{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# **imports**","metadata":{"execution":{"iopub.status.busy":"2022-10-17T14:59:47.499596Z","iopub.execute_input":"2022-10-17T14:59:47.500068Z","iopub.status.idle":"2022-10-17T14:59:47.596821Z","shell.execute_reply.started":"2022-10-17T14:59:47.500027Z","shell.execute_reply":"2022-10-17T14:59:47.594222Z"}}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nimport cv2\nimport sys\nimport random\nfrom matplotlib.pyplot import figure\nfrom sklearn.model_selection import train_test_split\n\nimport tensorflow as tf\nfrom tensorflow import keras\n\nseed = 2019\nrandom.seed = seed\nnp.random.seed = seed\ntf.seed = seed","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:56:57.580756Z","iopub.execute_input":"2023-01-24T17:56:57.581326Z","iopub.status.idle":"2023-01-24T17:57:02.795505Z","shell.execute_reply.started":"2023-01-24T17:56:57.581202Z","shell.execute_reply":"2023-01-24T17:57:02.794251Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# **Define**","metadata":{}},{"cell_type":"markdown","source":"### define decoder functions","metadata":{}},{"cell_type":"code","source":"def rle_decode(mask_rle, shape=(520,704)): # (height,width) \n '''\n mask_rle: run-length as string formated (start length)\n shape: (height,width) of array to return \n Returns numpy array, 1 - mask, 0 - background\n '''\n s = mask_rle.split()\n starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]\n starts -= 1\n ends = starts + lengths\n img = np.zeros(shape[0]*shape[1], dtype=np.uint8)\n \n for lo, hi in zip(starts, ends):\n img[lo:hi] = 1\n return img.reshape(shape) ","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:05.02884Z","iopub.execute_input":"2023-01-24T17:16:05.029239Z","iopub.status.idle":"2023-01-24T17:16:05.038137Z","shell.execute_reply.started":"2023-01-24T17:16:05.029192Z","shell.execute_reply":"2023-01-24T17:16:05.036962Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"markdown","source":"## read the trainig input data","metadata":{}},{"cell_type":"code","source":"masks = pd.read_csv(r'../input/sartorius-cell-instance-segmentation/train.csv')\nmasks","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:05.4801Z","iopub.execute_input":"2023-01-24T17:16:05.480816Z","iopub.status.idle":"2023-01-24T17:16:05.755246Z","shell.execute_reply.started":"2023-01-24T17:16:05.480779Z","shell.execute_reply":"2023-01-24T17:16:05.754184Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":" id annotation width \\\n0 0030fd0e6378 118145 6 118849 7 119553 8 120257 8 120961 9 1... 704 \n1 0030fd0e6378 189036 1 189739 3 190441 6 191144 7 191848 8 1... 704 \n2 0030fd0e6378 173567 3 174270 5 174974 5 175678 6 176382 7 1... 704 \n3 0030fd0e6378 196723 4 197427 6 198130 7 198834 8 199538 8 2... 704 \n4 0030fd0e6378 167818 3 168522 5 169225 7 169928 8 170632 9 1... 704 \n... ... ... ... \n73580 ffdb3cc02eef 3610 3 4311 7 5014 9 5717 11 6420 13 7123 15 7... 704 \n73581 ffdb3cc02eef 341585 2 342287 5 342988 10 343690 13 344394 1... 704 \n73582 ffdb3cc02eef 47788 3 48490 7 49192 11 49896 13 50599 14 513... 704 \n73583 ffdb3cc02eef 333290 1 333993 2 334696 4 335399 5 336102 6 3... 704 \n73584 ffdb3cc02eef 249775 2 250477 6 251180 8 251882 11 252585 12... 704 \n\n height cell_type plate_time sample_date \\\n0 520 shsy5y 11h30m00s 2019-06-16 \n1 520 shsy5y 11h30m00s 2019-06-16 \n2 520 shsy5y 11h30m00s 2019-06-16 \n3 520 shsy5y 11h30m00s 2019-06-16 \n4 520 shsy5y 11h30m00s 2019-06-16 \n... ... ... ... ... \n73580 520 cort 11h59m00s 2020-11-01 \n73581 520 cort 11h59m00s 2020-11-01 \n73582 520 cort 11h59m00s 2020-11-01 \n73583 520 cort 11h59m00s 2020-11-01 \n73584 520 cort 11h59m00s 2020-11-01 \n\n sample_id elapsed_timedelta \n0 shsy5y[diff]_E10-4_Vessel-714_Ph_3 0 days 11:30:00 \n1 shsy5y[diff]_E10-4_Vessel-714_Ph_3 0 days 11:30:00 \n2 shsy5y[diff]_E10-4_Vessel-714_Ph_3 0 days 11:30:00 \n3 shsy5y[diff]_E10-4_Vessel-714_Ph_3 0 days 11:30:00 \n4 shsy5y[diff]_E10-4_Vessel-714_Ph_3 0 days 11:30:00 \n... ... ... \n73580 cort[debris]_D9-3_Vessel-384_Ph_4 0 days 11:59:00 \n73581 cort[debris]_D9-3_Vessel-384_Ph_4 0 days 11:59:00 \n73582 cort[debris]_D9-3_Vessel-384_Ph_4 0 days 11:59:00 \n73583 cort[debris]_D9-3_Vessel-384_Ph_4 0 days 11:59:00 \n73584 cort[debris]_D9-3_Vessel-384_Ph_4 0 days 11:59:00 \n\n[73585 rows x 9 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idannotationwidthheightcell_typeplate_timesample_datesample_idelapsed_timedelta
00030fd0e6378118145 6 118849 7 119553 8 120257 8 120961 9 1...704520shsy5y11h30m00s2019-06-16shsy5y[diff]_E10-4_Vessel-714_Ph_30 days 11:30:00
10030fd0e6378189036 1 189739 3 190441 6 191144 7 191848 8 1...704520shsy5y11h30m00s2019-06-16shsy5y[diff]_E10-4_Vessel-714_Ph_30 days 11:30:00
20030fd0e6378173567 3 174270 5 174974 5 175678 6 176382 7 1...704520shsy5y11h30m00s2019-06-16shsy5y[diff]_E10-4_Vessel-714_Ph_30 days 11:30:00
30030fd0e6378196723 4 197427 6 198130 7 198834 8 199538 8 2...704520shsy5y11h30m00s2019-06-16shsy5y[diff]_E10-4_Vessel-714_Ph_30 days 11:30:00
40030fd0e6378167818 3 168522 5 169225 7 169928 8 170632 9 1...704520shsy5y11h30m00s2019-06-16shsy5y[diff]_E10-4_Vessel-714_Ph_30 days 11:30:00
..............................
73580ffdb3cc02eef3610 3 4311 7 5014 9 5717 11 6420 13 7123 15 7...704520cort11h59m00s2020-11-01cort[debris]_D9-3_Vessel-384_Ph_40 days 11:59:00
73581ffdb3cc02eef341585 2 342287 5 342988 10 343690 13 344394 1...704520cort11h59m00s2020-11-01cort[debris]_D9-3_Vessel-384_Ph_40 days 11:59:00
73582ffdb3cc02eef47788 3 48490 7 49192 11 49896 13 50599 14 513...704520cort11h59m00s2020-11-01cort[debris]_D9-3_Vessel-384_Ph_40 days 11:59:00
73583ffdb3cc02eef333290 1 333993 2 334696 4 335399 5 336102 6 3...704520cort11h59m00s2020-11-01cort[debris]_D9-3_Vessel-384_Ph_40 days 11:59:00
73584ffdb3cc02eef249775 2 250477 6 251180 8 251882 11 252585 12...704520cort11h59m00s2020-11-01cort[debris]_D9-3_Vessel-384_Ph_40 days 11:59:00
\n

73585 rows × 9 columns

\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"#### all types of the \"cell\"s we have and size of the \"id\"s","metadata":{}},{"cell_type":"code","source":"unique_masks = masks.id.unique()\nprint(\"the masks id's size is = \\n\" + str(len(unique_masks)))\ntest = masks.cell_type.unique()\nprint(\"the cell types are =\")\nprint(test)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:06.429028Z","iopub.execute_input":"2023-01-24T17:16:06.429413Z","iopub.status.idle":"2023-01-24T17:16:06.446489Z","shell.execute_reply.started":"2023-01-24T17:16:06.429379Z","shell.execute_reply":"2023-01-24T17:16:06.444898Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"the masks id's size is = \n606\nthe cell types are =\n['shsy5y' 'astro' 'cort']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### we need size 512 for using UNet","metadata":{}},{"cell_type":"code","source":"image_size = 512","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:07.113061Z","iopub.execute_input":"2023-01-24T17:16:07.113445Z","iopub.status.idle":"2023-01-24T17:16:07.118487Z","shell.execute_reply.started":"2023-01-24T17:16:07.113411Z","shell.execute_reply":"2023-01-24T17:16:07.117254Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"markdown","source":"### put every id's image in one list","metadata":{}},{"cell_type":"code","source":"# create a list variable for All image\nimagess = []\nfor i in range (len(unique_masks)):\n # read images\n ImageId = unique_masks[i]\n img = cv2.imread(r'../input/sartorius-cell-instance-segmentation/train/' + ImageId +'.png')\n # the size we want for UNet\n dim = (image_size, image_size) \n # resize image\n resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n imagess.append(resized)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:07.54852Z","iopub.execute_input":"2023-01-24T17:16:07.54889Z","iopub.status.idle":"2023-01-24T17:16:14.273113Z","shell.execute_reply.started":"2023-01-24T17:16:07.548858Z","shell.execute_reply":"2023-01-24T17:16:14.271737Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"print(np.mean(imagess[0]))","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:14.275306Z","iopub.execute_input":"2023-01-24T17:16:14.275711Z","iopub.status.idle":"2023-01-24T17:16:14.281954Z","shell.execute_reply.started":"2023-01-24T17:16:14.275675Z","shell.execute_reply":"2023-01-24T17:16:14.280921Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"127.9961166381836\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"the lentgh size of the id's images (imagess) is : \\n\" + str(len(imagess)))\nprint(\"the shape of every array in imagess : \\n\" + str(imagess[0].shape))","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:14.283437Z","iopub.execute_input":"2023-01-24T17:16:14.284213Z","iopub.status.idle":"2023-01-24T17:16:14.291246Z","shell.execute_reply.started":"2023-01-24T17:16:14.284175Z","shell.execute_reply":"2023-01-24T17:16:14.290265Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"the lentgh size of the id's images (imagess) is : \n606\nthe shape of every array in imagess : \n(512, 512, 3)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### turn our id's images(imagess) to numpy arrays","metadata":{}},{"cell_type":"code","source":"imagess = np.array(imagess)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:14.293819Z","iopub.execute_input":"2023-01-24T17:16:14.294842Z","iopub.status.idle":"2023-01-24T17:16:14.399108Z","shell.execute_reply.started":"2023-01-24T17:16:14.29479Z","shell.execute_reply":"2023-01-24T17:16:14.398053Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":"### plot every image we want from 0 to 606 to see how it works","metadata":{}},{"cell_type":"code","source":"fig, axarr = plt.subplots(figsize=(5,5))\naxarr.axis('off')\naxarr.imshow(imagess[0])\nplt.tight_layout(h_pad=0.1, w_pad=0.1)\nplt.show()","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2023-01-24T17:16:14.40048Z","iopub.execute_input":"2023-01-24T17:16:14.401653Z","iopub.status.idle":"2023-01-24T17:16:14.605061Z","shell.execute_reply.started":"2023-01-24T17:16:14.401614Z","shell.execute_reply":"2023-01-24T17:16:14.604194Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### read all 'annotation' in data and put them togheter","metadata":{}},{"cell_type":"code","source":"# create a list variable for All image mask ID\nAll_img_masks = []\nfor i in range (len(unique_masks)):\n # read all annotation image\n img_masks = masks[masks['id']==unique_masks[i]] ['annotation'].tolist()\n # put all annotation image together\n All_img_masks.append(img_masks)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:14.607131Z","iopub.execute_input":"2023-01-24T17:16:14.607949Z","iopub.status.idle":"2023-01-24T17:16:17.621255Z","shell.execute_reply.started":"2023-01-24T17:16:14.607908Z","shell.execute_reply":"2023-01-24T17:16:17.620274Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"print(\"the All_img_masks size is = \\n\" + str(len(All_img_masks)))","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:17.623127Z","iopub.execute_input":"2023-01-24T17:16:17.623721Z","iopub.status.idle":"2023-01-24T17:16:17.630287Z","shell.execute_reply.started":"2023-01-24T17:16:17.623684Z","shell.execute_reply":"2023-01-24T17:16:17.629246Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"the All_img_masks size is = \n606\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## define training decoder to make mask","metadata":{}},{"cell_type":"code","source":"def training_decoder(n):\n all_masks = np.zeros((520,704)) #(height,width) \n for mask in All_img_masks[n]:\n all_masks += rle_decode(mask)\n \n dim = (image_size, image_size) \n # resize image\n all_masks = cv2.resize(all_masks, dim, interpolation = cv2.INTER_AREA) \n \n return(all_masks)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:17.631859Z","iopub.execute_input":"2023-01-24T17:16:17.63306Z","iopub.status.idle":"2023-01-24T17:16:17.639984Z","shell.execute_reply.started":"2023-01-24T17:16:17.633031Z","shell.execute_reply":"2023-01-24T17:16:17.639114Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":"### make mask","metadata":{}},{"cell_type":"code","source":"# create a list variable for All image mask\nmask_imagess = []\nfor i in range (len(unique_masks)):\n num = training_decoder(i)\n mask_imagess.append(num)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:17.641511Z","iopub.execute_input":"2023-01-24T17:16:17.641875Z","iopub.status.idle":"2023-01-24T17:16:50.516235Z","shell.execute_reply.started":"2023-01-24T17:16:17.64184Z","shell.execute_reply":"2023-01-24T17:16:50.51502Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"markdown","source":"### plot every mask image we want from 0 to 606 to see how it works","metadata":{}},{"cell_type":"code","source":"fig, axarr = plt.subplots(figsize=(5,5))\naxarr.axis('off')\naxarr.imshow(mask_imagess[0])\nplt.tight_layout(h_pad=0.1, w_pad=0.1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:50.522619Z","iopub.execute_input":"2023-01-24T17:16:50.52482Z","iopub.status.idle":"2023-01-24T17:16:50.750243Z","shell.execute_reply.started":"2023-01-24T17:16:50.524779Z","shell.execute_reply":"2023-01-24T17:16:50.749238Z"},"trusted":true},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### turn our mask's images(mask_imagess) to numpy arrays","metadata":{}},{"cell_type":"code","source":"mask_imagess = np.array(mask_imagess) ","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:16:50.751687Z","iopub.execute_input":"2023-01-24T17:16:50.752243Z","iopub.status.idle":"2023-01-24T17:16:51.180844Z","shell.execute_reply.started":"2023-01-24T17:16:50.752192Z","shell.execute_reply":"2023-01-24T17:16:51.179462Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"np.sum(mask_imagess[23]>=1)/(512*512)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:42:46.482183Z","iopub.execute_input":"2023-01-24T17:42:46.482617Z","iopub.status.idle":"2023-01-24T17:42:46.490978Z","shell.execute_reply.started":"2023-01-24T17:42:46.482586Z","shell.execute_reply":"2023-01-24T17:42:46.489833Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"0.02886962890625"},"metadata":{}}]},{"cell_type":"code","source":"#///////// Normalize Masks\n\nnum = mask_imagess.shape[0]\ntemp_mask = np.zeros([606,512,512])\nfor i in range(num):\n temp_mask[i][:][:] = (mask_imagess[i][:][:]/np.max(mask_imagess[i]))\n \nmask_imagess = temp_mask\ntemp_mask = []","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:49:35.517188Z","iopub.execute_input":"2023-01-24T17:49:35.517943Z","iopub.status.idle":"2023-01-24T17:49:36.640451Z","shell.execute_reply.started":"2023-01-24T17:49:35.517907Z","shell.execute_reply":"2023-01-24T17:49:36.639426Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"markdown","source":"# UNet","metadata":{}},{"cell_type":"markdown","source":"### define UNet layers","metadata":{}},{"cell_type":"code","source":"def down_block(X, filters, kernel_size=(3, 3), padding = \"same\", strides = 1):\n c = keras.layers.Conv2D(filters, kernel_size, padding = padding, strides = strides, activation = \"relu\")(X) \n c = keras.layers.Conv2D(filters, kernel_size, padding = padding, strides = strides, activation = \"relu\")(c)\n p = keras.layers.MaxPool2D((2,2),(2,2))(c)\n return c, p\n\ndef up_block(X, skip, filters, kernel_size=(3, 3), padding = \"same\", strides = 1):\n us = keras.layers.UpSampling2D((2, 2))(X)\n concat = keras.layers.Concatenate()([us, skip])\n c = keras.layers.Conv2D(filters,kernel_size, padding = padding, strides = strides, activation = \"relu\")(concat) \n c = keras.layers.Conv2D(filters,kernel_size, padding = padding, strides = strides, activation = \"relu\")(c)\n p = keras.layers.MaxPool2D((2,2),(2,2))(c)\n return c\n\ndef bottleneck(X, filters,kernel_size=(3,3), padding = \"same\", strides = 1):\n c = keras.layers.Conv2D(filters,kernel_size, padding = padding, strides = strides, activation = \"relu\")(X) \n c = keras.layers.Conv2D(filters,kernel_size, padding = padding, strides = strides, activation = \"relu\")(c)\n return c\n","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:50:01.688992Z","iopub.execute_input":"2023-01-24T17:50:01.68942Z","iopub.status.idle":"2023-01-24T17:50:01.699229Z","shell.execute_reply.started":"2023-01-24T17:50:01.689384Z","shell.execute_reply":"2023-01-24T17:50:01.698231Z"},"trusted":true},"execution_count":75,"outputs":[]},{"cell_type":"markdown","source":"### define UNet algoritm","metadata":{}},{"cell_type":"code","source":"def UNet():\n f = [16,32,64,128,256,512,1024]\n inputs = keras.layers.Input((image_size, image_size, 3))\n \n p0 = inputs\n c1,p1 = down_block(p0 , f[0]) # 512 ---> 256\n c2,p2 = down_block(p1 , f[1]) # 256 ---> 128\n c3,p3 = down_block(p2 , f[2]) # 128 ---> 64\n c4,p4 = down_block(p3 , f[3]) # 64 ---> 32\n c5,p5 = down_block(p4 , f[4]) # 32 ---> 16\n c6,p6 = down_block(p5 , f[5]) # 16 ---> 8\n \n bn = bottleneck(p6, f[6])\n \n u1 = up_block(bn , c6, f[5]) # 512 ---> 256\n u2 = up_block(u1 , c5, f[4]) # 256 ---> 128\n u3 = up_block(u2 , c4, f[3]) # 128 ---> 64\n u4 = up_block(u3 , c3, f[2]) # 64 ---> 32\n u5 = up_block(u4 , c2, f[1]) # 32 ---> 16\n u6 = up_block(u5 , c1, f[0]) # 16 ---> 8\n \n outputs = keras.layers.Conv2D(1, (1,1), padding = 'same', activation = 'sigmoid' )(u6)\n model = keras.models.Model(inputs, outputs)\n return model","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:53:38.32263Z","iopub.execute_input":"2023-01-24T17:53:38.323056Z","iopub.status.idle":"2023-01-24T17:53:38.333401Z","shell.execute_reply.started":"2023-01-24T17:53:38.323021Z","shell.execute_reply":"2023-01-24T17:53:38.332284Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"markdown","source":"### define Model","metadata":{}},{"cell_type":"code","source":"model = UNet()\nmodel.compile(optimizer = \"adam\", loss = \"binary_crossentropy\", metrics = [\"acc\"])\nmodel.summary()","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2023-01-24T17:53:39.041724Z","iopub.execute_input":"2023-01-24T17:53:39.042471Z","iopub.status.idle":"2023-01-24T17:53:39.288308Z","shell.execute_reply.started":"2023-01-24T17:53:39.042434Z","shell.execute_reply":"2023-01-24T17:53:39.287289Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"Model: \"model_5\"\n__________________________________________________________________________________________________\nLayer (type) Output Shape Param # Connected to \n==================================================================================================\ninput_6 (InputLayer) [(None, 512, 512, 3) 0 \n__________________________________________________________________________________________________\nconv2d_135 (Conv2D) (None, 512, 512, 16) 448 input_6[0][0] \n__________________________________________________________________________________________________\nconv2d_136 (Conv2D) (None, 512, 512, 16) 2320 conv2d_135[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_60 (MaxPooling2D) (None, 256, 256, 16) 0 conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_137 (Conv2D) (None, 256, 256, 32) 4640 max_pooling2d_60[0][0] \n__________________________________________________________________________________________________\nconv2d_138 (Conv2D) (None, 256, 256, 32) 9248 conv2d_137[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_61 (MaxPooling2D) (None, 128, 128, 32) 0 conv2d_138[0][0] \n__________________________________________________________________________________________________\nconv2d_139 (Conv2D) (None, 128, 128, 64) 18496 max_pooling2d_61[0][0] \n__________________________________________________________________________________________________\nconv2d_140 (Conv2D) (None, 128, 128, 64) 36928 conv2d_139[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_62 (MaxPooling2D) (None, 64, 64, 64) 0 conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_141 (Conv2D) (None, 64, 64, 128) 73856 max_pooling2d_62[0][0] \n__________________________________________________________________________________________________\nconv2d_142 (Conv2D) (None, 64, 64, 128) 147584 conv2d_141[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_63 (MaxPooling2D) (None, 32, 32, 128) 0 conv2d_142[0][0] \n__________________________________________________________________________________________________\nconv2d_143 (Conv2D) (None, 32, 32, 256) 295168 max_pooling2d_63[0][0] \n__________________________________________________________________________________________________\nconv2d_144 (Conv2D) (None, 32, 32, 256) 590080 conv2d_143[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_64 (MaxPooling2D) (None, 16, 16, 256) 0 conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_145 (Conv2D) (None, 16, 16, 512) 1180160 max_pooling2d_64[0][0] \n__________________________________________________________________________________________________\nconv2d_146 (Conv2D) (None, 16, 16, 512) 2359808 conv2d_145[0][0] \n__________________________________________________________________________________________________\nmax_pooling2d_65 (MaxPooling2D) (None, 8, 8, 512) 0 conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_147 (Conv2D) (None, 8, 8, 1024) 4719616 max_pooling2d_65[0][0] \n__________________________________________________________________________________________________\nconv2d_148 (Conv2D) (None, 8, 8, 1024) 9438208 conv2d_147[0][0] \n__________________________________________________________________________________________________\nup_sampling2d_30 (UpSampling2D) (None, 16, 16, 1024) 0 conv2d_148[0][0] \n__________________________________________________________________________________________________\nconcatenate_30 (Concatenate) (None, 16, 16, 1536) 0 up_sampling2d_30[0][0] \n conv2d_146[0][0] \n__________________________________________________________________________________________________\nconv2d_149 (Conv2D) (None, 16, 16, 512) 7078400 concatenate_30[0][0] \n__________________________________________________________________________________________________\nconv2d_150 (Conv2D) (None, 16, 16, 512) 2359808 conv2d_149[0][0] \n__________________________________________________________________________________________________\nup_sampling2d_31 (UpSampling2D) (None, 32, 32, 512) 0 conv2d_150[0][0] \n__________________________________________________________________________________________________\nconcatenate_31 (Concatenate) (None, 32, 32, 768) 0 up_sampling2d_31[0][0] \n conv2d_144[0][0] \n__________________________________________________________________________________________________\nconv2d_151 (Conv2D) (None, 32, 32, 256) 1769728 concatenate_31[0][0] \n__________________________________________________________________________________________________\nconv2d_152 (Conv2D) (None, 32, 32, 256) 590080 conv2d_151[0][0] \n__________________________________________________________________________________________________\nup_sampling2d_32 (UpSampling2D) (None, 64, 64, 256) 0 conv2d_152[0][0] \n__________________________________________________________________________________________________\nconcatenate_32 (Concatenate) (None, 64, 64, 384) 0 up_sampling2d_32[0][0] \n conv2d_142[0][0] \n__________________________________________________________________________________________________\nconv2d_153 (Conv2D) (None, 64, 64, 128) 442496 concatenate_32[0][0] \n__________________________________________________________________________________________________\nconv2d_154 (Conv2D) (None, 64, 64, 128) 147584 conv2d_153[0][0] \n__________________________________________________________________________________________________\nup_sampling2d_33 (UpSampling2D) (None, 128, 128, 128 0 conv2d_154[0][0] \n__________________________________________________________________________________________________\nconcatenate_33 (Concatenate) (None, 128, 128, 192 0 up_sampling2d_33[0][0] \n conv2d_140[0][0] \n__________________________________________________________________________________________________\nconv2d_155 (Conv2D) (None, 128, 128, 64) 110656 concatenate_33[0][0] \n__________________________________________________________________________________________________\nconv2d_156 (Conv2D) (None, 128, 128, 64) 36928 conv2d_155[0][0] \n__________________________________________________________________________________________________\nup_sampling2d_34 (UpSampling2D) (None, 256, 256, 64) 0 conv2d_156[0][0] \n__________________________________________________________________________________________________\nconcatenate_34 (Concatenate) (None, 256, 256, 96) 0 up_sampling2d_34[0][0] \n conv2d_138[0][0] \n__________________________________________________________________________________________________\nconv2d_157 (Conv2D) (None, 256, 256, 32) 27680 concatenate_34[0][0] \n__________________________________________________________________________________________________\nconv2d_158 (Conv2D) (None, 256, 256, 32) 9248 conv2d_157[0][0] \n__________________________________________________________________________________________________\nup_sampling2d_35 (UpSampling2D) (None, 512, 512, 32) 0 conv2d_158[0][0] \n__________________________________________________________________________________________________\nconcatenate_35 (Concatenate) (None, 512, 512, 48) 0 up_sampling2d_35[0][0] \n conv2d_136[0][0] \n__________________________________________________________________________________________________\nconv2d_159 (Conv2D) (None, 512, 512, 16) 6928 concatenate_35[0][0] \n__________________________________________________________________________________________________\nconv2d_160 (Conv2D) (None, 512, 512, 16) 2320 conv2d_159[0][0] \n__________________________________________________________________________________________________\nconv2d_161 (Conv2D) (None, 512, 512, 1) 17 conv2d_160[0][0] \n==================================================================================================\nTotal params: 31,458,433\nTrainable params: 31,458,433\nNon-trainable params: 0\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### define loss variable for save every loss number","metadata":{}},{"cell_type":"code","source":"loss_functions = []","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:53:40.854568Z","iopub.execute_input":"2023-01-24T17:53:40.855304Z","iopub.status.idle":"2023-01-24T17:53:40.864166Z","shell.execute_reply.started":"2023-01-24T17:53:40.855257Z","shell.execute_reply":"2023-01-24T17:53:40.863032Z"},"trusted":true},"execution_count":83,"outputs":[]},{"cell_type":"markdown","source":"### start training","metadata":{}},{"cell_type":"code","source":"os.makedirs(\"tmp2\", exist_ok=True)\nos.chdir(\"tmp2\")","metadata":{"execution":{"iopub.status.busy":"2022-12-10T12:31:53.760808Z","iopub.execute_input":"2022-12-10T12:31:53.76138Z","iopub.status.idle":"2022-12-10T12:31:53.767923Z","shell.execute_reply.started":"2022-12-10T12:31:53.761289Z","shell.execute_reply":"2022-12-10T12:31:53.766834Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(imagess,mask_imagess,test_size=0.15, random_state=1)\nprint(\"X_train.shape\", X_train.shape, \"y_train.shape\", y_train.shape)\nprint(\"X_test.shape\", X_test.shape, \"y_test.shape\", y_test.shape)\nimagess = []\nmask_imagess = []","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:50:12.864922Z","iopub.execute_input":"2023-01-24T17:50:12.865327Z","iopub.status.idle":"2023-01-24T17:50:13.97848Z","shell.execute_reply.started":"2023-01-24T17:50:12.865291Z","shell.execute_reply":"2023-01-24T17:50:13.977305Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"X_train.shape (515, 512, 512, 3) y_train.shape (515, 512, 512)\nX_test.shape (91, 512, 512, 3) y_test.shape (91, 512, 512)\n","output_type":"stream"}]},{"cell_type":"code","source":"ALL_IoU_score_train = []\nALL_IoU_score_test = []","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:50:14.855654Z","iopub.execute_input":"2023-01-24T17:50:14.856018Z","iopub.status.idle":"2023-01-24T17:50:14.861144Z","shell.execute_reply.started":"2023-01-24T17:50:14.855988Z","shell.execute_reply":"2023-01-24T17:50:14.860195Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"\nhist = model.fit(X_train, y_train, batch_size = 32 , epochs = 60,shuffle=True)\n","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:53:44.259591Z","iopub.execute_input":"2023-01-24T17:53:44.259985Z","iopub.status.idle":"2023-01-24T17:53:58.152949Z","shell.execute_reply.started":"2023-01-24T17:53:44.259952Z","shell.execute_reply":"2023-01-24T17:53:58.151427Z"},"trusted":true},"execution_count":84,"outputs":[{"name":"stdout","text":"Epoch 1/60\n","output_type":"stream"},{"name":"stderr","text":"2023-01-24 17:53:58.080346: W tensorflow/core/common_runtime/bfc_allocator.cc:457] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.50GiB (rounded to 1610612736)requested by op model_5/concatenate_35/concat\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. \nCurrent allocation summary follows.\nCurrent allocation summary follows.\n2023-01-24 17:53:58.080433: I tensorflow/core/common_runtime/bfc_allocator.cc:1004] BFCAllocator dump for GPU_0_bfc\n2023-01-24 17:53:58.080457: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (256): \tTotal Chunks: 288, Chunks in use: 288. 72.0KiB allocated for chunks. 72.0KiB in use in bin. 24.1KiB client-requested in use in bin.\n2023-01-24 17:53:58.080471: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (512): \tTotal Chunks: 52, Chunks in use: 52. 26.0KiB allocated for chunks. 26.0KiB in use in bin. 26.0KiB client-requested in use in bin.\n2023-01-24 17:53:58.080482: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (1024): \tTotal Chunks: 66, Chunks in use: 66. 80.0KiB allocated for chunks. 80.0KiB in use in bin. 74.9KiB client-requested in use in bin.\n2023-01-24 17:53:58.080495: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (2048): \tTotal Chunks: 52, Chunks in use: 52. 110.2KiB allocated for chunks. 110.2KiB in use in bin. 104.0KiB client-requested in use in bin.\n2023-01-24 17:53:58.080507: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (4096): \tTotal Chunks: 26, Chunks in use: 26. 109.0KiB allocated for chunks. 109.0KiB in use in bin. 104.0KiB client-requested in use in bin.\n2023-01-24 17:53:58.080526: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (8192): \tTotal Chunks: 21, Chunks in use: 21. 207.8KiB allocated for chunks. 207.8KiB in use in bin. 189.0KiB client-requested in use in bin.\n2023-01-24 17:53:58.080537: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (16384): \tTotal Chunks: 27, Chunks in use: 27. 564.0KiB allocated for chunks. 564.0KiB in use in bin. 522.0KiB client-requested in use in bin.\n2023-01-24 17:53:58.080550: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (32768): \tTotal Chunks: 30, Chunks in use: 30. 1.09MiB allocated for chunks. 1.09MiB in use in bin. 1.02MiB client-requested in use in bin.\n2023-01-24 17:53:58.080564: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (65536): \tTotal Chunks: 21, Chunks in use: 21. 1.79MiB allocated for chunks. 1.79MiB in use in bin. 1.76MiB client-requested in use in bin.\n2023-01-24 17:53:58.080579: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (131072): \tTotal Chunks: 31, Chunks in use: 31. 4.61MiB allocated for chunks. 4.61MiB in use in bin. 4.18MiB client-requested in use in bin.\n2023-01-24 17:53:58.080593: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (262144): \tTotal Chunks: 21, Chunks in use: 21. 7.61MiB allocated for chunks. 7.61MiB in use in bin. 7.03MiB client-requested in use in bin.\n2023-01-24 17:53:58.080608: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (524288): \tTotal Chunks: 31, Chunks in use: 31. 18.42MiB allocated for chunks. 18.42MiB in use in bin. 16.73MiB client-requested in use in bin.\n2023-01-24 17:53:58.080624: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (1048576): \tTotal Chunks: 21, Chunks in use: 21. 29.81MiB allocated for chunks. 29.81MiB in use in bin. 28.12MiB client-requested in use in bin.\n2023-01-24 17:53:58.080640: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (2097152): \tTotal Chunks: 31, Chunks in use: 31. 73.69MiB allocated for chunks. 73.69MiB in use in bin. 66.94MiB client-requested in use in bin.\n2023-01-24 17:53:58.080657: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (4194304): \tTotal Chunks: 22, Chunks in use: 22. 123.25MiB allocated for chunks. 123.25MiB in use in bin. 116.50MiB client-requested in use in bin.\n2023-01-24 17:53:58.080692: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (8388608): \tTotal Chunks: 34, Chunks in use: 34. 313.63MiB allocated for chunks. 313.63MiB in use in bin. 291.75MiB client-requested in use in bin.\n2023-01-24 17:53:58.080725: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (16777216): \tTotal Chunks: 29, Chunks in use: 29. 621.25MiB allocated for chunks. 621.25MiB in use in bin. 611.00MiB client-requested in use in bin.\n2023-01-24 17:53:58.080745: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (33554432): \tTotal Chunks: 23, Chunks in use: 23. 865.96MiB allocated for chunks. 865.96MiB in use in bin. 794.00MiB client-requested in use in bin.\n2023-01-24 17:53:58.080774: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (67108864): \tTotal Chunks: 7, Chunks in use: 7. 544.00MiB allocated for chunks. 544.00MiB in use in bin. 512.00MiB client-requested in use in bin.\n2023-01-24 17:53:58.080805: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (134217728): \tTotal Chunks: 7, Chunks in use: 6. 1.00GiB allocated for chunks. 896.00MiB in use in bin. 832.00MiB client-requested in use in bin.\n2023-01-24 17:53:58.080836: I tensorflow/core/common_runtime/bfc_allocator.cc:1011] Bin (268435456): \tTotal Chunks: 23, Chunks in use: 20. 11.50GiB allocated for chunks. 8.98GiB in use in bin. 8.90GiB client-requested in use in bin.\n2023-01-24 17:53:58.080868: I tensorflow/core/common_runtime/bfc_allocator.cc:1027] Bin for 1.50GiB was 256.00MiB, Chunk State: \n2023-01-24 17:53:58.080904: I tensorflow/core/common_runtime/bfc_allocator.cc:1033] Size: 768.00MiB | Requested Size: 256.00MiB | in_use: 0 | bin_num: 20, prev: Size: 256.00MiB | Requested Size: 256.00MiB | in_use: 1 | bin_num: -1, next: Size: 768.00MiB | Requested Size: 768.00MiB | in_use: 1 | bin_num: -1\n2023-01-24 17:53:58.080937: I tensorflow/core/common_runtime/bfc_allocator.cc:1033] Size: 784.25MiB | Requested Size: 100.1KiB | in_use: 0 | bin_num: 20, prev: Size: 1.00GiB | Requested Size: 1.00GiB | in_use: 1 | bin_num: -1\n2023-01-24 17:53:58.080972: I tensorflow/core/common_runtime/bfc_allocator.cc:1033] Size: 1.00GiB | Requested Size: 1.00GiB | in_use: 0 | bin_num: 20, prev: Size: 768.00MiB | Requested Size: 768.00MiB | in_use: 1 | bin_num: -1, next: Size: 1.00GiB | Requested Size: 1.00GiB | in_use: 1 | bin_num: -1\n2023-01-24 17:53:58.080988: I tensorflow/core/common_runtime/bfc_allocator.cc:1040] Next region of size 16149905408\n2023-01-24 17:53:58.081004: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000000 of size 1280 next 1\n2023-01-24 17:53:58.081034: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000500 of size 256 next 2\n2023-01-24 17:53:58.081053: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000600 of size 256 next 3\n2023-01-24 17:53:58.081065: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000700 of size 256 next 4\n2023-01-24 17:53:58.081076: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000800 of size 256 next 5\n2023-01-24 17:53:58.081087: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000900 of size 256 next 8\n2023-01-24 17:53:58.081099: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000a00 of size 256 next 9\n2023-01-24 17:53:58.081110: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000b00 of size 256 next 10\n2023-01-24 17:53:58.081122: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000c00 of size 256 next 13\n2023-01-24 17:53:58.081134: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000d00 of size 256 next 14\n2023-01-24 17:53:58.081146: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000e00 of size 256 next 17\n2023-01-24 17:53:58.081157: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220000f00 of size 256 next 18\n2023-01-24 17:53:58.081169: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001000 of size 256 next 19\n2023-01-24 17:53:58.081191: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001100 of size 256 next 20\n2023-01-24 17:53:58.081209: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001200 of size 256 next 23\n2023-01-24 17:53:58.081238: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001300 of size 256 next 24\n2023-01-24 17:53:58.081248: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001400 of size 256 next 27\n2023-01-24 17:53:58.081261: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001500 of size 256 next 6\n2023-01-24 17:53:58.081271: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001600 of size 1792 next 7\n2023-01-24 17:53:58.081281: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001d00 of size 256 next 28\n2023-01-24 17:53:58.081292: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001e00 of size 256 next 29\n2023-01-24 17:53:58.081301: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220001f00 of size 256 next 32\n2023-01-24 17:53:58.081310: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002000 of size 256 next 33\n2023-01-24 17:53:58.081319: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002100 of size 512 next 36\n2023-01-24 17:53:58.081332: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002300 of size 256 next 37\n2023-01-24 17:53:58.081345: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002400 of size 256 next 38\n2023-01-24 17:53:58.081356: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002500 of size 512 next 39\n2023-01-24 17:53:58.081368: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002700 of size 256 next 42\n2023-01-24 17:53:58.081380: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002800 of size 256 next 43\n2023-01-24 17:53:58.081392: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002900 of size 1024 next 46\n2023-01-24 17:53:58.081404: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002d00 of size 256 next 47\n2023-01-24 17:53:58.081415: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002e00 of size 256 next 48\n2023-01-24 17:53:58.081427: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220002f00 of size 1024 next 49\n2023-01-24 17:53:58.081439: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220003300 of size 256 next 52\n2023-01-24 17:53:58.081453: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220003400 of size 256 next 53\n2023-01-24 17:53:58.081464: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220003500 of size 2048 next 56\n2023-01-24 17:53:58.081475: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220003d00 of size 256 next 57\n2023-01-24 17:53:58.081486: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220003e00 of size 256 next 58\n2023-01-24 17:53:58.081497: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220003f00 of size 2048 next 59\n2023-01-24 17:53:58.081509: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220004700 of size 256 next 62\n2023-01-24 