You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
211 lines
9.1 KiB
211 lines
9.1 KiB
import pytest
|
|
import torch
|
|
import platform
|
|
import os
|
|
import fnmatch
|
|
|
|
import timm
|
|
from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
|
|
get_model_default_value
|
|
|
|
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_*', 'swin_*']
|
|
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', '*101x3_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 not is_model_default_key(model_name, 'fixed_input_size'):
|
|
min_input_size = get_model_default_value(model_name, 'min_input_size')
|
|
if min_input_size is not None:
|
|
input_size = min_input_size
|
|
else:
|
|
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()
|
|
|
|
if has_model_default_key(model_name, 'fixed_input_size'):
|
|
input_size = get_model_default_value(model_name, 'input_size')
|
|
elif has_model_default_key(model_name, 'min_input_size'):
|
|
input_size = get_model_default_value(model_name, 'min_input_size')
|
|
else:
|
|
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']
|
|
|
|
|
|
@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
|
|
|
|
if has_model_default_key(model_name, 'fixed_input_size'):
|
|
input_size = get_model_default_value(model_name, 'input_size')
|
|
elif has_model_default_key(model_name, 'min_input_size'):
|
|
input_size = get_model_default_value(model_name, 'min_input_size')
|
|
else:
|
|
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()
|