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], - "metadata": { - "id": "wkBlmJT96jZj" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", + "metadata": { + "id": "UGxQdKZaF2NT" + }, "source": [ "This implementation enables ResNet retraining in SPG mode.\n", "\n", @@ -192,13 +201,15 @@ "\n", "3. gen_forward()\n", " - Purpose: Extended forward pass supporting both traditional inference and SPG retraining." - ], - "metadata": { - "id": "UGxQdKZaF2NT" - } + ] }, { "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "kTZWkoLr8cfE" + }, + "outputs": [], "source": [ "class ResNetConfig:\n", " @staticmethod\n", @@ -271,39 +282,37 @@ " return logits\n", "\n", " return func" - ], - "metadata": { - "id": "kTZWkoLr8cfE" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", - "source": [ - "Applies TRP modules to the base ResNet (main backbone). The k-th TRP module corresponding to a deeper ResNet variant with an additional depth of 3 * sum(depths[:k+1])." - ], "metadata": { "id": "cCn6vwItH1CW" - } + }, + "source": [ + "Applies TRP modules to the base ResNet (main backbone). The k-th TRP module corresponding to a deeper ResNet variant with an additional depth of 3 * sum(depths[:k+1])." + ] }, { "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "wXQF0oISH5Yp" + }, + "outputs": [], "source": [ "def apply_trp(model, depths: List[int], planes: int, lambdas: List[float], **kwargs):\n", " print(\"✅ Applying TRP to ResNet for Image Classification...\")\n", " model.trp_blocks = torch.nn.ModuleList([TPBlock(depths=d, inplanes=planes, planes=planes) for d in depths])\n", " model.forward = types.MethodType(ResNetConfig.gen_forward(lambdas), model)\n", " return model" - ], - "metadata": { - "id": "wXQF0oISH5Yp" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", + "metadata": { + "id": "kDjSAv3PJr7P" + }, "source": [ "The following is a training script for classification models, primarily based on the official TorchVision `train.py` reference implementation. We have made two modifications:\n", "\n", @@ -326,14 +335,11 @@ " utils.save_on_master(checkpoint, os.path.join(args.output_dir, f\"model_{epoch}.pth\"))\n", " utils.save_on_master(checkpoint, os.path.join(args.output_dir, \"checkpoint.pth\"))\n", "```" - ], - "metadata": { - "id": "kDjSAv3PJr7P" - } + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "id": "hK4Y7Sqv4xUa" }, @@ -507,6 +513,9 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "SV8s5k49KwgS" + }, "source": [ "Prepare the [ImageNet](http://image-net.org/) dataset manually and place it in `/path/to/imagenet`. For image classification examples, pass the argument `--data-path=/path/to/imagenet` to the training script. The extracted dataset directory should follow this structure:\n", "```setup\n", @@ -528,18 +537,15 @@ "**Implementation Note:**\n", "\n", "- This demonstration runs on Google Colab using a single GPU configuration\n", - "- Performance Improvement: Enhances ResNet18 validation accuracy (ACC@1) from XX.X% to YY.Y%\n", + "- Performance Improvement: Enhances ResNet18 validation accuracy (ACC@1) from 69.76% to 70.09%\n", "- For optimal results:\n", " - Refer to our README.md for complete setup instructions\n", " - Recommended hardware: 4× RTX A6000 GPUs" - ], - "metadata": { - "id": "SV8s5k49KwgS" - } + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "UDZxDNfT4xUb", "outputId": "bcf86aa0-eb77-4815-e0fa-05997f1e1f1b" @@ -549,67 +555,48 @@ "name": "stdout", "output_type": "stream", "text": [ - "namespace(data_path='/home/cs/Documents/datasets/imagenet', model='resnet18', device='cuda', batch_size=256, epochs=10, opt='sgd', lr=0.0004, momentum=0.9, weight_decay=0.0001, lr_warmup_epochs=1, lr_warmup_decay=0.0, lr_step_size=2, lr_gamma=0.5, print_freq=100, output_dir='resnet18', use_deterministic_algorithms=False, weights='ResNet18_Weights.IMAGENET1K_V1', apply_trp=True, trp_depths=[1, 1, 1], trp_planes=256, trp_lambdas=[0.4, 0.2, 0.1])\n", + "namespace(data_path='/home/cs/Documents/datasets/imagenet', model='resnet18', device='cuda', batch_size=512, epochs=6, lr=0.0004, momentum=0.9, weight_decay=0.0001, lr_warmup_epochs=1, lr_warmup_decay=0.0, lr_step_size=2, lr_gamma=0.5, print_freq=100, output_dir='resnet18', use_deterministic_algorithms=False, weights='ResNet18_Weights.IMAGENET1K_V1', apply_trp=True, trp_depths=[3, 3, 3], trp_planes=256, trp_lambdas=[0.4, 0.2, 0.1])\n", "Loading data\n", - "Loading training data\n", - "Took 2.6400649547576904\n", + "Loading training data\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Took 1.9062905311584473\n", "Loading validation data\n", "Creating data loaders\n", "Creating model\n", "✅ Applying TRP to ResNet for Image Classification...\n", "Start training\n", - "Epoch: [0] [ 0/5005] eta: 5:44:15 lr: 0.0 img/s: 492.1456954856547 loss: 0.7194 (0.7194) acc1: 69.1406 (69.1406) acc5: 86.3281 (86.3281) meanQV: 1.4840 (1.4840) stdQV: 0.3240 (0.3240) time: 4.1269 data: 3.6067 max mem: 8962\n", - "Epoch: [0] [ 100/5005] eta: 0:46:06 lr: 0.0 img/s: 487.81080383655046 loss: 0.7315 (0.7366) acc1: 68.7500 (68.9434) acc5: 87.1094 (87.2022) meanQV: 1.4813 (1.4826) stdQV: 0.3240 (0.3238) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 200/5005] eta: 0:43:26 lr: 0.0 img/s: 494.6728449606332 loss: 0.7197 (0.7298) acc1: 68.7500 (69.0318) acc5: 87.5000 (87.2785) meanQV: 1.4813 (1.4832) stdQV: 0.3251 (0.3236) time: 0.5211 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 300/5005] eta: 0:41:58 lr: 0.0 img/s: 487.94536242744385 loss: 0.7055 (0.7270) acc1: 70.7031 (69.1757) acc5: 86.7188 (87.3053) meanQV: 1.4949 (1.4842) stdQV: 0.3192 (0.3232) time: 0.5198 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 400/5005] eta: 0:40:48 lr: 0.0 img/s: 493.8419675273725 loss: 0.7652 (0.7331) acc1: 67.9688 (68.9867) acc5: 87.1094 (87.2409) meanQV: 1.4758 (1.4829) stdQV: 0.3262 (0.3237) time: 0.5219 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 500/5005] eta: 0:39:45 lr: 0.0 img/s: 490.99324013193245 loss: 0.7112 (0.7341) acc1: 69.1406 (68.9067) acc5: 87.1094 (87.2653) meanQV: 1.4840 (1.4823) stdQV: 0.3228 (0.3239) time: 0.5201 data: 0.0003 max mem: 8962\n", - "Epoch: [0] [ 600/5005] eta: 0:38:46 lr: 0.0 img/s: 493.63241471415074 loss: 0.7323 (0.7309) acc1: 69.5312 (69.0171) acc5: 87.8906 (87.3297) meanQV: 1.4867 (1.4831) stdQV: 0.3216 (0.3236) time: 0.5214 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 700/5005] eta: 0:37:49 lr: 0.0 img/s: 487.50804150353844 loss: 0.7489 (0.7310) acc1: 68.7500 (69.0442) acc5: 86.3281 (87.3167) meanQV: 1.4813 (1.4833) stdQV: 0.3240 (0.3235) time: 0.5206 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 800/5005] eta: 0:36:53 lr: 0.0 img/s: 491.46023226850616 loss: 0.7270 (0.7316) acc1: 69.1406 (69.0567) acc5: 87.8906 (87.3166) meanQV: 1.4840 (1.4834) stdQV: 0.3216 (0.3235) time: 0.5220 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [ 900/5005] eta: 0:35:58 lr: 0.0 img/s: 490.80023055787785 loss: 0.7573 (0.7338) acc1: 68.3594 (68.9898) acc5: 86.7188 (87.3010) meanQV: 1.4785 (1.4829) stdQV: 0.3262 (0.3237) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [1000/5005] eta: 0:35:03 lr: 0.0 img/s: 493.60609129495185 loss: 0.7472 (0.7342) acc1: 68.3594 (69.0087) acc5: 87.1094 (87.2924) meanQV: 1.4785 (1.4831) stdQV: 0.3262 (0.3237) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [1100/5005] eta: 0:34:09 lr: 0.0 img/s: 493.3883500186788 loss: 0.7273 (0.7337) acc1: 68.3594 (69.0157) acc5: 86.3281 (87.2832) meanQV: 1.4785 (1.4831) stdQV: 0.3251 (0.3236) time: 0.5200 data: 0.0003 max mem: 8962\n", - "Epoch: [0] [1200/5005] eta: 0:33:15 lr: 0.0 img/s: 488.9129516588334 loss: 0.7296 (0.7333) acc1: 67.9688 (69.0434) acc5: 87.1094 (87.2827) meanQV: 1.4758 (1.4833) stdQV: 0.3251 (0.3235) time: 0.5207 data: 0.0003 max mem: 8962\n", - "Epoch: [0] [1300/5005] eta: 0:32:22 lr: 0.0 img/s: 496.11964440830207 loss: 0.7214 (0.7334) acc1: 69.1406 (69.0587) acc5: 86.7188 (87.2835) meanQV: 1.4840 (1.4834) stdQV: 0.3240 (0.3235) time: 0.5209 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [1400/5005] eta: 0:31:29 lr: 0.0 img/s: 489.82739879256343 loss: 0.7335 (0.7330) acc1: 68.3594 (69.0832) acc5: 86.7188 (87.2881) meanQV: 1.4785 (1.4836) stdQV: 0.3240 (0.3234) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [1500/5005] eta: 0:30:35 lr: 0.0 img/s: 488.2193641437147 loss: 0.7221 (0.7324) acc1: 69.1406 (69.0979) acc5: 87.8906 (87.3184) meanQV: 1.4840 (1.4837) stdQV: 0.3228 (0.3234) time: 0.5221 data: 0.0004 max mem: 8962\n", - "Epoch: [0] [1600/5005] eta: 0:29:43 lr: 0.0 img/s: 491.9973130670899 loss: 0.7626 (0.7324) acc1: 68.3594 (69.0891) acc5: 86.7188 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0.3216 (0.3234) time: 0.5214 data: 0.0002 max mem: 8962\n", - "Epoch: [0] Total time: 0:43:17\n" + "Epoch: [0] [ 0/2503] eta: 10:05:09 lr: 0.0 img/s: 81.93631887515438 loss: 0.7334 (0.7334) acc1: 71.2891 (71.2891) acc5: 86.1328 (86.1328) time: 14.5065 data: 8.2577 max mem: 19119\n", + "Epoch: [0] [ 100/2503] eta: 0:29:06 lr: 0.0 img/s: 862.8257862120394 loss: 0.7145 (0.7308) acc1: 69.5312 (69.6105) acc5: 87.6953 (87.3704) time: 0.5927 data: 0.0003 max mem: 19119\n", + "Epoch: [0] [ 200/2503] eta: 0:25:23 lr: 0.0 img/s: 860.6862569301302 loss: 0.7355 (0.7353) acc1: 68.9453 (69.3427) acc5: 86.9141 (87.3125) time: 0.5966 data: 0.0003 max mem: 19119\n", + "Epoch: [0] [ 300/2503] eta: 0:23:29 lr: 0.0 img/s: 860.0754340960929 loss: 0.7159 (0.7314) acc1: 69.1406 (69.3463) acc5: 87.5000 (87.3676) time: 0.5967 data: 0.0003 max mem: 19119\n", + "Epoch: [0] [ 400/2503] eta: 0:22:03 lr: 0.0 img/s: 859.0790234707376 loss: 0.7594 (0.7361) acc1: 67.9688 (69.2283) acc5: 86.7188 (87.3232) time: 0.5960 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lr: 0.0 img/s: 859.5858857924882 loss: 0.7224 (0.7351) acc1: 69.3359 (69.2485) acc5: 87.3047 (87.3624) time: 0.5958 data: 0.0003 max mem: 19119\n", + "Epoch: [0] [1100/2503] eta: 0:14:12 lr: 0.0 img/s: 858.8670339725992 loss: 0.7240 (0.7360) acc1: 68.9453 (69.2212) acc5: 87.1094 (87.3361) time: 0.5958 data: 0.0002 max mem: 19119\n", + "Epoch: [0] [1200/2503] eta: 0:13:10 lr: 0.0 img/s: 861.4696676125856 loss: 0.7126 (0.7364) acc1: 68.3594 (69.1878) acc5: 87.3047 (87.3190) time: 0.5960 data: 0.0002 max mem: 19119\n", + "Epoch: [0] [1300/2503] eta: 0:12:09 lr: 0.0 img/s: 859.3643608581464 loss: 0.7291 (0.7367) acc1: 68.9453 (69.1669) acc5: 86.7188 (87.2990) time: 0.5959 data: 0.0002 max mem: 19119\n", + "Epoch: [0] [1400/2503] eta: 0:11:07 lr: 0.0 img/s: 861.1477063020853 loss: 0.7267 (0.7372) acc1: 69.9219 (69.1624) acc5: 87.1094 (87.2990) time: 0.5960 data: 0.0002 max mem: 19119\n", + "Epoch: [0] [1500/2503] eta: 0:10:06 lr: 0.0 img/s: 859.0494692253935 loss: 0.7234 (0.7374) acc1: 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0.0002 max mem: 19119\n", + "Epoch: [0] [2100/2503] eta: 0:04:02 lr: 0.0 img/s: 860.72592834858 loss: 0.7153 (0.7373) acc1: 69.3359 (69.1710) acc5: 87.3047 (87.3230) time: 0.5961 data: 0.0004 max mem: 19119\n", + "Epoch: [0] [2200/2503] eta: 0:03:02 lr: 0.0 img/s: 859.2460775467988 loss: 0.7307 (0.7371) acc1: 68.9453 (69.1861) acc5: 87.5000 (87.3380) time: 0.5960 data: 0.0004 max mem: 19119\n", + "Epoch: [0] [2300/2503] eta: 0:02:02 lr: 0.0 img/s: 859.2639554931892 loss: 0.7077 (0.7367) acc1: 69.3359 (69.1971) acc5: 87.6953 (87.3516) time: 0.5959 data: 0.0004 max mem: 19119\n", + "Epoch: [0] [2400/2503] eta: 0:01:01 lr: 0.0 img/s: 861.341130585524 loss: 0.7279 (0.7365) acc1: 68.5547 (69.1921) acc5: 86.9141 (87.3412) time: 0.5961 data: 0.0004 max mem: 19119\n", + "Epoch: [0] [2500/2503] eta: 0:00:01 lr: 0.0 img/s: 861.8382147793436 loss: 0.7469 (0.7368) acc1: 68.5547 (69.1894) acc5: 87.5000 (87.3423) time: 0.5955 data: 0.0005 max mem: 19119\n", + "Epoch: [0] Total time: 0:25:05\n" ] }, { @@ -624,573 +611,202 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test: [ 0/6250] eta: 2:26:50 loss: 0.8858 (0.8858) acc1: 75.0000 (75.0000) acc5: 100.0000 (100.0000) time: 1.4097 data: 0.7689 max mem: 8962\n", - "Test: [ 100/6250] eta: 0:02:03 loss: 0.2054 (0.6172) acc1: 100.0000 (83.7871) acc5: 100.0000 (95.6683) time: 0.0066 data: 0.0006 max mem: 8962\n", - "Test: [ 200/6250] eta: 0:01:22 loss: 0.6502 (0.6427) acc1: 87.5000 (84.3284) acc5: 87.5000 (95.0249) time: 0.0064 data: 0.0007 max mem: 8962\n", - "Test: [ 300/6250] eta: 0:01:08 loss: 1.1768 (0.8467) acc1: 62.5000 (78.3638) acc5: 87.5000 (93.2724) time: 0.0075 data: 0.0010 max mem: 8962\n", - "Test: [ 400/6250] eta: 0:00:59 loss: 1.1007 (0.9660) acc1: 75.0000 (75.4676) acc5: 87.5000 (92.2070) time: 0.0074 data: 0.0017 max mem: 8962\n", - "Test: [ 500/6250] eta: 0:00:55 loss: 1.1372 (1.0088) acc1: 75.0000 (74.0519) acc5: 87.5000 (91.8663) time: 0.0080 data: 0.0021 max mem: 8962\n", - "Test: [ 600/6250] eta: 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(93.4508) time: 0.0059 data: 0.0006 max mem: 8962\n", - "Test: [2000/6250] eta: 0:00:33 loss: 0.4827 (0.9227) acc1: 75.0000 (75.5622) acc5: 100.0000 (93.4283) time: 0.0062 data: 0.0005 max mem: 8962\n", - "Test: [2100/6250] eta: 0:00:32 loss: 0.4458 (0.9042) acc1: 87.5000 (76.1185) acc5: 100.0000 (93.5983) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [2200/6250] eta: 0:00:31 loss: 0.1983 (0.8953) acc1: 87.5000 (76.3233) acc5: 100.0000 (93.6620) time: 0.0058 data: 0.0005 max mem: 8962\n", - "Test: [2300/6250] eta: 0:00:30 loss: 0.6167 (0.8971) acc1: 87.5000 (76.2929) acc5: 100.0000 (93.6658) time: 0.0067 data: 0.0010 max mem: 8962\n", - "Test: [2400/6250] eta: 0:00:29 loss: 1.1606 (0.9060) acc1: 62.5000 (76.1245) acc5: 87.5000 (93.5652) time: 0.0069 data: 0.0006 max mem: 8962\n", - "Test: [2500/6250] eta: 0:00:29 loss: 0.9882 (0.9067) acc1: 75.0000 (76.2095) acc5: 100.0000 (93.5176) time: 0.0063 data: 0.0005 max mem: 8962\n", - "Test: [2600/6250] eta: 0:00:27 loss: 2.1575 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"Test: [6000/6250] eta: 0:00:01 loss: 0.6253 (1.2358) acc1: 87.5000 (69.8425) acc5: 87.5000 (89.1622) time: 0.0068 data: 0.0006 max mem: 8962\n", - "Test: [6100/6250] eta: 0:00:01 loss: 1.1478 (1.2438) acc1: 62.5000 (69.6423) acc5: 100.0000 (89.0776) time: 0.0069 data: 0.0006 max mem: 8962\n", - "Test: [6200/6250] eta: 0:00:00 loss: 0.2636 (1.2384) acc1: 87.5000 (69.7650) acc5: 100.0000 (89.1469) time: 0.0071 data: 0.0024 max mem: 8962\n", - "Test: Total time: 0:00:44\n", - "Test: Acc@1 69.802 Acc@5 89.166\n", - "Epoch: [1] [ 0/5005] eta: 5:42:56 lr: 0.0004 img/s: 480.9318095087941 loss: 0.7240 (0.7240) acc1: 67.1875 (67.1875) acc5: 89.4531 (89.4531) meanQV: 1.4703 (1.4703) stdQV: 0.3293 (0.3293) time: 4.1113 data: 3.5789 max mem: 8962\n", - "Epoch: [1] [ 100/5005] eta: 0:45:51 lr: 0.0004 img/s: 493.27547238807784 loss: 0.7663 (0.7544) acc1: 68.3594 (68.9047) acc5: 85.9375 (87.0166) meanQV: 1.4785 (1.4823) stdQV: 0.3262 (0.3240) time: 0.5211 data: 0.0004 max mem: 8962\n", - "Epoch: [1] 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489.9574830025097 loss: 0.7744 (0.7785) acc1: 68.7500 (68.8845) acc5: 87.5000 (87.0379) meanQV: 1.4813 (1.4821) stdQV: 0.3240 (0.3240) time: 0.5210 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [ 700/5005] eta: 0:37:48 lr: 0.0004 img/s: 490.1766813359385 loss: 0.7570 (0.7804) acc1: 69.9219 (68.9227) acc5: 87.1094 (87.0347) meanQV: 1.4895 (1.4824) stdQV: 0.3216 (0.3238) time: 0.5210 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [ 800/5005] eta: 0:36:53 lr: 0.0004 img/s: 488.54479381319 loss: 0.7706 (0.7828) acc1: 69.1406 (68.9124) acc5: 86.3281 (87.0435) meanQV: 1.4840 (1.4823) stdQV: 0.3226 (0.3239) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [ 900/5005] eta: 0:35:58 lr: 0.0004 img/s: 486.8855385165102 loss: 0.7941 (0.7848) acc1: 68.3594 (68.9390) acc5: 87.1094 (87.0465) meanQV: 1.4785 (1.4825) stdQV: 0.3251 (0.3238) time: 0.5219 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1000/5005] eta: 0:35:03 lr: 0.0004 img/s: 492.5010040446165 loss: 0.7671 (0.7867) acc1: 69.5312 (68.9233) acc5: 87.1094 (87.0430) meanQV: 1.4867 (1.4824) stdQV: 0.3228 (0.3238) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1100/5005] eta: 0:34:10 lr: 0.0004 img/s: 491.8478935923928 loss: 0.7918 (0.7885) acc1: 68.3594 (68.8866) acc5: 87.1094 (86.9948) meanQV: 1.4785 (1.4821) stdQV: 0.3251 (0.3240) time: 0.5219 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1200/5005] eta: 0:33:16 lr: 0.0004 img/s: 492.04127725035846 loss: 0.8054 (0.7904) acc1: 68.3594 (68.8863) acc5: 87.1094 (87.0059) meanQV: 1.4785 (1.4821) stdQV: 0.3240 (0.3240) time: 0.5212 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1300/5005] eta: 0:32:22 lr: 0.0004 img/s: 493.6503434775133 loss: 0.8003 (0.7921) acc1: 68.7500 (68.8953) acc5: 86.7188 (87.0238) meanQV: 1.4813 (1.4822) stdQV: 0.3240 (0.3239) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [1400/5005] eta: 0:31:29 lr: 0.0004 img/s: 493.7590786833788 loss: 0.8469 (0.7947) acc1: 67.5781 (68.8757) acc5: 86.3281 (87.0087) meanQV: 1.4730 (1.4821) stdQV: 0.3273 (0.3240) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1500/5005] eta: 0:30:36 lr: 0.0004 img/s: 493.85105292835783 loss: 0.8278 (0.7964) acc1: 67.9688 (68.8734) acc5: 87.1094 (87.0126) meanQV: 1.4758 (1.4820) stdQV: 0.3260 (0.3240) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1600/5005] eta: 0:29:43 lr: 0.0004 img/s: 489.0960134101013 loss: 0.8061 (0.7976) acc1: 69.5312 (68.8713) acc5: 86.7188 (87.0096) meanQV: 1.4867 (1.4820) stdQV: 0.3228 (0.3240) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1700/5005] eta: 0:28:50 lr: 0.0004 img/s: 487.0217960638814 loss: 0.8219 (0.7990) acc1: 69.1406 (68.8761) acc5: 86.3281 (87.0118) meanQV: 1.4840 (1.4821) stdQV: 0.3204 (0.3240) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [1800/5005] eta: 0:27:57 lr: 0.0004 img/s: 487.4064667244674 loss: 0.8217 (0.8005) acc1: 67.1875 (68.8660) acc5: 85.9375 (86.9964) meanQV: 1.4703 (1.4820) stdQV: 0.3273 (0.3240) time: 0.5210 data: 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(87.0036) meanQV: 1.4922 (1.4814) stdQV: 0.3192 (0.3242) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3200/5005] eta: 0:15:43 lr: 0.0004 img/s: 491.9060279391008 loss: 0.8449 (0.8176) acc1: 68.3594 (68.7768) acc5: 86.3281 (86.9960) meanQV: 1.4785 (1.4813) stdQV: 0.3262 (0.3242) time: 0.5214 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3300/5005] eta: 0:14:50 lr: 0.0004 img/s: 493.2963213941315 loss: 0.8492 (0.8186) acc1: 67.9688 (68.7659) acc5: 87.1094 (86.9994) meanQV: 1.4758 (1.4812) stdQV: 0.3248 (0.3243) time: 0.5203 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3400/5005] eta: 0:13:58 lr: 0.0004 img/s: 489.8253877234111 loss: 0.8006 (0.8190) acc1: 68.3594 (68.7695) acc5: 86.7188 (87.0057) meanQV: 1.4785 (1.4813) stdQV: 0.3192 (0.3243) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3500/5005] eta: 0:13:06 lr: 0.0004 img/s: 487.77468294972033 loss: 0.8505 (0.8198) acc1: 68.7500 (68.7579) acc5: 85.9375 (87.0026) meanQV: 1.4813 (1.4812) stdQV: 0.3248 (0.3243) time: 0.5211 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3600/5005] eta: 0:12:13 lr: 0.0004 img/s: 493.46703224484054 loss: 0.8441 (0.8204) acc1: 68.3594 (68.7684) acc5: 87.1094 (86.9979) meanQV: 1.4785 (1.4813) stdQV: 0.3262 (0.3243) time: 0.5203 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3700/5005] eta: 0:11:21 lr: 0.0004 img/s: 489.55359849141115 loss: 0.8019 (0.8213) acc1: 69.9219 (68.7655) acc5: 86.7188 (86.9992) meanQV: 1.4891 (1.4812) stdQV: 0.3214 (0.3243) time: 0.5221 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3800/5005] eta: 0:10:29 lr: 0.0004 img/s: 493.67462881592127 loss: 0.8116 (0.8217) acc1: 69.1406 (68.7733) acc5: 86.7188 (87.0059) meanQV: 1.4840 (1.4813) stdQV: 0.3216 (0.3243) time: 0.5207 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [3900/5005] eta: 0:09:37 lr: 0.0004 img/s: 491.10327860529725 loss: 0.8894 (0.8224) acc1: 67.5781 (68.7723) acc5: 85.9375 (87.0182) meanQV: 1.4730 (1.4813) stdQV: 0.3273 (0.3243) time: 0.5204 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [4000/5005] eta: 0:08:42 lr: 0.0004 img/s: 658.0155167215349 loss: 0.8130 (0.8233) acc1: 68.7500 (68.7736) acc5: 86.7188 (87.0157) meanQV: 1.4813 (1.4813) stdQV: 0.3228 (0.3243) time: 0.3829 data: 0.0022 max mem: 8962\n", - "Epoch: [1] [4100/5005] eta: 0:07:48 lr: 0.0004 img/s: 495.21469028956733 loss: 0.8295 (0.8240) acc1: 69.1406 (68.7658) acc5: 87.5000 (87.0174) meanQV: 1.4816 (1.4812) stdQV: 0.3235 (0.3243) time: 0.5208 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [4200/5005] eta: 0:06:57 lr: 0.0004 img/s: 492.8694949672994 loss: 0.8703 (0.8249) acc1: 68.3594 (68.7643) acc5: 87.1094 (87.0155) meanQV: 1.4773 (1.4812) stdQV: 0.3262 (0.3243) time: 0.5213 data: 0.0005 max mem: 8962\n", - "Epoch: [1] [4300/5005] eta: 0:06:05 lr: 0.0004 img/s: 490.85205892006695 loss: 0.8436 (0.8255) acc1: 67.5781 (68.7648) acc5: 87.1094 (87.0136) meanQV: 1.4730 (1.4812) stdQV: 0.3262 (0.3243) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [4400/5005] eta: 0:05:13 lr: 0.0004 img/s: 493.98669227672667 loss: 0.8510 (0.8258) acc1: 68.3594 (68.7773) acc5: 86.7188 (87.0168) meanQV: 1.4785 (1.4813) stdQV: 0.3251 (0.3242) time: 0.5209 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [4500/5005] eta: 0:04:21 lr: 0.0004 img/s: 493.84991723495295 loss: 0.8170 (0.8264) acc1: 68.3594 (68.7790) acc5: 88.2812 (87.0156) meanQV: 1.4785 (1.4813) stdQV: 0.3238 (0.3242) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [4600/5005] eta: 0:03:30 lr: 0.0004 img/s: 488.5125647070813 loss: 0.8377 (0.8271) acc1: 68.3594 (68.7688) acc5: 87.5000 (87.0158) meanQV: 1.4770 (1.4812) stdQV: 0.3262 (0.3243) time: 0.5200 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [4700/5005] eta: 0:02:38 lr: 0.0004 img/s: 491.9828855090406 loss: 0.8226 (0.8275) acc1: 68.3594 (68.7709) acc5: 87.8906 (87.0220) meanQV: 1.4785 (1.4812) stdQV: 0.3216 (0.3243) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [1] [4800/5005] eta: 0:01:46 lr: 0.0004 img/s: 493.26708795863277 loss: 0.8528 (0.8281) acc1: 68.7500 (68.7686) acc5: 86.3281 (87.0159) meanQV: 1.4809 (1.4812) stdQV: 0.3251 (0.3243) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [4900/5005] eta: 0:00:54 lr: 0.0004 img/s: 491.2724747144307 loss: 0.8068 (0.8286) acc1: 69.5312 (68.7636) acc5: 87.1094 (87.0159) meanQV: 1.4867 (1.4812) stdQV: 0.3228 (0.3243) time: 0.5217 data: 0.0003 max mem: 8962\n", - "Epoch: [1] [5000/5005] eta: 0:00:02 lr: 0.0004 img/s: 492.8498131172718 loss: 0.8547 (0.8292) acc1: 67.9688 (68.7634) acc5: 86.3281 (87.0148) meanQV: 1.4758 (1.4812) stdQV: 0.3262 (0.3243) time: 0.5210 data: 0.0002 max mem: 8962\n", - "Epoch: [1] Total time: 0:43:16\n", - "Test: [ 0/6250] eta: 1:21:57 loss: 0.8673 (0.8673) acc1: 62.5000 (62.5000) acc5: 100.0000 (100.0000) time: 0.7867 data: 0.7725 max mem: 8962\n", - "Test: [ 100/6250] eta: 0:01:29 loss: 0.1868 (0.6206) acc1: 100.0000 (83.5396) acc5: 100.0000 (95.4208) time: 0.0063 data: 0.0008 max mem: 8962\n", - "Test: [ 200/6250] eta: 0:01:03 loss: 0.7102 (0.6552) 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data: 0.0007 max mem: 8962\n", - "Test: [1600/6250] eta: 0:00:30 loss: 0.0993 (0.9286) acc1: 100.0000 (75.9135) acc5: 100.0000 (93.1371) time: 0.0053 data: 0.0005 max mem: 8962\n", - "Test: [1700/6250] eta: 0:00:29 loss: 1.1072 (0.9257) acc1: 62.5000 (75.8157) acc5: 100.0000 (93.2760) time: 0.0068 data: 0.0021 max mem: 8962\n", - "Test: [1800/6250] eta: 0:00:28 loss: 1.1913 (0.9373) acc1: 62.5000 (75.5622) acc5: 87.5000 (93.2537) time: 0.0054 data: 0.0006 max mem: 8962\n", - "Test: [1900/6250] eta: 0:00:28 loss: 1.0258 (0.9308) acc1: 62.5000 (75.8417) acc5: 100.0000 (93.3653) time: 0.0050 data: 0.0006 max mem: 8962\n", - "Test: [2000/6250] eta: 0:00:27 loss: 0.5154 (0.9364) acc1: 87.5000 (75.7871) acc5: 100.0000 (93.3221) time: 0.0055 data: 0.0006 max mem: 8962\n", - "Test: [2100/6250] eta: 0:00:27 loss: 0.3852 (0.9175) acc1: 87.5000 (76.3267) acc5: 100.0000 (93.4793) time: 0.0061 data: 0.0012 max mem: 8962\n", - "Test: [2200/6250] eta: 0:00:26 loss: 0.2291 (0.9102) acc1: 87.5000 (76.4652) acc5: 100.0000 (93.5654) time: 0.0063 data: 0.0007 max mem: 8962\n", - "Test: [2300/6250] eta: 0:00:25 loss: 0.6943 (0.9109) acc1: 87.5000 (76.4776) acc5: 100.0000 (93.5843) time: 0.0062 data: 0.0005 max mem: 8962\n", - "Test: [2400/6250] eta: 0:00:25 loss: 0.8836 (0.9201) acc1: 75.0000 (76.3432) acc5: 87.5000 (93.4975) time: 0.0056 data: 0.0011 max mem: 8962\n", - "Test: [2500/6250] eta: 0:00:24 loss: 0.7644 (0.9205) acc1: 87.5000 (76.4794) acc5: 100.0000 (93.4626) time: 0.0061 data: 0.0010 max mem: 8962\n", - "Test: [2600/6250] eta: 0:00:23 loss: 2.2800 (0.9451) acc1: 37.5000 (75.9948) acc5: 75.0000 (93.1469) time: 0.0055 data: 0.0006 max mem: 8962\n", - "Test: [2700/6250] eta: 0:00:23 loss: 0.7858 (0.9555) acc1: 75.0000 (75.7960) acc5: 87.5000 (93.0118) time: 0.0060 data: 0.0006 max mem: 8962\n", - "Test: [2800/6250] eta: 0:00:22 loss: 1.6738 (0.9770) acc1: 62.5000 (75.3481) acc5: 75.0000 (92.7704) time: 0.0052 data: 0.0004 max mem: 8962\n", - "Test: [2900/6250] eta: 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mem: 8962\n", - "Test: [3600/6250] eta: 0:00:16 loss: 0.4245 (1.0910) acc1: 87.5000 (73.1325) acc5: 100.0000 (91.2941) time: 0.0059 data: 0.0004 max mem: 8962\n", - "Test: [3700/6250] eta: 0:00:16 loss: 1.6126 (1.1052) acc1: 50.0000 (72.8519) acc5: 87.5000 (91.1105) time: 0.0121 data: 0.0065 max mem: 8962\n", - "Test: [3800/6250] eta: 0:00:15 loss: 0.7444 (1.1120) acc1: 87.5000 (72.7605) acc5: 87.5000 (90.9761) time: 0.0057 data: 0.0005 max mem: 8962\n", - "Test: [3900/6250] eta: 0:00:14 loss: 2.3107 (1.1276) acc1: 50.0000 (72.4494) acc5: 75.0000 (90.7652) time: 0.0052 data: 0.0005 max mem: 8962\n", - "Test: [4000/6250] eta: 0:00:14 loss: 1.4615 (1.1421) acc1: 62.5000 (72.1788) acc5: 87.5000 (90.5992) time: 0.0056 data: 0.0004 max mem: 8962\n", - "Test: [4100/6250] eta: 0:00:13 loss: 1.7447 (1.1522) acc1: 62.5000 (72.0129) acc5: 87.5000 (90.4840) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [4200/6250] eta: 0:00:12 loss: 0.4051 (1.1574) acc1: 87.5000 (71.8609) acc5: 100.0000 (90.4725) time: 0.0061 data: 0.0005 max mem: 8962\n", - "Test: [4300/6250] eta: 0:00:12 loss: 0.4819 (1.1662) acc1: 75.0000 (71.7420) acc5: 87.5000 (90.3191) time: 0.0058 data: 0.0007 max mem: 8962\n", - "Test: [4400/6250] eta: 0:00:11 loss: 0.8488 (1.1727) acc1: 75.0000 (71.5633) acc5: 100.0000 (90.2835) time: 0.0052 data: 0.0005 max mem: 8962\n", - "Test: [4500/6250] eta: 0:00:11 loss: 1.1410 (1.1786) acc1: 62.5000 (71.4619) acc5: 87.5000 (90.2383) time: 0.0058 data: 0.0004 max mem: 8962\n", - "Test: [4600/6250] eta: 0:00:10 loss: 1.5850 (1.1891) acc1: 50.0000 (71.2834) acc5: 87.5000 (90.0701) time: 0.0064 data: 0.0010 max mem: 8962\n", - "Test: [4700/6250] eta: 0:00:09 loss: 1.3859 (1.1988) acc1: 62.5000 (70.9982) acc5: 87.5000 (89.9463) time: 0.0051 data: 0.0005 max mem: 8962\n", - "Test: [4800/6250] eta: 0:00:09 loss: 1.2475 (1.2056) acc1: 62.5000 (70.8811) acc5: 87.5000 (89.8563) time: 0.0054 data: 0.0005 max mem: 8962\n", - "Test: [4900/6250] eta: 0:00:08 loss: 0.4116 (1.2104) acc1: 87.5000 (70.8121) acc5: 100.0000 (89.7750) time: 0.0058 data: 0.0009 max mem: 8962\n", - "Test: [5000/6250] eta: 0:00:07 loss: 2.0346 (1.2224) acc1: 62.5000 (70.6259) acc5: 75.0000 (89.6321) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [5100/6250] eta: 0:00:07 loss: 1.2888 (1.2271) acc1: 62.5000 (70.5327) acc5: 87.5000 (89.5952) time: 0.0069 data: 0.0006 max mem: 8962\n", - "Test: [5200/6250] eta: 0:00:06 loss: 1.0585 (1.2335) acc1: 75.0000 (70.4216) acc5: 87.5000 (89.5236) time: 0.0053 data: 0.0005 max mem: 8962\n", - "Test: [5300/6250] eta: 0:00:05 loss: 1.5391 (1.2470) acc1: 62.5000 (70.1401) acc5: 87.5000 (89.3440) time: 0.0058 data: 0.0006 max mem: 8962\n", - "Test: [5400/6250] eta: 0:00:05 loss: 0.9108 (1.2497) acc1: 75.0000 (70.0889) acc5: 87.5000 (89.2936) time: 0.0057 data: 0.0010 max mem: 8962\n", - "Test: [5500/6250] eta: 0:00:04 loss: 0.9651 (1.2539) acc1: 75.0000 (69.9668) acc5: 87.5000 (89.2383) time: 0.0060 data: 0.0006 max mem: 8962\n", - "Test: 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data: 0.0007 max mem: 8962\n", - "Test: Total time: 0:00:38\n", - "Test: Acc@1 69.816 Acc@5 89.174\n", - "Epoch: [2] [ 0/5005] eta: 5:24:29 lr: 0.0004 img/s: 488.37947021283276 loss: 0.7970 (0.7970) acc1: 72.2656 (72.2656) acc5: 87.1094 (87.1094) meanQV: 1.5059 (1.5059) stdQV: 0.3140 (0.3140) time: 3.8899 data: 3.3657 max mem: 8962\n", - "Epoch: [2] [ 100/5005] eta: 0:45:25 lr: 0.0004 img/s: 493.19209398441154 loss: 0.8641 (0.8689) acc1: 67.9688 (68.4483) acc5: 87.1094 (86.6530) meanQV: 1.4758 (1.4788) stdQV: 0.3251 (0.3251) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [ 200/5005] eta: 0:43:07 lr: 0.0004 img/s: 491.1342779344896 loss: 0.8738 (0.8655) acc1: 68.7500 (68.5595) acc5: 87.5000 (86.8898) meanQV: 1.4813 (1.4796) stdQV: 0.3240 (0.3248) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [ 300/5005] eta: 0:41:47 lr: 0.0004 img/s: 487.00059052814623 loss: 0.8533 (0.8685) acc1: 68.3594 (68.4191) acc5: 87.5000 (86.8005) meanQV: 1.4785 (1.4786) stdQV: 0.3251 (0.3252) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [ 400/5005] eta: 0:40:40 lr: 0.0004 img/s: 488.72224116185464 loss: 0.8569 (0.8636) acc1: 69.1406 (68.5932) acc5: 87.1094 (86.8931) meanQV: 1.4840 (1.4799) stdQV: 0.3216 (0.3247) time: 0.5220 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [ 500/5005] eta: 0:39:39 lr: 0.0004 img/s: 485.98511188265843 loss: 0.8240 (0.8635) acc1: 69.5312 (68.5403) acc5: 87.8906 (86.8723) meanQV: 1.4867 (1.4795) stdQV: 0.3215 (0.3249) time: 0.5207 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [ 600/5005] eta: 0:38:41 lr: 0.0004 img/s: 493.6265143933163 loss: 0.8603 (0.8641) acc1: 68.7500 (68.5791) acc5: 86.3281 (86.8962) meanQV: 1.4801 (1.4798) stdQV: 0.3251 (0.3248) time: 0.5222 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [ 700/5005] eta: 0:37:44 lr: 0.0004 img/s: 495.0733539463698 loss: 0.8482 (0.8641) acc1: 68.3594 (68.5656) acc5: 87.1094 (86.9194) meanQV: 1.4785 (1.4797) stdQV: 0.3260 (0.3249) time: 0.5209 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [ 800/5005] eta: 0:36:49 lr: 0.0004 img/s: 490.3255347956828 loss: 0.8578 (0.8649) acc1: 69.1406 (68.5959) acc5: 86.7188 (86.9270) meanQV: 1.4840 (1.4799) stdQV: 0.3240 (0.3248) time: 0.5212 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [ 900/5005] eta: 0:35:55 lr: 0.0004 img/s: 487.8480383248159 loss: 0.8300 (0.8626) acc1: 69.1406 (68.6646) acc5: 87.5000 (86.9767) meanQV: 1.4840 (1.4804) stdQV: 0.3228 (0.3246) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [1000/5005] eta: 0:35:01 lr: 0.0004 img/s: 491.5558526149826 loss: 0.8513 (0.8634) acc1: 69.1406 (68.6805) acc5: 87.5000 (86.9775) meanQV: 1.4840 (1.4805) stdQV: 0.3226 (0.3245) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [1100/5005] eta: 0:34:07 lr: 0.0004 img/s: 490.84757118315395 loss: 0.8577 (0.8636) acc1: 68.3594 (68.6847) acc5: 86.7188 (86.9710) meanQV: 1.4785 (1.4805) stdQV: 0.3251 (0.3245) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [1200/5005] eta: 0:33:14 lr: 0.0004 img/s: 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(68.6358) acc5: 87.5000 (86.9669) meanQV: 1.4891 (1.4802) stdQV: 0.3216 (0.3246) time: 0.5215 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [1700/5005] eta: 0:28:49 lr: 0.0004 img/s: 488.3163923205836 loss: 0.8374 (0.8661) acc1: 69.5312 (68.6529) acc5: 87.5000 (86.9794) meanQV: 1.4867 (1.4803) stdQV: 0.3228 (0.3246) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [1800/5005] eta: 0:27:56 lr: 0.0004 img/s: 493.57046837953203 loss: 0.8886 (0.8664) acc1: 68.3594 (68.6639) acc5: 86.7188 (86.9808) meanQV: 1.4785 (1.4804) stdQV: 0.3251 (0.3245) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [1900/5005] eta: 0:27:03 lr: 0.0004 img/s: 490.1453552810568 loss: 0.8548 (0.8668) acc1: 68.3594 (68.6598) acc5: 87.1094 (86.9768) meanQV: 1.4785 (1.4803) stdQV: 0.3249 (0.3245) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [2000/5005] eta: 0:26:11 lr: 0.0004 img/s: 488.1270344653269 loss: 0.9081 (0.8673) acc1: 67.9688 (68.6596) acc5: 85.9375 (86.9690) meanQV: 1.4754 (1.4803) stdQV: 0.3270 (0.3245) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [2100/5005] eta: 0:25:18 lr: 0.0004 img/s: 489.5038292141757 loss: 0.8766 (0.8678) acc1: 68.7500 (68.6416) acc5: 87.5000 (86.9612) meanQV: 1.4813 (1.4802) stdQV: 0.3240 (0.3246) time: 0.5213 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [2200/5005] eta: 0:24:26 lr: 0.0004 img/s: 493.80040608063206 loss: 0.8591 (0.8679) acc1: 68.3594 (68.6433) acc5: 86.7188 (86.9536) meanQV: 1.4785 (1.4802) stdQV: 0.3259 (0.3246) time: 0.5222 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [2300/5005] eta: 0:23:33 lr: 0.0004 img/s: 487.89902333117954 loss: 0.8695 (0.8681) acc1: 67.5781 (68.6313) acc5: 86.3281 (86.9520) meanQV: 1.4703 (1.4801) stdQV: 0.3262 (0.3246) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [2400/5005] eta: 0:22:41 lr: 0.0004 img/s: 495.53373554749567 loss: 0.9173 (0.8689) acc1: 67.5781 (68.6229) acc5: 86.3281 (86.9381) meanQV: 1.4730 (1.4801) stdQV: 0.3270 (0.3247) time: 0.5204 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [2500/5005] eta: 0:21:48 lr: 0.0004 img/s: 492.0162505212319 loss: 0.8747 (0.8695) acc1: 67.9688 (68.6191) acc5: 87.1094 (86.9432) meanQV: 1.4758 (1.4800) stdQV: 0.3262 (0.3247) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [2600/5005] eta: 0:20:56 lr: 0.0004 img/s: 489.42819036679475 loss: 0.8458 (0.8694) acc1: 69.9219 (68.6381) acc5: 86.7188 (86.9568) meanQV: 1.4895 (1.4802) stdQV: 0.3216 (0.3246) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [2700/5005] eta: 0:20:04 lr: 0.0004 img/s: 491.20055774275215 loss: 0.8641 (0.8696) acc1: 67.1875 (68.6336) acc5: 86.7188 (86.9582) meanQV: 1.4703 (1.4801) stdQV: 0.3277 (0.3246) time: 0.5221 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [2800/5005] eta: 0:19:11 lr: 0.0004 img/s: 494.1210418244721 loss: 0.8495 (0.8697) acc1: 69.5312 (68.6488) acc5: 87.5000 (86.9604) meanQV: 1.4867 (1.4802) stdQV: 0.3228 (0.3246) time: 0.5220 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [2900/5005] eta: 0:18:19 lr: 0.0004 img/s: 491.0172647329166 loss: 0.8790 (0.8703) acc1: 68.3594 (68.6341) acc5: 86.7188 (86.9494) meanQV: 1.4785 (1.4801) stdQV: 0.3251 (0.3246) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [3000/5005] eta: 0:17:27 lr: 0.0004 img/s: 485.94684164959335 loss: 0.8755 (0.8706) acc1: 68.3594 (68.6326) acc5: 87.5000 (86.9472) meanQV: 1.4785 (1.4801) stdQV: 0.3192 (0.3246) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3100/5005] eta: 0:16:34 lr: 0.0004 img/s: 494.1287731247124 loss: 0.9319 (0.8717) acc1: 66.4062 (68.6098) acc5: 84.7656 (86.9271) meanQV: 1.4645 (1.4800) stdQV: 0.3303 (0.3247) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3200/5005] eta: 0:15:42 lr: 0.0004 img/s: 489.1026970888204 loss: 0.8573 (0.8715) acc1: 67.1875 (68.6226) acc5: 87.1094 (86.9358) meanQV: 1.4699 (1.4801) stdQV: 0.3291 (0.3246) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3300/5005] eta: 0:14:50 lr: 0.0004 img/s: 493.99282848070345 loss: 0.9075 (0.8719) acc1: 68.3594 (68.6160) acc5: 85.9375 (86.9377) meanQV: 1.4770 (1.4800) stdQV: 0.3257 (0.3247) time: 0.5221 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3400/5005] eta: 0:13:58 lr: 0.0004 img/s: 491.0915987137075 loss: 0.8952 (0.8724) acc1: 67.1875 (68.6177) acc5: 87.5000 (86.9409) meanQV: 1.4688 (1.4800) stdQV: 0.3283 (0.3247) time: 0.5217 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [3500/5005] eta: 0:13:05 lr: 0.0004 img/s: 491.85172372724526 loss: 0.8616 (0.8726) acc1: 67.9688 (68.6169) acc5: 87.5000 (86.9428) meanQV: 1.4758 (1.4800) stdQV: 0.3262 (0.3247) time: 0.5212 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3600/5005] eta: 0:12:13 lr: 0.0004 img/s: 493.1658175040636 loss: 0.8484 (0.8728) acc1: 68.7500 (68.6238) acc5: 88.2812 (86.9481) meanQV: 1.4813 (1.4801) stdQV: 0.3237 (0.3246) time: 0.5210 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3700/5005] eta: 0:11:21 lr: 0.0004 img/s: 488.61460107584765 loss: 0.8767 (0.8732) acc1: 67.9688 (68.6150) acc5: 86.7188 (86.9436) meanQV: 1.4758 (1.4800) stdQV: 0.3240 (0.3247) time: 0.5216 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [3800/5005] eta: 0:10:29 lr: 0.0004 img/s: 494.11217386622536 loss: 0.8734 (0.8735) acc1: 66.7969 (68.6138) acc5: 86.3281 (86.9486) meanQV: 1.4676 (1.4800) stdQV: 0.3251 (0.3247) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [3900/5005] eta: 0:09:36 lr: 0.0004 img/s: 490.99728148911294 loss: 0.8477 (0.8733) acc1: 69.1406 (68.6243) acc5: 87.1094 (86.9573) meanQV: 1.4840 (1.4801) stdQV: 0.3240 (0.3246) time: 0.5215 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4000/5005] eta: 0:08:44 lr: 0.0004 img/s: 491.4019783456549 loss: 0.8635 (0.8735) acc1: 68.7500 (68.6289) acc5: 87.5000 (86.9596) meanQV: 1.4813 (1.4801) stdQV: 0.3240 (0.3246) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4100/5005] eta: 0:07:52 lr: 0.0004 img/s: 494.1733465144578 loss: 0.8801 (0.8738) acc1: 68.3594 (68.6256) acc5: 85.9375 (86.9581) meanQV: 1.4773 (1.4801) stdQV: 0.3228 (0.3246) time: 0.5216 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [4200/5005] eta: 0:07:00 lr: 0.0004 img/s: 489.6234710977453 loss: 0.8273 (0.8740) acc1: 70.3125 (68.6327) acc5: 87.8906 (86.9577) meanQV: 1.4922 (1.4801) stdQV: 0.3190 (0.3246) time: 0.5215 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4300/5005] eta: 0:06:08 lr: 0.0004 img/s: 494.2581946216013 loss: 0.8741 (0.8740) acc1: 68.7500 (68.6423) acc5: 87.5000 (86.9612) meanQV: 1.4813 (1.4802) stdQV: 0.3251 (0.3246) time: 0.5204 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4400/5005] eta: 0:05:15 lr: 0.0004 img/s: 745.4293803117672 loss: 0.8636 (0.8744) acc1: 68.7500 (68.6341) acc5: 