diff --git a/eval10_experiment5.ipynb b/eval10_experiment5.ipynb index b2b805a..e7faf20 100644 --- a/eval10_experiment5.ipynb +++ b/eval10_experiment5.ipynb @@ -88,7 +88,7 @@ }, "outputs": [], "source": [ - "img_dir = '../input/rxrxairgb'\n", + "img_dir = '../input/rxrxairgb512'\n", "path_data = '../input/rxrxaicsv'\n", "device = 'cuda'\n", "batch_size = 32\n", @@ -106,7 +106,7 @@ "class ImagesDS(D.Dataset):\n", " # taken textbook from https://arxiv.org/pdf/1812.01187.pdf\n", " transform_train = transforms.Compose([\n", - " transforms.RandomResizedCrop(224),\n", + " transforms.RandomResizedCrop(448),\n", " transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1),\n", " transforms.RandomHorizontalFlip(p=0.5),\n", " # PCA Noise should go here,\n", @@ -115,7 +115,7 @@ " ])\n", " \n", " transform_validation = transforms.Compose([\n", - " transforms.CenterCrop(224),\n", + " transforms.CenterCrop(448),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n", " ])\n", @@ -153,7 +153,7 @@ "\n", "class TestImagesDS(D.Dataset):\n", " transform = transforms.Compose([\n", - " transforms.RandomCrop(224),\n", + " transforms.RandomCrop(448),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n", " ])\n", @@ -179,7 +179,7 @@ " return [self._load_img_as_tensor(self._get_img_path(index, site)) for site in [1,2]]\n", " \n", " def __getitem__(self, index):\n", - " image_pairs = [self.get_image_pair(index) for _ in range(10)]\n", + " image_pairs = [self.get_image_pair(index) for _ in range(20)]\n", " \n", " return image_pairs, self.records[index].id_code\n", "\n", @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -252,13 +252,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68b16e5780184c4dbf02e5aae2777d81", + "model_id": "8248738073b1440fb805cea428e959f8", "version_major": 2, "version_minor": 0 }, @@ -268,11 +268,32 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0meval_model_10\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtloader\u001b[0m\u001b[0;34m,\u001b[0m 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numpy as np +import pandas as pd + +from PIL import Image + +import torch +import torch.nn as nn +import torch.utils.data as D +from torch.optim.lr_scheduler import ExponentialLR +import torch.nn.functional as F +from torch.autograd import Variable + +from torchvision import transforms + +from ignite.engine import Events +from scripts.ignite import create_supervised_evaluator, create_supervised_trainer +from ignite.metrics import Loss, Accuracy +from ignite.contrib.handlers.tqdm_logger import ProgressBar +from ignite.handlers import EarlyStopping, ModelCheckpoint +from ignite.contrib.handlers import LinearCyclicalScheduler, CosineAnnealingScheduler + +from tqdm import tqdm_notebook + +from sklearn.model_selection import train_test_split + +from efficientnet_pytorch import EfficientNet + +from scripts.evaluate import eval_model, eval_model_10 + +import warnings +warnings.filterwarnings('ignore') + + +# ## Define dataset and model + +# In[2]: + + +img_dir = '../input/rxrxairgb512' +path_data = '../input/rxrxaicsv' +device = 'cuda' +batch_size = 32 +torch.manual_seed(0) +model_name = 'efficientnet-b3' + + +# In[3]: + + +jitter = (0.6, 1.4) +class ImagesDS(D.Dataset): + # taken textbook from https://arxiv.org/pdf/1812.01187.pdf + transform_train = transforms.Compose([ + transforms.RandomResizedCrop(448), + transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1), + transforms.RandomHorizontalFlip(p=0.5), + # PCA Noise should go here, + transforms.ToTensor(), + transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375)) + ]) + + transform_validation = transforms.Compose([ + transforms.CenterCrop(448), + transforms.ToTensor(), + transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375)) + ]) + + def __init__(self, df, img_dir=img_dir, mode='train', validation=False, site=1): + self.records = df.to_records(index=False) + self.site = site + self.mode = mode + self.img_dir = img_dir + self.len = df.shape[0] + self.validation = validation + + @staticmethod + def _load_img_as_tensor(file_name, validation): + with Image.open(file_name) as img: + if not validation: + return ImagesDS.transform_train(img) + else: + return ImagesDS.transform_validation(img) + + def _get_img_path(self, index, site=1): + experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate + return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg' + + def __getitem__(self, index): + img1, img2 = [self._load_img_as_tensor(self._get_img_path(index, site), self.validation) for site in [1,2]] + if self.mode == 'train': + return img1, img2, int(self.records[index].sirna) + else: + return img1, img2, self.records[index].id_code + + def __len__(self): + return self.len + + +class TestImagesDS(D.Dataset): + transform = transforms.Compose([ + transforms.RandomCrop(448), + transforms.ToTensor(), + transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375)) + ]) + + def __init__(self, df, img_dir=img_dir, mode='test', validation=False, site=1): + self.records = df.to_records(index=False) + self.site = site + self.mode = mode + self.img_dir = img_dir + self.len = df.shape[0] + self.validation = validation + + @staticmethod + def _load_img_as_tensor(file_name): + with Image.open(file_name) as img: + return TestImagesDS.transform(img) + + def _get_img_path(self, index, site=1): + experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate + return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg' + + def get_image_pair(self, index): + return [self._load_img_as_tensor(self._get_img_path(index, site)) for site in [1,2]] + + def __getitem__(self, index): + image_pairs = [self.get_image_pair(index) for _ in range(20)] + + return image_pairs, self.records[index].id_code + + def __len__(self): + return self.len + + +# In[4]: + + +# dataframes for training, cross-validation, and testing +df_test = pd.read_csv(path_data+'/test.csv') + +# pytorch test dataset & loader +ds_test = TestImagesDS(df_test, mode='test', validation=True) +tloader = D.DataLoader(ds_test, batch_size=1, shuffle=False, num_workers=4) + + +# In[5]: + + +class EfficientNetTwoInputs(nn.Module): + def __init__(self): + super(EfficientNetTwoInputs, self).__init__() + self.classes = 1108 + + model = model = EfficientNet.from_pretrained(model_name, num_classes=1108) + num_ftrs = model._fc.in_features + model._fc = nn.Identity() + + self.resnet = model + self.fc = nn.Linear(num_ftrs * 2, self.classes) + + def forward(self, x1, x2): + x1_out = self.resnet(x1) + x2_out = self.resnet(x2) + + N, _, _, _ = x1.size() + x1_out = x1_out.view(N, -1) + x2_out = x2_out.view(N, -1) + + out = torch.cat((x1_out, x2_out), 1) + out = self.fc(out) + + return out + +model = EfficientNetTwoInputs() + + +# #### Evaluate + +# In[6]: + + +model.cuda() +eval_model_10(model, tloader, 'models/Model_efficientnet-b3_93.pth', path_data) + diff --git a/experiment7.ipynb b/experiment7.ipynb index 805cd82..e3144b4 100644 --- a/experiment7.ipynb +++ b/experiment7.ipynb @@ -91,7 +91,7 @@ "img_dir = '../input/rxrxairgb512'\n", "path_data = '../input/rxrxaicsv'\n", "device = 'cuda'\n", - "batch_size = 32\n", + "batch_size = 8\n", "torch.manual_seed(0)\n", "model_name = 'efficientnet-b3'" ] @@ -106,7 +106,7 @@ "class ImagesDS(D.Dataset):\n", " # taken textbook from https://arxiv.org/pdf/1812.01187.pdf\n", " transform_train = transforms.Compose([\n", - " transforms.RandomResizedCrop(224),\n", + " transforms.RandomResizedCrop(448),\n", " transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1),\n", " transforms.RandomHorizontalFlip(p=0.5),\n", " # PCA Noise should go here,\n", @@ -115,7 +115,7 @@ " ])\n", " \n", " transform_validation = transforms.Compose([\n", - " transforms.CenterCrop(224),\n", + " transforms.CenterCrop(448),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n", " ])\n", @@ -214,8 +214,7 @@ "\n", " return out \n", " \n", - "model = EfficientNetTwoInputs()\n", - "model.load_state_dict(torch.load('models/Model_efficientnet-b3_49.pth'))" + "model = EfficientNetTwoInputs()" ] }, { @@ -225,7 +224,7 @@ "outputs": [], "source": [ "criterion = nn.CrossEntropyLoss()\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=2e-4)" + "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)" ] }, { @@ -273,7 +272,7 @@ "metadata": {}, "outputs": [], "source": [ - "scheduler = CosineAnnealingScheduler(optimizer, 'lr', 2e-4, 1e-7, len(loader))\n", + "scheduler = CosineAnnealingScheduler(optimizer, 'lr', 3e-4, 1e-7, len(loader))\n", "trainer.add_event_handler(Events.ITERATION_STARTED, scheduler)\n", "\n", "@trainer.on(Events.ITERATION_COMPLETED)\n", @@ -398,26 +397,12 @@ ] }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "94c4e7cd554e488ba9278d4098093725", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(IntProgress(value=0, max=1027), HTML(value='')))" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", + "ename": "RuntimeError", + "evalue": "CUDA out of memory. Tried to allocate 112.00 MiB (GPU 0; 15.78 GiB total capacity; 14.40 GiB already allocated; 33.94 MiB free; 296.06 MiB cached)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training started\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 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Tried to allocate 112.00 MiB (GPU 0; 15.78 GiB total capacity; 14.40 GiB already allocated; 33.94 MiB free; 296.06 MiB cached)" ] } ], diff --git a/experiment7.py b/experiment7.py index 0e3ff8a..6c4b5d3 100644 --- a/experiment7.py +++ b/experiment7.py @@ -55,7 +55,7 @@ img_dir = '../input/rxrxairgb512' path_data = '../input/rxrxaicsv' device = 'cuda' -batch_size = 32 +batch_size = 8 torch.manual_seed(0) model_name = 'efficientnet-b3' @@ -67,7 +67,7 @@ class ImagesDS(D.Dataset): # taken textbook from https://arxiv.org/pdf/1812.01187.pdf transform_train = transforms.Compose([ - transforms.RandomResizedCrop(224), + transforms.RandomResizedCrop(448), transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1), transforms.RandomHorizontalFlip(p=0.5), # PCA Noise should go here, @@ -76,7 +76,7 @@ class ImagesDS(D.Dataset): ]) transform_validation = transforms.Compose([ - transforms.CenterCrop(224), + transforms.CenterCrop(448), transforms.ToTensor(), transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375)) ]) @@ -162,14 +162,13 @@ def forward(self, x1, x2): return out model = EfficientNetTwoInputs() -model.load_state_dict(torch.load('models/Model_efficientnet-b3_49.pth')) # In[6]: criterion = nn.CrossEntropyLoss() -optimizer = torch.optim.Adam(model.parameters(), lr=2e-4) +optimizer = torch.optim.Adam(model.parameters(), lr=3e-4) # In[7]: @@ -198,7 +197,7 @@ def forward(self, x1, x2): # In[9]: -scheduler = CosineAnnealingScheduler(optimizer, 'lr', 2e-4, 1e-7, len(loader)) +scheduler = CosineAnnealingScheduler(optimizer, 'lr', 3e-4, 1e-7, len(loader)) trainer.add_event_handler(Events.ITERATION_STARTED, scheduler) @trainer.on(Events.ITERATION_COMPLETED) diff --git a/experiment8.ipynb b/experiment8.ipynb index 4b8fa63..8253241 100644 --- a/experiment8.ipynb +++ b/experiment8.ipynb @@ -91,9 +91,9 @@ "img_dir = '../input/rxrxairgb'\n", "path_data = '../input/rxrxaicsv'\n", "device = 'cuda'\n", - "batch_size = 2\n", + "batch_size = 24\n", "torch.manual_seed(0)\n", - "model_name = 'efficientnet-b6'" + "model_name = 'efficientnet-b4'" ] }, { @@ -184,7 +184,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loaded pretrained weights for efficientnet-b3\n" + "Loaded pretrained weights for efficientnet-b4\n" ] } ], @@ -214,7 +214,8 @@ "\n", " return out \n", " \n", - "model = EfficientNetTwoInputs()" + "model = EfficientNetTwoInputs()\n", + "model.load_state_dict(torch.load('../input/rxrx_enetb4/Model_efficientnet-b4_22.pth'))" ] }, { @@ -224,7 +225,7 @@ "outputs": [], "source": [ "criterion = nn.CrossEntropyLoss()\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)" + "optimizer = torch.optim.Adam(model.parameters(), lr=3e-4)" ] }, { @@ -399,85 +400,17 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3ae3ea27298d489c9804b4498df966f0", + "model_id": "7a1420e7484847e68d2d3b788ca20b4a", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(IntProgress(value=0, max=1027), HTML(value='')))" + "HBox(children=(IntProgress(value=0, max=1370), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Iteration 10 | LR 0.00029994317585367494\n", - "Iteration 20 | LR 0.0002997468019973758\n", - "Iteration 30 | LR 0.0002994103605967601\n", - "Iteration 40 | LR 