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load_pytorch_weights.py
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load_pytorch_weights.py
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# Copyright (c) 2021 PPViT Authors. 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.
"""convert pytorch model weights to paddle pdparams"""
import os
import numpy as np
import paddle
import torch
import timm
from coat import build_coat as build_model
from config import get_config
def print_model_named_params(model):
print('----------------------------------')
for name, param in model.named_parameters():
print(name, param.shape)
print('----------------------------------')
def print_model_named_buffers(model):
print('----------------------------------')
for name, param in model.named_buffers():
print(name, param.shape)
print('----------------------------------')
def torch_to_paddle_mapping(model_name, config):
mapping = []
for idx in range(4):
layer_mapping = [
(f'cls_token{idx+1}', f'cls_tokens.{idx}'),
(f'patch_embed{idx+1}.proj', f'patch_embeds.{idx}.patch_embed'),
(f'patch_embed{idx+1}.norm', f'patch_embeds.{idx}.norm'),
(f'cpe{idx+1}.proj', f'cpes.{idx}.proj'),
(f'crpe{idx+1}.conv_list.0', f'crpes.{idx}.conv_list.0'),
(f'crpe{idx+1}.conv_list.1', f'crpes.{idx}.conv_list.1'),
(f'crpe{idx+1}.conv_list.2', f'crpes.{idx}.conv_list.2'),
]
mapping.extend(layer_mapping)
for stage_idx, serial_depth in enumerate(config.MODEL.SERIAL_DEPTHS):
for block_idx in range(serial_depth):
th_prefix = f'serial_blocks{stage_idx+1}.{block_idx}'
pp_prefix = f'serial_blocks.{stage_idx}.{block_idx}'
layer_mapping = [
(f'{th_prefix}.norm1', f'{pp_prefix}.norm1'),
(f'{th_prefix}.factoratt_crpe.qkv', f'{pp_prefix}.factoratt_crpe.qkv'),
(f'{th_prefix}.factoratt_crpe.proj', f'{pp_prefix}.factoratt_crpe.proj'),
(f'{th_prefix}.norm2', f'{pp_prefix}.norm2'),
(f'{th_prefix}.mlp.fc1', f'{pp_prefix}.mlp.fc1'),
(f'{th_prefix}.mlp.fc2', f'{pp_prefix}.mlp.fc2'),
]
mapping.extend(layer_mapping)
for block_idx in range(config.MODEL.PARALLEL_DEPTH):
th_prefix = f'parallel_blocks.{block_idx}'
pp_prefix = f'parallel_blocks.{block_idx}'
layer_mapping = [
(f'{th_prefix}.norm12', f'{pp_prefix}.norm12'),
(f'{th_prefix}.norm13', f'{pp_prefix}.norm13'),
(f'{th_prefix}.norm14', f'{pp_prefix}.norm14'),
(f'{th_prefix}.factoratt_crpe2.qkv', f'{pp_prefix}.factoratt_crpe2.qkv'),
(f'{th_prefix}.factoratt_crpe2.proj', f'{pp_prefix}.factoratt_crpe2.proj'),
(f'{th_prefix}.factoratt_crpe3.qkv', f'{pp_prefix}.factoratt_crpe3.qkv'),
(f'{th_prefix}.factoratt_crpe3.proj', f'{pp_prefix}.factoratt_crpe3.proj'),
(f'{th_prefix}.factoratt_crpe4.qkv', f'{pp_prefix}.factoratt_crpe4.qkv'),
(f'{th_prefix}.factoratt_crpe4.proj', f'{pp_prefix}.factoratt_crpe4.proj'),
(f'{th_prefix}.norm22', f'{pp_prefix}.norm22'),
(f'{th_prefix}.norm23', f'{pp_prefix}.norm23'),
(f'{th_prefix}.norm24', f'{pp_prefix}.norm24'),
(f'{th_prefix}.mlp2.fc1', f'{pp_prefix}.mlp2.fc1'),
(f'{th_prefix}.mlp2.fc2', f'{pp_prefix}.mlp2.fc2'),
(f'{th_prefix}.mlp2.fc1', f'{pp_prefix}.mlp3.fc1'),
(f'{th_prefix}.mlp2.fc2', f'{pp_prefix}.mlp3.fc2'),
(f'{th_prefix}.mlp2.fc1', f'{pp_prefix}.mlp4.fc1'),
(f'{th_prefix}.mlp2.fc2', f'{pp_prefix}.mlp4.fc2'),
]
mapping.extend(layer_mapping)
head_mapping = [
('norm2', 'norm2'),
('norm3', 'norm3'),
('norm4', 'norm4'),
('aggregate', 'aggregate'),
('head', 'head'),
]
mapping.extend(head_mapping)
return mapping
def convert(torch_model, paddle_model, model_name, config):
def _set_value(th_name, pd_name, transpose=True):
th_shape = th_params[th_name].shape
pd_shape = tuple(pd_params[pd_name].shape) # paddle shape default type is list
#assert th_shape == pd_shape, f'{th_shape} != {pd_shape}'
print(f'**SET** {th_name} {th_shape} **TO** {pd_name} {pd_shape}')
if isinstance(th_params[th_name], torch.nn.parameter.Parameter):
value = th_params[th_name].data.numpy()
else:
value = th_params[th_name].numpy()
if len(value.shape) == 2 and transpose:
value = value.transpose((1, 0))
pd_params[pd_name].set_value(value)
# 1. get paddle and torch model parameters
pd_params = {}
th_params = {}
for name, param in paddle_model.named_parameters():
pd_params[name] = param
for name, param in torch_model.named_parameters():
th_params[name] = param
for name, param in paddle_model.named_buffers():
pd_params[name] = param
for name, param in torch_model.named_buffers():
th_params[name] = param
# 2. get name mapping pairs
mapping = torch_to_paddle_mapping(model_name, config)
missing_keys_th = []
missing_keys_pd = []
zip_map = list(zip(*mapping))
th_keys = list(zip_map[0])
pd_keys = list(zip_map[1])
