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@ -1,15 +1,24 @@
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import pytest
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import pytest
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import torch
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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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from timm import list_models, create_model
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from timm import list_models, create_model
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MAX_FWD_SIZE = 320
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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 = ['*efficientnet_l2*']
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else:
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EXCLUDE_FILTERS = []
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MAX_FWD_SIZE = 384
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MAX_BWD_SIZE = 128
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MAX_BWD_SIZE = 128
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MAX_FWD_FEAT_SIZE = 448
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MAX_FWD_FEAT_SIZE = 448
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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())
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
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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(model_name, batch_size):
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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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"""Run a single forward pass with each model"""
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@ -28,7 +37,8 @@ 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='dla*')) # DLA models have an issue TBD
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# DLA models have an issue TBD, add them to exclusions
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@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + ['dla*']))
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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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@ -65,7 +75,8 @@ def test_model_default_cfgs(model_name, batch_size):
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pool_size = cfg['pool_size']
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pool_size = cfg['pool_size']
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input_size = model.default_cfg['input_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 'efficientnet_l2' not in model_name:
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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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# pool size only checked if default res <= 448 * 448 to keep resource down
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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, MAX_FWD_FEAT_SIZE) for x in input_size])
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input_size = tuple([min(x, MAX_FWD_FEAT_SIZE) for x in input_size])
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outputs = model.forward_features(torch.randn((batch_size, *input_size)))
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outputs = model.forward_features(torch.randn((batch_size, *input_size)))
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