File size: 7,836 Bytes
621619d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ee6e1b61-d3a5-45dd-9d2a-953d8acdd763",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.52, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.\n"
]
}
],
"source": [
"from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig\n",
"from PIL import Image\n",
"import requests\n",
"import torch\n",
"\n",
"# Can also be a local path if you have already cloned the hugging face repo\n",
"MODEL_PATH = \"C:/Users/reube/MolmoLocalQuant/Molmo_Quant\"\n",
"\n",
"# load the processor\n",
"processor = AutoProcessor.from_pretrained(\n",
" MODEL_PATH,\n",
" trust_remote_code=True,\n",
" device_map='auto'\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2ff193c-f148-400c-accb-d83ad58bfa73",
"metadata": {},
"outputs": [],
"source": [
"processor"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ee281cd8-527c-4686-94db-8c8b62686a80",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5959b4f7a5f245a18c04c848c3f0a2c5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = AutoModelForCausalLM.from_pretrained(\n",
" MODEL_PATH,\n",
" trust_remote_code=True,\n",
" device_map='auto'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ce1319d0-6d3a-41e1-99a7-5d92937bef12",
"metadata": {},
"outputs": [],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "e8ebdbbb-bc31-4aab-850b-81d464dadd93",
"metadata": {},
"outputs": [],
"source": [
"filename=\"C:/Users/reube/OneDrive/Pictures/dfmr4c7-d7c0ba06-f019-418f-a091-b247d44a738f.jpg\"\n",
"image = Image.open(filename).convert(\"RGB\")\n",
"inputs = processor.process(\n",
" images=[image],\n",
" text=\"Point to people in picture.\")\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "994719fb-ec2f-4c8f-83fc-44f0edfd65f2",
"metadata": {},
"outputs": [],
"source": [
"generated_text='Counting the <points x1=\"39.6\" y1=\"66.2\" x2=\"78.6\" y2=\"67.1\" alt=\"number of individual socks by pointing at their toe tips\">number of individual socks by pointing at their toe tips</points> shows a total of 2.'\n",
"label='test'"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "0e48eae4-8e1d-4f84-9cbd-dbc6adedd122",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"id": "c3361787-4f10-4fdb-a09a-a88535086039",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The following generation flags are not valid and may be ignored: ['temperature', 'top_p']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:/Users/reube/OneDrive/Pictures/dfmr4c7-d7c0ba06-f019-418f-a091-b247d44a738f.jpg| <points x1=\"16.8\" y1=\"76.9\" x2=\"46.2\" y2=\"66.9\" x3=\"66.8\" y3=\"59.6\" alt=\"people in picture\">people in picture</points>\n",
"\n"
]
}
],
"source": [
"\n",
"# move inputs to the correct device and make a batch of size 1\n",
"inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}\n",
"\n",
"# Compute is done in float16, while most weights are NF4\n",
"with torch.autocast(device_type=\"cuda\", enabled=True, dtype=torch.float16):\n",
" output = model.generate_from_batch(\n",
" inputs,\n",
" GenerationConfig(max_new_tokens=200, stop_strings=\"<|endoftext|>\"),\n",
" tokenizer=processor.tokenizer,\n",
" use_cache=False,\n",
" temperature=0.2,\n",
" top_p=0.5\n",
" )\n",
"\n",
"# only get generated tokens; decode them to text\n",
"generated_tokens = output[0, inputs['input_ids'].size(1):]\n",
"generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True) \n",
"\n",
"# print the generated text\n",
"print(filename+'|'+generated_text+'\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c44ec15e-849f-4dd3-87b6-a7017035b67c",
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import re\n",
"label='People'\n",
"cap = cv2.VideoCapture(0) # Use 0 for default webcam\n",
"\n",
"while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
" image = Image.fromarray(frame).convert(\"RGB\")\n",
" inputs = processor.process(\n",
" images=[image],\n",
" text=\"Point to the people.\")\n",
" # move inputs to the correct device and make a batch of size 1\n",
" inputs = {k: v.to(model.device).unsqueeze(0) for k, v in inputs.items()}\n",
"\n",
" # Compute is done in float16, while most weights are NF4\n",
" with torch.autocast(device_type=\"cuda\", enabled=True, dtype=torch.float16):\n",
" output = model.generate_from_batch(\n",
" inputs,\n",
" GenerationConfig(max_new_tokens=100, stop_strings=\"<|endoftext|>\"),\n",
" tokenizer=processor.tokenizer,\n",
" use_cache=False\n",
" )\n",
"\n",
" # only get generated tokens; decode them to text\n",
" generated_tokens = output[0, inputs['input_ids'].size(1):]\n",
" generated_text = processor.tokenizer.decode(generated_tokens, skip_special_tokens=True) \n",
" \n",
" # You’ll overlay annotations here\n",
" matches = re.findall(r'x\\d+=\"([\\d.]+)\"\\s*y\\d+=\"([\\d.]+)\"', generated_text)\n",
" \n",
" points = [{'x': float(x), 'y': float(y)} for x, y in matches]\n",
" if points:\n",
" for pt in points:\n",
" x, y = pt['x'], pt['y'] \n",
" h, w = frame.shape[:2]\n",
" x = int(pt['x'] / 100 * w)\n",
" y = int(pt['y'] / 100 * h)\n",
" cv2.circle(frame, (x, y), radius=5, color=(0, 255, 0), thickness=-1)\n",
" \n",
" cv2.putText(frame, label, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n",
" cv2.imshow('Live Feed', frame)\n",
" if cv2.waitKey(1) & 0xFF == ord('q'):\n",
" break\n",
"\n",
"cap.release()\n",
"cv2.destroyAllWindows()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a13bcb85-edab-49e1-a1a9-cd3144491f06",
"metadata": {},
"outputs": [],
"source": [
"torch.cuda.get_device_properties(0)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "base"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|