87.1094 (86.9601) meanQV: 1.4813 (1.4801) stdQV: 0.3240 (0.3246) time: 0.4351 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4500/5005] eta: 0:04:21 lr: 0.0004 img/s: 486.88907097457 loss: 0.8677 (0.8746) acc1: 67.9688 (68.6392) acc5: 86.3281 (86.9591) meanQV: 1.4758 (1.4802) stdQV: 0.3262 (0.3246) time: 0.3115 data: 0.0013 max mem: 8962\n", - "Epoch: [2] [4600/5005] eta: 0:03:29 lr: 0.0004 img/s: 489.810640387527 loss: 0.8474 (0.8748) acc1: 68.7500 (68.6407) acc5: 86.7188 (86.9571) meanQV: 1.4813 (1.4802) stdQV: 0.3240 (0.3246) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4700/5005] eta: 0:02:38 lr: 0.0004 img/s: 494.03101273102885 loss: 0.8843 (0.8749) acc1: 67.1875 (68.6427) acc5: 85.9375 (86.9571) meanQV: 1.4691 (1.4802) stdQV: 0.3270 (0.3246) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [4800/5005] eta: 0:01:46 lr: 0.0004 img/s: 491.7528352428831 loss: 0.9494 (0.8752) acc1: 68.3594 (68.6450) acc5: 85.9375 (86.9551) meanQV: 1.4785 (1.4802) stdQV: 0.3251 (0.3246) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [2] [4900/5005] eta: 0:00:54 lr: 0.0004 img/s: 489.231282062264 loss: 0.8820 (0.8755) acc1: 67.1875 (68.6437) acc5: 85.9375 (86.9490) meanQV: 1.4691 (1.4802) stdQV: 0.3290 (0.3246) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [2] [5000/5005] eta: 0:00:02 lr: 0.0004 img/s: 489.3046302811125 loss: 0.8896 (0.8759) acc1: 68.3594 (68.6439) acc5: 86.7188 (86.9525) meanQV: 1.4785 (1.4802) stdQV: 0.3248 (0.3246) time: 0.5207 data: 0.0002 max mem: 8962\n", - "Epoch: [2] Total time: 0:43:15\n", - "Test: [ 0/6250] eta: 1:13:07 loss: 0.9734 (0.9734) acc1: 62.5000 (62.5000) acc5: 100.0000 (100.0000) time: 0.7020 data: 0.6951 max mem: 8962\n", - "Test: [ 100/6250] eta: 0:01:26 loss: 0.1843 (0.6218) acc1: 87.5000 (83.2921) acc5: 100.0000 (95.2970) time: 0.0060 data: 0.0006 max mem: 8962\n", - "Test: [ 200/6250] eta: 0:01:02 loss: 0.7251 (0.6534) acc1: 87.5000 (84.0174) acc5: 87.5000 (94.8383) time: 0.0060 data: 0.0005 max mem: 8962\n", - "Test: [ 300/6250] eta: 0:00:53 loss: 1.2929 (0.8644) acc1: 62.5000 (78.9037) acc5: 87.5000 (93.1894) time: 0.0062 data: 0.0005 max mem: 8962\n", - "Test: [ 400/6250] eta: 0:00:48 loss: 0.8992 (0.9909) acc1: 75.0000 (76.0910) acc5: 87.5000 (92.3005) time: 0.0060 data: 0.0004 max mem: 8962\n", - "Test: [ 500/6250] eta: 0:00:45 loss: 1.1745 (1.0423) acc1: 75.0000 (74.8503) acc5: 87.5000 (91.9661) time: 0.0065 data: 0.0010 max mem: 8962\n", - "Test: [ 600/6250] eta: 0:00:42 loss: 0.4571 (0.9501) acc1: 87.5000 (76.8095) acc5: 100.0000 (92.5957) time: 0.0061 data: 0.0005 max mem: 8962\n", - "Test: [ 700/6250] eta: 0:00:40 loss: 0.4039 (0.9331) acc1: 87.5000 (77.1220) acc5: 100.0000 (92.5820) time: 0.0057 data: 0.0006 max mem: 8962\n", - "Test: [ 800/6250] eta: 0:00:39 loss: 0.7346 (0.9629) acc1: 75.0000 (76.5605) acc5: 87.5000 (92.1660) time: 0.0058 data: 0.0006 max mem: 8962\n", - "Test: [ 900/6250] eta: 0:00:37 loss: 0.3460 (0.9073) acc1: 87.5000 (77.8857) acc5: 100.0000 (92.6609) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [1000/6250] eta: 0:00:36 loss: 0.8425 (0.8922) acc1: 75.0000 (78.1094) acc5: 100.0000 (92.8322) time: 0.0057 data: 0.0008 max mem: 8962\n", - "Test: [1100/6250] eta: 0:00:35 loss: 0.9796 (0.9278) acc1: 75.0000 (77.2252) acc5: 100.0000 (92.6544) time: 0.0073 data: 0.0016 max mem: 8962\n", - "Test: [1200/6250] eta: 0:00:34 loss: 1.1206 (0.9326) acc1: 75.0000 (76.8110) acc5: 87.5000 (92.7560) time: 0.0060 data: 0.0011 max mem: 8962\n", - "Test: [1300/6250] eta: 0:00:33 loss: 0.4536 (0.9371) acc1: 87.5000 (76.5661) acc5: 100.0000 (92.8997) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [1400/6250] eta: 0:00:32 loss: 0.7222 (0.9336) acc1: 75.0000 (76.6149) acc5: 87.5000 (92.9425) time: 0.0059 data: 0.0006 max mem: 8962\n", - "Test: [1500/6250] eta: 0:00:31 loss: 0.9426 (0.9376) acc1: 62.5000 (76.3658) acc5: 100.0000 (93.0213) time: 0.0057 data: 0.0005 max mem: 8962\n", - "Test: [1600/6250] eta: 0:00:30 loss: 0.1496 (0.9321) acc1: 100.0000 (76.2336) acc5: 100.0000 (93.1839) time: 0.0053 data: 0.0005 max mem: 8962\n", - "Test: [1700/6250] eta: 0:00:29 loss: 1.0775 (0.9295) acc1: 75.0000 (76.0802) acc5: 100.0000 (93.3128) time: 0.0051 data: 0.0006 max mem: 8962\n", - "Test: [1800/6250] eta: 0:00:29 loss: 1.1900 (0.9398) acc1: 62.5000 (75.8537) acc5: 87.5000 (93.2954) time: 0.0047 data: 0.0004 max mem: 8962\n", - "Test: [1900/6250] eta: 0:00:28 loss: 0.9377 (0.9324) acc1: 75.0000 (76.1376) acc5: 100.0000 (93.4179) time: 0.0056 data: 0.0005 max mem: 8962\n", - "Test: [2000/6250] eta: 0:00:27 loss: 0.4859 (0.9373) acc1: 87.5000 (76.0557) acc5: 100.0000 (93.4158) time: 0.0057 data: 0.0005 max mem: 8962\n", - "Test: [2100/6250] eta: 0:00:27 loss: 0.3588 (0.9185) acc1: 87.5000 (76.5826) acc5: 100.0000 (93.5745) time: 0.0059 data: 0.0004 max mem: 8962\n", - "Test: [2200/6250] eta: 0:00:26 loss: 0.1941 (0.9123) acc1: 87.5000 (76.7208) acc5: 100.0000 (93.6336) time: 0.0066 data: 0.0011 max mem: 8962\n", - "Test: [2300/6250] eta: 0:00:25 loss: 0.6498 (0.9137) acc1: 87.5000 (76.6949) acc5: 100.0000 (93.5952) time: 0.0052 data: 0.0008 max mem: 8962\n", - "Test: [2400/6250] eta: 0:00:25 loss: 0.9289 (0.9226) acc1: 75.0000 (76.5723) acc5: 87.5000 (93.5079) time: 0.0067 data: 0.0004 max mem: 8962\n", - "Test: [2500/6250] eta: 0:00:24 loss: 0.9120 (0.9237) acc1: 87.5000 (76.6593) acc5: 100.0000 (93.4726) time: 0.0054 data: 0.0004 max mem: 8962\n", - "Test: [2600/6250] eta: 0:00:23 loss: 2.2991 (0.9477) acc1: 50.0000 (76.1774) acc5: 62.5000 (93.1565) time: 0.0057 data: 0.0005 max mem: 8962\n", - "Test: [2700/6250] eta: 0:00:23 loss: 0.7023 (0.9584) acc1: 75.0000 (76.0089) acc5: 100.0000 (93.0442) time: 0.0061 data: 0.0007 max mem: 8962\n", - "Test: [2800/6250] eta: 0:00:22 loss: 1.6450 (0.9808) acc1: 62.5000 (75.5400) acc5: 87.5000 (92.7749) time: 0.0056 data: 0.0007 max mem: 8962\n", - "Test: [2900/6250] eta: 0:00:21 loss: 1.8880 (1.0003) acc1: 50.0000 (75.1034) acc5: 87.5000 (92.5414) time: 0.0057 data: 0.0005 max mem: 8962\n", - "Test: [3000/6250] eta: 0:00:21 loss: 2.2391 (1.0214) acc1: 50.0000 (74.7959) acc5: 75.0000 (92.2193) time: 0.0051 data: 0.0004 max mem: 8962\n", - "Test: [3100/6250] eta: 0:00:20 loss: 1.8099 (1.0473) acc1: 50.0000 (74.2785) acc5: 75.0000 (91.9502) time: 0.0058 data: 0.0006 max mem: 8962\n", - "Test: [3200/6250] eta: 0:00:19 loss: 0.7831 (1.0671) acc1: 62.5000 (73.8558) acc5: 100.0000 (91.7565) time: 0.0058 data: 0.0009 max mem: 8962\n", - "Test: [3300/6250] eta: 0:00:18 loss: 1.2720 (1.0789) acc1: 62.5000 (73.5497) acc5: 87.5000 (91.6540) time: 0.0060 data: 0.0010 max mem: 8962\n", - "Test: [3400/6250] eta: 0:00:18 loss: 2.3960 (1.0930) acc1: 37.5000 (73.2946) acc5: 75.0000 (91.4363) time: 0.0070 data: 0.0010 max mem: 8962\n", - "Test: [3500/6250] eta: 0:00:17 loss: 1.8152 (1.0998) acc1: 50.0000 (73.1612) acc5: 75.0000 (91.3203) time: 0.0075 data: 0.0021 max mem: 8962\n", - "Test: [3600/6250] eta: 0:00:17 loss: 0.4449 (1.0965) acc1: 87.5000 (73.3026) acc5: 100.0000 (91.3219) time: 0.0058 data: 0.0004 max mem: 8962\n", - "Test: [3700/6250] eta: 0:00:16 loss: 1.6940 (1.1099) acc1: 62.5000 (73.0276) acc5: 87.5000 (91.1477) time: 0.0131 data: 0.0075 max mem: 8962\n", - "Test: [3800/6250] eta: 0:00:15 loss: 0.7985 (1.1155) acc1: 87.5000 (72.9644) acc5: 87.5000 (91.0484) time: 0.0054 data: 0.0006 max mem: 8962\n", - "Test: [3900/6250] eta: 0:00:15 loss: 2.2583 (1.1305) acc1: 50.0000 (72.6833) acc5: 75.0000 (90.8293) time: 0.0053 data: 0.0004 max mem: 8962\n", - "Test: [4000/6250] eta: 0:00:14 loss: 1.4313 (1.1455) acc1: 62.5000 (72.4256) acc5: 87.5000 (90.6492) time: 0.0056 data: 0.0005 max mem: 8962\n", - "Test: [4100/6250] eta: 0:00:13 loss: 1.7358 (1.1560) acc1: 62.5000 (72.2507) acc5: 87.5000 (90.5389) time: 0.0056 data: 0.0004 max mem: 8962\n", - "Test: [4200/6250] eta: 0:00:13 loss: 0.4747 (1.1604) acc1: 87.5000 (72.1168) acc5: 100.0000 (90.5380) time: 0.0064 data: 0.0013 max mem: 8962\n", - "Test: [4300/6250] eta: 0:00:12 loss: 0.5343 (1.1704) acc1: 75.0000 (71.9513) acc5: 87.5000 (90.3743) time: 0.0054 data: 0.0004 max mem: 8962\n", - "Test: [4400/6250] eta: 0:00:11 loss: 0.8444 (1.1773) acc1: 75.0000 (71.7422) acc5: 100.0000 (90.3175) time: 0.0050 data: 0.0003 max mem: 8962\n", - "Test: [4500/6250] eta: 0:00:11 loss: 1.2144 (1.1832) acc1: 62.5000 (71.6119) acc5: 87.5000 (90.2744) time: 0.0060 data: 0.0006 max mem: 8962\n", - "Test: [4600/6250] eta: 0:00:10 loss: 1.7535 (1.1929) acc1: 50.0000 (71.4600) acc5: 87.5000 (90.1190) time: 0.0061 data: 0.0005 max mem: 8962\n", - "Test: [4700/6250] eta: 0:00:09 loss: 1.4817 (1.2036) acc1: 62.5000 (71.1498) acc5: 87.5000 (89.9702) time: 0.0050 data: 0.0006 max mem: 8962\n", - "Test: [4800/6250] eta: 0:00:09 loss: 1.3074 (1.2111) acc1: 62.5000 (71.0034) acc5: 87.5000 (89.8563) time: 0.0054 data: 0.0005 max mem: 8962\n", - "Test: [4900/6250] eta: 0:00:08 loss: 0.4546 (1.2157) acc1: 75.0000 (70.9090) acc5: 100.0000 (89.7852) time: 0.0053 data: 0.0006 max mem: 8962\n", - "Test: [5000/6250] eta: 0:00:07 loss: 2.0130 (1.2274) acc1: 62.5000 (70.7359) acc5: 75.0000 (89.6296) time: 0.0059 data: 0.0004 max mem: 8962\n", - "Test: [5100/6250] eta: 0:00:07 loss: 1.3178 (1.2327) acc1: 62.5000 (70.6136) acc5: 87.5000 (89.5903) time: 0.0061 data: 0.0005 max mem: 8962\n", - "Test: [5200/6250] eta: 0:00:06 loss: 1.0106 (1.2388) acc1: 75.0000 (70.5177) acc5: 87.5000 (89.5236) time: 0.0052 data: 0.0004 max mem: 8962\n", - "Test: [5300/6250] eta: 0:00:05 loss: 1.5488 (1.2526) acc1: 62.5000 (70.2297) acc5: 87.5000 (89.3298) time: 0.0053 data: 0.0005 max mem: 8962\n", - "Test: [5400/6250] eta: 0:00:05 loss: 0.8032 (1.2557) acc1: 75.0000 (70.1768) acc5: 87.5000 (89.2728) time: 0.0058 data: 0.0004 max mem: 8962\n", - "Test: [5500/6250] eta: 0:00:04 loss: 0.8721 (1.2595) acc1: 75.0000 (70.0827) acc5: 87.5000 (89.2201) time: 0.0061 data: 0.0006 max mem: 8962\n", - "Test: [5600/6250] eta: 0:00:04 loss: 1.0362 (1.2646) acc1: 62.5000 (70.0009) acc5: 87.5000 (89.1269) time: 0.0053 data: 0.0004 max mem: 8962\n", - "Test: [5700/6250] eta: 0:00:03 loss: 2.2192 (1.2800) acc1: 37.5000 (69.7049) acc5: 75.0000 (88.9756) time: 0.0052 data: 0.0005 max mem: 8962\n", - "Test: [5800/6250] eta: 0:00:02 loss: 0.5529 (1.2755) acc1: 75.0000 (69.7897) acc5: 100.0000 (89.0321) time: 0.0052 data: 0.0005 max mem: 8962\n", - "Test: [5900/6250] eta: 0:00:02 loss: 1.1504 (1.2730) acc1: 75.0000 (69.8187) acc5: 87.5000 (89.0612) time: 0.0059 data: 0.0006 max mem: 8962\n", - "Test: [6000/6250] eta: 0:00:01 loss: 0.5121 (1.2652) acc1: 87.5000 (69.9779) acc5: 87.5000 (89.1518) time: 0.0060 data: 0.0005 max mem: 8962\n", - "Test: [6100/6250] eta: 0:00:00 loss: 1.3071 (1.2734) acc1: 62.5000 (69.7959) acc5: 87.5000 (89.0407) time: 0.0048 data: 0.0004 max mem: 8962\n", - "Test: [6200/6250] eta: 0:00:00 loss: 0.2227 (1.2671) acc1: 100.0000 (69.9161) acc5: 100.0000 (89.1207) time: 0.0062 data: 0.0005 max mem: 8962\n", - "Test: Total time: 0:00:38\n", - "Test: Acc@1 69.952 Acc@5 89.136\n", - "Epoch: [3] [ 0/5005] eta: 5:49:16 lr: 0.0002 img/s: 494.13332106139956 loss: 0.8680 (0.8680) acc1: 68.3594 (68.3594) acc5: 87.8906 (87.8906) meanQV: 1.4785 (1.4785) stdQV: 0.3262 (0.3262) time: 4.1871 data: 3.6690 max mem: 8962\n", - "Epoch: [3] [ 100/5005] eta: 0:45:34 lr: 0.0002 img/s: 493.44095322259034 loss: 0.8700 (0.8771) acc1: 69.5312 (68.9124) acc5: 87.1094 (87.1906) meanQV: 1.4867 (1.4820) stdQV: 0.3216 (0.3238) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [ 200/5005] eta: 0:43:11 lr: 0.0002 img/s: 491.6118921765009 loss: 0.8557 (0.8772) acc1: 69.1406 (69.1056) acc5: 87.1094 (87.0666) meanQV: 1.4840 (1.4834) stdQV: 0.3228 (0.3233) time: 0.5215 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [ 300/5005] eta: 0:41:50 lr: 0.0002 img/s: 488.24378408069873 loss: 0.8735 (0.8765) acc1: 68.3594 (69.1173) acc5: 87.1094 (87.2859) meanQV: 1.4773 (1.4834) stdQV: 0.3259 (0.3233) time: 0.5215 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [ 400/5005] eta: 0:40:42 lr: 0.0002 img/s: 491.1491050840666 loss: 0.8561 (0.8749) acc1: 69.1406 (69.1338) acc5: 87.1094 (87.3091) meanQV: 1.4816 (1.4835) stdQV: 0.3235 (0.3232) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [ 500/5005] eta: 0:39:40 lr: 0.0002 img/s: 494.98206480200105 loss: 0.8668 (0.8747) acc1: 68.3594 (69.2046) acc5: 87.5000 (87.3082) meanQV: 1.4785 (1.4840) stdQV: 0.3228 (0.3230) time: 0.5210 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [ 600/5005] eta: 0:38:42 lr: 0.0002 img/s: 492.9600064641953 loss: 0.8776 (0.8775) acc1: 69.1406 (69.1595) acc5: 87.5000 (87.2979) meanQV: 1.4840 (1.4837) stdQV: 0.3226 (0.3231) time: 0.5206 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [ 700/5005] eta: 0:37:46 lr: 0.0002 img/s: 488.8984818192125 loss: 0.8980 (0.8788) acc1: 69.1406 (69.1512) acc5: 86.3281 (87.2465) meanQV: 1.4836 (1.4837) stdQV: 0.3228 (0.3231) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [ 800/5005] eta: 0:36:50 lr: 0.0002 img/s: 489.088661574488 loss: 0.8472 (0.8815) acc1: 68.7500 (69.0777) acc5: 86.7188 (87.1776) meanQV: 1.4801 (1.4831) stdQV: 0.3248 (0.3233) time: 0.5208 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [ 900/5005] eta: 0:35:56 lr: 0.0002 img/s: 491.4127733969181 loss: 0.8806 (0.8807) acc1: 68.3594 (69.0921) acc5: 86.7188 (87.1657) meanQV: 1.4762 (1.4832) stdQV: 0.3262 (0.3233) time: 0.5213 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [1000/5005] eta: 0:35:01 lr: 0.0002 img/s: 491.22662521193246 loss: 0.9323 (0.8815) acc1: 67.9688 (69.0762) acc5: 86.7188 (87.1488) meanQV: 1.4758 (1.4831) stdQV: 0.3273 (0.3233) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [1100/5005] eta: 0:34:08 lr: 0.0002 img/s: 490.98717822090043 loss: 0.8727 (0.8828) acc1: 69.1406 (69.0541) acc5: 87.5000 (87.1292) meanQV: 1.4828 (1.4830) stdQV: 0.3237 (0.3234) time: 0.5207 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [1200/5005] eta: 0:33:14 lr: 0.0002 img/s: 489.31912417248043 loss: 0.8732 (0.8839) acc1: 68.7500 (69.0385) acc5: 86.3281 (87.0990) meanQV: 1.4801 (1.4829) stdQV: 0.3248 (0.3234) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [1300/5005] eta: 0:32:21 lr: 0.0002 img/s: 488.60570732215643 loss: 0.8841 (0.8833) acc1: 69.1406 (69.0139) acc5: 87.1094 (87.1034) meanQV: 1.4840 (1.4827) stdQV: 0.3228 (0.3235) time: 0.5211 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [1400/5005] eta: 0:31:27 lr: 0.0002 img/s: 489.7130175490766 loss: 0.8099 (0.8833) acc1: 69.9219 (69.0252) acc5: 87.1094 (87.0971) meanQV: 1.4895 (1.4828) stdQV: 0.3188 (0.3235) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [1500/5005] eta: 0:30:35 lr: 0.0002 img/s: 490.8552003847328 loss: 0.8780 (0.8834) acc1: 69.1406 (69.0147) acc5: 86.7188 (87.0748) meanQV: 1.4840 (1.4827) stdQV: 0.3228 (0.3235) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [1600/5005] eta: 0:29:42 lr: 0.0002 img/s: 492.7211280428486 loss: 0.9144 (0.8839) acc1: 69.1406 (69.0086) acc5: 86.7188 (87.0847) meanQV: 1.4828 (1.4827) stdQV: 0.3237 (0.3235) time: 0.5212 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [1700/5005] eta: 0:28:49 lr: 0.0002 img/s: 494.0639741221239 loss: 0.8737 (0.8834) acc1: 69.5312 (69.0187) acc5: 86.3281 (87.0809) meanQV: 1.4867 (1.4827) stdQV: 0.3228 (0.3235) time: 0.5216 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [1800/5005] eta: 0:27:56 lr: 0.0002 img/s: 494.14514608859565 loss: 0.9103 (0.8836) acc1: 67.5781 (69.0094) acc5: 87.1094 (87.0679) meanQV: 1.4730 (1.4827) stdQV: 0.3283 (0.3235) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [1900/5005] eta: 0:27:04 lr: 0.0002 img/s: 488.1301411462633 loss: 0.8608 (0.8841) acc1: 69.1406 (68.9943) acc5: 86.7188 (87.0798) meanQV: 1.4840 (1.4826) stdQV: 0.3228 (0.3236) time: 0.5210 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [2000/5005] eta: 0:26:11 lr: 0.0002 img/s: 493.9826015587656 loss: 0.9005 (0.8840) acc1: 69.1406 (69.0001) acc5: 87.1094 (87.0899) meanQV: 1.4840 (1.4826) stdQV: 0.3216 (0.3236) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [2100/5005] eta: 0:25:18 lr: 0.0002 img/s: 491.0731815518808 loss: 0.8610 (0.8835) acc1: 70.7031 (69.0287) acc5: 87.8906 (87.0908) meanQV: 1.4949 (1.4828) stdQV: 0.3180 (0.3235) time: 0.5210 data: 0.0005 max mem: 8962\n", - "Epoch: [3] [2200/5005] eta: 0:24:26 lr: 0.0002 img/s: 486.65824433670525 loss: 0.8879 (0.8840) acc1: 67.1875 (69.0139) acc5: 87.1094 (87.0943) meanQV: 1.4703 (1.4827) stdQV: 0.3262 (0.3235) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [2300/5005] eta: 0:23:33 lr: 0.0002 img/s: 489.28055002159914 loss: 0.8677 (0.8844) acc1: 69.1406 (69.0091) acc5: 88.6719 (87.0997) meanQV: 1.4840 (1.4826) stdQV: 0.3192 (0.3235) time: 0.5220 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [2400/5005] eta: 0:22:41 lr: 0.0002 img/s: 489.7322262906224 loss: 0.8478 (0.8846) acc1: 69.5312 (68.9997) acc5: 88.2812 (87.0944) meanQV: 1.4867 (1.4826) stdQV: 0.3226 (0.3236) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [2500/5005] eta: 0:21:49 lr: 0.0002 img/s: 494.49583330838766 loss: 0.9224 (0.8849) acc1: 68.7500 (68.9983) acc5: 86.3281 (87.0841) meanQV: 1.4813 (1.4826) stdQV: 0.3240 (0.3236) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [2600/5005] eta: 0:20:56 lr: 0.0002 img/s: 491.60829085668 loss: 0.8767 (0.8851) acc1: 69.9219 (69.0107) acc5: 87.8906 (87.0868) meanQV: 1.4883 (1.4826) stdQV: 0.3214 (0.3235) time: 0.5205 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [2700/5005] eta: 0:20:04 lr: 0.0002 img/s: 494.5233905012099 loss: 0.8684 (0.8851) acc1: 69.5312 (69.0087) acc5: 87.5000 (87.0942) meanQV: 1.4855 (1.4826) stdQV: 0.3216 (0.3235) time: 0.5203 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [2800/5005] eta: 0:19:11 lr: 0.0002 img/s: 491.0152438815262 loss: 0.9061 (0.8855) acc1: 69.1406 (68.9994) acc5: 86.7188 (87.0876) meanQV: 1.4840 (1.4826) stdQV: 0.3228 (0.3236) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [2900/5005] eta: 0:18:19 lr: 0.0002 img/s: 490.7789190394875 loss: 0.8594 (0.8853) acc1: 69.1406 (68.9987) acc5: 87.5000 (87.0913) meanQV: 1.4820 (1.4826) stdQV: 0.3215 (0.3236) time: 0.5220 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [3000/5005] eta: 0:17:27 lr: 0.0002 img/s: 490.72441483952787 loss: 0.8432 (0.8854) acc1: 69.5312 (68.9989) acc5: 87.8906 (87.0884) meanQV: 1.4867 (1.4826) stdQV: 0.3202 (0.3236) time: 0.5214 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [3100/5005] eta: 0:16:34 lr: 0.0002 img/s: 491.1839299405131 loss: 0.8755 (0.8852) acc1: 70.3125 (69.0101) acc5: 87.5000 (87.0990) meanQV: 1.4898 (1.4826) stdQV: 0.3192 (0.3235) time: 0.5210 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [3200/5005] eta: 0:15:42 lr: 0.0002 img/s: 491.87718437506726 loss: 0.8693 (0.8855) acc1: 69.5312 (69.0059) acc5: 86.3281 (87.0879) meanQV: 1.4863 (1.4826) stdQV: 0.3216 (0.3235) time: 0.5207 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [3300/5005] eta: 0:14:50 lr: 0.0002 img/s: 489.23663194863695 