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ChetanPatil28ChetanPatil28
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Fix code in training
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‎Image-Classification-in-PyTorch/image_classification_using_transfer_learning_in_pytorch.ipynb

+76-33
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@@ -4,7 +4,7 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
7-
"collapsed": true
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"id": "SrL47MttOLS3"
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},
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"outputs": [],
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"source": [
@@ -29,7 +29,7 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "V3Jwg9JJOLTE"
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},
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"outputs": [],
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"source": [
@@ -65,7 +65,11 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "LqB1PotzOLTF",
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"outputId": "2fef74c0-eba7-4dc5-a35a-b238432ee2c3"
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},
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"outputs": [],
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"source": [
@@ -83,7 +87,7 @@
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"bs = 32\n",
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"\n",
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"# Number of classes\n",
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"num_classes = len(os.listdir(valid_directory))-1 #10#2#257\n",
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"num_classes = len(os.listdir(valid_directory)) #10#2#257\n",
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"print(num_classes)\n",
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"\n",
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"# Load Data from folders\n",
@@ -112,31 +116,37 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "2mUcSMIMOLTG",
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"outputId": "88cc5e5d-2c37-41e6-ef9d-72ca9237efd8"
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},
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"outputs": [],
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"source": [
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"train_data_size, valid_data_size, test_data_size"
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"\n",
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"print(train_data_size, valid_data_size, test_data_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "ViY8viSLOLTH"
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},
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"outputs": [],
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"source": [
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"# Load pretrained ResNet50 Model\n",
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"resnet50 = models.resnet50(pretrained=True)\n",
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"resnet50 = resnet50.to('cuda:0')\n"
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"resnet50 = resnet50.to(device)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "3eVi7mJZOLTH"
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},
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"outputs": [],
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"source": [
@@ -149,7 +159,7 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "EqMKMqdWOLTH"
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},
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"outputs": [],
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"source": [
@@ -164,15 +174,17 @@
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" nn.LogSoftmax(dim=1) # For using NLLLoss()\n",
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")\n",
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"\n",
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"\n",
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"\n",
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"# Convert model to be used on GPU\n",
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"resnet50 = resnet50.to('cuda:0')\n"
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"resnet50 = resnet50.to(device)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "3Lske-iPOLTI"
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},
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"outputs": [],
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"source": [
@@ -185,7 +197,7 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "dyG4bCHHOLTI"
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},
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"outputs": [],
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"source": [
@@ -205,7 +217,8 @@
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" \n",
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" start = time.time()\n",
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" history = []\n",
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" best_acc = 0.0\n",
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" best_loss = 100000.0\n",
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" best_epoch = None\n",
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"\n",
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" for epoch in range(epochs):\n",
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" epoch_start = time.time()\n",
@@ -256,7 +269,7 @@
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" \n",
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" #print(\"Batch number: {:03d}, Training: Loss: {:.4f}, Accuracy: {:.4f}\".format(i, loss.item(), acc.item()))\n",
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"\n",
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" \n",
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" \n",
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" # Validation - No gradient tracking needed\n",
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" with torch.no_grad():\n",
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"\n",
@@ -288,7 +301,10 @@
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" valid_acc += acc.item() * inputs.size(0)\n",
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"\n",
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" #print(\"Validation Batch number: {:03d}, Validation: Loss: {:.4f}, Accuracy: {:.4f}\".format(j, loss.item(), acc.item()))\n",
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" \n",
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" if valid_loss < best_loss:\n",
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" best_loss = valid_loss\n",
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" best_epoch = epoch\n",
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"\n",
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" # Find average training loss and training accuracy\n",
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" avg_train_loss = train_loss/train_data_size \n",
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" avg_train_acc = train_acc/train_data_size\n",
@@ -301,31 +317,33 @@
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" \n",
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" epoch_end = time.time()\n",
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" \n",
