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214 lines
9.4 KiB
214 lines
9.4 KiB
import pytest
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import torch
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import platform
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import os
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import fnmatch
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import timm
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from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
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get_model_default_value
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if hasattr(torch._C, '_jit_set_profiling_executor'):
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# legacy executor is too slow to compile large models for unit tests
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# no need for the fusion performance here
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torch._C._jit_set_profiling_executor(True)
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torch._C._jit_set_profiling_mode(False)
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# transformer models don't support many of the spatial / feature based model functionalities
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NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*']
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NUM_NON_STD = len(NON_STD_FILTERS)
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# exclude models that cause specific test failures
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if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
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# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
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EXCLUDE_FILTERS = [
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'*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm',
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'*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*',
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'*resnetrs350*', '*resnetrs420*'] + NON_STD_FILTERS
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else:
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EXCLUDE_FILTERS = NON_STD_FILTERS
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MAX_FWD_SIZE = 384
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MAX_BWD_SIZE = 128
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MAX_FWD_FEAT_SIZE = 448
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS[:-NUM_NON_STD]))
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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 > MAX_FWD_SIZE for x in input_size]):
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if is_model_default_key(model_name, 'fixed_input_size'):
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pytest.skip("Fixed input size model > limit.")
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# cap forward test at max res 384 * 384 to keep resource down
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input_size = tuple([min(x, MAX_FWD_SIZE) 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=EXCLUDE_FILTERS))
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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 not is_model_default_key(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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if min_input_size is not None:
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input_size = min_input_size
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else:
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if any([x > MAX_BWD_SIZE 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, MAX_BWD_SIZE) 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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for n, x in model.named_parameters():
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assert x.grad is not None, f'No gradient for {n}'
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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(exclude_filters=NON_STD_FILTERS))
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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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pool_size = cfg['pool_size']
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input_size = model.default_cfg['input_size']
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if all([x <= MAX_FWD_FEAT_SIZE for x in input_size]) and \
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not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
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# output sizes only checked if default res <= 448 * 448 to keep resource down
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input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
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input_tensor = torch.randn((batch_size, *input_size))
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# test forward_features (always unpooled)
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outputs = model.forward_features(input_tensor)
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assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
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# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
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model.reset_classifier(0)
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outputs = model.forward(input_tensor)
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assert len(outputs.shape) == 2
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assert outputs.shape[-1] == model.num_features
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# test model forward without pooling and classifier
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model.reset_classifier(0, '') # reset classifier and set global pooling to pass-through
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outputs = model.forward(input_tensor)
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assert len(outputs.shape) == 4
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if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
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# FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
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assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
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# check classifier name matches default_cfg
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assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
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# check first conv(s) names match default_cfg
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first_conv = cfg['first_conv']
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if isinstance(first_conv, str):
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first_conv = (first_conv,)
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assert isinstance(first_conv, (tuple, list))
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for fc in first_conv:
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assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
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if 'GITHUB_ACTIONS' not in os.environ:
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(pretrained=True))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_load_pretrained(model_name, batch_size):
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"""Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
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in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change
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create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_features_pretrained(model_name, batch_size):
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"""Create that pretrained weights load when features_only==True."""
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create_model(model_name, pretrained=True, features_only=True)
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EXCLUDE_JIT_FILTERS = [
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'*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable
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'dla*', 'hrnet*', 'ghostnet*', # hopefully fix at some point
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]
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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('batch_size', [1])
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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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with set_scriptable(True):
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model = create_model(model_name, pretrained=False)
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model.eval()
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if has_model_default_key(model_name, 'fixed_input_size'):
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input_size = get_model_default_value(model_name, 'input_size')
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elif has_model_default_key(model_name, 'min_input_size'):
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input_size = get_model_default_value(model_name, 'min_input_size')
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else:
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input_size = (3, 128, 128) # jit compile is already a bit slow and we've tested normal res already...
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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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assert outputs.shape[0] == batch_size
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assert not torch.isnan(outputs).any(), 'Output included NaNs'
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EXCLUDE_FEAT_FILTERS = [
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'*pruned*', # hopefully fix at some point
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]
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if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system():
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# GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
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EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
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@pytest.mark.timeout(120)
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))
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@pytest.mark.parametrize('batch_size', [1])
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def test_model_forward_features(model_name, batch_size):
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"""Run a single forward pass with each model in feature extraction mode"""
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model = create_model(model_name, pretrained=False, features_only=True)
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model.eval()
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expected_channels = model.feature_info.channels()
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assert len(expected_channels) >= 4 # all models here should have at least 4 feature levels by default, some 5 or 6
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if has_model_default_key(model_name, 'fixed_input_size'):
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input_size = get_model_default_value(model_name, 'input_size')
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elif has_model_default_key(model_name, 'min_input_size'):
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input_size = get_model_default_value(model_name, 'min_input_size')
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else:
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input_size = (3, 96, 96) # jit compile is already a bit slow and we've tested normal res already...
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outputs = model(torch.randn((batch_size, *input_size)))
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assert len(expected_channels) == len(outputs)
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for e, o in zip(expected_channels, outputs):
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assert e == o.shape[1]
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assert o.shape[0] == batch_size
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assert not torch.isnan(o).any()
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