0.0002989341664516109\n", - "Iteration 50 | LR 0.0002983186651248503\n", - "Iteration 60 | LR 0.00029756443252563743\n", - "Iteration 70 | LR 0.0002966721743705047\n", - "Iteration 80 | LR 0.0002956427255230365\n", - "Iteration 90 | LR 0.0002944770492127082\n", - "Iteration 100 | LR 0.00029317623613361676\n", - "Iteration 110 | LR 0.0002917415034239452\n", - "Iteration 120 | LR 0.0002901741935271173\n", - "Iteration 130 | LR 0.00028847577293570676\n", - "Iteration 140 | LR 0.00028664783081927683\n", - "Iteration 150 | LR 0.0002846920775374343\n", - "Iteration 160 | LR 0.0002826103430394889\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Traceback (most recent call last):\n", - " File \"/usr/local/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n", - " send_bytes(obj)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n", - " self._send_bytes(m[offset:offset + size])\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n", - " self._send(header + buf)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n", - " n = write(self._handle, buf)\n", - "BrokenPipeError: [Errno 32] Broken pipe\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n", - " send_bytes(obj)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n", - " self._send_bytes(m[offset:offset + size])\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n", - " self._send(header + buf)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n", - " n = write(self._handle, buf)\n", - "BrokenPipeError: [Errno 32] Broken pipe\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n", - " send_bytes(obj)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n", - " self._send_bytes(m[offset:offset + size])\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n", - " self._send(header + buf)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n", - " n = write(self._handle, buf)\n", - "BrokenPipeError: [Errno 32] Broken pipe\n", - "Traceback (most recent call last):\n", - " File \"/usr/local/lib/python3.6/multiprocessing/queues.py\", line 240, in _feed\n", - " send_bytes(obj)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 200, in send_bytes\n", - " self._send_bytes(m[offset:offset + size])\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 404, in _send_bytes\n", - " self._send(header + buf)\n", - " File \"/usr/local/lib/python3.6/multiprocessing/connection.py\", line 368, in _send\n", - " n = write(self._handle, buf)\n", - "BrokenPipeError: [Errno 32] Broken pipe\n" - ] - }, { "ename": "KeyboardInterrupt", "evalue": "", @@ -485,23 +418,22 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training started\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training started\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.6/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, data, max_epochs)\u001b[0m\n\u001b[1;32m 357\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Engine run is terminating due to exception: %s.\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\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[0;32m--> 359\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 360\u001b[0m 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Time taken: %02d:%02d:%02d\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 348\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_terminate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.6/site-packages/ignite/engine/engine.py\u001b[0m in \u001b[0;36m_run_once_on_dataset\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 311\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m 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a/experiment8.py +++ b/experiment8.py @@ -55,9 +55,9 @@ img_dir = '../input/rxrxairgb' path_data = '../input/rxrxaicsv' device = 'cuda' -batch_size = 2 +batch_size = 24 torch.manual_seed(0) -model_name = 'efficientnet-b6' +model_name = 'efficientnet-b4' # In[3]: @@ -162,13 +162,14 @@ def forward(self, x1, x2): return out model = EfficientNetTwoInputs() +model.load_state_dict(torch.load('../input/rxrx_enetb4/Model_efficientnet-b4_22.pth')) # In[6]: criterion = nn.CrossEntropyLoss() -optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) +optimizer = torch.optim.Adam(model.parameters(), lr=3e-4) # In[7]: @@ -280,7 +281,7 @@ def save_best_epoch_only(engine): print('Training started\n') -trainer.run(loader, max_epochs=60) +trainer.run(loader, max_epochs=20) # #### Evaluate diff --git a/job_template.txt b/job_template.txt index 370d4f2..4ded244 100644 --- a/job_template.txt +++ b/job_template.txt @@ -1 +1 @@ -floyd run --gpu2 --data michelml/datasets/rxrxaicsv/1:rxrxaicsv --data michelml/datasets/rxrxairgb512/1:rxrxairgb --env pytorch-1.0 "ipython experiment8.py" --message "experiment 8 - same as experiment 5 but w/ efficientnet b6, 60 epochs" \ No newline at end of file +floyd run --gpu --env pytorch-1.0 --data michelml/datasets/rxrxaicsv/1:rxrxaicsv --data michelml/datasets/rxrxairgb512/1:rxrxairgb512 --message 'eval 20 w/ .313 model' 'ipython eval10_experiment5.py' \ No newline at end of file diff --git a/plates_leak7.ipynb b/plates_leak7.ipynb new file mode 100644 index 0000000..6c698d4 --- /dev/null +++ b/plates_leak7.ipynb @@ -0,0 +1,1531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mmenpo 0.8.1 has requirement matplotlib<2.0,>=1.4, but you'll have matplotlib 3.0.2 which is incompatible.\u001b[0m\n", + "\u001b[31mmenpo 0.8.1 has requirement pillow<5.0,>=3.0, but you'll have pillow 5.4.1 which is incompatible.\u001b[0m\n", + "\u001b[31mmenpo 0.8.1 has requirement scipy<1.0,>=0.16, but you'll have scipy 1.2.0 which is incompatible.\u001b[0m\n", + "\u001b[33mYou are using pip version 10.0.1, however version 19.2.2 is available.\n", + "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" + ] + } + ], + "source": [ + "!pip install -q -r requirements.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" + }, + "outputs": [], + "source": [ + "import sys\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from PIL import Image\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.utils.data as D\n", + "from torch.optim.lr_scheduler import ExponentialLR\n", + "import torch.nn.functional as F\n", + "from torch.autograd import Variable\n", + "\n", + "from torchvision import transforms\n", + "\n", + "from ignite.engine import Events\n", + "from scripts.ignite import create_supervised_evaluator, create_supervised_trainer\n", + "from ignite.metrics import Loss, Accuracy\n", + "from ignite.contrib.handlers.tqdm_logger import ProgressBar\n", + "from ignite.handlers import EarlyStopping, ModelCheckpoint\n", + "from ignite.contrib.handlers import LinearCyclicalScheduler, CosineAnnealingScheduler\n", + "\n", + "from tqdm import tqdm_notebook\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from efficientnet_pytorch import EfficientNet\n", + "\n", + "from scripts.evaluate import eval_model \n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define dataset and model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", + "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" + }, + "outputs": [], + "source": [ + "img_dir = '../input/rxrxairgb512'\n", + "path_data = '../input/rxrxaicsv'\n", + "device = 'cuda'\n", + "batch_size = 8\n", + "torch.manual_seed(0)\n", + "model_name = 'efficientnet-b3'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "jitter = (0.6, 1.4)\n", + "class ImagesDS(D.Dataset):\n", + " # taken textbook from https://arxiv.org/pdf/1812.01187.pdf\n", + " transform_train = transforms.Compose([\n", + " transforms.RandomResizedCrop(448),\n", + " transforms.ColorJitter(brightness=jitter, contrast=jitter, saturation=jitter, hue=.1),\n", + " transforms.RandomHorizontalFlip(p=0.5),\n", + " # PCA Noise should go here,\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n", + " ])\n", + " \n", + " transform_validation = transforms.Compose([\n", + " transforms.CenterCrop(448),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=(123.68, 116.779, 103.939), std=(58.393, 57.12, 57.375))\n", + " ])\n", + "\n", + " def __init__(self, df, img_dir=img_dir, mode='train', validation=False, site=1):\n", + " self.records = df.to_records(index=False)\n", + " self.site = site\n", + " self.mode = mode\n", + " self.img_dir = img_dir\n", + " self.len = df.shape[0]\n", + " self.validation = validation\n", + " \n", + " @staticmethod\n", + " def _load_img_as_tensor(file_name, validation):\n", + " with Image.open(file_name) as img:\n", + " if not validation:\n", + " return ImagesDS.transform_train(img)\n", + " else:\n", + " return ImagesDS.transform_validation(img)\n", + "\n", + " def _get_img_path(self, index, site=1):\n", + " experiment, well, plate = self.records[index].experiment, self.records[index].well, self.records[index].plate\n", + " return f'{self.img_dir}/{self.mode}/{experiment}_{plate}_{well}_s{site}.jpeg'\n", + " \n", + " def __getitem__(self, index):\n", + " img1, img2 = [self._load_img_as_tensor(self._get_img_path(index, site), self.validation) for site in [1,2]]\n", + " if self.mode == 'train':\n", + " return img1, img2, int(self.records[index].sirna)\n", + " else:\n", + " return img1, img2, self.records[index].id_code\n", + "\n", + " def __len__(self):\n", + " return self.len" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# dataframes for training, cross-validation, and testing\n", + "df = pd.read_csv(path_data+'/train.csv')\n", + "df_train, df_val = train_test_split(df, test_size = 0.1, random_state=42)\n", + "df_test = pd.read_csv(path_data+'/test.csv')\n", + "\n", + "# pytorch training dataset & loader\n", + "ds = ImagesDS(df_train, mode='train', validation=False)\n", + "loader = D.DataLoader(ds, batch_size=batch_size, shuffle=True, num_workers=4)\n", + "\n", + "# pytorch cross-validation dataset & loader\n", + "ds_val = ImagesDS(df_val, mode='train', validation=True)\n", + "val_loader = D.DataLoader(ds_val, batch_size=batch_size, shuffle=True, num_workers=4)\n", + "\n", + "# pytorch test dataset & loader\n", + "ds_test = ImagesDS(df_test, mode='test', validation=True)\n", + "tloader = D.DataLoader(ds_test, batch_size=1, shuffle=False, num_workers=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"http://storage.googleapis.com/public-models/efficientnet/efficientnet-b3-5fb5a3c3.pth\" to /root/.cache/torch/checkpoints/efficientnet-b3-5fb5a3c3.pth\n", + "100%|██████████| 49388949/49388949 [00:00<00:00, 51807325.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded pretrained weights for efficientnet-b3\n" + ] + }, + { + "data": { + "text/plain": [ + 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(11): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 576, 576, kernel_size=(3, 3), stride=(1, 1), groups=576, bias=False\n", + " (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(576, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 576, 24, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 24, 576, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 576, 96, kernel_size=(1, 1), stride=(1, 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)\n", + " (16): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 816, 816, kernel_size=(5, 5), stride=(1, 1), groups=816, bias=False\n", + " (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 816, 34, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 34, 816, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 816, 136, kernel_size=(1, 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Conv2dStaticSamePadding(\n", + " 34, 816, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 816, 136, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(136, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (18): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 136, 816, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 816, 816, kernel_size=(5, 5), stride=[2, 2], groups=816, bias=False\n", + " (static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(816, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 816, 34, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 34, 816, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 816, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (19): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False\n", + " (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 1392, 58, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 58, 1392, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (20): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False\n", + " (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 