Convert samples/targets in ParserImageInTar to numpy arrays, slightly less mem usage for massive datasets. Add a few more se/eca model defs to resnet.py

timm/data/parsers/parser_image_in_tar.py

@@ -155,9 +155,11 @@ def extract_tarinfos(root, class_name_to_idx=None, cache_tarinfo=None, extension
     samples_and_targets = [(s, class_name_to_idx[l]) for s, l in zip(samples, labels) if l in class_name_to_idx]
     if sort:
         samples_and_targets = sorted(samples_and_targets, key=lambda k: natural_key(k[0][0].path))
-
+    samples, targets = zip(*samples_and_targets)
-    _logger.info(f'Finished processing {len(samples_and_targets)} samples across {len(tarfiles)} tar files.')
+    samples = np.array(samples)
-    return samples_and_targets, class_name_to_idx, tarfiles
+    targets = np.array(targets)
+    _logger.info(f'Finished processing {len(samples)} samples across {len(tarfiles)} tar files.')
+    return samples, targets, class_name_to_idx, tarfiles
 
 
 class ParserImageInTar(Parser):
@@ -171,7 +173,7 @@ class ParserImageInTar(Parser):
         if class_map:
             class_name_to_idx = load_class_map(class_map, root)
         self.root = root
-        self.samples_and_targets, self.class_name_to_idx, tarfiles = extract_tarinfos(
+        self.samples, self.targets, self.class_name_to_idx, tarfiles = extract_tarinfos(
             self.root,
             class_name_to_idx=class_name_to_idx,
             cache_tarinfo=cache_tarinfo,
@@ -186,10 +188,11 @@ class ParserImageInTar(Parser):
         self.cache_tarfiles = cache_tarfiles
 
     def __len__(self):
-        return len(self.samples_and_targets)
+        return len(self.samples)
 
     def __getitem__(self, index):
-        sample, target = self.samples_and_targets[index]
+        sample = self.samples[index]
+        target = self.targets[index]
         sample_ti, parent_fn, child_ti = sample
         parent_abs = os.path.join(self.root, parent_fn) if parent_fn else self.root
 
@@ -213,7 +216,7 @@ class ParserImageInTar(Parser):
         return tf.extractfile(sample_ti), target
 
     def _filename(self, index, basename=False, absolute=False):
-        filename = self.samples_and_targets[index][0][0].name
+        filename = self.samples[index][0].name
         if basename:
             filename = os.path.basename(filename)
         return filename

timm/models/resnet.py

@@ -162,6 +162,12 @@ default_cfgs = {
     'seresnet152d_320': _cfg(
         url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
         interpolation='bicubic', first_conv='conv1.0', input_size=(3, 320, 320), crop_pct=1.0, pool_size=(10, 10)),
+    'seresnet200d': _cfg(
+        url='',
+        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
+    'seresnet269d': _cfg(
+        url='',
+        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
 
 
     #  Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
@@ -216,6 +222,12 @@ default_cfgs = {
         url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45610/outputs/ECAResNet101D_P_75a3370e.pth',
         interpolation='bicubic',
         first_conv='conv1.0'),
+    'ecaresnet200d': _cfg(
+        url='',
+        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
+    'ecaresnet269d': _cfg(
+        url='',
+        interpolation='bicubic', first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.94, pool_size=(8, 8)),
 
     # Efficient Channel Attention ResNeXts
     'ecaresnext26tn_32x4d': _cfg(
@@ -1123,6 +1135,26 @@ def ecaresnet101d_pruned(pretrained=False, **kwargs):
     return _create_resnet('ecaresnet101d_pruned', pretrained, pruned=True, **model_args)
 
 
+@register_model
+def ecaresnet200d(pretrained=False, **kwargs):
+    """Constructs a ResNet-200-D model with ECA.
+    """
+    model_args = dict(
+        block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
+        block_args=dict(attn_layer='eca'), **kwargs)
+    return _create_resnet('ecaresnet200d', pretrained, **model_args)
+
+
+@register_model
+def ecaresnet269d(pretrained=False, **kwargs):
+    """Constructs a ResNet-269-D model with ECA.
+    """
+    model_args = dict(
+        block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True,
+        block_args=dict(attn_layer='eca'), **kwargs)
+    return _create_resnet('ecaresnet269d', pretrained, **model_args)
+
+
 @register_model
 def ecaresnext26tn_32x4d(pretrained=False, **kwargs):
     """Constructs an ECA-ResNeXt-26-TN model.
@@ -1198,6 +1230,26 @@ def seresnet152d(pretrained=False, **kwargs):
     return _create_resnet('seresnet152d', pretrained, **model_args)
 
 
+@register_model
+def seresnet200d(pretrained=False, **kwargs):
+    """Constructs a ResNet-200-D model with SE attn.
+    """
+    model_args = dict(
+        block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
+        block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('seresnet200d', pretrained, **model_args)
+
+
+@register_model
+def seresnet269d(pretrained=False, **kwargs):
+    """Constructs a ResNet-269-D model with SE attn.
+    """
+    model_args = dict(
+        block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True,
+        block_args=dict(attn_layer='se'), **kwargs)
+    return _create_resnet('seresnet269d', pretrained, **model_args)
+
+
 @register_model
 def seresnet152d_320(pretrained=False, **kwargs):
     model_args = dict(