New YOLOv5 Classification Models (#8956)
* Update * Logger step fix: Increment step with epochs (#8654) * enhance * revert * allow training from scratch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update --img argument from train.py single line * fix image size from 640 to 128 * suport custom dataloader and augmentation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * format * Update dataloaders.py * Single line return, single line comment, remove unused argument * address PR comments * fix spelling * don't augment eval set * use fstring * update augmentations.py * new maning convention for transforms * reverse if statement, inline ops * reverse if statement, inline ops * updates * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update dataloaders * Remove additional if statement * Remove is_train as redundant * Cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Cleanup2 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update classifier.py * Update augmentations.py * fix: imshow clip warning * update * Revert ToTensorV2 removal * Update classifier.py * Update normalize values, revert uint8 * normalize image using cv2 * remove dedundant comment * Update classifier.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * replace print with logger * commit steps * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Allow logging models from GenericLogger (#8676) * enhance * revert * allow training from scratch * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update --img argument from train.py single line * fix image size from 640 to 128 * suport custom dataloader and augmentation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * format * Update dataloaders.py * Single line return, single line comment, remove unused argument * address PR comments * fix spelling * don't augment eval set * use fstring * update augmentations.py * new maning convention for transforms * reverse if statement, inline ops * reverse if statement, inline ops * updates * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update dataloaders * Remove additional if statement * Remove is_train as redundant * Cleanup * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Cleanup2 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update classifier.py * Update augmentations.py * fix: imshow clip warning * update * Revert ToTensorV2 removal * Update classifier.py * Update normalize values, revert uint8 * normalize image using cv2 * remove dedundant comment * Update classifier.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * replace print with logger * commit steps * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * support final model logging * update * update * update * update * remove curses * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update classifier.py * Update __init__.py Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> * Update * Update * Update * Update * Update dataset download * Update dataset download * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Pass imgsz to classify_transforms() * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Update * Cos scheduler * Cos scheduler * Remove unused args * Update * Add seed * Add seed * Update * Update * Add run(), main() * Merge master * Merge master * Update * Update * Update * Update * Update * Update * Update * Create YOLOv5 BaseModel class (#8829) * Create BaseModel * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * fix * Hub load device fix * Update Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * Add experiment * Merge master * Attach names * weight decay = 1e-4 * weight decay = 5e-5 * update smart_optimizer console printout * fashion-mnist fix * Merge master * Update Table * Update Table * Remove destroy process group * add kwargs to forward() * fuse fix for resnet50 * nc, names fix for resnet50 * nc, names fix for resnet50 * ONNX CPU inference fix * revert * cuda * if augment or visualize * if augment or visualize * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * New smart_inference_mode() * Update README * Refactor into /classify dir * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * reset defaults * reset defaults * fix gpu predict * warmup * ema half fix * spacing * remove data * remove cache * remove denormalize * save run settings * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * verbose false on initial plots * new save_yaml() function * Update ci-testing.yml * Path(data) CI fix * Separate classification CI * fix val * fix val * fix val * smartCrossEntropyLoss * skip validation on hub load * autodownload with working dir root * str(data) * Dataset usage example * im_show normalize * im_show normalize * add imagenet simple names to multibackend * Add validation speeds * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * 24-space names * Update bash scripts * Update permissions * Add bash script arguments * remove verbose * TRT data fix * names generator fix * optimize if names * update usage * Add local loading * Verbose=False * update names printing * Add Usage examples * Add Usage examples * Add Usage examples * Add Usage examples * named_children * reshape_classifier_outputs * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * update * update * fix CI * fix incorrect class substitution * fix incorrect class substitution * remove denormalize * ravel fix * cleanup * update opt file printing * update opt file printing * update defaults * add opt to checkpoint * Add warning * Add comment * plot half bug fix * Use NotImplementedError * fix export shape report * Fix TRT load * cleanup CI * profile comment * CI fix * Add cls models * avoid inplace error * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix usage examples * Update README * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README * Update README Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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@@ -17,13 +17,12 @@ import pandas as pd
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import requests
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import torch
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import torch.nn as nn
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import yaml
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from PIL import Image
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from torch.cuda import amp
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from utils.dataloaders import exif_transpose, letterbox
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from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path,
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make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
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from utils.general import (LOGGER, ROOT, check_requirements, check_suffix, check_version, colorstr, increment_path,
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make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh, yaml_load)
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from utils.plots import Annotator, colors, save_one_box
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from utils.torch_utils import copy_attr, smart_inference_mode, time_sync
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@@ -322,13 +321,10 @@ class DetectMultiBackend(nn.Module):
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super().__init__()
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w = str(weights[0] if isinstance(weights, list) else weights)
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pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w) # get backend
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pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self._model_type(w) # get backend
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w = attempt_download(w) # download if not local
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fp16 &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16
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stride, names = 32, [f'class{i}' for i in range(1000)] # assign defaults
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if data: # assign class names (optional)
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with open(data, errors='ignore') as f:
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names = yaml.safe_load(f)['names']
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fp16 &= pt or jit or onnx or engine # FP16
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stride = 32 # default stride
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if pt: # PyTorch
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model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
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@@ -350,7 +346,7 @@ class DetectMultiBackend(nn.Module):
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net = cv2.dnn.readNetFromONNX(w)
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elif onnx: # ONNX Runtime
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LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
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cuda = torch.cuda.is_available()
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cuda = torch.cuda.is_available() and device.type != 'cpu'
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check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
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import onnxruntime
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providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
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@@ -380,6 +376,8 @@ class DetectMultiBackend(nn.Module):
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LOGGER.info(f'Loading {w} for TensorRT inference...')
