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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "1e99de7a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2024-06-20 13:18:56-- https://docs-assets.developer.apple.com/ml-research/datasets/mobileclip/mobileclip_s0.pt\n",
"Resolving docs-assets.developer.apple.com (docs-assets.developer.apple.com)... 17.253.73.203, 17.253.73.201\n",
"Connecting to docs-assets.developer.apple.com (docs-assets.developer.apple.com)|17.253.73.203|:443... connected.\n",
"HTTP request sent, awaiting response... 416 Requested Range Not Satisfiable\n",
"\n",
" The file is already fully retrieved; nothing to do.\n",
"\n",
"--2024-06-20 13:18:58-- https://raw.githubusercontent.com/apple/ml-mobileclip/main/mobileclip/configs/mobileclip_s0.json\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
"HTTP request sent, awaiting response... 416 Range Not Satisfiable\n",
"\n",
" The file is already fully retrieved; nothing to do.\n",
"\n"
]
}
],
"source": [
"#!git clone https://huggingface.co/spaces/depth-anything/Depth-Anything-V2\n",
"#!pip install -r Depth-Anything-V2/requirements.txt\n",
"#!pip install -q --upgrade coremltools"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d6cb8a61",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "801db364",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"scikit-learn version 1.6.0 is not supported. Minimum required version: 0.17. Maximum required version: 1.5.1. Disabling scikit-learn conversion API.\n"
]
}
],
"source": [
"import torch\n",
"import coremltools as ct\n",
"import numpy as np\n",
"from PIL import Image\n",
"import tempfile\n",
"from huggingface_hub import hf_hub_download\n",
"import sys\n",
"sys.path.append('./Depth-Anything-V2')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "73882c02",
"metadata": {},
"outputs": [],
"source": [
"from depth_anything_v2.dpt import DepthAnythingV2\n",
"from depth_anything_v2.util.transform import Resize, NormalizeImage, PrepareForNet\n",
"\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "markdown",
"id": "26f7dcff",
"metadata": {},
"source": [
"# 1. Load Depth-Anything-V2's vitl checkpoint"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e67aa722",
"metadata": {},
"outputs": [],
"source": [
"DEVICE = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'\n",
"model_configs = {\n",
" 'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},\n",
" 'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},\n",
" 'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},\n",
" 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}\n",
"}\n",
"encoder2name = {\n",
" 'vits': 'Small',\n",
" 'vitb': 'Base',\n",
" 'vitl': 'Large',\n",
" 'vitg': 'Giant', # we are undergoing company review procedures to release our giant model checkpoint\n",
"}\n",
"encoder = 'vitl'\n",
"model_name = encoder2name[encoder]\n",
"model = DepthAnythingV2(**model_configs[encoder])\n",
"filepath = hf_hub_download(repo_id=f\"depth-anything/Depth-Anything-V2-{model_name}\", filename=f\"depth_anything_v2_{encoder}.pth\", repo_type=\"model\")\n",
"state_dict = torch.load(filepath, map_location=\"cpu\")\n",
"model.load_state_dict(state_dict)\n",
"model = model.to(DEVICE).eval()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a632e6b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3024, 4032, 3)\n"
]
}
],
"source": [
"image = Image.open(\"./sample_images/IMG_4061.jpeg\")\n",
"img = np.array(image)\n",
"print(img.shape)\n",
"h, w = img.shape[:2]\n",
"depth = model.infer_image(img)\n",
"depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0\n",
"depth = depth.astype(np.uint8)\n",