17:53:58.081530: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220004800 of size 256 next 63\n2023-01-24 17:53:58.081543: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220004900 of size 16384 next 11\n2023-01-24 17:53:58.081554: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220008900 of size 256 next 66\n2023-01-24 17:53:58.081565: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220008a00 of size 256 next 67\n2023-01-24 17:53:58.081579: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa220008b00 of size 12800 next 93\n2023-01-24 17:53:58.081592: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000bd00 of size 256 next 94\n2023-01-24 17:53:58.081605: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000be00 of size 256 next 96\n2023-01-24 17:53:58.081616: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000bf00 of size 512 next 98\n2023-01-24 17:53:58.081628: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c100 of size 256 next 99\n2023-01-24 17:53:58.081641: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c200 of size 512 next 101\n2023-01-24 17:53:58.081654: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c400 of size 256 next 102\n2023-01-24 17:53:58.081670: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c500 of size 256 next 105\n2023-01-24 17:53:58.081681: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c600 of size 256 next 106\n2023-01-24 17:53:58.081691: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c700 of size 256 next 107\n2023-01-24 17:53:58.081700: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c800 of size 256 next 108\n2023-01-24 17:53:58.081709: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000c900 of size 256 next 109\n2023-01-24 17:53:58.081719: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000ca00 of size 256 next 110\n2023-01-24 17:53:58.081731: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000cb00 of size 256 next 111\n2023-01-24 17:53:58.081742: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000cc00 of size 256 next 112\n2023-01-24 17:53:58.081753: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa22000cd00 of size 405012480 next 536\n2023-01-24 17:53:58.081763: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa23824cd00 of size 540016640 next 537\n2023-01-24 17:53:58.081773: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa25854cd00 of size 18874368 next 729\n2023-01-24 17:53:58.081783: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa25974cd00 of size 9437184 next 734\n2023-01-24 17:53:58.081792: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa25a04cd00 of size 9437184 next 647\n2023-01-24 17:53:58.081802: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa25a94cd00 of size 37748736 next 646\n2023-01-24 17:53:58.081814: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa25cd4cd00 of size 28311552 next 690\n2023-01-24 17:53:58.081825: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa25e84cd00 of size 28311552 next 732\n2023-01-24 17:53:58.081835: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26034cd00 of size 2359296 next 736\n2023-01-24 17:53:58.081845: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26058cd00 of size 1769472 next 738\n2023-01-24 17:53:58.081854: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26073cd00 of size 589824 next 740\n2023-01-24 17:53:58.081864: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2607ccd00 of size 442368 next 741\n2023-01-24 17:53:58.081873: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260838d00 of size 256 next 742\n2023-01-24 17:53:58.081883: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260838e00 of size 147456 next 743\n2023-01-24 17:53:58.081892: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26085ce00 of size 256 next 744\n2023-01-24 17:53:58.081902: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26085cf00 of size 110592 next 745\n2023-01-24 17:53:58.081911: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260877f00 of size 256 next 746\n2023-01-24 17:53:58.081923: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260878000 of size 36864 next 747\n2023-01-24 17:53:58.081932: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260881000 of size 256 next 748\n2023-01-24 17:53:58.081942: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260881100 of size 27648 next 749\n2023-01-24 17:53:58.081952: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260887d00 of size 256 next 750\n2023-01-24 17:53:58.081961: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260887e00 of size 9216 next 751\n2023-01-24 17:53:58.081971: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088a200 of size 256 next 752\n2023-01-24 17:53:58.081980: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088a300 of size 256 next 753\n2023-01-24 17:53:58.081990: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088a400 of size 256 next 754\n2023-01-24 17:53:58.081999: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088a500 of size 1792 next 755\n2023-01-24 17:53:58.082010: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088ac00 of size 256 next 756\n2023-01-24 17:53:58.082020: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088ad00 of size 9216 next 757\n2023-01-24 17:53:58.082030: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088d100 of size 256 next 758\n2023-01-24 17:53:58.082039: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26088d200 of size 18432 next 759\n2023-01-24 17:53:58.082048: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260891a00 of size 256 next 760\n2023-01-24 17:53:58.082063: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260891b00 of size 36864 next 761\n2023-01-24 17:53:58.082079: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26089ab00 of size 256 next 762\n2023-01-24 17:53:58.082095: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26089ac00 of size 73728 next 763\n2023-01-24 17:53:58.082106: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2608acc00 of size 256 next 764\n2023-01-24 17:53:58.082118: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2608acd00 of size 147456 next 765\n2023-01-24 17:53:58.082129: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2608d0d00 of size 256 next 766\n2023-01-24 17:53:58.082140: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2608d0e00 of size 294912 next 767\n2023-01-24 17:53:58.082150: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260918e00 of size 512 next 768\n2023-01-24 17:53:58.082162: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260919000 of size 589824 next 769\n2023-01-24 17:53:58.082173: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2609a9000 of size 512 next 770\n2023-01-24 17:53:58.082184: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2609a9200 of size 1179648 next 771\n2023-01-24 17:53:58.082196: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260ac9200 of size 1024 next 772\n2023-01-24 17:53:58.082208: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260ac9600 of size 2359296 next 773\n2023-01-24 17:53:58.082232: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260d09600 of size 1024 next 774\n2023-01-24 17:53:58.082246: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa260d09a00 of size 4718592 next 775\n2023-01-24 17:53:58.082258: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa261189a00 of size 2048 next 776\n2023-01-24 17:53:58.082269: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26118a200 of size 9437184 next 777\n2023-01-24 17:53:58.082280: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa261a8a200 of size 2048 next 778\n2023-01-24 17:53:58.082292: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa261a8aa00 of size 18874368 next 779\n2023-01-24 17:53:58.082305: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa262c8aa00 of size 4096 next 780\n2023-01-24 17:53:58.082316: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa262c8ba00 of size 37748736 next 781\n2023-01-24 17:53:58.082328: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26508ba00 of size 4096 next 782\n2023-01-24 17:53:58.082349: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26508ca00 of size 28311552 next 783\n2023-01-24 17:53:58.082364: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa266b8ca00 of size 2048 next 784\n2023-01-24 17:53:58.082375: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa266b8d200 of size 9437184 next 785\n2023-01-24 17:53:58.082385: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26748d200 of size 2048 next 786\n2023-01-24 17:53:58.082396: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26748da00 of size 7077888 next 787\n2023-01-24 17:53:58.082406: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267b4da00 of size 1024 next 788\n2023-01-24 17:53:58.082416: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267b4de00 of size 2359296 next 789\n2023-01-24 17:53:58.082428: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267d8de00 of size 1024 next 790\n2023-01-24 17:53:58.082441: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267d8e200 of size 1769472 next 791\n2023-01-24 17:53:58.082454: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267f3e200 of size 512 next 792\n2023-01-24 17:53:58.082466: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267f3e400 of size 589824 next 793\n2023-01-24 17:53:58.082478: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267fce400 of size 512 next 794\n2023-01-24 17:53:58.082490: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa267fce600 of size 442368 next 795\n2023-01-24 17:53:58.082503: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26803a600 of size 256 next 796\n2023-01-24 17:53:58.082514: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26803a700 of size 147456 next 797\n2023-01-24 17:53:58.082540: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26805e700 of size 256 next 798\n2023-01-24 17:53:58.082552: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26805e800 of size 110592 next 799\n2023-01-24 17:53:58.082563: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa268079800 of size 256 next 800\n2023-01-24 17:53:58.082577: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa268079900 of size 36864 next 801\n2023-01-24 17:53:58.082588: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa268082900 of size 256 next 802\n2023-01-24 17:53:58.082600: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa268082a00 of size 27648 next 803\n2023-01-24 17:53:58.082613: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa268089600 of size 256 next 804\n2023-01-24 17:53:58.082625: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa268089700 of size 9216 next 805\n2023-01-24 17:53:58.082637: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808bb00 of size 256 next 806\n2023-01-24 17:53:58.082649: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808bc00 of size 256 next 807\n2023-01-24 17:53:58.082659: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808bd00 of size 256 next 808\n2023-01-24 17:53:58.082669: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808be00 of size 256 next 809\n2023-01-24 17:53:58.082681: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808bf00 of size 256 next 810\n2023-01-24 17:53:58.082692: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c000 of size 256 next 811\n2023-01-24 17:53:58.082704: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c100 of size 256 next 812\n2023-01-24 17:53:58.082716: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c200 of size 256 next 813\n2023-01-24 17:53:58.082730: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c300 of size 256 next 814\n2023-01-24 17:53:58.082741: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c400 of size 256 next 815\n2023-01-24 17:53:58.082752: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c500 of size 256 next 816\n2023-01-24 17:53:58.082766: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c600 of size 256 next 817\n2023-01-24 17:53:58.082778: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c700 of size 256 next 818\n2023-01-24 17:53:58.082789: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c800 of size 256 next 821\n2023-01-24 17:53:58.082801: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808c900 of size 256 next 823\n2023-01-24 17:53:58.082814: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808ca00 of size 256 next 826\n2023-01-24 17:53:58.082825: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808cb00 of size 256 next 827\n2023-01-24 17:53:58.082838: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26808cc00 of size 25164800 next 819\n2023-01-24 17:53:58.082851: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26988c800 of size 33554432 next 820\n2023-01-24 17:53:58.082863: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26b88c800 of size 33554432 next 824\n2023-01-24 17:53:58.082877: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa26d88c800 of size 50007808 next 209\n2023-01-24 17:53:58.082889: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083d700 of size 256 next 210\n2023-01-24 17:53:58.082902: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083d800 of size 2816 next 247\n2023-01-24 17:53:58.082914: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083e300 of size 256 next 273\n2023-01-24 17:53:58.082926: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083e400 of size 256 next 277\n2023-01-24 17:53:58.082939: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083e500 of size 256 next 257\n2023-01-24 17:53:58.082951: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083e600 of size 256 next 229\n2023-01-24 17:53:58.082963: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083e700 of size 256 next 275\n2023-01-24 17:53:58.082975: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083e800 of size 512 next 238\n2023-01-24 17:53:58.082987: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083ea00 of size 512 next 237\n2023-01-24 17:53:58.082998: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083ec00 of size 1280 next 268\n2023-01-24 17:53:58.083009: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083f100 of size 1792 next 221\n2023-01-24 17:53:58.083019: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083f800 of size 1024 next 222\n2023-01-24 17:53:58.083031: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27083fc00 of size 2048 next 228\n2023-01-24 17:53:58.083042: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270840400 of size 2048 next 256\n2023-01-24 17:53:58.083053: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270840c00 of size 4096 next 241\n2023-01-24 17:53:58.083065: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270841c00 of size 4096 next 227\n2023-01-24 17:53:58.083079: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270842c00 of size 2048 next 235\n2023-01-24 17:53:58.083091: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270843400 of size 3072 next 280\n2023-01-24 17:53:58.083102: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270844000 of size 9216 next 248\n2023-01-24 17:53:58.083116: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270846400 of size 1024 next 233\n2023-01-24 17:53:58.083129: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270846800 of size 1024 next 244\n2023-01-24 17:53:58.083142: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270846c00 of size 512 next 245\n2023-01-24 17:53:58.083154: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270846e00 of size 512 next 274\n2023-01-24 17:53:58.083166: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270847000 of size 256 next 246\n2023-01-24 17:53:58.083177: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270847100 of size 256 next 231\n2023-01-24 17:53:58.083190: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270847200 of size 256 next 226\n2023-01-24 17:53:58.083203: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270847300 of size 256 next 260\n2023-01-24 17:53:58.083215: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270847400 of size 256 next 271\n2023-01-24 17:53:58.083240: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270847500 of size 14080 next 254\n2023-01-24 17:53:58.083256: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084ac00 of size 18432 next 263\n2023-01-24 17:53:58.083269: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084f400 of size 256 next 285\n2023-01-24 17:53:58.083281: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084f500 of size 256 next 287\n2023-01-24 17:53:58.083292: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084f600 of size 256 next 288\n2023-01-24 17:53:58.083304: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084f700 of size 256 next 423\n2023-01-24 17:53:58.083315: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084f800 of size 256 next 290\n2023-01-24 17:53:58.083327: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084f900 of size 256 next 293\n2023-01-24 17:53:58.083339: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27084fa00 of size 1792 next 294\n2023-01-24 17:53:58.083356: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850100 of size 256 next 295\n2023-01-24 17:53:58.083368: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850200 of size 256 next 296\n2023-01-24 17:53:58.083379: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850300 of size 256 next 298\n2023-01-24 17:53:58.083391: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850400 of size 256 next 300\n2023-01-24 17:53:58.083402: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850500 of size 256 next 301\n2023-01-24 17:53:58.083413: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850600 of size 256 next 302\n2023-01-24 17:53:58.083424: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850700 of size 512 next 303\n2023-01-24 17:53:58.083436: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850900 of size 512 next 304\n2023-01-24 17:53:58.083447: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850b00 of size 1024 next 305\n2023-01-24 17:53:58.083459: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270850f00 of size 1024 next 306\n2023-01-24 17:53:58.083471: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270851300 of size 1536 next 286\n2023-01-24 17:53:58.083483: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270851900 of size 18176 next 265\n2023-01-24 17:53:58.083495: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270856000 of size 46080 next 255\n2023-01-24 17:53:58.083506: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270861400 of size 36864 next 224\n2023-01-24 17:53:58.083557: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27086a400 of size 36864 next 278\n2023-01-24 17:53:58.083595: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270873400 of size 18432 next 297\n2023-01-24 17:53:58.083628: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270877c00 of size 2048 next 307\n2023-01-24 17:53:58.083658: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270878400 of size 2048 next 308\n2023-01-24 17:53:58.083688: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270878c00 of size 4096 next 309\n2023-01-24 17:53:58.083718: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270879c00 of size 4096 next 311\n2023-01-24 17:53:58.083749: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27087ac00 of size 2048 next 312\n2023-01-24 17:53:58.083797: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27087b400 