def test_function(X, Y, network):
"""
Evaluation of network on test and valid set
Parameters
----------
X : input images (B,3,h,w)
Y : ground truth (B,1,h/8,w/8)
network : BSDR object
"""
X = torch.autograd.Variable(torch.from_numpy(X)).cuda()
Y = torch.autograd.Variable(torch.from_numpy(Y)).cuda()
network = network.cuda()
network.eval()
output = network(X) # (B,1,h,w)
loss = 0.0
loss_criterion = nn.MSELoss(size_average=True)
# bp()
loss = loss_criterion(output, Y)
count_error = torch.abs(
torch.sum(Y.view(Y.size(0), -1), dim=1) -
torch.sum(output.view(output.size(0), -1), dim=1))
network.train()
network = set_batch_norm_to_eval(network)
return loss.item(), output.cpu().detach().numpy(), count_error.cpu(
).detach().numpy()
def test_function(X, Y, network):
X = torch.autograd.Variable(torch.from_numpy(X)).cuda()
Y = torch.autograd.Variable(torch.from_numpy(Y)).cuda()
network = network.cuda()
network.eval()
output = network(X) # (B,1,h,w)
loss = 0.0
loss_criterion = get_loss_criterion()
avg_pool = nn.AvgPool2d(kernel_size=args.kernel_size,
stride=args.kernel_size)
output_reshape = avg_pool(output) * (args.kernel_size * args.kernel_size)
loss = loss_criterion(output_reshape.view(-1, 1),
torch.cuda.FloatTensor(sampled_GT).view(-1,
1)) * 0.01
count_error = torch.abs(
torch.sum(Y.view(Y.size(0), -1), dim=1) -
torch.sum(output.view(output.size(0), -1), dim=1))
network.train()
network = set_batch_norm_to_eval(network)
return loss.item(), output.cpu().detach().numpy(), count_error.cpu(
).detach().numpy()
def test_function(X, Y, network):
X = torch.autograd.Variable(torch.from_numpy(X)).cuda()
Y = torch.autograd.Variable(torch.from_numpy(Y)).cuda()
network = network.cuda()
network.eval()
output = network(X) # (B,1,h,w)
count_error = torch.abs(torch.sum(Y.view(Y.size(0), -1), dim=1) - torch.sum(output.view(output.size(0), -1), dim=1))
network.train()
network = set_batch_norm_to_eval(network)
return output.cpu().detach().numpy(), count_error.cpu().detach().numpy()
def test_function(Xs, Ys, network, set_name=None):
assert(set_name is not None)
X = torch.autograd.Variable(torch.from_numpy(Xs)).cuda()
Y = torch.autograd.Variable(torch.from_numpy(Ys)).float().cuda()
network = network.cuda()
network.eval()
output = network(X) # (B,1,h,w)
loss = 0.0
loss_criterion = get_loss_criterion()
avg_pool = nn.AvgPool2d(kernel_size=args.kernel_size,
stride=args.kernel_size)
output_reshape_ = avg_pool(output) * (args.kernel_size * args.kernel_size)
if set_name == 'test_valid':
pseudo_density_maps = create_pseudo_density(Xs)
pseudo_density_maps = torch.from_numpy(pseudo_density_maps).cuda()
pseudo_reshape_ = avg_pool(pseudo_density_maps) * (args.kernel_size * args.kernel_size)
output_reshape = output_reshape_.view(-1, 1)
pseudo_reshape = pseudo_reshape_.view(-1, 1)
pseudo_median = pseudo_reshape.topk(int(args.percentile_thresh*len(pseudo_reshape)), dim=0)[0][-1:][0]
Y_median = Y.topk(int(args.percentile_thresh*(len(Y))), dim=0)[0][-1:][0]
a_output_indices = pseudo_reshape < pseudo_median
a_Y_indices = Y < Y_median
if a_output_indices.sum() > 2:
loss_sparse = loss_criterion(output_reshape[a_output_indices].view(-1, 1), Y[a_Y_indices].view(-1, 1))
loss_dense = loss_criterion(output_reshape[~a_output_indices].view(-1, 1), Y[~a_Y_indices].view(-1, 1))
loss = (loss_sparse + loss_dense)* 0.01
else:
loss = loss_criterion(output_reshape, Y.view(-1, 1)) * 0.01
else:
output_reshape = output_reshape_.view(-1, 1)
loss = loss_criterion(output_reshape, Y.view(-1, 1)) * 0.01
count_error = torch.abs(torch.sum(Y.view(Y.size(0), -1), dim=1) - torch.sum(output.view(output.size(0), -1), dim=1))
network.train()
network = set_batch_norm_to_eval(network)
if set_name == "test_valid":
return loss.item(), loss_sparse.item(), loss_dense.item(), output.cpu().detach().numpy()
else:
return loss.item(), output.cpu().detach().numpy(), count_error.cpu().detach().numpy()