mirror of
https://github.com/open-mmlab/mmcv.git
synced 2025-06-03 21:54:52 +08:00
32e09f4933
* [Feature] Choose storage backend by the prefix of filepath * refactor FileClient and add unittest * support loading from different backends * polish docstring * fix unittet * rename attribute str_like_obj to is_str_like_obj * [Docs] Upload checkpoint to petrel oss * add infer_client method * Support uploading checkpoint to petrel oss * add check_exist method * refactor CheckpointHook * support uploading logs to ceph * rename var client to file_client * polish docstring * enhance load_from_ceph * refactor load_from_ceph * refactor TextLoggerHook * change the meaning of out_dir argument * fix test_checkpoint_hook.py * add join_paths method * remove join_paths and add _format_path * enhance unittest * refactor unittest * add a unittest for EvalHook when file backend is petrel * singleton pattern * fix test_clientio.py * deprecate CephBackend * add warning in load_from_ceph * fix type of out_suffix * enhance docstring * refactor unittest for petrel * refactor unittest for disk backend * update io.md * add concat_paths method * fix CI * mock check_exist * improve docstring * improve docstring * improve docstring * improve docstring * add isdir and copyfile for file backend * delete copyfile and add get_local_path * remove isdir method of petrel * fix typo * rename check_exists to exists * refactor code and polish docstring * fix windows ci * add comment and polish docstring * polish docstring * polish docstring * rename _path_mapping to _map_path * polish docstring and fix typo * refactor get_local_path * add list_dir_or_file for FileClient * add list_dir_or_file for PetrelBackend * fix windows ci * Add return docstring * polish docstring * fix typo * fix typo * fix typo * fix error when mocking PetrelBackend * deprecate the conversion from Path to str * add docs for loading checkpoints with FileClient * rename keep_log to keep_local * refactor map_path * add _ensure_methods to ensure methods have been implemented * fix list_dir_or_file * rename _ensure_method_implemented to has_method * refactor * polish information * format information
433 lines
16 KiB
Python
433 lines
16 KiB
Python
import sys
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from collections import OrderedDict
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from tempfile import TemporaryDirectory
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from unittest.mock import MagicMock, patch
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import pytest
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.nn.parallel import DataParallel
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from mmcv.fileio.file_client import PetrelBackend
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from mmcv.parallel.registry import MODULE_WRAPPERS
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from mmcv.runner.checkpoint import (_load_checkpoint_with_prefix,
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get_state_dict, load_checkpoint,
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load_from_pavi, save_checkpoint)
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sys.modules['petrel_client'] = MagicMock()
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sys.modules['petrel_client.client'] = MagicMock()
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@MODULE_WRAPPERS.register_module()
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class DDPWrapper(object):
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def __init__(self, module):
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self.module = module
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class Block(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv = nn.Conv2d(3, 3, 1)
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self.norm = nn.BatchNorm2d(3)
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class Model(nn.Module):
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def __init__(self):
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super().__init__()
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self.block = Block()
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self.conv = nn.Conv2d(3, 3, 1)
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class Mockpavimodel(object):
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def __init__(self, name='fakename'):
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self.name = name
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def download(self, file):
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pass
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def assert_tensor_equal(tensor_a, tensor_b):
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assert tensor_a.eq(tensor_b).all()
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def test_get_state_dict():
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if torch.__version__ == 'parrots':
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state_dict_keys = set([
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'block.conv.weight', 'block.conv.bias', 'block.norm.weight',
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'block.norm.bias', 'block.norm.running_mean',
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'block.norm.running_var', 'conv.weight', 'conv.bias'
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])
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else:
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state_dict_keys = set([
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'block.conv.weight', 'block.conv.bias', 'block.norm.weight',
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'block.norm.bias', 'block.norm.running_mean',
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'block.norm.running_var', 'block.norm.num_batches_tracked',
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'conv.weight', 'conv.bias'
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])
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model = Model()
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state_dict = get_state_dict(model)
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assert isinstance(state_dict, OrderedDict)
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assert set(state_dict.keys()) == state_dict_keys
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assert_tensor_equal(state_dict['block.conv.weight'],
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model.block.conv.weight)
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assert_tensor_equal(state_dict['block.conv.bias'], model.block.conv.bias)
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assert_tensor_equal(state_dict['block.norm.weight'],
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model.block.norm.weight)
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assert_tensor_equal(state_dict['block.norm.bias'], model.block.norm.bias)
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assert_tensor_equal(state_dict['block.norm.running_mean'],
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model.block.norm.running_mean)
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assert_tensor_equal(state_dict['block.norm.running_var'],
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model.block.norm.running_var)
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if torch.__version__ != 'parrots':
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assert_tensor_equal(state_dict['block.norm.num_batches_tracked'],
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model.block.norm.num_batches_tracked)
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assert_tensor_equal(state_dict['conv.weight'], model.conv.weight)
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assert_tensor_equal(state_dict['conv.bias'], model.conv.bias)
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wrapped_model = DDPWrapper(model)
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state_dict = get_state_dict(wrapped_model)
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assert isinstance(state_dict, OrderedDict)
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assert set(state_dict.keys()) == state_dict_keys
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assert_tensor_equal(state_dict['block.conv.weight'],
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wrapped_model.module.block.conv.weight)
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assert_tensor_equal(state_dict['block.conv.bias'],
