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Transformers
Inference Endpoints
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1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": null,
6
+ "metadata": {
7
+ "colab": {
8
+ "base_uri": "https://localhost:8080/"
9
+ },
10
+ "id": "e9tLVH2veLrg",
11
+ "outputId": "d7d0d808-46aa-40db-be05-5d27d2e26bce"
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+ },
13
+ "outputs": [
14
+ {
15
+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
18
+ "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cu121)\n",
19
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.25.2)\n",
20
+ "Requirement already satisfied: torch==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.3.0+cu121)\n",
21
+ "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
22
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (3.14.0)\n",
23
+ "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (4.12.2)\n",
24
+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (1.12.1)\n",
25
+ "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (3.3)\n",
26
+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (3.1.4)\n",
27
+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (2023.6.0)\n",
28
+ "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch==2.3.0->torchvision)\n",
29
+ " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
30
+ "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch==2.3.0->torchvision)\n",
31
+ " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
32
+ "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch==2.3.0->torchvision)\n",
33
+ " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
34
+ "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch==2.3.0->torchvision)\n",
35
+ " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
36
+ "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch==2.3.0->torchvision)\n",
37
+ " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
38
+ "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch==2.3.0->torchvision)\n",
39
+ " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
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+ "Collecting nvidia-curand-cu12==10.3.2.106 (from torch==2.3.0->torchvision)\n",
41
+ " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
42
+ "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch==2.3.0->torchvision)\n",
43
+ " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
44
+ "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch==2.3.0->torchvision)\n",
45
+ " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
46
+ "Collecting nvidia-nccl-cu12==2.20.5 (from torch==2.3.0->torchvision)\n",
47
+ " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
48
+ "Collecting nvidia-nvtx-cu12==12.1.105 (from torch==2.3.0->torchvision)\n",
49
+ " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
50
+ "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.3.0->torchvision) (2.3.0)\n",
51
+ "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch==2.3.0->torchvision)\n",
52
+ " Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.3.0->torchvision) (2.1.5)\n",
55
+ "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.3.0->torchvision) (1.3.0)\n",
56
+ "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
57
+ "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105\n"
58
+ ]
59
+ },
60
+ {
61
+ "output_type": "execute_result",
62
+ "data": {
63
+ "text/plain": [
64
+ "['AlexNet',\n",
65
+ " 'AlexNet_Weights',\n",
66
+ " 'ConvNeXt',\n",
67
+ " 'ConvNeXt_Base_Weights',\n",
68
+ " 'ConvNeXt_Large_Weights',\n",
69
+ " 'ConvNeXt_Small_Weights',\n",
70
+ " 'ConvNeXt_Tiny_Weights',\n",
71
+ " 'DenseNet',\n",
72
+ " 'DenseNet121_Weights',\n",
73
+ " 'DenseNet161_Weights',\n",
74
+ " 'DenseNet169_Weights',\n",
75
+ " 'DenseNet201_Weights',\n",
76
+ " 'EfficientNet',\n",
77
+ " 'EfficientNet_B0_Weights',\n",
78
+ " 'EfficientNet_B1_Weights',\n",
79
+ " 'EfficientNet_B2_Weights',\n",
80
+ " 'EfficientNet_B3_Weights',\n",
81
+ " 'EfficientNet_B4_Weights',\n",
82
+ " 'EfficientNet_B5_Weights',\n",
83
+ " 'EfficientNet_B6_Weights',\n",
84
+ " 'EfficientNet_B7_Weights',\n",
85
+ " 'EfficientNet_V2_L_Weights',\n",
86
+ " 'EfficientNet_V2_M_Weights',\n",
87
+ " 'EfficientNet_V2_S_Weights',\n",
88
+ " 'GoogLeNet',\n",
89
+ " 'GoogLeNetOutputs',\n",
90
+ " 'GoogLeNet_Weights',\n",
91
+ " 'Inception3',\n",
92
+ " 'InceptionOutputs',\n",
93
+ " 'Inception_V3_Weights',\n",
94
+ " 'MNASNet',\n",
95
+ " 'MNASNet0_5_Weights',\n",
96
+ " 'MNASNet0_75_Weights',\n",
97
+ " 'MNASNet1_0_Weights',\n",
98
+ " 'MNASNet1_3_Weights',\n",
99
+ " 'MaxVit',\n",
100
+ " 'MaxVit_T_Weights',\n",
101
+ " 'MobileNetV2',\n",
102
+ " 'MobileNetV3',\n",
103
+ " 'MobileNet_V2_Weights',\n",
104
+ " 'MobileNet_V3_Large_Weights',\n",
105
+ " 'MobileNet_V3_Small_Weights',\n",
106
+ " 'RegNet',\n",
107
+ " 'RegNet_X_16GF_Weights',\n",
108
+ " 'RegNet_X_1_6GF_Weights',\n",
109
+ " 'RegNet_X_32GF_Weights',\n",
110
+ " 'RegNet_X_3_2GF_Weights',\n",
111
+ " 'RegNet_X_400MF_Weights',\n",
112
+ " 'RegNet_X_800MF_Weights',\n",
113
+ " 'RegNet_X_8GF_Weights',\n",
114
+ " 'RegNet_Y_128GF_Weights',\n",
115
+ " 'RegNet_Y_16GF_Weights',\n",
116
+ " 'RegNet_Y_1_6GF_Weights',\n",
117
+ " 'RegNet_Y_32GF_Weights',\n",
118
+ " 'RegNet_Y_3_2GF_Weights',\n",
119
+ " 'RegNet_Y_400MF_Weights',\n",
120