1392, 58, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 58, 1392, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (21): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False\n", + " (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 1392, 58, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 58, 1392, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (22): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False\n", + " (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 1392, 58, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 58, 1392, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (23): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 1392, 1392, kernel_size=(5, 5), stride=(1, 1), groups=1392, bias=False\n", + " (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 1392, 58, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 58, 1392, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 1392, 232, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(232, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (24): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 232, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 1392, 1392, kernel_size=(3, 3), stride=[1, 1], groups=1392, bias=False\n", + " (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(1392, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 1392, 58, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 58, 1392, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 1392, 384, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " (25): MBConvBlock(\n", + " (_expand_conv): Conv2dStaticSamePadding(\n", + " 384, 2304, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn0): BatchNorm2d(2304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_depthwise_conv): Conv2dStaticSamePadding(\n", + " 2304, 2304, kernel_size=(3, 3), stride=(1, 1), groups=2304, bias=False\n", + " (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)\n", + " )\n", + " (_bn1): BatchNorm2d(2304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_se_reduce): Conv2dStaticSamePadding(\n", + " 2304, 96, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_se_expand): Conv2dStaticSamePadding(\n", + " 96, 2304, kernel_size=(1, 1), stride=(1, 1)\n", + " (static_padding): Identity()\n", + " )\n", + " (_project_conv): Conv2dStaticSamePadding(\n", + " 2304, 384, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn2): BatchNorm2d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (_conv_head): Conv2dStaticSamePadding(\n", + " 384, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False\n", + " (static_padding): Identity()\n", + " )\n", + " (_bn1): BatchNorm2d(1536, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n", + " (_fc): Identity()\n", + " )\n", + " (fc): Linear(in_features=3072, out_features=1108, bias=True)\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class EfficientNetTwoInputs(nn.Module):\n", + " def __init__(self):\n", + " super(EfficientNetTwoInputs, self).__init__()\n", + " self.classes = 1108\n", + " \n", + " model = model = EfficientNet.from_pretrained(model_name, num_classes=1108) \n", + " num_ftrs = model._fc.in_features\n", + " model._fc = nn.Identity()\n", + " \n", + " self.resnet = model\n", + " self.fc = nn.Linear(num_ftrs * 2, self.classes)\n", + "\n", + " def forward(self, x1, x2):\n", + " x1_out = self.resnet(x1)\n", + " x2_out = self.resnet(x2)\n", + " \n", + " N, _, _, _ = x1.size()\n", + " x1_out = x1_out.view(N, -1)\n", + " x2_out = x2_out.view(N, -1)\n", + " \n", + " out = torch.cat((x1_out, x2_out), 1)\n", + " out = self.fc(out)\n", + "\n", + " return out \n", + " \n", + "model = EfficientNetTwoInputs()\n", + "model.load_state_dict(torch.load('models/Model_efficientnet-b3_93.pth'))\n", + "model.cuda()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "plate_groups = np.zeros((1108,4), int)\n", + "for sirna in range(1108):\n", + " grp = df.loc[df.sirna==sirna,:].plate.value_counts().index.values\n", + " assert len(grp) == 3\n", + " plate_groups[sirna,0:3] = grp\n", + " plate_groups[sirna,3] = 10 - grp.