loss: 0.8853 (0.8859) acc1: 69.9219 (69.0043) acc5: 85.9375 (87.0807) meanQV: 1.4871 (1.4826) stdQV: 0.3202 (0.3235) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [3400/5005] eta: 0:13:58 lr: 0.0002 img/s: 487.7279331806508 loss: 0.8678 (0.8862) acc1: 68.3594 (68.9936) acc5: 86.3281 (87.0718) meanQV: 1.4785 (1.4825) stdQV: 0.3262 (0.3236) time: 0.5219 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [3500/5005] eta: 0:13:05 lr: 0.0002 img/s: 492.66031437817423 loss: 0.8921 (0.8862) acc1: 68.3594 (68.9935) acc5: 86.3281 (87.0740) meanQV: 1.4781 (1.4825) stdQV: 0.3260 (0.3236) time: 0.5205 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [3600/5005] eta: 0:12:13 lr: 0.0002 img/s: 491.45280918624405 loss: 0.8688 (0.8865) acc1: 69.5312 (68.9917) acc5: 87.1094 (87.0669) meanQV: 1.4855 (1.4825) stdQV: 0.3226 (0.3236) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [3700/5005] eta: 0:11:21 lr: 0.0002 img/s: 491.6204455225456 loss: 0.8652 (0.8867) acc1: 68.3594 (68.9841) acc5: 86.3281 (87.0616) meanQV: 1.4773 (1.4824) stdQV: 0.3262 (0.3236) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [3800/5005] eta: 0:10:29 lr: 0.0002 img/s: 487.71309034173106 loss: 0.8378 (0.8872) acc1: 69.1406 (68.9696) acc5: 86.7188 (87.0521) meanQV: 1.4840 (1.4823) stdQV: 0.3228 (0.3236) time: 0.5206 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [3900/5005] eta: 0:09:36 lr: 0.0002 img/s: 493.498784340653 loss: 0.9257 (0.8877) acc1: 68.3594 (68.9631) acc5: 87.1094 (87.0480) meanQV: 1.4785 (1.4823) stdQV: 0.3240 (0.3237) time: 0.5218 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4000/5005] eta: 0:08:44 lr: 0.0002 img/s: 494.29778520681486 loss: 0.8728 (0.8877) acc1: 68.3594 (68.9672) acc5: 87.5000 (87.0530) meanQV: 1.4785 (1.4823) stdQV: 0.3240 (0.3236) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4100/5005] eta: 0:07:52 lr: 0.0002 img/s: 491.56395393020676 loss: 0.9223 (0.8879) acc1: 68.7500 (68.9693) acc5: 86.7188 (87.0528) meanQV: 1.4789 (1.4823) stdQV: 0.3249 (0.3236) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4200/5005] eta: 0:07:00 lr: 0.0002 img/s: 489.26494371188943 loss: 0.8813 (0.8883) acc1: 67.5781 (68.9555) acc5: 86.3281 (87.0487) meanQV: 1.4730 (1.4822) stdQV: 0.3273 (0.3237) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4300/5005] eta: 0:06:07 lr: 0.0002 img/s: 493.6955115781962 loss: 0.8671 (0.8883) acc1: 70.7031 (68.9589) acc5: 86.3281 (87.0572) meanQV: 1.4949 (1.4823) stdQV: 0.3192 (0.3237) time: 0.5204 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4400/5005] eta: 0:05:15 lr: 0.0002 img/s: 493.47927899772964 loss: 0.9207 (0.8885) acc1: 68.3594 (68.9580) acc5: 85.9375 (87.0487) meanQV: 1.4785 (1.4823) stdQV: 0.3262 (0.3237) time: 0.5198 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4500/5005] eta: 0:04:23 lr: 0.0002 img/s: 492.8457412066241 loss: 0.8825 (0.8885) acc1: 69.5312 (68.9623) acc5: 87.1094 (87.0524) meanQV: 1.4867 (1.4823) stdQV: 0.3228 (0.3237) time: 0.5207 data: 0.0004 max mem: 8962\n", - "Epoch: [3] [4600/5005] eta: 0:03:31 lr: 0.0002 img/s: 491.5558526149826 loss: 0.8631 (0.8888) acc1: 68.7500 (68.9587) acc5: 86.7188 (87.0511) meanQV: 1.4813 (1.4823) stdQV: 0.3240 (0.3237) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4700/5005] eta: 0:02:39 lr: 0.0002 img/s: 491.1044017010751 loss: 0.9060 (0.8890) acc1: 67.9688 (68.9607) acc5: 86.7188 (87.0516) meanQV: 1.4754 (1.4823) stdQV: 0.3262 (0.3237) time: 0.5205 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4800/5005] eta: 0:01:46 lr: 0.0002 img/s: 488.3679195209023 loss: 0.8826 (0.8891) acc1: 70.3125 (68.9605) acc5: 87.5000 (87.0509) meanQV: 1.4922 (1.4823) stdQV: 0.3204 (0.3237) time: 0.5207 data: 0.0003 max mem: 8962\n", - "Epoch: [3] [4900/5005] eta: 0:00:54 lr: 0.0002 img/s: 657.254661723217 loss: 0.8709 (0.8890) acc1: 69.1406 (68.9624) acc5: 87.1094 (87.0525) meanQV: 1.4840 (1.4823) stdQV: 0.3208 (0.3237) time: 0.3795 data: 0.0027 max mem: 8962\n", - "Epoch: [3] [5000/5005] eta: 0:00:02 lr: 0.0002 img/s: 489.617889485732 loss: 0.8860 (0.8894) acc1: 67.5781 (68.9478) acc5: 86.3281 (87.0502) meanQV: 1.4730 (1.4822) stdQV: 0.3259 (0.3237) time: 0.5212 data: 0.0002 max mem: 8962\n", - "Epoch: [3] Total time: 0:43:15\n", - "Test: [ 0/6250] eta: 1:13:37 loss: 0.8178 (0.8178) acc1: 62.5000 (62.5000) acc5: 100.0000 (100.0000) time: 0.7068 data: 0.7000 max mem: 8962\n", - "Test: [ 100/6250] eta: 0:01:28 loss: 0.1440 (0.6006) acc1: 87.5000 (83.4158) acc5: 100.0000 (95.4208) time: 0.0070 data: 0.0009 max mem: 8962\n", - "Test: [ 200/6250] eta: 0:01:06 loss: 0.6774 (0.6502) acc1: 87.5000 (84.2040) acc5: 100.0000 (94.8383) time: 0.0073 data: 0.0006 max mem: 8962\n", - "Test: [ 300/6250] eta: 0:00:56 loss: 1.2668 (0.8598) acc1: 62.5000 (79.2774) acc5: 87.5000 (93.1478) time: 0.0066 data: 0.0007 max mem: 8962\n", - "Test: [ 400/6250] eta: 0:00:51 loss: 0.8958 (0.9858) acc1: 75.0000 (76.1845) acc5: 100.0000 (92.2693) time: 0.0062 data: 0.0008 max mem: 8962\n", - "Test: [ 500/6250] eta: 0:00:48 loss: 1.1632 (1.0374) acc1: 75.0000 (74.9002) acc5: 87.5000 (91.9411) time: 0.0061 data: 0.0007 max mem: 8962\n", - "Test: [ 600/6250] eta: 0:00:45 loss: 0.3993 (0.9455) acc1: 87.5000 (76.9343) acc5: 100.0000 (92.6165) time: 0.0064 data: 0.0006 max mem: 8962\n", - "Test: [ 700/6250] eta: 0:00:43 loss: 0.4326 (0.9321) acc1: 87.5000 (77.1576) acc5: 100.0000 (92.6712) time: 0.0067 data: 0.0006 max mem: 8962\n", - "Test: [ 800/6250] eta: 0:00:42 loss: 0.8489 (0.9613) acc1: 75.0000 (76.5918) acc5: 87.5000 (92.2441) time: 0.0066 data: 0.0008 max mem: 8962\n", - "Test: [ 900/6250] eta: 0:00:41 loss: 0.3832 (0.9033) acc1: 87.5000 (77.9550) acc5: 100.0000 (92.7303) time: 0.0068 data: 0.0005 max mem: 8962\n", - "Test: [1000/6250] eta: 0:00:39 loss: 0.9345 (0.8886) acc1: 75.0000 (78.1094) acc5: 100.0000 (92.9196) time: 0.0061 data: 0.0009 max mem: 8962\n", - "Test: [1100/6250] eta: 0:00:38 loss: 1.0317 (0.9263) acc1: 75.0000 (77.0777) acc5: 100.0000 (92.7679) time: 0.0071 data: 0.0008 max mem: 8962\n", - "Test: [1200/6250] eta: 0:00:37 loss: 1.0881 (0.9341) acc1: 75.0000 (76.5820) acc5: 87.5000 (92.8393) time: 0.0069 data: 0.0007 max mem: 8962\n", - "Test: [1300/6250] eta: 0:00:36 loss: 0.4206 (0.9358) acc1: 75.0000 (76.3259) acc5: 100.0000 (93.0150) time: 0.0074 data: 0.0009 max mem: 8962\n", - "Test: [1400/6250] eta: 0:00:35 loss: 0.8187 (0.9322) acc1: 75.0000 (76.4632) acc5: 87.5000 (93.0496) time: 0.0061 data: 0.0009 max mem: 8962\n", - "Test: [1500/6250] eta: 0:00:34 loss: 0.8115 (0.9372) acc1: 75.0000 (76.2408) acc5: 100.0000 (93.1379) time: 0.0061 data: 0.0006 max mem: 8962\n", - "Test: [1600/6250] eta: 0:00:33 loss: 0.1571 (0.9323) acc1: 100.0000 (76.1165) acc5: 100.0000 (93.2933) time: 0.0064 data: 0.0006 max mem: 8962\n", - "Test: [1700/6250] eta: 0:00:32 loss: 0.9674 (0.9303) acc1: 75.0000 (76.0068) acc5: 100.0000 (93.3936) time: 0.0071 data: 0.0007 max mem: 8962\n", - "Test: [1800/6250] eta: 0:00:32 loss: 1.2235 (0.9404) acc1: 62.5000 (75.7843) acc5: 87.5000 (93.3717) time: 0.0083 data: 0.0008 max mem: 8962\n", - "Test: [1900/6250] eta: 0:00:31 loss: 1.0283 (0.9336) acc1: 62.5000 (76.0652) acc5: 100.0000 (93.4903) time: 0.0054 data: 0.0006 max mem: 8962\n", - "Test: [2000/6250] eta: 0:00:30 loss: 0.4590 (0.9386) acc1: 87.5000 (76.0057) acc5: 100.0000 (93.4533) time: 0.0056 data: 0.0009 max mem: 8962\n", - "Test: [2100/6250] eta: 0:00:29 loss: 0.3773 (0.9199) acc1: 87.5000 (76.5469) acc5: 100.0000 (93.6042) time: 0.0062 data: 0.0007 max mem: 8962\n", - "Test: [2200/6250] eta: 0:00:28 loss: 0.2202 (0.9123) acc1: 87.5000 (76.7208) acc5: 100.0000 (93.6733) time: 0.0054 data: 0.0006 max mem: 8962\n", - "Test: [2300/6250] eta: 0:00:27 loss: 0.6553 (0.9135) acc1: 87.5000 (76.7058) acc5: 100.0000 (93.6821) time: 0.0067 data: 0.0020 max mem: 8962\n", - "Test: [2400/6250] eta: 0:00:27 loss: 0.8205 (0.9230) acc1: 75.0000 (76.5775) acc5: 87.5000 (93.5964) time: 0.0078 data: 0.0017 max mem: 8962\n", - "Test: [2500/6250] eta: 0:00:26 loss: 0.7730 (0.9230) acc1: 87.5000 (76.6693) acc5: 100.0000 (93.5726) time: 0.0055 data: 0.0006 max mem: 8962\n", - "Test: [2600/6250] eta: 0:00:25 loss: 2.1682 (0.9463) acc1: 50.0000 (76.2351) acc5: 75.0000 (93.2526) time: 0.0053 data: 0.0004 max mem: 8962\n", - "Test: [2700/6250] eta: 0:00:24 loss: 0.8635 (0.9574) acc1: 75.0000 (76.0459) acc5: 87.5000 (93.1137) time: 0.0055 data: 0.0005 max mem: 8962\n", - "Test: [2800/6250] eta: 0:00:23 loss: 1.4083 (0.9782) acc1: 62.5000 (75.6025) acc5: 87.5000 (92.8865) time: 0.0055 data: 0.0004 max mem: 8962\n", - "Test: [2900/6250] eta: 0:00:22 loss: 1.8681 (0.9967) acc1: 37.5000 (75.2025) acc5: 87.5000 (92.6706) time: 0.0063 data: 0.0009 max mem: 8962\n", - "Test: [3000/6250] eta: 0:00:22 loss: 2.3759 (1.0186) acc1: 50.0000 (74.8542) acc5: 75.0000 (92.3484) time: 0.0055 data: 0.0006 max mem: 8962\n", - "Test: [3100/6250] eta: 0:00:21 loss: 1.6813 (1.0453) acc1: 50.0000 (74.2986) acc5: 75.0000 (92.0872) time: 0.0058 data: 0.0007 max mem: 8962\n", - "Test: [3200/6250] eta: 0:00:20 loss: 0.7106 (1.0641) acc1: 62.5000 (73.9261) acc5: 100.0000 (91.8658) time: 0.0050 data: 0.0004 max mem: 8962\n", - "Test: [3300/6250] eta: 0:00:19 loss: 1.2310 (1.0768) acc1: 62.5000 (73.5800) acc5: 87.5000 (91.7487) time: 0.0060 data: 0.0005 max mem: 8962\n", - "Test: [3400/6250] eta: 0:00:19 loss: 2.4905 (1.0913) acc1: 50.0000 (73.3203) acc5: 75.0000 (91.5466) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [3500/6250] eta: 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mem: 8962\n", - "Test: [4200/6250] eta: 0:00:13 loss: 0.3974 (1.1608) acc1: 87.5000 (72.0930) acc5: 100.0000 (90.6362) time: 0.0068 data: 0.0005 max mem: 8962\n", - "Test: [4300/6250] eta: 0:00:12 loss: 0.4784 (1.1701) acc1: 75.0000 (71.9542) acc5: 100.0000 (90.4935) time: 0.0052 data: 0.0006 max mem: 8962\n", - "Test: [4400/6250] eta: 0:00:12 loss: 0.9728 (1.1769) acc1: 75.0000 (71.7763) acc5: 100.0000 (90.4368) time: 0.0052 data: 0.0005 max mem: 8962\n", - "Test: [4500/6250] eta: 0:00:11 loss: 1.1849 (1.1823) acc1: 75.0000 (71.6785) acc5: 87.5000 (90.3855) time: 0.0058 data: 0.0004 max mem: 8962\n", - "Test: [4600/6250] eta: 0:00:10 loss: 1.6483 (1.1923) acc1: 50.0000 (71.5116) acc5: 87.5000 (90.2413) time: 0.0056 data: 0.0007 max mem: 8962\n", - "Test: [4700/6250] eta: 0:00:10 loss: 1.3664 (1.2023) acc1: 62.5000 (71.2189) acc5: 87.5000 (90.1032) time: 0.0055 data: 0.0007 max mem: 8962\n", - "Test: [4800/6250] eta: 0:00:09 loss: 1.3209 (1.2095) acc1: 62.5000 (71.0841) acc5: 87.5000 (89.9969) time: 0.0051 data: 0.0008 max mem: 8962\n", - "Test: [4900/6250] eta: 0:00:08 loss: 0.4727 (1.2146) acc1: 87.5000 (71.0161) acc5: 100.0000 (89.9077) time: 0.0049 data: 0.0004 max mem: 8962\n", - "Test: [5000/6250] eta: 0:00:08 loss: 2.0785 (1.2263) acc1: 62.5000 (70.8483) acc5: 75.0000 (89.7520) time: 0.0054 data: 0.0005 max mem: 8962\n", - "Test: [5100/6250] eta: 0:00:07 loss: 1.2702 (1.2315) acc1: 62.5000 (70.7410) acc5: 87.5000 (89.7153) time: 0.0059 data: 0.0007 max mem: 8962\n", - "Test: [5200/6250] eta: 0:00:06 loss: 1.0106 (1.2380) acc1: 75.0000 (70.6138) acc5: 87.5000 (89.6510) time: 0.0047 data: 0.0004 max mem: 8962\n", - "Test: [5300/6250] eta: 0:00:06 loss: 1.4912 (1.2517) acc1: 50.0000 (70.3122) acc5: 87.5000 (89.4619) time: 0.0056 data: 0.0005 max mem: 8962\n", - "Test: [5400/6250] eta: 0:00:05 loss: 0.9010 (1.2548) acc1: 75.0000 (70.2671) acc5: 100.0000 (89.4071) time: 0.0057 data: 0.0006 max mem: 8962\n", - "Test: [5500/6250] eta: 0:00:04 loss: 0.9291 (1.2585) acc1: 75.0000 (70.1622) acc5: 87.5000 (89.3656) time: 0.0056 data: 0.0005 max mem: 8962\n", - "Test: [5600/6250] eta: 0:00:04 loss: 0.9468 (1.2638) acc1: 62.5000 (70.0857) acc5: 87.5000 (89.2586) time: 0.0060 data: 0.0016 max mem: 8962\n", - "Test: [5700/6250] eta: 0:00:03 loss: 2.1460 (1.2784) acc1: 37.5000 (69.7926) acc5: 75.0000 (89.1203) time: 0.0050 data: 0.0006 max mem: 8962\n", - "Test: [5800/6250] eta: 0:00:02 loss: 0.6562 (1.2747) acc1: 75.0000 (69.8522) acc5: 100.0000 (89.1657) time: 0.0053 data: 0.0008 max mem: 8962\n", - "Test: [5900/6250] eta: 0:00:02 loss: 1.0713 (1.2722) acc1: 75.0000 (69.8759) acc5: 100.0000 (89.2095) time: 0.0053 data: 0.0007 max mem: 8962\n", - "Test: [6000/6250] eta: 0:00:01 loss: 0.5802 (1.2647) acc1: 87.5000 (70.0342) acc5: 87.5000 (89.2955) time: 0.0057 data: 0.0006 max mem: 8962\n", - "Test: [6100/6250] eta: 0:00:00 loss: 1.2877 (1.2731) acc1: 62.5000 (69.8328) acc5: 87.5000 (89.2046) time: 0.0052 data: 0.0008 max mem: 8962\n", - "Test: 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0.9149 (0.9015) acc1: 67.9688 (68.7227) acc5: 87.8906 (87.0367) meanQV: 1.4758 (1.4805) stdQV: 0.3260 (0.3243) time: 0.5222 data: 0.0005 max mem: 8962\n", - "Epoch: [4] [ 400/5005] eta: 0:40:46 lr: 0.0002 img/s: 489.97201559708594 loss: 0.8910 (0.8983) acc1: 68.7500 (68.8055) acc5: 86.3281 (87.0305) meanQV: 1.4797 (1.4812) stdQV: 0.3246 (0.3240) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [ 500/5005] eta: 0:39:44 lr: 0.0002 img/s: 494.2242973791529 loss: 0.8698 (0.8962) acc1: 69.9219 (68.8599) acc5: 86.7188 (87.0556) meanQV: 1.4895 (1.4815) stdQV: 0.3214 (0.3239) time: 0.5205 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [ 600/5005] eta: 0:38:45 lr: 0.0002 img/s: 491.0853097714258 loss: 0.8708 (0.8966) acc1: 70.3125 (68.8962) acc5: 87.1094 (87.0457) meanQV: 1.4922 (1.4818) stdQV: 0.3188 (0.3238) time: 0.5203 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [ 700/5005] eta: 0:37:48 lr: 0.0002 img/s: 493.5375728016555 loss: 0.9078 (0.8985) acc1: 68.7500 (68.8353) acc5: 87.1094 (87.0018) meanQV: 1.4813 (1.4814) stdQV: 0.3248 (0.3240) time: 0.5213 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [ 800/5005] eta: 0:36:52 lr: 0.0002 img/s: 490.2532232540004 loss: 0.9188 (0.8979) acc1: 67.9688 (68.8904) acc5: 86.3281 (86.9953) meanQV: 1.4746 (1.4818) stdQV: 0.3270 (0.3238) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [ 900/5005] eta: 0:35:57 lr: 0.0002 img/s: 493.18552460181996 loss: 0.9238 (0.8994) acc1: 67.9688 (68.8575) acc5: 86.3281 (86.9680) meanQV: 1.4758 (1.4815) stdQV: 0.3262 (0.3239) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [1000/5005] eta: 0:35:03 lr: 0.0002 img/s: 490.8677666455156 loss: 0.8737 (0.8983) acc1: 69.9219 (68.8835) acc5: 87.1094 (86.9892) meanQV: 1.4895 (1.4817) stdQV: 0.3192 (0.3238) time: 0.5206 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [1100/5005] eta: 0:34:09 lr: 0.0002 img/s: 494.22497982810273 loss: 0.8912 (0.8984) acc1: 69.1406 (68.9065) acc5: 86.7188 (87.0005) meanQV: 1.4840 (1.4819) stdQV: 0.3204 (0.3237) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [1200/5005] eta: 0:33:16 lr: 0.0002 img/s: 491.8136503316876 loss: 0.9000 (0.8996) acc1: 68.7500 (68.8476) acc5: 87.5000 (86.9653) meanQV: 1.4801 (1.4814) stdQV: 0.3248 (0.3239) time: 0.5211 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [1300/5005] eta: 0:32:22 lr: 0.0002 img/s: 494.33123736827326 loss: 0.8564 (0.8992) acc1: 70.3125 (68.8515) acc5: 87.5000 (86.9686) meanQV: 1.4918 (1.4815) stdQV: 0.3192 (0.3239) time: 0.5202 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [1400/5005] eta: 0:31:29 lr: 0.0002 img/s: 493.6809842522038 loss: 0.8581 (0.8993) acc1: 69.5312 (68.8392) acc5: 87.5000 (86.9747) meanQV: 1.4867 (1.4814) stdQV: 0.3216 (0.3239) time: 0.5203 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [1500/5005] eta: 0:30:36 lr: 0.0002 img/s: 492.017828708216 loss: 0.8798 (0.8990) acc1: 68.7500 (68.8564) acc5: 86.7188 (86.9821) meanQV: 1.4801 (1.4815) stdQV: 0.3216 (0.3239) time: 0.5223 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [1600/5005] eta: 0:29:43 lr: 0.0002 img/s: 494.6657802925495 loss: 0.8522 (0.8990) acc1: 69.9219 (68.8637) acc5: 87.1094 (86.9842) meanQV: 1.4887 (1.4816) stdQV: 0.3213 (0.3239) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [1700/5005] eta: 0:28:50 lr: 0.0002 img/s: 499.96709108899364 loss: 0.8898 (0.8983) acc1: 69.5312 (68.8800) acc5: 86.7188 (86.9978) meanQV: 1.4867 (1.4817) stdQV: 0.3215 (0.3238) time: 0.5219 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [1800/5005] eta: 0:27:57 lr: 0.0002 img/s: 491.00491534530653 loss: 0.9142 (0.8985) acc1: 68.3594 (68.8823) acc5: 86.7188 (86.9988) meanQV: 1.4781 (1.4817) stdQV: 0.3251 (0.3238) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [1900/5005] eta: 0:27:05 lr: 0.0002 img/s: 489.01270554630025 loss: 0.8833 (0.8986) acc1: 68.7500 (68.8708) acc5: 87.1094 (86.9945) meanQV: 1.4801 (1.4816) stdQV: 0.3248 (0.3238) time: 0.5220 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [2000/5005] eta: 0:26:12 lr: 0.0002 img/s: 485.6457289761854 loss: 0.8689 (0.8982) acc1: 69.1406 (68.8986) acc5: 87.5000 (87.0073) meanQV: 1.4824 (1.4818) stdQV: 0.3235 (0.3238) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [2100/5005] eta: 0:25:19 lr: 0.0002 img/s: 490.798211492559 loss: 0.8893 (0.8982) acc1: 68.7500 (68.8917) acc5: 87.5000 (87.0095) meanQV: 1.4813 (1.4817) stdQV: 0.3251 (0.3238) time: 0.5210 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [2200/5005] eta: 0:24:27 lr: 0.0002 img/s: 493.2516794802064 loss: 0.8902 (0.8984) acc1: 68.3594 (68.8870) acc5: 87.1094 (87.0112) meanQV: 1.4785 (1.4817) stdQV: 0.3248 (0.3238) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [2300/5005] eta: 0:23:34 lr: 0.0002 img/s: 487.6654664365519 loss: 0.8884 (0.8983) acc1: 68.7500 (68.8970) acc5: 86.3281 (87.0163) meanQV: 1.4813 (1.4818) stdQV: 0.3240 (0.3238) time: 0.5224 data: 0.0002 max mem: 8962\n", - "Epoch: [4] [2400/5005] eta: 0:22:42 lr: 0.0002 img/s: 487.97818203963027 loss: 0.9129 (0.8987) acc1: 68.3594 (68.9003) acc5: 87.5000 (87.0210) meanQV: 1.4785 (1.4818) stdQV: 0.3251 (0.3238) time: 0.5218 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [2500/5005] eta: 0:21:49 lr: 0.0002 img/s: 493.4107957036287 loss: 0.8948 (0.8988) acc1: 68.7500 (68.9018) acc5: 86.3281 (87.0080) meanQV: 1.4809 (1.4818) stdQV: 0.3240 (0.3238) time: 0.5208 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [2600/5005] eta: 0:20:57 lr: 0.0002 img/s: 491.60288897587867 loss: 0.8879 (0.8991) acc1: 68.7500 (68.9072) acc5: 86.3281 (87.0161) meanQV: 1.4813 (1.4818) stdQV: 0.3226 (0.3237) time: 0.5208 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [2700/5005] eta: 0:20:04 lr: 0.0002 img/s: 491.1488804236057 loss: 0.8520 (0.8993) acc1: 69.9219 (68.9277) acc5: 87.8906 (87.0213) meanQV: 1.4895 (1.4820) stdQV: 0.3204 (0.3237) time: 0.5217 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [2800/5005] eta: 0:19:12 lr: 0.0002 img/s: 493.3804151641137 loss: 0.8855 (0.8992) acc1: 69.1406 (68.9271) acc5: 87.1094 (87.0320) meanQV: 1.4828 (1.4820) stdQV: 0.3228 (0.3237) time: 0.5209 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [2900/5005] eta: 0:18:20 lr: 0.0002 img/s: 494.2067818344083 loss: 0.9216 (0.8993) acc1: 68.7500 (68.9377) acc5: 86.3281 (87.0301) meanQV: 1.4813 (1.4821) stdQV: 0.3251 (0.3237) time: 0.5215 