Upload folder using huggingface_hub

.gitattributes

@@ -1,35 +1,17 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Auto detect text files and perform LF normalization
+* text=auto
+assets/840_iSXIa0hE8Ek.zip filter=lfs diff=lfs merge=lfs -text
+assets/cars.mp4 filter=lfs diff=lfs merge=lfs -text
+assets/cell.mp4 filter=lfs diff=lfs merge=lfs -text
+assets/demo_3x2.gif filter=lfs diff=lfs merge=lfs -text
+assets/top.gif filter=lfs diff=lfs merge=lfs -text
+ckpt/groundingdino_swint_ogc.pth filter=lfs diff=lfs merge=lfs -text
+ckpt/sam_vit_b_01ec64.pth filter=lfs diff=lfs merge=lfs -text
+sam/assets/masks1.png filter=lfs diff=lfs merge=lfs -text
+sam/assets/notebook2.png filter=lfs diff=lfs merge=lfs -text
+src/groundingdino/.asset/GD_GLIGEN.png filter=lfs diff=lfs merge=lfs -text
+src/groundingdino/.asset/GD_SD.png filter=lfs diff=lfs merge=lfs -text
+src/groundingdino/.asset/hero_figure.png filter=lfs diff=lfs merge=lfs -text
+tutorial/img/click_segment.jpg filter=lfs diff=lfs merge=lfs -text
+tutorial/img/input_video.jpg filter=lfs diff=lfs merge=lfs -text
+tutorial/img/start_tracking.jpg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,14 @@
+.vscode
+ckpt/*
+assets/*masks
+assets/*mp4
+# assets/*zip
+assets/*gif
+*.pyc
+debug
+cym_utils
+/src
+/tracking_results
+/aot/results
+/aot/pretrain_models
+/aot/datasets

LICENSE.txt

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
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Pytorch-Correlation-extension/.gitignore

@@ -0,0 +1 @@
+*.egg*

Pytorch-Correlation-extension/Correlation_Module/correlation.cpp

@@ -0,0 +1,178 @@
+#include <torch/extension.h>
+using namespace torch;
+
+#include <vector>
+
+#define WITHIN_BOUNDS(x, y, H, W) (x >= 0 && x < H && y >= 0 && y < W)
+
+template <typename scalar_t>
+static void correlate_patch(
+    TensorAccessor<scalar_t,3> input1,
+    TensorAccessor<scalar_t,3> input2,
+    scalar_t *dst,
+    int kH, int kW,
+    int dilationH, int dilationW,
+    int u, int v,
+    int shiftU, int shiftV){
+  const int C = input1.size(0);
+  const int iH = input1.size(1);
+  const int iW = input1.size(2);
+  for (int c=0; c<C; ++c){
+    for (int i=0; i<kH; ++i){
+      int i1 = u + i * dilationH;
+      int i2 = i1 + shiftU;
+      if WITHIN_BOUNDS(i1, i2, iH, iH){
+        for (int j=0; j<kW; ++j){
+          int j1 = v + j * dilationW;
+          int j2 = j1 + shiftV;
+          if WITHIN_BOUNDS(j1, j2, iW, iW){
+            scalar_t v1 = input1[c][i1][j1];
+            scalar_t v2 = input2[c][i2][j2];
+            *dst += v1 * v2;
+          }
+        }
+      }
+    }
+  }
+}
+
+template <typename scalar_t>
+static void correlate_patch_grad(
+    TensorAccessor<scalar_t,3> input1,
+    TensorAccessor<scalar_t,3> gradInput1,
+    TensorAccessor<scalar_t,3> input2,
+    TensorAccessor<scalar_t,3> gradInput2,
+    scalar_t gradOutput,
+    int kH, int kW,
+    int dilationH, int dilationW,
+    int u, int v,
+    int shiftU, int shiftV){
+
+  const int C = input1.size(0);
+  const int iH = input1.size(1);
+  const int iW = input1.size(2);
+
+  for (int c=0; c<C; ++c){
+    for (int i=0; i<kH; ++i){
+      int i1 = u + i * dilationH;
+      int i2 = i1 + shiftU;
+      if WITHIN_BOUNDS(i1, i2, iH, iH){
+        for (int j=0; j<kW; ++j){
+          int j1 = v + j * dilationW;
+          int j2 = j1 + shiftV;
+          if WITHIN_BOUNDS(j1, j2, iW, iW){
+            scalar_t v1 = input1[c][i1][j1];
+            scalar_t v2 = input2[c][i2][j2];
+            gradInput2[c][i2][j2] += gradOutput * v1;
+            gradInput1[c][i1][j1] += gradOutput * v2;
+          }
+        }
+      }
+    }
+  }
+}
+
+torch::Tensor correlation_cpp_forward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+
+  const auto batch_size = input1.size(0);
+  const auto iH = input1.size(2);
+  const auto iW = input1.size(3);
+  const int patchRadH = (patchH - 1) / 2;
+  const int patchRadW = (patchW - 1) / 2;
+  const int dilatedKH = (kH - 1) * dilationH + 1;
+  const int dilatedKW = (kW - 1) * dilationW + 1;
+
+  const auto oH = (iH + 2 * padH - dilatedKH) / dH + 1;
+  const auto oW = (iW + 2 * padW - dilatedKW) / dW + 1;
+  auto output = at::zeros({batch_size, patchH, patchW, oH, oW}, input1.options());
+
+  int n, ph, pw, h, w;
+  #pragma omp parallel for private(n, ph, pw, h, w) collapse(2)
+    for (n = 0; n < batch_size; ++n) {
+      for(ph = 0; ph < patchH; ++ph){
+        for(pw = 0; pw < patchW; ++pw){
+          AT_DISPATCH_FLOATING_TYPES(input1.scalar_type(), "correlation_forward_cpp", ([&] {
+            auto input1_acc = input1.accessor<scalar_t, 4>();
+            auto input2_acc = input2.accessor<scalar_t, 4>();
+            auto output_acc = output.accessor<scalar_t, 5>();
+            for (h = 0; h < oH; ++h) {
+              for (w = 0; w < oW; ++w) {
+                correlate_patch(input1_acc[n],
+                                input2_acc[n],
+                                &output_acc[n][ph][pw][h][w],
+                                kH, kW,
+                                dilationH, dilationW,
+                                -padH + h * dH,
+                                -padW + w * dW,
+                                (ph - patchRadH)  * dilation_patchH,
+                                (pw - patchRadW)  * dilation_patchW);
+              }
+            }
+          }));
+        }
+      }
+    }
+  return output;
+}
+
+std::vector<torch::Tensor> correlation_cpp_backward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    torch::Tensor gradOutput,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+  
+  const int batch_size = input1.size(0);
+  const int patchRadH = (patchH - 1) / 2;
+  const int patchRadW = (patchW - 1) / 2;
+  const int oH = gradOutput.size(3);
+  const int oW = gradOutput.size(4);
+  
+  auto gradInput1 = torch::zeros_like(input1);
+
+  auto gradInput2 = torch::zeros_like(input2);
+
+  int n, ph, pw, h, w;
+  #pragma omp parallel for private(n, ph, pw, h, w)
+    for (n = 0; n < batch_size; ++n) {
+      AT_DISPATCH_FLOATING_TYPES(input1.scalar_type(), "correlation_backward_cpp", ([&] {
+        auto input1_acc = input1.accessor<scalar_t, 4>();
+        auto gradInput1_acc = gradInput1.accessor<scalar_t, 4>();
+        auto input2_acc = input2.accessor<scalar_t, 4>();
+        auto gradInput2_acc = gradInput2.accessor<scalar_t, 4>();
+        auto gradOutput_acc = gradOutput.accessor<scalar_t, 5>();
+
+        for(ph = 0; ph < patchH; ++ph){
+          for(pw = 0; pw < patchW; ++pw){
+            for (h = 0; h < oH; ++h) {
+              for (w = 0; w < oW; ++w) {
+                correlate_patch_grad(input1_acc[n], gradInput1_acc[n],
+                                     input2_acc[n], gradInput2_acc[n],
+                                     gradOutput_acc[n][ph][pw][h][w],
+                                     kH, kW,
+                                     dilationH, dilationW,
+                                     -padH + h * dH,
+                                     -padW + w * dW,
+                                     (ph - patchRadH)  * dilation_patchH,
+                                     (pw - patchRadW)  * dilation_patchW);
+              }
+            }
+          }
+        }
+      }));
+    }
+
+  return {gradInput1, gradInput2};
+}

Pytorch-Correlation-extension/Correlation_Module/correlation_cuda_kernel.cu

@@ -0,0 +1,327 @@
+#include <torch/types.h>
+using namespace torch;
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+#include <vector>
+#include <iostream>
+
+// Cuda tensor accessor definitions
+// restrict pointer traits piroritize speed over memory consumption
+#define TensorAcc4R PackedTensorAccessor32<scalar_t,4,RestrictPtrTraits>
+#define TensorAcc5R PackedTensorAccessor32<scalar_t,5,RestrictPtrTraits>
+#define WITHIN_BOUNDS(x, y, H, W) (x >= 0 && x < H && y >= 0 && y < W)
+
+#define THREADS_FORWARD 32
+#define THREADS_BACKWARD 5
+
+
+namespace corr {
+template <typename scalar_t>
+__global__ void correlation_cuda_forward_kernel(
+    const TensorAcc4R rInput1,
+    const TensorAcc4R rInput2,
+    TensorAcc5R output,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+  
+  const int iH = rInput1.size(1);
+  const int iW = rInput1.size(2);
+  const int C = rInput1.size(3);
+
+  const int n = blockIdx.x;
+  const int h = blockIdx.y;
+  const int w = blockIdx.z;
+  const int thread = threadIdx.x;
+
+  const int start_i = -padH + h * dH;
+  const int start_j = -padW + w * dW;
+
+  const int patchRadH = dilation_patchH * (patchH - 1) / 2;
+  const int patchRadW = dilation_patchW * (patchW - 1) / 2;
+
+  __shared__ scalar_t prod_sum[THREADS_FORWARD];
+
+  for(int ph = 0; ph < patchH; ++ph){
+    int ph_dilated = ph * dilation_patchH - patchRadH;
+    for(int pw = 0; pw < patchW; ++pw){
+      int pw_dilated = pw * dilation_patchW - patchRadW;
+      prod_sum[thread] = 0;
+      for (int i=0; i<kH; ++i){
+        int i1 = start_i + i * dilationH;
+        int i2 = i1 + ph_dilated;
+        if WITHIN_BOUNDS(i1, i2, iH, iH){
+          for (int j=0; j<kW; ++j){
+            int j1 = start_j + j * dilationW;
+            int j2 = j1 + pw_dilated;
+            if WITHIN_BOUNDS(j1, j2, iW, iW){
+              for (int c=thread; c<C; c += THREADS_FORWARD){
+                scalar_t v1 = rInput1[n][i1][j1][c];
+                scalar_t v2 = rInput2[n][i2][j2][c];
+                prod_sum[thread] += v1 * v2;
+              }
+            }
+          }
+        }
+      }
+      // accumulate 
+      __syncthreads();
+      if (thread == 0) {
+        scalar_t reduce_sum = 0;
+        for (int index = 0; index < THREADS_FORWARD; ++index) {
+          reduce_sum += prod_sum[index];
+        }
+        output[n][ph][pw][h][w] = reduce_sum;
+      }
+    }
+  }
+}
+
+
+template <typename scalar_t>
+__global__ void correlation_cuda_backward_kernel_input1(
+    const TensorAcc5R gradOutput,
+    const TensorAcc4R input2,
+    TensorAcc4R gradInput1,
+    const int kH, const int kW,
+    const int patchH, const int patchW,
+    const int padH, const int padW,
+    const int dilationH, const int dilationW,
+    const int dilation_patchH, const int dilation_patchW,
+    const int dH, const int dW,
+    const int batch) {
+  const int iH = input2.size(2);
+  const int iW = input2.size(3);
+
+  const int H = gradOutput.size(3);
+  const int W = gradOutput.size(4);
+
+  const int patchRadH = (patchH - 1) / 2;
+  const int patchRadW = (patchW - 1) / 2;
+  
+  const int n = batch;
+  const int c = blockIdx.x;
+  const int h = blockIdx.y;
+  const int w = blockIdx.z;
+  const int ph_off = threadIdx.x;
+  const int pw_off = threadIdx.y;
+
+  const int h_2 = h + padH;
+  const int w_2 = w + padW;
+  const int min_h = h_2 - kH * dilationH;
+  const int min_w = w_2 - kW * dilationW;
+
+  __shared__ scalar_t prod_sum[THREADS_BACKWARD][THREADS_BACKWARD];
+  prod_sum[ph_off][pw_off] = 0;
+
+  for (int ph = ph_off; ph < patchH; ph += THREADS_BACKWARD) {
+    int i1 = h + dilation_patchH * (ph - patchRadH);
+    for (int pw = pw_off; pw < patchW; pw += THREADS_BACKWARD) {
+      int j1 = w + dilation_patchW * (pw - patchRadW);
+      if (WITHIN_BOUNDS(i1, j1, iH, iW)){
+        scalar_t val = input2[n][c][i1][j1];
+        for(int h_3 = h_2; h_3 > min_h; h_3 -= dilationH) {
+          int i2 = (h_3)/dH;
+          if (i2 * dH != h_3)
+            continue;
+          for(int w_3 = w_2; w_3 > min_w; w_3 -= dilationW) {
+            int j2 = (w_3) / dW;
+            if(j2 * dW != w_3)
+              continue;
+            if WITHIN_BOUNDS(i2, j2, H, W) {
+              prod_sum[ph_off][pw_off] += gradOutput[n][ph][pw][i2][j2] * val;
+            }
+          }
+        }
+      }
+    }
+  }
+
+  __syncthreads();
+
+  if (ph_off == 0 && pw_off == 0){
+    scalar_t reduce_sum =0;
+    for (int ph = 0; ph < THREADS_BACKWARD; ++ph){
+      for (int pw = 0; pw < THREADS_BACKWARD; ++pw){
+        reduce_sum += prod_sum[ph][pw];
+      }
+    }
+    gradInput1[n][c][h][w] = reduce_sum;
+  }
+}
+
+
+template <typename scalar_t>
+__global__ void correlation_cuda_backward_kernel_input2(
+    const TensorAcc5R gradOutput,
+    const TensorAcc4R input1,
+    TensorAcc4R gradInput2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW,
+    int batch) {
+  const int iH = input1.size(2);
+  const int iW = input1.size(3);
+
+  const int patchRadH = (patchH - 1) / 2;
+  const int patchRadW = (patchW - 1) / 2;
+
+  const int H = gradOutput.size(3);
+  const int W = gradOutput.size(4);
+
+  const int dilatedKH = kH * dilationH;
+  const int dilatedKW = kW * dilationW;
+  
+  const int n = batch;
+  const int c = blockIdx.x;
+  const int h = blockIdx.y;
+  const int w = blockIdx.z;
+  const int ph_off = threadIdx.x;
+  const int pw_off = threadIdx.y;
+
+  __shared__ scalar_t prod_sum[THREADS_BACKWARD][THREADS_BACKWARD];
+  prod_sum[ph_off][pw_off] = 0;
+
+  for (int ph = ph_off; ph < patchH; ph += THREADS_BACKWARD) {
+    int i1 = h - dilation_patchH * (ph - patchRadH);
+    for (int pw = pw_off; pw < patchW; pw += THREADS_BACKWARD) {
+      int j1 = w - dilation_patchW * (pw - patchRadW);
+      if WITHIN_BOUNDS(i1, j1, iH, iW) {
+        scalar_t val = input1[n][c][i1][j1];
+        
+        const int h_2 = i1 + padH;
+        const int w_2 = j1 + padW;
+        const int min_h = h_2 - dilatedKH;
+        const int min_w = w_2 - dilatedKW;
+        
+        for(int h_3 = h_2; h_3 > min_h; h_3 -= dilationH) {
+          int i2 = (h_3)/dH;
+          if (i2 * dH != h_3)
+            continue;
+          for(int w_3 = w_2; w_3 > min_w; w_3 -= dilationW) {
+            int j2 = (w_3) / dW;
+            if(j2 * dW != w_3)
+              continue;
+            if WITHIN_BOUNDS(i2, j2, H, W) {
+              prod_sum[ph_off][pw_off] += gradOutput[n][ph][pw][i2][j2] * val;
+            }
+          }
+        }
+      }
+    }
+  }
+
+  __syncthreads();
+
+  if (ph_off == 0 && pw_off == 0){
+    scalar_t reduce_sum =0;
+    for (int ph = 0; ph < THREADS_BACKWARD; ++ph){
+      for (int pw = 0; pw < THREADS_BACKWARD; ++pw){
+        reduce_sum += prod_sum[ph][pw];
+      }
+    }
+    gradInput2[n][c][h][w] = reduce_sum;
+  }
+}
+} // namsepace corr
+
+torch::Tensor correlation_cuda_forward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+  
+  const int batch_size = input1.size(0);
+  const int iH = input1.size(2);
+  const int iW = input1.size(3);
+  const int dilatedKH = (kH - 1) * dilationH + 1;
+  const int dilatedKW = (kW - 1) * dilationW + 1;
+
+  const auto oH = (iH + 2 * padH - dilatedKH) / dH + 1;
+  const auto oW = (iW + 2 * padW - dilatedKW) / dW + 1;
+  auto output = torch::zeros({batch_size, patchH, patchW, oH, oW}, input1.options());
+  
+  auto trInput1 = input1.permute({0, 2, 3, 1}).contiguous();
+  auto trInput2 = input2.permute({0, 2, 3, 1}).contiguous();
+  
+  const int threads = THREADS_FORWARD;
+  const dim3 blocks(batch_size, oH, oW);
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input1.scalar_type(), "correlation_forward_cuda", ([&] {
+    TensorAcc4R trInput1_acc  = trInput1.packed_accessor32<scalar_t,4,RestrictPtrTraits>();
+    TensorAcc4R trInput2_acc = trInput2.packed_accessor32<scalar_t,4,RestrictPtrTraits>();
+    TensorAcc5R output_acc = output.packed_accessor32<scalar_t,5,RestrictPtrTraits>();
+    corr::correlation_cuda_forward_kernel<scalar_t><<<blocks, threads>>>(
+        trInput1_acc, trInput2_acc, output_acc,
+        kH, kW, patchH, patchW, padH, padW, dilationH, dilationW,
+        dilation_patchH, dilation_patchW, dH, dW);
+  }));
+
+  return output;
+}
+
+std::vector<torch::Tensor> correlation_cuda_backward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    torch::Tensor gradOutput,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+  
+  auto gradInput1 = torch::zeros_like(input1);
+  auto gradInput2 = torch::zeros_like(input2);
+
+  const int batch_size = input1.size(0);
+  const int iH = input1.size(2);
+  const int iW = input1.size(3);
+  const int C = input1.size(1);
+
+  const dim3 blocks(C, iH, iW);
+  const dim3 threads(THREADS_BACKWARD, THREADS_BACKWARD);
+
+  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input1.scalar_type(), "correlation_backward_cuda", ([&] {
+    TensorAcc4R input1_acc = input1.packed_accessor32<scalar_t,4,RestrictPtrTraits>();
+    TensorAcc4R input2_acc = input2.packed_accessor32<scalar_t,4,RestrictPtrTraits>();
+    TensorAcc4R gradInput1_acc = gradInput1.packed_accessor32<scalar_t,4,RestrictPtrTraits>();
+    TensorAcc4R gradInput2_acc = gradInput2.packed_accessor32<scalar_t,4,RestrictPtrTraits>();
+    TensorAcc5R gradOutput_acc = gradOutput.packed_accessor32<scalar_t,5,RestrictPtrTraits>();
+
+
+    for (int n = 0; n < batch_size; ++n){
+      corr::correlation_cuda_backward_kernel_input1<scalar_t><<<blocks, threads>>>(
+          gradOutput_acc, input2_acc, gradInput1_acc,
+          kH, kW, patchH, patchW, padH, padW,
+          dilationH, dilationW,
+          dilation_patchH, dilation_patchW, 
+          dH, dW,
+          n);
+    }
+
+    for (int n = 0; n < batch_size; ++n){
+      corr::correlation_cuda_backward_kernel_input2<scalar_t><<<blocks, threads>>>(
+          gradOutput_acc, input1_acc, gradInput2_acc,
+          kH, kW, patchH, patchW, padH, padW,
+          dilationH, dilationW,
+          dilation_patchH, dilation_patchW, 
+          dH, dW,
+          n);
+    }
+  }));
+
+  return {gradInput1, gradInput2};
+}