for key in th_params:
missing = False
if key not in th_keys:
missing = True
if key.endswith('.weight'):
if key[:-7] in th_keys:
missing = False
if key.endswith('.bias'):
if key[:-5] in th_keys:
missing = False
if key.endswith('.running_mean'):
if key[:-13] in th_keys:
missing = False
if key.endswith('.running_var'):
if key[:-12] in th_keys:
missing = False
if key.endswith('num_batches_tracked'):
missing = False
if missing:
missing_keys_th.append(key)
for key in pd_params:
missing = False
if key not in pd_keys:
missing = True
if key.endswith('.weight'):
if key[:-7] in pd_keys:
missing = False
if key.endswith('.bias'):
if key[:-5] in pd_keys:
missing = False
if key.endswith('._mean'):
if key[:-6] in pd_keys:
missing = False
if key.endswith('._variance'):
if key[:-10] in pd_keys:
missing = False
if missing:
missing_keys_pd.append(key)
print('====================================')
print('missing_keys_pytorch:')
print(missing_keys_th)
print('missing_keys_paddle:')
print(missing_keys_pd)
print('====================================')
# 3. set torch param values to paddle params: may needs transpose on weights
for th_name, pd_name in mapping:
if th_name in th_params and pd_name in pd_params: # nn.Parameters
#if 'attention_biases' in th_name or 'attention_bias_idxs' in th_name:
# _set_value(th_name, pd_name, transpose=False)
#else:
# _set_value(th_name, pd_name)
_set_value(th_name, pd_name)
else:
if f'{th_name}.weight' in th_params and f'{pd_name}.weight' in pd_params:
th_name_w = f'{th_name}.weight'
pd_name_w = f'{pd_name}.weight'
_set_value(th_name_w, pd_name_w)
if f'{th_name}.bias' in th_params and f'{pd_name}.bias' in pd_params:
th_name_b = f'{th_name}.bias'
pd_name_b = f'{pd_name}.bias'
_set_value(th_name_b, pd_name_b)
if f'{th_name}.running_mean' in th_params and f'{pd_name}._mean' in pd_params:
th_name_w = f'{th_name}.running_mean'
pd_name_w = f'{pd_name}._mean'
_set_value(th_name_w, pd_name_w)
if f'{th_name}.running_var' in th_params and f'{pd_name}._variance' in pd_params:
th_name_b = f'{th_name}.running_var'
pd_name_b = f'{pd_name}._variance'
_set_value(th_name_b, pd_name_b)
return paddle_model
def main():
paddle.set_device('cpu')
model_name_list = [
#"coat_tiny",
#"coat_mini",
#"coat_small",
"coat_lite_tiny",
#"coat_lite_mini",
#"coat_lite_small",
#"coat_lite_medium",
]
for model_name in model_name_list:
print(f'============= NOW: {model_name} =============')
sz = 224
config = get_config(f'./configs/{model_name}.yaml')
paddle_model = build_model(config)
paddle_model.eval()
print_model_named_params(paddle_model)
print_model_named_buffers(paddle_model)
#print(paddle_model)
print('+++++++++++++++++++++++++++++++++++')
print('+++++++++++++++++++++++++++++++++++')
print('+++++++++++++++++++++++++++++++++++')
device = torch.device('cpu')
torch_model = timm.create_model(model_name, pretrained=True)
#torch_model.load_state_dict(torch.load('../../../coat_pth/coat_mini_40667eec.pth')['model_ema'])
#torch_model.load_state_dict(torch.load('../../../coat_pth/coat_small_7479cf9b.pth')['model'])
#torch_model.load_state_dict(torch.load('../../../coat_pth/coat_lite_mini_6b4a8ae5.pth')['model'])
#torch_model.load_state_dict(torch.load('../../../coat_pth/coat_lite_small_8d362f48.pth')['model'])
#torch_model.load_state_dict(torch.load('../../../coat_pth/coat_lite_medium_a750cd63.pth')['model'])
#torch_model.load_state_dict(torch.load('../../../coat_pth/coat_tiny_c6efc33c.pth')['model'])
torch_model.load_state_dict(torch.load('../../../coat_pth/coat_lite_tiny_e88e96b0.pth')['model'])
torch_model.eval()
torch_model = torch_model.to(device)
print_model_named_params(torch_model)
print_model_named_buffers(torch_model)
print(torch_model)
# convert weights
paddle_model = convert(torch_model, paddle_model, model_name, config)
# check correctness
x = np.random.randn(2, 3, sz, sz).astype('float32')
x_paddle = paddle.to_tensor(x)
x_torch = torch.Tensor(x).to(device)
out_torch = torch_model(x_torch)
print('+++++++++++++++++++++++++++++++++++')
print('+++++++++++++++++++++++++++++++++++')
out_paddle = paddle_model(x_paddle)
out_torch = out_torch.data.cpu().numpy()
out_paddle = out_paddle.cpu().numpy()
print(out_torch.shape, out_paddle.shape)
print(out_torch[0, 0:100])
print('========================================================')
print(out_paddle[0, 0:100])
assert np.allclose(out_torch, out_paddle, atol = 1e-5)
# save weights for paddle model
model_path = os.path.join(f'./{model_name}.pdparams')
paddle.save(paddle_model.state_dict(), model_path)
print(f'{model_name} done')
print('all done')
if __name__ == "__main__":
main()