+ " 'RegNet_Y_800MF_Weights',\n",
121
+ " 'RegNet_Y_8GF_Weights',\n",
122
+ " 'ResNeXt101_32X8D_Weights',\n",
123
+ " 'ResNeXt101_64X4D_Weights',\n",
124
+ " 'ResNeXt50_32X4D_Weights',\n",
125
+ " 'ResNet',\n",
126
+ " 'ResNet101_Weights',\n",
127
+ " 'ResNet152_Weights',\n",
128
+ " 'ResNet18_Weights',\n",
129
+ " 'ResNet34_Weights',\n",
130
+ " 'ResNet50_Weights',\n",
131
+ " 'ShuffleNetV2',\n",
132
+ " 'ShuffleNet_V2_X0_5_Weights',\n",
133
+ " 'ShuffleNet_V2_X1_0_Weights',\n",
134
+ " 'ShuffleNet_V2_X1_5_Weights',\n",
135
+ " 'ShuffleNet_V2_X2_0_Weights',\n",
136
+ " 'SqueezeNet',\n",
137
+ " 'SqueezeNet1_0_Weights',\n",
138
+ " 'SqueezeNet1_1_Weights',\n",
139
+ " 'SwinTransformer',\n",
140
+ " 'Swin_B_Weights',\n",
141
+ " 'Swin_S_Weights',\n",
142
+ " 'Swin_T_Weights',\n",
143
+ " 'Swin_V2_B_Weights',\n",
144
+ " 'Swin_V2_S_Weights',\n",
145
+ " 'Swin_V2_T_Weights',\n",
146
+ " 'VGG',\n",
147
+ " 'VGG11_BN_Weights',\n",
148
+ " 'VGG11_Weights',\n",
149
+ " 'VGG13_BN_Weights',\n",
150
+ " 'VGG13_Weights',\n",
151
+ " 'VGG16_BN_Weights',\n",
152
+ " 'VGG16_Weights',\n",
153
+ " 'VGG19_BN_Weights',\n",
154
+ " 'VGG19_Weights',\n",
155
+ " 'ViT_B_16_Weights',\n",
156
+ " 'ViT_B_32_Weights',\n",
157
+ " 'ViT_H_14_Weights',\n",
158
+ " 'ViT_L_16_Weights',\n",
159
+ " 'ViT_L_32_Weights',\n",
160
+ " 'VisionTransformer',\n",
161
+ " 'Weights',\n",
162
+ " 'WeightsEnum',\n",
163
+ " 'Wide_ResNet101_2_Weights',\n",
164
+ " 'Wide_ResNet50_2_Weights',\n",
165
+ " '_GoogLeNetOutputs',\n",
166
+ " '_InceptionOutputs',\n",
167
+ " '__builtins__',\n",
168
+ " '__cached__',\n",
169
+ " '__doc__',\n",
170
+ " '__file__',\n",
171
+ " '__loader__',\n",
172
+ " '__name__',\n",
173
+ " '__package__',\n",
174
+ " '__path__',\n",
175
+ " '__spec__',\n",
176
+ " '_api',\n",
177
+ " '_meta',\n",
178
+ " '_utils',\n",
179
+ " 'alexnet',\n",
180
+ " 'convnext',\n",
181
+ " 'convnext_base',\n",
182
+ " 'convnext_large',\n",
183
+ " 'convnext_small',\n",
184
+ " 'convnext_tiny',\n",
185
+ " 'densenet',\n",
186
+ " 'densenet121',\n",
187
+ " 'densenet161',\n",
188
+ " 'densenet169',\n",
189
+ " 'densenet201',\n",
190
+ " 'detection',\n",
191
+ " 'efficientnet',\n",
192
+ " 'efficientnet_b0',\n",
193
+ " 'efficientnet_b1',\n",
194
+ " 'efficientnet_b2',\n",
195
+ " 'efficientnet_b3',\n",
196
+ " 'efficientnet_b4',\n",
197
+ " 'efficientnet_b5',\n",
198
+ " 'efficientnet_b6',\n",
199
+ " 'efficientnet_b7',\n",
200
+ " 'efficientnet_v2_l',\n",
201
+ " 'efficientnet_v2_m',\n",
202
+ " 'efficientnet_v2_s',\n",
203
+ " 'get_model',\n",
204
+ " 'get_model_builder',\n",
205
+ " 'get_model_weights',\n",
206
+ " 'get_weight',\n",
207
+ " 'googlenet',\n",
208
+ " 'inception',\n",
209
+ " 'inception_v3',\n",
210
+ " 'list_models',\n",
211
+ " 'maxvit',\n",
212
+ " 'maxvit_t',\n",
213
+ " 'mnasnet',\n",
214
+ " 'mnasnet0_5',\n",
215
+ " 'mnasnet0_75',\n",
216
+ " 'mnasnet1_0',\n",
217
+ " 'mnasnet1_3',\n",
218
+ " 'mobilenet',\n",
219
+ " 'mobilenet_v2',\n",
220
+ " 'mobilenet_v3_large',\n",
221
+ " 'mobilenet_v3_small',\n",
222
+ " 'mobilenetv2',\n",
223
+ " 'mobilenetv3',\n",
224
+ " 'optical_flow',\n",
225
+ " 'quantization',\n",
226
+ " 'regnet',\n",
227
+ " 'regnet_x_16gf',\n",
228
+ " 'regnet_x_1_6gf',\n",
229
+ " 'regnet_x_32gf',\n",
230
+ " 'regnet_x_3_2gf',\n",
231
+ " 'regnet_x_400mf',\n",
232
+ " 'regnet_x_800mf',\n",
233
+ " 'regnet_x_8gf',\n",
234
+ " 'regnet_y_128gf',\n",
235
+ " 'regnet_y_16gf',\n",
236
+ " 'regnet_y_1_6gf',\n",
237
+ " 'regnet_y_32gf',\n",
238
+ " 'regnet_y_3_2gf',\n",
239
+ " 'regnet_y_400mf',\n",
240
+ " 'regnet_y_800mf',\n",
241
+ " 'regnet_y_8gf',\n",
242
+ " 'resnet',\n",
243
+ " 'resnet101',\n",
244
+ " 'resnet152',\n",
245
+ " 'resnet18',\n",
246
+ " 'resnet34',\n",
247
+ " 'resnet50',\n",
248
+ " 'resnext101_32x8d',\n",
249
+ " 'resnext101_64x4d',\n",
250
+ " 'resnext50_32x4d',\n",
251
+ " 'segmentation',\n",
252
+ " 'shufflenet_v2_x0_5',\n",
253
+ " 'shufflenet_v2_x1_0',\n",
254
+ " 'shufflenet_v2_x1_5',\n",
255
+ " 'shufflenet_v2_x2_0',\n",
256
+ " 'shufflenetv2',\n",
257
+ " 'squeezenet',\n",
258
+ " 'squeezenet1_0',\n",
259
+ " 'squeezenet1_1',\n",
260
+ " 'swin_b',\n",
261
+ " 'swin_s',\n",
262
+ " 'swin_t',\n",
263
+ " 'swin_transformer',\n",
264
+ " 'swin_v2_b',\n",
265
+ " 'swin_v2_s',\n",
266
+ " 'swin_v2_t',\n",
267
+ " 'vgg',\n",
268
+ " 'vgg11',\n",
269
+ " 'vgg11_bn',\n",
270
+ " 'vgg13',\n",
271
+ " 'vgg13_bn',\n",
272
+ " 'vgg16',\n",
273
+ " 'vgg16_bn',\n",
274
+ " 'vgg19',\n",
275
+ " 'vgg19_bn',\n",
276
+ " 'video',\n",
277
+ " 'vision_transformer',\n",
278
+ " 'vit_b_16',\n",
279
+ " 'vit_b_32',\n",
280
+ " 'vit_h_14',\n",
281
+ " 'vit_l_16',\n",
282
+ " 'vit_l_32',\n",
283
+ " 'wide_resnet101_2',\n",
284
+ " 'wide_resnet50_2']"
285
+ ]
286
+ },
287
+ "metadata": {},
288
+ "execution_count": 1
289
+ }
290
+ ],
291
+ "source": [
292
+ "#Loading Pre-Trained Network using TorchVision\n",
293
+ "!pip install torchvision\n",
294
+ "from torchvision import models\n",
295
+ "import torch\n",
296
+ "\n",
297
+ "dir(models)"
298
+ ]
299
+ },
300
+ {
301
+ "cell_type": "code",
302
+ "execution_count": null,
303
+ "metadata": {
304
+ "id": "48_BUp-Kfrn-",
305
+ "colab": {
306
+ "base_uri": "https://localhost:8080/"
307
+ },
308
+ "outputId": "8c759ed5-ead3-4490-e22b-39c9066aa86a"
309
+ },
310
+ "outputs": [
311
+ {
312
+ "output_type": "stream",
313
+ "name": "stderr",
314
+ "text": [
315
