diff --git "a/testing.ipynb" "b/testing.ipynb" --- "a/testing.ipynb" +++ "b/testing.ipynb" @@ -2,17 +2,113 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "593f43b1-8665-42e0-bac7-c8b5e073ce6d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+http://hf.co/usm3d/tools.git\n", + " Cloning http://hf.co/usm3d/tools.git to /tmp/pip-req-build-g_xe_urh\n", + " Running command git clone --filter=blob:none --quiet http://hf.co/usm3d/tools.git /tmp/pip-req-build-g_xe_urh\n", + " warning: redirecting to https://huggingface.co/usm3d/tools.git/\n", + " Resolved http://hf.co/usm3d/tools.git to commit 0614af07256edd3fc13b8b606b58ee2c3f0804d0\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: datasets in /local/home/denysr/anaconda3/envs/pytorch3d/lib/python3.9/site-packages (from hoho==0.0.4) (2.19.1)\n", + "Requirement 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hoho\n", + " Attempting uninstall: hoho\n", + " Found existing installation: hoho 0.0.3\n", + " Uninstalling hoho-0.0.3:\n", + " Successfully uninstalled hoho-0.0.3\n", + "Successfully installed hoho-0.0.4\n" + ] + } + ], "source": [ "!pip install git+http://hf.co/usm3d/tools.git " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "c2c7762c-9a4f-44c0-b069-e0065ccacefa", "metadata": {}, "outputs": [], @@ -22,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "aa3534be-e92f-4ae7-bd21-e2d4fd49cd3a", "metadata": {}, "outputs": [], @@ -44,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "503c6bcb-aa46-46c6-8b86-566b0a470b43", "metadata": {}, "outputs": [], @@ -71,14 +167,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "1acb8e09-d954-43bb-8d1b-ee5b4ab387ce", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c04ba592fc2f489984c3ef0b84ab6066", + "model_id": "09bbcac8a1204162ac8f5e129e49ea42", "version_major": 2, "version_minor": 0 }, @@ -115,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 60, "id": "133f15dd-3378-4215-863d-e7c331e11ddf", "metadata": {}, "outputs": [], @@ -123,203 +219,116 @@ "from scipy.spatial.distance import cdist\n", "from scipy.optimize import linear_sum_assignment\n", "\n", - "def my_compute_WED(pd_vertices, pd_edges, gt_vertices, gt_edges, cv=1.0, ce=1.0, normalized=True, squared=False):\n", + "def preregister_mean_std(verts_to_transform, target_verts, single_scale=True):\n", + " mu_target = target_verts.mean(axis=0)\n", + " mu_in = verts_to_transform.mean(axis=0)\n", + " std_target = np.std(target_verts, axis=0)\n", + " std_in = np.std(verts_to_transform, axis=0)\n", + " \n", + " if np.any(std_in == 0):\n", + " std_in[std_in == 0] = 1\n", + " if np.any(std_target == 0):\n", + " std_target[std_target == 0] = 1\n", + " if np.any(np.isnan(std_in)):\n", + " std_in[np.isnan(std_in)] = 1\n", + " if np.any(np.isnan(std_target)):\n", + " std_target[np.isnan(std_target)] = 1\n", + " \n", + " if single_scale:\n", + " std_target = np.linalg.norm(std_target)\n", + " std_in = np.linalg.norm(std_in)\n", + " \n", + " transformed_verts = (verts_to_transform - mu_in) / std_in\n", + " transformed_verts = transformed_verts * std_target + mu_target\n", + " \n", + " return transformed_verts\n", + "\n", + "\n", + "def update_cv(cv, gt_vertices):\n", + " if cv < 0:\n", + " diameter = cdist(gt_vertices, gt_vertices).max()\n", + " # Cost of adding or deleting a vertex is set to -cv times the diameter of the ground truth wireframe\n", + " cv = -cv * diameter\n", + " return cv\n", + "\n", + "def my_compute_WED(pd_vertices, pd_edges, gt_vertices, gt_edges, cv_ins=-1/2, cv_del=-1/4, ce=1.0, normalized=True, preregister=True, single_scale=True):\n", + " '''The function computes the Wireframe Edge Distance (WED) between two graphs.