of size 2048 next 314\n2023-01-24 17:53:58.083832: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27087bc00 of size 1024 next 317\n2023-01-24 17:53:58.083862: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27087c000 of size 512 next 319\n2023-01-24 17:53:58.083892: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27087c200 of size 512 next 239\n2023-01-24 17:53:58.083922: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27087c400 of size 73728 next 240\n2023-01-24 17:53:58.083951: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27088e400 of size 36864 next 299\n2023-01-24 17:53:58.083981: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270897400 of size 73728 next 281\n2023-01-24 17:53:58.084014: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2708a9400 of size 184320 next 234\n2023-01-24 17:53:58.084047: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2708d6400 of size 147456 next 267\n2023-01-24 17:53:58.084079: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2708fa400 of size 147456 next 253\n2023-01-24 17:53:58.084109: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27091e400 of size 147456 next 232\n2023-01-24 17:53:58.084141: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270942400 of size 294912 next 264\n2023-01-24 17:53:58.084170: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27098a400 of size 442368 next 283\n2023-01-24 17:53:58.084199: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2709f6400 of size 737280 next 225\n2023-01-24 17:53:58.084241: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270aaa400 of size 589824 next 261\n2023-01-24 17:53:58.084272: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270b3a400 of size 589824 next 284\n2023-01-24 17:53:58.084305: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270bca400 of size 589824 next 249\n2023-01-24 17:53:58.084339: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270c5a400 of size 1179648 next 266\n2023-01-24 17:53:58.084370: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270d7a400 of size 1769472 next 258\n2023-01-24 17:53:58.084400: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa270f2a400 of size 2949120 next 252\n2023-01-24 17:53:58.084432: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2711fa400 of size 2359296 next 223\n2023-01-24 17:53:58.084461: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27143a400 of size 2359296 next 279\n2023-01-24 17:53:58.084495: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27167a400 of size 2359296 next 236\n2023-01-24 17:53:58.084544: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2718ba400 of size 4718592 next 269\n2023-01-24 17:53:58.084579: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa271d3a400 of size 9437184 next 250\n2023-01-24 17:53:58.084611: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27263a400 of size 9437184 next 262\n2023-01-24 17:53:58.084645: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa272f3a400 of size 9437184 next 230\n2023-01-24 17:53:58.084679: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27383a400 of size 7077888 next 259\n2023-01-24 17:53:58.084715: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa273efa400 of size 9716224 next 242\n2023-01-24 17:53:58.084750: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27483e600 of size 256 next 251\n2023-01-24 17:53:58.084784: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27483e700 of size 18874368 next 270\n2023-01-24 17:53:58.084819: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa275a3e700 of size 18874368 next 276\n2023-01-24 17:53:58.084850: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa276c3e700 of size 37748736 next 310\n2023-01-24 17:53:58.084884: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27903e700 of size 37748736 next 243\n2023-01-24 17:53:58.084916: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27b43e700 of size 37748736 next 282\n2023-01-24 17:53:58.084950: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27d83e700 of size 28311552 next 272\n2023-01-24 17:53:58.084984: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa27f33e700 of size 405012480 next 291\n2023-01-24 17:53:58.085017: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa29757e700 of size 540016640 next 292\n2023-01-24 17:53:58.085052: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b787e700 of size 9437184 next 313\n2023-01-24 17:53:58.085084: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b817e700 of size 7077888 next 315\n2023-01-24 17:53:58.085119: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b883e700 of size 2359296 next 316\n2023-01-24 17:53:58.085153: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8a7e700 of size 1769472 next 318\n2023-01-24 17:53:58.085187: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8c2e700 of size 589824 next 320\n2023-01-24 17:53:58.085231: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8cbe700 of size 442368 next 321\n2023-01-24 17:53:58.085270: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d2a700 of size 256 next 322\n2023-01-24 17:53:58.085304: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d2a800 of size 147456 next 323\n2023-01-24 17:53:58.085343: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d4e800 of size 256 next 324\n2023-01-24 17:53:58.085379: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d4e900 of size 110592 next 325\n2023-01-24 17:53:58.085412: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d69900 of size 256 next 326\n2023-01-24 17:53:58.085451: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d69a00 of size 36864 next 327\n2023-01-24 17:53:58.085486: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d72a00 of size 256 next 328\n2023-01-24 17:53:58.085526: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d72b00 of size 27648 next 329\n2023-01-24 17:53:58.085563: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d79700 of size 256 next 330\n2023-01-24 17:53:58.085599: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d79800 of size 9216 next 331\n2023-01-24 17:53:58.085631: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7bc00 of size 256 next 332\n2023-01-24 17:53:58.085665: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7bd00 of size 256 next 333\n2023-01-24 17:53:58.085699: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7be00 of size 256 next 334\n2023-01-24 17:53:58.085732: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7bf00 of size 1792 next 335\n2023-01-24 17:53:58.085765: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7c600 of size 256 next 336\n2023-01-24 17:53:58.085800: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7c700 of size 9216 next 337\n2023-01-24 17:53:58.085835: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7eb00 of size 256 next 338\n2023-01-24 17:53:58.085868: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d7ec00 of size 18432 next 339\n2023-01-24 17:53:58.085900: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d83400 of size 256 next 340\n2023-01-24 17:53:58.085935: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d83500 of size 36864 next 341\n2023-01-24 17:53:58.085967: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d8c500 of size 256 next 342\n2023-01-24 17:53:58.086001: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d8c600 of size 73728 next 343\n2023-01-24 17:53:58.086033: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d9e600 of size 256 next 344\n2023-01-24 17:53:58.086068: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8d9e700 of size 147456 next 345\n2023-01-24 17:53:58.086102: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8dc2700 of size 256 next 346\n2023-01-24 17:53:58.086135: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8dc2800 of size 294912 next 347\n2023-01-24 17:53:58.086171: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8e0a800 of size 512 next 348\n2023-01-24 17:53:58.086206: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8e0aa00 of size 589824 next 349\n2023-01-24 17:53:58.086254: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8e9aa00 of size 512 next 350\n2023-01-24 17:53:58.086289: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8e9ac00 of size 1179648 next 351\n2023-01-24 17:53:58.086325: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8fbac00 of size 1024 next 352\n2023-01-24 17:53:58.086358: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b8fbb000 of size 2359296 next 353\n2023-01-24 17:53:58.086392: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b91fb000 of size 1024 next 354\n2023-01-24 17:53:58.086425: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b91fb400 of size 4718592 next 355\n2023-01-24 17:53:58.086458: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b967b400 of size 2048 next 356\n2023-01-24 17:53:58.086492: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b967bc00 of size 9437184 next 357\n2023-01-24 17:53:58.086532: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b9f7bc00 of size 2048 next 358\n2023-01-24 17:53:58.086566: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2b9f7c400 of size 18874368 next 359\n2023-01-24 17:53:58.086607: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bb17c400 of size 4096 next 360\n2023-01-24 17:53:58.086641: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bb17d400 of size 37748736 next 361\n2023-01-24 17:53:58.086675: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bd57d400 of size 4096 next 362\n2023-01-24 17:53:58.086709: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bd57e400 of size 28311552 next 363\n2023-01-24 17:53:58.086742: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bf07e400 of size 2048 next 364\n2023-01-24 17:53:58.086775: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bf07ec00 of size 9437184 next 365\n2023-01-24 17:53:58.086809: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bf97ec00 of size 2048 next 366\n2023-01-24 17:53:58.086844: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2bf97f400 of size 7077888 next 367\n2023-01-24 17:53:58.086878: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c003f400 of size 1024 next 368\n2023-01-24 17:53:58.086911: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c003f800 of size 2359296 next 369\n2023-01-24 17:53:58.086945: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c027f800 of size 1024 next 370\n2023-01-24 17:53:58.086978: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c027fc00 of size 1769472 next 371\n2023-01-24 17:53:58.087013: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c042fc00 of size 512 next 372\n2023-01-24 17:53:58.087047: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c042fe00 of size 589824 next 373\n2023-01-24 17:53:58.087081: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c04bfe00 of size 512 next 374\n2023-01-24 17:53:58.087115: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c04c0000 of size 442368 next 375\n2023-01-24 17:53:58.087148: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c052c000 of size 256 next 376\n2023-01-24 17:53:58.087183: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c052c100 of size 147456 next 377\n2023-01-24 17:53:58.087226: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c0550100 of size 256 next 378\n2023-01-24 17:53:58.087263: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c0550200 of size 110592 next 379\n2023-01-24 17:53:58.087298: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c056b200 of size 256 next 380\n2023-01-24 17:53:58.087329: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c056b300 of size 36864 next 381\n2023-01-24 17:53:58.087363: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c0574300 of size 256 next 382\n2023-01-24 17:53:58.087397: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c0574400 of size 27648 next 383\n2023-01-24 17:53:58.087431: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057b000 of size 256 next 384\n2023-01-24 17:53:58.087464: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057b100 of size 9216 next 385\n2023-01-24 17:53:58.087497: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057d500 of size 256 next 386\n2023-01-24 17:53:58.087539: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057d600 of size 256 next 387\n2023-01-24 17:53:58.087574: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057d700 of size 256 next 388\n2023-01-24 17:53:58.087613: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057d800 of size 256 next 389\n2023-01-24 17:53:58.087649: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057d900 of size 256 next 390\n2023-01-24 17:53:58.087683: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057da00 of size 256 next 391\n2023-01-24 17:53:58.087717: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057db00 of size 256 next 392\n2023-01-24 17:53:58.087752: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057dc00 of size 256 next 393\n2023-01-24 17:53:58.087785: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057dd00 of size 256 next 394\n2023-01-24 17:53:58.087819: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057de00 of size 256 next 395\n2023-01-24 17:53:58.087854: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057df00 of size 256 next 396\n2023-01-24 17:53:58.087887: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057e000 of size 256 next 397\n2023-01-24 17:53:58.087921: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057e100 of size 256 next 398\n2023-01-24 17:53:58.087954: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2c057e200 of size 405012480 next 473\n2023-01-24 17:53:58.087989: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2d87be200 of size 540016640 next 474\n2023-01-24 17:53:58.088028: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa2f8abe200 of size 405012480 next 74\n2023-01-24 17:53:58.088061: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa310cfe200 of size 540016640 next 84\n2023-01-24 17:53:58.088095: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa330ffe200 of size 7077888 next 55\n2023-01-24 17:53:58.088127: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3316be200 of size 7077888 next 514\n2023-01-24 17:53:58.088162: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa331d7e200 of size 11796480 next 505\n2023-01-24 17:53:58.088197: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3328be200 of size 11796480 next 87\n2023-01-24 17:53:58.088244: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3333fe200 of size 37748736 next 131\n2023-01-24 17:53:58.088280: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3357fe200 of size 18874368 next 557\n2023-01-24 17:53:58.088315: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3369fe200 of size 9437184 next 560\n2023-01-24 17:53:58.088349: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3372fe200 of size 9437184 next 507\n2023-01-24 17:53:58.088383: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa337bfe200 of size 37748736 next 504\n2023-01-24 17:53:58.088417: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa339ffe200 of size 28311552 next 509\n2023-01-24 17:53:58.088453: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa33bafe200 of size 45876224 next 91\n2023-01-24 17:53:58.088488: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa33e6be600 of size 405012480 next 123\n2023-01-24 17:53:58.088530: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3568fe600 of size 2359296 next 563\n2023-01-24 17:53:58.088566: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356b3e600 of size 1769472 next 565\n2023-01-24 17:53:58.088602: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356cee600 of size 512 next 591\n2023-01-24 17:53:58.088636: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356cee800 of size 589824 next 592\n2023-01-24 17:53:58.088670: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356d7e800 of size 512 next 593\n2023-01-24 17:53:58.088704: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356d7ea00 of size 1179648 next 594\n2023-01-24 17:53:58.088740: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356e9ea00 of size 1024 next 595\n2023-01-24 17:53:58.088773: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa356e9ee00 of size 2359296 next 596\n2023-01-24 17:53:58.088807: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3570dee00 of size 1024 next 597\n2023-01-24 17:53:58.088849: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3570df200 of size 4718592 next 598\n2023-01-24 17:53:58.088885: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35755f200 of size 2048 next 599\n2023-01-24 17:53:58.088920: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35755fa00 of size 9437184 next 600\n2023-01-24 17:53:58.088954: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa357e5fa00 of size 2048 next 601\n2023-01-24 17:53:58.088989: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa357e60200 of size 18874368 next 602\n2023-01-24 17:53:58.089023: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa359060200 of size 4096 next 603\n2023-01-24 17:53:58.089058: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa359061200 of size 37748736 next 604\n2023-01-24 17:53:58.089091: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35b461200 of size 4096 next 605\n2023-01-24 17:53:58.089125: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35b462200 of size 28311552 next 606\n2023-01-24 17:53:58.089160: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35cf62200 of size 2048 next 607\n2023-01-24 17:53:58.089194: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35cf62a00 of size 9437184 next 608\n2023-01-24 17:53:58.089240: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35d862a00 of size 2048 next 609\n2023-01-24 17:53:58.089278: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35d863200 of size 7077888 next 610\n2023-01-24 17:53:58.089311: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35df23200 of size 1024 next 611\n2023-01-24 17:53:58.089344: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35df23600 of size 2359296 next 612\n2023-01-24 17:53:58.089378: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e163600 of size 1024 next 613\n2023-01-24 17:53:58.089411: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e163a00 of size 1769472 next 614\n2023-01-24 17:53:58.089445: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e313a00 of size 512 next 615\n2023-01-24 17:53:58.089479: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e313c00 of size 589824 next 616\n2023-01-24 17:53:58.089512: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e3a3c00 of size 512 next 617\n2023-01-24 17:53:58.089556: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e3a3e00 of size 442368 next 618\n2023-01-24 17:53:58.089591: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e40fe00 of size 256 next 619\n2023-01-24 17:53:58.089626: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e40ff00 of size 147456 next 620\n2023-01-24 17:53:58.089661: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e433f00 of size 256 next 621\n2023-01-24 17:53:58.089696: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e434000 of size 110592 next 622\n2023-01-24 17:53:58.089737: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e44f000 of size 256 next 623\n2023-01-24 17:53:58.089774: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e44f100 of size 36864 next 624\n2023-01-24 