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from typing import List |
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from data.dataloader import build_dataloader |
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from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel |
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import torch |
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import sys |
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from torch import nn |
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from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel |
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from new_impl.cv.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg |
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from new_impl.cv.elasticdnn.model.base import ElasticDNNUtil |
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from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util |
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from sam import FM_to_MD_sam_Util |
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from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util |
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from sam import FMLoRA_sam_Util |
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from sam import ElasticsamUtil |
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from utils.common.file import ensure_dir |
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from utils.dl.common.model import LayerActivation, get_module, get_parameter |
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from utils.common.exp import save_models_dict_for_init, get_res_save_dir |
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from data import build_scenario |
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from utils.dl.common.loss import CrossEntropyLossSoft |
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import torch.nn.functional as F |
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from utils.dl.common.env import create_tbwriter |
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import os |
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from utils.common.log import logger |
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from utils.common.data_record import write_json |
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from new_impl.cv.feat_align.main import OnlineFeatAlignModel, FeatAlignAlg |
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import tqdm |
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from new_impl.cv.feat_align.mmd import mmd_rbf |
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from new_impl.cv.utils.baseline_da import baseline_da |
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device = 'cuda' |
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app_name = 'cls' |
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scenario = build_scenario( |
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source_datasets_name=['GTA5', 'SuperviselyPerson'], |
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target_datasets_order=['Cityscapes', 'BaiduPerson'] * 10, |
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da_mode='close_set', |
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data_dirs={ |
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'GTA5': '/data/zql/datasets/GTA-ls-copy/GTA5', |
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'SuperviselyPerson': '/data/zql/datasets/supervisely_person/Supervisely Person Dataset', |
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'Cityscapes': '/data/zql/datasets/cityscape/', |
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'BaiduPerson': '/data/zql/datasets/baidu_person/clean_images/' |
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}, |
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) |
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class SegOnlineFeatAlignModel(OnlineFeatAlignModel): |
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def __init__(self, name: str, models_dict_path: str, device: str, num_classes): |
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super().__init__(name, models_dict_path, device) |
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self.num_classes = num_classes |
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def get_feature_hook(self): |
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return LayerActivation(get_module(self.models_dict['main'], 'head'), False, self.device) |
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def forward_to_get_task_loss(self, x, y): |
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return F.cross_entropy(self.infer(x), y) |
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def get_mmd_loss(self, f1, f2): |
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return mmd_rbf(f1.flatten(1), f2.flatten(1)) |
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def infer(self, x, *args, **kwargs): |
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return self.models_dict['main'](x) |
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def get_trained_params(self): |
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qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'qkv.weight' in n or 'norm' in n or 'mlp' in n] |
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return qkv_and_norm_params |
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def infer(self, x, *args, **kwargs): |
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return self.models_dict['main'](x) |
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def get_accuracy(self, test_loader, *args, **kwargs): |
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device = self.device |
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self.to_eval_mode() |
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from methods.elasticdnn.api.model import StreamSegMetrics |
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metrics = StreamSegMetrics(self.num_classes) |
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metrics.reset() |
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import tqdm |
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pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), leave=False, dynamic_ncols=True) |
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with torch.no_grad(): |
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for batch_index, (x, y) in pbar: |
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x, y = x.to(device, dtype=x.dtype, non_blocking=True, copy=False), \ |
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y.to(device, dtype=y.dtype, non_blocking=True, copy=False) |
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output = self.infer(x) |
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pred = output.detach().max(dim=1)[1].cpu().numpy() |
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metrics.update((y + 0).cpu().numpy(), pred) |
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res = metrics.get_results() |
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pbar.set_description(f'cur batch mIoU: {res["Mean Acc"]:.4f}') |
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res = metrics.get_results() |
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return res['Mean Acc'] |
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da_alg = FeatAlignAlg |
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da_model = SegOnlineFeatAlignModel( |
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app_name, |
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'new_impl/cv/sam/results/seg_wo_fbs.py/20231130/999999-144157/models/md_best.pt', |
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device, |
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scenario.num_classes |
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) |
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da_alg_hyp = {'Cityscapes': { |
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'train_batch_size': 16, |
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'val_batch_size': 128, |
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'num_workers': 16, |
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'optimizer': 'AdamW', |
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'optimizer_args': {'lr': 1e-9, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, |
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'scheduler': '', |
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'scheduler_args': {}, |
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'num_iters': 10, |
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'val_freq': 20, |
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'feat_align_loss_weight': 3.0 |
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}, 'BaiduPerson': { |
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'train_batch_size': 16, |
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'val_batch_size': 128, |
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'num_workers': 16, |
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'optimizer': 'AdamW', |
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'optimizer_args': {'lr': 1e-2, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, |
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'scheduler': '', |
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'scheduler_args': {}, |
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'num_iters': 10, |
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'val_freq': 20, |
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'feat_align_loss_weight': 0.3 |
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}} |
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baseline_da( |
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app_name, |
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scenario, |
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da_alg, |
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da_alg_hyp, |
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da_model, |
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device, |
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__file__, |
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sys.argv[0] |
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) |
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