import pytest import torch import platform import os import fnmatch import timm from timm import list_models, create_model, set_scriptable if hasattr(torch._C, '_jit_set_profiling_executor'): # legacy executor is too slow to compile large models for unit tests # no need for the fusion performance here torch._C._jit_set_profiling_executor(True) torch._C._jit_set_profiling_mode(False) # transformer models don't support many of the spatial / feature based model functionalities NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*'] NUM_NON_STD = len(NON_STD_FILTERS) # exclude models that cause specific test failures 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 EXCLUDE_FILTERS = [ '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*'] + NON_STD_FILTERS else: EXCLUDE_FILTERS = NON_STD_FILTERS MAX_FWD_SIZE = 384 MAX_BWD_SIZE = 128 MAX_FWD_FEAT_SIZE = 448 # @pytest.mark.timeout(120) # @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS[:-NUM_NON_STD])) # @pytest.mark.parametrize('batch_size', [1]) # def test_model_forward(model_name, batch_size): # """Run a single forward pass with each model""" # model = create_model(model_name, pretrained=False) # model.eval() # input_size = model.default_cfg['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 # input_size = tuple([min(x, MAX_FWD_SIZE) for x in input_size]) # inputs = torch.randn((batch_size, *input_size)) # outputs = model(inputs) # assert outputs.shape[0] == batch_size # assert not torch.isnan(outputs).any(), 'Output included NaNs' # @pytest.mark.timeout(120) # @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS)) # @pytest.mark.parametrize('batch_size', [2]) # def test_model_backward(model_name, batch_size): # """Run a single forward pass with each model""" # model = create_model(model_name, pretrained=False, num_classes=42) # num_params = sum([x.numel() for x in model.parameters()]) # model.eval() # input_size = model.default_cfg['input_size'] # if any([x > MAX_BWD_SIZE for x in input_size]): # # cap backward test at 128 * 128 to keep resource usage down # input_size = tuple([min(x, MAX_BWD_SIZE) for x in input_size]) # inputs = torch.randn((batch_size, *input_size)) # outputs = model(inputs) # outputs.mean().backward() # for n, x in model.named_parameters(): # 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]) # assert outputs.shape[-1] == 42 # assert num_params == num_grad, 'Some parameters are missing gradients' # assert not torch.isnan(outputs).any(), 'Output included NaNs' # @pytest.mark.timeout(120) # @pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS)) # @pytest.mark.parametrize('batch_size', [1]) # def test_model_default_cfgs(model_name, batch_size): # """Run a single forward pass with each model""" # model = create_model(model_name, pretrained=False) # model.eval() # state_dict = model.state_dict() # cfg = model.default_cfg # classifier = cfg['classifier'] # pool_size = cfg['pool_size'] # input_size = model.default_cfg['input_size'] # 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]): # # 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_tensor = torch.randn((batch_size, *input_size)) # # test forward_features (always unpooled) # outputs = model.forward_features(input_tensor) # 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 # model.reset_classifier(0) # outputs = model.forward(input_tensor) # assert len(outputs.shape) == 2 # assert outputs.shape[-1] == model.num_features # # test model forward without pooling and classifier # model.reset_classifier(0, '') # reset classifier and set global pooling to pass-through # outputs = model.forward(input_tensor) # assert len(outputs.shape) == 4 # if not isinstance(model, timm.models.MobileNetV3): # # FIXME mobilenetv3 forward_features vs removed pooling differ # assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2] # # check classifier name matches default_cfg # assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params' # # check first conv(s) names match default_cfg # first_conv = cfg['first_conv'] # if isinstance(first_conv, str): # first_conv = (first_conv,) # assert isinstance(first_conv, (tuple, list)) # for fc in first_conv: # assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params' if 'GITHUB_ACTIONS' not in os.environ: @pytest.mark.timeout(120) @pytest.mark.parametrize('model_name', list_models(pretrained=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_load_pretrained(model_name, batch_size): """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 create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5) @pytest.mark.timeout(120) @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS)) @pytest.mark.parametrize('batch_size', [1]) def test_model_features_pretrained(model_name, batch_size): """Create that pretrained weights load when features_only==True.""" create_model(model_name, pretrained=True, features_only=True) EXCLUDE_JIT_FILTERS = [ '*iabn*', 'tresnet*', # models using inplace abn unlikely to ever be scriptable 'dla*', 'hrnet*', # hopefully fix at some point ] # @pytest.mark.timeout(120) # @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS)) # @pytest.mark.parametrize('batch_size', [1]) # def test_model_forward_torchscript(model_name, batch_size): # """Run a single forward pass with each model""" # with set_scriptable(True): # model = create_model(model_name, pretrained=False) # model.eval() # input_size = (3, 128, 128) # jit compile is already a bit slow and we've tested normal res already... # model = torch.jit.script(model) # outputs = model(torch.randn((batch_size, *input_size))) # assert outputs.shape[0] == batch_size # assert not torch.isnan(outputs).any(), 'Output included NaNs' EXCLUDE_FEAT_FILTERS = [ '*pruned*', # hopefully fix at some point ] 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 EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d', 'resnetv2_101x1_bitm'] @pytest.mark.timeout(120) @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS)) @pytest.mark.parametrize('batch_size', [1]) def test_model_forward_features(model_name, batch_size): """Run a single forward pass with each model in feature extraction mode""" model = create_model(model_name, pretrained=False, features_only=True) model.eval() expected_channels = model.feature_info.channels() assert len(expected_channels) >= 4 # all models here should have at least 4 feature levels by default, some 5 or 6 input_size = (3, 96, 96) # jit compile is already a bit slow and we've tested normal res already... outputs = model(torch.randn((batch_size, *input_size))) assert len(expected_channels) == len(outputs) for e, o in zip(expected_channels, outputs): assert e == o.shape[1] assert o.shape[0] == batch_size assert not torch.isnan(o).any()