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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Jupyternote Cheatsheet.ipynb","provenance":[],"mount_file_id":"1rMSETYdooFC6fVgT0PaOovnBrB4ZWoys","authorship_tag":"ABX9TyN4O59ZYPVT0rGiUB3bfznT"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Models"],"metadata":{"id":"ODx9TIOB4tCe"}},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BelRHeLw4qyQ","executionInfo":{"status":"ok","timestamp":1654537166220,"user_tz":-60,"elapsed":22,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"60695f20-3957-4958-aabd-c2ecff870977"},"outputs":[{"output_type":"stream","name":"stdout","text":["Writing models.py\n"]}],"source":["%%writefile models.py\n","from __future__ import division\n","\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch.autograd import Variable\n","import numpy as np\n","\n","from PIL import Image\n","\n","from utils.parse_config import *\n","from utils.utils import build_targets\n","from collections import defaultdict\n","\n","##import matplotlib.pyplot as plt\n","##import matplotlib.patches as patches\n","\n","\n","def create_modules(module_defs):\n"," \"\"\"\n"," Constructs module list of layer blocks from module configuration in module_defs\n"," \"\"\"\n"," hyperparams = module_defs.pop(0)\n"," output_filters = [int(hyperparams[\"channels\"])]\n"," module_list = nn.ModuleList()\n"," for i, module_def in enumerate(module_defs):\n"," modules = nn.Sequential()\n","\n"," if module_def[\"type\"] == \"convolutional\":\n"," bn = int(module_def[\"batch_normalize\"])\n"," filters = int(module_def[\"filters\"])\n"," kernel_size = int(module_def[\"size\"])\n"," pad = (kernel_size - 1) // 2 if int(module_def[\"pad\"]) else 0\n"," modules.add_module(\n"," \"conv_%d\" % i,\n"," nn.Conv2d(\n"," in_channels=output_filters[-1],\n"," out_channels=filters,\n"," kernel_size=kernel_size,\n"," stride=int(module_def[\"stride\"]),\n"," padding=pad,\n"," bias=not bn,\n"," ),\n"," )\n"," if bn:\n"," modules.add_module(\"batch_norm_%d\" % i, nn.BatchNorm2d(filters))\n"," if module_def[\"activation\"] == \"leaky\":\n"," modules.add_module(\"leaky_%d\" % i, nn.LeakyReLU(0.1))\n","\n"," elif module_def[\"type\"] == \"maxpool\":\n"," kernel_size = int(module_def[\"size\"])\n"," stride = int(module_def[\"stride\"])\n"," if kernel_size == 2 and stride == 1:\n"," padding = nn.ZeroPad2d((0, 1, 0, 1))\n"," modules.add_module(\"_debug_padding_%d\" % i, padding)\n"," maxpool = nn.MaxPool2d(\n"," kernel_size=int(module_def[\"size\"]),\n"," stride=int(module_def[\"stride\"]),\n"," padding=int((kernel_size - 1) // 2),\n"," )\n"," modules.add_module(\"maxpool_%d\" % i, maxpool)\n","\n"," elif module_def[\"type\"] == \"upsample\":\n"," upsample = nn.Upsample(scale_factor=int(module_def[\"stride\"]), mode=\"nearest\")\n"," modules.add_module(\"upsample_%d\" % i, upsample)\n","\n"," elif module_def[\"type\"] == \"route\":\n"," layers = [int(x) for x in module_def[\"layers\"].split(\",\")]\n"," filters = sum([output_filters[layer_i] for layer_i in layers])\n"," modules.add_module(\"route_%d\" % i, EmptyLayer())\n","\n"," elif module_def[\"type\"] == \"shortcut\":\n"," filters = output_filters[int(module_def[\"from\"])]\n"," modules.add_module(\"shortcut_%d\" % i, EmptyLayer())\n","\n"," elif module_def[\"type\"] == \"yolo\":\n"," anchor_idxs = [int(x) for x in module_def[\"mask\"].split(\",\")]\n"," # Extract anchors\n"," anchors = [int(x) for x in module_def[\"anchors\"].split(\",\")]\n"," anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]\n"," anchors = [anchors[i] for i in anchor_idxs]\n"," num_classes = int(module_def[\"classes\"])\n"," img_height = int(hyperparams[\"height\"])\n"," # Define detection layer\n"," yolo_layer = YOLOLayer(anchors, num_classes, img_height)\n"," modules.add_module(\"yolo_%d\" % i, yolo_layer)\n"," # Register module list and number of output filters\n"," module_list.append(modules)\n"," output_filters.append(filters)\n","\n"," return hyperparams, module_list\n","\n","\n","class EmptyLayer(nn.Module):\n"," \"\"\"Placeholder for 'route' and 'shortcut' layers\"\"\"\n","\n"," def __init__(self):\n"," super(EmptyLayer, self).