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wrapped_model.module.block.conv.bias)
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assert_tensor_equal(state_dict['block.norm.weight'],
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wrapped_model.module.block.norm.weight)
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assert_tensor_equal(state_dict['block.norm.bias'],
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wrapped_model.module.block.norm.bias)
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assert_tensor_equal(state_dict['block.norm.running_mean'],
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wrapped_model.module.block.norm.running_mean)
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assert_tensor_equal(state_dict['block.norm.running_var'],
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wrapped_model.module.block.norm.running_var)
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if torch.__version__ != 'parrots':
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assert_tensor_equal(
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state_dict['block.norm.num_batches_tracked'],
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wrapped_model.module.block.norm.num_batches_tracked)
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assert_tensor_equal(state_dict['conv.weight'],
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wrapped_model.module.conv.weight)
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assert_tensor_equal(state_dict['conv.bias'],
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wrapped_model.module.conv.bias)
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# wrapped inner module
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for name, module in wrapped_model.module._modules.items():
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module = DataParallel(module)
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wrapped_model.module._modules[name] = module
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state_dict = get_state_dict(wrapped_model)
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assert isinstance(state_dict, OrderedDict)
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assert set(state_dict.keys()) == state_dict_keys
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assert_tensor_equal(state_dict['block.conv.weight'],
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wrapped_model.module.block.module.conv.weight)
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assert_tensor_equal(state_dict['block.conv.bias'],
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wrapped_model.module.block.module.conv.bias)
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assert_tensor_equal(state_dict['block.norm.weight'],
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wrapped_model.module.block.module.norm.weight)
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assert_tensor_equal(state_dict['block.norm.bias'],
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wrapped_model.module.block.module.norm.bias)
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assert_tensor_equal(state_dict['block.norm.running_mean'],
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wrapped_model.module.block.module.norm.running_mean)
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assert_tensor_equal(state_dict['block.norm.running_var'],
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wrapped_model.module.block.module.norm.running_var)
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if torch.__version__ != 'parrots':
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assert_tensor_equal(
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state_dict['block.norm.num_batches_tracked'],
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wrapped_model.module.block.module.norm.num_batches_tracked)
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assert_tensor_equal(state_dict['conv.weight'],
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wrapped_model.module.conv.module.weight)
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assert_tensor_equal(state_dict['conv.bias'],
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wrapped_model.module.conv.module.bias)
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def test_load_pavimodel_dist():
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sys.modules['pavi'] = MagicMock()
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sys.modules['pavi.modelcloud'] = MagicMock()
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pavimodel = Mockpavimodel()
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import pavi
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pavi.modelcloud.get = MagicMock(return_value=pavimodel)
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with pytest.raises(AssertionError):
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# test pavi prefix
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_ = load_from_pavi('MyPaviFolder/checkpoint.pth')
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with pytest.raises(FileNotFoundError):
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# there is not such checkpoint for us to load
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_ = load_from_pavi('pavi://checkpoint.pth')
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def test_load_checkpoint_with_prefix():
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class FooModule(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = nn.Linear(1, 2)
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self.conv2d = nn.Conv2d(3, 1, 3)
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self.conv2d_2 = nn.Conv2d(3, 2, 3)
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model = FooModule()
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nn.init.constant_(model.linear.weight, 1)
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nn.init.constant_(model.linear.bias, 2)
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nn.init.constant_(model.conv2d.weight, 3)
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nn.init.constant_(model.conv2d.bias, 4)
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nn.init.constant_(model.conv2d_2.weight, 5)
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nn.init.constant_(model.conv2d_2.bias, 6)
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with TemporaryDirectory():
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torch.save(model.state_dict(), 'model.pth')
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prefix = 'conv2d'
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state_dict = _load_checkpoint_with_prefix(prefix, 'model.pth')
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assert torch.equal(model.conv2d.state_dict()['weight'],
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state_dict['weight'])
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assert torch.equal(model.conv2d.state_dict()['bias'],
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state_dict['bias'])
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# test whether prefix is in pretrained model
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with pytest.raises(AssertionError):
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prefix = 'back'
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_load_checkpoint_with_prefix(prefix, 'model.pth')
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def test_load_checkpoint():
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import os
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import tempfile
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import re
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class PrefixModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.backbone = Model()
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pmodel = PrefixModel()
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model = Model()
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checkpoint_path = os.path.join(tempfile.gettempdir(), 'checkpoint.pth')
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# add prefix
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torch.save(model.state_dict(), checkpoint_path)
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state_dict = load_checkpoint(
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pmodel, checkpoint_path, revise_keys=[(r'^', 'backbone.')])