"depth_image = Image.fromarray(depth)\n",
"depth_image.save(\"depth_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "77477217",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3024, 4032, 3)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/dadler/Projects/Glide/ai-bots/depth/./Depth-Anything-V2/depth_anything_v2/dinov2_layers/patch_embed.py:73: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" assert H % patch_H == 0, f\"Input image height {H} is not a multiple of patch height {patch_H}\"\n",
"/Users/dadler/Projects/Glide/ai-bots/depth/./Depth-Anything-V2/depth_anything_v2/dinov2_layers/patch_embed.py:74: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" assert W % patch_W == 0, f\"Input image width {W} is not a multiple of patch width: {patch_W}\"\n",
"/Users/dadler/Projects/Glide/ai-bots/depth/./Depth-Anything-V2/depth_anything_v2/dinov2.py:183: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if npatch == N and w == h:\n",
"/Users/dadler/Projects/Glide/ai-bots/depth/./Depth-Anything-V2/depth_anything_v2/dpt.py:147: TracerWarning: Converting a tensor to a Python integer might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode=\"bilinear\", align_corners=True)\n"
]
}
],
"source": [
"original_image = Image.open(\"./sample_images/IMG_4061.jpeg\")\n",
"origina_img = np.array(original_image)\n",
"print(origina_img.shape)\n",
"original_h, original_w = origina_img.shape[:2]\n",
"input_size = 518\n",
"image = original_image.resize((input_size,input_size), Image.Resampling.BILINEAR)\n",
"img = np.array(image)\n",
"input_image, (h, w) = model.image2tensor(img, input_size)\n",
"input_image = input_image.to(DEVICE)\n",
"with torch.no_grad():\n",
" depth = model(input_image)\n",
" depth = F.interpolate(depth[:, None], (h, w), mode=\"bilinear\", align_corners=True)[0, 0]\n",
" depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0\n",
" depth = depth.cpu().numpy().astype(np.uint8)\n",
"depth_image = Image.fromarray(depth).resize((original_w,original_h), Image.Resampling.BILINEAR)\n",
"depth_image.save(\"depth_image_2.jpg\")\n",
"\n",
"traced_model = torch.jit.trace(model, input_image)\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "42632870",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traced PyTorch ImageEncoder ckpt out for jpg:\n",
">>> tensor([[3.8735, 3.9076, 4.0226, ..., 1.8554, 1.7260, 2.5633],\n",
" [4.3636, 4.1100, 4.1624, ..., 2.1774, 2.2929, 2.2913],\n",
" [4.3914, 4.2280, 4.2901, ..., 2.3076, 2.3133, 2.2698],\n",
" ...,\n",
" [5.8771, 5.8192, 5.8249, ..., 3.9578, 3.9079, 3.7710],\n",
" [6.1631, 6.1475, 6.1688, ..., 4.2481, 4.2320, 4.0410],\n",
" [6.4769, 6.4864, 6.4850, ..., 4.6766, 4.6218, 4.4442]],\n",
" device='mps:0', grad_fn=<SliceBackward0>)\n"
]
}
],
"source": [
"example_output = traced_model(input_image)\n",
"print(\"Traced PyTorch ImageEncoder ckpt out for jpg:\\n>>>\", example_output[0, :10])"
]
},
{
"cell_type": "markdown",
"id": "3c0d9c70",
"metadata": {},
"source": [
"You can see that there is some loss in precision, but it is still acceptable."
]
},
{
"cell_type": "markdown",
"id": "ca182b4a",
"metadata": {},
"source": [
"# 2. Export ImageEncoder"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "ef7af5c5",
"metadata": {},
"outputs": [],
"source": [
"image_means = [0.485, 0.456, 0.406]\n",
"image_stds = [0.229, 0.224, 0.225]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"id": "8f66a99c",
"metadata": {},
"outputs": [],
"source": [
"import torchvision.transforms as transforms\n",
"\n",
"class Wrapper(torch.nn.Module): \n",
" def __init__(self, model):\n",
" super().__init__()\n",