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import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
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check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
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if device.type == 'cpu':
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device = torch.device('cuda:0')
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Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
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logger = trt.Logger(trt.Logger.INFO)
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with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
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@@ -398,8 +396,8 @@ class DetectMultiBackend(nn.Module):
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if dtype == np.float16:
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fp16 = True
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shape = tuple(context.get_binding_shape(index))
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data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device)
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bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
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im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
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bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
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binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
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batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size
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elif coreml: # CoreML
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@@ -445,9 +443,16 @@ class DetectMultiBackend(nn.Module):
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input_details = interpreter.get_input_details() # inputs
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output_details = interpreter.get_output_details() # outputs
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elif tfjs:
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raise Exception('ERROR: YOLOv5 TF.js inference is not supported')
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raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
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else:
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raise Exception(f'ERROR: {w} is not a supported format')
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raise NotImplementedError(f'ERROR: {w} is not a supported format')
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# class names
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if 'names' not in locals():
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names = yaml_load(data)['names'] if data else [f'class{i}' for i in range(999)]
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if names[0] == 'n01440764' and len(names) == 1000: # ImageNet
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names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names
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self.__dict__.update(locals()) # assign all variables to self
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def forward(self, im, augment=False, visualize=False, val=False):
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@@ -457,7 +462,9 @@ class DetectMultiBackend(nn.Module):
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im = im.half() # to FP16
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if self.pt: # PyTorch
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y = self.model(im, augment=augment, visualize=visualize)[0]
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y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
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if isinstance(y, tuple):
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y = y[0]
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elif self.jit: # TorchScript
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y = self.model(im)[0]
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elif self.dnn: # ONNX OpenCV DNN
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@@ -526,7 +533,7 @@ class DetectMultiBackend(nn.Module):
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self.forward(im) # warmup
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@staticmethod
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def model_type(p='path/to/model.pt'):
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def _model_type(p='path/to/model.pt'):
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# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
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from export import export_formats
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suffixes = list(export_formats().Suffix) + ['.xml'] # export suffixes
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@@ -540,8 +547,7 @@ class DetectMultiBackend(nn.Module):
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@staticmethod
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def _load_metadata(f='path/to/meta.yaml'):
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# Load metadata from meta.yaml if it exists
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with open(f, errors='ignore') as f:
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d = yaml.safe_load(f)
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d = yaml_load(f)
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return d['stride'], d['names'] # assign stride, names
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@@ -753,10 +759,13 @@ class Classify(nn.Module):
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# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
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super().__init__()
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self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
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self.flat = nn.Flatten()
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c_ = 1280 # efficientnet_b0 size
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self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
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self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1)
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self.drop = nn.Dropout(p=0.0, inplace=True)
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self.linear = nn.Linear(c_, c2) # to x(b,c2)
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def forward(self, x):
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z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
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return self.flat(self.conv(z)) # flatten to x(b,c2)
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if isinstance(x, list):
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x = torch.cat(x, 1)
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return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
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@@ -79,7 +79,9 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
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for w in weights if isinstance(weights, list) else [weights]:
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ckpt = torch.load(attempt_download(w), map_location='cpu') # load
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ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
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model.append(ckpt.fuse().eval() if fuse else ckpt.eval()) # fused or un-fused model in eval mode
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if not hasattr(ckpt, 'stride'):
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ckpt.stride = torch.tensor([32.]) # compatibility update for ResNet etc.
