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pull/554/head
Aman Arora 4 years ago
parent 973b8801c4
commit bf63f0cc7b
1 changed files with 139 additions and 139 deletions
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tests/test_models.py
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@ -21,7 +21,7 @@ NUM_NON_STD = len(NON_STD_FILTERS)
if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system(): if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
EXCLUDE_FILTERS = [ EXCLUDE_FILTERS = [
'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', 'resnetv2_101x1_bitm'
'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*'] + NON_STD_FILTERS '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*'] + NON_STD_FILTERS
else: else:
EXCLUDE_FILTERS = NON_STD_FILTERS EXCLUDE_FILTERS = NON_STD_FILTERS
@ -31,144 +31,144 @@ MAX_BWD_SIZE = 128
MAX_FWD_FEAT_SIZE = 448 MAX_FWD_FEAT_SIZE = 448
# @pytest.mark.timeout(120) @pytest.mark.timeout(120)
# @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS[:-NUM_NON_STD])) @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS[:-NUM_NON_STD]))
# @pytest.mark.parametrize('batch_size', [1]) @pytest.mark.parametrize('batch_size', [1])
# def test_model_forward(model_name, batch_size): def test_model_forward(model_name, batch_size):
# """Run a single forward pass with each model""" """Run a single forward pass with each model"""
# model = create_model(model_name, pretrained=False) model = create_model(model_name, pretrained=False)
# model.eval() model.eval()
# input_size = model.default_cfg['input_size'] input_size = model.default_cfg['input_size']
# if any([x > MAX_FWD_SIZE for x in input_size]): if any([x > MAX_FWD_SIZE for x in input_size]):
# # cap forward test at max res 448 * 448 to keep resource down # cap forward test at max res 448 * 448 to keep resource down
# input_size = tuple([min(x, MAX_FWD_SIZE) for x in input_size]) input_size = tuple([min(x, MAX_FWD_SIZE) for x in input_size])
# inputs = torch.randn((batch_size, *input_size)) inputs = torch.randn((batch_size, *input_size))
# outputs = model(inputs) outputs = model(inputs)
# assert outputs.shape[0] == batch_size assert outputs.shape[0] == batch_size
# assert not torch.isnan(outputs).any(), 'Output included NaNs' assert not torch.isnan(outputs).any(), 'Output included NaNs'
# @pytest.mark.timeout(120) @pytest.mark.timeout(120)
# @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS)) @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
# @pytest.mark.parametrize('batch_size', [2]) @pytest.mark.parametrize('batch_size', [2])
# def test_model_backward(model_name, batch_size): def test_model_backward(model_name, batch_size):
# """Run a single forward pass with each model""" """Run a single forward pass with each model"""
# model = create_model(model_name, pretrained=False, num_classes=42) model = create_model(model_name, pretrained=False, num_classes=42)
# num_params = sum([x.numel() for x in model.parameters()]) num_params = sum([x.numel() for x in model.parameters()])
# model.eval() model.eval()
# input_size = model.default_cfg['input_size'] input_size = model.default_cfg['input_size']
# if any([x > MAX_BWD_SIZE for x in input_size]): if any([x > MAX_BWD_SIZE for x in input_size]):
# # cap backward test at 128 * 128 to keep resource usage down # cap backward test at 128 * 128 to keep resource usage down
# input_size = tuple([min(x, MAX_BWD_SIZE) for x in input_size]) input_size = tuple([min(x, MAX_BWD_SIZE) for x in input_size])
# inputs = torch.randn((batch_size, *input_size)) inputs = torch.randn((batch_size, *input_size))
# outputs = model(inputs) outputs = model(inputs)
# outputs.mean().backward() outputs.mean().backward()
# for n, x in model.named_parameters(): for n, x in model.named_parameters():
# assert x.grad is not None, f'No gradient for {n}' assert x.grad is not None, f'No gradient for {n}'
# num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None]) num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
# assert outputs.shape[-1] == 42 assert outputs.shape[-1] == 42
# assert num_params == num_grad, 'Some parameters are missing gradients' assert num_params == num_grad, 'Some parameters are missing gradients'
# assert not torch.isnan(outputs).any(), 'Output included NaNs' assert not torch.isnan(outputs).any(), 'Output included NaNs'
# @pytest.mark.timeout(120) @pytest.mark.timeout(120)
# @pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS)) @pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS))
# @pytest.mark.parametrize('batch_size', [1]) @pytest.mark.parametrize('batch_size', [1])
# def test_model_default_cfgs(model_name, batch_size): def test_model_default_cfgs(model_name, batch_size):
# """Run a single forward pass with each model""" """Run a single forward pass with each model"""
# model = create_model(model_name, pretrained=False) model = create_model(model_name, pretrained=False)
# model.eval() model.eval()
# state_dict = model.state_dict() state_dict = model.state_dict()
# cfg = model.default_cfg cfg = model.default_cfg
# classifier = cfg['classifier'] classifier = cfg['classifier']
# pool_size = cfg['pool_size'] pool_size = cfg['pool_size']
# input_size = model.default_cfg['input_size'] input_size = model.default_cfg['input_size']
# if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \ if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
# not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]): not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
# # output sizes only checked if default res <= 448 * 448 to keep resource down # output sizes only checked if default res <= 448 * 448 to keep resource down
# input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size]) input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
# input_tensor = torch.randn((batch_size, *input_size)) input_tensor = torch.randn((batch_size, *input_size))
# # test forward_features (always unpooled) # test forward_features (always unpooled)
# outputs = model.forward_features(input_tensor) outputs = model.forward_features(input_tensor)
# assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2] assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
# # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
# model.reset_classifier(0) model.reset_classifier(0)
# outputs = model.forward(input_tensor) outputs = model.forward(input_tensor)
# assert len(outputs.shape) == 2 assert len(outputs.shape) == 2
# assert outputs.shape[-1] == model.num_features assert outputs.shape[-1] == model.num_features
# # test model forward without pooling and classifier # test model forward without pooling and classifier
# model.reset_classifier(0, '') # reset classifier and set global pooling to pass-through model.reset_classifier(0, '') # reset classifier and set global pooling to pass-through
# outputs = model.forward(input_tensor) outputs = model.forward(input_tensor)
# assert len(outputs.shape) == 4 assert len(outputs.shape) == 4
# if not isinstance(model, timm.models.MobileNetV3): if not isinstance(model, timm.models.MobileNetV3):
# # FIXME mobilenetv3 forward_features vs removed pooling differ # FIXME mobilenetv3 forward_features vs removed pooling differ
# assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2] assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
# # check classifier name matches default_cfg # check classifier name matches default_cfg
# assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params' assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
# # check first conv(s) names match default_cfg # check first conv(s) names match default_cfg
# first_conv = cfg['first_conv'] first_conv = cfg['first_conv']
# if isinstance(first_conv, str): if isinstance(first_conv, str):
# first_conv = (first_conv,) first_conv = (first_conv,)
# assert isinstance(first_conv, (tuple, list)) assert isinstance(first_conv, (tuple, list))
# for fc in first_conv: for fc in first_conv:
# assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params' assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
# if 'GITHUB_ACTIONS' not in os.environ: if 'GITHUB_ACTIONS' not in os.environ:
# @pytest.mark.timeout(120) @pytest.mark.timeout(120)
# @pytest.mark.parametrize('model_name', list_models(pretrained=True)) @pytest.mark.parametrize('model_name', list_models(pretrained=True))
# @pytest.mark.parametrize('batch_size', [1]) @pytest.mark.parametrize('batch_size', [1])
# def test_model_load_pretrained(model_name, batch_size): def test_model_load_pretrained(model_name, batch_size):
# """Create that pretrained weights load, verify support for in_chans != 3 while doing so.""" """Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
# in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change
# create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5) create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
# @pytest.mark.timeout(120) @pytest.mark.timeout(120)
# @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS)) @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
# @pytest.mark.parametrize('batch_size', [1]) @pytest.mark.parametrize('batch_size', [1])
# def test_model_features_pretrained(model_name, batch_size): def test_model_features_pretrained(model_name, batch_size):
# """Create that pretrained weights load when features_only==True.""" """Create that pretrained weights load when features_only==True."""
# create_model(model_name, pretrained=True, features_only=True) create_model(model_name, pretrained=True, features_only=True)
# EXCLUDE_JIT_FILTERS = [ EXCLUDE_JIT_FILTERS = [
# '*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable '*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable
# 'dla*', 'hrnet*', # hopefully fix at some point 'dla*', 'hrnet*', # hopefully fix at some point
# ] ]
# @pytest.mark.timeout(120) @pytest.mark.timeout(120)
# @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS)) @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
# @pytest.mark.parametrize('batch_size', [1]) @pytest.mark.parametrize('batch_size', [1])
# def test_model_forward_torchscript(model_name, batch_size): def test_model_forward_torchscript(model_name, batch_size):
# """Run a single forward pass with each model""" """Run a single forward pass with each model"""
# with set_scriptable(True): with set_scriptable(True):
# model = create_model(model_name, pretrained=False) model = create_model(model_name, pretrained=False)
# model.eval() model.eval()
# input_size = (3, 128, 128) # jit compile is already a bit slow and we've tested normal res already... input_size = (3, 128, 128) # jit compile is already a bit slow and we've tested normal res already...
# model = torch.jit.script(model) model = torch.jit.script(model)
# outputs = model(torch.randn((batch_size, *input_size))) outputs = model(torch.randn((batch_size, *input_size)))
# assert outputs.shape[0] == batch_size assert outputs.shape[0] == batch_size
# assert not torch.isnan(outputs).any(), 'Output included NaNs' assert not torch.isnan(outputs).any(), 'Output included NaNs'
# EXCLUDE_FEAT_FILTERS = [ EXCLUDE_FEAT_FILTERS = [
# '*pruned*', # hopefully fix at some point '*pruned*', # hopefully fix at some point
# ] ]
# if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system(): if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
# # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
# EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d'] EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
@pytest.mark.timeout(210) @pytest.mark.timeout(210)

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