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import torch
from new_impl.cv.elasticdnn.api.model import ElasticDNN_OfflineSegFMModel, ElasticDNN_OfflineSegMDModel
from new_impl.cv.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg
from new_impl.cv.elasticdnn.model.base import ElasticDNNUtil
from new_impl.cv.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util
from sam import FMLoRA_sam_Util
from new_impl.cv.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util
from sam import FM_to_MD_sam_Util
from sam import ElasticsamUtil
from utils.dl.common.model import LayerActivation, get_module, get_parameter, set_module
from utils.common.exp import save_models_dict_for_init, get_res_save_dir
from data import build_scenario
import torch.nn.functional as F
class ElasticDNN_sam_OfflineSegFMModel(ElasticDNN_OfflineSegFMModel):
def generate_md_by_reducing_width(self, reducing_width_ratio, samples: torch.Tensor):
return FM_to_MD_sam_Util().init_md_from_fm_by_reducing_width_with_perf_test(self.models_dict['main'],
reducing_width_ratio, samples)
def get_feature_hook(self) -> LayerActivation:
return LayerActivation(get_module(self.models_dict['main'], 'head'), True, self.device)
def get_elastic_dnn_util(self) -> ElasticDNNUtil:
return ElasticsamUtil()
def forward_to_get_task_loss(self, x, y, *args, **kwargs):
return F.cross_entropy(self.infer(x), y)
def get_lora_util(self) -> FMLoRA_Util:
return FMLoRA_sam_Util()
def get_task_head_params(self):
head = get_module(self.models_dict['main'], 'head')
return list(head.parameters())
class ElasticDNN_sam_OfflineSegMDModel(ElasticDNN_OfflineSegMDModel):
def get_feature_hook(self) -> LayerActivation:
return LayerActivation(get_module(self.models_dict['main'], 'head'), True, self.device)
def forward_to_get_task_loss(self, x, y, *args, **kwargs):
return F.cross_entropy(self.infer(x).pred_masks, y)
if __name__ == '__main__':
# 1. init scenario
scenario = build_scenario(
source_datasets_name=['GTA5', 'SuperviselyPerson'],
target_datasets_order=['Cityscapes', 'BaiduPerson'] * 10,
da_mode='close_set',
data_dirs={
'GTA5': '/data/zql/datasets/GTA-ls-copy/GTA5',
'SuperviselyPerson': '/data/zql/datasets/supervisely_person/Supervisely Person Dataset',
'Cityscapes': '/data/zql/datasets/cityscape/',
'BaiduPerson': '/data/zql/datasets/baidu_person/clean_images/'
},
)
# 2. init model\
torch.cuda.set_device(1)
device = 'cuda'
# from dnns.vit import vit_b_16
# seg_model = vit_b_16(pretrained=True, num_classes=scenario.num_classes)
# from dnns.deeplabv3.head import DecoderLinear
# head = DecoderLinear(scenario.num_classes, 16, 768, (224, 224)).to(device)
# set_module(seg_model, 'head', head)
# from types import MethodType
# from timm.models.vision_transformer import VisionTransformer
# def forward_head(self, x, pre_logits: bool = False):
# return self.head(x)
# VisionTransformer.forward_head = MethodType(forward_head, seg_model)
from sam import Sammodel
seg_model = Sammodel.from_pretrained('new_impl/cv/sam/sam_pretrained',ignore_mismatched_sizes=True,num_classes=scenario.num_classes)
# from dnns.deeplabv3.head import DecoderLinear
# head = DecoderLinear(scenario.num_classes, 16, 256, (224, 224)).to(device)
# set_module(seg_model, 'mask_decoder.iou_prediction_head', head)
fm_models_dict_path = save_models_dict_for_init({
'main': seg_model
}, __file__, 'fm_sam_pretrained_with_seg_head')
fm_model = ElasticDNN_sam_OfflineSegFMModel('fm', fm_models_dict_path, device, scenario.num_classes)
# 3. init alg
models = {
'fm': fm_model
}
fm_lora_alg = ElasticDNN_FMLoRAAlg(models, get_res_save_dir(__file__))
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# 4. run alg
from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup
fm_lora_alg.run(scenario, hyps={
'launch_tbboard': False,
'samples_size': (1, 3, 224, 224),
'ab_r': 8,
'train_batch_size': 16,
'val_batch_size': 256,
'num_workers': 16,
'optimizer': 'Adam',
'optimizer_args': {'lr': 5e-3, 'betas': [0.9, 0.999]},
'scheduler': 'LambdaLR',
'scheduler_args': {'lr_lambda': get_linear_schedule_with_warmup(10000, 70000)},
'num_iters': 80000,
'val_freq': 400
}) |