17:53:58.089808: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e458100 of size 256 next 625\n2023-01-24 17:53:58.089842: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e458200 of size 27648 next 626\n2023-01-24 17:53:58.089877: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e45ee00 of size 256 next 627\n2023-01-24 17:53:58.089913: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e45ef00 of size 9216 next 628\n2023-01-24 17:53:58.089946: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461300 of size 256 next 629\n2023-01-24 17:53:58.089979: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461400 of size 256 next 630\n2023-01-24 17:53:58.090015: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461500 of size 256 next 631\n2023-01-24 17:53:58.090050: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461600 of size 256 next 632\n2023-01-24 17:53:58.090090: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461700 of size 256 next 633\n2023-01-24 17:53:58.090123: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461800 of size 256 next 634\n2023-01-24 17:53:58.090158: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461900 of size 256 next 635\n2023-01-24 17:53:58.090192: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461a00 of size 256 next 636\n2023-01-24 17:53:58.090238: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461b00 of size 256 next 637\n2023-01-24 17:53:58.090274: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461c00 of size 256 next 638\n2023-01-24 17:53:58.090310: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461d00 of size 256 next 639\n2023-01-24 17:53:58.090345: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461e00 of size 256 next 640\n2023-01-24 17:53:58.090381: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e461f00 of size 256 next 641\n2023-01-24 17:53:58.090415: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462000 of size 256 next 679\n2023-01-24 17:53:58.090450: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462100 of size 256 next 681\n2023-01-24 17:53:58.090486: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462200 of size 256 next 648\n2023-01-24 17:53:58.090528: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462300 of size 256 next 695\n2023-01-24 17:53:58.090564: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462400 of size 256 next 676\n2023-01-24 17:53:58.090599: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462500 of size 256 next 672\n2023-01-24 17:53:58.090634: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462600 of size 256 next 671\n2023-01-24 17:53:58.090669: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462700 of size 512 next 666\n2023-01-24 17:53:58.090705: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462900 of size 512 next 665\n2023-01-24 17:53:58.090739: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e462b00 of size 1792 next 678\n2023-01-24 17:53:58.090776: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e463200 of size 1792 next 683\n2023-01-24 17:53:58.090811: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e463900 of size 1024 next 660\n2023-01-24 17:53:58.090845: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e463d00 of size 2048 next 656\n2023-01-24 17:53:58.090879: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e464500 of size 2048 next 650\n2023-01-24 17:53:58.090915: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e464d00 of size 4096 next 649\n2023-01-24 17:53:58.090950: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e465d00 of size 4096 next 698\n2023-01-24 17:53:58.090984: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e466d00 of size 2048 next 692\n2023-01-24 17:53:58.091017: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e467500 of size 3072 next 644\n2023-01-24 17:53:58.091051: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e468100 of size 9216 next 645\n2023-01-24 17:53:58.091084: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46a500 of size 1024 next 688\n2023-01-24 17:53:58.091119: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46a900 of size 1024 next 691\n2023-01-24 17:53:58.091154: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46ad00 of size 512 next 687\n2023-01-24 17:53:58.091188: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46af00 of size 512 next 643\n2023-01-24 17:53:58.091234: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46b100 of size 256 next 699\n2023-01-24 17:53:58.091272: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46b200 of size 256 next 694\n2023-01-24 17:53:58.091307: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46b300 of size 256 next 696\n2023-01-24 17:53:58.091349: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46b400 of size 256 next 701\n2023-01-24 17:53:58.091384: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46b500 of size 256 next 703\n2023-01-24 17:53:58.091419: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46b600 of size 14080 next 680\n2023-01-24 17:53:58.091454: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e46ed00 of size 18432 next 682\n2023-01-24 17:53:58.091488: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473500 of size 256 next 705\n2023-01-24 17:53:58.091530: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473600 of size 256 next 707\n2023-01-24 17:53:58.091566: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473700 of size 256 next 708\n2023-01-24 17:53:58.091600: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473800 of size 256 next 709\n2023-01-24 17:53:58.091636: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473900 of size 256 next 710\n2023-01-24 17:53:58.091669: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473a00 of size 256 next 713\n2023-01-24 17:53:58.091705: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e473b00 of size 1792 next 714\n2023-01-24 17:53:58.091739: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474200 of size 256 next 715\n2023-01-24 17:53:58.091773: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474300 of size 256 next 716\n2023-01-24 17:53:58.091809: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474400 of size 256 next 718\n2023-01-24 17:53:58.091844: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474500 of size 256 next 720\n2023-01-24 17:53:58.091880: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474600 of size 256 next 721\n2023-01-24 17:53:58.091916: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474700 of size 256 next 722\n2023-01-24 17:53:58.091950: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474800 of size 512 next 723\n2023-01-24 17:53:58.091985: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474a00 of size 512 next 724\n2023-01-24 17:53:58.092018: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e474c00 of size 1024 next 725\n2023-01-24 17:53:58.092052: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e475000 of size 1024 next 726\n2023-01-24 17:53:58.092087: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e475400 of size 1536 next 706\n2023-01-24 17:53:58.092127: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e475a00 of size 18176 next 704\n2023-01-24 17:53:58.092162: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e47a100 of size 46080 next 674\n2023-01-24 17:53:58.092196: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e485500 of size 36864 next 675\n2023-01-24 17:53:58.092243: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e48e500 of size 36864 next 702\n2023-01-24 17:53:58.092280: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e497500 of size 18432 next 717\n2023-01-24 17:53:58.092314: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49bd00 of size 2048 next 727\n2023-01-24 17:53:58.092347: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49c500 of size 2048 next 728\n2023-01-24 17:53:58.092383: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49cd00 of size 4096 next 730\n2023-01-24 17:53:58.092417: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49dd00 of size 4096 next 731\n2023-01-24 17:53:58.092452: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49ed00 of size 2048 next 733\n2023-01-24 17:53:58.092485: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49f500 of size 2048 next 735\n2023-01-24 17:53:58.092527: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e49fd00 of size 1024 next 737\n2023-01-24 17:53:58.092563: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4a0100 of size 512 next 739\n2023-01-24 17:53:58.092597: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4a0300 of size 512 next 673\n2023-01-24 17:53:58.092631: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4a0500 of size 73728 next 677\n2023-01-24 17:53:58.092667: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4b2500 of size 36864 next 719\n2023-01-24 17:53:58.092701: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4bb500 of size 73728 next 700\n2023-01-24 17:53:58.092737: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4cd500 of size 184320 next 670\n2023-01-24 17:53:58.092771: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e4fa500 of size 147456 next 667\n2023-01-24 17:53:58.092806: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e51e500 of size 147456 next 693\n2023-01-24 17:53:58.092841: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e542500 of size 147456 next 668\n2023-01-24 17:53:58.092876: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e566500 of size 294912 next 669\n2023-01-24 17:53:58.092909: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e5ae500 of size 442368 next 697\n2023-01-24 17:53:58.092950: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e61a500 of size 737280 next 663\n2023-01-24 17:53:58.092984: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e6ce500 of size 589824 next 664\n2023-01-24 17:53:58.093018: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e75e500 of size 589824 next 653\n2023-01-24 17:53:58.093052: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e7ee500 of size 589824 next 661\n2023-01-24 17:53:58.093085: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e87e500 of size 1179648 next 662\n2023-01-24 17:53:58.093119: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35e99e500 of size 1769472 next 689\n2023-01-24 17:53:58.093153: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35eb4e500 of size 2949120 next 654\n2023-01-24 17:53:58.093188: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35ee1e500 of size 2359296 next 659\n2023-01-24 17:53:58.093231: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35f05e500 of size 2359296 next 685\n2023-01-24 17:53:58.093269: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35f29e500 of size 2359296 next 658\n2023-01-24 17:53:58.093304: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35f4de500 of size 4718592 next 642\n2023-01-24 17:53:58.093339: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa35f95e500 of size 7077888 next 686\n2023-01-24 17:53:58.093379: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36001e500 of size 11796480 next 655\n2023-01-24 17:53:58.093415: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa360b5e500 of size 9437184 next 657\n2023-01-24 17:53:58.093450: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36145e500 of size 9437184 next 684\n2023-01-24 17:53:58.093486: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa361d5e500 of size 9437184 next 652\n2023-01-24 17:53:58.093555: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36265e500 of size 18874368 next 651\n2023-01-24 17:53:58.093594: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36385e500 of size 44304640 next 400\n2023-01-24 17:53:58.093630: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629ee00 of size 256 next 434\n2023-01-24 17:53:58.093665: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629ef00 of size 256 next 422\n2023-01-24 17:53:58.093699: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f000 of size 256 next 441\n2023-01-24 17:53:58.093733: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f100 of size 256 next 455\n2023-01-24 17:53:58.093770: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f200 of size 256 next 430\n2023-01-24 17:53:58.093810: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f300 of size 256 next 421\n2023-01-24 17:53:58.093847: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f400 of size 256 next 439\n2023-01-24 17:53:58.093880: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f500 of size 512 next 402\n2023-01-24 17:53:58.093915: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f700 of size 512 next 460\n2023-01-24 17:53:58.093949: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629f900 of size 1280 next 435\n2023-01-24 17:53:58.093984: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36629fe00 of size 1792 next 408\n2023-01-24 17:53:58.094017: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a0500 of size 1024 next 464\n2023-01-24 17:53:58.094061: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a0900 of size 2048 next 447\n2023-01-24 17:53:58.094095: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a1100 of size 2048 next 403\n2023-01-24 17:53:58.094128: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a1900 of size 4096 next 465\n2023-01-24 17:53:58.094163: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a2900 of size 4096 next 443\n2023-01-24 17:53:58.094196: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a3900 of size 2048 next 444\n2023-01-24 17:53:58.094242: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a4100 of size 3072 next 438\n2023-01-24 17:53:58.094278: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a4d00 of size 9216 next 459\n2023-01-24 17:53:58.094313: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7100 of size 1024 next 425\n2023-01-24 17:53:58.094346: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7500 of size 1024 next 419\n2023-01-24 17:53:58.094378: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7900 of size 512 next 413\n2023-01-24 17:53:58.094413: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7b00 of size 512 next 463\n2023-01-24 17:53:58.094447: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7d00 of size 256 next 462\n2023-01-24 17:53:58.094482: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7e00 of size 256 next 415\n2023-01-24 17:53:58.094525: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a7f00 of size 256 next 426\n2023-01-24 17:53:58.094562: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a8000 of size 256 next 428\n2023-01-24 17:53:58.094596: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a8100 of size 256 next 407\n2023-01-24 17:53:58.094629: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a8200 of size 2048 next 216\n2023-01-24 17:53:58.094664: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a8a00 of size 1024 next 214\n2023-01-24 17:53:58.094697: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a8e00 of size 1024 next 212\n2023-01-24 17:53:58.094732: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9200 of size 256 next 207\n2023-01-24 17:53:58.094767: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9300 of size 256 next 205\n2023-01-24 17:53:58.094801: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9400 of size 256 next 203\n2023-01-24 17:53:58.094835: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9500 of size 256 next 201\n2023-01-24 17:53:58.094871: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9600 of size 256 next 200\n2023-01-24 17:53:58.094905: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9700 of size 256 next 199\n2023-01-24 17:53:58.094942: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9800 of size 256 next 198\n2023-01-24 17:53:58.094976: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662a9900 of size 1792 next 197\n2023-01-24 17:53:58.095012: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa000 of size 256 next 196\n2023-01-24 17:53:58.095046: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa100 of size 256 next 194\n2023-01-24 17:53:58.095081: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa200 of size 256 next 192\n2023-01-24 17:53:58.095116: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa300 of size 256 next 190\n2023-01-24 17:53:58.095151: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa400 of size 256 next 188\n2023-01-24 17:53:58.095190: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa500 of size 256 next 186\n2023-01-24 17:53:58.095237: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa600 of size 512 next 184\n2023-01-24 17:53:58.095273: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aa800 of size 512 next 182\n2023-01-24 17:53:58.095307: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aaa00 of size 1024 next 180\n2023-01-24 17:53:58.095342: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662aae00 of size 1024 next 178\n2023-01-24 17:53:58.095378: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662ab200 of size 1792 next 412\n2023-01-24 17:53:58.095412: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662ab900 of size 18432 next 427\n2023-01-24 17:53:58.095447: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b0100 of size 256 next 467\n2023-01-24 17:53:58.095482: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b0200 of size 256 next 469\n2023-01-24 17:53:58.095524: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b0300 of size 256 next 470\n2023-01-24 17:53:58.095562: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b0400 of size 256 next 89\n2023-01-24 17:53:58.095597: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b0500 of size 256 next 472\n2023-01-24 17:53:58.095633: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b0600 of size 4096 next 220\n2023-01-24 17:53:58.095667: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b1600 of size 4096 next 468\n2023-01-24 17:53:58.095701: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b2600 of size 18176 next 453\n2023-01-24 17:53:58.095736: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662b6d00 of size 46080 next 440\n2023-01-24 17:53:58.095776: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662c2100 of size 36864 next 399\n2023-01-24 17:53:58.095812: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662cb100 of size 36864 next 431\n2023-01-24 17:53:58.095847: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662d4100 of size 18432 next 72\n2023-01-24 17:53:58.095881: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662d8900 of size 9216 next 195\n2023-01-24 17:53:58.095916: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662dad00 of size 2048 next 176\n2023-01-24 17:53:58.095950: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662db500 of size 2048 next 174\n2023-01-24 17:53:58.095985: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662dbd00 of size 5120 next 452\n2023-01-24 17:53:58.096018: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662dd100 of size 73728 next 432\n2023-01-24 17:53:58.096052: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662ef100 of size 36864 next 103\n2023-01-24 17:53:58.096086: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3662f8100 of size 73728 next 451\n2023-01-24 17:53:58.096121: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36630a100 of size 184320 next 449\n2023-01-24 17:53:58.096154: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa366337100 