" _means = image_means\n",
" _stds = image_stds\n",
" self.model = model \n",
" self.stds = torch.tensor(_stds).half()[:,None,None]\n",
" self.means = torch.tensor(_means).half()[:,None,None]\n",
"\n",
" transform_model = torch.nn.Sequential(\n",
" transforms.Normalize(mean=image_means, std=image_stds)\n",
" )\n",
"\n",
" def forward(self, input): \n",
" input = input/255.0\n",
" intput = self.transform_model(input)\n",
" output = self.model(input)\n",
" output = (output - output.min()) / (output.max() - output.min()) \n",
" # Fix \"Image output, 'depthOutput', must have rank 4. Instead it has rank 3\"\n",
" output = output.unsqueeze(0)\n",
" # Fix \"Shape of the RGB/BGR image output, 'depthOutput', must be of kind (1, 3, H, W), i.e., first two dimensions must be (1, 3), instead they are: (1, 1)\"ArithmeticError\n",
" output = output.repeat(1, 3, 1, 1)\n",
" output = output * 255.0\n",
" return output\n",
"\n",
"# Instantiate the Wrapper model passing the original PyTorch FCN model\n",
"wrapped_model = Wrapper(traced_model)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "b3da3350",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wrapped PyTorch ImageEncoder ckpt out for jpg:\n",
">>> tensor([[[1.3479e+00, 1.3024e+00, 1.3246e+00, ..., 3.6170e-02,\n",
" 1.2884e-01, 4.5228e-01],\n",
" [1.5584e+00, 1.4481e+00, 1.4059e+00, ..., 3.4862e-01,\n",
" 3.9270e-01, 3.3447e-01],\n",
" [1.6099e+00, 1.5023e+00, 1.5238e+00, ..., 3.6392e-01,\n",
" 3.8963e-01, 4.5296e-01],\n",
" ...,\n",
" [1.0288e+02, 1.0318e+02, 1.0304e+02, ..., 1.0168e+02,\n",
" 1.0194e+02, 1.0191e+02],\n",
" [1.0353e+02, 1.0333e+02, 1.0334e+02, ..., 1.0216e+02,\n",
" 1.0219e+02, 1.0212e+02],\n",
" [1.0339e+02, 1.0290e+02, 1.0300e+02, ..., 1.0180e+02,\n",
" 1.0220e+02, 1.0189e+02]],\n",
"\n",
" [[1.3479e+00, 1.3024e+00, 1.3246e+00, ..., 3.6170e-02,\n",
" 1.2884e-01, 4.5228e-01],\n",
" [1.5584e+00, 1.4481e+00, 1.4059e+00, ..., 3.4862e-01,\n",
" 3.9270e-01, 3.3447e-01],\n",
" [1.6099e+00, 1.5023e+00, 1.5238e+00, ..., 3.6392e-01,\n",
" 3.8963e-01, 4.5296e-01],\n",
" ...,\n",
" [1.0288e+02, 1.0318e+02, 1.0304e+02, ..., 1.0168e+02,\n",
" 1.0194e+02, 1.0191e+02],\n",
" [1.0353e+02, 1.0333e+02, 1.0334e+02, ..., 1.0216e+02,\n",
" 1.0219e+02, 1.0212e+02],\n",
" [1.0339e+02, 1.0290e+02, 1.0300e+02, ..., 1.0180e+02,\n",
" 1.0220e+02, 1.0189e+02]],\n",
"\n",
" [[1.3479e+00, 1.3024e+00, 1.3246e+00, ..., 3.6170e-02,\n",
" 1.2884e-01, 4.5228e-01],\n",
" [1.5584e+00, 1.4481e+00, 1.4059e+00, ..., 3.4862e-01,\n",
" 3.9270e-01, 3.3447e-01],\n",
" [1.6099e+00, 1.5023e+00, 1.5238e+00, ..., 3.6392e-01,\n",
" 3.8963e-01, 4.5296e-01],\n",
" ...,\n",
" [1.0288e+02, 1.0318e+02, 1.0304e+02, ..., 1.0168e+02,\n",
" 1.0194e+02, 1.0191e+02],\n",
" [1.0353e+02, 1.0333e+02, 1.0334e+02, ..., 1.0216e+02,\n",
" 1.0219e+02, 1.0212e+02],\n",
" [1.0339e+02, 1.0290e+02, 1.0300e+02, ..., 1.0180e+02,\n",
" 1.0220e+02, 1.0189e+02]]], device='mps:0')\n",
"Traced wrapped PyTorch ImageEncoder ckpt out for jpg:\n",
">>> tensor([[[1.3479e+00, 1.3024e+00, 1.3246e+00, ..., 3.6170e-02,\n",
" 1.2884e-01, 4.5228e-01],\n",
" [1.5584e+00, 1.4481e+00, 1.4059e+00, ..., 3.4862e-01,\n",
" 3.9270e-01, 3.3447e-01],\n",
" [1.6099e+00, 1.5023e+00, 1.5238e+00, ..., 3.6392e-01,\n",
" 3.8963e-01, 4.5296e-01],\n",
" ...,\n",
" [1.0288e+02, 1.0318e+02, 1.0304e+02, ..., 1.0168e+02,\n",
" 1.0194e+02, 1.0191e+02],\n",
" [1.0353e+02, 1.0333e+02, 1.0334e+02, ..., 1.0216e+02,\n",
" 1.0219e+02, 1.0212e+02],\n",
" [1.0339e+02, 1.0290e+02, 1.0300e+02, ..., 1.0180e+02,\n",
" 1.0220e+02, 1.0189e+02]],\n",
"\n",
" [[1.3479e+00, 1.3024e+00, 1.3246e+00, ..., 3.6170e-02,\n",
" 1.2884e-01, 4.5228e-01],\n",