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [3000/5005] eta: 0:17:27 lr: 0.0002 img/s: 491.1448365704542 loss: 0.9209 (0.8991) acc1: 68.3594 (68.9410) acc5: 86.7188 (87.0258) meanQV: 1.4785 (1.4821) stdQV: 0.3249 (0.3237) time: 0.5223 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [3100/5005] eta: 0:16:35 lr: 0.0002 img/s: 492.0568356895722 loss: 0.8835 (0.8994) acc1: 68.7500 (68.9353) acc5: 87.1094 (87.0167) meanQV: 1.4813 (1.4820) stdQV: 0.3226 (0.3237) time: 0.5203 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [3200/5005] eta: 0:15:43 lr: 0.0002 img/s: 491.44133759107467 loss: 0.8832 (0.8996) acc1: 69.9219 (68.9327) acc5: 87.1094 (87.0172) meanQV: 1.4883 (1.4820) stdQV: 0.3214 (0.3237) time: 0.5212 data: 0.0005 max mem: 8962\n", - "Epoch: [4] [3300/5005] eta: 0:14:50 lr: 0.0002 img/s: 487.73613035951706 loss: 0.8594 (0.8997) acc1: 69.1406 (68.9283) acc5: 87.8906 (87.0199) meanQV: 1.4840 (1.4820) stdQV: 0.3228 (0.3237) time: 0.5213 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [3400/5005] eta: 0:13:58 lr: 0.0002 img/s: 491.204377802288 loss: 0.9090 (0.8999) acc1: 68.7500 (68.9294) acc5: 86.3281 (87.0150) meanQV: 1.4789 (1.4820) stdQV: 0.3246 (0.3237) time: 0.5213 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [3500/5005] eta: 0:13:06 lr: 0.0002 img/s: 495.01811999100084 loss: 0.9384 (0.9003) acc1: 68.3594 (68.9144) acc5: 86.3281 (87.0097) meanQV: 1.4785 (1.4819) stdQV: 0.3262 (0.3237) time: 0.5219 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [3600/5005] eta: 0:12:13 lr: 0.0002 img/s: 488.85240665181243 loss: 0.8764 (0.9000) acc1: 69.9219 (68.9205) acc5: 87.8906 (87.0174) meanQV: 1.4895 (1.4819) stdQV: 0.3204 (0.3237) time: 0.5217 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [3700/5005] eta: 0:11:21 lr: 0.0002 img/s: 488.62905411616515 loss: 0.8644 (0.8999) acc1: 69.9219 (68.9198) acc5: 86.7188 (87.0206) meanQV: 1.4895 (1.4819) stdQV: 0.3204 (0.3237) time: 0.5218 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [3800/5005] eta: 0:10:29 lr: 0.0002 img/s: 488.65351505542134 loss: 0.8901 (0.8997) acc1: 68.7500 (68.9341) acc5: 87.5000 (87.0288) meanQV: 1.4789 (1.4820) stdQV: 0.3248 (0.3237) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [3900/5005] eta: 0:09:37 lr: 0.0002 img/s: 493.65601740808734 loss: 0.8595 (0.8997) acc1: 68.3594 (68.9418) acc5: 87.1094 (87.0309) meanQV: 1.4785 (1.4821) stdQV: 0.3262 (0.3237) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [4000/5005] eta: 0:08:44 lr: 0.0002 img/s: 492.87718715199964 loss: 0.8968 (0.8997) acc1: 69.9219 (68.9440) acc5: 87.5000 (87.0334) meanQV: 1.4895 (1.4821) stdQV: 0.3204 (0.3237) time: 0.5208 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [4100/5005] eta: 0:07:52 lr: 0.0002 img/s: 488.15255480193474 loss: 0.8810 (0.8995) acc1: 68.3594 (68.9461) acc5: 87.5000 (87.0394) meanQV: 1.4785 (1.4821) stdQV: 0.3251 (0.3237) time: 0.5210 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [4200/5005] eta: 0:07:00 lr: 0.0002 img/s: 490.5603024844127 loss: 0.9360 (0.8999) acc1: 68.7500 (68.9386) acc5: 85.9375 (87.0355) meanQV: 1.4813 (1.4820) stdQV: 0.3240 (0.3237) time: 0.5213 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [4300/5005] eta: 0:06:08 lr: 0.0002 img/s: 493.6328685908266 loss: 0.8927 (0.9000) acc1: 67.9688 (68.9293) acc5: 87.1094 (87.0393) meanQV: 1.4758 (1.4820) stdQV: 0.3273 (0.3237) time: 0.5209 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [4400/5005] eta: 0:05:15 lr: 0.0002 img/s: 500.0339136176978 loss: 0.8938 (0.9000) acc1: 67.9688 (68.9281) acc5: 87.5000 (87.0415) meanQV: 1.4746 (1.4820) stdQV: 0.3237 (0.3237) time: 0.5203 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [4500/5005] eta: 0:04:23 lr: 0.0002 img/s: 491.36240054840806 loss: 0.8792 (0.9000) acc1: 69.5312 (68.9270) acc5: 87.1094 (87.0380) meanQV: 1.4832 (1.4820) stdQV: 0.3213 (0.3237) time: 0.5218 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [4600/5005] eta: 0:03:31 lr: 0.0002 img/s: 494.30324647494075 loss: 0.9115 (0.9000) acc1: 69.1406 (68.9302) acc5: 86.7188 (87.0371) meanQV: 1.4828 (1.4820) stdQV: 0.3238 (0.3237) time: 0.5208 data: 0.0003 max mem: 8962\n", - "Epoch: [4] [4700/5005] eta: 0:02:39 lr: 0.0002 img/s: 494.0173748211402 loss: 0.8860 (0.9000) acc1: 69.9219 (68.9322) acc5: 87.1094 (87.0404) meanQV: 1.4895 (1.4820) stdQV: 0.3202 (0.3237) time: 0.5212 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [4800/5005] eta: 0:01:47 lr: 0.0002 img/s: 493.9932830203957 loss: 0.9224 (0.9002) acc1: 68.7500 (68.9312) acc5: 87.1094 (87.0430) meanQV: 1.4801 (1.4820) stdQV: 0.3248 (0.3237) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [4900/5005] eta: 0:00:54 lr: 0.0002 img/s: 494.8479308631688 loss: 0.8930 (0.9002) acc1: 67.9688 (68.9332) acc5: 86.7188 (87.0443) meanQV: 1.4758 (1.4820) stdQV: 0.3262 (0.3237) time: 0.5202 data: 0.0004 max mem: 8962\n", - "Epoch: [4] [5000/5005] eta: 0:00:02 lr: 0.0002 img/s: 492.44182560982995 loss: 0.8594 (0.8999) acc1: 70.3125 (68.9486) acc5: 87.5000 (87.0523) meanQV: 1.4922 (1.4821) stdQV: 0.3192 (0.3236) time: 0.5203 data: 0.0002 max mem: 8962\n", - "Epoch: [4] Total time: 0:43:33\n", - "Test: [ 0/6250] eta: 1:16:02 loss: 0.7909 (0.7909) acc1: 75.0000 (75.0000) acc5: 100.0000 (100.0000) time: 0.7299 data: 0.7206 max mem: 8962\n", - "Test: [ 100/6250] eta: 0:01:29 loss: 0.1878 (0.6247) acc1: 87.5000 (83.4158) acc5: 100.0000 (95.2970) time: 0.0065 data: 0.0006 max mem: 8962\n", - "Test: [ 200/6250] eta: 0:01:04 loss: 0.6666 (0.6644) acc1: 87.5000 (84.3284) acc5: 100.0000 (94.5274) time: 0.0077 data: 0.0020 max mem: 8962\n", - "Test: [ 300/6250] eta: 0:00:54 loss: 1.2709 (0.8699) acc1: 62.5000 (78.9037) acc5: 87.5000 (93.0648) time: 0.0064 data: 0.0008 max mem: 8962\n", - "Test: [ 400/6250] eta: 0:00:49 loss: 0.8989 (0.9948) acc1: 75.0000 (76.1222) acc5: 100.0000 (92.1135) time: 0.0065 data: 0.0005 max mem: 8962\n", - "Test: [ 500/6250] eta: 0:00:45 loss: 1.1087 (1.0409) acc1: 75.0000 (74.9251) acc5: 87.5000 (91.7914) time: 0.0068 data: 0.0025 max mem: 8962\n", - "Test: [ 600/6250] eta: 0:00:43 loss: 0.4547 (0.9505) acc1: 87.5000 (76.8927) acc5: 100.0000 (92.4293) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [ 700/6250] eta: 0:00:40 loss: 0.3772 (0.9356) acc1: 87.5000 (77.1755) acc5: 100.0000 (92.4215) time: 0.0055 data: 0.0004 max mem: 8962\n", - "Test: [ 800/6250] eta: 0:00:40 loss: 0.8068 (0.9664) acc1: 75.0000 (76.5762) acc5: 87.5000 (92.0568) time: 0.0060 data: 0.0004 max mem: 8962\n", - "Test: [ 900/6250] eta: 0:00:38 loss: 0.3764 (0.9114) acc1: 87.5000 (77.8996) acc5: 100.0000 (92.5361) time: 0.0062 data: 0.0004 max mem: 8962\n", - "Test: [1000/6250] eta: 0:00:37 loss: 0.9310 (0.8980) acc1: 75.0000 (78.1094) acc5: 100.0000 (92.7572) time: 0.0063 data: 0.0006 max mem: 8962\n", - "Test: [1100/6250] eta: 0:00:37 loss: 1.1327 (0.9363) acc1: 75.0000 (77.1571) acc5: 100.0000 (92.5636) time: 0.0073 data: 0.0006 max mem: 8962\n", - "Test: [1200/6250] eta: 0:00:36 loss: 1.0381 (0.9410) acc1: 75.0000 (76.7902) acc5: 87.5000 (92.6728) time: 0.0076 data: 0.0006 max mem: 8962\n", - "Test: [1300/6250] eta: 0:00:35 loss: 0.4042 (0.9409) acc1: 87.5000 (76.5853) acc5: 100.0000 (92.8997) time: 0.0066 data: 0.0007 max mem: 8962\n", - "Test: [1400/6250] eta: 0:00:34 loss: 0.7884 (0.9356) acc1: 75.0000 (76.6774) acc5: 87.5000 (92.9961) time: 0.0063 data: 0.0005 max mem: 8962\n", - "Test: [1500/6250] eta: 0:00:33 loss: 0.9547 (0.9392) acc1: 75.0000 (76.4074) acc5: 100.0000 (93.1046) time: 0.0058 data: 0.0005 max mem: 8962\n", - "Test: [1600/6250] eta: 0:00:32 loss: 0.1298 (0.9348) acc1: 87.5000 (76.2492) acc5: 100.0000 (93.2464) time: 0.0063 data: 0.0006 max mem: 8962\n", - "Test: [1700/6250] eta: 0:00:32 loss: 1.0550 (0.9324) acc1: 75.0000 (76.1023) acc5: 100.0000 (93.3715) time: 0.0092 data: 0.0041 max mem: 8962\n", - "Test: [1800/6250] eta: 0:00:31 loss: 1.2747 (0.9422) acc1: 62.5000 (75.8537) acc5: 87.5000 (93.3648) time: 0.0064 data: 0.0004 max mem: 8962\n", - "Test: [1900/6250] eta: 0:00:30 loss: 0.9273 (0.9352) acc1: 75.0000 (76.1244) acc5: 100.0000 (93.4705) time: 0.0065 data: 0.0005 max mem: 8962\n", - "Test: [2000/6250] eta: 0:00:30 loss: 0.4722 (0.9411) acc1: 87.5000 (76.0120) acc5: 100.0000 (93.4658) time: 0.0064 data: 0.0006 max mem: 8962\n", - "Test: [2100/6250] eta: 0:00:29 loss: 0.3972 (0.9220) acc1: 87.5000 (76.5469) acc5: 100.0000 (93.6161) time: 0.0064 data: 0.0005 max mem: 8962\n", - "Test: [2200/6250] eta: 0:00:28 loss: 0.1838 (0.9145) acc1: 87.5000 (76.6924) acc5: 100.0000 (93.6733) time: 0.0052 data: 0.0004 max mem: 8962\n", - "Test: [2300/6250] eta: 0:00:27 loss: 0.7064 (0.9173) acc1: 87.5000 (76.6515) acc5: 100.0000 (93.6441) time: 0.0061 data: 0.0019 max mem: 8962\n", - "Test: [2400/6250] eta: 0:00:26 loss: 0.9312 (0.9272) acc1: 75.0000 (76.5618) acc5: 87.5000 (93.5392) time: 0.0057 data: 0.0006 max mem: 8962\n", - "Test: [2500/6250] eta: 0:00:26 loss: 0.9157 (0.9291) acc1: 75.0000 (76.6144) acc5: 100.0000 (93.4976) time: 0.0060 data: 0.0006 max mem: 8962\n", - "Test: [2600/6250] eta: 0:00:25 loss: 2.2484 (0.9531) acc1: 50.0000 (76.1246) acc5: 62.5000 (93.1949) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [2700/6250] eta: 0:00:24 loss: 0.8119 (0.9641) acc1: 75.0000 (75.9210) acc5: 87.5000 (93.0766) time: 0.0057 data: 0.0004 max mem: 8962\n", - "Test: [2800/6250] eta: 0:00:23 loss: 1.5186 (0.9860) acc1: 62.5000 (75.4418) acc5: 75.0000 (92.8240) time: 0.0058 data: 0.0004 max mem: 8962\n", - "Test: [2900/6250] eta: 0:00:23 loss: 1.7899 (1.0040) acc1: 37.5000 (75.0603) acc5: 87.5000 (92.6060) time: 0.0078 data: 0.0007 max mem: 8962\n", - "Test: [3000/6250] eta: 0:00:22 loss: 2.3188 (1.0252) acc1: 50.0000 (74.7418) acc5: 75.0000 (92.2817) time: 0.0062 data: 0.0006 max mem: 8962\n", - "Test: [3100/6250] eta: 0:00:21 loss: 1.8096 (1.0517) acc1: 50.0000 (74.2341) acc5: 75.0000 (92.0106) time: 0.0056 data: 0.0004 max mem: 8962\n", - "Test: [3200/6250] eta: 0:00:20 loss: 0.6736 (1.0715) acc1: 75.0000 (73.8402) acc5: 100.0000 (91.8072) time: 0.0057 data: 0.0004 max mem: 8962\n", - "Test: [3300/6250] eta: 0:00:19 loss: 1.2702 (1.0843) acc1: 62.5000 (73.4853) acc5: 87.5000 (91.6919) time: 0.0060 data: 0.0009 max mem: 8962\n", - "Test: [3400/6250] eta: 0:00:19 loss: 2.4354 (1.0978) acc1: 37.5000 (73.2468) acc5: 75.0000 (91.5062) time: 0.0059 data: 0.0005 max mem: 8962\n", - "Test: [3500/6250] eta: 0:00:18 loss: 1.7555 (1.1037) acc1: 50.0000 (73.1720) acc5: 75.0000 (91.3846) time: 0.0058 data: 0.0006 max mem: 8962\n", - "Test: [3600/6250] eta: 0:00:17 loss: 0.5104 (1.0999) acc1: 87.5000 (73.3164) acc5: 100.0000 (91.3948) time: 0.0054 data: 0.0005 max mem: 8962\n", - "Test: [3700/6250] eta: 0:00:17 loss: 1.7609 (1.1145) acc1: 62.5000 (73.0276) acc5: 87.5000 (91.2017) time: 0.0116 data: 0.0071 max mem: 8962\n", - "Test: [3800/6250] eta: 0:00:16 loss: 0.6589 (1.1207) acc1: 87.5000 (72.9446) acc5: 87.5000 (91.1076) time: 0.0069 data: 0.0008 max mem: 8962\n", - "Test: [3900/6250] eta: 0:00:15 loss: 2.3705 (1.1368) acc1: 50.0000 (72.6224) acc5: 75.0000 (90.8837) time: 0.0063 data: 0.0010 max mem: 8962\n", - "Test: [4000/6250] eta: 0:00:14 loss: 1.3760 (1.1507) acc1: 62.5000 (72.3663) acc5: 87.5000 (90.7179) time: 0.0052 data: 0.0004 max mem: 8962\n", - "Test: [4100/6250] eta: 0:00:14 loss: 1.3587 (1.1609) acc1: 62.5000 (72.1836) acc5: 87.5000 (90.6059) time: 0.0056 data: 0.0005 max mem: 8962\n", - "Test: [4200/6250] eta: 0:00:13 loss: 0.4211 (1.1651) acc1: 87.5000 (72.0543) acc5: 100.0000 (90.5975) time: 0.0050 data: 0.0004 max mem: 8962\n", - "Test: [4300/6250] eta: 0:00:12 loss: 0.4845 (1.1744) acc1: 75.0000 (71.9339) acc5: 87.5000 (90.4354) time: 0.0056 data: 0.0006 max mem: 8962\n", - "Test: [4400/6250] eta: 0:00:12 loss: 0.9234 (1.1813) acc1: 75.0000 (71.7422) acc5: 100.0000 (90.3885) time: 0.0054 data: 0.0005 max mem: 8962\n", - "Test: [4500/6250] eta: 0:00:11 loss: 1.2129 (1.1870) acc1: 62.5000 (71.6230) acc5: 87.5000 (90.3299) time: 0.0060 data: 0.0008 max mem: 8962\n", - "Test: [4600/6250] eta: 0:00:10 loss: 1.6169 (1.1973) acc1: 50.0000 (71.4519) acc5: 87.5000 (90.1652) time: 0.0059 data: 0.0006 max mem: 8962\n", - "Test: [4700/6250] eta: 0:00:10 loss: 1.4572 (1.2073) acc1: 62.5000 (71.1471) acc5: 87.5000 (90.0181) time: 0.0054 data: 0.0005 max mem: 8962\n", - "Test: [4800/6250] eta: 0:00:09 loss: 1.1656 (1.2140) acc1: 62.5000 (71.0347) acc5: 87.5000 (89.9188) time: 0.0057 data: 0.0005 max mem: 8962\n", - "Test: [4900/6250] eta: 0:00:08 loss: 0.4505 (1.2183) acc1: 87.5000 (70.9779) acc5: 100.0000 (89.8312) time: 0.0050 data: 0.0005 max mem: 8962\n", - "Test: [5000/6250] eta: 0:00:08 loss: 2.1536 (1.2300) acc1: 62.5000 (70.8008) acc5: 75.0000 (89.6821) time: 0.0061 data: 0.0004 max mem: 8962\n", - "Test: [5100/6250] eta: 0:00:07 loss: 1.3107 (1.2352) acc1: 62.5000 (70.6920) acc5: 100.0000 (89.6442) time: 0.0064 data: 0.0008 max mem: 8962\n", - "Test: [5200/6250] eta: 0:00:06 loss: 1.0626 (1.2412) acc1: 75.0000 (70.5850) acc5: 87.5000 (89.5741) time: 0.0053 data: 0.0005 max mem: 8962\n", - "Test: [5300/6250] eta: 0:00:06 loss: 1.4655 (1.2552) acc1: 62.5000 (70.2650) acc5: 87.5000 (89.3864) time: 0.0063 data: 0.0005 max mem: 8962\n", - "Test: [5400/6250] eta: 0:00:05 loss: 0.8139 (1.2579) acc1: 75.0000 (70.2162) acc5: 87.5000 (89.3469) time: 0.0053 data: 0.0004 max mem: 8962\n", - "Test: [5500/6250] eta: 0:00:04 loss: 0.9340 (1.2610) acc1: 75.0000 (70.1213) acc5: 87.5000 (89.3065) time: 0.0055 data: 0.0004 max mem: 8962\n", - "Test: [5600/6250] eta: 0:00:04 loss: 1.0789 (1.2661) acc1: 62.5000 (70.0433) acc5: 87.5000 (89.2140) time: 0.0064 data: 0.0013 max mem: 8962\n", - "Test: [5700/6250] eta: 0:00:03 loss: 2.0526 (1.2814) acc1: 37.5000 (69.7422) acc5: 75.0000 (89.0567) time: 0.0050 data: 0.0004 max mem: 8962\n", - "Test: [5800/6250] eta: 0:00:02 loss: 0.5955 (1.2775) acc1: 75.0000 (69.8220) acc5: 100.0000 (89.0989) time: 0.0068 data: 0.0007 max mem: 8962\n", - "Test: [5900/6250] eta: 0:00:02 loss: 1.1023 (1.2751) acc1: 62.5000 (69.8483) acc5: 100.0000 (89.1353) time: 0.0056 data: 0.0006 max mem: 8962\n", - "Test: [6000/6250] eta: 0:00:01 loss: 0.5431 (1.2677) acc1: 87.5000 (69.9988) acc5: 87.5000 (89.2289) time: 0.0058 data: 0.0004 max mem: 8962\n", - "Test: [6100/6250] eta: 0:00:00 loss: 1.3317 (1.2758) acc1: 62.5000 (69.8164) acc5: 87.5000 (89.1370) time: 0.0054 data: 0.0004 max mem: 8962\n", - "Test: [6200/6250] eta: 0:00:00 loss: 0.2130 (1.2696) acc1: 87.5000 (69.9444) acc5: 100.0000 (89.2114) time: 0.0065 data: 0.0005 max mem: 8962\n", - "Test: Total time: 0:00:40\n", - "Test: Acc@1 69.982 Acc@5 89.216\n", - "Epoch: [5] [ 0/5005] eta: 6:26:06 lr: 0.0001 img/s: 472.08087977640673 loss: 0.9772 (0.9772) acc1: 70.3125 (70.3125) acc5: 87.8906 (87.8906) meanQV: 1.4922 (1.4922) stdQV: 0.3204 (0.3204) time: 4.6287 data: 4.0864 max mem: 8962\n", - "Epoch: [5] [ 100/5005] eta: 0:45:55 lr: 0.0001 img/s: 493.412156113808 loss: 0.8671 (0.8977) acc1: 69.1406 (69.1445) acc5: 87.1094 (87.3028) meanQV: 1.4840 (1.4833) stdQV: 0.3228 (0.3230) time: 0.5215 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [ 200/5005] eta: 0:42:34 lr: 0.0001 img/s: 633.4831033504211 loss: 0.8743 (0.9023) acc1: 69.5312 (69.1465) acc5: 87.1094 (87.1774) meanQV: 1.4867 (1.4834) stdQV: 0.3216 (0.3230) time: 0.4189 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [ 300/5005] eta: 0:37:51 lr: 0.0001 img/s: 491.4145726182402 loss: 0.8887 (0.9009) acc1: 69.5312 (69.1938) acc5: 87.8906 (87.1872) meanQV: 1.4867 (1.4838) stdQV: 0.3204 (0.3229) time: 0.3729 data: 0.0032 max mem: 8962\n", - "Epoch: [5] [ 400/5005] eta: 0:37:47 lr: 0.0001 img/s: 488.7140108034497 loss: 0.9087 (0.9030) acc1: 68.3594 (69.0539) acc5: 87.5000 (87.1396) meanQV: 1.4781 (1.4828) stdQV: 0.3251 (0.3233) time: 0.5207 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [ 500/5005] eta: 0:37:23 lr: 0.0001 img/s: 489.01871879991273 loss: 0.8969 (0.9043) acc1: 67.5781 (68.9605) acc5: 87.1094 (87.0493) meanQV: 1.4730 (1.4821) stdQV: 0.3249 (0.3237) time: 0.5216 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [ 600/5005] eta: 0:36:50 lr: 0.0001 img/s: 494.24204166440353 loss: 0.9185 (0.9048) acc1: 68.3594 (68.9502) acc5: 87.1094 (87.0873) meanQV: 1.4785 (1.4821) stdQV: 0.3262 (0.3237) time: 0.5209 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [ 700/5005] eta: 0:36:12 lr: 0.0001 img/s: 490.8462248780818 loss: 0.9053 (0.9051) acc1: 68.3594 (68.9840) acc5: 85.9375 (87.0927) meanQV: 1.4781 (1.4823) stdQV: 0.3240 (0.3236) time: 0.5222 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [ 800/5005] eta: 0:35:30 lr: 0.0001 img/s: 493.49674301159627 loss: 0.8652 (0.9059) acc1: 70.3125 (68.9992) acc5: 87.5000 (87.0879) meanQV: 1.4918 (1.4824) stdQV: 0.3202 (0.3235) time: 0.5209 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [ 900/5005] eta: 0:34:46 lr: 0.0001 img/s: 492.24203409939537 loss: 0.8904 (0.9059) acc1: 69.1406 (68.9659) acc5: 87.5000 (87.0920) meanQV: 1.4840 (1.4822) stdQV: 0.3224 (0.3236) time: 0.5210 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [1000/5005] eta: 0:34:00 lr: 0.0001 img/s: 493.5423367172554 loss: 0.9307 (0.9063) acc1: 69.1406 (68.9541) acc5: 85.9375 (87.0731) meanQV: 1.4840 (1.4821) stdQV: 0.3228 (0.3236) time: 0.5217 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [1100/5005] eta: 0:33:14 lr: 0.0001 img/s: 489.2419819520163 loss: 0.8769 (0.9064) acc1: 69.9219 (68.9540) acc5: 87.1094 (87.0718) meanQV: 1.4891 (1.4821) stdQV: 0.3212 (0.3236) time: 0.5218 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [1200/5005] eta: 0:32:26 lr: 0.0001 img/s: 499.6392909558434 loss: 0.8655 (0.9056) acc1: 67.9688 (68.9608) acc5: 86.7188 (87.0716) meanQV: 1.4746 (1.4821) stdQV: 0.3257 (0.3236) time: 0.5207 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [1300/5005] eta: 0:31:37 lr: 0.0001 img/s: 493.8163030755964 loss: 0.9314 (0.9051) acc1: 67.5781 (68.9860) acc5: 86.7188 (87.0968) meanQV: 1.4727 (1.4823) stdQV: 0.3280 (0.3235) time: 0.5206 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [1400/5005] eta: 0:30:49 lr: 0.0001 img/s: 494.9975815812936 loss: 0.8642 (0.9041) acc1: 69.5312 (69.0129) acc5: 87.8906 (87.0929) meanQV: 1.4844 (1.4825) stdQV: 0.3216 (0.3234) time: 0.5214 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [1500/5005] eta: 0:29:59 lr: 0.0001 img/s: 488.4738953923197 loss: 0.9535 (0.9047) acc1: 67.9688 (68.9798) acc5: 86.3281 (87.0698) meanQV: 1.4758 (1.4823) stdQV: 0.3262 (0.3235) time: 0.5208 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [1600/5005] eta: 0:29:09 lr: 0.0001 img/s: 489.3487835801782 loss: 0.8809 (0.9042) acc1: 69.1406 (69.0108) acc5: 87.1094 (87.0811) meanQV: 1.4828 (1.4825) stdQV: 0.3237 (0.3235) time: 0.5214 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [1700/5005] eta: 0:28:19 lr: 0.0001 img/s: 489.42685183665805 loss: 0.8938 (0.9043) acc1: 68.3594 (68.9845) acc5: 85.9375 (87.0756) meanQV: 1.4785 (1.4823) stdQV: 0.3262 (0.3235) time: 0.5206 data: 0.0003 max mem: 8962\n", - "Epoch: [5] [1800/5005] eta: 0:27:29 lr: 0.0001 img/s: 489.34900659686224 loss: 0.8771 (0.9038) acc1: 69.9219 (69.0081) acc5: 89.0625 (87.1057) meanQV: 1.4867 (1.4825) stdQV: 0.3192 (0.3235) time: 0.5219 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [1900/5005] eta: 0:26:39 lr: 0.0001 img/s: 488.80233479720977 loss: 0.9229 (0.9041) acc1: 67.9688 (69.0192) acc5: 86.7188 (87.1049) meanQV: 1.4746 (1.4825) stdQV: 0.3236 (0.3234) time: 0.5216 data: 0.0004 max mem: 8962\n", - "Epoch: [5] [2000/5005] eta: 0:25:48 lr: 0.0001 img/s: 492.5014558430955 loss: 0.9266 (0.9045) acc1: 67.9688 (69.0048) acc5: 86.7188 (87.0990) meanQV: 1.4758 (1.4824) stdQV: 0.3262 (0.3235) time: 0.5205 data: 0.0004 max mem: 8962\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 27\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mtypes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m SimpleNamespace\n\u001b[1;32m 3\u001b[0m args \u001b[38;5;241m=\u001b[39m SimpleNamespace(\n\u001b[1;32m 4\u001b[0m data_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/home/cs/Documents/datasets/imagenet\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m model\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresnet18\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 24\u001b[0m trp_lambdas\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m0.4\u001b[39m, \u001b[38;5;241m0.2\u001b[39m, \u001b[38;5;241m0.1\u001b[39m],\n\u001b[1;32m 25\u001b[0m )\n\u001b[0;32m---> 27\u001b[0m \u001b[43mmain\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[7], line 151\u001b[0m, in \u001b[0;36mmain\u001b[0;34m(args)\u001b[0m\n\u001b[1;32m 149\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(args\u001b[38;5;241m.