead"
): # for multi head
if config["PostProcess"]["name"] == "DistillationSARLabelDecode":
char_num = char_num - 2
if config["PostProcess"]["name"] == "DistillationNRTRLabelDecode":
char_num = char_num - 3
out_channels_list = {}
out_channels_list["CTCLabelDecode"] = char_num
# update SARLoss params
if (
list(config["Loss"]["loss_config_list"][-1].keys())[0]
== "DistillationSARLoss"
):
config["Loss"]["loss_config_list"][-1]["DistillationSARLoss"][
"ignore_index"
] = (char_num + 1)
out_channels_list["SARLabelDecode"] = char_num + 2
elif (
list(config["Loss"]["loss_config_list"][-1].keys())[0]
== "DistillationNRTRLoss"
):
out_channels_list["NRTRLabelDecode"] = char_num + 3
config["Architecture"]["Models"][key]["Head"][
"out_channels_list"
] = out_channels_list
else:
config["Architecture"]["Models"][key]["Head"][
"out_channels"
] = char_num
elif config["Architecture"]["Head"]["name"] == "MultiHead": # for multi head
if config["PostProcess"]["name"] == "SARLabelDecode":
char_num = char_num - 2
if config["PostProcess"]["name"] == "NRTRLabelDecode":
char_num = char_num - 3
out_channels_list = {}
out_channels_list["CTCLabelDecode"] = char_num
# update SARLoss params
if list(config["Loss"]["loss_config_list"][1].keys())[0] == "SARLoss":
if config["Loss"]["loss_config_list"][1]["SARLoss"] is None:
config["Loss"]["loss_config_list"][1]["SARLoss"] = {
"ignore_index": char_num + 1
}
else:
config["Loss"]["loss_config_list"][1]["SARLoss"]["ignore_index"] = (
char_num + 1
)
out_channels_list["SARLabelDecode"] = char_num + 2
elif list(config["Loss"]["loss_config_list"][1].keys())[0] == "NRTRLoss":
out_channels_list["NRTRLabelDecode"] = char_num + 3
config["Architecture"]["Head"]["out_channels_list"] = out_channels_list
else: # base rec model
config["Architecture"]["Head"]["out_channels"] = char_num
if config["PostProcess"]["name"] == "SARLabelDecode": # for SAR model
config["Loss"]["ignore_index"] = char_num - 1
return config
def conver_params(model_config, paddle_params_path, tmp_dir, show_log=False):
from padiff import assign_weight, create_model
torch_model = build_model(model_config)
paddle_model = build_model_paddle(model_config)
if os.path.exists(paddle_params_path):
paddle_model.set_state_dict(paddle.load(paddle_params_path))
print(f'load paddle ckpt from {paddle_params_path}')
torch_model.eval()
paddle_model.eval()
torch_model_warp = create_model(torch_model)
paddle_model_warp = create_model(paddle_model)
torch_model_warp.auto_layer_map("base", show_log=show_log)
paddle_model_warp.auto_layer_map("raw", show_log=show_log)
assign_weight(torch_model_warp, paddle_model_warp)
# for recv4 rec and det
# torch2paddle(torch_model, paddle_model)
if not os.path.exists(paddle_params_path):
paddle_params_path = os.path.join(tmp_dir, 'paddle.pdparams')
paddle.save(paddle_model.state_dict(), paddle_params_path)
print(f"save default paddle params success to {paddle_params_path}")
torch_params_path = paddle_params_path.replace(".pdparams", ".pth")
torch.save({"state_dict": torch_model.state_dict()}, torch_params_path)
print(f"save convert torch params to {torch_params_path}")
return paddle_params_path, torch_params_path
def torch2paddle(torch_model: torch.nn.Module, paddle_model: paddle.nn.Layer):
paddle_state_dict = paddle_model.state_dict()
torch_dict = torch_model.state_dict()
# paddle_state_dict = paddle.load(paddle_model)
fc_names = ["qkv",'fc', 'kv', 'tgt_word_prj','q','out_proj','linear','proj'] # v4 rec
# fc_names = []
torch_state_dict = {}
for k in paddle_state_dict:
v = paddle_state_dict[k].detach().cpu().numpy()
flag = [i in k for i in fc_names]
if any(flag) and "weight" in k: # ignore bias
new_shape = [1, 0] + list(range(2, v.ndim))
print(f"name: {k}, ori shape: {v.shape}, new shape: {v.transpose(new_shape).shape}")
v = v.transpose(new_shape)
k = k.replace("_variance", "running_var")
k = k.replace("_mean", "running_mean")
# if torch_dict[k].numpy().shape != v.shape:
# print(torch_dict[k].numpy().shape, v.shape)
torch_state_dict[k] = torch.from_numpy(v)
for k in torch_state_dict:
if k not in torch_model.state_dict():
print(f'{k} is not in torch model')
for k in torch_model.state_dict():
if 'num_batches_tracked' in k:
continue
if k not in torch_state_dict:
print(f'{k} is not in torch params')
torch_model.load_state_dict(torch_state_dict)
def get_input(w, h, color=True):
img = cv2.imread("doc/imgs/1.jpg", 1 if color else 0)
img = cv2.resize(img, (w, h))
if len(img.shape) == 2:
img = np.expand_dims(img, axis=2)
img = np.expand_dims(img, 0).transpose([0, 3, 1, 2])
img = img.astype("float32")
img /= 255
img-=0.5
img/=0.5
return img
def diff_func(torch_out, paddle_out, prex=""):
if isinstance(torch_out, dict):
for k in torch_out:
diff_func(torch_out[k], paddle_out[k], f"{prex}_{k}")
elif isinstance(torch_out, list):
for k in range(len(torch_out)):
diff_func(torch_out[k], paddle_out[k], f"{prex}_{k}")
elif isinstance(torch_out, torch.Tensor):
diff = paddle_out.detach().cpu().numpy() - torch_out.detach().cpu().numpy()
print(prex[1:], np.abs(diff).mean())
def paddle_infer(config, input_np, device, params_path):
print(f"paddle version: {paddle.__version__}")
print(f"input shape of paddle is {input_np.shape}")
paddle.device.set_device(device)
x = paddle.to_tensor(input_np)
model = build_model_paddle(config)
model.set_state_dict(paddle.load(params_path))
model.eval()
y = model(x)
return y
def torch_infer(config, input_np, device, params_path):
print(f"torch version: {torch.__version__}")
print(f"input shape of torch is {input_np.shape}")
if device == 'gpu':
device = 'cuda'
x = torch.from_numpy(input_np)
x = x.to(device)
model = build_model(config)
model.load_state_dict(torch.load(params_path)["state_dict"])
model.eval()
model = model.to(device)
y = model(x)
return y
def main():
device = "cpu"
input_np = get_input(320, 48, True)
tmp_dir = './tmp'
os.makedirs(tmp_dir, exist_ok=True)
config_path = "configs/rec/rec_r34_vd_tps_bilstm_ctc.yml"
paddle_params_path = r''
torch_params_path = paddle_params_path.replace('.pdparams', '.pth')
config = load_config(config_path)
config = init_head(config)
model_config = config["Architecture"]
print(model_config)
# step 1 convert params and run paddle and save result
paddle_params_path, torch_params_path = conver_params(model_config, paddle_params_path, tmp_dir, show_log=False)
# step 2 run paddle
paddle_out = paddle_infer(model_config, input_np, device, paddle_params_path)
# step 2 run torch
torch_out = torch_infer(model_config, input_np, device, torch_params_path)
diff_func(torch_out, paddle_out)
if __name__ == "__main__":
main()