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "all_test_exp = df_test.experiment.unique()\n", + "sub = pd.read_csv(\"./submission (13).csv\")\n", + "\n", + "group_plate_probs = np.zeros((len(all_test_exp),4))\n", + "for idx in range(len(all_test_exp)):\n", + " preds = sub.loc[df_test.experiment == all_test_exp[idx],'sirna'].values\n", + " pp_mult = np.zeros((len(preds),1108))\n", + " pp_mult[range(len(preds)),preds] = 1\n", + " \n", + " sub_test = df_test.loc[df_test.experiment == all_test_exp[idx],:]\n", + " assert len(pp_mult) == len(sub_test)\n", + " \n", + " for j in range(4):\n", + " mask = np.repeat(plate_groups[np.newaxis, :, j], len(pp_mult), axis=0) == \\\n", + " np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)\n", + " \n", + " group_plate_probs[idx,j] = np.array(pp_mult)[mask].sum()/len(pp_mult)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + 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HEPG2-100.5830320.1407940.1398920.136282
HEPG2-110.6157320.1238700.1184450.141953
HUVEC-170.6642600.1010830.1128160.121841
HUVEC-180.5700090.1427280.1427280.144535
HUVEC-190.0722020.0866430.7833940.057762
HUVEC-200.0460290.0388090.8456680.069495
HUVEC-210.1218410.1444040.1290610.604693
HUVEC-220.7129960.0929600.1028880.091155
HUVEC-230.6587810.1119200.1119200.117379
HUVEC-240.0990910.1000000.1000000.700909
RPE-080.1489170.4900720.1687730.192238
RPE-090.6034330.1463410.1192410.130985
RPE-100.6507220.1182310.1110110.120036
RPE-110.5968920.1243140.1471660.131627
U2OS-040.2581230.2545130.2355600.251805
U2OS-050.1877850.2069280.2060160.399271
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"metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(group_plate_probs, index = all_test_exp)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [], + "source": [ + "exp_to_group = group_plate_probs.argmax(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4e3ad1c050d04550abdea3b2d6fba22a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=19897), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with torch.no_grad():\n", + " preds = []\n", + " for x1, x2, _ in tqdm_notebook(tloader): \n", + " x1 = x1.to(device)\n", + " x2 = x2.to(device)\n", + " output = model(x1,x2)\n", + " idx = output.cpu().numpy()\n", + " preds.append(idx[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "pre_preds = np.stack(preds).squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "def select_plate_group(pp_mult, idx):\n", + " sub_test = df_test.loc[df_test.experiment == all_test_exp[idx],:]\n", + " assert len(pp_mult) == len(sub_test)\n", + " mask = np.repeat(plate_groups[np.newaxis, :, exp_to_group[idx]], len(pp_mult), axis=0) != \\\n", + " np.repeat(sub_test.plate.values[:, np.newaxis], 1108, axis=1)\n", + " pp_mult[mask] = 0\n", + " return pp_mult" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "too many indices for array", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperiment\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mall_test_exp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfinal_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpre_preds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\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 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mfinal_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect_plate_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_preds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: too many indices for array" + ] + } + ], + "source": [ + "for idx in range(len(all_test_exp)):\n", + " indices = (df_test.experiment == all_test_exp[idx])\n", + "\n", + " final_preds = pre_preds[indices,:].copy()\n", + " \n", + " final_preds = select_plate_group(final_preds, idx)\n", + " sub.loc[indices,'sirna'] = final_preds.argmax(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0004020706639191838" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(sub.sirna == pd.read_csv(\"./submission (13).csv\").sirna).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": {}, + "outputs": [], + 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