+ "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
316
+ " warnings.warn(\n",
317
+ "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
318
+ " warnings.warn(msg)\n",
319
+ "Downloading: \"https://download.pytorch.org/models/alexnet-owt-7be5be79.pth\" to /root/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth\n",
320
+ "100%|██████████| 233M/233M [00:01<00:00, 124MB/s]\n"
321
+ ]
322
+ }
323
+ ],
324
+ "source": [
325
+ "# Using AlexNet for Image Classification\n",
326
+ "# Load the pre-trained model\n",
327
+ "alexnet = models.alexnet(pretrained=True)"
328
+ ]
329
+ },
330
+ {
331
+ "cell_type": "code",
332
+ "execution_count": null,
333
+ "metadata": {
334
+ "colab": {
335
+ "base_uri": "https://localhost:8080/"
336
+ },
337
+ "id": "M4Lkuk4lgDMJ",
338
+ "outputId": "190a9c05-9c27-46ec-fac4-602cc6dddce7"
339
+ },
340
+ "outputs": [
341
+ {
342
+ "output_type": "stream",
343
+ "name": "stdout",
344
+ "text": [
345
+ "AlexNet(\n",
346
+ " (features): Sequential(\n",
347
+ " (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
348
+ " (1): ReLU(inplace=True)\n",
349
+ " (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
350
+ " (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
351
+ " (4): ReLU(inplace=True)\n",
352
+ " (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
353
+ " (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
354
+ " (7): ReLU(inplace=True)\n",
355
+ " (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
356
+ " (9): ReLU(inplace=True)\n",
357
+ " (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
358
+ " (11): ReLU(inplace=True)\n",
359
+ " (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
360
+ " )\n",
361
+ " (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
362
+ " (classifier): Sequential(\n",
363
+ " (0): Dropout(p=0.5, inplace=False)\n",
364
+ " (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
365
+ " (2): ReLU(inplace=True)\n",
366
+ " (3): Dropout(p=0.5, inplace=False)\n",
367
+ " (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
368
+ " (5): ReLU(inplace=True)\n",
369
+ " (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
370
+ " )\n",
371
+ ")\n"
372
+ ]
373
+ }
374
+ ],
375
+ "source": [
376
+ "print(alexnet)"
377
+ ]
378
+ },
379
+ {
380
+ "cell_type": "code",
381
+ "execution_count": null,
382
+ "metadata": {
383
+ "id": "BYGBAm4bgRrG"
384
+ },
385
+ "outputs": [],
386
+ "source": [
387
+ "#make sure that data transforms into the right shape\n",
388
+ "from torchvision import transforms\n",
389
+ "transform = transforms.Compose([\n",
390
+ " transforms.Resize(256),\n",
391
+ " transforms.CenterCrop(224),\n",
392
+ " transforms.ToTensor(),\n",
393
+ " transforms.Normalize(\n",
394
+ " mean=[0.485, 0.456, 0.406],\n",
395
+ " std=[0.229, 0.224, 0.225]\n",
396
+ " )])"
397
+ ]
398
+ },
399
+ {
400
+ "cell_type": "code",
401
+ "execution_count": null,
402
+ "metadata": {
403
+ "colab": {
404
+ "base_uri": "https://localhost:8080/"
405
+ },
406
+ "id": "QjAr0XAw4y8S",
407
+ "outputId": "523c603e-f6e0-4ee5-a4c4-736f97e5975e"
408
+ },
409
+ "outputs": [
410
+ {
411
+ "output_type": "stream",
412
+ "name": "stdout",
413
+ "text": [
414
+ "Mounted at /content/gdrive\n"
415
+ ]
416
+ }
417
+ ],
418
+ "source": [
419
+ "#connect colab to google drive\n",
420
+ "from google.colab import drive\n",
421
+ "drive.mount('/content/gdrive')"
422
+ ]
423
+ },
424
+ {
425
+ "cell_type": "code",
426
+ "execution_count": null,
427
+ "metadata": {
428
+ "colab": {
429
+ "base_uri": "https://localhost:8080/"
430
+ },
431
+ "id": "UrVD0SbACHrd",
432
+ "outputId": "4ef343c7-2bc2-4d34-ea31-d9c7a11ef3e3"
433
+ },
434
+ "outputs": [
435
+ {
436
+ "output_type": "stream",
437
+ "name": "stdout",
438
+ "text": [
439
+ "Collecting pixellib==0.5.2\n",
440
+ " Downloading pixellib-0.5.2-py3-none-any.whl (66 kB)\n",
441
+ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/66.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/66.9 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.9/66.9 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "Requirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from pixellib==0.5.2) (0.19.3)\n",
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+ " Downloading labelme2coco-0.2.6-py3-none-any.whl (19 kB)\n",
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+ "Collecting imantics (from pixellib==0.5.2)\n",
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+ " Downloading imantics-0.1.12.tar.gz (13 kB)\n",
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+ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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+ "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from imantics->pixellib==0.5.2) (1.25.2)\n",
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+ "Collecting xmljson (from imantics->pixellib==0.5.2)\n",
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+ " Downloading xmljson-0.2.1-py2.py3-none-any.whl (10 kB)\n",
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+ "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from imgaug->pixellib==0.5.2) (1.16.0)\n",
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+ "Collecting sahi>=0.8.19 (from labelme2coco->pixellib==0.5.2)\n",
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+ " Downloading sahi-0.11.16-py3-none-any.whl (112 kB)\n",