Pytorch-Correlation-extension/Correlation_Module/correlation_sampler.cpp

@@ -0,0 +1,138 @@
+#include <torch/extension.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <vector>
+#include <iostream>
+
+// declarations
+
+torch::Tensor correlation_cpp_forward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW);
+
+std::vector<torch::Tensor> correlation_cpp_backward(
+    torch::Tensor grad_output,
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW);
+
+#ifdef USE_CUDA
+
+#define CHECK_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x, " must be a CUDA tensor")
+#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x, " must be contiguous")
+#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
+#define CHECK_SAME_DEVICE(x, y) TORCH_CHECK(x.device() == y.device(), #x " is not on same device as " #y)
+
+torch::Tensor correlation_cuda_forward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW);
+
+std::vector<torch::Tensor> correlation_cuda_backward(
+    torch::Tensor grad_output,
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW);
+
+// C++ interface
+
+torch::Tensor correlation_sample_forward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+  if (input1.device().is_cuda()){
+    CHECK_INPUT(input1);
+    CHECK_INPUT(input2);
+
+    // set device of input1 as default CUDA device
+    // https://pytorch.org/cppdocs/api/structc10_1_1cuda_1_1_optional_c_u_d_a_guard.html
+    const at::cuda::OptionalCUDAGuard guard_input1(device_of(input1));
+    CHECK_SAME_DEVICE(input1, input2);
+
+    return correlation_cuda_forward(input1, input2, kH, kW, patchH, patchW,
+                             padH, padW, dilationH, dilationW,
+                             dilation_patchH, dilation_patchW,
+                             dH, dW);
+  }else{
+    return correlation_cpp_forward(input1, input2, kH, kW, patchH, patchW,
+                             padH, padW, dilationH, dilationW,
+                             dilation_patchH, dilation_patchW,
+                             dH, dW);
+  }
+}
+
+std::vector<torch::Tensor> correlation_sample_backward(
+    torch::Tensor input1,
+    torch::Tensor input2,
+    torch::Tensor grad_output,
+    int kH, int kW,
+    int patchH, int patchW,
+    int padH, int padW,
+    int dilationH, int dilationW,
+    int dilation_patchH, int dilation_patchW,
+    int dH, int dW) {
+
+  if(grad_output.device().is_cuda()){
+    CHECK_INPUT(input1);
+    CHECK_INPUT(input2);
+
+    // set device of input1 as default CUDA device
+    const at::cuda::OptionalCUDAGuard guard_input1(device_of(input1));
+    CHECK_SAME_DEVICE(input1, input2);
+    CHECK_SAME_DEVICE(input1, grad_output);
+
+    return correlation_cuda_backward(input1, input2, grad_output,
+                              kH, kW, patchH, patchW,
+                              padH, padW,
+                              dilationH, dilationW,
+                              dilation_patchH, dilation_patchW,
+                              dH, dW);
+  }else{
+    return correlation_cpp_backward(
+                              input1, input2, grad_output,
+                              kH, kW, patchH, patchW,
+                              padH, padW,
+                              dilationH, dilationW,
+                              dilation_patchH, dilation_patchW,
+                              dH, dW);
+  }
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &correlation_sample_forward, "Spatial Correlation Sampler Forward");
+  m.def("backward", &correlation_sample_backward, "Spatial Correlation Sampler backward");
+}
+
+#else
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("forward", &correlation_cpp_forward, "Spatial Correlation Sampler Forward");
+  m.def("backward", &correlation_cpp_backward, "Spatial Correlation Sampler backward");
+}
+
+#endif

Pytorch-Correlation-extension/Correlation_Module/spatial_correlation_sampler/__init__.py

@@ -0,0 +1 @@
+from .spatial_correlation_sampler import SpatialCorrelationSampler, spatial_correlation_sample

Pytorch-Correlation-extension/Correlation_Module/spatial_correlation_sampler/spatial_correlation_sampler.py

@@ -0,0 +1,107 @@
+from torch import nn
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn.modules.utils import _pair
+
+import spatial_correlation_sampler_backend as correlation
+
+
+def spatial_correlation_sample(input1,
+                               input2,
+                               kernel_size=1,
+                               patch_size=1,
+                               stride=1,
+                               padding=0,
+                               dilation=1,
+                               dilation_patch=1):
+    """Apply spatial correlation sampling on from input1 to input2,
+
+    Every parameter except input1 and input2 can be either single int
+    or a pair of int. For more information about Spatial Correlation
+    Sampling, see this page.
+    https://lmb.informatik.uni-freiburg.de/Publications/2015/DFIB15/
+
+    Args:
+        input1 : The first parameter.
+        input2 : The second parameter.
+        kernel_size : total size of your correlation kernel, in pixels
+        patch_size : total size of your patch, determining how many
+            different shifts will be applied
+        stride : stride of the spatial sampler, will modify output
+            height and width
+        padding : padding applied to input1 and input2 before applying
+            the correlation sampling, will modify output height and width
+        dilation_patch : step for every shift in patch
+
+    Returns:
+        Tensor: Result of correlation sampling
+
+    """
+    return SpatialCorrelationSamplerFunction.apply(input1, input2,
+                                                   kernel_size, patch_size,
+                                                   stride, padding, dilation, dilation_patch)
+
+
+class SpatialCorrelationSamplerFunction(Function):
+
+    @staticmethod
+    def forward(ctx,
+                input1,
+                input2,
+                kernel_size=1,
+                patch_size=1,
+                stride=1,
+                padding=0,
+                dilation=1,
+                dilation_patch=1):
+
+        ctx.save_for_backward(input1, input2)
+        kH, kW = ctx.kernel_size = _pair(kernel_size)
+        patchH, patchW = ctx.patch_size = _pair(patch_size)
+        padH, padW = ctx.padding = _pair(padding)
+        dilationH, dilationW = ctx.dilation = _pair(dilation)
+        dilation_patchH, dilation_patchW = ctx.dilation_patch = _pair(dilation_patch)
+        dH, dW = ctx.stride = _pair(stride)
+
+        output = correlation.forward(input1, input2,
+                                     kH, kW, patchH, patchW,
+                                     padH, padW, dilationH, dilationW,
+                                     dilation_patchH, dilation_patchW,
+                                     dH, dW)
+
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        input1, input2 = ctx.saved_variables
+
+        kH, kW = ctx.kernel_size
+        patchH, patchW = ctx.patch_size
+        padH, padW = ctx.padding
+        dilationH, dilationW = ctx.dilation
+        dilation_patchH, dilation_patchW = ctx.dilation_patch
+        dH, dW = ctx.stride
+
+        grad_input1, grad_input2 = correlation.backward(input1, input2, grad_output,
+                                                        kH, kW, patchH, patchW,
+                                                        padH, padW, dilationH, dilationW,
+                                                        dilation_patchH, dilation_patchW,
+                                                        dH, dW)
+        return grad_input1, grad_input2, None, None, None, None, None, None
+
+
+class SpatialCorrelationSampler(nn.Module):
+    def __init__(self, kernel_size=1, patch_size=1, stride=1, padding=0, dilation=1, dilation_patch=1):
+        super(SpatialCorrelationSampler, self).__init__()
+        self.kernel_size = kernel_size
+        self.patch_size = patch_size
+        self.stride = stride
+        self.padding = padding
+        self.dilation = dilation
+        self.dilation_patch = dilation_patch
+
+    def forward(self, input1, input2):
+        return SpatialCorrelationSamplerFunction.apply(input1, input2, self.kernel_size,
+                                                       self.patch_size, self.stride,
+                                                       self.padding, self.dilation, self.dilation_patch)

Pytorch-Correlation-extension/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) [year] [fullname]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Pytorch-Correlation-extension/README.md

