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ieee8023 merged 5 commits into from
Apr 27, 2025
Merged

Only change input resolution if loading model weights #168

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only change input resolution if loading model weights
ieee8023 Apr 27, 2025
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only change input resolution if loading model weights
ieee8023 Apr 27, 2025
0b652a3
only change input resolution if loading model weights
ieee8023 Apr 27, 2025
2c6f4d3
only change input resolution if loading model weights
ieee8023 Apr 27, 2025
0321e84
only change input resolution if loading model weights
ieee8023 Apr 27, 2025
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37 changes: 27 additions & 10 deletions torchxrayvision/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,8 @@
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/nih-pc-chex-mimic_ch-google-openi-kaggle-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
"op_threshs": [0.07422872, 0.038290843, 0.09814756, 0.0098118475, 0.023601074, 0.0022490358, 0.010060724, 0.103246614, 0.056810737, 0.026791653, 0.050318155, 0.023985857, 0.01939503, 0.042889766, 0.053369623, 0.035975814, 0.20204692, 0.05015312],
"ppv80_thres": [0.72715247, 0.8885005, 0.92493945, 0.6527224, 0.68707734, 0.46127197, 0.7272054, 0.6127343, 0.9878492, 0.61979693, 0.66309816, 0.7853459, 0.930661, 0.93645346, 0.6788558, 0.6547198, 0.61614525, 0.8489876]
"ppv80_thres": [0.72715247, 0.8885005, 0.92493945, 0.6527224, 0.68707734, 0.46127197, 0.7272054, 0.6127343, 0.9878492, 0.61979693, 0.66309816, 0.7853459, 0.930661, 0.93645346, 0.6788558, 0.6547198, 0.61614525, 0.8489876],
"input_resolution": 224,
}
model_urls['densenet121-res224-all'] = model_urls['all']

Expand All @@ -32,43 +33,49 @@
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/nih-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', '', '', '', ''],
"op_threshs": [0.039117552, 0.0034529066, 0.11396341, 0.0057298196, 0.00045666535, 0.0018880932, 0.012037827, 0.038744126, 0.0037213727, 0.014730946, 0.016149804, 0.054241467, 0.037198864, 0.0004403434, np.nan, np.nan, np.nan, np.nan],
"input_resolution": 224,
}
model_urls['densenet121-res224-nih'] = model_urls['nih']


model_urls['pc'] = {
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/pc-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', '', 'Fracture', '', ''],
"op_threshs": [0.031012505, 0.013347598, 0.081435576, 0.001262615, 0.002587246, 0.0035944257, 0.0023071, 0.055412333, 0.044385884, 0.042766232, 0.043258056, 0.037629247, 0.005658899, 0.0091741895, np.nan, 0.026507627, np.nan, np.nan]
"op_threshs": [0.031012505, 0.013347598, 0.081435576, 0.001262615, 0.002587246, 0.0035944257, 0.0023071, 0.055412333, 0.044385884, 0.042766232, 0.043258056, 0.037629247, 0.005658899, 0.0091741895, np.nan, 0.026507627, np.nan, np.nan],
"input_resolution": 224,
}
model_urls['densenet121-res224-pc'] = model_urls['pc']

model_urls['chex'] = {
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/chex-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['Atelectasis', 'Consolidation', '', 'Pneumothorax', 'Edema', '', '', 'Effusion', 'Pneumonia', '', 'Cardiomegaly', '', '', '', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
"op_threshs": [0.1988969, 0.05710573, np.nan, 0.0531293, 0.1435217, np.nan, np.nan, 0.27212676, 0.07749717, np.nan, 0.19712369, np.nan, np.nan, np.nan, 0.09932402, 0.09273402, 0.3270967, 0.10888247],
"input_resolution": 224,
}
model_urls['densenet121-res224-chex'] = model_urls['chex']


model_urls['rsna'] = {
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/kaggle-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['', '', '', '', '', '', '', '', 'Pneumonia', '', '', '', '', '', '', '', 'Lung Opacity', ''],
"op_threshs": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.13486601, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.13511065, np.nan]
"op_threshs": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.13486601, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 0.13511065, np.nan],
"input_resolution": 224,
}
model_urls['densenet121-res224-rsna'] = model_urls['rsna']

