def collect_torch_weights(output_dir, config, num_layers):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model = load_torch_model(config)
model_e = model.eval()
data_to_compare = OrderedDict()
register_print_hooks(output_dir,
model_e,
num_layers=num_layers,
data_to_compare=data_to_compare,
dump_activations=False)
input_ = randn(config['input_sample_size'])
model_e(input_)
for _, module in enumerate(model_e.modules()):
paths = get_full_dump_paths(module)
if paths is not None:
for dump_path in paths:
if os.path.isfile(dump_path):
data_to_compare[os.path.splitext(
os.path.basename(dump_path))[0]] = np.load(dump_path)
return data_to_compare
def validate_torch_model(output_dir, config, num_layers, dump, val_loader=None, cuda=False):
from tools.debug.common import load_torch_model, register_print_hooks
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model = load_torch_model(config, cuda)
model_e = model.eval()
if dump:
register_print_hooks(output_dir, model_e, num_layers=num_layers, data_to_compare=None, dump_activations=True)
validate_general(val_loader, model_e, infer_pytorch_model, cuda)