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for key in pmodel.backbone.state_dict().keys():
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assert torch.equal(pmodel.backbone.state_dict()[key], state_dict[key])
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# strip prefix
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torch.save(pmodel.state_dict(), checkpoint_path)
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state_dict = load_checkpoint(
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model, checkpoint_path, revise_keys=[(r'^backbone\.', '')])
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for key in state_dict.keys():
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key_stripped = re.sub(r'^backbone\.', '', key)
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assert torch.equal(model.state_dict()[key_stripped], state_dict[key])
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os.remove(checkpoint_path)
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def test_load_checkpoint_metadata():
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import os
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import tempfile
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from mmcv.runner import load_checkpoint, save_checkpoint
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class ModelV1(nn.Module):
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def __init__(self):
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super().__init__()
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self.block = Block()
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self.conv1 = nn.Conv2d(3, 3, 1)
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self.conv2 = nn.Conv2d(3, 3, 1)
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nn.init.normal_(self.conv1.weight)
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nn.init.normal_(self.conv2.weight)
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class ModelV2(nn.Module):
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_version = 2
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def __init__(self):
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super().__init__()
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self.block = Block()
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self.conv0 = nn.Conv2d(3, 3, 1)
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self.conv1 = nn.Conv2d(3, 3, 1)
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nn.init.normal_(self.conv0.weight)
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nn.init.normal_(self.conv1.weight)
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def _load_from_state_dict(self, state_dict, prefix, local_metadata,
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*args, **kwargs):
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"""load checkpoints."""
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# Names of some parameters in has been changed.
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version = local_metadata.get('version', None)
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if version is None or version < 2:
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state_dict_keys = list(state_dict.keys())
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convert_map = {'conv1': 'conv0', 'conv2': 'conv1'}
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for k in state_dict_keys:
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for ori_str, new_str in convert_map.items():
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if k.startswith(prefix + ori_str):
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new_key = k.replace(ori_str, new_str)
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state_dict[new_key] = state_dict[k]
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del state_dict[k]
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super()._load_from_state_dict(state_dict, prefix, local_metadata,
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*args, **kwargs)
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model_v1 = ModelV1()
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model_v1_conv0_weight = model_v1.conv1.weight.detach()
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model_v1_conv1_weight = model_v1.conv2.weight.detach()
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model_v2 = ModelV2()
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model_v2_conv0_weight = model_v2.conv0.weight.detach()
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model_v2_conv1_weight = model_v2.conv1.weight.detach()
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ckpt_v1_path = os.path.join(tempfile.gettempdir(), 'checkpoint_v1.pth')
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ckpt_v2_path = os.path.join(tempfile.gettempdir(), 'checkpoint_v2.pth')
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# Save checkpoint
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save_checkpoint(model_v1, ckpt_v1_path)
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save_checkpoint(model_v2, ckpt_v2_path)
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# test load v1 model
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load_checkpoint(model_v2, ckpt_v1_path)
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assert torch.allclose(model_v2.conv0.weight, model_v1_conv0_weight)
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assert torch.allclose(model_v2.conv1.weight, model_v1_conv1_weight)
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# test load v2 model
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load_checkpoint(model_v2, ckpt_v2_path)
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assert torch.allclose(model_v2.conv0.weight, model_v2_conv0_weight)
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assert torch.allclose(model_v2.conv1.weight, model_v2_conv1_weight)
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def test_load_classes_name():
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import os
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import tempfile
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from mmcv.runner import load_checkpoint, save_checkpoint
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checkpoint_path = os.path.join(tempfile.gettempdir(), 'checkpoint.pth')
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model = Model()
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save_checkpoint(model, checkpoint_path)
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checkpoint = load_checkpoint(model, checkpoint_path)
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assert 'meta' in checkpoint and 'CLASSES' not in checkpoint['meta']
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model.CLASSES = ('class1', 'class2')
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save_checkpoint(model, checkpoint_path)
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checkpoint = load_checkpoint(model, checkpoint_path)
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assert 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']
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assert checkpoint['meta']['CLASSES'] == ('class1', 'class2')
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model = Model()
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wrapped_model = DDPWrapper(model)
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save_checkpoint(wrapped_model, checkpoint_path)
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checkpoint = load_checkpoint(wrapped_model, checkpoint_path)
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assert 'meta' in checkpoint and 'CLASSES' not in checkpoint['meta']
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wrapped_model.module.CLASSES = ('class1', 'class2')
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save_checkpoint(wrapped_model, checkpoint_path)
