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import pytest
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import torch
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from timm import list_models, create_model
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models())
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_forward(model_name, batch_size):
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"""Run a single forward pass with each model"""
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model = create_model(model_name, pretrained=False)
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model.eval()
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input_size = model.default_cfg['input_size']
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if any([x > 448 for x in input_size]):
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# cap forward test at max res 448 * 448 to keep resource down
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input_size = tuple([min(x, 448) for x in input_size])
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inputs = torch.randn((batch_size, *input_size))
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outputs = model(inputs)
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assert outputs.shape[0] == batch_size
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assert not torch.isnan(outputs).any(), 'Output included NaNs'
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters='dla*')) # DLA models have an issue TBD
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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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"""Run a single forward pass with each model"""
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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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model.eval()
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input_size = model.default_cfg['input_size']
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if any([x > 128 for x in input_size]):
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# cap backward test at 128 * 128 to keep resource usage down
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input_size = tuple([min(x, 128) for x in 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.mean().backward()
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num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
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assert outputs.shape[-1] == 42
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assert num_params == num_grad, 'Some parameters are missing gradients'
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assert not torch.isnan(outputs).any(), 'Output included NaNs'
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models())
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_default_cfgs(model_name, batch_size):
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"""Run a single forward pass with each model"""
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model = create_model(model_name, pretrained=False)
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model.eval()
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state_dict = model.state_dict()
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cfg = model.default_cfg
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classifier = cfg['classifier']
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first_conv = cfg['first_conv']
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pool_size = cfg['pool_size']
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input_size = model.default_cfg['input_size']
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if all([x <= 448 for x in input_size]):
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# pool size only checked if default res <= 448 * 448 to keep resource down
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input_size = tuple([min(x, 448) for x in input_size])
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outputs = model.forward_features(torch.randn((batch_size, *input_size)))
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assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
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assert any([k.startswith(classifier) for k in state_dict.keys()]), f'{classifier} not in model params'
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assert any([k.startswith(first_conv) for k in state_dict.keys()]), f'{first_conv} not in model params'
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