add new model resnet50v1.5

image_classification/mixup.py

+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the BSD 3-Clause License  (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+import torch.nn as nn
+import numpy as np
+
+
+def mixup(alpha, data, target):
+    with torch.no_grad():
+        bs = data.size(0)
+        c = np.random.beta(alpha, alpha)
+
+        perm = torch.randperm(bs).cuda()
+
+        md = c * data + (1 - c) * data[perm, :]
+        mt = c * target + (1 - c) * target[perm, :]
+        return md, mt
+
+
+class MixUpWrapper(object):
+    def __init__(self, alpha, dataloader):
+        self.alpha = alpha
+        self.dataloader = dataloader
+
+    def mixup_loader(self, loader):
+        for input, target in loader:
+            i, t = mixup(self.alpha, input, target)
+            yield i, t
+
+    def __iter__(self):
+        return self.mixup_loader(self.dataloader)
+
+    def __len__(self):
+        return len(self.dataloader)
+
+
+class NLLMultiLabelSmooth(nn.Module):
+    def __init__(self, smoothing=0.0):
+        super(NLLMultiLabelSmooth, self).__init__()
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+
+    def forward(self, x, target):
+        if self.training:
+            x = x.float()
+            target = target.float()
+            logprobs = torch.nn.functional.log_softmax(x, dim=-1)
+
+            nll_loss = -logprobs * target
+            nll_loss = nll_loss.sum(-1)
+
+            smooth_loss = -logprobs.mean(dim=-1)
+
+            loss = self.confidence * nll_loss + self.smoothing * smooth_loss
+
+            return loss.mean()
+        else:
+            return torch.nn.functional.cross_entropy(x, target)

image_classification/models/__init__.py

+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the BSD 3-Clause License  (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# https://opensource.org/licenses/BSD-3-Clause
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .entrypoints import nvidia_convnets_processing_utils, nvidia_efficientnet
+from .resnet import resnet50, resnext101_32x4d, se_resnext101_32x4d
+from .efficientnet import (
+    efficientnet_b0,
+    efficientnet_b4,
+    efficientnet_widese_b0,
+    efficientnet_widese_b4,
+    efficientnet_quant_b0,
+    efficientnet_quant_b4,
+)