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checkpoint = load_checkpoint(wrapped_model, checkpoint_path)
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assert 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']
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assert checkpoint['meta']['CLASSES'] == ('class1', 'class2')
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# remove the temp file
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os.remove(checkpoint_path)
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def test_checkpoint_loader():
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from mmcv.runner import _load_checkpoint, save_checkpoint, CheckpointLoader
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import tempfile
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import os
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checkpoint_path = os.path.join(tempfile.gettempdir(), 'checkpoint.pth')
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model = Model()
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save_checkpoint(model, checkpoint_path)
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checkpoint = _load_checkpoint(checkpoint_path)
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assert 'meta' in checkpoint and 'CLASSES' not in checkpoint['meta']
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# remove the temp file
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os.remove(checkpoint_path)
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filenames = [
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'http://xx.xx/xx.pth', 'https://xx.xx/xx.pth',
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'modelzoo://xx.xx/xx.pth', 'torchvision://xx.xx/xx.pth',
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'open-mmlab://xx.xx/xx.pth', 'openmmlab://xx.xx/xx.pth',
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'mmcls://xx.xx/xx.pth', 'pavi://xx.xx/xx.pth', 's3://xx.xx/xx.pth',
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'ss3://xx.xx/xx.pth', ' s3://xx.xx/xx.pth'
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]
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fn_names = [
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'load_from_http', 'load_from_http', 'load_from_torchvision',
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'load_from_torchvision', 'load_from_openmmlab', 'load_from_openmmlab',
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'load_from_mmcls', 'load_from_pavi', 'load_from_ceph',
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'load_from_local', 'load_from_local'
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]
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for filename, fn_name in zip(filenames, fn_names):
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loader = CheckpointLoader._get_checkpoint_loader(filename)
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assert loader.__name__ == fn_name
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@CheckpointLoader.register_scheme(prefixes='ftp://')
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def load_from_ftp(filename, map_location):
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return dict(filename=filename)
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# test register_loader
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filename = 'ftp://xx.xx/xx.pth'
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loader = CheckpointLoader._get_checkpoint_loader(filename)
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assert loader.__name__ == 'load_from_ftp'
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def load_from_ftp1(filename, map_location):
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return dict(filename=filename)
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# test duplicate registered error
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with pytest.raises(KeyError):
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CheckpointLoader.register_scheme('ftp://', load_from_ftp1)
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# test force param
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CheckpointLoader.register_scheme('ftp://', load_from_ftp1, force=True)
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checkpoint = CheckpointLoader.load_checkpoint(filename)
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assert checkpoint['filename'] == filename
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# test print function name
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loader = CheckpointLoader._get_checkpoint_loader(filename)
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assert loader.__name__ == 'load_from_ftp1'
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# test sort
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@CheckpointLoader.register_scheme(prefixes='a/b')
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def load_from_ab(filename, map_location):
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return dict(filename=filename)
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@CheckpointLoader.register_scheme(prefixes='a/b/c')
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def load_from_abc(filename, map_location):
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return dict(filename=filename)
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filename = 'a/b/c/d'
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loader = CheckpointLoader._get_checkpoint_loader(filename)
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assert loader.__name__ == 'load_from_abc'
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def test_save_checkpoint(tmp_path):
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model = Model()
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optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
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# meta is not a dict
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with pytest.raises(TypeError):
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save_checkpoint(model, '/path/of/your/filename', meta='invalid type')
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# 1. save to disk
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filename = str(tmp_path / 'checkpoint1.pth')
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save_checkpoint(model, filename)
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filename = str(tmp_path / 'checkpoint2.pth')
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save_checkpoint(model, filename, optimizer)
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filename = str(tmp_path / 'checkpoint3.pth')
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save_checkpoint(model, filename, meta={'test': 'test'})
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filename = str(tmp_path / 'checkpoint4.pth')
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save_checkpoint(model, filename, file_client_args={'backend': 'disk'})
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# 2. save to petrel oss
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with patch.object(PetrelBackend, 'put') as mock_method:
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filename = 's3://path/of/your/checkpoint1.pth'
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save_checkpoint(model, filename)
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mock_method.assert_called()
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with patch.object(PetrelBackend, 'put') as mock_method:
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filename = 's3://path//of/your/checkpoint2.pth'
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save_checkpoint(
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model, filename, file_client_args={'backend': 'petrel'})
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mock_method.assert_called()
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