\u001b[39mepochs):\n\u001b[0;32m--> 151\u001b[0m \u001b[43mtrain_one_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 152\u001b[0m lr_scheduler\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 153\u001b[0m evaluate(model, nn\u001b[38;5;241m.\u001b[39mCrossEntropyLoss(), data_loader_test, device\u001b[38;5;241m=\u001b[39mdevice)\n", - "Cell \u001b[0;32mIn[7], line 61\u001b[0m, in \u001b[0;36mtrain_one_epoch\u001b[0;34m(model, optimizer, data_loader, device, epoch, args)\u001b[0m\n\u001b[1;32m 59\u001b[0m acc1, acc5 \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39maccuracy(output, target, topk\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m5\u001b[39m))\n\u001b[1;32m 60\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m image\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m---> 61\u001b[0m metric_logger\u001b[38;5;241m.\u001b[39mupdate(loss\u001b[38;5;241m=\u001b[39m\u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m, lr\u001b[38;5;241m=\u001b[39moptimizer\u001b[38;5;241m.\u001b[39mparam_groups[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 62\u001b[0m metric_logger\u001b[38;5;241m.\u001b[39mmeters[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124macc1\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(acc1\u001b[38;5;241m.\u001b[39mitem(), n\u001b[38;5;241m=\u001b[39mbatch_size)\n\u001b[1;32m 63\u001b[0m metric_logger\u001b[38;5;241m.\u001b[39mmeters[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124macc5\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mupdate(acc5\u001b[38;5;241m.\u001b[39mitem(), n\u001b[38;5;241m=\u001b[39mbatch_size)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "Test: [ 0/782] eta: 0:23:05 loss: 0.6283 (0.6283) acc1: 89.0625 (89.0625) acc5: 95.3125 (95.3125) time: 1.7719 data: 1.3111 max mem: 19119\n", + "Test: [100/782] eta: 0:00:30 loss: 1.0688 (0.9382) acc1: 76.5625 (76.2840) acc5: 89.0625 (92.1875) time: 0.0399 data: 0.0263 max mem: 19119\n", + "Test: [200/782] eta: 0:00:21 loss: 0.9244 (0.9143) acc1: 73.4375 (75.8240) acc5: 95.3125 (93.2369) time: 0.0244 data: 0.0107 max mem: 19119\n", + "Test: [300/782] eta: 0:00:17 loss: 0.8615 (0.9072) acc1: 76.5625 (76.1991) acc5: 92.1875 (93.5008) time: 0.0381 data: 0.0244 max mem: 19119\n", + "Test: [400/782] eta: 0:00:13 loss: 1.6977 (1.0440) acc1: 59.3750 (73.6323) acc5: 82.8125 (91.7472) time: 0.0313 data: 0.0176 max mem: 19119\n", + "Test: [500/782] eta: 0:00:09 loss: 1.6021 (1.1237) acc1: 54.6875 (72.0964) acc5: 85.9375 (90.5845) time: 0.0247 data: 0.0109 max mem: 19119\n", + "Test: [600/782] eta: 0:00:06 loss: 1.3631 (1.1858) acc1: 64.0625 (70.8741) acc5: 84.3750 (89.7853) time: 0.0291 data: 0.0153 max mem: 19119\n", + "Test: [700/782] eta: 0:00:02 loss: 1.2494 (1.2361) acc1: 68.7500 (69.9313) acc5: 87.5000 (89.1115) time: 0.0391 data: 0.0254 max mem: 19119\n", + "Test: Total time: 0:00:26\n", + "Test: Acc@1 69.846 Acc@5 89.136\n", + "Epoch: [1] [ 0/2503] eta: 4:27:27 lr: 0.0004 img/s: 861.3684242192573 loss: 0.7611 (0.7611) acc1: 68.5547 (68.5547) acc5: 86.1328 (86.1328) time: 6.4115 data: 5.8170 max mem: 19119\n", + "Epoch: [1] [ 100/2503] eta: 0:26:25 lr: 0.0004 img/s: 856.9149263546342 loss: 0.7538 (0.7542) acc1: 70.5078 (69.0536) acc5: 87.5000 (87.1364) time: 0.5982 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [ 200/2503] eta: 0:24:10 lr: 0.0004 img/s: 854.0172713632207 loss: 0.7744 (0.7573) acc1: 69.7266 (69.2990) acc5: 88.0859 (87.3785) time: 0.5998 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [ 300/2503] eta: 0:22:45 lr: 0.0004 img/s: 856.0483105922384 loss: 0.7551 (0.7613) acc1: 69.1406 (69.2834) acc5: 87.3047 (87.4611) time: 0.5996 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [ 400/2503] eta: 0:21:32 lr: 0.0004 img/s: 854.8386016604893 loss: 0.7931 (0.7645) acc1: 68.5547 (69.3004) acc5: 87.3047 (87.4698) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [ 500/2503] eta: 0:20:24 lr: 0.0004 img/s: 855.3431965742996 loss: 0.7744 (0.7684) acc1: 68.1641 (69.2853) acc5: 86.9141 (87.4361) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [ 600/2503] eta: 0:19:19 lr: 0.0004 img/s: 855.1112541063571 loss: 0.7860 (0.7730) acc1: 69.1406 (69.2310) acc5: 86.7188 (87.3941) time: 0.5988 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [ 700/2503] eta: 0:18:15 lr: 0.0004 img/s: 856.4904515045232 loss: 0.7908 (0.7773) acc1: 68.7500 (69.1746) acc5: 86.7188 (87.3543) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [ 800/2503] eta: 0:17:13 lr: 0.0004 img/s: 858.0146361031335 loss: 0.8157 (0.7805) acc1: 68.5547 (69.1660) acc5: 87.6953 (87.3181) time: 0.5991 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [ 900/2503] eta: 0:16:11 lr: 0.0004 img/s: 854.9138104963116 loss: 0.7641 (0.7825) acc1: 69.5312 (69.1807) acc5: 88.8672 (87.3346) time: 0.5989 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [1000/2503] eta: 0:15:09 lr: 0.0004 img/s: 855.7491430488731 loss: 0.8024 (0.7852) acc1: 68.1641 (69.1506) acc5: 86.5234 (87.3234) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1100/2503] eta: 0:14:08 lr: 0.0004 img/s: 856.0848253972304 loss: 0.8099 (0.7872) acc1: 69.1406 (69.1564) acc5: 86.7188 (87.3231) time: 0.5992 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1200/2503] eta: 0:13:07 lr: 0.0004 img/s: 855.6028761225017 loss: 0.8307 (0.7894) acc1: 68.5547 (69.1569) acc5: 87.1094 (87.3258) time: 0.5989 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1300/2503] eta: 0:12:06 lr: 0.0004 img/s: 855.8589613399885 loss: 0.8206 (0.7913) acc1: 68.9453 (69.1304) acc5: 87.3047 (87.3177) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [1400/2503] eta: 0:11:05 lr: 0.0004 img/s: 856.6045604019511 loss: 0.8454 (0.7936) acc1: 68.1641 (69.1019) acc5: 86.9141 (87.2906) time: 0.5989 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1500/2503] eta: 0:10:04 lr: 0.0004 img/s: 854.944442321167 loss: 0.8428 (0.7960) acc1: 68.1641 (69.0905) acc5: 87.3047 (87.2706) time: 0.5990 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1600/2503] eta: 0:09:04 lr: 0.0004 img/s: 855.0727794914757 loss: 0.7906 (0.7974) acc1: 69.5312 (69.0922) acc5: 87.1094 (87.2686) time: 0.5990 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1700/2503] eta: 0:08:03 lr: 0.0004 img/s: 855.4958499669949 loss: 0.8199 (0.7989) acc1: 69.7266 (69.0854) acc5: 87.1094 (87.2704) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [1800/2503] eta: 0:07:03 lr: 0.0004 img/s: 855.0251166287029 loss: 0.8257 (0.8007) acc1: 70.1172 (69.0869) acc5: 87.5000 (87.2656) time: 0.5988 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [1900/2503] eta: 0:06:03 lr: 0.0004 img/s: 856.9867390518363 loss: 0.7952 (0.8018) acc1: 68.7500 (69.0943) acc5: 87.3047 (87.2670) time: 0.5989 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [2000/2503] eta: 0:05:02 lr: 0.0004 img/s: 854.3927252530574 loss: 0.8402 (0.8032) acc1: 68.5547 (69.0964) acc5: 87.1094 (87.2747) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [2100/2503] eta: 0:04:02 lr: 0.0004 img/s: 855.2427067851231 loss: 0.8451 (0.8042) acc1: 68.3594 (69.1089) acc5: 87.3047 (87.2816) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [2200/2503] eta: 0:03:02 lr: 0.0004 img/s: 853.8318747507567 loss: 0.8314 (0.8058) acc1: 68.9453 (69.1012) acc5: 87.3047 (87.2716) time: 0.5989 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [2300/2503] eta: 0:02:02 lr: 0.0004 img/s: 855.3312728222841 loss: 0.8350 (0.8070) acc1: 68.7500 (69.0993) acc5: 86.3281 (87.2549) time: 0.5988 data: 0.0003 max mem: 19119\n", + "Epoch: [1] [2400/2503] eta: 0:01:01 lr: 0.0004 img/s: 855.1613103361218 loss: 0.8206 (0.8084) acc1: 68.5547 (69.0927) acc5: 86.9141 (87.2551) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [1] [2500/2503] eta: 0:00:01 lr: 0.0004 img/s: 856.7190414886559 loss: 0.8286 (0.8094) acc1: 69.1406 (69.1000) acc5: 87.1094 (87.2599) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [1] Total time: 0:25:05\n", + "Test: [ 0/782] eta: 0:16:19 loss: 0.5636 (0.5636) acc1: 87.5000 (87.5000) acc5: 96.8750 (96.8750) time: 1.2525 data: 1.2385 max mem: 19119\n", + "Test: [100/782] eta: 0:00:31 loss: 1.0393 (0.9414) acc1: 76.5625 (76.9647) acc5: 90.6250 (92.2958) time: 0.0417 data: 0.0280 max mem: 19119\n", + "Test: [200/782] eta: 0:00:22 loss: 0.8964 (0.9176) acc1: 73.4375 (76.4614) acc5: 95.3125 (93.3147) time: 0.0249 data: 0.0112 max mem: 19119\n", + "Test: [300/782] eta: 0:00:17 loss: 0.7984 (0.9094) acc1: 79.6875 (76.7130) acc5: 92.1875 (93.6150) time: 0.0311 data: 0.0173 max mem: 19119\n", + "Test: [400/782] eta: 0:00:13 loss: 1.7745 (1.0483) acc1: 57.8125 (73.9635) acc5: 84.3750 (91.8758) time: 0.0328 data: 0.0190 max mem: 19119\n", + "Test: [500/782] eta: 0:00:09 loss: 1.6435 (1.1264) acc1: 59.3750 (72.4239) acc5: 84.3750 (90.7934) time: 0.0328 data: 0.0190 max mem: 19119\n", + "Test: [600/782] eta: 0:00:06 loss: 1.3057 (1.1915) acc1: 62.5000 (71.0483) acc5: 85.9375 (90.0010) time: 0.0400 data: 0.0261 max mem: 19119\n", + "Test: [700/782] eta: 0:00:02 loss: 1.2212 (1.2428) acc1: 70.3125 (70.0985) acc5: 87.5000 (89.3010) time: 0.0253 data: 0.0115 max mem: 19119\n", + "Test: Total time: 0:00:26\n", + "Test: Acc@1 70.000 Acc@5 89.320\n", + "Epoch: [2] [ 0/2503] eta: 4:06:15 lr: 0.0004 img/s: 867.4756359685 loss: 0.8414 (0.8414) acc1: 67.9688 (67.9688) acc5: 86.1328 (86.1328) time: 5.9030 data: 5.3128 max mem: 19119\n", + "Epoch: [2] [ 100/2503] eta: 0:25:53 lr: 0.0004 img/s: 859.1872918530421 loss: 0.8472 (0.8456) acc1: 68.9453 (69.1194) acc5: 86.7188 (87.2892) time: 0.5963 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [ 200/2503] eta: 0:23:52 lr: 0.0004 img/s: 857.5945509684602 loss: 0.8563 (0.8443) acc1: 68.1641 (69.1649) acc5: 86.3281 (87.1852) time: 0.5972 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [ 300/2503] eta: 0:22:31 lr: 0.0004 img/s: 859.3399450658505 loss: 0.8386 (0.8440) acc1: 69.1406 (69.0790) acc5: 87.3047 (87.1762) time: 0.5967 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [ 400/2503] eta: 0:21:21 lr: 0.0004 img/s: 859.825426282471 loss: 0.8455 (0.8446) acc1: 69.3359 (69.0422) acc5: 87.3047 (87.1591) time: 0.5963 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [ 500/2503] eta: 0:20:15 lr: 0.0004 img/s: 858.6002202991031 loss: 0.8400 (0.8448) acc1: 67.9688 (69.0373) acc5: 87.5000 (87.1640) time: 0.5962 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [ 600/2503] eta: 0:19:11 lr: 0.0004 img/s: 859.6997885462696 loss: 0.8544 (0.8467) acc1: 68.3594 (69.0217) acc5: 86.9141 (87.1347) time: 0.5963 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [ 700/2503] eta: 0:18:08 lr: 0.0004 img/s: 858.0379481409502 loss: 0.8386 (0.8466) acc1: 68.7500 (69.0314) acc5: 87.1094 (87.1459) time: 0.5966 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [ 800/2503] eta: 0:17:06 lr: 0.0004 img/s: 859.3574830298114 loss: 0.8607 (0.8472) acc1: 69.5312 (69.0338) acc5: 87.1094 (87.1364) time: 0.5965 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [ 900/2503] eta: 0:16:05 lr: 0.0004 img/s: 858.1785328651912 loss: 0.8502 (0.8474) acc1: 68.5547 (69.0273) acc5: 87.1094 (87.1404) time: 0.5966 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [1000/2503] eta: 0:15:04 lr: 0.0004 img/s: 858.6554923981921 loss: 0.8213 (0.8468) acc1: 70.1172 (69.0737) acc5: 87.6953 (87.1601) time: 0.5966 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [1100/2503] eta: 0:14:03 lr: 0.0004 img/s: 858.942266240932 loss: 0.8322 (0.8466) acc1: 68.9453 (69.0824) acc5: 87.5000 (87.1826) time: 0.5965 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [1200/2503] eta: 0:13:02 lr: 0.0004 img/s: 859.9796839014982 loss: 0.8353 (0.8468) acc1: 68.5547 (69.0858) acc5: 87.1094 (87.1886) time: 0.5962 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [1300/2503] eta: 0:12:02 lr: 0.0004 img/s: 858.8996673563247 loss: 0.8654 (0.8471) acc1: 68.3594 (69.0645) acc5: 86.7188 (87.1876) time: 0.5966 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [1400/2503] eta: 0:11:01 lr: 0.0004 img/s: 859.7879032225777 loss: 0.8277 (0.8466) acc1: 70.1172 (69.0861) acc5: 88.6719 (87.2244) time: 0.5963 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [1500/2503] eta: 0:10:01 lr: 0.0004 img/s: 859.6271763544084 loss: 0.8703 (0.8471) acc1: 68.7500 (69.0763) acc5: 86.5234 (87.2101) time: 0.5962 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [1600/2503] eta: 0:09:01 lr: 0.0004 img/s: 859.8206066235718 loss: 0.8818 (0.8481) acc1: 68.5547 (69.0523) acc5: 86.7188 (87.1975) time: 0.5965 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [1700/2503] eta: 0:08:01 lr: 0.0004 img/s: 858.3188206070029 loss: 0.8447 (0.8487) acc1: 69.3359 (69.0259) acc5: 87.3047 (87.1857) time: 0.5965 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [1800/2503] eta: 0:07:01 lr: 0.0004 img/s: 860.4651988761577 loss: 0.8492 (0.8489) acc1: 67.5781 (69.0295) acc5: 86.7188 (87.1785) time: 0.5965 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [1900/2503] eta: 0:06:01 lr: 0.0004 img/s: 858.5823699612625 loss: 0.8559 (0.8491) acc1: 67.9688 (69.0192) acc5: 87.5000 (87.1776) time: 0.5963 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [2000/2503] eta: 0:05:01 lr: 0.0004 img/s: 858.4468002775832 loss: 0.8712 (0.8498) acc1: 68.9453 (69.0207) acc5: 87.6953 (87.1836) time: 0.5963 data: 0.0003 max mem: 19119\n", + "Epoch: [2] [2100/2503] eta: 0:04:01 lr: 0.0004 img/s: 858.6208177650899 loss: 0.8782 (0.8507) acc1: 68.3594 (69.0053) acc5: 85.9375 (87.1818) time: 0.5964 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [2200/2503] eta: 0:03:01 lr: 0.0004 img/s: 858.769492116456 loss: 0.8845 (0.8514) acc1: 68.3594 (68.9953) acc5: 86.5234 (87.1744) time: 0.5963 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [2300/2503] eta: 0:02:01 lr: 0.0004 img/s: 860.1050589782235 loss: 0.8664 (0.8522) acc1: 68.7500 (68.9914) acc5: 87.5000 (87.1735) time: 0.5962 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [2400/2503] eta: 0:01:01 lr: 0.0004 img/s: 859.3210323775423 loss: 0.8824 (0.8529) acc1: 67.7734 (68.9772) acc5: 86.5234 (87.1693) time: 0.5963 data: 0.0002 max mem: 19119\n", + "Epoch: [2] [2500/2503] eta: 0:00:01 lr: 0.0004 img/s: 860.1956686665284 loss: 0.8302 (0.8531) acc1: 69.9219 (68.9880) acc5: 87.5000 (87.1751) time: 0.5962 data: 0.0002 max mem: 19119\n", + "Epoch: [2] Total time: 0:24:57\n", + "Test: [ 0/782] eta: 0:14:49 loss: 0.6400 (0.6400) acc1: 82.8125 (82.8125) acc5: 93.7500 (93.7500) time: 1.1370 data: 1.1232 max mem: 19119\n", + "Test: [100/782] eta: 0:00:28 loss: 1.0691 (0.9495) acc1: 75.0000 (76.8874) acc5: 89.0625 (92.2184) time: 0.0422 data: 0.0284 max mem: 19119\n", + "Test: [200/782] eta: 0:00:20 loss: 0.8384 (0.9253) acc1: 75.0000 (76.3293) acc5: 95.3125 (93.2292) time: 0.0298 data: 0.0161 max mem: 19119\n", + "Test: [300/782] eta: 0:00:16 loss: 0.8140 (0.9153) acc1: 78.1250 (76.6092) acc5: 92.1875 (93.5631) time: 0.0281 data: 0.0143 max mem: 19119\n", + "Test: [400/782] eta: 0:00:12 loss: 1.7029 (1.0528) acc1: 62.5000 (73.9479) acc5: 84.3750 (91.8797) time: 0.0260 data: 0.0123 max mem: 19119\n", + "Test: [500/782] eta: 0:00:09 loss: 1.7149 (1.1295) acc1: 59.3750 (72.4894) acc5: 84.3750 (90.7997) time: 0.0315 data: 0.0177 max mem: 19119\n", + "Test: [600/782] eta: 0:00:06 loss: 1.3215 (1.1949) acc1: 65.6250 (71.1288) acc5: 85.9375 (90.0192) time: 0.0343 data: 0.0204 max mem: 19119\n", + "Test: [700/782] eta: 0:00:02 loss: 1.3000 (1.2468) acc1: 70.3125 (70.1386) acc5: 85.9375 (89.2809) time: 0.0246 data: 0.0108 max mem: 19119\n", + "Test: Total time: 0:00:25\n", + "Test: Acc@1 70.034 Acc@5 89.306\n", + "Epoch: [3] [ 0/2503] eta: 3:48:40 lr: 0.0002 img/s: 868.6651772838787 loss: 0.9922 (0.9922) acc1: 65.8203 (65.8203) acc5: 84.3750 (84.3750) time: 5.4818 data: 4.8924 max mem: 19119\n", + "Epoch: [3] [ 100/2503] eta: 0:25:56 lr: 0.0002 img/s: 857.1146638568258 loss: 0.8599 (0.8484) acc1: 69.7266 (69.1851) acc5: 86.7188 (87.2660) time: 0.5978 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [ 200/2503] eta: 0:23:56 lr: 0.0002 img/s: 854.6256384868216 loss: 0.8801 (0.8570) acc1: 68.7500 (69.0182) acc5: 86.3281 (87.1521) time: 0.5998 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [ 300/2503] eta: 0:22:36 lr: 0.0002 img/s: 855.2042203405152 loss: 0.8260 (0.8538) acc1: 69.3359 (69.0959) acc5: 87.6953 (87.2262) time: 0.5990 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [ 400/2503] eta: 0:21:25 lr: 0.0002 img/s: 856.2763231713128 loss: 0.8881 (0.8553) acc1: 68.3594 (69.1733) acc5: 86.9141 (87.2141) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [ 500/2503] eta: 0:20:19 lr: 0.0002 img/s: 856.0431919432866 loss: 0.8596 (0.8573) acc1: 68.3594 (69.1281) acc5: 87.3047 (87.2291) time: 0.5982 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [ 600/2503] eta: 0:19:15 lr: 0.0002 img/s: 855.2682527981125 loss: 0.8779 (0.8592) acc1: 68.1641 (69.1153) acc5: 86.7188 (87.2007) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [ 700/2503] eta: 0:18:12 lr: 0.0002 img/s: 855.3551206587658 loss: 0.8727 (0.8601) acc1: 68.7500 (69.0902) acc5: 87.8906 (87.2033) time: 0.5988 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [ 800/2503] eta: 0:17:10 lr: 0.0002 img/s: 854.5804059835012 loss: 0.8775 (0.8608) acc1: 