def _test_serialization(self, module, inputs):
with TemporaryFileName() as fname:
torch.jit.save(module, fname)
loaded = torch.jit.load(fname)
self.assertEqual(
module(*inputs).to_dense(),
loaded(*inputs).to_dense())
def getExportImportCopy(self, m, also_test_file=True, map_location=None):
buffer = io.BytesIO()
torch.jit.save(m, buffer)
buffer.seek(0)
imported = torch.jit.load(buffer, map_location=map_location)
if not also_test_file:
return imported
with TemporaryFileName() as fname:
torch.jit.save(imported, fname)
return torch.jit.load(fname, map_location=map_location)
def compare_enabled_disabled(self, src):
"""
Runs the script in `src` with PYTORCH_JIT enabled and disabled and
compares their stdout for equality.
"""
# Write `src` out to a temporary so our source inspection logic works
# correctly.
with TemporaryFileName() as fname:
with open(fname, 'w') as f:
f.write(src)
with _jit_disabled():
out_disabled = subprocess.check_output(
[sys.executable, fname])
out_enabled = subprocess.check_output([sys.executable, fname])
self.assertEqual(out_disabled, out_enabled)
def getExportImportCopy(self, m, also_test_file=True, map_location=None):
if isinstance(m, torch._C.Function):
src, constants = _jit_python_print(m)
cu = torch.jit.CompilationUnit()._import(src, constants)
return getattr(cu, m.name)
buffer = io.BytesIO()
torch.jit.save(m, buffer)
buffer.seek(0)
imported = torch.jit.load(buffer, map_location=map_location)
if not also_test_file:
return imported
with TemporaryFileName() as fname:
imported.save(fname)
return torch.jit.load(fname, map_location=map_location)
def test_save_load_with_extra_files(self):
class MyMod(torch.jit.ScriptModule):
@torch.jit.script_method
def forward(self, a):
return a
expected_extra_files = torch._C.ExtraFilesMap()
expected_extra_files['foo'] = 'bar'
m = MyMod()
# Save to file.
with TemporaryFileName() as fname:
m.save(fname, _extra_files=expected_extra_files)
extra_files = torch._C.ExtraFilesMap()
extra_files['foo'] = ''
torch.jit.load(fname, _extra_files=extra_files)
self.assertEqual('bar', extra_files['foo'])
# Use torch.jit API
torch.jit.save(m, fname, _extra_files=expected_extra_files)
extra_files['foo'] = ''
torch.jit.load(fname, _extra_files=extra_files)
self.assertEqual('bar', extra_files['foo'])
# Save to buffer.
buffer = io.BytesIO(
m.save_to_buffer(_extra_files=expected_extra_files))
extra_files = torch._C.ExtraFilesMap()
extra_files['foo'] = ''
torch.jit.load(buffer, _extra_files=extra_files)
self.assertEqual('bar', extra_files['foo'])
# Use torch.jit API
buffer = io.BytesIO()
torch.jit.save(m, buffer, _extra_files=expected_extra_files)
buffer.seek(0)
extra_files = torch._C.ExtraFilesMap()
extra_files['foo'] = ''
torch.jit.load(buffer, _extra_files=extra_files)
self.assertEqual('bar', extra_files['foo'])
# Non-existent file 'bar'
with self.assertRaises(RuntimeError):
extra_files['bar'] = ''
torch.jit.load(buffer, _extra_files=extra_files)