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+ "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco->pixellib==0.5.2) (23.2.0)\n",
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+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco->pixellib==0.5.2) (2023.12.1)\n",
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+ "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco->pixellib==0.5.2) (0.35.1)\n",
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+ "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco->pixellib==0.5.2) (0.18.1)\n",
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+ "Requirement already satisfied: tqdm>=4.48.2 in /usr/local/lib/python3.10/dist-packages (from sahi>=0.8.19->labelme2coco->pixellib==0.5.2) (4.66.4)\n",
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+ "Collecting pybboxes==0.1.6 (from sahi>=0.8.19->labelme2coco->pixellib==0.5.2)\n",
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+ " Downloading pybboxes-0.1.6-py3-none-any.whl (24 kB)\n",
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+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from sahi>=0.8.19->labelme2coco->pixellib==0.5.2) (6.0.1)\n",
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+ "Collecting fire (from sahi>=0.8.19->labelme2coco->pixellib==0.5.2)\n",
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+ " Downloading fire-0.6.0.tar.gz (88 kB)\n",
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+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.4/88.4 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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+ "Collecting terminaltables (from sahi>=0.8.19->labelme2coco->pixellib==0.5.2)\n",
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+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->sahi>=0.8.19->labelme2coco->pixellib==0.5.2) (3.3.2)\n",
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+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->sahi>=0.8.19->labelme2coco->pixellib==0.5.2) (3.7)\n",
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+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->sahi>=0.8.19->labelme2coco->pixellib==0.5.2) (2.0.7)\n",
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+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->sahi>=0.8.19->labelme2coco->pixellib==0.5.2) (2024.6.2)\n",
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+ "Building wheels for collected packages: imantics, fire\n",
496
+ " Building wheel for imantics (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
497
+ " Created wheel for imantics: filename=imantics-0.1.12-py3-none-any.whl size=16010 sha256=7d817e9d50811bc57f5430a56a85b68bdd80eb6c1bf29c0162c164ea5437ecc8\n",
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+ " Stored in directory: /root/.cache/pip/wheels/56/6a/be/4c60e88b14abec4e93234a1f7f91ce8abe1ae88a2b3eaad3ac\n",
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+ " Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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+ " Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117029 sha256=7d054fa215963197dcfe0f04d1643078abd6b3849d6506532cce27e2499d896d\n",
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+ " Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
502
+ "Successfully built imantics fire\n",
503
+ "Installing collected packages: xmljson, terminaltables, pybboxes, fire, sahi, imantics, labelme2coco, pixellib\n",
504
+ "Successfully installed fire-0.6.0 imantics-0.1.12 labelme2coco-0.2.6 pixellib-0.5.2 pybboxes-0.1.6 sahi-0.11.16 terminaltables-3.1.10 xmljson-0.2.1\n",
505
+ "Collecting labelme2coco==0.1.0\n",
506
+ " Downloading labelme2coco-0.1.0-py3-none-any.whl (19 kB)\n",
507
+ "Requirement already satisfied: numpy>=1.15.1 in /usr/local/lib/python3.10/dist-packages (from labelme2coco==0.1.0) (1.25.2)\n",
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+ "Requirement already satisfied: pillow>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from labelme2coco==0.1.0) (9.4.0)\n",
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+ "Requirement already satisfied: jsonschema>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from labelme2coco==0.1.0) (4.19.2)\n",
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+ "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco==0.1.0) (23.2.0)\n",
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+ "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco==0.1.0) (2023.12.1)\n",
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+ "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco==0.1.0) (0.35.1)\n",
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+ "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6.0->labelme2coco==0.1.0) (0.18.1)\n",
514
+ "Installing collected packages: labelme2coco\n",
515
+ " Attempting uninstall: labelme2coco\n",
516
+ " Found existing installation: labelme2coco 0.2.6\n",
517
+ " Uninstalling labelme2coco-0.2.6:\n",
518
+ " Successfully uninstalled labelme2coco-0.2.6\n",
519
+ "Successfully installed labelme2coco-0.1.0\n"
520
+ ]
521
+ }
522
+ ],
523
+ "source": [
524
+ "!pip install pixellib==0.5.2\n",
525
+ "!pip install labelme2coco==0.1.0"
526
+ ]
527
+ },
528
+ {
529
+ "cell_type": "code",
530
+ "execution_count": null,
531
+ "metadata": {
532
+ "id": "LM8K9V9VEpeb"