__init__()\n","\n","\n","class YOLOLayer(nn.Module):\n"," \"\"\"Detection layer\"\"\"\n","\n"," def __init__(self, anchors, num_classes, img_dim):\n"," super(YOLOLayer, self).__init__()\n"," self.anchors = anchors\n"," self.num_anchors = len(anchors)\n"," self.num_classes = num_classes\n"," self.bbox_attrs = 5 + num_classes\n"," self.image_dim = img_dim\n"," self.ignore_thres = 0.5\n"," self.lambda_coord = 1\n","\n"," self.mse_loss = nn.MSELoss(size_average=True) # Coordinate loss\n"," self.bce_loss = nn.BCELoss(size_average=True) # Confidence loss\n"," self.ce_loss = nn.CrossEntropyLoss() # Class loss\n","\n"," def forward(self, x, targets=None):\n"," nA = self.num_anchors\n"," nB = x.size(0)\n"," nG = x.size(2)\n"," stride = self.image_dim / nG\n","\n"," # Tensors for cuda support\n"," FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor\n"," LongTensor = torch.cuda.LongTensor if x.is_cuda else torch.LongTensor\n"," ByteTensor = torch.cuda.ByteTensor if x.is_cuda else torch.ByteTensor\n","\n"," prediction = x.view(nB, nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous()\n","\n"," # Get outputs\n"," x = torch.sigmoid(prediction[..., 0]) # Center x\n"," y = torch.sigmoid(prediction[..., 1]) # Center y\n"," w = prediction[..., 2] # Width\n"," h = prediction[..., 3] # Height\n"," pred_conf = torch.sigmoid(prediction[..., 4]) # Conf\n"," pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred.\n","\n"," # Calculate offsets for each grid\n"," grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).type(FloatTensor)\n"," grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).type(FloatTensor)\n"," scaled_anchors = FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in self.anchors])\n"," anchor_w = scaled_anchors[:, 0:1].view((1, nA, 1, 1))\n"," anchor_h = scaled_anchors[:, 1:2].view((1, nA, 1, 1))\n","\n"," # Add offset and scale with anchors\n"," pred_boxes = FloatTensor(prediction[..., :4].shape)\n"," pred_boxes[..., 0] = x.data + grid_x\n"," pred_boxes[..., 1] = y.data + grid_y\n"," pred_boxes[..., 2] = torch.exp(w.data) * anchor_w\n"," pred_boxes[..., 3] = torch.exp(h.data) * anchor_h\n","\n"," # Training\n"," if targets is not None:\n","\n"," if x.is_cuda:\n"," self.mse_loss = self.mse_loss.cuda()\n"," self.bce_loss = self.bce_loss.cuda()\n"," self.ce_loss = self.ce_loss.cuda()\n","\n"," nGT, nCorrect, mask, conf_mask, tx, ty, tw, th, tconf, tcls = build_targets(\n"," pred_boxes=pred_boxes.cpu().data,\n"," pred_conf=pred_conf.cpu().data,\n"," pred_cls=pred_cls.cpu().data,\n"," target=targets.cpu().data,\n"," anchors=scaled_anchors.cpu().data,\n"," num_anchors=nA,\n"," num_classes=self.num_classes,\n"," grid_size=nG,\n"," ignore_thres=self.ignore_thres,\n"," img_dim=self.image_dim,\n"," )\n","\n"," nProposals = int((pred_conf > 0.5).sum().item())\n"," recall = float(nCorrect / nGT) if nGT else 1\n"," precision = float(nCorrect / nProposals)\n","\n"," # Handle masks\n"," mask = Variable(mask.type(ByteTensor))\n"," conf_mask = Variable(conf_mask.type(ByteTensor))\n","\n"," # Handle target variables\n"," tx = Variable(tx.type(FloatTensor), requires_grad=False)\n"," ty = Variable(ty.type(FloatTensor), requires_grad=False)\n"," tw = Variable(tw.type(FloatTensor), requires_grad=False)\n"," th = Variable(th.type(FloatTensor), requires_grad=False)\n"," tconf = Variable(tconf.type(FloatTensor), requires_grad=False)\n"," tcls = Variable(tcls.type(LongTensor), requires_grad=False)\n","\n"," # Get conf mask where gt and where there is no gt\n"," conf_mask_true = mask\n"," conf_mask_false = conf_mask - mask\n","\n"," # Mask outputs to ignore non-existing objects\n"," loss_x = self.mse_loss(x[mask], tx[mask])\n"," loss_y = self.mse_loss(y[mask], ty[mask])\n"," loss_w = self.mse_loss(w[mask], tw[mask])\n"," loss_h = self.mse_loss(h[mask], th[mask])\n"," loss_conf = self.bce_loss(pred_conf[conf_mask_false], tconf[conf_mask_false]) + self.bce_loss(\n"," pred_conf[conf_mask_true], tconf[conf_mask_true]\n"," )\n"," loss_cls = (1 / nB) * self.ce_loss(pred_cls[mask], torch.argmax(tcls[mask], 1))\n"," loss = loss_x + loss_y + loss_w + loss_h + loss_conf + loss_cls\n","\n"," return (\n"," loss,\n"," loss_x.item(),\n"," loss_y.item(),\n"," loss_w.item(),\n"," loss_h.item(),\n"," loss_conf.item(),\n"," loss_cls.item(),\n"," recall,\n"," precision,\n"," )\n","\n"," else:\n"," # If not in training phase return predictions\n"," output = torch.cat(\n"," (\n"," pred_boxes.view(nB, -1, 4) * stride,\n"," pred_conf.view(nB, -1, 1),\n"," pred_cls.view(nB, -1, self.num_classes),\n"," ),\n"," -1,\n"," )\n"," return output\n","\n","\n","class Darknet(nn.Module):\n"," \"\"\"YOLOv3 object detection model\"\"\"\n","\n"," def __init__(self, config_path, img_size=416):\n"," super(Darknet, self).__init__()\n"," self.module_defs = parse_model_config(config_path)\n"," self.hyperparams, self.module_list = create_modules(self.module_defs)\n"," self.img_size = img_size\n"," self.seen = 0\n"," self.header_info = np.array([0, 0, 0, self.seen, 0])\n"," self.loss_names = [\"x\", \"y\", \"w\", \"h\", \"conf\", \"cls\", \"recall\", \"precision\"]\n","\n"," def forward(self, x, targets=None):\n"," is_training = targets is not None\n"," output = []\n"," self.losses = defaultdict(float)\n"," layer_outputs = []\n"," for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):\n"," if module_def[\"type\"] in [\"convolutional\", \"upsample\", \"maxpool\"]:\n"," x = module(x)\n"," elif module_def[\"type\"] == \"route\":\n"," layer_i = [int(x) for x in module_def[\"layers\"].split(\",\")]\n"," x = torch.cat([layer_outputs[i] for i in layer_i], 1)\n"," elif module_def[\"type\"] == \"shortcut\":\n"," layer_i = int(module_def[\"from\"])\n"," x = layer_outputs[-1] + layer_outputs[layer_i]\n"," elif module_def[\"type\"] == \"yolo\":\n"," # Train phase: get loss\n"," if is_training:\n"," x, *losses = module[0](x, targets)\n"," for name, loss in zip(self.loss_names, losses):\n"," self.losses[name] += loss\n"," # Test phase: Get detections\n"," else:\n"," x = module(x)\n"," output.append(x)\n"," layer_outputs.append(x)\n","\n"," self.losses[\"recall\"] /= 3\n"," self.losses[\"precision\"] /= 3\n"," return sum(output) if is_training else torch.cat(output, 1)\n","\n"," def load_weights(self, weights_path):\n"," \"\"\"Parses and loads the weights stored in 'weights_path'\"\"\"\n","\n"," # Open the weights file\n"," fp = open(weights_path, \"rb\")\n"," header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values\n","\n"," # Needed to write header when saving weights\n"," self.header_info = header\n","\n"," self.seen = header[3]\n"," weights = np.fromfile(fp, dtype=np.float32) # The rest are weights\n"," fp.close()\n","\n"," ptr = 0\n"," for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)):\n"," if module_def[\"type\"] == \"convolutional\":\n"," conv_layer = module[0]\n"," if module_def[\"batch_normalize\"]:\n"," # Load BN bias, weights, running mean and running variance\n"," bn_layer = module[1]\n"," num_b = bn_layer.bias.numel() # Number of biases\n"," # Bias\n"," bn_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.bias)\n"," bn_layer.bias.data.copy_(bn_b)\n"," ptr += num_b\n"," # Weight\n"," bn_w = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.weight)\n"," bn_layer.weight.data.copy_(bn_w)\n"," ptr += num_b\n"," # Running Mean\n"," bn_rm = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_mean)\n"," bn_layer.running_mean.data.copy_(bn_rm)\n"," ptr += num_b\n"," # Running Var\n"," bn_rv = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(bn_layer.running_var)\n"," bn_layer.running_var.data.copy_(bn_rv)\n"," ptr += num_b\n"," else:\n"," # Load conv. bias\n"," num_b = conv_layer.bias.numel()\n"," conv_b = torch.from_numpy(weights[ptr : ptr + num_b]).view_as(conv_layer.bias)\n"," conv_layer.bias.data.copy_(conv_b)\n"," ptr += num_b\n"," # Load conv. weights\n"," num_w = conv_layer.weight.numel()\n"," conv_w = torch.from_numpy(weights[ptr : ptr + num_w]).view_as(conv_layer.weight)\n"," conv_layer.weight.data.copy_(conv_w)\n"," ptr += num_w\n","\n"," \"\"\"\n"," @:param path - path of the new weights file\n"," @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved)\n"," \"\"\"\n","\n"," def save_weights(self, path, cutoff=-1):\n","\n"," fp = open(path, \"wb\")\n"," self.header_info[3] = self.seen\n"," self.header_info.tofile(fp)\n","\n"," # Iterate through layers\n"," for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])):\n"," if module_def[\"type\"] == \"convolutional\":\n"," conv_layer = module[0]\n"," # If batch norm, load bn first\n"," if module_def[\"batch_normalize\"]:\n"," bn_layer = module[1]\n"," bn_layer.bias.data.cpu().numpy().tofile(fp)\n"," bn_layer.weight.data.cpu().numpy().tofile(fp)\n"," bn_layer.running_mean.data.cpu().numpy().tofile(fp)\n"," bn_layer.running_var.data.cpu().numpy().tofile(fp)\n"," # Load conv bias\n"," else:\n"," conv_layer.bias.data.cpu().numpy().tofile(fp)\n"," # Load conv weights\n"," conv_layer.weight.data.cpu().numpy().tofile(fp)\n","\n"," fp.close()"]},{"cell_type":"code","source":["!ls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ar8FuY3z43Fk","executionInfo":{"status":"ok","timestamp":1654537174809,"user_tz":-60,"elapsed":16,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"ce227d02-75a3-477d-becf-e1c2702c7001"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["models.py sample_data\n"]}]},{"cell_type":"code","source":["!pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hRxa6vyoGbla","executionInfo":{"status":"ok","timestamp":1654537258168,"user_tz":-60,"elapsed":26,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"ccaaf1dc-6769-4093-8769-c8aa3b809bdf"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n"]}]},{"cell_type":"code","source":["%%writefile Readme.md\n","Are you for real!!"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cVKDwgGtGv7g","executionInfo":{"status":"ok","timestamp":1654537404197,"user_tz":-60,"elapsed":21,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"41cdc392-059d-42be-b267-2a7f66d0a1f6"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Overwriting Readme.md\n"]}]},{"cell_type":"code","source":["%cd Computer Vision"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"780vJiykHTmT","executionInfo":{"status":"ok","timestamp":1654537643123,"user_tz":-60,"elapsed":16,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"159eb128-2a7a-41b3-b84c-7d517ff92454"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Python/Machine Learning/Computer Vision\n"]}]},{"cell_type":"code","source":["!pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WeA417NzHe0W","executionInfo":{"status":"ok","timestamp":1654537646111,"user_tz":-60,"elapsed":408,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"036a3c8e-b106-46a8-b5de-b7adf66938ab"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/Python/Machine Learning/Computer Vision\n"]}]},{"cell_type":"code","source":["%%writefile test.and\n","\n","Really I can now write to my drive!"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hrSVQd-fHzai","executionInfo":{"status":"ok","timestamp":1654537570112,"user_tz":-60,"elapsed":24,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"c58a5849-aaba-4fe3-c596-681a5e7df731"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["Writing test.and\n"]}]},{"cell_type":"code","source":["!ls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jRtg6b1IH8KV","executionInfo":{"status":"ok","timestamp":1654537654214,"user_tz":-60,"elapsed":24,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"dd49447d-6924-4176-f5a9-ca184b671be8"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["cnn-resnet-CIFAR10 darknet-COCO-object_detection feedforward-cnn-MNIST\n"]}]},{"cell_type":"code","source":["%%bash\n","\n","ls -la\n","python --version"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iUpVW1oZIQnl","executionInfo":{"status":"ok","timestamp":1654537857269,"user_tz":-60,"elapsed":14,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"ff54c93a-9f2c-4453-d82f-c6c1683f61b8"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["total 12\n","drwx------ 2 root root 4096 May 17 21:02 cnn-resnet-CIFAR10\n","drwx------ 2 root root 4096 Jun 6 16:38 darknet-COCO-object_detection\n","drwx------ 2 root root 4096 May 17 21:01 feedforward-cnn-MNIST\n","Python 3.7.13\n"]}]},{"cell_type":"code","source":["%cd ../"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NJ7riTtCI2-V","executionInfo":{"status":"ok","timestamp":1654537984381,"user_tz":-60,"elapsed":14,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"713f2de8-ae10-46b9-d5e9-bbfa779de2c8"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n"]}]},{"cell_type":"code","source":["%%bash\n","\n","cd \"drive/MyDrive/Python/Machine Learning\"\n","ls"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZAOqxQzPJc1k","executionInfo":{"status":"ok","timestamp":1654538084191,"user_tz":-60,"elapsed":14,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"7b82c13f-3e14-47b5-bc12-25bdf0dee540"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Articles\n","Computer Vision\n","Datasets\n","Deep-Learning-with-PyTorch-Jovian\n","Deep RL\n","FastAI Course\n","Generative Models\n","HuggingFace-Deep-RL\n","PyTorch\n","ZeroToGANS_Revision\n"]}]},{"cell_type":"code","source":["%run models.py"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"id":"HvI6SRX8JsS7","executionInfo":{"status":"ok","timestamp":1654538109961,"user_tz":-60,"elapsed":2355,"user":{"displayName":"Adejumo Daniel","userId":"02925977078148845759"}},"outputId":"08a28f6a-76c2-4eaa-fa36-36d5a8e145ea"},"execution_count":27,"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/content/models.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mPIL\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_config\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbuild_targets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 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