\n", + " pd_vertices: list of predicted vertices\n", + " pd_edges: list of predicted edges\n", + " gt_vertices: list of ground truth vertices\n", + " gt_edges: list of ground truth edges\n", + " cv_ins: vertex insertion cost: if positive, the cost in centimeters of inserting vertex, if negative, multiplies diameter to compute cost (default is -1/2)\n", + " cv_del: vertex deletion cost: if positive, the cost in centimeters of deleting a vertex, if negative, multiplies diameter to compute cost (default is -1/4)\n", + " ce: edge cost (multiplier of the edge length for edge deletion and insertion, default is 1.0)\n", + " normalized: if True, the WED is normalized by the total length of the ground truth edges\n", + " preregister: if True, the predicted vertices have their mean and scale matched to the ground truth vertices\n", + " '''\n", + " \n", " pd_vertices = np.array(pd_vertices)\n", " gt_vertices = np.array(gt_vertices)\n", " pd_edges = np.array(pd_edges)\n", - " gt_edges = np.array(gt_edges)\n", + " gt_edges = np.array(gt_edges) \n", + " \n", + " \n", + " cv_del = update_cv(cv_del, gt_vertices)\n", + " cv_ins = update_cv(cv_ins, gt_vertices)\n", + " \n", + " # Step 0: Prenormalize / preregister\n", + " if preregister:\n", + " pd_vertices = preregister_mean_std(pd_vertices, gt_vertices, single_scale=single_scale)\n", " \n", - " # Step 1: Bipartite Matching\n", - " if squared:\n", - " distances = cdist(pd_vertices, gt_vertices, metric='sqeuclidean')\n", - " else:\n", - " distances = cdist(pd_vertices, gt_vertices, metric='euclidean')\n", "\n", + " # Step 1: Bipartite Matching\n", + " distances = cdist(pd_vertices, gt_vertices, metric='euclidean')\n", " row_ind, col_ind = linear_sum_assignment(distances)\n", - " # print(row_ind, col_ind)\n", + " print(row_ind, col_ind)\n", " \n", " # Step 2: Vertex Translation\n", + " translation_costs = np.sum(distances[row_ind, col_ind])\n", " \n", - " if squared:\n", - " translation_costs = cv * np.sqrt(np.sum(distances[row_ind, col_ind]))\n", - " else:\n", - " translation_costs = cv * np.sum(distances[row_ind, col_ind])\n", - " \n", - " # Additional: Vertex Deletion\n", + " # Step 3: Vertex Deletion\n", " unmatched_pd_indices = set(range(len(pd_vertices))) - set(row_ind)\n", - " deletion_costs = cv * len(unmatched_pd_indices) # Assuming a fixed cost for vertex deletion\n", + " deletion_costs = cv_del * len(unmatched_pd_indices) \n", " \n", - " # Step 3: Vertex Insertion\n", + " # Step 4: Vertex Insertion\n", " unmatched_gt_indices = set(range(len(gt_vertices))) - set(col_ind)\n", - " insertion_costs = cv * len(unmatched_gt_indices) # Assuming a fixed cost for vertex insertion\n", + " insertion_costs = cv_ins * len(unmatched_gt_indices) \n", " \n", - " # Step 4: Edge Deletion and Insertion\n", - " \n", - " # updated_pd_edges = [(row_ind[np.where(col_ind == edge[0])[0][0]], row_ind[np.where(col_ind == edge[1])[0][0]]) for edge in pd_edges if edge[0] in col_ind and edge[1] in col_ind]\n", - " updated_pd_edges = [(col_ind[np.where(row_ind == edge[0])[0][0]], col_ind[np.where(row_ind == edge[1])[0][0]]) for edge in pd_edges if edge[0] in row_ind and edge[1] in row_ind]\n", - "\n", + " # Step 5: Edge Deletion and Insertion\n", + " updated_pd_edges = [(col_ind[np.where(row_ind == edge[0])[0][0]], col_ind[np.where(row_ind == edge[1])[0][0]]) for edge in pd_edges if len(edge)==2 and edge[0] in row_ind and edge[1] in row_ind]\n", " pd_edges_set = set(map(tuple, [set(edge) for edge in updated_pd_edges]))\n", " gt_edges_set = set(map(tuple, [set(edge) for edge in gt_edges]))\n", - " # print(pd_edges_set)\n", + "\n", " \n", " # Delete edges not in ground truth\n", " edges_to_delete = pd_edges_set - gt_edges_set\n", - " # print(edges_to_delete)\n", - "\n", - " vert_tf = [np.where(col_ind == v)[0][0] if v in col_ind else 0 for v in range(len(gt_vertices))]\n", - " deletion_edge_costs = ce * sum(np.linalg.norm(pd_vertices[vert_tf[edge[0]]] - pd_vertices[vert_tf[edge[1]]]) for edge in edges_to_delete)\n", " \n", - " deletion_edge_costs = ce * sum(np.linalg.norm(pd_vertices[vert_tf[edge[0]]] - pd_vertices[vert_tf[edge[1]]]) for edge in edges_to_delete)\n", + " vert_tf = [np.where(col_ind == v)[0][0] if v in col_ind else 0 for v in range(len(gt_vertices))]\n", + " deletion_edge_costs = ce * sum(np.linalg.norm(pd_vertices[vert_tf[edge[0]]] - pd_vertices[vert_tf[edge[1]]]) for edge in edges_to_delete if len(edge) == 2)\n", "\n", + " \n", " # Insert missing edges from ground truth\n", " edges_to_insert = gt_edges_set - pd_edges_set\n", - " insertion_edge_costs = ce * sum(np.linalg.norm(gt_vertices[edge[0]] - gt_vertices[edge[1]]) for edge in edges_to_insert) \n", + " insertion_edge_costs = ce * sum(np.linalg.norm(gt_vertices[edge[0]] - gt_vertices[edge[1]]) for edge in edges_to_insert if len(edge) == 2) \n", " \n", " # Step 5: Calculation of WED\n", " WED = translation_costs + deletion_costs + insertion_costs + deletion_edge_costs + insertion_edge_costs\n", - " # print(translation_costs, deletion_costs, insertion_costs, deletion_edge_costs, insertion_edge_costs, len(gt_vertices))\n", + " print(translation_costs, deletion_costs, insertion_costs, deletion_edge_costs, insertion_edge_costs)\n", + " \n", " if normalized:\n", " total_length_of_gt_edges = np.linalg.norm((gt_vertices[gt_edges[:, 0]] - gt_vertices[gt_edges[:, 1]]), axis=1).sum()\n", " WED = WED / total_length_of_gt_edges\n", - " \n", + " \n", + " # print (\"Total length\", total_length_of_gt_edges)\n", " return WED" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "88f4fc8f-efa9-404b-9073-c7d4a73f9075", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0752621279596126 1.055505745670705\n", - "1.1062107320558674 1.0343857334182223\n", - "1.1048251985131585 1.0885542677978308\n", - "1.0957678077416388 1.0723141151199471\n", - "1.1130233980683475 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\u001b[0;32mIn[8], line 12\u001b[0m\n\u001b[1;32m 9\u001b[0m sc \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ki, entry \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(dataset):\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# print (entry.keys())\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m solver \u001b[38;5;241m=\u001b[39m \u001b[43mGeomSolver\u001b[49m\u001b[43m(\u001b[49m\u001b[43mentry\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m vertices, edges \u001b[38;5;241m=\u001b[39m solver\u001b[38;5;241m.\u001b[39mget_vertices()\n\u001b[1;32m 15\u001b[0m scores0 \u001b[38;5;241m=\u001b[39m (compute_WED(np\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m)),\n\u001b[1;32m 16\u001b[0m [(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m)],\n\u001b[1;32m 17\u001b[0m np\u001b[38;5;241m.\u001b[39marray(entry[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_vertices\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 18\u001b[0m np\u001b[38;5;241m.\u001b[39marray(entry[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwf_edges\u001b[39m\u001b[38;5;124m'\u001b[39m]),\n\u001b[1;32m 19\u001b[0m cv\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, ce\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m))\n", - "File \u001b[0;32m~/src/DRAK/geom_solver.py:67\u001b[0m, in \u001b[0;36mGeomSolver.__init__\u001b[0;34m(self, entry)\u001b[0m\n\u001b[1;32m 64\u001b[0m ndist \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(dist)\n\u001b[1;32m 65\u001b[0m ndist \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mnormalize(dist, ndist, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1.0\u001b[39m, cv2\u001b[38;5;241m.\u001b[39mNORM_MINMAX)\n\u001b[0;32m---> 67\u001b[0m in_this_image \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([cki \u001b[38;5;129;01min\u001b[39;00m p\u001b[38;5;241m.\u001b[39mimage_ids \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m points3D\u001b[38;5;241m.\u001b[39mvalues()])\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# tempind = 2103\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# print(in_this_image[tempind-1], cki, points3D[tempind].image_ids)\u001b[39;00m\n\u001b[1;32m 70\u001b[0m uv \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mround(pyt_cameras[ki]\u001b[38;5;241m.