69.1406 (69.0684) acc5: 86.5234 (87.1713) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [ 900/2503] eta: 0:16:08 lr: 0.0002 img/s: 855.525160200547 loss: 0.8299 (0.8601) acc1: 69.5312 (69.0866) acc5: 87.5000 (87.1883) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1000/2503] eta: 0:15:07 lr: 0.0002 img/s: 855.1732293498484 loss: 0.8740 (0.8600) acc1: 68.3594 (69.0608) acc5: 86.7188 (87.1773) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1100/2503] eta: 0:14:06 lr: 0.0002 img/s: 855.7201584498974 loss: 0.8490 (0.8600) acc1: 69.5312 (69.0574) acc5: 87.6953 (87.1810) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [1200/2503] eta: 0:13:05 lr: 0.0002 img/s: 855.6761738093758 loss: 0.8551 (0.8598) acc1: 70.1172 (69.0749) acc5: 87.3047 (87.1956) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1300/2503] eta: 0:12:04 lr: 0.0002 img/s: 855.1391759063549 loss: 0.8736 (0.8596) acc1: 68.9453 (69.1111) acc5: 87.5000 (87.2038) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1400/2503] eta: 0:11:04 lr: 0.0002 img/s: 855.9558431039926 loss: 0.8849 (0.8602) acc1: 69.1406 (69.1073) acc5: 86.3281 (87.2021) time: 0.5989 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1500/2503] eta: 0:10:03 lr: 0.0002 img/s: 856.2879318505251 loss: 0.8493 (0.8600) acc1: 69.7266 (69.1198) acc5: 87.3047 (87.2114) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [1600/2503] eta: 0:09:03 lr: 0.0002 img/s: 855.3885098640291 loss: 0.8944 (0.8605) acc1: 67.9688 (69.1188) acc5: 86.5234 (87.2106) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [1700/2503] eta: 0:08:03 lr: 0.0002 img/s: 855.653671788276 loss: 0.8327 (0.8606) acc1: 69.7266 (69.1132) acc5: 87.3047 (87.1988) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1800/2503] eta: 0:07:02 lr: 0.0002 img/s: 854.6603313202065 loss: 0.8716 (0.8606) acc1: 69.5312 (69.1106) acc5: 87.3047 (87.2095) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [1900/2503] eta: 0:06:02 lr: 0.0002 img/s: 855.8654421819135 loss: 0.8433 (0.8607) acc1: 68.9453 (69.1149) acc5: 86.9141 (87.1973) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [2000/2503] eta: 0:05:02 lr: 0.0002 img/s: 858.0228637365412 loss: 0.8635 (0.8613) acc1: 69.1406 (69.0981) acc5: 86.7188 (87.1936) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [2100/2503] eta: 0:04:02 lr: 0.0002 img/s: 855.1837864680422 loss: 0.8389 (0.8614) acc1: 69.7266 (69.1067) acc5: 87.3047 (87.2038) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [2200/2503] eta: 0:03:02 lr: 0.0002 img/s: 854.9267436657309 loss: 0.8588 (0.8618) acc1: 69.5312 (69.1018) acc5: 86.9141 (87.2006) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [2300/2503] eta: 0:02:01 lr: 0.0002 img/s: 857.5592770650364 loss: 0.8385 (0.8623) acc1: 69.7266 (69.1041) acc5: 87.6953 (87.1965) time: 0.5985 data: 0.0003 max mem: 19119\n", + "Epoch: [3] [2400/2503] eta: 0:01:01 lr: 0.0002 img/s: 854.3804880688189 loss: 0.8534 (0.8625) acc1: 68.9453 (69.1074) acc5: 87.3047 (87.1914) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [3] [2500/2503] eta: 0:00:01 lr: 0.0002 img/s: 855.3973686621443 loss: 0.8348 (0.8625) acc1: 69.3359 (69.1134) acc5: 87.6953 (87.1933) time: 0.5984 data: 0.0002 max mem: 19119\n", + "Epoch: [3] Total time: 0:25:03\n", + "Test: [ 0/782] eta: 0:13:34 loss: 0.6298 (0.6298) acc1: 84.3750 (84.3750) acc5: 95.3125 (95.3125) time: 1.0412 data: 1.0273 max mem: 19119\n", + "Test: [100/782] eta: 0:00:28 loss: 1.0908 (0.9514) acc1: 75.0000 (76.9957) acc5: 89.0625 (92.2339) time: 0.0397 data: 0.0260 max mem: 19119\n", + "Test: [200/782] eta: 0:00:21 loss: 0.9058 (0.9231) acc1: 73.4375 (76.3137) acc5: 95.3125 (93.2680) time: 0.0258 data: 0.0121 max mem: 19119\n", + "Test: [300/782] eta: 0:00:17 loss: 0.8269 (0.9143) acc1: 79.6875 (76.5988) acc5: 92.1875 (93.5735) time: 0.0356 data: 0.0218 max mem: 19119\n", + "Test: [400/782] eta: 0:00:13 loss: 1.8047 (1.0535) acc1: 60.9375 (73.9207) acc5: 82.8125 (91.8329) time: 0.0270 data: 0.0133 max mem: 19119\n", + "Test: [500/782] eta: 0:00:09 loss: 1.6839 (1.1303) acc1: 59.3750 (72.5050) acc5: 85.9375 (90.7622) time: 0.0334 data: 0.0196 max mem: 19119\n", + "Test: [600/782] eta: 0:00:06 loss: 1.3633 (1.1951) acc1: 64.0625 (71.1704) acc5: 87.5000 (89.9776) time: 0.0256 data: 0.0118 max mem: 19119\n", + "Test: [700/782] eta: 0:00:02 loss: 1.2720 (1.2480) acc1: 71.8750 (70.1632) acc5: 85.9375 (89.2876) time: 0.0280 data: 0.0142 max mem: 19119\n", + "Test: Total time: 0:00:26\n", + "Test: Acc@1 70.092 Acc@5 89.308\n", + "Epoch: [4] [ 0/2503] eta: 3:53:04 lr: 0.0002 img/s: 868.854963030395 loss: 0.9245 (0.9245) acc1: 67.5781 (67.5781) acc5: 87.3047 (87.3047) time: 5.5871 data: 4.9977 max mem: 19119\n", + "Epoch: [4] [ 100/2503] eta: 0:25:58 lr: 0.0002 img/s: 856.1991675966316 loss: 0.8765 (0.8657) acc1: 68.9453 (69.2605) acc5: 86.5234 (87.1171) time: 0.5978 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [ 200/2503] eta: 0:23:57 lr: 0.0002 img/s: 853.9622551773969 loss: 0.8653 (0.8621) acc1: 69.7266 (69.4321) acc5: 87.3047 (87.2464) time: 0.5998 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [ 300/2503] eta: 0:22:37 lr: 0.0002 img/s: 855.0397553711638 loss: 0.8689 (0.8659) acc1: 69.3359 (69.2879) acc5: 87.3047 (87.2275) time: 0.5990 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [ 400/2503] eta: 0:21:26 lr: 0.0002 img/s: 857.3206551376953 loss: 0.8390 (0.8645) acc1: 68.7500 (69.3432) acc5: 86.5234 (87.2808) time: 0.5983 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [ 500/2503] eta: 0:20:19 lr: 0.0002 img/s: 856.0698095863008 loss: 0.8473 (0.8638) acc1: 69.5312 (69.3262) acc5: 87.5000 (87.3148) time: 0.5984 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [ 600/2503] eta: 0:19:15 lr: 0.0002 img/s: 856.6852065793925 loss: 0.8467 (0.8628) acc1: 69.3359 (69.3753) acc5: 87.3047 (87.3388) time: 0.5985 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [ 700/2503] eta: 0:18:12 lr: 0.0002 img/s: 856.1111043339286 loss: 0.8966 (0.8657) acc1: 68.1641 (69.3106) acc5: 86.7188 (87.2924) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [ 800/2503] eta: 0:17:10 lr: 0.0002 img/s: 856.0469456131993 loss: 0.8508 (0.8662) acc1: 69.3359 (69.2959) acc5: 87.6953 (87.2976) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [ 900/2503] eta: 0:16:08 lr: 0.0002 img/s: 854.9645243753337 loss: 0.8663 (0.8662) acc1: 70.3125 (69.2997) acc5: 87.6953 (87.2860) time: 0.5986 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1000/2503] eta: 0:15:07 lr: 0.0002 img/s: 855.0186485068215 loss: 0.8554 (0.8671) acc1: 68.3594 (69.2827) acc5: 86.9141 (87.2858) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1100/2503] eta: 0:14:06 lr: 0.0002 img/s: 855.2873281496619 loss: 0.8594 (0.8670) acc1: 69.7266 (69.2912) acc5: 87.5000 (87.2926) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [1200/2503] eta: 0:13:05 lr: 0.0002 img/s: 856.2193086400064 loss: 0.8768 (0.8677) acc1: 68.7500 (69.2626) acc5: 86.7188 (87.2596) time: 0.5982 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1300/2503] eta: 0:12:04 lr: 0.0002 img/s: 856.0534293024039 loss: 0.8824 (0.8688) acc1: 68.7500 (69.2354) acc5: 87.1094 (87.2479) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [1400/2503] eta: 0:11:04 lr: 0.0002 img/s: 857.5664685962558 loss: 0.8628 (0.8691) acc1: 68.7500 (69.2155) acc5: 87.5000 (87.2509) time: 0.5984 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [1500/2503] eta: 0:10:03 lr: 0.0002 img/s: 854.9161928928554 loss: 0.8838 (0.8696) acc1: 69.1406 (69.1992) acc5: 87.5000 (87.2396) time: 0.5983 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1600/2503] eta: 0:09:03 lr: 0.0002 img/s: 858.2563886134587 loss: 0.8541 (0.8697) acc1: 68.5547 (69.1823) acc5: 87.3047 (87.2258) time: 0.5985 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1700/2503] eta: 0:08:03 lr: 0.0002 img/s: 855.3152614496619 loss: 0.8702 (0.8694) acc1: 68.9453 (69.1954) acc5: 86.9141 (87.2312) time: 0.5988 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1800/2503] eta: 0:07:02 lr: 0.0002 img/s: 856.2275018779335 loss: 0.8691 (0.8696) acc1: 68.9453 (69.1939) acc5: 87.1094 (87.2242) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [1900/2503] eta: 0:06:02 lr: 0.0002 img/s: 854.6021714024656 loss: 0.8867 (0.8703) acc1: 68.3594 (69.1812) acc5: 86.5234 (87.2186) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [4] [2000/2503] eta: 0:05:02 lr: 0.0002 img/s: 856.027153906284 loss: 0.8680 (0.8710) acc1: 67.9688 (69.1654) acc5: 87.1094 (87.2148) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [2100/2503] eta: 0:04:02 lr: 0.0002 img/s: 854.944442321167 loss: 0.8930 (0.8715) acc1: 68.9453 (69.1539) acc5: 87.1094 (87.2134) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [2200/2503] eta: 0:03:02 lr: 0.0002 img/s: 857.4733304797255 loss: 0.8344 (0.8713) acc1: 69.7266 (69.1614) acc5: 87.6953 (87.2185) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [2300/2503] eta: 0:02:01 lr: 0.0002 img/s: 854.5545609884018 loss: 0.8644 (0.8712) acc1: 69.1406 (69.1611) acc5: 86.9141 (87.2154) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [2400/2503] eta: 0:01:01 lr: 0.0002 img/s: 855.9186570165338 loss: 0.8843 (0.8714) acc1: 69.1406 (69.1609) acc5: 86.9141 (87.2155) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [4] [2500/2503] eta: 0:00:01 lr: 0.0002 img/s: 855.5220927564314 loss: 0.8757 (0.8719) acc1: 68.9453 (69.1569) acc5: 87.1094 (87.2136) time: 0.5979 data: 0.0002 max mem: 19119\n", + "Epoch: [4] Total time: 0:25:03\n", + "Test: [ 0/782] eta: 0:15:24 loss: 0.5899 (0.5899) acc1: 85.9375 (85.9375) acc5: 95.3125 (95.3125) time: 1.1827 data: 1.1689 max mem: 19119\n", + "Test: [100/782] eta: 0:00:27 loss: 1.0514 (0.9553) acc1: 76.5625 (76.7327) acc5: 89.0625 (92.1411) time: 0.0322 data: 0.0185 max mem: 19119\n", + "Test: [200/782] eta: 0:00:20 loss: 0.8755 (0.9239) acc1: 75.0000 (76.3682) acc5: 95.3125 (93.2369) time: 0.0254 data: 0.0116 max mem: 19119\n", + "Test: [300/782] eta: 0:00:16 loss: 0.7986 (0.9160) acc1: 78.1250 (76.6352) acc5: 92.1875 (93.5424) time: 0.0298 data: 0.0161 max mem: 19119\n", + "Test: [400/782] eta: 0:00:12 loss: 1.7921 (1.0555) acc1: 60.9375 (73.8817) acc5: 84.3750 (91.8095) time: 0.0308 data: 0.0171 max mem: 19119\n", + "Test: [500/782] eta: 0:00:09 loss: 1.7681 (1.1332) acc1: 59.3750 (72.4613) acc5: 84.3750 (90.7248) time: 0.0302 data: 0.0164 max mem: 19119\n", + "Test: [600/782] eta: 0:00:06 loss: 1.3149 (1.1978) acc1: 65.6250 (71.1340) acc5: 85.9375 (89.9880) time: 0.0445 data: 0.0307 max mem: 19119\n", + "Test: [700/782] eta: 0:00:02 loss: 1.2842 (1.2500) acc1: 70.3125 (70.1297) acc5: 87.5000 (89.2899) time: 0.0292 data: 0.0154 max mem: 19119\n", + "Test: Total time: 0:00:25\n", + "Test: Acc@1 70.056 Acc@5 89.296\n", + "Epoch: [5] [ 0/2503] eta: 3:36:03 lr: 0.0001 img/s: 868.6398787818482 loss: 0.9304 (0.9304) acc1: 68.1641 (68.1641) acc5: 84.5703 (84.5703) time: 5.1790 data: 4.5895 max mem: 19119\n", + "Epoch: [5] [ 100/2503] eta: 0:26:17 lr: 0.0001 img/s: 856.2223810858537 loss: 0.8658 (0.8735) acc1: 68.7500 (69.3843) acc5: 86.5234 (87.1229) time: 0.5978 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [ 200/2503] eta: 0:24:06 lr: 0.0001 img/s: 854.6059124455048 loss: 0.8757 (0.8726) acc1: 68.3594 (69.2893) acc5: 87.3047 (87.2027) time: 0.5998 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [ 300/2503] eta: 0:22:42 lr: 0.0001 img/s: 854.7457148952566 loss: 0.9211 (0.8775) acc1: 68.3594 (69.1964) acc5: 86.5234 (87.1548) time: 0.5994 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [ 400/2503] eta: 0:21:30 lr: 0.0001 img/s: 855.9967856527867 loss: 0.8790 (0.8763) acc1: 69.5312 (69.2220) acc5: 87.1094 (87.1795) time: 0.5981 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [ 500/2503] eta: 0:20:22 lr: 0.0001 img/s: 856.1715178770771 loss: 0.8828 (0.8767) acc1: 68.5547 (69.1835) acc5: 87.1094 (87.1916) time: 0.5981 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [ 600/2503] eta: 0:19:17 lr: 0.0001 img/s: 857.1064536316845 loss: 0.8499 (0.8745) acc1: 70.1172 (69.2495) acc5: 87.6953 (87.2351) time: 0.5985 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [ 700/2503] eta: 0:18:14 lr: 0.0001 img/s: 855.5684475906053 loss: 0.8724 (0.8738) acc1: 68.7500 (69.2543) acc5: 87.3047 (87.2381) time: 0.5986 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [ 800/2503] eta: 0:17:11 lr: 0.0001 img/s: 855.3483068555046 loss: 0.8648 (0.8747) acc1: 69.3359 (69.2645) acc5: 87.1094 (87.2354) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [ 900/2503] eta: 0:16:10 lr: 0.0001 img/s: 855.0516709967052 loss: 0.8869 (0.8744) acc1: 68.9453 (69.2787) acc5: 87.1094 (87.2609) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [1000/2503] eta: 0:15:08 lr: 0.0001 img/s: 856.4016452607827 loss: 0.8660 (0.8746) acc1: 69.5312 (69.2706) acc5: 87.3047 (87.2602) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [1100/2503] eta: 0:14:07 lr: 0.0001 img/s: 855.5752649016649 loss: 0.8801 (0.8741) acc1: 69.3359 (69.2911) acc5: 86.7188 (87.2653) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [1200/2503] eta: 0:13:06 lr: 0.0001 img/s: 857.7295089955442 loss: 0.9027 (0.8741) acc1: 68.1641 (69.2950) acc5: 86.7188 (87.2543) time: 0.5983 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [1300/2503] eta: 0:12:05 lr: 0.0001 img/s: 857.4562117036577 loss: 0.8591 (0.8742) acc1: 69.3359 (69.2867) acc5: 87.3047 (87.2523) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [1400/2503] eta: 0:11:04 lr: 0.0001 img/s: 856.1711765336744 loss: 0.8659 (0.8747) acc1: 69.7266 (69.2756) acc5: 87.1094 (87.2657) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [1500/2503] eta: 0:10:04 lr: 0.0001 img/s: 855.6918577361109 loss: 0.8848 (0.8744) acc1: 67.5781 (69.2883) acc5: 86.9141 (87.2744) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [1600/2503] eta: 0:09:03 lr: 0.0001 img/s: 856.2199914038447 loss: 0.8476 (0.8740) acc1: 69.1406 (69.2962) acc5: 87.5000 (87.2808) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [1700/2503] eta: 0:08:03 lr: 0.0001 img/s: 856.7518536501099 loss: 0.8676 (0.8745) acc1: 68.3594 (69.2823) acc5: 87.6953 (87.2798) time: 0.5986 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [1800/2503] eta: 0:07:03 lr: 0.0001 img/s: 855.5047109885885 loss: 0.8884 (0.8745) acc1: 68.1641 (69.2810) acc5: 87.3047 (87.2716) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [1900/2503] eta: 0:06:02 lr: 0.0001 img/s: 855.3728370553123 loss: 0.8565 (0.8744) acc1: 68.9453 (69.2820) acc5: 88.2812 (87.2709) time: 0.5987 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [2000/2503] eta: 0:05:02 lr: 0.0001 img/s: 856.9477535227904 loss: 0.8799 (0.8749) acc1: 68.7500 (69.2718) acc5: 86.7188 (87.2586) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [2100/2503] eta: 0:04:02 lr: 0.0001 img/s: 855.2914158356967 loss: 0.8914 (0.8745) acc1: 68.9453 (69.2829) acc5: 87.5000 (87.2690) time: 0.5987 data: 0.0003 max mem: 19119\n", + "Epoch: [5] [2200/2503] eta: 0:03:02 lr: 0.0001 img/s: 855.3816955287992 loss: 0.8659 (0.8744) acc1: 68.9453 (69.2853) acc5: 87.1094 (87.2749) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [2300/2503] eta: 0:02:02 lr: 0.0001 img/s: 857.8035141651501 loss: 0.8525 (0.8745) acc1: 69.7266 (69.2810) acc5: 87.5000 (87.2740) time: 0.5986 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [2400/2503] eta: 0:01:01 lr: 0.0001 img/s: 855.2345323832691 loss: 0.8424 (0.8745) acc1: 69.5312 (69.2709) acc5: 87.3047 (87.2737) time: 0.5988 data: 0.0002 max mem: 19119\n", + "Epoch: [5] [2500/2503] eta: 0:00:01 lr: 0.0001 img/s: 856.8721864153102 loss: 0.8785 (0.8748) acc1: 68.7500 (69.2651) acc5: 87.5000 (87.2714) time: 0.5982 data: 0.0002 max mem: 19119\n", + "Epoch: [5] Total time: 0:25:04\n", + "Test: [ 0/782] eta: 0:16:48 loss: 0.6137 (0.6137) acc1: 85.9375 (85.9375) acc5: 95.3125 (95.3125) time: 1.2890 data: 1.2749 max mem: 19119\n", + "Test: [100/782] eta: 0:00:29 loss: 1.0820 (0.9476) acc1: 76.5625 (77.0885) acc5: 89.0625 (92.3113) time: 0.0337 data: 0.0200 max mem: 19119\n", + "Test: [200/782] eta: 0:00:21 loss: 0.8791 (0.9212) acc1: 75.0000 (76.4537) acc5: 95.3125 (93.3613) time: 0.0276 data: 0.0139 max mem: 19119\n", + "Test: [300/782] eta: 0:00:16 loss: 0.8066 (0.9144) acc1: 76.5625 (76.6923) acc5: 92.1875 (93.6306) time: 0.0281 data: 0.0144 max mem: 19119\n", + "Test: [400/782] eta: 0:00:13 loss: 1.8165 (1.0555) acc1: 60.9375 (73.9596) acc5: 84.3750 (91.9031) time: 0.0350 data: 0.0214 max mem: 19119\n", + "Test: [500/782] eta: 0:00:09 loss: 1.7107 (1.1325) acc1: 59.3750 (72.5299) acc5: 84.3750 (90.7934) time: 0.0344 data: 0.0206 max mem: 19119\n", + "Test: [600/782] eta: 0:00:06 loss: 1.3799 (1.1970) acc1: 64.0625 (71.2068) acc5: 84.3750 (89.9880) time: 0.0266 data: 0.0127 max mem: 19119\n", + "Test: [700/782] eta: 0:00:02 loss: 1.2741 (1.2493) acc1: 68.7500 (70.1966) acc5: 85.9375 (89.2765) time: 0.0262 data: 0.0124 max mem: 19119\n", + "Test: Total time: 0:00:25\n", + "Test: Acc@1 70.120 Acc@5 89.284\n", + "Training time 2:33:01\n" ] } ], @@ -1201,8 +817,8 @@ " data_path=\"/home/cs/Documents/datasets/imagenet\", # Replace with your /path/to/imagenet\n", " model=\"resnet18\",\n", " device=\"cuda\",\n", - " batch_size=256,\n", - " epochs=10,\n", + " batch_size=512,\n", + " epochs=6,\n", " lr=0.0004,\n", " momentum=0.9,\n", " weight_decay=1e-4,\n", @@ -1215,834 +831,13 @@ " use_deterministic_algorithms=False,\n", " weights=\"ResNet18_Weights.IMAGENET1K_V1\",\n", " apply_trp=True,\n", - " trp_depths=[1, 1, 1],\n", + " trp_depths=[3, 3, 3],\n", " trp_planes=256,\n", " trp_lambdas=[0.4, 0.2, 0.1],\n", ")\n", "\n", "main(args)" ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "a3KD3WXU3l-O" - }, - "source": [ - "# Fine-tuning a language model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JAscNNUD3l-P" - }, - "source": [ - "In this notebook, we'll see how to fine-tune one of the [🤗 Transformers](https://github.com/huggingface/transformers) model on a language modeling tasks. We will cover two types of language modeling tasks which are:\n", - "\n", - "- Causal language modeling: the model has to predict the next token in the sentence (so the labels are the same as the inputs shifted to the right). To make sure the model does not cheat, it gets an attention mask that will prevent it to access the tokens after token i when trying to predict the token i+1 in the sentence.\n", - "\n", - "![Widget inference representing the causal language modeling task](https://github.com/huggingface/notebooks/blob/main/examples/images/causal_language_modeling.png?raw=1)\n", - "\n", - "- Masked language modeling: the model has to predict some tokens that are masked in the input. It still has access to the whole sentence, so it can use the tokens before and after the tokens masked to predict their value.