of size 147456 next 405\n2023-01-24 17:53:58.096188: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36635b100 of size 147456 next 417\n2023-01-24 17:53:58.096230: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36637f100 of size 147456 next 401\n2023-01-24 17:53:58.096268: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3663a3100 of size 294912 next 450\n2023-01-24 17:53:58.096304: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3663eb100 of size 442368 next 429\n2023-01-24 17:53:58.096339: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa366457100 of size 737280 next 461\n2023-01-24 17:53:58.096372: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36650b100 of size 589824 next 420\n2023-01-24 17:53:58.096407: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36659b100 of size 589824 next 416\n2023-01-24 17:53:58.096442: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36662b100 of size 589824 next 418\n2023-01-24 17:53:58.096476: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3666bb100 of size 1179648 next 456\n2023-01-24 17:53:58.096525: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3667db100 of size 1769472 next 454\n2023-01-24 17:53:58.096562: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36698b100 of size 2949120 next 448\n2023-01-24 17:53:58.096597: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa366c5b100 of size 2359296 next 289\n2023-01-24 17:53:58.096630: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa366e9b100 of size 2359296 next 437\n2023-01-24 17:53:58.096665: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3670db100 of size 2359296 next 436\n2023-01-24 17:53:58.096699: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36731b100 of size 4718592 next 409\n2023-01-24 17:53:58.096730: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36779b100 of size 11550464 next 410\n2023-01-24 17:53:58.096764: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36829f000 of size 256 next 406\n2023-01-24 17:53:58.096798: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36829f100 of size 4718592 next 104\n2023-01-24 17:53:58.096831: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36871f100 of size 2359296 next 213\n2023-01-24 17:53:58.096864: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36895f100 of size 2359296 next 411\n2023-01-24 17:53:58.096899: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa368b9f100 of size 9437184 next 446\n2023-01-24 17:53:58.096932: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36949f100 of size 9437184 next 442\n2023-01-24 17:53:58.096966: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa369d9f100 of size 7077888 next 433\n2023-01-24 17:53:58.097002: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36a45f100 of size 21233664 next 445\n2023-01-24 17:53:58.097034: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36b89f100 of size 18874368 next 466\n2023-01-24 17:53:58.097067: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36ca9f100 of size 37748736 next 424\n2023-01-24 17:53:58.097101: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa36ee9f100 of size 37748736 next 414\n2023-01-24 17:53:58.097133: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37129f100 of size 28311552 next 404\n2023-01-24 17:53:58.097167: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa372d9f100 of size 28311552 next 217\n2023-01-24 17:53:58.097202: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37489f100 of size 9437184 next 218\n2023-01-24 17:53:58.097249: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37519f100 of size 7077888 next 215\n2023-01-24 17:53:58.097288: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37585f100 of size 589824 next 211\n2023-01-24 17:53:58.097322: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3758ef100 of size 442368 next 208\n2023-01-24 17:53:58.097355: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37595b100 of size 147456 next 100\n2023-01-24 17:53:58.097391: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37597f100 of size 110592 next 206\n2023-01-24 17:53:58.097424: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37599a100 of size 36864 next 204\n2023-01-24 17:53:58.097458: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3759a3100 of size 27648 next 202\n2023-01-24 17:53:58.097491: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3759a9d00 of size 18432 next 193\n2023-01-24 17:53:58.097532: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3759ae500 of size 36864 next 191\n2023-01-24 17:53:58.097567: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3759b7500 of size 73728 next 189\n2023-01-24 17:53:58.097600: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3759c9500 of size 147456 next 187\n2023-01-24 17:53:58.097634: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3759ed500 of size 294912 next 185\n2023-01-24 17:53:58.097668: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa375a35500 of size 589824 next 183\n2023-01-24 17:53:58.097702: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa375ac5500 of size 1179648 next 181\n2023-01-24 17:53:58.097737: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa375be5500 of size 2359296 next 179\n2023-01-24 17:53:58.097770: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa375e25500 of size 4718592 next 177\n2023-01-24 17:53:58.097802: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3762a5500 of size 9437184 next 175\n2023-01-24 17:53:58.097836: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa376ba5500 of size 18874368 next 173\n2023-01-24 17:53:58.097869: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa377da5500 of size 37748736 next 172\n2023-01-24 17:53:58.097904: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37a1a5500 of size 4096 next 171\n2023-01-24 17:53:58.097941: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37a1a6500 of size 28311552 next 170\n2023-01-24 17:53:58.097974: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37bca6500 of size 2048 next 169\n2023-01-24 17:53:58.098013: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37bca6d00 of size 9437184 next 168\n2023-01-24 17:53:58.098048: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37c5a6d00 of size 2048 next 167\n2023-01-24 17:53:58.098081: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37c5a7500 of size 7077888 next 166\n2023-01-24 17:53:58.098114: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37cc67500 of size 2359296 next 165\n2023-01-24 17:53:58.098148: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37cea7500 of size 1024 next 164\n2023-01-24 17:53:58.098179: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37cea7900 of size 1769472 next 163\n2023-01-24 17:53:58.098213: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d057900 of size 512 next 162\n2023-01-24 17:53:58.098259: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d057b00 of size 589824 next 161\n2023-01-24 17:53:58.098293: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d0e7b00 of size 512 next 160\n2023-01-24 17:53:58.098327: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d0e7d00 of size 442368 next 159\n2023-01-24 17:53:58.098361: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d153d00 of size 256 next 158\n2023-01-24 17:53:58.098393: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d153e00 of size 147456 next 157\n2023-01-24 17:53:58.098427: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d177e00 of size 256 next 156\n2023-01-24 17:53:58.098461: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d177f00 of size 110592 next 155\n2023-01-24 17:53:58.098494: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d192f00 of size 256 next 154\n2023-01-24 17:53:58.098534: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d193000 of size 36864 next 153\n2023-01-24 17:53:58.098566: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d19c000 of size 256 next 152\n2023-01-24 17:53:58.098601: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d19c100 of size 27648 next 151\n2023-01-24 17:53:58.098633: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a2d00 of size 256 next 150\n2023-01-24 17:53:58.098666: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a2e00 of size 9216 next 149\n2023-01-24 17:53:58.098700: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5200 of size 256 next 148\n2023-01-24 17:53:58.098733: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5300 of size 256 next 147\n2023-01-24 17:53:58.098765: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5400 of size 256 next 146\n2023-01-24 17:53:58.098799: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5500 of size 256 next 132\n2023-01-24 17:53:58.098832: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5600 of size 256 next 144\n2023-01-24 17:53:58.098865: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5700 of size 256 next 143\n2023-01-24 17:53:58.098899: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5800 of size 256 next 142\n2023-01-24 17:53:58.098937: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5900 of size 256 next 141\n2023-01-24 17:53:58.098969: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5a00 of size 256 next 140\n2023-01-24 17:53:58.099003: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5b00 of size 256 next 139\n2023-01-24 17:53:58.099035: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5c00 of size 256 next 138\n2023-01-24 17:53:58.099067: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5d00 of size 256 next 137\n2023-01-24 17:53:58.099102: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5e00 of size 256 next 133\n2023-01-24 17:53:58.099136: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a5f00 of size 256 next 45\n2023-01-24 17:53:58.099168: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6000 of size 256 next 21\n2023-01-24 17:53:58.099202: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6100 of size 256 next 115\n2023-01-24 17:53:58.099245: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6200 of size 256 next 68\n2023-01-24 17:53:58.099279: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6300 of size 256 next 78\n2023-01-24 17:53:58.099313: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6400 of size 256 next 22\n2023-01-24 17:53:58.099346: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6500 of size 256 next 88\n2023-01-24 17:53:58.099378: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6600 of size 512 next 26\n2023-01-24 17:53:58.099410: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6800 of size 512 next 31\n2023-01-24 17:53:58.099444: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6a00 of size 1280 next 119\n2023-01-24 17:53:58.099478: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a6f00 of size 1792 next 121\n2023-01-24 17:53:58.099511: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a7600 of size 1024 next 80\n2023-01-24 17:53:58.099552: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a7a00 of size 2048 next 113\n2023-01-24 17:53:58.099586: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a8200 of size 2048 next 117\n2023-01-24 17:53:58.099618: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a8a00 of size 4096 next 44\n2023-01-24 17:53:58.099652: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1a9a00 of size 4096 next 95\n2023-01-24 17:53:58.099685: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1aaa00 of size 2048 next 128\n2023-01-24 17:53:58.099718: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1ab200 of size 3072 next 34\n2023-01-24 17:53:58.099749: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1abe00 of size 9216 next 135\n2023-01-24 17:53:58.099781: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1ae200 of size 1024 next 471\n2023-01-24 17:53:58.099815: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1ae600 of size 1024 next 54\n2023-01-24 17:53:58.099848: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1aea00 of size 512 next 16\n2023-01-24 17:53:58.099882: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1aec00 of size 512 next 73\n2023-01-24 17:53:58.099915: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1aee00 of size 256 next 61\n2023-01-24 17:53:58.099951: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1aef00 of size 256 next 83\n2023-01-24 17:53:58.099984: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1af000 of size 256 next 30\n2023-01-24 17:53:58.100016: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1af100 of size 256 next 79\n2023-01-24 17:53:58.100047: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1af200 of size 256 next 60\n2023-01-24 17:53:58.100079: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1af300 of size 4096 next 508\n2023-01-24 17:53:58.100117: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b0300 of size 2048 next 510\n2023-01-24 17:53:58.100149: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b0b00 of size 2048 next 511\n2023-01-24 17:53:58.100184: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1300 of size 1024 next 513\n2023-01-24 17:53:58.100228: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1700 of size 512 next 517\n2023-01-24 17:53:58.100264: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1900 of size 512 next 518\n2023-01-24 17:53:58.100295: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1b00 of size 256 next 520\n2023-01-24 17:53:58.100327: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1c00 of size 256 next 522\n2023-01-24 17:53:58.100360: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1d00 of size 256 next 524\n2023-01-24 17:53:58.100391: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1e00 of size 256 next 526\n2023-01-24 17:53:58.100424: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b1f00 of size 256 next 528\n2023-01-24 17:53:58.100454: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2000 of size 256 next 530\n2023-01-24 17:53:58.100488: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2100 of size 256 next 532\n2023-01-24 17:53:58.100531: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2200 of size 256 next 533\n2023-01-24 17:53:58.100566: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2300 of size 256 next 534\n2023-01-24 17:53:58.100598: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2400 of size 256 next 535\n2023-01-24 17:53:58.100633: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2500 of size 256 next 538\n2023-01-24 17:53:58.100666: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2600 of size 256 next 539\n2023-01-24 17:53:58.100698: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2700 of size 256 next 541\n2023-01-24 17:53:58.100731: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2800 of size 256 next 543\n2023-01-24 17:53:58.100762: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2900 of size 256 next 92\n2023-01-24 17:53:58.100795: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b2a00 of size 18432 next 65\n2023-01-24 17:53:58.100828: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7200 of size 256 next 40\n2023-01-24 17:53:58.100860: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7300 of size 256 next 475\n2023-01-24 17:53:58.100897: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7400 of size 256 next 476\n2023-01-24 17:53:58.100929: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7500 of size 256 next 477\n2023-01-24 17:53:58.100963: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7600 of size 256 next 478\n2023-01-24 17:53:58.100996: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7700 of size 1024 next 498\n2023-01-24 17:53:58.101028: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7b00 of size 1024 next 499\n2023-01-24 17:53:58.101061: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b7f00 of size 1792 next 481\n2023-01-24 17:53:58.101092: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b8600 of size 1792 next 482\n2023-01-24 17:53:58.101125: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b8d00 of size 256 next 479\n2023-01-24 17:53:58.101159: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b8e00 of size 256 next 484\n2023-01-24 17:53:58.101191: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b8f00 of size 256 next 487\n2023-01-24 17:53:58.101234: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b9000 of size 256 next 488\n2023-01-24 17:53:58.101269: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b9100 of size 256 next 491\n2023-01-24 17:53:58.101302: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b9200 of size 256 next 492\n2023-01-24 17:53:58.101336: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b9300 of size 512 next 495\n2023-01-24 17:53:58.101378: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b9500 of size 512 next 12\n2023-01-24 17:53:58.101415: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1b9700 of size 18176 next 50\n2023-01-24 17:53:58.101446: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1bde00 of size 46080 next 64\n2023-01-24 17:53:58.101480: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1c9200 of size 36864 next 85\n2023-01-24 17:53:58.101514: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1d2200 of size 36864 next 51\n2023-01-24 17:53:58.101556: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1db200 of size 2048 next 500\n2023-01-24 17:53:58.101588: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1dba00 of size 2048 next 503\n2023-01-24 17:53:58.101622: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1dc200 of size 5120 next 483\n2023-01-24 17:53:58.101654: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1dd600 of size 9216 next 480\n2023-01-24 17:53:58.101693: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1dfa00 of size 9216 next 542\n2023-01-24 17:53:58.101729: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1e1e00 of size 2048 next 556\n2023-01-24 17:53:58.101761: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1e2600 of size 7168 next 136\n2023-01-24 17:53:58.101798: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1e4200 of size 73728 next 15\n2023-01-24 17:53:58.101833: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f6200 of size 9216 next 531\n2023-01-24 17:53:58.101872: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f8600 of size 1792 next 540\n2023-01-24 17:53:58.101913: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f8d00 of size 256 next 546\n2023-01-24 17:53:58.101952: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f8e00 of size 256 next 547\n2023-01-24 17:53:58.101983: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f8f00 of size 256 next 549\n2023-01-24 17:53:58.102016: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f9000 of size 512 next 550\n2023-01-24 17:53:58.102051: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f9200 of size 512 next 552\n2023-01-24 17:53:58.102084: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f9400 of size 1024 next 554\n2023-01-24 17:53:58.102115: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f9800 of size 1024 next 555\n2023-01-24 17:53:58.102148: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1f9c00 of size 3584 next 486\n2023-01-24 17:53:58.102183: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1faa00 of size 18432 next 485\n2023-01-24 17:53:58.102213: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d1ff200 of size 36864 next 545\n2023-01-24 