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@ -40,14 +40,25 @@ TARGET_FFEAT_SIZE = 96
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MAX_FFEAT_SIZE = 256
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MAX_FFEAT_SIZE = 256
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def _get_input_size(model, target=None):
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def _get_input_size(model=None, model_name='', target=None):
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if model is None:
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assert model_name, "One of model or model_name must be provided"
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input_size = get_model_default_value(model_name, 'input_size')
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fixed_input_size = get_model_default_value(model_name, 'fixed_input_size')
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min_input_size = get_model_default_value(model_name, 'min_input_size')
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else:
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default_cfg = model.default_cfg
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default_cfg = model.default_cfg
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input_size = default_cfg['input_size']
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input_size = default_cfg['input_size']
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if 'fixed_input_size' in default_cfg and default_cfg['fixed_input_size']:
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fixed_input_size = default_cfg.get('fixed_input_size', None)
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min_input_size = default_cfg.get('min_input_size', None)
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assert input_size is not None
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if fixed_input_size:
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return input_size
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return input_size
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if 'min_input_size' in default_cfg:
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if min_input_size:
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if target and max(input_size) > target:
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if target and max(input_size) > target:
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input_size = default_cfg['min_input_size']
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input_size = min_input_size
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else:
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else:
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if target and max(input_size) > target:
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if target and max(input_size) > target:
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input_size = tuple([min(x, target) for x in input_size])
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input_size = tuple([min(x, target) for x in input_size])
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@ -73,18 +84,18 @@ def test_model_forward(model_name, batch_size):
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
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@pytest.mark.parametrize('batch_size', [2])
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@pytest.mark.parametrize('batch_size', [2])
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def test_model_backward(model_name, batch_size):
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def test_model_backward(model_name, batch_size):
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"""Run a single forward pass with each model"""
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"""Run a single forward pass with each model"""
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input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
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if max(input_size) > MAX_BWD_SIZE:
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pytest.skip("Fixed input size model > limit.")
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model = create_model(model_name, pretrained=False, num_classes=42)
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model = create_model(model_name, pretrained=False, num_classes=42)
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num_params = sum([x.numel() for x in model.parameters()])
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num_params = sum([x.numel() for x in model.parameters()])
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model.train()
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model.train()
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input_size = _get_input_size(model, TARGET_BWD_SIZE)
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if max(input_size) > MAX_BWD_SIZE:
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pytest.skip("Fixed input size model > limit.")
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inputs = torch.randn((batch_size, *input_size))
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inputs = torch.randn((batch_size, *input_size))
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outputs = model(inputs)
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outputs = model(inputs)
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if isinstance(outputs, tuple):
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if isinstance(outputs, tuple):
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@ -172,18 +183,19 @@ EXCLUDE_JIT_FILTERS = [
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@pytest.mark.timeout(120)
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
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@pytest.mark.parametrize(
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'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
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@pytest.mark.parametrize('batch_size', [1])
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_forward_torchscript(model_name, batch_size):
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def test_model_forward_torchscript(model_name, batch_size):
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"""Run a single forward pass with each model"""
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"""Run a single forward pass with each model"""
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input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
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if max(input_size) > MAX_JIT_SIZE: # NOTE using MAX_FWD_SIZE as the final limit is intentional
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pytest.skip("Fixed input size model > limit.")
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with set_scriptable(True):
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with set_scriptable(True):
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model = create_model(model_name, pretrained=False)
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model = create_model(model_name, pretrained=False)
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model.eval()
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model.eval()
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input_size = _get_input_size(model, TARGET_JIT_SIZE)
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if max(input_size) > MAX_JIT_SIZE: # NOTE using MAX_FWD_SIZE as the final limit is intentional
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pytest.skip("Fixed input size model > limit.")
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model = torch.jit.script(model)
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model = torch.jit.script(model)
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outputs = model(torch.randn((batch_size, *input_size)))
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outputs = model(torch.randn((batch_size, *input_size)))
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