update app.py, add resnet50.py

app.py

@@ -8,10 +8,18 @@ import numpy as np
 import gradio as gr
 import torch
 
+from resnet50 import resnet18
 from sampling_util import furthest_neighbours
 from video_reader import video_reader
 
-model = torch.load("model").eval()
+model = resnet18(
+    output_dim=0,
+    nmb_prototypes=0,
+    eval_mode=True,
+    hidden_mlp=0,
+    normalize=False)
+model.load_state_dict(torch.load("model.pt"))
+model.eval()
 avg_pool = torch.nn.AdaptiveAvgPool2d((1, 1))
 
 
@@ -77,4 +85,4 @@ demo = gr.Interface(
     inputs=[gr.inputs.Video(label="Upload Video File"), gr.inputs.Number(Label="Downsample size")],
     outputs=gr.outputs.File(label="Zip"))
 
-demo.launch()
+demo.launch()

model

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:61fdeced8efaa14180ef11f528c92cd8dff7077490f967aef32c5ec7d7e8d15c
-size 55943397

resnet50.py

@@ -0,0 +1,355 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import torch
+import torch.nn as nn
+
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=dilation,
+        groups=groups,
+        bias=False,
+        dilation=dilation,
+    )
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+    __constants__ = ["downsample"]
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+    __constants__ = ["downsample"]
+
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width / 64.0)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+    def __init__(
+            self,
+            block,
+            layers,
+            zero_init_residual=False,
+            groups=1,
+            widen=1,
+            width_per_group=64,
+            replace_stride_with_dilation=None,
+            norm_layer=None,
+            normalize=False,
+            output_dim=0,
+            hidden_mlp=0,
+            nmb_prototypes=0,
+            eval_mode=False,
+    ):
+        super(ResNet, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+
+        self.eval_mode = eval_mode
+        self.padding = nn.ConstantPad2d(1, 0.0)
+
+        self.inplanes = width_per_group * widen
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError(
+                "replace_stride_with_dilation should be None "
+                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
+            )
+        self.groups = groups
+        self.base_width = width_per_group
+
+        # change padding 3 -> 2 compared to original torchvision code because added a padding layer
+        num_out_filters = width_per_group * widen
+        self.conv1 = nn.Conv2d(
+            3, num_out_filters, kernel_size=3, stride=1, padding=1, bias=False
+        )
+        self.bn1 = norm_layer(num_out_filters)
+        self.relu = nn.ReLU(inplace=True)
+        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, num_out_filters, layers[0])
+        num_out_filters *= 2
+        self.layer2 = self._make_layer(
+            block, num_out_filters, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
+        )
+        num_out_filters *= 2
+        self.layer3 = self._make_layer(
+            block, num_out_filters, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
+        )
+        num_out_filters *= 2
+        self.layer4 = self._make_layer(
+            block, num_out_filters, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
+        )
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+        # normalize output features
+        self.l2norm = normalize
+
+        # projection head
+        if output_dim == 0:
+            self.projection_head = None
+        elif hidden_mlp == 0:
+            self.projection_head = nn.Linear(num_out_filters * block.expansion, output_dim)
+        else:
+            self.projection_head = nn.Sequential(
+                nn.Linear(num_out_filters * block.expansion, hidden_mlp),
+                nn.BatchNorm1d(hidden_mlp),
+                nn.ReLU(inplace=True),
+                nn.Linear(hidden_mlp, output_dim),
+            )
+
+        # prototype layer
+        self.prototypes = None
+        if isinstance(nmb_prototypes, list):
+            self.prototypes = MultiPrototypes(output_dim, nmb_prototypes)
+        elif nmb_prototypes > 0:
+            self.prototypes = nn.Linear(output_dim, nmb_prototypes, bias=False)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample,
+                self.groups,
+                self.base_width,
+                previous_dilation,
+                norm_layer,
+            )
+        )
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    dilation=self.dilation,
+                    norm_layer=norm_layer,
+                )
+            )
+
+        return nn.Sequential(*layers)
+
+    def forward_backbone(self, x):
+        x = self.padding(x)
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        # x = self.maxpool(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        if self.eval_mode:
+            return x
+
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+
+        return x
+
+    def forward_head(self, x):
+        if self.projection_head is not None:
+            x = self.projection_head(x)
+
+        if self.l2norm:
+            x = nn.functional.normalize(x, dim=1, p=2)
+
+        if self.prototypes is not None:
+            return x, self.prototypes(x)
+        return x
+
+    def forward(self, inputs):
+        if not isinstance(inputs, list):
+            inputs = [inputs]
+        idx_crops = torch.cumsum(torch.unique_consecutive(
+            torch.tensor([inp.shape[-1] for inp in inputs]),
+            return_counts=True,
+        )[1], 0)
+        start_idx = 0
+        for end_idx in idx_crops:
+            _out = self.forward_backbone(torch.cat(inputs[start_idx: end_idx]).cuda(non_blocking=True))
+            if start_idx == 0:
+                output = _out
+            else:
+                output = torch.cat((output, _out))
+            start_idx = end_idx
+        return self.forward_head(output)
+
+
+class MultiPrototypes(nn.Module):
+    def __init__(self, output_dim, nmb_prototypes):
+        super(MultiPrototypes, self).__init__()
+        self.nmb_heads = len(nmb_prototypes)
+        for i, k in enumerate(nmb_prototypes):
+            self.add_module("prototypes" + str(i), nn.Linear(output_dim, k, bias=False))
+
+    def forward(self, x):
+        out = []
+        for i in range(self.nmb_heads):
+            out.append(getattr(self, "prototypes" + str(i))(x))
+        return out
+
+def resnet18(**kwargs):
+    return ResNet(Bottleneck, [2, 2, 2, 2], **kwargs)
+
+def resnet50(**kwargs):
+    return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+
+
+def resnet50w2(**kwargs):
+    return ResNet(Bottleneck, [3, 4, 6, 3], widen=2, **kwargs)
+
+
+def resnet50w4(**kwargs):
+    return ResNet(Bottleneck, [3, 4, 6, 3], widen=4, **kwargs)
+
+
+def resnet50w5(**kwargs):
+    return ResNet(Bottleneck, [3, 4, 6, 3], widen=5, **kwargs)