533
+ },
534
+ "outputs": [],
535
+ "source": [
536
+ "# Import necessary libraries\n",
537
+ "import os\n",
538
+ "from PIL import Image\n",
539
+ "\n",
540
+ "# Initialize an empty list to store image information\n",
541
+ "info = []\n",
542
+ "# Define the path to the main folder containing the images\n",
543
+ "main_folder_path = \"/content/gdrive/MyDrive/Orchid-Flowers-Dataset-v1.1/img\"\n",
544
+ "# Walk through the directory structure starting from the main folder\n",
545
+ "for root, _, files in os.walk(main_folder_path):\n",
546
+ " # Iterate over each file in the current directory\n",
547
+ " for file in files:\n",
548
+ " # Check if the file is a JPEG image\n",
549
+ " if file.endswith(\".jpg\"):\n",
550
+ " # Construct the full path to the image file\n",
551
+ " img_path = os.path.join(root, file)\n",
552
+ " # Open the image using PIL\n",
553
+ " img = Image.open(img_path)\n",
554
+ " # Append a dictionary containing the image object and its path to the info list\n",
555
+ " info.append({\"img\": img, \"path\": img_path})\n"
556
+ ]
557
+ },
558
+ {
559
+ "cell_type": "code",
560
+ "execution_count": null,
561
+ "metadata": {
562
+ "id": "cAJhT7DhhOb2"
563
+ },
564
+ "outputs": [],
565
+ "source": [
566
+ "#preprocess\n",
567
+ "## prepares the image 'img' according to the requirements of the model\n",
568
+ "img_t = transform(img)\n",
569
+ "batch_t = torch.unsqueeze(img_t, 0)"
570
+ ]
571
+ },
572
+ {
573
+ "cell_type": "code",
574
+ "execution_count": null,
575
+ "metadata": {
576
+ "colab": {
577
+ "base_uri": "https://localhost:8080/"
578
+ },
579
+ "id": "_4pQvaLEhVGG",
580
+ "outputId": "f04abab6-dca9-456a-f81c-6a6a2b2574df"
581
+ },
582
+ "outputs": [
583
+ {
584
+ "output_type": "stream",
585
+ "name": "stdout",
586
+ "text": [
587
+ "torch.Size([1, 1000])\n"
588
+ ]
589
+ }
590
+ ],
591
+ "source": [
592
+ "#Model Inference\n",
593
+ "#put our model in eval mode.\n",
594
+ "alexnet.eval()\n",
595
+ "\n",
596
+ "out = alexnet(batch_t)\n",
597
+ "print(out.shape)"
598
+ ]
599
+ },
600
+ {
601
+ "cell_type": "code",
602
+ "execution_count": null,
603
+ "metadata": {
604
+ "id": "6vwXgG-Khp4g"
605
+ },
606
+ "outputs": [],
607
+ "source": [
608
+ "#get images class\n",
609
+ "with open('/content/gdrive/MyDrive/Orchid-Flowers-Dataset-v1.1/Multi-label Classification/Multilabel_training.csv') as f:\n",
610
+ " classes = [line.strip() for line in f.readlines()]"
611
+ ]
612
+ },
613
+ {
614
+ "cell_type": "code",
615
+ "execution_count": null,
616
+ "metadata": {
617
+ "colab": {
618
+ "base_uri": "https://localhost:8080/"
619
+ },
620
+ "id": "oRLrd5igi72e",
621
+ "outputId": "2c9eb2b8-0e34-4fe1-d201-db8e889202ee"
622
+ },
623
+ "outputs": [
624
+ {
625
+ "output_type": "stream",
626
+ "name": "stdout",
627
+ "text": [
628
+ "1328.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24 15.88855266571045\n"
629
+ ]
630
+ }
631
+ ],
632
+ "source": [
633
+ "#find out the max prediction.\n",
634
+ "_, index = torch.max(out, 1)\n",
635
+ "\n",
636
+ "percentage = torch.nn.functional.softmax(out, dim=1)[0] * 100\n",
637
+ "\n",
638
+ "print(classes[index[0]], percentage[index[0]].item())\n"
639
+ ]
640
+ },
641
+ {
642
+ "cell_type": "code",
643
+ "execution_count": null,
644
+ "metadata": {
645
+ "colab": {
646
+ "base_uri": "https://localhost:8080/"
647
+ },
648
+ "id": "UVpTbwpLjk1u",
649
+ "outputId": "eb37e1df-4673-47b0-fff1-f841c3c82525"
650
+ },
651
+ "outputs": [
652
+ {
653
+ "output_type": "execute_result",
654
+ "data": {
655
+ "text/plain": [
656
+ "[('1328.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24', 15.88855266571045),\n",
657
+ " ('59.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
658
+ " 13.1360502243042),\n",
659
+ " ('1253.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 12.89087963104248),\n",
660
+ " ('1292.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 8.40053653717041),\n",
661
+ " ('381.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 7.234877586364746),\n",
662
+ " ('366.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 2.4095914363861084),\n",
663
+ " ('367.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 1.6216557025909424),\n",
664
+ " ('61.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
665
+ " 1.5760637521743774),\n",
666
+ " ('1336.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24', 1.5720475912094116),\n",
667
+ " ('21.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 1.1183711290359497),\n",
668
+ " ('379.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 1.050938606262207),\n",
669
+ " ('60.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
670
+ " 0.8608635663986206),\n",
671
+ " ('358.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.819441020488739),\n",
672
+ " ('19.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.7847009301185608),\n",
673
+ " ('81.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
674
+ " 0.7471103072166443),\n",
675
+ " ('1260.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.7245150804519653),\n",
676
+ " ('770.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.7173356413841248),\n",
677
+ " ('157.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.6922218203544617),\n",
678
+ " ('172.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Purple;7', 0.6557213068008423),\n",
679