@@ -0,0 +1,146 @@
+
+[![PyPI](https://img.shields.io/pypi/v/spatial-correlation-sampler.svg)](https://pypi.org/project/spatial-correlation-sampler/)
+
+
+# Pytorch Correlation module
+
+this is a custom C++/Cuda implementation of Correlation module, used e.g. in [FlowNetC](https://arxiv.org/abs/1504.06852)
+
+This [tutorial](http://pytorch.org/tutorials/advanced/cpp_extension.html) was used as a basis for implementation, as well as
+[NVIDIA's cuda code](https://github.com/NVIDIA/flownet2-pytorch/tree/master/networks/correlation_package)
+
+- Build and Install C++ and CUDA extensions by executing `python setup.py install`,
+- Benchmark C++ vs. CUDA by running `python benchmark.py {cpu, cuda}`,
+- Run gradient checks on the code by running `python grad_check.py --backend {cpu, cuda}`.
+
+# Requirements
+
+This module is expected to compile for Pytorch `1.6`.
+
+# Installation
+
+this module is available on pip
+
+`pip install spatial-correlation-sampler`
+
+For a cpu-only version, you can install from source with
+
+`python setup_cpu.py install`
+
+# Known Problems
+
+This module needs compatible gcc version and CUDA to be compiled.
+Namely, CUDA 9.1 and below will need gcc5, while CUDA 9.2 and 10.0 will need gcc7
+See [this issue](https://github.com/ClementPinard/Pytorch-Correlation-extension/issues/1) for more information
+
+# Usage
+
+API has a few difference with NVIDIA's module
+ * output is now a 5D tensor, which reflects the shifts horizontal and vertical.
+ ```
+input (B x C x H x W) -> output (B x PatchH x PatchW x oH x oW)
+ ```
+ * Output sizes `oH` and `oW` are no longer dependant of patch size, but only of kernel size and padding
+ * Patch size `patch_size` is now the whole patch, and not only the radii.
+ * `stride1` is now `stride` and`stride2` is `dilation_patch`, which behave like dilated convolutions
+ * equivalent `max_displacement` is then `dilation_patch * (patch_size - 1) / 2`.
+ * `dilation` is a new parameter, it acts the same way as dilated convolution regarding the correlation kernel
+ * to get the right parameters for FlowNetC, you would have
+ ```
+kernel_size=1
+patch_size=21,
+stride=1,
+padding=0,
+dilation=1
+dilation_patch=2
+ ```
+
+
+## Example
+```python
+import torch
+from spatial_correlation_sampler import SpatialCorrelationSampler, 
+
+device = "cuda"
+batch_size = 1
+channel = 1
+H = 10
+W = 10
+dtype = torch.float32
+
+input1 = torch.randint(1, 4, (batch_size, channel, H, W), dtype=dtype, device=device, requires_grad=True)
+input2 = torch.randint_like(input1, 1, 4).requires_grad_(True)
+
+#You can either use the function or the module. Note that the module doesn't contain any parameter tensor.
+
+#function
+
+out = spatial_correlation_sample(input1,
+	                         input2,
+                                 kernel_size=3,
+                                 patch_size=1,
+                                 stride=2,
+                                 padding=0,
+                                 dilation=2,
+                                 dilation_patch=1)
+
+#module
+
+correlation_sampler = SpatialCorrelationSampler(
+    kernel_size=3,
+    patch_size=1,
+    stride=2,
+    padding=0,
+    dilation=2,
+    dilation_patch=1)
+out = correlation_sampler(input1, input2)
+
+```
+
+# Benchmark
+
+ * default parameters are from `benchmark.py`, FlowNetC parameters are same as use in `FlowNetC` with a batch size of 4, described in [this paper](https://arxiv.org/abs/1504.06852), implemented [here](https://github.com/lmb-freiburg/flownet2) and [here](https://github.com/NVIDIA/flownet2-pytorch/blob/master/networks/FlowNetC.py).
+ * Feel free to file an issue to add entries to this with your hardware !
+
+## CUDA Benchmark
+
+ * See [here](https://gist.github.com/ClementPinard/270e910147119831014932f67fb1b5ea) for a benchmark script working with [NVIDIA](https://github.com/NVIDIA/flownet2-pytorch/tree/master/networks/correlation_package)'s code, and Pytorch.
+ * Benchmark are launched with environment variable `CUDA_LAUNCH_BLOCKING` set to `1`.
+ * Only `float32` is benchmarked.
+ * FlowNetC correlation parameters where launched with the following command:
+ 
+ ```bash
+ CUDA_LAUNCH_BLOCKING=1 python benchmark.py --scale ms -k1 --patch 21 -s1 -p0 --patch_dilation 2 -b4 --height 48 --width 64 -c256 cuda -d float
+ 
+ CUDA_LAUNCH_BLOCKING=1 python NV_correlation_benchmark.py --scale ms -k1 --patch 21 -s1 -p0 --patch_dilation 2 -b4 --height 48 --width 64 -c256
+ ```
+
+ | implementation | Correlation parameters |  device |     pass |      min time |      avg time |
+ | -------------- | ---------------------- | ------- | -------- | ------------: | ------------: |
+ |           ours |                default | 980 GTX |  forward |  **5.745 ms** |  **5.851 ms** |
+ |           ours |                default | 980 GTX | backward |     77.694 ms |     77.957 ms |
+ |         NVIDIA |                default | 980 GTX |  forward |     13.779 ms |     13.853 ms |
+ |         NVIDIA |                default | 980 GTX | backward | **73.383 ms** | **73.708 ms** |
+ |                |                        |         |          |               |               |
+ |           ours |               FlowNetC | 980 GTX |  forward |  **26.102 ms** |  **26.179 ms** |
+ |           ours |               FlowNetC | 980 GTX | backward | **208.091 ms** | **208.510 ms** |
+ |         NVIDIA |               FlowNetC | 980 GTX |  forward |      35.363 ms |      35.550 ms |
+ |         NVIDIA |               FlowNetC | 980 GTX | backward |     283.748 ms |     284.346 ms |
+ 
+### Notes
+ * The overhead of our implementation regarding `kernel_size` > 1 during backward needs some investigation, feel free to
+ dive in the code to improve it !
+ * The backward pass of NVIDIA is not entirely correct when stride1 > 1 and kernel_size > 1, because not everything
+ is computed, see [here](https://github.com/NVIDIA/flownet2-pytorch/blob/master/networks/correlation_package/src/correlation_cuda_kernel.cu#L120).
+
+## CPU Benchmark
+
+  * No other implementation is avalaible on CPU.
+  * It is obviously not recommended to run it on CPU if you have a GPU.
+
+ | Correlation parameters |               device |     pass |    min time |    avg time |
+ | ---------------------- | -------------------- | -------- | ----------: | ----------: |
+ |                default | E5-2630 v3 @ 2.40GHz |  forward |  159.616 ms |  188.727 ms |
+ |                default | E5-2630 v3 @ 2.40GHz | backward |  282.641 ms |  294.194 ms |
+ |               FlowNetC | E5-2630 v3 @ 2.40GHz |  forward |  2.138 s |  2.144 s |
+ |               FlowNetC | E5-2630 v3 @ 2.40GHz | backward | 7.006 s | 7.075 s |

Pytorch-Correlation-extension/benchmark.py

@@ -0,0 +1,90 @@
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import time
+
+import torch
+from spatial_correlation_sampler import SpatialCorrelationSampler
+from tqdm import trange
+
+TIME_SCALES = {'s': 1, 'ms': 1000, 'us': 1000000}
+
+parser = argparse.ArgumentParser()
+parser.add_argument('backend', choices=['cpu', 'cuda'], default='cuda')
+parser.add_argument('-b', '--batch-size', type=int, default=16)
+parser.add_argument('-k', '--kernel-size', type=int, default=3)
+parser.add_argument('--patch', type=int, default=3)
+parser.add_argument('--patch_dilation', type=int, default=2)
+parser.add_argument('-c', '--channel', type=int, default=64)
+parser.add_argument('--height', type=int, default=100)
+parser.add_argument('-w', '--width', type=int, default=100)
+parser.add_argument('-s', '--stride', type=int, default=2)
+parser.add_argument('-p', '--pad', type=int, default=1)
+parser.add_argument('--scale', choices=['s', 'ms', 'us'], default='us')
+parser.add_argument('-r', '--runs', type=int, default=100)
+parser.add_argument('--dilation', type=int, default=2)
+parser.add_argument('-d', '--dtype', choices=['half', 'float', 'double'])
+
+args = parser.parse_args()
+
+device = torch.device(args.backend)
+
+if args.dtype == 'half':
+    dtype = torch.float16
+elif args.dtype == 'float':
+    dtype = torch.float32
+else:
+    dtype = torch.float64
+
+
+input1 = torch.randn(args.batch_size,
+                     args.channel,
+                     args.height,
+                     args.width,
+                     dtype=dtype,
+                     device=device,
+                     requires_grad=True)
+input2 = torch.randn_like(input1)
+
+correlation_sampler = SpatialCorrelationSampler(
+    args.kernel_size,
+    args.patch,
+    args.stride,
+    args.pad,
+    args.dilation,
+    args.patch_dilation)
+
+# Force CUDA initialization
+output = correlation_sampler(input1, input2)
+print(output.size())
+output.mean().backward()
+forward_min = float('inf')
+forward_time = 0
+backward_min = float('inf')
+backward_time = 0
+for _ in trange(args.runs):
+    correlation_sampler.zero_grad()
+
+    start = time.time()
+    output = correlation_sampler(input1, input2)
+    elapsed = time.time() - start
+    forward_min = min(forward_min, elapsed)
+    forward_time += elapsed
+    output = output.mean()
+
+    start = time.time()
+    (output.mean()).backward()
+    elapsed = time.time() - start
+    backward_min = min(backward_min, elapsed)
+    backward_time += elapsed
+
+scale = TIME_SCALES[args.scale]
+forward_min *= scale
+backward_min *= scale
+forward_average = forward_time / args.runs * scale
+backward_average = backward_time / args.runs * scale
+
+print('Forward: {0:.3f}/{1:.3f} {4} | Backward {2:.3f}/{3:.3f} {4}'.format(
+    forward_min, forward_average, backward_min, backward_average,
+    args.scale))

Pytorch-Correlation-extension/check.py

@@ -0,0 +1,119 @@
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import numpy as np
+import torch
+
+from spatial_correlation_sampler import SpatialCorrelationSampler
+
+
+def check_equal(first, second, verbose):
+    if verbose:
+        print()
+    for i, (x, y) in enumerate(zip(first, second)):
+        x = x.cpu().detach().numpy()
+        y = y.cpu().detach().numpy()
+        if verbose:
+            print("x = {}".format(x.flatten()))
+            print("y = {}".format(y.flatten()))
+            print('-' * 80)
+        np.testing.assert_allclose(x, y, err_msg="Index: {}".format(i))
+
+
+def zero_grad(variables):
+    for variable in variables:
+        if variable.grad is not None: variable.grad.zero_()
+
+
+def get_grads(variables):
+    return [var.grad.clone() for var in variables]
+
+
+def check_forward(input1, input2, correlation_sampler, verbose, gpu_index=0):
+    device = torch.device(f"cuda:{gpu_index}")
+
+    cpu_values = correlation_sampler(input1, input2)
+    cuda_values = correlation_sampler(input1.to(device), input2.to(device))
+
+    print(f"Forward: CPU vs. CUDA device:{gpu_index} ... ", end='')
+    check_equal(cpu_values, cuda_values, verbose)
+    print('Ok')
+
+
+def check_backward(input1, input2, correlation_sampler, verbose, gpu_index=0):
+    device = torch.device(f"cuda:{gpu_index}")
+
+    zero_grad([input1, input2])
+
+    cpu_values = correlation_sampler(input1, input2)
+    cpu_values.sum().backward()
+    grad_cpu = get_grads([input1, input2])
+
+    zero_grad([input1, input2])
+
+    cuda_values = correlation_sampler(input1.to(device), input2.to(device))
+    cuda_values.sum().backward()
+    grad_cuda = get_grads([input1, input2])
+
+    print(f"Backward: CPU vs. CUDA device:{gpu_index} ... ", end='')
+    check_equal(grad_cpu, grad_cuda, verbose)
+    print('Ok')
+
+
+def check_multi_gpu_forward(correlation_sampler, verbose):
+    print("Multi-GPU forward")
+    total_gpus = torch.cuda.device_count()
+    for gpu in range(total_gpus):
+        check_forward(input1, input2, correlation_sampler, verbose, gpu_index=gpu)
+
+def check_multi_gpu_backward(correlation_sampler, verbose):
+    print("Multi-GPU backward")
+    total_gpus = torch.cuda.device_count()
+    for gpu in range(total_gpus):
+        check_backward(input1, input2, correlation_sampler, verbose, gpu_index=gpu)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('direction', choices=['forward', 'backward'], nargs='+')
+parser.add_argument('-b', '--batch-size', type=int, default=1)
+parser.add_argument('-k', '--kernel-size', type=int, default=3)
+parser.add_argument('--patch', type=int, default=3)
+parser.add_argument('--patch_dilation', type=int, default=2)
+parser.add_argument('-c', '--channel', type=int, default=10)
+parser.add_argument('--height', type=int, default=10)
+parser.add_argument('-w', '--width', type=int, default=10)
+parser.add_argument('-s', '--stride', type=int, default=2)
+parser.add_argument('-p', '--pad', type=int, default=5)
+parser.add_argument('-v', '--verbose', action='store_true', default=False)
+parser.add_argument('-d', '--dilation', type=int, default=2)
+args = parser.parse_args()
+print(args)
+
+assert(torch.cuda.is_available()), "no comparison to make"
+input1 = torch.randn(args.batch_size,
+                     args.channel,
+                     args.height,
+                     args.width).double()
+input2 = torch.randn(args.batch_size,
+                     args.channel,
+                     args.height,
+                     args.width).double()
+input1.requires_grad = True
+input2.requires_grad = True
+
+correlation_sampler = SpatialCorrelationSampler(
+    args.kernel_size,
+    args.patch,
+    args.stride,
+    args.pad,
+    args.dilation,
+    args.patch_dilation)
+
+if 'forward' in args.direction:
+    check_forward(input1, input2, correlation_sampler, args.verbose)
+    if torch.cuda.device_count() > 1: check_multi_gpu_forward(correlation_sampler, args.verbose)
+
+if 'backward' in args.direction:
+    check_backward(input1, input2, correlation_sampler, args.verbose)
+    if torch.cuda.device_count() > 1: check_multi_gpu_backward(correlation_sampler, args.verbose)

Pytorch-Correlation-extension/grad_check.py

@@ -0,0 +1,47 @@
+import argparse
+import torch
+# torch.set_printoptions(precision=1, threshold=10000)
+from torch.autograd import gradcheck
+from spatial_correlation_sampler import SpatialCorrelationSampler
+
+parser = argparse.ArgumentParser()
+parser.add_argument('backend', choices=['cpu', 'cuda'], default='cuda')
+parser.add_argument('-b', '--batch-size', type=int, default=2)
+parser.add_argument('-k', '--kernel-size', type=int, default=3)
+parser.add_argument('--patch', type=int, default=3)
+parser.add_argument('--patch_dilation', type=int, default=2)
+parser.add_argument('-c', '--channel', type=int, default=2)
+parser.add_argument('--height', type=int, default=10)
+parser.add_argument('-w', '--width', type=int, default=10)
+parser.add_argument('-s', '--stride', type=int, default=2)
+parser.add_argument('-p', '--pad', type=int, default=1)
+parser.add_argument('-d', '--dilation', type=int, default=2)
+
+args = parser.parse_args()
+
+input1 = torch.randn(args.batch_size,
+                     args.channel,
+                     args.height,
+                     args.width,
+                     dtype=torch.float64,
+                     device=torch.device(args.backend))
+input2 = torch.randn(args.batch_size,
+                     args.channel,
+                     args.height,
+                     args.width,
+                     dtype=torch.float64,
+                     device=torch.device(args.backend))
+
+input1.requires_grad = True
+input2.requires_grad = True
+
+correlation_sampler = SpatialCorrelationSampler(args.kernel_size,
+                                                args.patch,
+                                                args.stride,
+                                                args.pad,
+                                                args.dilation,
+                                                args.patch_dilation)
+
+
+if gradcheck(correlation_sampler, [input1, input2]):
+    print('Ok')

Pytorch-Correlation-extension/requirements.txt

@@ -0,0 +1,2 @@
+torch>=1.0.1
+numpy

Pytorch-Correlation-extension/setup.py

@@ -0,0 +1,69 @@
+import os
+from setuptools import setup
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension
+from os.path import join
+
+CPU_ONLY = False
+project_root = 'Correlation_Module'
+
+source_files = ['correlation.cpp', 'correlation_sampler.cpp']
+
+cxx_args = ['-std=c++14', '-fopenmp']
+
+def generate_nvcc_args(gpu_archs):
+    nvcc_args = []
+    for arch in gpu_archs:
+        nvcc_args.extend(['-gencode', f'arch=compute_{arch},code=sm_{arch}'])
+    return nvcc_args
+
+gpu_arch = os.environ.get('GPU_ARCH', '').split()
+nvcc_args = generate_nvcc_args(gpu_arch)
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+
+def launch_setup():
+    if CPU_ONLY:
+        Extension = CppExtension
+        macro = []
+    else:
+        Extension = CUDAExtension
+        source_files.append('correlation_cuda_kernel.cu')
+        macro = [("USE_CUDA", None)]
+
+    sources = [join(project_root, file) for file in source_files]
+
+    setup(
+        name='spatial_correlation_sampler',
+        version="0.4.0",
+        author="Clément Pinard",
+        author_email="[email protected]",
+        description="Correlation module for pytorch",
+        long_description=long_description,
+        long_description_content_type="text/markdown",
+        url="https://github.com/ClementPinard/Pytorch-Correlation-extension",
+        install_requires=['torch>=1.1', 'numpy'],
+        ext_modules=[
+            Extension('spatial_correlation_sampler_backend',
+                      sources,
+                      define_macros=macro,
+                      extra_compile_args={'cxx': cxx_args, 'nvcc': nvcc_args},
+                      extra_link_args=['-lgomp'])
+        ],
+        package_dir={'': project_root},
+        packages=['spatial_correlation_sampler'],
+        cmdclass={
+            'build_ext': BuildExtension
+        },
+        classifiers=[
+            "Programming Language :: Python :: 3",
+            "License :: OSI Approved :: MIT License",
+            "Operating System :: POSIX :: Linux",
+            "Intended Audience :: Science/Research",
+            "Topic :: Scientific/Engineering :: Artificial Intelligence"
+        ])
+
+
+if __name__ == '__main__':
+    launch_setup()