\u001b[39mtransform_points(verts)[:, :\u001b[38;5;241m2\u001b[39m])\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n", - "File \u001b[0;32m~/src/DRAK/geom_solver.py:67\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 64\u001b[0m ndist \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros_like(dist)\n\u001b[1;32m 65\u001b[0m ndist \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mnormalize(dist, ndist, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1.0\u001b[39m, cv2\u001b[38;5;241m.\u001b[39mNORM_MINMAX)\n\u001b[0;32m---> 67\u001b[0m in_this_image \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([cki \u001b[38;5;129;01min\u001b[39;00m p\u001b[38;5;241m.\u001b[39mimage_ids \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m points3D\u001b[38;5;241m.\u001b[39mvalues()])\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# tempind = 2103\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# print(in_this_image[tempind-1], cki, points3D[tempind].image_ids)\u001b[39;00m\n\u001b[1;32m 70\u001b[0m uv \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mround(pyt_cameras[ki]\u001b[38;5;241m.\u001b[39mtransform_points(verts)[:, :\u001b[38;5;241m2\u001b[39m])\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mnumpy()\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "# One shard of the dataset\n", "dataset = wds.WebDataset(hf_hub_download(repo_id='usm3d/hoho-train-set',\n", @@ -332,56 +341,89 @@ "sc = []\n", "for ki, entry in enumerate(dataset):\n", " # print (entry.keys())\n", - " solver = GeomSolver(entry)\n", - " vertices, edges = solver.get_vertices()\n", + "# solver = GeomSolver(entry)\n", + "# vertices, edges = solver.get_vertices()\n", " \n", - " scores0 = (compute_WED(np.zeros((2,3)),\n", - " [(0, 1)],\n", - " np.array(entry['wf_vertices']),\n", - " np.array(entry['wf_edges']),\n", - " cv=1, ce=1))\n", + "# scores0 = (compute_WED(np.zeros((2,3)),\n", + "# [(0, 1)],\n", + "# np.array(entry['wf_vertices']),\n", + "# np.array(entry['wf_edges']),\n", + "# cv=1, ce=1))\n", "\n", - " scores = (compute_WED(vertices,\n", - " edges,\n", - " np.array(entry['wf_vertices']),\n", - " np.array(entry['wf_edges']),\n", - " cv=1, ce=1))\n", + "# scores = (compute_WED(vertices,\n", + "# edges,\n", + "# np.array(entry['wf_vertices']),\n", + "# np.array(entry['wf_edges']),\n", + "# cv=1, ce=1))\n", " # print(scores0, scores)\n", - " sc0.append(scores0)\n", - " sc.append(scores)\n", - " print(np.mean(sc0), np.mean(sc))\n", + "# sc0.append(scores0)\n", + "# sc.append(scores)\n", + "# print(np.mean(sc0), np.mean(sc))\n", " \n", - " # if ki >= 17:\n", - " # break\n", + " if ki >= 33:\n", + " break\n", "\n", "human_entry = convert_entry_to_human_readable(entry)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "70c53366-f1fe-47d7-b815-1eb2f1431f69", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "from geom_solver import GeomSolver\n", - "from my_solution import predict\n", - "# solver = GeomSolver(entry)\n", - "# vertices, edges = solver.get_vertices(True)\n", - "key, vertices, edges = predict(entry, True)\n" + "# from geom_solver import GeomSolver\n", + "# from my_solution import predict\n", + "# # solver = GeomSolver(entry)\n", + "# # vertices, edges = solver.get_vertices(True)\n", + "# key, vertices, edges = predict(entry, True)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "7b0fa0e0-86ae-4207-8104-2b78a833265a", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-570.19119263, 862.20397949, 265.06669521],\n", + " [-577.40931511, 203.39889526, 265.06669521],\n", + " [-172.1023798 , -147.6531148 , 360.22200584],\n", + " [-585.00356674, -489.74051476, 265.06669521],\n", + " [-587.94884682, -758.55937004, 265.06669521],\n", + " [-157.08510876, -763.28005791, 365.28317928],\n", + " [-139.32743073, 857.48329163, 365.28317928],\n", + " [ 273.77865314, -768.00074577, 265.06669521],\n", + " [ 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", 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