def _05_05(): res = [] for label in [NEGATIVE_NETWORK, STREAKS, GLOBULES]: for lr in [1e-5]: res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2), balanced=True)) res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion='none', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2), balanced=True)) return res
def _02_05(): res = [] for label in LABELS: for lr in [1e-4, 1e-5]: res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2))) res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion='none', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2))) return res
def _14_05_neg(): res = [] for label in [GLOBULES]: for lr in [1e-4]: for ae_loss in ['none', 'mse']: for neg_p in [80, 60, 50]: res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion=ae_loss, device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, attention_loss_lambda=1, no_rand_dataloader=False, train_on_test=False, neg_percent=neg_p)) return res
def _09_05_2(): res = [] for label in [GLOBULES]: for ae_loss in ['none', 'mse']: res.append( RunConfig(lr=1e-5, label=label, epochs=100, when_present=True, tags=['train_new', 'represent'], images_size=512, cl_criterion='weight-bce', attention_criterion=ae_loss, device='cuda:1', no_scheduler=True, seed=3521001796, balanced=False, train_on_test=True, no_rand_dataloader=False, grouping_label='manual-seed')) res.append( RunConfig(lr=1e-5, label=label, epochs=100, when_present=True, tags=['train_new', 'represent'], images_size=512, cl_criterion='weight-bce', attention_criterion=ae_loss, device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, train_on_test=True, no_rand_dataloader=True, grouping_label='not-random-dataloader')) return res
def _04_05(): res = [] for label in LABELS: for lr in [1e-4, 1e-5]: res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2), balanced=True)) res.append( RunConfig(lr=lr, label=label, epochs=100, when_present=True, tags=['train_new'], images_size=512, cl_criterion='weight-bce', attention_criterion='none', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2), balanced=True)) res = [ x for x in res if x.label != PIGMENT_NETWORK and ( x.label != MILIA_LIKE_CYST and x.lr != 1e-5) ] return res
def check_03_05(): return [ RunConfig(lr=1e-4, label=PIGMENT_NETWORK, epochs=2, when_present=True, tags=['checking-run'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2), balanced=True) ]
def _08_04_2(): res = [] for label in [GLOBULES]: for ae_loss in ['none', 'mse']: res.append( RunConfig(lr=1e-5, label=label, epochs=100, when_present=True, tags=['train_new', 'correct-globules-lr'], images_size=512, cl_criterion='weight-bce', attention_criterion=ae_loss, device='cuda:1', no_scheduler=True, seed=numpy.random.randint(low=1, high=2**32 - 2), balanced=False, train_on_test=True)) return res
def _09_05(): res = [] for label in [GLOBULES]: for ae_loss in ['none', 'mse']: res.append( RunConfig(lr=1e-5, label=label, epochs=100, when_present=True, tags=['train_new', 'manual-seed'], images_size=512, cl_criterion='weight-bce', attention_criterion=ae_loss, device='cuda:1', no_scheduler=True, seed=3521001796, balanced=False, train_on_test=True, no_rand_dataloader=True)) return res
def _11_05(): return [ RunConfig( lr=1e-4, label=GLOBULES, epochs=100, when_present=True, tags=[ 'train_new', '11_05_balanced', '11_05_bce_ae', '11_05_lambda' ], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, ), RunConfig( lr=1e-4, label=GLOBULES, epochs=100, when_present=True, tags=['train_new', '11_05_bce_ae'], images_size=512, cl_criterion='weight-bce', attention_criterion='bce', device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, ), RunConfig( lr=1e-4, label=GLOBULES, epochs=100, when_present=True, tags=['train_new', '11_05_balanced'], images_size=512, cl_criterion='weight-bce', attention_criterion='none', device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, ), RunConfig(lr=1e-4, label=GLOBULES, epochs=100, when_present=True, tags=['train_new', '11_05_lambda'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, attention_loss_lambda=10), RunConfig(lr=1e-4, label=GLOBULES, epochs=100, when_present=True, tags=['train_new', '11_05_lambda'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, attention_loss_lambda=100), RunConfig(lr=1e-4, label=GLOBULES, epochs=100, when_present=True, tags=['train_new', '11_05_lambda'], images_size=512, cl_criterion='weight-bce', attention_criterion='mse', device='cuda:1', seed=numpy.random.randint(low=1, high=2**32 - 2), no_scheduler=True, balanced=False, attention_loss_lambda=1000), ]