Pytorch-Correlation-extension/setup_cpu.py

@@ -0,0 +1,4 @@
+import setup
+
+setup.CPU_ONLY = True
+setup.launch_setup()

README.md

@@ -1,12 +1,217 @@
 ---
-title: Segment And Track Anything
-emoji: 🐨
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.44.3
+title: Segment_and_track_Anything
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.35.2
 ---
+# Segment and Track Anything (SAM-Track)
+**Online Demo:** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1R10N70AJaslzADFqb-a5OihYkllWEVxB?usp=sharing)
+**Technical Report**: [![](https://img.shields.io/badge/Report-arXiv:2305.06558-green)](https://arxiv.org/abs/2305.06558)
+
+**Tutorial:** [tutorial-v1.5 (Text)](./tutorial/tutorial%20for%20WebUI-1.5-Version.md), [tutorial-v1.0 (Click & Brush)](./tutorial/tutorial%20for%20WebUI-1.0-Version.md)
+
+<p align="center">
+<img src="./assets/top.gif" width="880">
+</p>
+
+**Segment and Track Anything** is an open-source project that focuses on the segmentation and tracking of any objects in videos, utilizing both automatic and interactive methods. The primary algorithms utilized include the [**SAM** (Segment Anything Models)](https://github.com/facebookresearch/segment-anything) for automatic/interactive key-frame segmentation and the [**DeAOT** (Decoupling features in Associating Objects with Transformers)](https://github.com/yoxu515/aot-benchmark) (NeurIPS2022) for efficient multi-object tracking and propagation. The SAM-Track pipeline enables dynamic and automatic detection and segmentation of new objects by SAM, while DeAOT is responsible for tracking all identified objects.
+
+## :loudspeaker:New Features
+- [2023/5/12] We have authored a technical report for SAM-Track.
+- [2023/5/7] We have added `demo_instseg.ipynb`, which uses Grounding-DINO to detect new objects in the key frames of a video. It can be applied in the fields of smart cities and autonomous driving.
+- [2023/4/29] We have added advanced arguments for AOT-L: `long_term_memory_gap` and `max_len_long_term`.
+   - `long_term_memory_gap` controls the frequency at which the AOT model adds new reference frames to its long-term memory. During mask propagation, AOT matches the current frame with the reference frames stored in the long-term memory. 
+   - Setting the gap value to a proper value helps to obtain better performance. To avoid memory explosion in long videos, we set a `max_len_long_term` value for the long-term memory storage, i.e. when the number of memory frames reaches the `max_len_long_term value`, the oldest memory frame will be discarded and a new frame will be added.
+
+- [2023/4/26] **Interactive WebUI 1.5-Version**: We have added new features based on Interactive WebUI-1.0 Version.
+   - We have added a new form of interactivity—text prompts—to SAMTrack.
+   - From now on, multiple objects that need to be tracked can be interactively added.
+   - Check out [tutorial](./tutorial/tutorial%20for%20WebUI-1.5-Version.md) for Interactive WebUI 1.5-Version. More demos will be released in the next few days.
+- [2023/4/26] **Image-Sequence input**: The WebUI now has a new feature that allows for input of image sequences, which can be used to test video segmentation datasets. Get started with the [tutorial](./tutorial/tutorial%20for%20Image-Sequence%20input.md) for Image-Sequence input. 
+- [2023/4/25] **Online Demo:** You can easily use SAMTrack in [Colab](https://colab.research.google.com/drive/1R10N70AJaslzADFqb-a5OihYkllWEVxB?usp=sharing) for visual tracking tasks.
+
+- [2023/4/23] **Interactive WebUI:** We have introduced a new WebUI that allows interactive user segmentation through strokes and clicks. Feel free to explore and have fun with the [tutorial](./tutorial/tutorial%20for%20WebUI-1.0-Version.md)!
+    - [2023/4/24] **Tutorial V1.0:** Check out our new video tutorials!
+      - YouTube-Link: [Tutorial for Interactively modify single-object mask for first frame of video](https://www.youtube.com/watch?v=DF0iFSsX8KY)、[Tutorial for Interactively add object by click](https://www.youtube.com/watch?v=UJvKPng9_DA)、[Tutorial for Interactively add object by stroke](https://www.youtube.com/watch?v=m1oFavjIaCM).
+      - Bilibili Video Link:[Tutorial for Interactively modify single-object mask for first frame of video](https://www.bilibili.com/video/BV1tM4115791/?spm_id_from=333.999.0.0)、[Tutorial for Interactively add object by click](https://www.bilibili.com/video/BV1Qs4y1A7d1/)、[Tutorial for Interactively add object by stroke](https://www.bilibili.com/video/BV1Lm4y117J4/?spm_id_from=333.999.0.0).
+    - 1.0-Version is a developer version, please feel free to contact us if you encounter any bugs :bug:.
+
+- [2023/4/17] **SAMTrack**: Automatically segment and track anything in video!
+
+## :fire:Demos
+<div align=center>
+
+[![Segment-and-Track-Anything Versatile Demo](https://res.cloudinary.com/marcomontalbano/image/upload/v1681713095/video_to_markdown/images/youtube--UPhtpf1k6HA-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://youtu.be/UPhtpf1k6HA "Segment-and-Track-Anything Versatile Demo")
+</div>
+
+This video showcases the segmentation and tracking capabilities of SAM-Track in various scenarios, such as street views, AR, cells, animations, aerial shots, and more.
+
+## :calendar:TODO
+ - [x] Colab notebook: Completed on April 25th, 2023.
+ - [x] 1.0-Version Interactive WebUI: Completed on April 23rd, 2023.
+    - We will create a feature that enables users to interactively modify the mask for the initial video frame according to their needs. The interactive segmentation capabilities of Segment-and-Track-Anything is demonstrated in [Demo8](https://www.youtube.com/watch?v=Xyd54AngvV8&feature=youtu.be) and [Demo9](https://www.youtube.com/watch?v=eZrdna8JkoQ).
+    - Bilibili Video Link: [Demo8](https://www.bilibili.com/video/BV1JL411v7uE/), [Demo9](https://www.bilibili.com/video/BV1Qs4y1w763/).
+ - [x] 1.5-Version Interactive WebUI: Completed on April 26th, 2023.
+    - We will develop a function that allows interactive modification of multi-object masks for the first frame of a video. This function will be based on Version 1.0.  YouTube: [Demo4](https://www.youtube.com/watch?v=UFtwFaOfx2I&feature=youtu.be), [Demo5](https://www.youtube.com/watch?v=cK5MPFdJdSY&feature=youtu.be); Bilibili: [Demo4](https://www.bilibili.com/video/BV17X4y127mJ/), [Demo5](https://www.bilibili.com/video/BV1Pz4y1a7mC/)
+    - Furthermore, we plan to include text prompts as an additional form of interaction. YouTube: [Demo1](https://www.youtube.com/watch?v=5oieHqFIJPc&feature=youtu.be), [Demo2](https://www.youtube.com/watch?v=nXfq17X6ohk); Bilibili: [Demo1](https://www.bilibili.com/video/BV1hg4y157yd/?vd_source=fe3b5c0215d05cc44c8eb3d94abae3ca), [Demo2](https://www.bilibili.com/video/BV1RV4y1k7i5/)
+ - [ ] 2.x-Version Interactive WebUI
+    - In version 2.x, the segmentation model will offer two options: SAM and SEEM.
+    - We will develop a new function where the fixed-category object detection result can be displayed as a prompt.
+    - We will enable SAM-Track to add and modify objects during tracking. YouTube: [Demo6](https://www.youtube.com/watch?v=l7hXM1a3nEA&feature=youtu.be
+), [Demo7](https://www.youtube.com/watch?v=hPjw28Ul4cw&feature=youtu.be); Bilibili: [Demo6](https://www.bilibili.com/video/BV1nk4y1j7Am), [Demo7](https://www.bilibili.com/video/BV1mk4y1E78s/?vd_source=fe3b5c0215d05cc44c8eb3d94abae3ca)
+
+**Demo1** showcases SAM-Track's ability to take the class of objects as prompt. The user gives the category text 'panda' to enable instance-level segmentation and tracking of all objects belonging to this category.
+<div align=center>
+ 
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683347297/video_to_markdown/images/youtube--5oieHqFIJPc-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=5oieHqFIJPc&feature=youtu.be "demo1")
+</div>
+
+**Demo2** showcases SAM-Track's ability to take the text description as prompt. SAM-Track could segment and track target objects given the input that 'panda on the far left'.
+<div align=center>
+ 
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683347643/video_to_markdown/images/youtube--nXfq17X6ohk-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=nXfq17X6ohk "demo1")
+</div>
+
+
+**Demo3** showcases SAM-Track's ability to track numerous objects at the same time. SAM-Track is capable of automatically detecting newly appearing objects.
+<div align=center>
+ 
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683347961/video_to_markdown/images/youtube--jMqFMq0tRP0-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=jMqFMq0tRP0 "demo1")
+</div>
+
+**Demo4** showcases SAM-Track's ability to take multiple modes of interactions as prompt. The user specified human and skateboard with click and brushstroke, respectively.  
+<div align=center>
+ 
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683348115/video_to_markdown/images/youtube--UFtwFaOfx2I-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=UFtwFaOfx2I&feature=youtu.be "demo1")
+</div>
+
+
+**Demo5** showcases SAM-Track's ability to refine the results of segment-everything. The user merges the tram as a whole with a single click.
+<div align=center>
+ 
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683348276/video_to_markdown/images/youtube--cK5MPFdJdSY-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=cK5MPFdJdSY&feature=youtu.be "demo1")
+</div>
+
+**Demo6** showcases SAM-Track's ability to add new objects during tracking. The user annotates another car by rolling back to an intermediate frame.
+<div align=center>
+ 
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683348411/video_to_markdown/images/youtube--l7hXM1a3nEA-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=l7hXM1a3nEA "demo1")
+</div>
+
+**Demo7** showcases SAM-Track's ability to refine the prediction during tracking. This feature is highly advantageous for segmentation and tracking under complex environments.
+<div align=center>
+
+[![demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1683348621/video_to_markdown/images/youtube--hPjw28Ul4cw-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=hPjw28Ul4cw&feature=youtu.be "demo1")
+</div>
+
+**Demo8** showcases SAM-Track's ability to interactively segment and track individual objects.  The user specified that SAM-Track tracked a man playing street basketball.
+<div align=center>
+
+[![Interactive Segment-and-Track-Anything Demo1](https://res.cloudinary.com/marcomontalbano/image/upload/v1681712022/video_to_markdown/images/youtube--Xyd54AngvV8-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=Xyd54AngvV8 "Interactive Segment-and-Track-Anything Demo1")
+</div>
+
+**Demo9** showcases SAM-Track's ability to interactively add specified objects for tracking.The user customized the addition of objects to be tracked on top of the segmentation of everything in the scene using SAM-Track.
+<div align=center>
+ 
+[![Interactive Segment-and-Track-Anything Demo2](https://res.cloudinary.com/marcomontalbano/image/upload/v1681712071/video_to_markdown/images/youtube--eZrdna8JkoQ-c05b58ac6eb4c4700831b2b3070cd403.jpg)](https://www.youtube.com/watch?v=eZrdna8JkoQ "Interactive Segment-and-Track-Anything Demo2")
+</div>
+
+## :computer:Getting Started
+### :bookmark_tabs:Requirements
+
+The [Segment-Anything](https://github.com/facebookresearch/segment-anything) repository has been cloned and renamed as sam, and the [aot-benchmark](https://github.com/yoxu515/aot-benchmark) repository has been cloned and renamed as aot.
+
+Please check the dependency requirements in [SAM](https://github.com/facebookresearch/segment-anything) and [DeAOT](https://github.com/yoxu515/aot-benchmark).
+
+The implementation is tested under python 3.9, as well as pytorch 1.10 and torchvision 0.11. **We recommend equivalent or higher pytorch version**.
+
+Use the `install.sh` to install the necessary libs for SAM-Track
+```
+bash script/install.sh
+```
+
+### :star:Model Preparation
+Download SAM model to ckpt, the default model is SAM-VIT-B ([sam_vit_b_01ec64.pth](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth)).
+
+Download DeAOT/AOT model to ckpt, the default model is R50-DeAOT-L ([R50_DeAOTL_PRE_YTB_DAV.pth](https://drive.google.com/file/d/1QoChMkTVxdYZ_eBlZhK2acq9KMQZccPJ/view)).
+
+Download Grounding-Dino model to ckpt, the default model is GroundingDINO-T ([groundingdino_swint_ogc](https://huggingface.co/ShilongLiu/GroundingDINO/resolve/main/groundingdino_swint_ogc.pth)).
+
+You can download the default weights using the command line as shown below.
+```
+bash script/download_ckpt.sh
+```
+
+### :heart:Run Demo
+- The video to be processed can be put in ./assets. 
+- Then run **demo.ipynb** step by step to generate results. 
+- The results will be saved as masks for each frame and a gif file for visualization.
+
+The arguments for SAM-Track, DeAOT and SAM can be manually modified in model_args.py for purpose of using other models or controling the behavior of each model.
+
+### :muscle:WebUI App
+Our user-friendly visual interface allows you to easily obtain the results of your experiments. Simply initiate it using the command line.
+
+```
+python app.py
+```
+Users can upload the video directly on the UI and use SegTracker to automatically/interactively track objects within that video. We use a video of a man playing basketball as an example.
+
+![Interactive WebUI](./assets/interactive_webui.jpg)
+
+SegTracker-Parameters:
+ - **aot_model**: used to select which version of DeAOT/AOT to use for tracking and propagation.
+ - **sam_gap**: used to control how often SAM is used to add newly appearing objects at specified frame intervals. Increase to decrease the frequency of discovering new targets, but significantly improve speed of inference.
+ - **points_per_side**: used to control the number of points per side used for generating masks by sampling a grid over the image. Increasing the size enhances the ability to detect small objects, but larger targets may be segmented into finer granularity.
+ - **max_obj_num**: used to limit the maximum number of objects that SAM-Track can detect and track. A larger number of objects necessitates a greater utilization of memory, with approximately 16GB of memory capable of processing a maximum of 255 objects.
+
+Usage: To see the details, please refer to the [tutorial for 1.0-Version WebUI](./tutorial/tutorial%20for%20WebUI-1.0-Version.md).
+
+### :school:About us
+Thank you for your interest in this project. The project is supervised by the ReLER Lab at Zhejiang University’s College of Computer Science and Technology. ReLER was established by Yang Yi, a Qiu Shi Distinguished Professor at Zhejiang University. Our dedicated team of contributors includes [Yangming Cheng](https://github.com/yamy-cheng), [Yuanyou Xu](https://github.com/yoxu515), [Liulei Li](https://github.com/lingorX), Xiaodi Li, [Zongxin Yang](https://z-x-yang.github.io/), [Wenguan Wang](https://sites.google.com/view/wenguanwang) and [Yi Yang](https://scholar.google.com/citations?user=RMSuNFwAAAAJ&hl=en).
+
+### :full_moon_with_face:Credits
+Licenses for borrowed code can be found in [licenses.md](https://github.com/z-x-yang/Segment-and-Track-Anything/blob/main/licenses.md) file. 
+
+* DeAOT/AOT - [https://github.com/yoxu515/aot-benchmark](https://github.com/yoxu515/aot-benchmark)
+* SAM - [https://github.com/facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything)
+* Gradio (for building WebUI) - [https://github.com/gradio-app/gradio](https://github.com/gradio-app/gradio)
+* Grounding-Dino - [https://github.com/yamy-cheng/GroundingDINO](https://github.com/yamy-cheng/GroundingDINO)
+
+### License
+The project is licensed under the [AGPL-3.0 license](https://github.com/z-x-yang/Segment-and-Track-Anything/blob/main/LICENSE.txt). To utilize or further develop this project for commercial purposes through proprietary means, permission must be granted by us (as well as the owners of any borrowed code).
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+### Citations
+Please consider citing the related paper(s) in your publications if it helps your research.
+```
+@article{cheng2023segment,
+  title={Segment and Track Anything},
+  author={Cheng, Yangming and Li, Liulei and Xu, Yuanyou and Li, Xiaodi and Yang, Zongxin and Wang, Wenguan and Yang, Yi},
+  journal={arXiv preprint arXiv:2305.06558},
+  year={2023}
+}
+@article{kirillov2023segment,
+  title={Segment anything},
+  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C and Lo, Wan-Yen and others},
+  journal={arXiv preprint arXiv:2304.02643},
+  year={2023}
+}
+@inproceedings{yang2022deaot,
+  title={Decoupling Features in Hierarchical Propagation for Video Object Segmentation},
+  author={Yang, Zongxin and Yang, Yi},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2022}
+}
+@inproceedings{yang2021aot,
+  title={Associating Objects with Transformers for Video Object Segmentation},
+  author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2021}
+}
+@article{liu2023grounding,
+  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
+  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
+  journal={arXiv preprint arXiv:2303.05499},
+  year={2023}
+}
+```