+ " ('136.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Pink;4', 0.6397452354431152),\n",
680
+ " ('382.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.6052159070968628),\n",
681
+ " ('95.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
682
+ " 0.5833677649497986),\n",
683
+ " ('668.jpg;Nospot;Lobed;Spike;AFew;White;White;13', 0.5295686721801758),\n",
684
+ " ('76.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
685
+ " 0.5227769017219543),\n",
686
+ " ('140.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.5217817425727844),\n",
687
+ " ('380.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.4721907079219818),\n",
688
+ " ('656.jpg;Nospot;Lobed;Spike;AFew;White;White;13', 0.45688581466674805),\n",
689
+ " ('370.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.4555966258049011),\n",
690
+ " ('374.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.44236528873443604),\n",
691
+ " ('378.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.44061797857284546),\n",
692
+ " ('543.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
693
+ " 0.38045161962509155),\n",
694
+ " ('497.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
695
+ " 0.379602313041687),\n",
696
+ " ('1254.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.3571322560310364),\n",
697
+ " ('1092.jpg;Nospot;Simple;Raceme;Many;PinkRed;YellowPurple;22',\n",
698
+ " 0.3147432208061218),\n",
699
+ " ('368.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.3100665211677551),\n",
700
+ " ('6.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.2809280753135681),\n",
701
+ " ('167.jpg;Spot;Lobed;Raceme;AFew;Green;GreenBrown;6', 0.27919310331344604),\n",
702
+ " ('1295.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.2738247513771057),\n",
703
+ " ('66.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
704
+ " 0.2725987136363983),\n",
705
+ " ('418.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.26582586765289307),\n",
706
+ " ('1278.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.26240798830986023),\n",
707
+ " ('166.jpg;Spot;Lobed;Raceme;AFew;Green;GreenBrown;6', 0.2560056149959564),\n",
708
+ " ('72.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
709
+ " 0.25269821286201477),\n",
710
+ " ('3.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.23681098222732544),\n",
711
+ " ('1291.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.22926069796085358),\n",
712
+ " ('2.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.2286931574344635),\n",
713
+ " ('383.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.22630411386489868),\n",
714
+ " ('1332.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24', 0.2254563570022583),\n",
715
+ " ('1330.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24', 0.21111786365509033),\n",
716
+ " ('52.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
717
+ " 0.2055903971195221),\n",
718
+ " ('152.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.20558702945709229),\n",
719
+ " ('879.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.20312798023223877),\n",
720
+ " ('356.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.20167100429534912),\n",
721
+ " ('8.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.19924338161945343),\n",
722
+ " ('24.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.19896815717220306),\n",
723
+ " ('110.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
724
+ " 0.19074273109436035),\n",
725
+ " ('45.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
726
+ " 0.18984097242355347),\n",
727
+ " ('1333.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24', 0.1873064637184143),\n",
728
+ " ('7.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.18209609389305115),\n",
729
+ " ('361.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.17600329220294952),\n",
730
+ " ('364.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.16741099953651428),\n",
731
+ " ('365.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.16448485851287842),\n",
732
+ " ('20.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.16104918718338013),\n",
733
+ " ('79.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
734
+ " 0.1599152684211731),\n",
735
+ " ('1250.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.15917754173278809),\n",
736
+ " ('602.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
737
+ " 0.15614819526672363),\n",
738
+ " ('1251.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.1506175547838211),\n",
739
+ " ('154.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.1481093168258667),\n",
740
+ " ('153.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.1388826221227646),\n",
741
+ " ('156.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.1335545927286148),\n",
742
+ " ('57.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
743
+ " 0.1283245086669922),\n",
744
+ " ('4.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.12153234332799911),\n",
745
+ " ('1294.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.12126421928405762),\n",
746
+ " ('360.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.11958467215299606),\n",
747
+ " ('149.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.11674829572439194),\n",
748
+ " ('77.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
749
+ " 0.11610187590122223),\n",
750
+ " ('44.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
751