" [1.5584e+00, 1.4481e+00, 1.4059e+00, ..., 3.4862e-01,\n",
" 3.9270e-01, 3.3447e-01],\n",
" [1.6099e+00, 1.5023e+00, 1.5238e+00, ..., 3.6392e-01,\n",
" 3.8963e-01, 4.5296e-01],\n",
" ...,\n",
" [1.0288e+02, 1.0318e+02, 1.0304e+02, ..., 1.0168e+02,\n",
" 1.0194e+02, 1.0191e+02],\n",
" [1.0353e+02, 1.0333e+02, 1.0334e+02, ..., 1.0216e+02,\n",
" 1.0219e+02, 1.0212e+02],\n",
" [1.0339e+02, 1.0290e+02, 1.0300e+02, ..., 1.0180e+02,\n",
" 1.0220e+02, 1.0189e+02]],\n",
"\n",
" [[1.3479e+00, 1.3024e+00, 1.3246e+00, ..., 3.6170e-02,\n",
" 1.2884e-01, 4.5228e-01],\n",
" [1.5584e+00, 1.4481e+00, 1.4059e+00, ..., 3.4862e-01,\n",
" 3.9270e-01, 3.3447e-01],\n",
" [1.6099e+00, 1.5023e+00, 1.5238e+00, ..., 3.6392e-01,\n",
" 3.8963e-01, 4.5296e-01],\n",
" ...,\n",
" [1.0288e+02, 1.0318e+02, 1.0304e+02, ..., 1.0168e+02,\n",
" 1.0194e+02, 1.0191e+02],\n",
" [1.0353e+02, 1.0333e+02, 1.0334e+02, ..., 1.0216e+02,\n",
" 1.0219e+02, 1.0212e+02],\n",
" [1.0339e+02, 1.0290e+02, 1.0300e+02, ..., 1.0180e+02,\n",
" 1.0220e+02, 1.0189e+02]]], device='mps:0')\n"
]
}
],
"source": [
"i = np.asarray(original_image.resize((518, 518)))\n",
"i = i.astype(\"float32\")\n",
"i = np.transpose(i, (2, 0, 1))\n",
"i = np.expand_dims(i, 0)\n",
"i = torch.from_numpy(i).to(DEVICE)\n",
"\n",
"with torch.no_grad():\n",
" out = wrapped_model(i)\n",
"\n",
"print(\"wrapped PyTorch ImageEncoder ckpt out for jpg:\\n>>>\", out[0, :10])\n",
"\n",
"traced_model_w = torch.jit.trace(wrapped_model, i)\n",
"\n",
"with torch.no_grad():\n",
" out = traced_model_w(i)\n",
"\n",
"print(\"Traced wrapped PyTorch ImageEncoder ckpt out for jpg:\\n>>>\", out[0, :10])"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "db5cb9b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([1, 3, 518, 518]), torch.Size([1, 3, 518, 518]))"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i.shape, out.shape"
]
},
{
"cell_type": "code",
"execution_count": 92,
"id": "681683aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 3, 518, 518) 255.0 0.0 104.07214\n",
"(518, 518, 3) 255 0 103.57204722648738\n"
]
}
],
"source": [
"tmp = out.cpu().numpy()\n",
"\n",
"print(tmp.shape, tmp.max(), tmp.min(), tmp.mean())\n",
"# Convert to 3, 256, 256\n",
"tmp = np.transpose(tmp, (0, 2, 3, 1)).astype(np.uint8)\n",
"tmp = tmp.squeeze()\n",
"print(tmp.shape, tmp.max(), tmp.min(), tmp.mean())\n",
"Image.fromarray(tmp)\n",
"tmp_image = Image.fromarray(tmp).resize((original_w,original_h))\n",
"tmp_image.save(\"depth_image_3.png\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "9e4f00bd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([1, 3, 518, 518])"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "304ae7b0",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Converting PyTorch Frontend ==> MIL Ops: 100%|█████████▉| 1247/1248 [00:00<00:00, 6927.17 ops/s]\n",
"Running MIL frontend_pytorch pipeline: 100%|██████████| 5/5 [00:00<00:00, 90.46 passes/s]\n",
"Running MIL default pipeline: 100%|██████████| 89/89 [00:06<00:00, 13.75 passes/s]\n",
"Running MIL backend_mlprogram pipeline: 100%|██████████| 12/12 [00:00<00:00, 99.10 passes/s]\n"
]
}
],
"source": [
"traced_model_w.eval()\n",
"image_input = ct.ImageType(name=\"colorImage\", shape=i.shape)\n",
"image_encoder_model = ct.converters.convert(\n",
" traced_model_w,\n",
" convert_to=\"mlprogram\",\n",
" inputs=[image_input],\n",
" outputs=[ct.ImageType(name=\"depthOutput\")],\n",
" minimum_deployment_target=ct.target.iOS16,\n",
")\n",
"image_encoder_model.save(\"DepthAnything_v2_large.mlpackage\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pytorch2",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|