\n", - "\n", - "![Widget inference representing the masked language modeling task](https://github.com/huggingface/notebooks/blob/main/examples/images/masked_language_modeling.png?raw=1)\n", - "\n", - "We will see how to easily load and preprocess the dataset for each one of those tasks, and how to use the `Trainer` API to fine-tune a model on it.\n", - "\n", - "A script version of this notebook you can directly run on a distributed environment or on TPU is available in our [examples folder](https://github.com/huggingface/transformers/tree/master/examples)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1r_n9OWV3l-Q" - }, - "source": [ - "## Preparing the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kswRMhPc3l-Q" - }, - "source": [ - "For each of those tasks, we will use the [Wikitext 2]() dataset as an example. You can load it very easily with the 🤗 Datasets library." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "n2ZRs1cL3l-R" - }, - "outputs": [], - "source": [ - "from datasets import load_dataset\n", - "datasets = load_dataset('wikitext', 'wikitext-2-raw-v1')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f1-9jepM3l-W" - }, - "source": [ - "You can replace the dataset above with any dataset hosted on [the hub](https://huggingface.co/datasets) or use your own files. Just uncomment the following cell and replace the paths with values that will lead to your files:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "uxSaGa_l3l-W" - }, - "outputs": [], - "source": [ - "# datasets = load_dataset(\"text\", data_files={\"train\": path_to_train.txt, \"validation\": path_to_validation.txt}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jY1SwIrY3l-a" - }, - "source": [ - "You can also load datasets from a csv or a JSON file, see the [full documentation](https://huggingface.co/docs/datasets/loading_datasets.html#from-local-files) for more information." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u3EtYfeHIrIz" - }, - "source": [ - "To access an actual element, you need to select a split first, then give an index:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "X6HrpprwIrIz" - }, - "outputs": [], - "source": [ - "datasets[\"train\"][10]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WHUmphG3IrI3" - }, - "source": [ - "To get a sense of what the data looks like, the following function will show some examples picked randomly in the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ur5sNUcZ3l-g" - }, - "outputs": [], - "source": [ - "from datasets import ClassLabel\n", - "import random\n", - "import pandas as pd\n", - "from IPython.display import display, HTML\n", - "\n", - "def show_random_elements(dataset, num_examples=10):\n", - " assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n", - " picks = []\n", - " for _ in range(num_examples):\n", - " pick = random.randint(0, len(dataset)-1)\n", - " while pick in picks:\n", - " pick = random.randint(0, len(dataset)-1)\n", - " picks.append(pick)\n", - "\n", - " df = pd.DataFrame(dataset[picks])\n", - " for column, typ in dataset.features.items():\n", - " if isinstance(typ, ClassLabel):\n", - " df[column] = df[column].transform(lambda i: typ.names[i])\n", - " display(HTML(df.to_html()))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "1Uk8NROQ3l-k" - }, - "outputs": [], - "source": [ - "show_random_elements(datasets[\"train\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "CKerdF353l-o" - }, - "source": [ - "As we can see, some of the texts are a full paragraph of a Wikipedia article while others are just titles or empty lines." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JEA1ju653l-p" - }, - "source": [ - "## Causal Language modeling" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v5GTGKZS3l-q" - }, - "source": [ - "For causal language modeling (CLM) we are going to take all the texts in our dataset and concatenate them after they are tokenized. Then we will split them in examples of a certain sequence length. This way the model will receive chunks of contiguous text that may look like:\n", - "```\n", - "part of text 1\n", - "```\n", - "or\n", - "```\n", - "end of text 1 [BOS_TOKEN] beginning of text 2\n", - "```\n", - "depending on whether they span over several of the original texts in the dataset or not. The labels will be the same as the inputs, shifted to the left.\n", - "\n", - "We will use the [`distilgpt2`](https://huggingface.co/distilgpt2) model for this example. You can pick any of the checkpoints listed [here](https://huggingface.co/models?filter=causal-lm) instead:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-WGBCO343l-q" - }, - "outputs": [], - "source": [ - "model_checkpoint = \"distilgpt2\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5io6fY_d3l-u" - }, - "source": [ - "To tokenize all our texts with the same vocabulary that was used when training the model, we have to download a pretrained tokenizer. This is all done by the `AutoTokenizer` class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iAYlS40Z3l-v" - }, - "outputs": [], - "source": [ - "from transformers import AutoTokenizer\n", - "\n", - "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "rpOiBrJ13l-y" - }, - "source": [ - "We can now call the tokenizer on all our texts. This is very simple, using the [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) method from the Datasets library. First we define a function that call the tokenizer on our texts:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lS2m25YM3l-z" - }, - "outputs": [], - "source": [ - "def tokenize_function(examples):\n", - " return tokenizer(examples[\"text\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "M9xVAa3s3l-2" - }, - "source": [ - "Then we apply it to all the splits in our `datasets` object, using `batched=True` and 4 processes to speed up the preprocessing. We won't need the `text` column afterward, so we discard it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NVAO0H8u3l-3" - }, - "outputs": [], - "source": [ - "tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8qik3J_C3l-7" - }, - "source": [ - "If we now look at an element of our datasets, we will see the text have been replaced by the `input_ids` the model will need:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nYv_mcKk3l-7" - }, - "outputs": [], - "source": [ - "tokenized_datasets[\"train\"][1]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "obvgcXda3l--" - }, - "source": [ - "Now for the harder part: we need to concatenate all our texts together then split the result in small chunks of a certain `block_size`. To do this, we will use the `map` method again, with the option `batched=True`. This option actually lets us change the number of examples in the datasets by returning a different number of examples than we got. This way, we can create our new samples from a batch of examples.\n", - "\n", - "First, we grab the maximum length our model was pretrained with. This might be a big too big to fit in your GPU RAM, so here we take a bit less at just 128." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "DVHs5aCA3l-_" - }, - "outputs": [], - "source": [ - "# block_size = tokenizer.model_max_length\n", - "block_size = 128" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RpNfGiMw3l_A" - }, - "source": [ - "Then we write the preprocessing function that will group our texts:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "iaAJy5Hu3l_B" - }, - "outputs": [], - "source": [ - "def group_texts(examples):\n", - " # Concatenate all texts.\n", - " concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n", - " total_length = len(concatenated_examples[list(examples.keys())[0]])\n", - " # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n", - " # customize this part to your needs.\n", - " total_length = (total_length // block_size) * block_size\n", - " # Split by chunks of max_len.\n", - " result = {\n", - " k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n", - " for k, t in concatenated_examples.items()\n", - " }\n", - " result[\"labels\"] = result[\"input_ids\"].copy()\n", - " return result" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LGJWXtNv3l_C" - }, - "source": [ - "First note that we duplicate the inputs for our labels. This is because the model of the 🤗 Transformers library apply the shifting to the right, so we don't need to do it manually.\n", - "\n", - "Also note that by default, the `map` method will send a batch of 1,000 examples to be treated by the preprocessing function. So here, we will drop the remainder to make the concatenated tokenized texts a multiple of `block_size` every 1,000 examples. You can adjust this behavior by passing a higher batch size (which will also be processed slower). You can also speed-up the preprocessing by using multiprocessing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gXUSfBrq3l_C" - }, - "outputs": [], - "source": [ - "lm_datasets = tokenized_datasets.map(\n", - " group_texts,\n", - " batched=True,\n", - " batch_size=1000,\n", - " num_proc=4,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6n84V8Gc3l_G" - }, - "source": [ - "And we can check our datasets have changed: now the samples contain chunks of `block_size` contiguous tokens, potentially spanning over several of our original texts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hTeGCLl_3l_G" - }, - "outputs": [], - "source": [ - "tokenizer.decode(lm_datasets[\"train\"][1][\"input_ids\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iEmeQ7Xm3l_H" - }, - "source": [ - "Now that the data has been cleaned, we're ready to instantiate our `Trainer`. We will a model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "sPqQA3TT3l_I" - }, - "outputs": [], - "source": [ - "from transformers import AutoModelForCausalLM\n", - "model = AutoModelForCausalLM.from_pretrained(model_checkpoint)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VyPQTOF_3l_J" - }, - "source": [ - "And some `TrainingArguments`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jElf8LJ33l_K" - }, - "outputs": [], - "source": [ - "from transformers import Trainer, TrainingArguments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "YbSwEhQ63l_L" - }, - "outputs": [], - "source": [ - "model_name = model_checkpoint.split(\"/\")[-1]\n", - "training_args = TrainingArguments(\n", - " f\"{model_name}-finetuned-wikitext2\",\n", - " eval_strategy = \"epoch\",\n", - " learning_rate=2e-5,\n", - " weight_decay=0.01,\n", - " push_to_hub=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dsx-8ebng3FI" - }, - "source": [ - "The last argument to setup everything so we can push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally in a name that is different than the name of the repository it will be pushed, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"sgugger/gpt-finetuned-wikitext2\"` or `\"huggingface/gpt-finetuned-wikitext2\"`)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "sZRbT9ui3l_N" - }, - "source": [ - "We pass along all of those to the `Trainer` class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "OEuqwIra3l_N" - }, - "outputs": [], - "source": [ - "trainer = Trainer(\n", - " model=model,\n", - " args=training_args,\n", - " train_dataset=lm_datasets[\"train\"],\n", - " eval_dataset=lm_datasets[\"validation\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Vvz34Td3l_O" - }, - "source": [ - "And we can train our model:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NyZvu_MF3l_P" - }, - "outputs": [], - "source": [ - "trainer.train()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3APq-vUc3l_R" - }, - "source": [ - "Once the training is completed, we can evaluate our model and get its perplexity on the validation set like this:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "diKZnB1I3l_R" - }, - "outputs": [], - "source": [ - "import math\n", - "eval_results = trainer.evaluate()\n", - "print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wY82caEX3l_i" - }, - "source": [ - "You can now upload the result of the training to the Hub, just execute this instruction:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "KjTnsZXwg3FI" - }, - "outputs": [], - "source": [ - "trainer.push_to_hub()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lKIc9Jr8g3FI" - }, - "source": [ - "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n", - "\n", - "```python\n", - "from transformers import AutoModelForCausalLM\n", - "\n", - "model = AutoModelForCausalLM.from_pretrained(\"sgugger/my-awesome-model\")\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "q-EIELH43l_T" - }, - "source": [ - "## Masked language modeling" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LWk97-Ny3l_T" - }, - "source": [ - "For masked language modeling (MLM) we are going to use the same preprocessing as before for our dataset with one additional step: we will randomly mask some tokens (by replacing them by `[MASK]`) and the labels will be adjusted to only include the masked tokens (we don't have to predict the non-masked tokens).\n", - "\n", - "We will use the [`distilroberta-base`](https://huggingface.co/distilroberta-base) model for this example. You can pick any of the checkpoints listed [here](https://huggingface.co/models?filter=masked-lm) instead:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QRTpmyCc3l_T" - }, - "outputs": [], - "source": [ - "model_checkpoint = \"distilroberta-base\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "12F1ulgT3l_V" - }, - "source": [ - "We can apply the same tokenization function as before, we just need to update our tokenizer to use the checkpoint we just picked:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "h8RCYcvr3l_V" - }, - "outputs": [], - "source": [ - "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n", - "tokenized_datasets = datasets.map(tokenize_function, batched=True, num_proc=4, remove_columns=[\"text\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MTuy8UUs3l_X" - }, - "source": [ - "And like before, we group texts together and chunk them in samples of length `block_size`. You can skip that step if your dataset is composed of individual sentences." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LVYPMwEs3l_X" - }, - "outputs": [], - "source": [ - "lm_datasets = tokenized_datasets.map(\n", - " group_texts,\n", - " batched=True,\n", - " batch_size=1000,\n", - " num_proc=4,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nFJ49iHJ3l_Z" - }, - "source": [ - "The rest is very similar to what we had, with two exceptions. First we use a model suitable for masked LM:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PM10A9Za3l_Z" - }, - "outputs": [], - "source": [ - "from transformers import AutoModelForMaskedLM\n", - "model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "emlofW7Yg3FJ" - }, - "source": [ - "We redefine our `TrainingArguments`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Hgd9wpjHg3FJ" - }, - "outputs": [], - "source": [ - "model_name = model_checkpoint.split(\"/\")[-1]\n", - "training_args = TrainingArguments(\n", - " f\"{model_name}-finetuned-wikitext2\",\n", - " eval_strategy = \"epoch\",\n", - " learning_rate=2e-5,\n", - " weight_decay=0.01,\n", - " push_to_hub=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3tP4SZ3rg3FJ" - }, - "source": [ - "Like before, the last argument to setup everything so we can push the model to the [Hub](https://huggingface.co/models) regularly during training. Remove it if you didn't follow the installation steps at the top of the notebook. If you want to save your model locally in a name that is different than the name of the repository it will be pushed, or if you want to push your model under an organization and not your name space, use the `hub_model_id` argument to set the repo name (it needs to be the full name, including your namespace: for instance `\"sgugger/bert-finetuned-wikitext2\"` or `\"huggingface/bert-finetuned-wikitext2\"`)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "z6uuUnvz3l_b" - }, - "source": [ - "Finally, we use a special `data_collator`. The `data_collator` is a function that is responsible of taking the samples and batching them in tensors. In the previous example, we had nothing special to do, so we just used the default for this argument. Here we want to do the random-masking. We could do it as a pre-processing step (like the tokenization) but then the tokens would always be masked the same way at each epoch. By doing this step inside the `data_collator`, we ensure this random masking is done in a new way each time we go over the data.\n", - "\n", - "To do this masking for us, the library provides a `DataCollatorForLanguageModeling`. We can adjust the probability of the masking:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nRZ-5v_P3l_b" - }, - "outputs": [], - "source": [ - "from transformers import DataCollatorForLanguageModeling\n", - "data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bqHnWcYC3l_d" - }, - "source": [ - "Then we just have to pass everything to `Trainer` and begin training:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "V-Y3gNqV3l_d" - }, - "outputs": [], - "source": [ - "trainer = Trainer(\n", - " model=model,\n", - " args=training_args,\n", - " train_dataset=lm_datasets[\"train\"],\n", - " eval_dataset=lm_datasets[\"validation\"],\n", - " data_collator=data_collator,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Y9TFqDG_3l_e" - }, - "outputs": [], - "source": [ - "trainer.train()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "KDBi0reX3l_g" - }, - "source": [ - "Like before, we can evaluate our model on the validation set. The perplexity is much lower than for the CLM objective because for the MLM objective, we only have to make predictions for the masked tokens (which represent 15% of the total here) while having access to the rest of the tokens. It's thus an easier task for the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4hSaANqj3l_g" - }, - "outputs": [], - "source": [ - "eval_results = trainer.evaluate()\n", - "print(f\"Perplexity: {math.exp(eval_results['eval_loss']):.2f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TDmITtEYg3FK" - }, - "source": [ - "You can now upload the result of the training to the Hub, just execute this instruction:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-3CFIYUrg3FK" - }, - "outputs": [], - "source": [ - "trainer.push_to_hub()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Da10yeZNg3FK" - }, - "source": [ - "You can now share this model with all your friends, family, favorite pets: they can all load it with the identifier `\"your-username/the-name-you-picked\"` so for instance:\n", - "\n", - "```python\n", - "from transformers import AutoModelForMaskedLM\n", - "\n", - "model = AutoModelForMaskedLM.from_pretrained(\"sgugger/my-awesome-model\")\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RqqSgjeSg3FK" - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -2069,4 +864,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +}