def test_partly_pretrained_fit_step(self):
path_to_pretrained_json = os.path.join(get_project_root(),
'res_experiments',
'hps_common_mlp.json')
path_to_pretrained_model = os.path.join(get_project_root(),
'res_experiments',
'trained_models', 'test.pt')
hps_pretrained = HyperParams.from_file(
path_to_json=path_to_pretrained_json)
model_common = Model(hps_pretrained)
model_common.load_model(path_to_pretrained_model)
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_partly_independent_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model_part = Model(hps)
model_part.load_pretrained_bottom(path_to_pretrained_model,
path_to_pretrained_json)
train_loader, val_loader = model_part.make_loader(
filename='../data/small_parameters_base.fits')
loss = model_part.fit_step(train_loader, pretrained_bottom=True)
par_0_com = list(zip(*model_common.net.bottom.mlp.named_parameters())
)[1][0].detach().numpy()
par_0_par = list(zip(*model_part.net.bottom.mlp.named_parameters())
)[1][0].detach().numpy()
assert par_0_com == pytest.approx(par_0_par)
def test_partly_pretrained_init(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_common_mlp.json')
path_to_model = os.path.join(get_project_root(), 'res_experiments',
'trained_models', 'test.pt')
hps = HyperParams.from_file(path_to_json=path_to_json)
model_common = Model(hps)
model_common.load_model(path_to_model)
pretrained_dict = model_common.net.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if 'bottom' in k
}
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_partly_independent_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model_part = Model(hps)
model_dict = model_part.net.state_dict()
model_dict.update(pretrained_dict)
model_part.net.load_state_dict(model_dict)
par_0_part = list(
zip(*model_part.net.bottom.mlp.named_parameters()))[1][0]
par_0_com = list(
zip(*model_common.net.bottom.mlp.named_parameters()))[1][0]
def test_model_train(self):
hps = HyperParams()
hps.per_epoch = 2
model = Model(hps)
model.net = Conv1dModel(hps)
history = model.train()
assert True
def test_partly_pretrained_train(self):
path_to_pretrained_json = os.path.join(get_project_root(),
'res_experiments',
'hps_common_mlp.json')
path_to_pretrained_model = os.path.join(get_project_root(),
'res_experiments',
'trained_models', 'test.pt')
hps_pretrained = HyperParams.from_file(
path_to_json=path_to_pretrained_json)
model_common = Model(hps_pretrained)
model_common.load_model(path_to_pretrained_model)
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_partly_independent_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model_part = Model(hps)
model_part.load_pretrained_bottom(path_to_pretrained_model,
path_to_pretrained_json)
filename = '../data/small_parameters_base.fits'
history = model_part.train(
filename=filename,
path_to_save='../res_experiments/trained_models/partly_test.pt',
pretrained_bottom=True,
logdir='../res_experiments/')
par_0_com = list(zip(*model_common.net.bottom.mlp.named_parameters())
)[1][0].detach().numpy()
par_0_par = list(zip(*model_part.net.bottom.mlp.named_parameters())
)[1][0].detach().numpy()
assert par_0_com == pytest.approx(par_0_par)
def sample_batch(self):
hps = HyperParams()
model = Model(hps)
train_loader = model.make_loader()
it = iter(train_loader)
sample_batch = next(it)
return sample_batch
def test_resnet_train(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_base_resnet.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
hps.trainset = 5
model = Model(hps)
history = model.train()
assert history[0][0] > 0
def test_model_conv_train(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_base_conv.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model = Model(hps)
x = model.make_loader()
x_ = next(iter(x))
history = model.train()
assert history[0][0] > 0
def test_model_make_loader(self):
hps = HyperParams()
model = Model(hps)
model.net = Conv1dModel(hps)
train_loader = model.make_loader()
it = iter(train_loader)
sample_batch = next(it)
assert sample_batch['X'].size() == (20, 225)
assert sample_batch['Y'].size() == (20, 11)
assert isinstance(sample_batch['X'][0], torch.Tensor)
assert isinstance(sample_batch['Y'][0], torch.Tensor)
def test_model_partly_ind_train(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_partly_independent_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model = Model(hps)
filename = '../data/small_parameters_base.fits'
history = model.train(
filename=filename,
path_to_save='../res_experiments/trained_models/partly_test.pt',
logdir='../res_experiments/')
# model.save_model(path_to_save='../res_experiments/trained_models/test.pt')
assert True
def test_common_model_train(self, common_mlp_rescale_hps):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_common_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model = Model(hps)
filename = '../data/small_parameters_base.fits'
history = model.train(
filename=filename,
pregen=True,
path_to_save='../res_experiments/trained_models/test.pt',
logdir='../res_experiments/')
model.save_model(path='../res_experiments/trained_models/common.pt')
assert True
def test_load_pretrained_bottom(self):
path_to_pretrained_json = os.path.join(get_project_root(),
'res_experiments',
'hps_common_mlp.json')
path_to_pretrained_model = os.path.join(get_project_root(),
'res_experiments',
'trained_models', 'test.pt')
hps_pretrained = HyperParams.from_file(
path_to_json=path_to_pretrained_json)
model_common = Model(hps_pretrained)
model_common.load_model(path_to_pretrained_model)
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_partly_independent_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model_part = Model(hps)
model_part.load_pretrained_bottom(path_to_pretrained_model,
path_to_pretrained_json)
par_0_part = list(
zip(*model_part.net.bottom.mlp.named_parameters()))[1][0]
par_0_com = list(
zip(*model_common.net.bottom.mlp.named_parameters()))[1][0]
assert True
def test_predict_one_pixel_conv(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_base_conv.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
hps.trainset = 1
hps.valset = 1
hps.n_epochs = 1
hps.batch_size = 1
model = Model(hps)
history = model.train()
filename = Path(os.getcwd()).parent / 'data' / "20170905_030404.fits"
ref = fits.open(filename)
predicted, y, x, _ = model.predict_one_pixel(ref, 3, 4)
assert predicted[0].shape == torch.Size([1, 3])
def test_predict_full_image_conv(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_base_conv.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
hps.trainset = 1
hps.valset = 1
hps.n_epochs = 1
hps.batch_size = 1
model = Model(hps)
history = model.train()
filename = Path(os.getcwd()).parent / 'data' / "20170905_030404.fits"
ref = fits.open(filename)
predicted, params, lines, cont = model.predict_full_image(ref,
cnn=True)
assert predicted.shape == (ref[1].data.shape + (3, ))
def test_model_ind_train(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_independent_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
model = Model(hps)
filename = '../data/small_parameters_base.fits'
history = model.train(
filename=filename,
path_to_save='../res_experiments/trained_models/test.pt',
logdir='../res_experiments/')
# model.save_model(path_to_save='../res_experiments/trained_models/test.pt')
# list(zip(*self.net.top.task_layers[0].named_parameters()))[1][0].grad
params_groups_0 = list(
zip(*model.net.top.task_layers[0].named_parameters()))
params_groups_3 = list(
zip(*model.net.top.task_layers[3].named_parameters()))
assert True
def base_mlp_standard_hps(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_base_mlp_standard.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
return hps
def test_model_eval_step(self, sample_batch):
hps = HyperParams()
model = Model(hps)
model.net = Conv1dModel(hps)
loss = model.eval_step(sample_batch)
assert True
def test_model_init(self):
hps = HyperParams()
model = Model(hps)
model.net = Conv1dModel(hps)
assert True
def common_mlp_rescale_hps(self):
path_to_json = os.path.join(get_project_root(), 'res_experiments',
'hps_common_mlp.json')
hps = HyperParams.from_file(path_to_json=path_to_json)
return hps