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" print(\"Epoch : {:03d}, Training: Loss: {:.4f}, Accuracy: {:.4f}%, \\n\\t\\tValidation : Loss : {:.4f}, Accuracy: {:.4f}%, Time: {:.4f}s\".format(epoch, avg_train_loss, avg_train_acc*100, avg_valid_loss, avg_valid_acc*100, epoch_end-epoch_start))\n",
320+
" print(\"Epoch : {:03d}, Training: Loss - {:.4f}, Accuracy - {:.4f}%, \\n\\t\\tValidation : Loss - {:.4f}, Accuracy - {:.4f}%, Time: {:.4f}s\".format(epoch, avg_train_loss, avg_train_acc*100, avg_valid_loss, avg_valid_acc*100, epoch_end-epoch_start))\n",
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" \n",
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" # Save if the model has best accuracy till now\n",
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" torch.save(model, dataset+'_model_'+str(epoch)+'.pt')\n",
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" \n",
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" return model, history\n",
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" return model, history, best_epoch\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "GIVZTrxAOLTN",
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"outputId": "ebdcc4b0-a98e-4488-dddb-bd948e452cc0"
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},
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"outputs": [],
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"source": [
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"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"\n",
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"# Print the model to be trained\n",
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"#summary(resnet50, input_size=(3, 224, 224), batch_size=bs, device='cuda')\n",
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"\n",
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"# Train the model for 25 epochs\n",
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"num_epochs = 30\n",
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"trained_model, history = train_and_validate(resnet50, loss_func, optimizer, num_epochs)\n",
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"trained_model, history, best_epoch = train_and_validate(resnet50, loss_func, optimizer, num_epochs)\n",
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"\n",
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"torch.save(history, dataset+'_history.pt')"
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]
@@ -334,7 +352,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 283
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},
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"id": "pGcRnfbiOLTS",
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"outputId": "1326a7fe-4daf-45ea-9f69-7219e23d277b"
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},
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"outputs": [],
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"source": [
@@ -352,7 +375,12 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 283
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},
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"id": "1e8oWgPWOLTS",
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"outputId": "c74fcaa4-1b41-436f-de02-8be4ee5c1a11"
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "FGHpMyvHOLTS"
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"id": "QBHeFS7QOLTT"
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},
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"outputs": [],
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"source": [
@@ -444,11 +472,11 @@
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" \n",
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" transform = image_transforms['test']\n",
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"\n",
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"\n",
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" test_image = Image.open(test_image_name)\n",
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" plt.imshow(test_image)\n",
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" \n",
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" test_image_tensor = transform(test_image)\n",
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"\n",
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" if torch.cuda.is_available():\n",
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" test_image_tensor = test_image_tensor.view(1, 3, 224, 224).cuda()\n",
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" else:\n",
@@ -459,7 +487,11 @@
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" # Model outputs log probabilities\n",
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" out = model(test_image_tensor)\n",
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" ps = torch.exp(out)\n",
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"\n",
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" topk, topclass = ps.topk(3, dim=1)\n",
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" cls = idx_to_class[topclass.cpu().numpy()[0][0]]\n",
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" score = topk.cpu().numpy()[0][0]\n",
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"\n",
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" for i in range(3):\n",
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" print(\"Predcition\", i+1, \":\", idx_to_class[topclass.cpu().numpy()[0][i]], \", Score: \", topk.cpu().numpy()[0][i])\n",
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"\n",
@@ -470,15 +502,20 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 507
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},
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"id": "vK8eHndnOLTU",
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"outputId": "581a2e73-b90f-446e-fc73-d4820adc4b68"
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},
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"outputs": [],
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"source": [
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"# Test a particular model on a test image\n",
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"\n",
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"! wget https://cdn.pixabay.com/photo/2018/10/01/12/28/skunk-3716043_1280.jpg -O skunk.jpg\n",
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"dataset = 'caltech_10'\n",
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"model = torch.load('caltech_10_model_8.pt')\n",
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"predict(model, 'pixabay-test-animals/triceratops-954293_640.jpg')\n",
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"model = torch.load(\"{}_model_{}.pt\".format(dataset, best_epoch))\n",
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"predict(model, 'skunk.jpg')\n",
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"\n",
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"# Load Data from folders\n",
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"#computeTestSetAccuracy(model, loss_func)\n",
@@ -488,6 +525,12 @@
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"name": "image_classification_using_transfer_learning_in_pytorch.ipynb",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.7"
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"version": "3.6.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 1
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}

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