17:53:58.102259: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d208200 of size 36864 next 77\n2023-01-24 17:53:58.102299: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d211200 of size 184320 next 82\n2023-01-24 17:53:58.102336: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d23e200 of size 147456 next 219\n2023-01-24 17:53:58.102366: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d262200 of size 147456 next 41\n2023-01-24 17:53:58.102399: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d286200 of size 36864 next 489\n2023-01-24 17:53:58.102432: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d28f200 of size 36864 next 527\n2023-01-24 17:53:58.102474: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d298200 of size 27648 next 529\n2023-01-24 17:53:58.102521: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d29ee00 of size 18432 next 544\n2023-01-24 17:53:58.102554: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a3600 of size 4096 next 558\n2023-01-24 17:53:58.102586: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a4600 of size 2048 next 559\n2023-01-24 17:53:58.102624: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a4e00 of size 2048 next 561\n2023-01-24 17:53:58.102662: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a5600 of size 1024 next 562\n2023-01-24 17:53:58.102693: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a5a00 of size 1024 next 564\n2023-01-24 17:53:58.102729: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a5e00 of size 512 next 566\n2023-01-24 17:53:58.102759: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6000 of size 512 next 567\n2023-01-24 17:53:58.102790: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6200 of size 256 next 569\n2023-01-24 17:53:58.102830: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6300 of size 256 next 570\n2023-01-24 17:53:58.102869: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6400 of size 256 next 572\n2023-01-24 17:53:58.102906: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6500 of size 256 next 573\n2023-01-24 17:53:58.102938: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6600 of size 256 next 575\n2023-01-24 17:53:58.102982: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2a6700 of size 15104 next 122\n2023-01-24 17:53:58.103011: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2aa200 of size 294912 next 70\n2023-01-24 17:53:58.103051: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d2f2200 of size 73728 next 490\n2023-01-24 17:53:58.103083: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d304200 of size 147456 next 548\n2023-01-24 17:53:58.103117: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d328200 of size 221184 next 81\n2023-01-24 17:53:58.103152: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d35e200 of size 737280 next 90\n2023-01-24 17:53:58.103193: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d412200 of size 589824 next 125\n2023-01-24 17:53:58.103241: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d4a2200 of size 589824 next 75\n2023-01-24 17:53:58.103274: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d532200 of size 147456 next 493\n2023-01-24 17:53:58.103306: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d556200 of size 147456 next 523\n2023-01-24 17:53:58.103339: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d57a200 of size 110592 next 525\n2023-01-24 17:53:58.103382: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d595200 of size 184320 next 86\n2023-01-24 17:53:58.103417: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d5c2200 of size 1179648 next 120\n2023-01-24 17:53:58.103448: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d6e2200 of size 294912 next 494\n2023-01-24 17:53:58.103481: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d72a200 of size 589824 next 551\n2023-01-24 17:53:58.103537: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d7ba200 of size 884736 next 69\n2023-01-24 17:53:58.103573: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37d892200 of size 2949120 next 114\n2023-01-24 17:53:58.103605: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37db62200 of size 2359296 next 35\n2023-01-24 17:53:58.103645: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37dda2200 of size 2359296 next 145\n2023-01-24 17:53:58.103680: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37dfe2200 of size 589824 next 497\n2023-01-24 17:53:58.103710: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37e072200 of size 589824 next 519\n2023-01-24 17:53:58.103741: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37e102200 of size 442368 next 521\n2023-01-24 17:53:58.103781: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37e16e200 of size 737280 next 129\n2023-01-24 17:53:58.103826: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37e222200 of size 4718592 next 130\n2023-01-24 17:53:58.103860: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa37e6a2200 of size 28311552 next 124\n2023-01-24 17:53:58.103895: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3801a2200 of size 54803968 next 25\n2023-01-24 17:53:58.103928: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3835e6000 of size 256 next 97\n2023-01-24 17:53:58.103958: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3835e6100 of size 1179648 next 496\n2023-01-24 17:53:58.103998: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa383706100 of size 1769472 next 516\n2023-01-24 17:53:58.104037: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3838b6100 of size 2949120 next 501\n2023-01-24 17:53:58.104070: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa383b86100 of size 3538944 next 134\n2023-01-24 17:53:58.104104: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa383ee6100 of size 9437184 next 127\n2023-01-24 17:53:58.104143: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3847e6100 of size 9437184 next 512\n2023-01-24 17:53:58.104175: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3850e6100 of size 9437184 next 506\n2023-01-24 17:53:58.104209: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3859e6100 of size 18874368 next 126\n2023-01-24 17:53:58.104258: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa386be6100 of size 18874368 next 116\n2023-01-24 17:53:58.104291: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa387de6100 of size 2359296 next 515\n2023-01-24 17:53:58.104326: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa388026100 of size 1179648 next 553\n2023-01-24 17:53:58.104366: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa388146100 of size 442368 next 568\n2023-01-24 17:53:58.104407: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881b2100 of size 110592 next 571\n2023-01-24 17:53:58.104440: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881cd100 of size 27648 next 574\n2023-01-24 17:53:58.104475: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d3d00 of size 256 next 576\n2023-01-24 17:53:58.104509: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d3e00 of size 256 next 577\n2023-01-24 17:53:58.104549: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d3f00 of size 256 next 578\n2023-01-24 17:53:58.104585: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d4000 of size 1792 next 579\n2023-01-24 17:53:58.104624: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d4700 of size 256 next 580\n2023-01-24 17:53:58.104655: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d4800 of size 9216 next 581\n2023-01-24 17:53:58.104691: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d6c00 of size 256 next 582\n2023-01-24 17:53:58.104721: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881d6d00 of size 18432 next 583\n2023-01-24 17:53:58.104753: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881db500 of size 256 next 584\n2023-01-24 17:53:58.104793: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881db600 of size 36864 next 585\n2023-01-24 17:53:58.104830: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881e4600 of size 256 next 586\n2023-01-24 17:53:58.104865: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881e4700 of size 73728 next 587\n2023-01-24 17:53:58.104899: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881f6700 of size 256 next 588\n2023-01-24 17:53:58.104931: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3881f6800 of size 147456 next 589\n2023-01-24 17:53:58.104965: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa38821a800 of size 256 next 590\n2023-01-24 17:53:58.104999: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa38821a900 of size 309248 next 502\n2023-01-24 17:53:58.105031: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa388266100 of size 4718592 next 118\n2023-01-24 17:53:58.105065: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3886e6100 of size 11109888 next 458\n2023-01-24 17:53:58.105101: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa38917e700 of size 256 next 457\n2023-01-24 17:53:58.105141: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa38917e800 of size 405012480 next 711\n2023-01-24 17:53:58.105174: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3a13be800 of size 540016640 next 712\n2023-01-24 17:53:58.105210: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3c16be800 of size 100663296 next 822\n2023-01-24 17:53:58.105255: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3c76be800 of size 536870912 next 825\n2023-01-24 17:53:58.105289: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3e76be800 of size 134217728 next 829\n2023-01-24 17:53:58.105322: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa3ef6be800 of size 351012352 next 76\n2023-01-24 17:53:58.105358: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa40457ee00 of size 256 next 71\n2023-01-24 17:53:58.105392: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa40457ef00 of size 536870912 next 828\n2023-01-24 17:53:58.105429: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa42457ef00 of size 268435456 next 830\n2023-01-24 17:53:58.105463: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa43457ef00 of size 67108864 next 831\n2023-01-24 17:53:58.105503: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa43857ef00 of size 134217728 next 832\n2023-01-24 17:53:58.105545: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa44057ef00 of size 134217728 next 833\n2023-01-24 17:53:58.105577: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa44857ef00 of size 67108864 next 834\n2023-01-24 17:53:58.105614: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa44c57ef00 of size 67108864 next 835\n2023-01-24 17:53:58.105648: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45057ef00 of size 33554432 next 836\n2023-01-24 17:53:58.105682: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45257ef00 of size 33554432 next 837\n2023-01-24 17:53:58.105723: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45457ef00 of size 8388608 next 838\n2023-01-24 17:53:58.105773: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa454d7ef00 of size 16777216 next 839\n2023-01-24 17:53:58.105808: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa455d7ef00 of size 16777216 next 840\n2023-01-24 17:53:58.105845: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa456d7ef00 of size 4194304 next 841\n2023-01-24 17:53:58.105879: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45717ef00 of size 8388608 next 842\n2023-01-24 17:53:58.105914: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45797ef00 of size 8388608 next 843\n2023-01-24 17:53:58.105950: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45817ef00 of size 16777216 next 845\n2023-01-24 17:53:58.105980: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45917ef00 of size 16777216 next 847\n2023-01-24 17:53:58.106014: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45a17ef00 of size 41943040 next 846\n2023-01-24 17:53:58.106053: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45c97ef00 of size 50331648 next 844\n2023-01-24 17:53:58.106086: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa45f97ef00 of size 33554432 next 849\n2023-01-24 17:53:58.106120: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa46197ef00 of size 100663296 next 848\n2023-01-24 17:53:58.106153: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa46797ef00 of size 100663296 next 850\n2023-01-24 17:53:58.106189: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa46d97ef00 of size 67108864 next 852\n2023-01-24 17:53:58.106233: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa47197ef00 of size 201326592 next 851\n2023-01-24 17:53:58.106276: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa47d97ef00 of size 201326592 next 853\n2023-01-24 17:53:58.106317: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa48997ef00 of size 134217728 next 855\n2023-01-24 17:53:58.106351: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa49197ef00 of size 268435456 next 858\n2023-01-24 17:53:58.106384: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] Free at 7fa4a197ef00 of size 134217728 next 854\n2023-01-24 17:53:58.106421: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa4a997ef00 of size 402653184 next 856\n2023-01-24 17:53:58.106450: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa4c197ef00 of size 268435456 next 860\n2023-01-24 17:53:58.106485: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] Free at 7fa4d197ef00 of size 805306368 next 857\n2023-01-24 17:53:58.106532: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa50197ef00 of size 805306368 next 859\n2023-01-24 17:53:58.106567: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] Free at 7fa53197ef00 of size 1073741824 next 862\n2023-01-24 17:53:58.106603: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] InUse at 7fa57197ef00 of size 1073741824 next 861\n2023-01-24 17:53:58.106636: I tensorflow/core/common_runtime/bfc_allocator.cc:1060] Free at 7fa5b197ef00 of size 822350080 next 18446744073709551615\n2023-01-24 17:53:58.106669: I tensorflow/core/common_runtime/bfc_allocator.cc:1065] Summary of in-use Chunks by size: \n2023-01-24 17:53:58.106715: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 288 Chunks of size 256 totalling 72.0KiB\n2023-01-24 17:53:58.106750: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 52 Chunks of size 512 totalling 26.0KiB\n2023-01-24 17:53:58.106787: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 44 Chunks of size 1024 totalling 44.0KiB\n2023-01-24 17:53:58.106830: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 1280 totalling 5.0KiB\n2023-01-24 17:53:58.106864: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 2 Chunks of size 1536 totalling 3.0KiB\n2023-01-24 17:53:58.106901: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 16 Chunks of size 1792 totalling 28.0KiB\n2023-01-24 17:53:58.106935: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 46 Chunks of size 2048 totalling 92.0KiB\n2023-01-24 17:53:58.106969: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 2816 totalling 2.8KiB\n2023-01-24 17:53:58.107005: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 3072 totalling 12.0KiB\n2023-01-24 17:53:58.107038: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 3584 totalling 3.5KiB\n2023-01-24 17:53:58.107077: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 23 Chunks of size 4096 totalling 92.0KiB\n2023-01-24 17:53:58.107114: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 2 Chunks of size 5120 totalling 10.0KiB\n2023-01-24 17:53:58.107149: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 7168 totalling 7.0KiB\n2023-01-24 17:53:58.107184: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 17 Chunks of size 9216 totalling 153.0KiB\n2023-01-24 17:53:58.107236: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 12800 totalling 12.5KiB\n2023-01-24 17:53:58.107273: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 2 Chunks of size 14080 totalling 27.5KiB\n2023-01-24 17:53:58.107311: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 15104 totalling 14.8KiB\n2023-01-24 17:53:58.107347: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 16384 totalling 16.0KiB\n2023-01-24 17:53:58.107381: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 18176 totalling 71.0KiB\n2023-01-24 17:53:58.107418: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 13 Chunks of size 18432 totalling 234.0KiB\n2023-01-24 17:53:58.107451: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 9 Chunks of size 27648 totalling 243.0KiB\n2023-01-24 17:53:58.107487: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 26 Chunks of size 36864 totalling 936.0KiB\n2023-01-24 17:53:58.107532: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 46080 totalling 180.0KiB\n2023-01-24 17:53:58.107569: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 12 Chunks of size 73728 totalling 864.0KiB\n2023-01-24 17:53:58.107605: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 9 Chunks of size 110592 totalling 972.0KiB\n2023-01-24 17:53:58.107638: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 25 Chunks of size 147456 totalling 3.52MiB\n2023-01-24 17:53:58.107674: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 5 Chunks of size 184320 totalling 900.0KiB\n2023-01-24 17:53:58.107710: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 221184 totalling 216.0KiB\n2023-01-24 17:53:58.107744: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 8 Chunks of size 294912 totalling 2.25MiB\n2023-01-24 17:53:58.107779: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 309248 totalling 302.0KiB\n2023-01-24 17:53:58.107821: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 12 Chunks of size 442368 totalling 5.06MiB\n2023-01-24 17:53:58.107863: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 25 Chunks of size 589824 totalling 14.06MiB\n2023-01-24 17:53:58.107896: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 5 Chunks of size 737280 totalling 3.52MiB\n2023-01-24 17:53:58.107932: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 884736 totalling 864.0KiB\n2023-01-24 17:53:58.107980: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 10 Chunks of size 1179648 totalling 11.25MiB\n2023-01-24 17:53:58.108017: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 11 Chunks of size 1769472 totalling 18.56MiB\n2023-01-24 17:53:58.108052: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 25 Chunks of size 2359296 totalling 56.25MiB\n2023-01-24 17:53:58.108088: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 5 Chunks of size 2949120 totalling 14.06MiB\n2023-01-24 17:53:58.108128: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 3538944 totalling 3.38MiB\n2023-01-24 17:53:58.108167: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 4194304 totalling 4.00MiB\n2023-01-24 17:53:58.108202: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 10 Chunks of size 4718592 totalling 45.00MiB\n2023-01-24 17:53:58.108250: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 11 Chunks of size 7077888 totalling 74.25MiB\n2023-01-24 17:53:58.108285: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 3 Chunks of size 8388608 totalling 24.00MiB\n2023-01-24 17:53:58.108322: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 25 Chunks of size 9437184 totalling 225.00MiB\n2023-01-24 17:53:58.108354: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 9716224 totalling 9.27MiB\n2023-01-24 17:53:58.108390: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 11109888 totalling 10.59MiB\n2023-01-24 17:53:58.108426: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 11550464 totalling 11.01MiB\n2023-01-24 17:53:58.108466: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 3 Chunks of size 11796480 totalling 33.75MiB\n2023-01-24 17:53:58.108506: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 16777216 totalling 64.00MiB\n2023-01-24 17:53:58.108547: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 12 Chunks of size 18874368 totalling 216.00MiB\n2023-01-24 17:53:58.108583: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 21233664 totalling 20.25MiB\n2023-01-24 17:53:58.108626: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 25164800 totalling 24.00MiB\n2023-01-24 17:53:58.108668: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 11 Chunks of size 28311552 totalling 297.00MiB\n2023-01-24 17:53:58.108704: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 5 Chunks of size 33554432 totalling 160.00MiB\n2023-01-24 17:53:58.108752: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 12 Chunks of size 37748736 totalling 432.00MiB\n2023-01-24 17:53:58.108792: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 41943040 totalling 40.00MiB\n2023-01-24 17:53:58.108828: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 44304640 totalling 42.25MiB\n2023-01-24 17:53:58.108864: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 45876224 totalling 43.75MiB\n2023-01-24 17:53:58.108902: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 50007808 totalling 47.69MiB\n2023-01-24 17:53:58.108945: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 50331648 totalling 48.00MiB\n2023-01-24 17:53:58.108979: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 54803968 totalling 52.26MiB\n2023-01-24 17:53:58.109015: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 67108864 totalling 256.00MiB\n2023-01-24 17:53:58.109051: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 3 Chunks of size 100663296 totalling 288.00MiB\n2023-01-24 17:53:58.109083: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 4 Chunks of size 134217728 totalling 512.00MiB\n2023-01-24 17:53:58.109119: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 2 Chunks of size 201326592 totalling 384.00MiB\n2023-01-24 17:53:58.109156: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 3 Chunks of size 268435456 totalling 768.00MiB\n2023-01-24 17:53:58.109188: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 351012352 totalling 334.75MiB\n2023-01-24 17:53:58.109234: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 402653184 totalling 384.00MiB\n2023-01-24 17:53:58.109278: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 6 Chunks of size 405012480 totalling 2.26GiB\n2023-01-24 17:53:58.109319: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 2 Chunks of size 536870912 totalling 1.00GiB\n2023-01-24 17:53:58.109354: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 5 Chunks of size 540016640 totalling 2.51GiB\n2023-01-24 17:53:58.109392: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 805306368 totalling 768.00MiB\n2023-01-24 17:53:58.109429: I tensorflow/core/common_runtime/bfc_allocator.cc:1068] 1 Chunks of size 1073741824 totalling 1.00GiB\n2023-01-24 17:53:58.109469: I tensorflow/core/common_runtime/bfc_allocator.cc:1072] Sum Total of in-use chunks: 12.40GiB\n2023-01-24 17:53:58.109502: I tensorflow/core/common_runtime/bfc_allocator.cc:1074] total_region_allocated_bytes_: 16149905408 memory_limit_: 16149905408 available bytes: 0 curr_region_allocation_bytes_: 32299810816\n2023-01-24 17:53:58.109555: I tensorflow/core/common_runtime/bfc_allocator.cc:1080] Stats: \nLimit: 16149905408\nInUse: 13314289408\nMaxInUse: 15178384640\nNumAllocs: 459283\nMaxAllocSize: 4093640704\nReserved: 0\nPeakReserved: 0\nLargestFreeBlock: 0\n\n2023-01-24 17:53:58.109646: W tensorflow/core/common_runtime/bfc_allocator.cc:468] ************************************************************************____******______*******_____\n2023-01-24 17:53:58.110633: W tensorflow/core/framework/op_kernel.cc:1692] OP_REQUIRES failed at concat_op.cc:158 : Resource exhausted: OOM when allocating tensor with shape[32,48,512,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mResourceExhaustedError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_17/2787884476.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m32\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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 2\u001b[0m \u001b[0mresult_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mresult_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mresult_train_threshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult_train\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mresult_test_threshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult_test\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1182\u001b[0m _r=1):\n\u001b[1;32m 1183\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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 1185\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 885\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 886\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0;31m# Lifting succeeded, so variables are initialized and we can run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;31m# stateless function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 951\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 952\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiltered_flat_args\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3038\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m 3039\u001b[0m return graph_function._call_flat(\n\u001b[0;32m-> 3040\u001b[0;31m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m 3041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3042\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1962\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1963\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1964\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1965\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1966\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 594\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 597\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 598\u001b[0m outputs = execute.execute_with_cancellation(\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mResourceExhaustedError\u001b[0m: OOM when allocating tensor with shape[32,48,512,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[node model_5/concatenate_35/concat (defined at tmp/ipykernel_17/2787884476.py:1) ]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_function_18714]\n\nFunction call stack:\ntrain_function\n"],"ename":"ResourceExhaustedError","evalue":" OOM when allocating tensor with shape[32,48,512,512] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc\n\t [[node model_5/concatenate_35/concat (defined at tmp/ipykernel_17/2787884476.py:1) ]]\nHint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.\n [Op:__inference_train_function_18714]\n\nFunction call stack:\ntrain_function\n","output_type":"error"}]},{"cell_type":"code","source":"result_train = model.predict(X_train)\nresult_test = model.predict(X_test)\nresult_train_threshold = result_train > 0.5\nresult_test_threshold = result_test > 0.5\n\nresult_train_threshold = result_train_threshold.reshape(X_train.shape[0], image_size, image_size)\nresult_test_threshold = result_test_threshold.reshape(X_test.shape[0], image_size, image_size)\n\nintersection_train = np.logical_and(y_train,result_train_threshold)\nunion_train = np.logical_or(y_train,result_train_threshold)\nIoU_score_train = np.sum(intersection_train) / np.sum(union_train)\n\nintersection_test = np.logical_and(y_test,result_test_threshold)\nunion_test = np.logical_or(y_test,result_test_threshold)\nIoU_score_test = np.sum(intersection_test) / np.sum(union_test)\n\nALL_IoU_score_train.append(IoU_score_train)\nALL_IoU_score_test.append(IoU_score_test)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:55:04.675535Z","iopub.execute_input":"2023-01-24T17:55:04.675952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print (ALL_IoU_score_train)\nprint (ALL_IoU_score_test)","metadata":{"execution":{"iopub.status.busy":"2023-01-24T17:38:45.888185Z","iopub.execute_input":"2023-01-24T17:38:45.888586Z","iopub.status.idle":"2023-01-24T17:38:45.895033Z","shell.execute_reply.started":"2023-01-24T17:38:45.888546Z","shell.execute_reply":"2023-01-24T17:38:45.893895Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"[0.1749192169366493]\n[0.18573219081724004]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### print loss and save it","metadata":{}},{"cell_type":"code","source":"print(len(hist.history['loss']))\nloss_functions.append(hist.history['loss'])\nhist.history['loss']\n","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:52:21.202965Z","iopub.execute_input":"2022-11-08T14:52:21.203425Z","iopub.status.idle":"2022-11-08T14:52:21.328002Z","shell.execute_reply.started":"2022-11-08T14:52:21.203327Z","shell.execute_reply":"2022-11-08T14:52:21.3265Z"},"trusted":true},"execution_count":1,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_17/4249788558.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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 2\u001b[0m \u001b[0mloss_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'hist' is not defined"],"ename":"NameError","evalue":"name 'hist' is not defined","output_type":"error"}]},{"cell_type":"code","source":"result_train = model.predict(X_train)\nresult_test = model.predict(X_test)\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:49:36.381226Z","iopub.execute_input":"2022-11-08T14:49:36.382411Z","iopub.status.idle":"2022-11-08T14:49:43.281568Z","shell.execute_reply.started":"2022-11-08T14:49:36.382359Z","shell.execute_reply":"2022-11-08T14:49:43.280475Z"},"trusted":true},"execution_count":67,"outputs":[{"name":"stdout","text":"done\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result_train_threshold = result_train > 0.5\nresult_test_threshold = result_test > 0.5\n\nresult_train_threshold = result_train_threshold.reshape(X_train.shape[0], image_size, image_size)\nresult_test_threshold = result_test_threshold.reshape(X_test.shape[0], image_size, image_size)\n\nintersection_train = np.logical_and(y_train,result_train_threshold)\nunion_train = np.logical_or(y_train,result_train_threshold)\nIoU_score_train = np.sum(intersection_train) / np.sum(union_train)\n\nintersection_test = np.logical_and(y_test,result_test_threshold)\nunion_test = np.logical_or(y_test,result_test_threshold)\nIoU_score_test = np.sum(intersection_test) / np.sum(union_test)\n\nALL_IoU_score_train.append(IoU_score_train)\nALL_IoU_score_test.append(IoU_score_test)\n\n\nprint (IoU_score_train)\nprint (IoU_score_test)\n\n","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:49:44.908976Z","iopub.execute_input":"2022-11-08T14:49:44.909743Z","iopub.status.idle":"2022-11-08T14:49:45.972682Z","shell.execute_reply.started":"2022-11-08T14:49:44.909696Z","shell.execute_reply":"2022-11-08T14:49:45.971601Z"},"trusted":true},"execution_count":68,"outputs":[{"name":"stdout","text":"0.6080031884964875\n0.5905192005379913\n","output_type":"stream"}]},{"cell_type":"code","source":"# ALL_IoU_score_train = []\n# ALL_IoU_score_test = []\nprint (ALL_IoU_score_train)\nprint (ALL_IoU_score_test)","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:49:48.342707Z","iopub.execute_input":"2022-11-08T14:49:48.343076Z","iopub.status.idle":"2022-11-08T14:49:48.352036Z","shell.execute_reply.started":"2022-11-08T14:49:48.343044Z","shell.execute_reply":"2022-11-08T14:49:48.350764Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"[0.34786736160859255,\n 0.35139044087606913,\n 0.5097814599805182,\n 0.6120825202907157,\n 0.5905192005379913]"},"metadata":{}}]},{"cell_type":"markdown","source":"### save loss in csv and txt file","metadata":{}},{"cell_type":"code","source":"from numpy import savetxt\nsavetxt('loss_functions.csv', loss_functions, delimiter=',', fmt='%s')\nsavetxt('loss_functions.txt', loss_functions, delimiter=',', fmt='%s')","metadata":{"execution":{"iopub.status.busy":"2022-10-17T22:55:23.429358Z","iopub.execute_input":"2022-10-17T22:55:23.430118Z","iopub.status.idle":"2022-10-17T22:55:23.43726Z","shell.execute_reply.started":"2022-10-17T22:55:23.430075Z","shell.execute_reply":"2022-10-17T22:55:23.436127Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":"### read the trainig output data","metadata":{}},{"cell_type":"code","source":"testCSV = pd.read_csv(r'../input/sartorius-cell-instance-segmentation/sample_submission.csv')\ntestCSV","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:29.794282Z","iopub.execute_input":"2022-11-08T14:46:29.795409Z","iopub.status.idle":"2022-11-08T14:46:29.815343Z","shell.execute_reply.started":"2022-11-08T14:46:29.795356Z","shell.execute_reply":"2022-11-08T14:46:29.814225Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":" id predicted\n0 7ae19de7bc2a NaN\n1 d48ec7815252 NaN\n2 d8bfd1dafdc4 NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idpredicted
07ae19de7bc2aNaN
1d48ec7815252NaN
2d8bfd1dafdc4NaN
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### number of the output data","metadata":{}},{"cell_type":"code","source":"unique_testCSV = testCSV.id.unique()\nprint(\"the masks id's size is = \\n\" + str(len(unique_testCSV)))","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:31.781663Z","iopub.execute_input":"2022-11-08T14:46:31.782052Z","iopub.status.idle":"2022-11-08T14:46:31.788991Z","shell.execute_reply.started":"2022-11-08T14:46:31.782022Z","shell.execute_reply":"2022-11-08T14:46:31.78776Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stdout","text":"the masks id's size is = \n3\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### read all test images","metadata":{}},{"cell_type":"code","source":"# create a list variable for All test image\ntest_imagess = []\nfor i in range (len(unique_testCSV)):\n test_ImageId = unique_testCSV[i]\n test_img = cv2.imread('../input/sartorius-cell-instance-segmentation/test/' + test_ImageId +'.png')\n dim = (image_size, image_size) \n # resize image\n test_resized = cv2.resize(test_img, dim, interpolation = cv2.INTER_AREA)\n test_imagess.append(test_resized)","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:33.329868Z","iopub.execute_input":"2022-11-08T14:46:33.33097Z","iopub.status.idle":"2022-11-08T14:46:33.404994Z","shell.execute_reply.started":"2022-11-08T14:46:33.330922Z","shell.execute_reply":"2022-11-08T14:46:33.403907Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"markdown","source":"### plot every mask image we want from 0 to 3 to see how it works","metadata":{}},{"cell_type":"code","source":"fig, axarr = plt.subplots(figsize=(5,5))\naxarr.axis('off')\naxarr.imshow(test_imagess[0])\nplt.tight_layout(h_pad=0.1, w_pad=0.1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:34.864944Z","iopub.execute_input":"2022-11-08T14:46:34.866154Z","iopub.status.idle":"2022-11-08T14:46:35.212751Z","shell.execute_reply.started":"2022-11-08T14:46:34.866106Z","shell.execute_reply":"2022-11-08T14:46:35.211865Z"},"trusted":true},"execution_count":58,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### turn our mask's images(mask_imagess) to numpy arrays","metadata":{}},{"cell_type":"code","source":"test_imagess = np.array(test_imagess)","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:38.275218Z","iopub.execute_input":"2022-11-08T14:46:38.276369Z","iopub.status.idle":"2022-11-08T14:46:38.281986Z","shell.execute_reply.started":"2022-11-08T14:46:38.276317Z","shell.execute_reply":"2022-11-08T14:46:38.280874Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"markdown","source":"### test images shape","metadata":{}},{"cell_type":"code","source":"test_imagess.shape","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:43.204857Z","iopub.execute_input":"2022-11-08T14:46:43.205261Z","iopub.status.idle":"2022-11-08T14:46:43.212764Z","shell.execute_reply.started":"2022-11-08T14:46:43.205224Z","shell.execute_reply":"2022-11-08T14:46:43.211747Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"(3, 512, 512, 3)"},"metadata":{}}]},{"cell_type":"markdown","source":"### Save the Weights","metadata":{}},{"cell_type":"code","source":"model.save_weights(\"UNet.h5\")","metadata":{"execution":{"iopub.status.busy":"2022-10-17T23:07:55.860759Z","iopub.execute_input":"2022-10-17T23:07:55.861463Z","iopub.status.idle":"2022-10-17T23:07:56.181239Z","shell.execute_reply.started":"2022-10-17T23:07:55.861421Z","shell.execute_reply":"2022-10-17T23:07:56.180144Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"markdown","source":"### predict Dataset ","metadata":{}},{"cell_type":"code","source":"result = model.predict(test_imagess)\nprint(\"done\")","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:47.177885Z","iopub.execute_input":"2022-11-08T14:46:47.178417Z","iopub.status.idle":"2022-11-08T14:46:48.046582Z","shell.execute_reply.started":"2022-11-08T14:46:47.178374Z","shell.execute_reply":"2022-11-08T14:46:48.045405Z"},"trusted":true},"execution_count":61,"outputs":[{"name":"stdout","text":"done\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### plot predict (test)","metadata":{}},{"cell_type":"code","source":"fig, axarr = plt.subplots(figsize=(5,5))\naxarr.axis('off')\naxarr.imshow(result[0])\nplt.tight_layout(h_pad=0.1, w_pad=0.1)\nplt.savefig('output512_(batch=10)(Epoch=300)84 percent.png')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:51.324455Z","iopub.execute_input":"2022-11-08T14:46:51.325336Z","iopub.status.idle":"2022-11-08T14:46:51.748489Z","shell.execute_reply.started":"2022-11-08T14:46:51.325284Z","shell.execute_reply":"2022-11-08T14:46:51.747566Z"},"trusted":true},"execution_count":62,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# UoI","metadata":{}},{"cell_type":"markdown","source":"### predict input data","metadata":{}},{"cell_type":"code","source":"y_pred = model.predict(imagess)\ny_pred_threshold = y_pred > 0.5\ny_pred_threshold = y_pred_threshold.reshape(606, image_size, image_size)\ny_true = mask_imagess","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:46:57.834816Z","iopub.execute_input":"2022-11-08T14:46:57.835914Z","iopub.status.idle":"2022-11-08T14:47:06.513697Z","shell.execute_reply.started":"2022-11-08T14:46:57.835874Z","shell.execute_reply":"2022-11-08T14:47:06.512621Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"intersection = np.logical_and(y_true,y_pred_threshold)\nunion = np.logical_or(y_true,y_pred_threshold)","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:47:11.811224Z","iopub.execute_input":"2022-11-08T14:47:11.811593Z","iopub.status.idle":"2022-11-08T14:47:12.504901Z","shell.execute_reply.started":"2022-11-08T14:47:11.811563Z","shell.execute_reply":"2022-11-08T14:47:12.503852Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"markdown","source":"### IoU score","metadata":{}},{"cell_type":"code","source":"IoU_score = np.sum(intersection) / np.sum(union)\nprint (IoU_score)","metadata":{"execution":{"iopub.status.busy":"2022-11-08T14:47:12.51069Z","iopub.execute_input":"2022-11-08T14:47:12.511008Z","iopub.status.idle":"2022-11-08T14:47:12.815383Z","shell.execute_reply.started":"2022-11-08T14:47:12.51098Z","shell.execute_reply":"2022-11-08T14:47:12.814281Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"0.6121328114258593\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}