SegTracker.py

@@ -0,0 +1,264 @@
+import sys
+sys.path.append("..")
+sys.path.append("./sam")
+from sam.segment_anything import sam_model_registry, SamAutomaticMaskGenerator
+from aot_tracker import get_aot
+import numpy as np
+from tool.segmentor import Segmentor
+from tool.detector import Detector
+from tool.transfer_tools import draw_outline, draw_points
+import cv2
+from seg_track_anything import draw_mask
+
+
+class SegTracker():
+    def __init__(self,segtracker_args, sam_args, aot_args) -> None:
+        """
+         Initialize SAM and AOT.
+        """
+        self.sam = Segmentor(sam_args)
+        self.tracker = get_aot(aot_args)
+        self.detector = Detector(self.sam.device)
+        self.sam_gap = segtracker_args['sam_gap']
+        self.min_area = segtracker_args['min_area']
+        self.max_obj_num = segtracker_args['max_obj_num']
+        self.min_new_obj_iou = segtracker_args['min_new_obj_iou']
+        self.reference_objs_list = []
+        self.object_idx = 1
+        self.curr_idx = 1
+        self.origin_merged_mask = None  # init by segment-everything or update
+        self.first_frame_mask = None
+
+        # debug
+        self.everything_points = []
+        self.everything_labels = []
+        print("SegTracker has been initialized")
+
+    def seg(self,frame):
+        '''
+        Arguments:
+            frame: numpy array (h,w,3)
+        Return:
+            origin_merged_mask: numpy array (h,w)
+        '''
+        frame = frame[:, :, ::-1]
+        anns = self.sam.everything_generator.generate(frame)
+
+        # anns is a list recording all predictions in an image
+        if len(anns) == 0:
+            return
+        # merge all predictions into one mask (h,w)
+        # note that the merged mask may lost some objects due to the overlapping
+        self.origin_merged_mask = np.zeros(anns[0]['segmentation'].shape,dtype=np.uint8)
+        idx = 1
+        for ann in anns:
+            if ann['area'] > self.min_area:
+                m = ann['segmentation']
+                self.origin_merged_mask[m==1] = idx
+                idx += 1
+                self.everything_points.append(ann["point_coords"][0])
+                self.everything_labels.append(1)
+
+        obj_ids = np.unique(self.origin_merged_mask)
+        obj_ids = obj_ids[obj_ids!=0]
+
+        self.object_idx = 1
+        for id in obj_ids:
+            if np.sum(self.origin_merged_mask==id) < self.min_area or self.object_idx > self.max_obj_num:
+                self.origin_merged_mask[self.origin_merged_mask==id] = 0
+            else:
+                self.origin_merged_mask[self.origin_merged_mask==id] = self.object_idx
+                self.object_idx += 1
+
+        self.first_frame_mask = self.origin_merged_mask
+        return self.origin_merged_mask
+
+    def update_origin_merged_mask(self, updated_merged_mask):
+        self.origin_merged_mask = updated_merged_mask
+        # obj_ids = np.unique(updated_merged_mask)
+        # obj_ids = obj_ids[obj_ids!=0]
+        # self.object_idx = int(max(obj_ids)) + 1
+
+    def reset_origin_merged_mask(self, mask, id):
+        self.origin_merged_mask = mask
+        self.curr_idx = id
+
+    def add_reference(self,frame,mask,frame_step=0):
+        '''
+        Add objects in a mask for tracking.
+        Arguments:
+            frame: numpy array (h,w,3)
+            mask: numpy array (h,w)
+        '''
+        self.reference_objs_list.append(np.unique(mask))
+        self.curr_idx = self.get_obj_num() + 1
+        self.tracker.add_reference_frame(frame,mask, self.curr_idx - 1, frame_step)
+
+    def track(self,frame,update_memory=False):
+        '''
+        Track all known objects.
+        Arguments:
+            frame: numpy array (h,w,3)
+        Return:
+            origin_merged_mask: numpy array (h,w)
+        '''
+        pred_mask = self.tracker.track(frame)
+        if update_memory:
+            self.tracker.update_memory(pred_mask)
+        return pred_mask.squeeze(0).squeeze(0).detach().cpu().numpy().astype(np.uint8)
+    
+    def get_tracking_objs(self):
+        objs = set()
+        for ref in self.reference_objs_list:
+            objs.update(set(ref))
+        objs = list(sorted(list(objs)))
+        objs = [i for i in objs if i!=0]
+        return objs
+    
+    def get_obj_num(self):
+        objs = self.get_tracking_objs()
+        if len(objs) == 0: return 0
+        return int(max(objs))
+
+    def find_new_objs(self, track_mask, seg_mask):
+        '''
+        Compare tracked results from AOT with segmented results from SAM. Select objects from background if they are not tracked.
+        Arguments:
+            track_mask: numpy array (h,w)
+            seg_mask: numpy array (h,w)
+        Return:
+            new_obj_mask: numpy array (h,w)
+        '''
+        new_obj_mask = (track_mask==0) * seg_mask
+        new_obj_ids = np.unique(new_obj_mask)
+        new_obj_ids = new_obj_ids[new_obj_ids!=0]
+        # obj_num = self.get_obj_num() + 1
+        obj_num = self.curr_idx
+        for idx in new_obj_ids:
+            new_obj_area = np.sum(new_obj_mask==idx)
+            obj_area = np.sum(seg_mask==idx)
+            if new_obj_area/obj_area < self.min_new_obj_iou or new_obj_area < self.min_area\
+                or obj_num > self.max_obj_num:
+                new_obj_mask[new_obj_mask==idx] = 0
+            else:
+                new_obj_mask[new_obj_mask==idx] = obj_num
+                obj_num += 1
+        return new_obj_mask
+        
+    def restart_tracker(self):
+        self.tracker.restart()
+
+    def seg_acc_bbox(self, origin_frame: np.ndarray, bbox: np.ndarray,):
+        ''''
+        Use bbox-prompt to get mask
+        Parameters:
+            origin_frame: H, W, C
+            bbox: [[x0, y0], [x1, y1]]
+        Return:
+            refined_merged_mask: numpy array (h, w)
+            masked_frame: numpy array (h, w, c)
+        '''
+        # get interactive_mask
+        interactive_mask = self.sam.segment_with_box(origin_frame, bbox)[0]
+        refined_merged_mask = self.add_mask(interactive_mask)
+
+        # draw mask
+        masked_frame = draw_mask(origin_frame.copy(), refined_merged_mask)
+
+        # draw bbox
+        masked_frame = cv2.rectangle(masked_frame, bbox[0], bbox[1], (0, 0, 255))
+
+        return refined_merged_mask, masked_frame
+
+    def seg_acc_click(self, origin_frame: np.ndarray, coords: np.ndarray, modes: np.ndarray, multimask=True):
+        '''
+        Use point-prompt to get mask
+        Parameters:
+            origin_frame: H, W, C
+            coords: nd.array [[x, y]]
+            modes: nd.array [[1]]
+        Return:
+            refined_merged_mask: numpy array (h, w)
+            masked_frame: numpy array (h, w, c)
+        '''
+        # get interactive_mask
+        interactive_mask = self.sam.segment_with_click(origin_frame, coords, modes, multimask)
+
+        refined_merged_mask = self.add_mask(interactive_mask)
+
+        # draw mask
+        masked_frame = draw_mask(origin_frame.copy(), refined_merged_mask)
+
+        # draw points
+        # self.everything_labels = np.array(self.everything_labels).astype(np.int64)
+        # self.everything_points = np.array(self.everything_points).astype(np.int64)
+
+        masked_frame = draw_points(coords, modes, masked_frame)
+
+        # draw outline
+        masked_frame = draw_outline(interactive_mask, masked_frame)
+
+        return refined_merged_mask, masked_frame
+
+    def add_mask(self, interactive_mask: np.ndarray):
+        '''
+        Merge interactive mask with self.origin_merged_mask
+        Parameters:
+            interactive_mask: numpy array (h, w)
+        Return:
+            refined_merged_mask: numpy array (h, w)
+        '''
+        if self.origin_merged_mask is None:
+            self.origin_merged_mask = np.zeros(interactive_mask.shape,dtype=np.uint8)
+
+        refined_merged_mask = self.origin_merged_mask.copy()
+        refined_merged_mask[interactive_mask > 0] = self.curr_idx
+
+        return refined_merged_mask
+    
+    def detect_and_seg(self, origin_frame: np.ndarray, grounding_caption, box_threshold, text_threshold, box_size_threshold=1, reset_image=False):
+        '''
+        Using Grounding-DINO to detect object acc Text-prompts
+        Retrun:
+            refined_merged_mask: numpy array (h, w)
+            annotated_frame: numpy array (h, w, 3)
+        '''
+        # backup id and origin-merged-mask
+        bc_id = self.curr_idx
+        bc_mask = self.origin_merged_mask
+
+        # get annotated_frame and boxes
+        annotated_frame, boxes = self.detector.run_grounding(origin_frame, grounding_caption, box_threshold, text_threshold)
+        for i in range(len(boxes)):
+            bbox = boxes[i]
+            if (bbox[1][0] - bbox[0][0]) * (bbox[1][1] - bbox[0][1]) > annotated_frame.shape[0] * annotated_frame.shape[1] * box_size_threshold:
+                continue
+            interactive_mask = self.sam.segment_with_box(origin_frame, bbox, reset_image)[0]
+            refined_merged_mask = self.add_mask(interactive_mask)
+            self.update_origin_merged_mask(refined_merged_mask)
+            self.curr_idx += 1
+
+        # reset origin_mask
+        self.reset_origin_merged_mask(bc_mask, bc_id)
+
+        return refined_merged_mask, annotated_frame
+
+if __name__ == '__main__':
+    from model_args import segtracker_args,sam_args,aot_args
+
+    Seg_Tracker = SegTracker(segtracker_args, sam_args, aot_args)
+    
+    # ------------------ detect test ----------------------
+    
+    origin_frame = cv2.imread('/data2/cym/Seg_Tra_any/Segment-and-Track-Anything/debug/point.png')
+    origin_frame = cv2.cvtColor(origin_frame, cv2.COLOR_BGR2RGB)
+    grounding_caption = "swan.water"
+    box_threshold = 0.25
+    text_threshold = 0.25
+
+    predicted_mask, annotated_frame = Seg_Tracker.detect_and_seg(origin_frame, grounding_caption, box_threshold, text_threshold)
+    masked_frame = draw_mask(annotated_frame, predicted_mask)
+    origin_frame = cv2.cvtColor(origin_frame, cv2.COLOR_RGB2BGR)
+
+    cv2.imwrite('./debug/masked_frame.png', masked_frame)
+    cv2.imwrite('./debug/x.png', annotated_frame)

aot/LICENSE

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2020, z-x-yang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

