def _init_model_and_optimizer(self):
if self.config['hyperbolic']:
model = HResNetSimCLR(**self.config["model"]).to(self.device)
else:
model = ResNetSimCLR(**self.config["model"]).to(self.device)
model, loaded_iter = self._load_pre_trained_weights(model)
# if self.config['hyperbolic']:
# optimizer = geoopt.optim.RiemannianAdam(
# [p for p in self.model.parameters() if p.requires_grad],
# 1e-4, weight_decay=eval(self.config['weight_decay']))
# else:
lr = float(self.config['lr'])
optimizer = torch.optim.Adam(
[p for p in model.parameters() if p.requires_grad],
lr,
weight_decay=eval(self.config['weight_decay']))
self.model = model
self.optimizer = optimizer
return loaded_iter
def _save_dt_features(self):
from eval.feature_saver import LvisSaver
import torch
config = self.config
if self.config['hyperbolic']:
from models.hyperbolic_resnet import HResNetSimCLR
model = HResNetSimCLR(config['model']['base_model'], config['model']['out_dim'])
else:
from models.resnet_simclr import ResNetSimCLR
model = ResNetSimCLR(config['model']['base_model'], config['model']['out_dim'])
state_dict = torch.load(os.path.join(self.run_path, self.model_ckpt)) # , map_location=device)i
model.load_state_dict(state_dict)
model.eval()
saver = LvisSaver(model, self.lvis_dt, GT_FEATS)
saver.save()
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.manifold import TSNE
import os
import multiprocessing
from itertools import product
from models.hyperbolic_resnet import HResNetSimCLR
"""
cmodel = HResNetSimCLR('resnet18', 256)
state = torch.load(r'/scratch/users/zzweng/runs/checkpoints/freeze_coco_pretrain_hyp=True_zdim=64_loss=triplet/model_50000.pth')
cmodel.load_state_dict(state)
print(cmodel)
"""
PATH = 'features_lvis_dt_hyperbolic'
os.makedirs(PATH, exist_ok=True)
checkpoint_dir = r'/scratch/users/zzweng/runs/checkpoints/all_hyp=True_zdim=2_loss=triplet_maskloss=False/'
cmodel = HResNetSimCLR('resnet101', 2)
state_dict = torch.load(os.path.join(checkpoint_dir, 'model_11500.pth')) #, map_location=device)i
cmodel.load_state_dict(state_dict)
cmodel.eval()
#cmodel_ = torchvision.models.resnet101(pretrained=True)
#cmodel = nn.Sequential(*list(cmodel_.children())[:-1])
#cmodel.eval()
lvis = LVIS('/scratch/users/zzweng/datasets/lvis/lvis_v0.5_val.json')
lvis_dt = LVISResults(lvis, r'output/inference/lvis_instances_results.json')
def collect_features_from_dt(start, end, folder=PATH):
print('Collecting {} to {}'.format(start, end))
img_ids = lvis_dt.get_img_ids()[start:end]
feats = []