def eval_models(models_paths: list, path_to_data: str):
if len(models_paths) == 0:
return 0.0
# getting test loader
ds = FashionMnistHandler(path_to_data, False)
ds.download()
ds.load()
# noise parameters are not relevant since test loader shouldn't have noise
_, _, test_loader = ds.get_noisy_loaders(0, '1', 0.2, 128, 128, 128)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
test_acc = []
for model_file in models_paths:
# creating model
checkpoint = torch.load(model_file, map_location=device)
model_name = model_file.split("/")[-1]
# loading from checkpoint
model = CNNModel()
model.load_state_dict(checkpoint['model_state_dict'])
model.to(device)
loss_fn = torch.nn.CrossEntropyLoss()
# evaluating
_, acc = Solver.eval(model,
device,
loss_fn=loss_fn,
data_loader=test_loader)
test_acc.append(acc)
print(f"Model {model_name} has {acc:.4f} acc in test dataset")
return test_acc
def solve(solver_name, problem_name, options):
'Solve the problem by solver with options.'
# Set cpp_compiler options
parameters["form_compiler"]["cpp_optimize"] = True
# Set debug level
set_log_active(options['debug'])
# Set refinement level
options['N'] = mesh_sizes[options['refinement_level']]
# Create problem and solver
problem = Problem(problem_name, options)
solver = Solver(solver_name, options)
time_step = solver.get_timestep(problem)[0]
if MPI.process_number() == 0 and options['verbose']:
print 'Problem: ' + str(problem)
print 'Solver: ' + str(solver)
# Solve problem with solver
wct = time.time()
u, p = solver.solve(problem)
# Compute elapsed time
wct = time.time() - wct
# Compute number of degrees of freedom
num_dofs = u.vector().size() + p.vector().size()
# Get the mesh size
mesh_size = u.function_space().mesh().hmin()
# Get functional value and error
functional, error = solver.eval()
# Save results
cpu_time = solver.cputime()
save_results(problem, solver, num_dofs, mesh_size, time_step, functional,
error)
return 0
def solve(solver_name, problem_name, options):
'Solve the problem by solver with options.'
# Set cpp_compiler options
parameters["form_compiler"]["cpp_optimize"] = True
# Set debug level
set_log_active(options['debug'])
# Set refinement level
options['N'] = mesh_sizes[options['refinement_level']]
# Create problem and solver
problem = Problem(problem_name, options)
solver = Solver(solver_name, options)
time_step = solver.get_timestep(problem)[0]
if MPI.process_number() == 0 and options['verbose']:
print 'Problem: ' + str(problem)
print 'Solver: ' + str(solver)
# Solve problem with solver
wct = time.time()
u, p = solver.solve(problem)
# Compute elapsed time
wct = time.time() - wct
# Compute number of degrees of freedom
num_dofs = u.vector().size() + p.vector().size()
# Get the mesh size
mesh_size = u.function_space().mesh().hmin()
# Get functional value and error
functional, error = solver.eval()
# Save results
cpu_time = solver.cputime()
save_results(problem, solver, num_dofs, mesh_size, time_step, functional, error)
return 0