+ " 0.11539264768362045),\n",
752
+ " ('375.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.1144905537366867),\n",
753
+ " ('16.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.1133173331618309),\n",
754
+ " ('31.jpg;Spot;Lobed;Raceme;AFew;White;GreenBrown;2', 0.11253408342599869),\n",
755
+ " ('934.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.11123257130384445),\n",
756
+ " ('377.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.1095474436879158),\n",
757
+ " ('345.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.10801921784877777),\n",
758
+ " ('71.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
759
+ " 0.10731980204582214),\n",
760
+ " ('1329.jpg;Spot;Lobed;Raceme;AFew;White;YellowBrown;24', 0.1048896461725235),\n",
761
+ " ('347.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.1000528559088707),\n",
762
+ " ('415.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.10003134608268738),\n",
763
+ " ('393.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.09839722514152527),\n",
764
+ " ('67.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
765
+ " 0.09760170429944992),\n",
766
+ " ('372.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.09713171422481537),\n",
767
+ " ('9.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.09567029029130936),\n",
768
+ " ('1129.jpg;Nospot;Simple;Raceme;Many;PinkRed;YellowPurple;22',\n",
769
+ " 0.09349559247493744),\n",
770
+ " ('29.jpg;Spot;Lobed;Raceme;AFew;White;GreenBrown;2', 0.0917716920375824),\n",
771
+ " ('43.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
772
+ " 0.09113944321870804),\n",
773
+ " ('151.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.09085232764482498),\n",
774
+ " ('68.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
775
+ " 0.08954086899757385),\n",
776
+ " ('1205.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.08864416182041168),\n",
777
+ " ('397.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.08842142671346664),\n",
778
+ " ('335.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.08576169610023499),\n",
779
+ " ('419.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.08285453915596008),\n",
780
+ " ('75.jpg;Spot;Simple;SingleOrPair;SinglePair;White;White;3',\n",
781
+ " 0.08281706273555756),\n",
782
+ " ('394.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.0826164186000824),\n",
783
+ " ('388.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.0812898576259613),\n",
784
+ " ('117.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
785
+ " 0.08076874911785126),\n",
786
+ " ('764.jpg;Nospot;Lobed;Raceme;AFew;Yellow;Yellow;18', 0.08051265776157379),\n",
787
+ " ('357.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.07831750810146332),\n",
788
+ " ('1262.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.07743849605321884),\n",
789
+ " ('376.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.0759877860546112),\n",
790
+ " ('158.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.07545885443687439),\n",
791
+ " ('344.jpg;Nospot;Lobed;Raceme;AFew;White;White;11', 0.07462375611066818),\n",
792
+ " ('385.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.07386453449726105),\n",
793
+ " ('103.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
794
+ " 0.07346448302268982),\n",
795
+ " ('812.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.07209622859954834),\n",
796
+ " ('1149.jpg;Nospot;Simple;Raceme;Many;PinkRed;YellowPurple;22',\n",
797
+ " 0.07033940404653549),\n",
798
+ " ('423.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.07011250406503677),\n",
799
+ " ('369.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.06953710317611694),\n",
800
+ " ('28.jpg;Spot;Lobed;Raceme;AFew;White;GreenBrown;2', 0.06891264021396637),\n",
801
+ " ('226.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Purple;9', 0.06838587671518326),\n",
802
+ " ('776.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.06826956570148468),\n",
803
+ " ('334.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.06826279312372208),\n",
804
+ " ('69.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
805
+ " 0.06743894517421722),\n",
806
+ " ('818.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.06660064309835434),\n",
807
+ " ('488.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
808
+ " 0.06591498851776123),\n",
809
+ " ('177.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Purple;7', 0.06477275490760803),\n",
810
+ " ('674.jpg;Nospot;Lobed;Spike;AFew;White;White;13', 0.06421098113059998),\n",
811
+ " ('603.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
812
+ " 0.06350540369749069),\n",
813
+ " ('697.jpg;Nospot;Lobed;SingleOrPair;SinglePair;PinkRed;White;15',\n",
814
+ " 0.06278358399868011),\n",
815
+ " ('175.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Purple;7', 0.06274962425231934),\n",
816
+ " ('1265.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.06253521144390106),\n",
817
+ " ('328.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.06139958277344704),\n",
818
+ " ('1160.jpg;Nospot;Simple;Raceme;Many;PinkRed;YellowPurple;22',\n",
819
+ " 0.061397090554237366),\n",
820
+ " ('350.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.058440323919057846),\n",
821
+ " ('230.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;10', 0.057940512895584106),\n",
822
+ " ('5.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.057657621800899506),\n",
823
+ " ('171.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Purple;7', 0.057468231767416),\n",
824
+ " ('498.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
825
+ " 0.05653461068868637),\n",
826
+ " ('1002.jpg;Spot;Lobed;Raceme;Many;White;YellowPurple;20',\n",
827
+ " 0.05630265921354294),\n",
828
+ " ('482.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
829
+ " 0.056098878383636475),\n",
830
+ " ('168.jpg;Spot;Lobed;Raceme;AFew;Green;GreenBrown;6', 0.05599924549460411),\n",
831
+ " ('391.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.053999386727809906),\n",
832
+ " ('264.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.05180278792977333),\n",
833
+ " ('1244.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.05118359625339508),\n",
834
+ " ('78.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
835
+ " 0.05117344483733177),\n",
836
+ " ('1075.jpg;Spot;Simple;Raceme;AFew;White;GreenBrown;21',\n",
837
+ " 0.050270598381757736),\n",
838
+ " ('1282.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.050155527889728546),\n",
839
+ " ('413.jpg;Nospot;Lobed;Raceme;AFew;White;White;11', 0.049014657735824585),\n",
840
+ " ('17.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.0488186813890934),\n",
841
+ " ('64.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
842
+ " 0.04768126830458641),\n",
843
+ " ('90.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
844
+ " 0.04746171087026596),\n",
845
+ " ('412.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.04722648113965988),\n",
846
+ " ('1095.jpg;Nospot;Simple;Raceme;Many;PinkRed;YellowPurple;22',\n",
847
+ " 0.046545252203941345),\n",
848
+ " ('1256.jpg;Spot;Lobed;Raceme;AFew;White;Green;23', 0.04631029814481735),\n",
849
+ " ('262.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.04623924568295479),\n",
850
+ " ('174.jpg;Nospot;Lobed;Raceme;AFew;BluePurple;Purple;7',\n",
851
+ " 0.046176716685295105),\n",
852
+ " ('240.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;White;10', 0.045038942247629166),\n",
853
+ " ('286.jpg;Nospot;Lobed;Raceme;AFew;PinkRed;Pink;11', 0.044957708567380905)]"
854
+ ]
855
+ },
856
+ "metadata": {},
857
+ "execution_count": 14
858
+ }
859
+ ],
860
+ "source": [
861
+ "#sorts the output tensor out in descending order to identify the classes ranked by their predicted probabilities\n",
862
+ "_, indices = torch.sort(out, descending=True)\n",
863
+ "[(classes[idx], percentage[idx].item()) for idx in indices[0][:156]]"
864
+ ]
865
+ },
866
+ {
867
+ "cell_type": "code",
868
+ "execution_count": null,
869
+ "metadata": {
870
+ "colab": {
871
+ "base_uri": "https://localhost:8080/"
872
+ },
873
+ "id": "eCB_8WaAlH81",
874
+ "outputId": "5b1892c4-c998-4d56-9858-774a0af872d3"
875
+ },
876
+ "outputs": [
877
+ {
878
+ "output_type": "stream",
879
+ "name": "stderr",
880
+ "text": [
881
+ "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.\n",
882
+ " warnings.warn(msg)\n",
883
+ "Downloading: \"https://download.pytorch.org/models/resnet101-63fe2227.pth\" to /root/.cache/torch/hub/checkpoints/resnet101-63fe2227.pth\n",
884
+ "100%|██████████| 171M/171M [00:01<00:00, 150MB/s]\n"
885
+ ]
886
+ },
887
+ {
888
+ "output_type": "execute_result",
889
+ "data": {
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+ " 0.00870033074170351),\n",
1083
+ " ('856.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.008633521385490894),\n",
1084
+ " ('103.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
1085
+ " 0.008289733901619911),\n",
1086
+ " ('73.jpg;Spot;Simple;SingleOrPair;SinglePair;PinkRed;Pink;3',\n",
1087
+ " 0.008183479309082031),\n",
1088
+ " ('904.jpg;Spot;Lobed;Raceme;Many;White;YellowBrown;19', 0.008113854564726353),\n",
1089
+ " ('597.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
1090
+ " 0.00803594570606947),\n",
1091
+ " ('535.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
1092
+ " 0.008010867051780224),\n",
1093
+ " ('153.jpg;Nospot;Lobed;Raceme;Many;BluePurple;Pink;5', 0.008003998547792435),\n",
1094
+ " ('498.jpg;Spot;Pouched;SingleOrPair;SinglePair;PinkRedWhite;Pink;12',\n",
1095
+ " 0.007863061502575874),\n",
1096
+ " ('23.jpg;Spot;Lobed;Raceme;AFew;PinkRedWhite;White;1', 0.007817661389708519)]"
1097
+ ]
1098
+ },
1099
+ "metadata": {},
1100
+ "execution_count": 15
1101
+ }
1102
+ ],
1103
+ "source": [
1104
+ "# Using ResNet for Image Classification\n",
1105
+ "# First, load the model\n",
1106
+ "resnet = models.resnet101(pretrained=True)\n",
1107
+ "\n",
1108
+ "# Second, put the network in eval mode\n",
1109
+ "resnet.eval()\n",
1110
+ "\n",
1111
+ "# Third, carry out model inference\n",
1112
+ "out = resnet(batch_t)\n",
1113
+ "\n",
1114
+ "# Forth, print the top 156 classes predicted by the model\n",
1115
+ "_, indices = torch.sort(out, descending=True)\n",
1116
+ "percentage = torch.nn.functional.softmax(out, dim=1)[0] * 100\n",
1117
+ "[(classes[idx], percentage[idx].item()) for idx in indices[0][:156]]"
1118
+ ]
1119
+ }
1120
+ ],
1121
+ "metadata": {
1122
+ "accelerator": "GPU",
1123
+ "colab": {
1124
+ "gpuType": "T4",
1125
+ "provenance": []
1126
+ },
1127
+ "kernelspec": {
1128
+ "display_name": "Python 3",
1129
+ "name": "python3"
1130
+ },
1131
+ "language_info": {
1132
+ "name": "python"
1133
+ }
1134
+ },
1135
+ "nbformat": 4,
1136
+ "nbformat_minor": 0
1137
+ }