aot/MODEL_ZOO.md

@@ -0,0 +1,115 @@
+## Model Zoo and Results
+
+### Environment and Settings
+- 4/1 NVIDIA V100 GPUs for training/evaluation.
+- Auto-mixed precision was enabled in training but disabled in evaluation.
+- Test-time augmentations were not used.
+- The inference resolution of DAVIS/YouTube-VOS was 480p/1.3x480p as [CFBI](https://github.com/z-x-yang/CFBI).
+- Fully online inference. We passed all the modules frame by frame.
+- Multi-object FPS was recorded instead of single-object one.
+
+### Pre-trained Models
+Stages:
+
+- `PRE`: the pre-training stage with static images.
+
+- `PRE_YTB_DAV`: the main-training stage with YouTube-VOS and DAVIS. All the kinds of evaluation share an **identical** model and the **same** parameters.
+
+
+| Model      | Param (M) |                                             PRE                                              |                                         PRE_YTB_DAV                                          |
+|:---------- |:---------:|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|
+| AOTT       |    5.7    | [gdrive](https://drive.google.com/file/d/1_513h8Hok9ySQPMs_dHgX5sPexUhyCmy/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1owPmwV4owd_ll6GuilzklqTyAd0ZvbCu/view?usp=sharing) |
+| AOTS       |    7.0    | [gdrive](https://drive.google.com/file/d/1QUP0-VED-lOF1oX_ppYWnXyBjvUzJJB7/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1beU5E6Mdnr_pPrgjWvdWurKAIwJSz1xf/view?usp=sharing) |
+| AOTB       |    8.3    | [gdrive](https://drive.google.com/file/d/11Bx8n_INAha1IdpHjueGpf7BrKmCJDvK/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1hH-GOn4GAxHkV8ARcQzsUy8Ax6ndot-A/view?usp=sharing) |
+| AOTL       |    8.3    | [gdrive](https://drive.google.com/file/d/1WL6QCsYeT7Bt-Gain9ZIrNNXpR2Hgh29/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1L1N2hkSPqrwGgnW9GyFHuG59_EYYfTG4/view?usp=sharing) |
+| R50-AOTL   |   14.9    | [gdrive](https://drive.google.com/file/d/1hS4JIvOXeqvbs-CokwV6PwZV-EvzE6x8/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1qJDYn3Ibpquu4ffYoQmVjg1YCbr2JQep/view?usp=sharing) |
+| SwinB-AOTL |   65.4    | [gdrive](https://drive.google.com/file/d/1LlhKQiXD8JyZGGs3hZiNzcaCLqyvL9tj/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/192jCGQZdnuTsvX-CVra-KVZl2q1ZR0vW/view?usp=sharing) |
+
+| Model      | Param (M) |                                             PRE                                              |                                         PRE_YTB_DAV                                          |
+|:---------- |:---------:|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|
+| DeAOTT       |    7.2   | [gdrive](https://drive.google.com/file/d/11C1ZBoFpL3ztKtINS8qqwPSldfYXexFK/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1ThWIZQS03cYWx1EKNN8MIMnJS5eRowzr/view?usp=sharing) |
+| DeAOTS       |    10.2   | [gdrive](https://drive.google.com/file/d/1uUidrWVoaP9A5B5-EzQLbielUnRLRF3j/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1YwIAV5tBtn5spSFxKLBQBEQGwPHyQlHi/view?usp=sharing) |
+| DeAOTB       |    13.2   | [gdrive](https://drive.google.com/file/d/1bEQr6vIgQMVITrSOtxWTMgycKpS0cor9/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1BHxsonnvJXylqHlZ1zJHHc-ymKyq-CFf/view?usp=sharing) |
+| DeAOTL       |    13.2   | [gdrive](https://drive.google.com/file/d/1_vBL4KJlmBy0oBE4YFDOvsYL1ZtpEL32/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/18elNz_wi9JyVBcIUYKhRdL08MA-FqHD5/view?usp=sharing) |
+| R50-DeAOTL   |    19.8   | [gdrive](https://drive.google.com/file/d/1sTRQ1g0WCpqVCdavv7uJiZNkXunBt3-R/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1QoChMkTVxdYZ_eBlZhK2acq9KMQZccPJ/view?usp=sharing) |
+| SwinB-DeAOTL |    70.3   | [gdrive](https://drive.google.com/file/d/16BZEE53no8CxT-pPLDC2q1d6Xlg8mWPU/view?usp=sharing) | [gdrive](https://drive.google.com/file/d/1g4E-F0RPOx9Nd6J7tU9AE1TjsouL4oZq/view?usp=sharing) |
+
+To use our pre-trained model to infer, a simple way is to set `--model` and `--ckpt_path` to your downloaded checkpoint's model type and file path when running `eval.py`.
+
+### YouTube-VOS 2018 val
+`ALL-F`: all frames. The default evaluation setting of YouTube-VOS is 6fps, but 30fps sequences (all the frames) are also supplied by the dataset organizers. We noticed that many VOS methods prefer to evaluate with 30fps videos. Thus, we also supply our results here. Denser video sequences can significantly improve VOS performance when using the memory reading strategy (like AOTL, R50-AOTL, and SwinB-AOTL), but the efficiency will be influenced since more memorized frames are stored for object matching.
+| Model        |    Stage    |   FPS    | All-F |   Mean   |  J Seen  |  F Seen  | J Unseen | F Unseen |                                         Predictions                                          |
+|:------------ |:-----------:|:--------:|:-----:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------------------------------------------------------------------------------------------:|
+| AOTT         | PRE_YTB_DAV |   41.0   |       |   80.2   |   80.4   |   85.0   |   73.6   |   81.7   | [gdrive](https://drive.google.com/file/d/1u8mvPRT08ENZHsw9Xf_4C6Sv9BoCzENR/view?usp=sharing) |
+| AOTT         | PRE_YTB_DAV |   41.0   |   √   |   80.9   |   80.0   |   84.7   |   75.2   |   83.5   | [gdrive](https://drive.google.com/file/d/1RGMI5-29Z0odq73rt26eCxOUYUd-fvVv/view?usp=sharing) |
+| DeAOTT       | PRE_YTB_DAV | **53.4** |       | **82.0** | **81.6** | **86.3** | **75.8** | **84.2** |    -                                                                                          |
+| AOTS         | PRE_YTB_DAV |   27.1   |       |   82.9   |   82.3   |   87.0   |   77.1   |   85.1   | [gdrive](https://drive.google.com/file/d/1a4-rNnxjMuPBq21IKo31WDYZXMPgS7r2/view?usp=sharing) |
+| AOTS         | PRE_YTB_DAV |   27.1   |   √   |   83.0   |   82.2   |   87.0   |   77.3   |   85.7   | [gdrive](https://drive.google.com/file/d/1Z0cndyoCw5Na6u-VFRE8CyiIG2RbMIUO/view?usp=sharing) |
+| DeAOTS       | PRE_YTB_DAV | **38.7** |       | **84.0** | **83.3** | **88.3** | **77.9** | **86.6** |    -                                                                                          |
+| AOTB         | PRE_YTB_DAV |   20.5   |       |   84.0   |   83.2   |   88.1   |   78.0   |   86.5   | [gdrive](https://drive.google.com/file/d/1J5nhuQbbjVLYNXViBIgo21ddQy-MiOLG/view?usp=sharing) |
+| AOTB         | PRE_YTB_DAV |   20.5   |   √   |   84.1   |   83.6   |   88.5   |   78.0   |   86.5   | [gdrive](https://drive.google.com/file/d/1gFaweB_GTJjHzSD61v_ZsY9K7UEND30O/view?usp=sharing) |
+| DeAOTB       | PRE_YTB_DAV | **30.4** |       | **84.6** | **83.9** | **88.9** | **78.5** | **87.0** |    -                                                                                          |
+| AOTL         | PRE_YTB_DAV |   16.0   |       |   84.1   |   83.2   |   88.2   |   78.2   |   86.8   | [gdrive](https://drive.google.com/file/d/1kS8KWQ2L3wzxt44ROLTxwZOT7ZpT8Igc/view?usp=sharing) |
+| AOTL         | PRE_YTB_DAV |   6.5    |   √   |   84.5   |   83.7   |   88.8   |   78.4   | **87.1** | [gdrive](https://drive.google.com/file/d/1Rpm3e215kJOUvb562lJ2kYg2I3hkrxiM/view?usp=sharing) |
+| DeAOTL       | PRE_YTB_DAV | **24.7** |       | **84.8** | **84.2** | **89.4** | **78.6** |   87.0   |    -                                                                                          |
+| R50-AOTL     | PRE_YTB_DAV |   14.9   |       |   84.6   |   83.7   |   88.5   |   78.8   |   87.3   | [gdrive](https://drive.google.com/file/d/1nbJZ1bbmEgyK-bg6HQ8LwCz5gVJ6wzIZ/view?usp=sharing) |
+| R50-AOTL     | PRE_YTB_DAV |   6.4    |   √   |   85.5   |   84.5   |   89.5   |   79.6   |   88.2   | [gdrive](https://drive.google.com/file/d/1NbB54ZhYvfJh38KFOgovYYPjWopd-2TE/view?usp=sharing) |
+| R50-DeAOTL   | PRE_YTB_DAV | **22.4** |       | **86.0** | **84.9** | **89.9** | **80.4** | **88.7** |    -                                                                                         |
+| SwinB-AOTL   | PRE_YTB_DAV |   9.3    |       |   84.7   |   84.5   |   89.5   |   78.1   |   86.7   | [gdrive](https://drive.google.com/file/d/1QFowulSY0LHfpsjUV8ZE9rYc55L9DOC7/view?usp=sharing) |
+| SwinB-AOTL   | PRE_YTB_DAV |   5.2    |   √   |   85.1   |   85.1   |   90.1   |   78.4   |   86.9   | [gdrive](https://drive.google.com/file/d/1TulhVOhh01rkssNYbOQASeWKu7CQ5Azx/view?usp=sharing) |
+| SwinB-DeAOTL | PRE_YTB_DAV | **11.9** |       | **86.2** | **85.6** | **90.6** | **80.0** | **88.4** |    -                                                                                  |
+
+### YouTube-VOS 2019 val
+| Model        |    Stage    |   FPS    | All-F |   Mean   |  J Seen  |  F Seen  | J Unseen | F Unseen |                                         Predictions                                          |
+|:------------ |:-----------:|:--------:|:-----:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------------------------------------------------------------------------------------------:|
+| AOTT         | PRE_YTB_DAV |   41.0   |       |   80.0   |   79.8   |   84.2   |   74.1   |   82.1   | [gdrive](https://drive.google.com/file/d/1zzyhN1XYtajte5nbZ7opOdfXeDJgCxC5/view?usp=sharing) |
+| AOTT         | PRE_YTB_DAV |   41.0   |   √   |   80.9   |   79.9   |   84.4   |   75.6   |   83.8   | [gdrive](https://drive.google.com/file/d/1V_5vi9dAXOis_WrDieacSESm7OX20Bv-/view?usp=sharing) |
+| DeAOTT       | PRE_YTB_DAV | **53.4** |       | **82.0** | **81.2** | **85.6** | **76.4** | **84.7** |     -                                                                                         |
+| AOTS         | PRE_YTB_DAV |   27.1   |       |   82.7   |   81.9   |   86.5   |   77.3   |   85.2   | [gdrive](https://drive.google.com/file/d/11YdkUeyjkTv8Uw7xMgPCBzJs6v5SDt6n/view?usp=sharing) |
+| AOTS         | PRE_YTB_DAV |   27.1   |   √   |   82.8   |   81.9   |   86.5   |   77.3   |   85.6   | [gdrive](https://drive.google.com/file/d/1UhyurGTJeAw412czU3_ebzNwF8xQ4QG_/view?usp=sharing) |
+| DeAOTS       | PRE_YTB_DAV | **38.7** |       | **83.8** | **82.8** | **87.5** | **78.1** | **86.8** |     -                                                                                         |
+| AOTB         | PRE_YTB_DAV |   20.5   |       |   84.0   |   83.1   |   87.7   |   78.5   |   86.8   | [gdrive](https://drive.google.com/file/d/1NeI8cT4kVqTqVWAwtwiga1rkrvksNWaO/view?usp=sharing) |
+| AOTB         | PRE_YTB_DAV |   20.5   |   √   |   84.1   |   83.3   |   88.0   |   78.2   |   86.7   | [gdrive](https://drive.google.com/file/d/1kpYV2XFR0sOfLWD-wMhd-nUO6CFiLjlL/view?usp=sharing) |
+| DeAOTB       | PRE_YTB_DAV | **30.4** |       | **84.6** | **83.5** | **88.3** | **79.1** | **87.5** |     -                                                                                         |
+| AOTL         | PRE_YTB_DAV |   16.0   |       |   84.0   |   82.8   |   87.6   |   78.6   |   87.1   | [gdrive](https://drive.google.com/file/d/1qKLlNXxmT31bW0weEHI_zAf4QwU8Lhou/view?usp=sharing) |
+| AOTL         | PRE_YTB_DAV |   6.5    |   √   |   84.2   |   83.0   |   87.8   |   78.7   |   87.3   | [gdrive](https://drive.google.com/file/d/1o3fwZ0cH71bqHSA3bYNjhP4GGv9Vyuwa/view?usp=sharing) |
+| DeAOTL       | PRE_YTB_DAV | **24.7** |       | **84.7** | **83.8** | **88.8** | **79.0** | **87.2** |     -                                                                                         |
+| R50-AOTL     | PRE_YTB_DAV |   14.9   |       |   84.4   |   83.4   |   88.1   |   78.7   |   87.2   | [gdrive](https://drive.google.com/file/d/1I7ooSp8EYfU6fvkP6QcCMaxeencA68AH/view?usp=sharing) |
+| R50-AOTL     | PRE_YTB_DAV |   6.4    |   √   |   85.3   |   83.9   |   88.8   |   79.9   |   88.5   | [gdrive](https://drive.google.com/file/d/1OGqlkEu0uXa8QVWIVz_M5pmXXiYR2sh3/view?usp=sharing) |
+| R50-DeAOTL   | PRE_YTB_DAV | **22.4** |       | **85.9** | **84.6** | **89.4** | **80.8** | **88.9** |     -                                                                                         |
+| SwinB-AOTL   | PRE_YTB_DAV |   9.3    |       |   84.7   |   84.0   |   88.8   |   78.7   |   87.1   | [gdrive](https://drive.google.com/file/d/1fPzCxi5GM7N2sLKkhoTC2yoY_oTQCHp1/view?usp=sharing) |
+| SwinB-AOTL   | PRE_YTB_DAV |   5.2    |   √   |   85.3   |   84.6   |   89.5   |   79.3   |   87.7   | [gdrive](https://drive.google.com/file/d/1e3D22s_rJ7Y2X2MHo7x5lcNtwmHFlwYB/view?usp=sharing) |
+| SwinB-DeAOTL | PRE_YTB_DAV | **11.9** |       | **86.1** | **85.3** | **90.2** | **80.4** | **88.6** |     -                                                                                         |
+
+### DAVIS-2017 test
+
+| Model      |    Stage    | FPS  |   Mean   | J Score  | F Score  | Predictions |
+| ---------- |:-----------:|:----:|:--------:|:--------:|:--------:|:----:|
+| AOTT       | PRE_YTB_DAV | **51.4** |   73.7   |   70.0   |   77.3   | [gdrive](https://drive.google.com/file/d/14Pu-6Uz4rfmJ_WyL2yl57KTx_pSSUNAf/view?usp=sharing) |
+| AOTS       | PRE_YTB_DAV | 40.0 |   75.2   |   71.4   |   78.9   | [gdrive](https://drive.google.com/file/d/1zzAPZCRLgnBWuAXqejPPEYLqBxu67Rj1/view?usp=sharing) |
+| AOTB       | PRE_YTB_DAV | 29.6 |   77.4   |   73.7   |   81.1   | [gdrive](https://drive.google.com/file/d/1WpQ-_Jrs7Ssfw0oekrejM2OVWEx_tBN1/view?usp=sharing) |
+| AOTL       | PRE_YTB_DAV | 18.7 |   79.3   |   75.5   |   83.2   | [gdrive](https://drive.google.com/file/d/1rP1Zdgc0N1d8RR2EaXMz3F-o5zqcNVe8/view?usp=sharing) |
+| R50-AOTL   | PRE_YTB_DAV | 18.0 |   79.5   |   76.0   |   83.0   | [gdrive](https://drive.google.com/file/d/1iQ5iNlvlS-In586ZNc4LIZMSdNIWDvle/view?usp=sharing) |
+| SwinB-AOTL | PRE_YTB_DAV | 12.1  | **82.1** | **78.2** | **85.9** | [gdrive](https://drive.google.com/file/d/1oVt4FPcZdfVHiOxjYYKef0q7Ovy4f5Q_/view?usp=sharing) |
+
+### DAVIS-2017 val
+
+| Model      |    Stage    | FPS  |   Mean   | J Score  |  F Score  | Predictions |
+| ---------- |:-----------:|:----:|:--------:|:--------:|:---------:|:----:|
+| AOTT       | PRE_YTB_DAV | **51.4** |   79.2   |   76.5   |   81.9    | [gdrive](https://drive.google.com/file/d/10OUFhK2Sz-hOJrTDoTI0mA45KO1qodZt/view?usp=sharing) |
+| AOTS       | PRE_YTB_DAV | 40.0 |   82.1   |   79.3   |   84.8    | [gdrive](https://drive.google.com/file/d/1T-JTYyksWlq45jxcLjnRaBvvYUhWgHFH/view?usp=sharing) |
+| AOTB       | PRE_YTB_DAV | 29.6 |   83.3   |   80.6   |   85.9    | [gdrive](https://drive.google.com/file/d/1EVUnxQm9TLBTuwK82QyiSKk9R9V8NwRL/view?usp=sharing) |
+| AOTL       | PRE_YTB_DAV | 18.7 |   83.6   |   80.8   |   86.3    | [gdrive](https://drive.google.com/file/d/1CFauSni2BxAe_fcl8W_6bFByuwJRbDYm/view?usp=sharing) |
+| R50-AOTL   | PRE_YTB_DAV | 18.0 |   85.2   |   82.5   |   87.9    | [gdrive](https://drive.google.com/file/d/1vjloxnP8R4PZdsH2DDizfU2CrkdRHHyo/view?usp=sharing) |
+| SwinB-AOTL | PRE_YTB_DAV | 12.1  | **85.9** | **82.9** | **88.9** | [gdrive](https://drive.google.com/file/d/1tYCbKOas0i7Et2iyUAyDwaXnaD9YWxLr/view?usp=sharing) |
+
+### DAVIS-2016 val
+
+| Model      |    Stage    | FPS  |   Mean   | J Score  | F Score  | Predictions |
+| ---------- |:-----------:|:----:|:--------:|:--------:|:--------:|:----:|
+| AOTT       | PRE_YTB_DAV | **51.4** |   87.5   |   86.5   |   88.4   | [gdrive](https://drive.google.com/file/d/1LeW8WQhnylZ3umT7E379KdII92uUsGA9/view?usp=sharing) |
+| AOTS       | PRE_YTB_DAV | 40.0 |   89.6   |   88.6   |   90.5   | [gdrive](https://drive.google.com/file/d/1vqGei5tLu1FPVrTi5bwRAsaGy3Upf7B1/view?usp=sharing) |
+| AOTB       | PRE_YTB_DAV | 29.6 |   90.9   |   89.6   |   92.1   | [gdrive](https://drive.google.com/file/d/1qAppo2uOVu0FbE9t1FBUpymC3yWgw1LM/view?usp=sharing) |
+| AOTL       | PRE_YTB_DAV | 18.7 |   91.1   |   89.5   |   92.7   | [gdrive](https://drive.google.com/file/d/1g6cjYhgBWjMaY3RGAm31qm3SPEF3QcKV/view?usp=sharing) |
+| R50-AOTL   | PRE_YTB_DAV | 18.0 |   91.7   |   90.4   |   93.0   | [gdrive](https://drive.google.com/file/d/1QzxojqWKsvRf53K2AgKsK523ZVuYU4O-/view?usp=sharing) |
+| SwinB-AOTL | PRE_YTB_DAV | 12.1  | **92.2** | **90.6** | **93.8** | [gdrive](https://drive.google.com/file/d/1RIqUtAyVnopeogfT520d7a0yiULg1obp/view?usp=sharing) |