model_urls['mimic_nb'] = {
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/mimic_nb-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['Atelectasis', 'Consolidation', '', 'Pneumothorax', 'Edema', '', '', 'Effusion', 'Pneumonia', '', 'Cardiomegaly', '', '', '', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
"op_threshs": [0.08558747, 0.011884617, np.nan, 0.0040595434, 0.010733786, np.nan, np.nan, 0.118761964, 0.022924708, np.nan, 0.06358637, np.nan, np.nan, np.nan, 0.022143636, 0.017476924, 0.1258702, 0.014020768],
"input_resolution": 224,
}
model_urls['densenet121-res224-mimic_nb'] = model_urls['mimic_nb']

model_urls['mimic_ch'] = {
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/mimic_ch-densenet121-d121-tw-lr001-rot45-tr15-sc15-seed0-best.pt',
"labels": ['Atelectasis', 'Consolidation', '', 'Pneumothorax', 'Edema', '', '', 'Effusion', 'Pneumonia', '', 'Cardiomegaly', '', '', '', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
"op_threshs": [0.09121389, 0.010573786, np.nan, 0.005023008, 0.003698257, np.nan, np.nan, 0.08001232, 0.037242252, np.nan, 0.05006329, np.nan, np.nan, np.nan, 0.019866971, 0.03823637, 0.11303808, 0.0069147074],
"input_resolution": 224,
}
model_urls['densenet121-res224-mimic_ch'] = model_urls['mimic_ch']

Expand All @@ -77,7 +84,8 @@
"weights_url": 'https://github.com/mlmed/torchxrayvision/releases/download/v1/pc-nih-rsna-siim-vin-resnet50-test512-e400-state.pt',
"labels": ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening', 'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'Lung Lesion', 'Fracture', 'Lung Opacity', 'Enlarged Cardiomediastinum'],
"op_threshs": [0.51570356, 0.50444704, 0.53787947, 0.50723547, 0.5025118, 0.5035252, 0.5038076, 0.51862943, 0.5078151, 0.50724894, 0.5056339, 0.510706, 0.5053923, 0.5020846, np.nan, 0.5080557, 0.5138526, np.nan],
"ppv80_thres": [0.690908, 0.720028, 0.7303882, 0.7235838, 0.6787441, 0.7304924, 0.73105824, 0.6839408, 0.7241559, 0.7219969, 0.6346738, 0.72764945, 0.7285066, 0.5735704, np.nan, 0.69684714, 0.7135549, np.nan]
"ppv80_thres": [0.690908, 0.720028, 0.7303882, 0.7235838, 0.6787441, 0.7304924, 0.73105824, 0.6839408, 0.7241559, 0.7219969, 0.6346738, 0.72764945, 0.7285066, 0.5735704, np.nan, 0.69684714, 0.7135549, np.nan],
"input_resolution": 512,
}

# Just created for documentation
Expand Down Expand Up @@ -313,24 +321,29 @@ def __init__(self,
if "op_threshs" in model_urls[weights]:
self.op_threshs = torch.tensor(model_urls[weights]["op_threshs"])

if "input_resolution" in model_urls[weights]:
self.input_resolution = model_urls[weights]["input_resolution"]

def __repr__(self):
if self.weights is not None:
return "XRV-DenseNet121-{}".format(self.weights)
else:
return "XRV-DenseNet"

def features2(self, x):
x = utils.fix_resolution(x, 224, self)
utils.warn_normalization(x)
if hasattr(self, 'input_resolution'):
x = utils.fix_resolution(x, self.input_resolution, self)
utils.warn_normalization(x)

features = self.features(x)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
return out

def forward(self, x):
x = utils.fix_resolution(x, 224, self)
utils.warn_normalization(x)
if hasattr(self, 'input_resolution'):
x = utils.fix_resolution(x, self.input_resolution, self)
utils.warn_normalization(x)

features = self.features2(x)
out = self.classifier(features)
Expand Down Expand Up @@ -419,6 +432,9 @@ def __init__(self, weights: str = None, apply_sigmoid: bool = False, cache_dir:
if "op_threshs" in model_urls[weights]:
self.register_buffer('op_threshs', torch.tensor(model_urls[weights]["op_threshs"]))

if "input_resolution" in model_urls[weights]:
self.input_resolution = model_urls[weights]["input_resolution"]

self.eval()

def __repr__(self):
Expand Down Expand Up @@ -446,8 +462,9 @@ def features(self, x):
return x

def forward(self, x):
x = utils.fix_resolution(x, 512, self)
utils.warn_normalization(x)
if hasattr(self, 'input_resolution'):
x = utils.fix_resolution(x, self.input_resolution, self)
utils.warn_normalization(x)

out = self.model(x)

Expand Down