aot/README.md

@@ -0,0 +1,152 @@
+# AOT Series Frameworks in PyTorch
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/decoupling-features-in-hierarchical/semi-supervised-video-object-segmentation-on-15)](https://paperswithcode.com/sota/semi-supervised-video-object-segmentation-on-15?p=decoupling-features-in-hierarchical)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/associating-objects-with-scalable/video-object-segmentation-on-youtube-vos)](https://paperswithcode.com/sota/video-object-segmentation-on-youtube-vos?p=associating-objects-with-scalable)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/associating-objects-with-scalable/semi-supervised-video-object-segmentation-on-18)](https://paperswithcode.com/sota/semi-supervised-video-object-segmentation-on-18?p=associating-objects-with-scalable)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/associating-objects-with-scalable/semi-supervised-video-object-segmentation-on-1)](https://paperswithcode.com/sota/semi-supervised-video-object-segmentation-on-1?p=associating-objects-with-scalable)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/associating-objects-with-scalable/visual-object-tracking-on-davis-2017)](https://paperswithcode.com/sota/visual-object-tracking-on-davis-2017?p=associating-objects-with-scalable)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/associating-objects-with-scalable/visual-object-tracking-on-davis-2016)](https://paperswithcode.com/sota/visual-object-tracking-on-davis-2016?p=associating-objects-with-scalable)
+
+A modular reference PyTorch implementation of AOT series frameworks:
+- **DeAOT**: Decoupling Features in Hierachical Propagation for Video Object Segmentation (NeurIPS 2022, Spotlight) [[OpenReview](https://openreview.net/forum?id=DgM7-7eMkq0)][[PDF](https://arxiv.org/pdf/2210.09782.pdf)]
+<img src="source/overview_deaot.png" width="90%"/>
+
+- **AOT**: Associating Objects with Transformers for Video Object Segmentation (NeurIPS 2021, Score 8/8/7/8) [[OpenReview](https://openreview.net/forum?id=hl3v8io3ZYt)][[PDF](https://arxiv.org/abs/2106.02638)]
+<img src="source/overview.png" width="90%"/>
+
+An extension of AOT, [AOST](https://arxiv.org/abs/2203.11442) (under review), is available now. AOST is a more robust and flexible framework, supporting run-time speed-accuracy trade-offs.
+
+## Examples
+Benchmark examples:
+
+<img src="source/some_results.png" width="81%"/>
+
+General examples (Messi and Kobe):
+
+<img src="source/messi.gif" width="45%"/> <img src="source/kobe.gif" width="45%"/>
+
+## Highlights
+- **High performance:** up to **85.5%** ([R50-AOTL](MODEL_ZOO.md#youtube-vos-2018-val)) on YouTube-VOS 2018 and **82.1%** ([SwinB-AOTL]((MODEL_ZOO.md#youtube-vos-2018-val))) on DAVIS-2017 Test-dev under standard settings (without any test-time augmentation and post processing). 
+- **High efficiency:** up to **51fps** ([AOTT](MODEL_ZOO.md#davis-2017-test)) on DAVIS-2017 (480p) even with **10** objects and **41fps** on YouTube-VOS (1.3x480p). AOT can process multiple objects (less than a pre-defined number, 10 is the default) as efficiently as processing a single object. This project also supports inferring any number of objects together within a video by automatic separation and aggregation.
+- **Multi-GPU training and inference**
+- **Mixed precision training and inference** 
+- **Test-time augmentation:** multi-scale and flipping augmentations are supported.
+
+## Requirements
+   * Python3
+   * pytorch >= 1.7.0 and torchvision
+   * opencv-python
+   * Pillow
+   * Pytorch Correlation (Recommend to install from [source](https://github.com/ClementPinard/Pytorch-Correlation-extension) instead of using `pip`. **The project can also work without this module but will lose some efficiency of the short-term attention**.)
+
+Optional:
+   * scikit-image (if you want to run our **Demo**, please install)
+
+## Model Zoo and Results
+Pre-trained models, benckmark scores, and pre-computed results reproduced by this project can be found in [MODEL_ZOO.md](MODEL_ZOO.md).
+
+## Demo - Panoptic Propagation
+We provide a simple demo to demonstrate AOT's effectiveness. The demo will propagate more than **40** objects, including semantic regions (like sky) and instances (like person), together within a single complex scenario and predict its video panoptic segmentation.
+
+To run the demo, download the [checkpoint](https://drive.google.com/file/d/1qJDYn3Ibpquu4ffYoQmVjg1YCbr2JQep/view?usp=sharing) of R50-AOTL into [pretrain_models](pretrain_models), and then run:
+```bash
+python tools/demo.py
+```
+which will predict the given scenarios in the resolution of 1.3x480p. You can also run this demo with other AOTs ([MODEL_ZOO.md](MODEL_ZOO.md)) by setting `--model` (model type) and `--ckpt_path` (checkpoint path).
+
+Two scenarios from [VSPW](https://www.vspwdataset.com/home) are supplied in [datasets/Demo](datasets/Demo):
+
+- 1001_3iEIq5HBY1s: 44 objects. 1080P.
+- 1007_YCTBBdbKSSg: 43 objects. 1080P.
+
+Results:
+
+<img src="source/1001_3iEIq5HBY1s.gif" width="45%"/>  <img src="source/1007_YCTBBdbKSSg.gif" width="45%"/>
+
+
+## Getting Started
+0. Prepare a valid environment follow the [requirements](#requirements).
+
+1. Prepare datasets:
+
+    Please follow the below instruction to prepare datasets in each corresponding folder.
+    * **Static** 
+    
+        [datasets/Static](datasets/Static): pre-training dataset with static images. Guidance can be found in [AFB-URR](https://github.com/xmlyqing00/AFB-URR), which we referred to in the implementation of the pre-training.
+    * **YouTube-VOS**
+
+        A commonly-used large-scale VOS dataset.
+
+        [datasets/YTB/2019](datasets/YTB/2019): version 2019, download [link](https://drive.google.com/drive/folders/1BWzrCWyPEmBEKm0lOHe5KLuBuQxUSwqz?usp=sharing). `train` is required for training. `valid` (6fps) and `valid_all_frames` (30fps, optional) are used for evaluation.
+
+        [datasets/YTB/2018](datasets/YTB/2018): version 2018, download [link](https://drive.google.com/drive/folders/1bI5J1H3mxsIGo7Kp-pPZU8i6rnykOw7f?usp=sharing). Only `valid` (6fps) and `valid_all_frames` (30fps, optional) are required for this project and used for evaluation.
+
+    * **DAVIS**
+
+        A commonly-used small-scale VOS dataset.
+
+        [datasets/DAVIS](datasets/DAVIS): [TrainVal](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-trainval-480p.zip) (480p) contains both the training and validation split. [Test-Dev](https://data.vision.ee.ethz.ch/csergi/share/davis/DAVIS-2017-test-dev-480p.zip) (480p) contains the Test-dev split. The [full-resolution version](https://davischallenge.org/davis2017/code.html) is also supported for training and evaluation but not required.
+
+
+2. Prepare ImageNet pre-trained encoders
+
+    Select and download below checkpoints into [pretrain_models](pretrain_models):
+
+    - [MobileNet-V2](https://download.pytorch.org/models/mobilenet_v2-b0353104.pth) (default encoder)
+    - [MobileNet-V3](https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth)
+    - [ResNet-50](https://download.pytorch.org/models/resnet50-0676ba61.pth)
+    - [ResNet-101](https://download.pytorch.org/models/resnet101-63fe2227.pth)
+    - [ResNeSt-50](https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest50-528c19ca.pth)
+    - [ResNeSt-101](https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest101-22405ba7.pth)
+    - [Swin-Base](https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth)
+
+    The current default training configs are not optimized for encoders larger than ResNet-50. If you want to use larger encoders, we recommend early stopping the main-training stage at 80,000 iterations (100,000 in default) to avoid over-fitting on the seen classes of YouTube-VOS.
+
+
+
+3. Training and Evaluation
+
+    The [example script](train_eval.sh) will train AOTT with 2 stages using 4 GPUs and auto-mixed precision (`--amp`). The first stage is a pre-training stage using `Static` dataset, and the second stage is a main-training stage, which uses both `YouTube-VOS 2019 train` and `DAVIS-2017 train` for training, resulting in a model that can generalize to different domains (YouTube-VOS and DAVIS) and different frame rates (6fps, 24fps, and 30fps).
+
+    Notably, you can use only the `YouTube-VOS 2019 train` split in the second stage by changing `pre_ytb_dav` to `pre_ytb`, which leads to better YouTube-VOS performance on unseen classes. Besides, if you don't want to do the first stage, you can start the training from stage `ytb`, but the performance will drop about 1~2% absolutely.
+
+    After the training is finished (about 0.6 days for each stage with 4 Tesla V100 GPUs), the [example script](train_eval.sh) will evaluate the model on YouTube-VOS and DAVIS, and the results will be packed into Zip files. For calculating scores, please use official YouTube-VOS servers ([2018 server](https://competitions.codalab.org/competitions/19544) and [2019 server](https://competitions.codalab.org/competitions/20127)), official [DAVIS toolkit](https://github.com/davisvideochallenge/davis-2017) (for Val), and official [DAVIS server](https://competitions.codalab.org/competitions/20516#learn_the_details) (for Test-dev).
+
+## Adding your own dataset
+Coming
+
+## Troubleshooting
+Waiting
+
+## TODO
+- [ ] Code documentation
+- [ ] Adding your own dataset
+- [ ] Results with test-time augmentations in Model Zoo
+- [ ] Support gradient accumulation
+- [x] Demo tool
+
+## Citations
+Please consider citing the related paper(s) in your publications if it helps your research.
+```
+@inproceedings{yang2022deaot,
+  title={Decoupling Features in Hierarchical Propagation for Video Object Segmentation},
+  author={Yang, Zongxin and Yang, Yi},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2022}
+}
+@article{yang2021aost,
+  title={Scalable Multi-object Identification for Video Object Segmentation},
+  author={Yang, Zongxin and Miao, Jiaxu and Wang, Xiaohan and Wei, Yunchao and Yang, Yi},
+  journal={arXiv preprint arXiv:2203.11442},
+  year={2022}
+}
+@inproceedings{yang2021aot,
+  title={Associating Objects with Transformers for Video Object Segmentation},
+  author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},
+  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2021}
+}
+```
+
+## License
+This project is released under the BSD-3-Clause license. See [LICENSE](LICENSE) for additional details.

aot/configs/default.py

@@ -0,0 +1,138 @@
+import os
+import importlib
+
+
+class DefaultEngineConfig():
+    def __init__(self, exp_name='default', model='aott'):
+        model_cfg = importlib.import_module('configs.models.' +
+                                            model).ModelConfig()
+        self.__dict__.update(model_cfg.__dict__)  # add model config
+
+        self.EXP_NAME = exp_name + '_' + self.MODEL_NAME
+
+        self.STAGE_NAME = 'YTB'
+
+        self.DATASETS = ['youtubevos']
+        self.DATA_WORKERS = 8
+        self.DATA_RANDOMCROP = (465,
+                                465) if self.MODEL_ALIGN_CORNERS else (464,
+                                                                       464)
+        self.DATA_RANDOMFLIP = 0.5
+        self.DATA_MAX_CROP_STEPS = 10
+        self.DATA_SHORT_EDGE_LEN = 480
+        self.DATA_MIN_SCALE_FACTOR = 0.7
+        self.DATA_MAX_SCALE_FACTOR = 1.3
+        self.DATA_RANDOM_REVERSE_SEQ = True
+        self.DATA_SEQ_LEN = 5
+        self.DATA_DAVIS_REPEAT = 5
+        self.DATA_RANDOM_GAP_DAVIS = 12  # max frame interval between two sampled frames for DAVIS (24fps)
+        self.DATA_RANDOM_GAP_YTB = 3  # max frame interval between two sampled frames for YouTube-VOS (6fps)
+        self.DATA_DYNAMIC_MERGE_PROB = 0.3
+
+        self.PRETRAIN = True
+        self.PRETRAIN_FULL = False  # if False, load encoder only
+        self.PRETRAIN_MODEL = './data_wd/pretrain_model/mobilenet_v2.pth'
+        # self.PRETRAIN_MODEL = './pretrain_models/mobilenet_v2-b0353104.pth'
+
+        self.TRAIN_TOTAL_STEPS = 100000
+        self.TRAIN_START_STEP = 0
+        self.TRAIN_WEIGHT_DECAY = 0.07
+        self.TRAIN_WEIGHT_DECAY_EXCLUSIVE = {
+            # 'encoder.': 0.01
+        }
+        self.TRAIN_WEIGHT_DECAY_EXEMPTION = [
+            'absolute_pos_embed', 'relative_position_bias_table',
+            'relative_emb_v', 'conv_out'
+        ]
+        self.TRAIN_LR = 2e-4
+        self.TRAIN_LR_MIN = 2e-5 if 'mobilenetv2' in self.MODEL_ENCODER else 1e-5
+        self.TRAIN_LR_POWER = 0.9
+        self.TRAIN_LR_ENCODER_RATIO = 0.1
+        self.TRAIN_LR_WARM_UP_RATIO = 0.05
+        self.TRAIN_LR_COSINE_DECAY = False
+        self.TRAIN_LR_RESTART = 1
+        self.TRAIN_LR_UPDATE_STEP = 1
+        self.TRAIN_AUX_LOSS_WEIGHT = 1.0
+        self.TRAIN_AUX_LOSS_RATIO = 1.0
+        self.TRAIN_OPT = 'adamw'
+        self.TRAIN_SGD_MOMENTUM = 0.9
+        self.TRAIN_GPUS = 4
+        self.TRAIN_BATCH_SIZE = 16
+        self.TRAIN_TBLOG = False
+        self.TRAIN_TBLOG_STEP = 50
+        self.TRAIN_LOG_STEP = 20
+        self.TRAIN_IMG_LOG = True
+        self.TRAIN_TOP_K_PERCENT_PIXELS = 0.15
+        self.TRAIN_SEQ_TRAINING_FREEZE_PARAMS = ['patch_wise_id_bank']
+        self.TRAIN_SEQ_TRAINING_START_RATIO = 0.5
+        self.TRAIN_HARD_MINING_RATIO = 0.5
+        self.TRAIN_EMA_RATIO = 0.1
+        self.TRAIN_CLIP_GRAD_NORM = 5.
+        self.TRAIN_SAVE_STEP = 5000
+        self.TRAIN_MAX_KEEP_CKPT = 8
+        self.TRAIN_RESUME = False
+        self.TRAIN_RESUME_CKPT = None
+        self.TRAIN_RESUME_STEP = 0
+        self.TRAIN_AUTO_RESUME = True
+        self.TRAIN_DATASET_FULL_RESOLUTION = False
+        self.TRAIN_ENABLE_PREV_FRAME = False
+        self.TRAIN_ENCODER_FREEZE_AT = 2
+        self.TRAIN_LSTT_EMB_DROPOUT = 0.
+        self.TRAIN_LSTT_ID_DROPOUT = 0.
+        self.TRAIN_LSTT_DROPPATH = 0.1
+        self.TRAIN_LSTT_DROPPATH_SCALING = False
+        self.TRAIN_LSTT_DROPPATH_LST = False
+        self.TRAIN_LSTT_LT_DROPOUT = 0.
+        self.TRAIN_LSTT_ST_DROPOUT = 0.
+
+        self.TEST_GPU_ID = 0
+        self.TEST_GPU_NUM = 1
+        self.TEST_FRAME_LOG = False
+        self.TEST_DATASET = 'youtubevos'
+        self.TEST_DATASET_FULL_RESOLUTION = False
+        self.TEST_DATASET_SPLIT = 'val'
+        self.TEST_CKPT_PATH = None
+        # if "None", evaluate the latest checkpoint.
+        self.TEST_CKPT_STEP = None
+        self.TEST_FLIP = False
+        self.TEST_MULTISCALE = [1]
+        self.TEST_MAX_SHORT_EDGE = None
+        self.TEST_MAX_LONG_EDGE = 800 * 1.3
+        self.TEST_WORKERS = 4
+
+        # GPU distribution
+        self.DIST_ENABLE = True
+        self.DIST_BACKEND = "nccl"  # "gloo"
+        self.DIST_URL = "tcp://127.0.0.1:13241"
+        self.DIST_START_GPU = 0
+
+    def init_dir(self):
+        self.DIR_DATA = '../VOS02/datasets'#'./datasets'
+        self.DIR_DAVIS = os.path.join(self.DIR_DATA, 'DAVIS')
+        self.DIR_YTB = os.path.join(self.DIR_DATA, 'YTB')
+        self.DIR_STATIC = os.path.join(self.DIR_DATA, 'Static')
+
+        self.DIR_ROOT = './'#'./data_wd/youtube_vos_jobs'
+
+        self.DIR_RESULT = os.path.join(self.DIR_ROOT, 'result', self.EXP_NAME,
+                                       self.STAGE_NAME)
+        self.DIR_CKPT = os.path.join(self.DIR_RESULT, 'ckpt')
+        self.DIR_EMA_CKPT = os.path.join(self.DIR_RESULT, 'ema_ckpt')
+        self.DIR_LOG = os.path.join(self.DIR_RESULT, 'log')
+        self.DIR_TB_LOG = os.path.join(self.DIR_RESULT, 'log', 'tensorboard')
+        # self.DIR_IMG_LOG = os.path.join(self.DIR_RESULT, 'log', 'img')
+        # self.DIR_EVALUATION = os.path.join(self.DIR_RESULT, 'eval')
+        self.DIR_IMG_LOG = './img_logs'
+        self.DIR_EVALUATION = './results'
+
+        for path in [
+                self.DIR_RESULT, self.DIR_CKPT, self.DIR_EMA_CKPT,
+                self.DIR_LOG, self.DIR_EVALUATION, self.DIR_IMG_LOG,
+                self.DIR_TB_LOG
+        ]:
+            if not os.path.isdir(path):
+                try:
+                    os.makedirs(path)
+                except Exception as inst:
+                    print(inst)
+                    print('Failed to make dir: {}.'.format(path))

aot/configs/models/aotb.py

@@ -0,0 +1,9 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'AOTB'
+
+        self.MODEL_LSTT_NUM = 3

aot/configs/models/aotl.py

@@ -0,0 +1,13 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'AOTL'
+
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/aots.py

@@ -0,0 +1,9 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'AOTS'
+
+        self.MODEL_LSTT_NUM = 2

aot/configs/models/aott.py

@@ -0,0 +1,7 @@
+import os
+from .default import DefaultModelConfig
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'AOTT'

aot/configs/models/deaotb.py

@@ -0,0 +1,9 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'DeAOTB'
+
+        self.MODEL_LSTT_NUM = 3

aot/configs/models/deaotl.py

@@ -0,0 +1,13 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'DeAOTL'
+
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/deaots.py

@@ -0,0 +1,9 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'DeAOTS'
+
+        self.MODEL_LSTT_NUM = 2

aot/configs/models/deaott.py

@@ -0,0 +1,7 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'DeAOTT'

aot/configs/models/default.py

@@ -0,0 +1,27 @@
+class DefaultModelConfig():
+    def __init__(self):
+        self.MODEL_NAME = 'AOTDefault'
+
+        self.MODEL_VOS = 'aot'
+        self.MODEL_ENGINE = 'aotengine'
+        self.MODEL_ALIGN_CORNERS = True
+        self.MODEL_ENCODER = 'mobilenetv2'
+        self.MODEL_ENCODER_PRETRAIN = './pretrain_models/mobilenet_v2-b0353104.pth'
+        self.MODEL_ENCODER_DIM = [24, 32, 96, 1280]  # 4x, 8x, 16x, 16x
+        self.MODEL_ENCODER_EMBEDDING_DIM = 256
+        self.MODEL_DECODER_INTERMEDIATE_LSTT = True
+        self.MODEL_FREEZE_BN = True
+        self.MODEL_FREEZE_BACKBONE = False
+        self.MODEL_MAX_OBJ_NUM = 10
+        self.MODEL_SELF_HEADS = 8
+        self.MODEL_ATT_HEADS = 8
+        self.MODEL_LSTT_NUM = 1
+        self.MODEL_EPSILON = 1e-5
+        self.MODEL_USE_PREV_PROB = False
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 9999
+        self.TRAIN_AUG_TYPE = 'v1'
+
+        self.TEST_LONG_TERM_MEM_GAP = 9999
+
+        self.TEST_SHORT_TERM_MEM_SKIP = 1

aot/configs/models/default_deaot.py

@@ -0,0 +1,17 @@
+from .default import DefaultModelConfig as BaseConfig
+
+
+class DefaultModelConfig(BaseConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'DeAOTDefault'
+
+        self.MODEL_VOS = 'deaot'
+        self.MODEL_ENGINE = 'deaotengine'
+
+        self.MODEL_DECODER_INTERMEDIATE_LSTT = False
+
+        self.MODEL_SELF_HEADS = 1
+        self.MODEL_ATT_HEADS = 1
+
+        self.TRAIN_AUG_TYPE = 'v2'

aot/configs/models/r101_aotl.py

@@ -0,0 +1,16 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'R101_AOTL'
+
+        self.MODEL_ENCODER = 'resnet101'
+        self.MODEL_ENCODER_PRETRAIN = './pretrain_models/resnet101-63fe2227.pth'  # https://download.pytorch.org/models/resnet101-63fe2227.pth
+        self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024]  # 4x, 8x, 16x, 16x
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/r50_aotl.py

@@ -0,0 +1,16 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'R50_AOTL'
+
+        self.MODEL_ENCODER = 'resnet50'
+        self.MODEL_ENCODER_PRETRAIN = './pretrain_models/resnet50-0676ba61.pth'  # https://download.pytorch.org/models/resnet50-0676ba61.pth
+        self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024]  # 4x, 8x, 16x, 16x
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/r50_deaotl.py

@@ -0,0 +1,16 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'R50_DeAOTL'
+
+        self.MODEL_ENCODER = 'resnet50'
+        self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024]  # 4x, 8x, 16x, 16x
+
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/rs101_aotl.py

@@ -0,0 +1,16 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'R101_AOTL'
+
+        self.MODEL_ENCODER = 'resnest101'
+        self.MODEL_ENCODER_PRETRAIN = './pretrain_models/resnest101-22405ba7.pth'  # https://github.com/zhanghang1989/ResNeSt/releases/download/weights_step1/resnest101-22405ba7.pth
+        self.MODEL_ENCODER_DIM = [256, 512, 1024, 1024]  # 4x, 8x, 16x, 16x
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/swinb_aotl.py

@@ -0,0 +1,17 @@
+from .default import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'SwinB_AOTL'
+
+        self.MODEL_ENCODER = 'swin_base'
+        self.MODEL_ENCODER_PRETRAIN = './pretrain_models/swin_base_patch4_window7_224_22k.pth'  # https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22k.pth
+        self.MODEL_ALIGN_CORNERS = False
+        self.MODEL_ENCODER_DIM = [128, 256, 512, 512]  # 4x, 8x, 16x, 16x
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/models/swinb_deaotl.py

@@ -0,0 +1,17 @@
+from .default_deaot import DefaultModelConfig
+
+
+class ModelConfig(DefaultModelConfig):
+    def __init__(self):
+        super().__init__()
+        self.MODEL_NAME = 'SwinB_DeAOTL'
+
+        self.MODEL_ENCODER = 'swin_base'
+        self.MODEL_ALIGN_CORNERS = False
+        self.MODEL_ENCODER_DIM = [128, 256, 512, 512]  # 4x, 8x, 16x, 16x
+
+        self.MODEL_LSTT_NUM = 3
+
+        self.TRAIN_LONG_TERM_MEM_GAP = 2
+
+        self.TEST_LONG_TERM_MEM_GAP = 5

aot/configs/pre.py

@@ -0,0 +1,19 @@
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+    def __init__(self, exp_name='default', model='AOTT'):
+        super().__init__(exp_name, model)
+        self.STAGE_NAME = 'PRE'
+
+        self.init_dir()
+
+        self.DATASETS = ['static']
+
+        self.DATA_DYNAMIC_MERGE_PROB = 1.0
+
+        self.TRAIN_LR = 4e-4
+        self.TRAIN_LR_MIN = 2e-5
+        self.TRAIN_WEIGHT_DECAY = 0.03
+        self.TRAIN_SEQ_TRAINING_START_RATIO = 1.0
+        self.TRAIN_AUX_LOSS_RATIO = 0.1

aot/configs/pre_dav.py

@@ -0,0 +1,21 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+    def __init__(self, exp_name='default', model='AOTT'):
+        super().__init__(exp_name, model)
+        self.STAGE_NAME = 'PRE_DAV'
+
+        self.init_dir()
+
+        self.DATASETS = ['davis2017']
+
+        self.TRAIN_TOTAL_STEPS = 50000
+
+        pretrain_stage = 'PRE'
+        pretrain_ckpt = 'save_step_100000.pth'
+        self.PRETRAIN_FULL = True  # if False, load encoder only
+        self.PRETRAIN_MODEL = os.path.join(self.DIR_ROOT, 'result',
+                                           self.EXP_NAME, pretrain_stage,
+                                           'ema_ckpt', pretrain_ckpt)

aot/configs/pre_ytb.py

@@ -0,0 +1,17 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+    def __init__(self, exp_name='default', model='AOTT'):
+        super().__init__(exp_name, model)
+        self.STAGE_NAME = 'PRE_YTB'
+
+        self.init_dir()
+
+        pretrain_stage = 'PRE'
+        pretrain_ckpt = 'save_step_100000.pth'
+        self.PRETRAIN_FULL = True  # if False, load encoder only
+        self.PRETRAIN_MODEL = os.path.join(self.DIR_ROOT, 'result',
+                                           self.EXP_NAME, pretrain_stage,
+                                           'ema_ckpt', pretrain_ckpt)

aot/configs/pre_ytb_dav.py

@@ -0,0 +1,19 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+    def __init__(self, exp_name='default', model='AOTT'):
+        super().__init__(exp_name, model)
+        self.STAGE_NAME = 'PRE_YTB_DAV'
+
+        self.init_dir()
+
+        self.DATASETS = ['youtubevos', 'davis2017']
+
+        pretrain_stage = 'PRE'
+        pretrain_ckpt = 'save_step_100000.pth'
+        self.PRETRAIN_FULL = True  # if False, load encoder only
+        self.PRETRAIN_MODEL = os.path.join(self.DIR_ROOT, 'result',
+                                           self.EXP_NAME, pretrain_stage,
+                                           'ema_ckpt', pretrain_ckpt)

aot/configs/ytb.py

@@ -0,0 +1,10 @@
+import os
+from .default import DefaultEngineConfig
+
+
+class EngineConfig(DefaultEngineConfig):
+    def __init__(self, exp_name='default', model='AOTT'):
+        super().__init__(exp_name, model)
+        self.STAGE_NAME = 'YTB'
+
+        self.init_dir()