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model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
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# Compatibility updates
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for m in model.modules():
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@@ -92,11 +94,14 @@ def attempt_load(weights, device=None, inplace=True, fuse=True):
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elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
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m.recompute_scale_factor = None # torch 1.11.0 compatibility
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# Return model
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if len(model) == 1:
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return model[-1] # return model
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return model[-1]
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# Return detection ensemble
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print(f'Ensemble created with {weights}\n')
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for k in 'names', 'nc', 'yaml':
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setattr(model, k, getattr(model[0], k))
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model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
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assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
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return model # return ensemble
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return model
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+83
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@@ -90,8 +90,64 @@ class Detect(nn.Module):
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return grid, anchor_grid
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class Model(nn.Module):
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# YOLOv5 model
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class BaseModel(nn.Module):
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# YOLOv5 base model
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def forward(self, x, profile=False, visualize=False):
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return self._forward_once(x, profile, visualize) # single-scale inference, train
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def _forward_once(self, x, profile=False, visualize=False):
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y, dt = [], [] # outputs
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for m in self.model:
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if m.f != -1: # if not from previous layer
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x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
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if profile:
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self._profile_one_layer(m, x, dt)
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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return x
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def _profile_one_layer(self, m, x, dt):
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c = m == self.model[-1] # is final layer, copy input as inplace fix
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o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
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t = time_sync()
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for _ in range(10):
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m(x.copy() if c else x)
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dt.append((time_sync() - t) * 100)
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if m == self.model[0]:
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LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
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LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
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if c:
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LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
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def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
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LOGGER.info('Fusing layers... ')
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for m in self.model.modules():
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if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
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m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
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delattr(m, 'bn') # remove batchnorm
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m.forward = m.forward_fuse # update forward
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self.info()
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return self
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def info(self, verbose=False, img_size=640): # print model information
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model_info(self, verbose, img_size)
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def _apply(self, fn):
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# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
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self = super()._apply(fn)
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m = self.model[-1] # Detect()
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if isinstance(m, Detect):
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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class DetectionModel(BaseModel):
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# YOLOv5 detection model
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def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
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super().__init__()
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if isinstance(cfg, dict):
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@@ -149,19 +205,6 @@ class Model(nn.Module):
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y = self._clip_augmented(y) # clip augmented tails
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return torch.cat(y, 1), None # augmented inference, train
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def _forward_once(self, x, profile=False, visualize=False):
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y, dt = [], [] # outputs
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for m in self.model:
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if m.f != -1: # if not from previous layer
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x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
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if profile:
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self._profile_one_layer(m, x, dt)
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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return x
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def _descale_pred(self, p, flips, scale, img_size):
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# de-scale predictions following augmented inference (inverse operation)
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if self.inplace:
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@@ -190,19 +233,6 @@ class Model(nn.Module):
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y[-1] = y[-1][:, i:] # small
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return y
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def _profile_one_layer(self, m, x, dt):
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c = isinstance(m, Detect) # is final layer, copy input as inplace fix
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o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
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t = time_sync()
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for _ in range(10):
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m(x.copy() if c else x)
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dt.append((time_sync() - t) * 100)
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if m == self.model[0]:
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LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
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LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
|
||||
if c:
|
||||
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
||||
|
||||
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
|
||||
# https://arxiv.org/abs/1708.02002 section 3.3
|
||||
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
|
||||
@@ -213,41 +243,34 @@ class Model(nn.Module):
|
||||
b[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls
|
||||
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
||||
|
||||
def _print_biases(self):
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi in m.m: # from
|
||||
b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
|
||||
LOGGER.info(
|
||||
('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
|
||||
|
||||
# def _print_weights(self):
|
||||
# for m in self.model.modules():
|
||||
# if type(m) is Bottleneck:
|
||||
# LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
|
||||
Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility
|
||||
|
||||
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
|
||||
LOGGER.info('Fusing layers... ')
|
||||
for m in self.model.modules():
|
||||
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.forward_fuse # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
def info(self, verbose=False, img_size=640): # print model information
|
||||
model_info(self, verbose, img_size)
|
||||
class ClassificationModel(BaseModel):
|
||||
# YOLOv5 classification model
|
||||
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index
|
||||
super().__init__()
|
||||
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
|
||||
|
||||
def _apply(self, fn):
|
||||
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
||||
self = super()._apply(fn)
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, Detect):
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
def _from_detection_model(self, model, nc=1000, cutoff=10):
|
||||
# Create a YOLOv5 classification model from a YOLOv5 detection model
|
||||
if isinstance(model, DetectMultiBackend):
|
||||
model = model.model # unwrap DetectMultiBackend
|
||||
model.model = model.model[:cutoff] # backbone
|
||||
m = model.model[-1] # last layer
|
||||
ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module
|
||||
c = Classify(ch, nc) # Classify()
|
||||
c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type
|
||||
model.model[-1] = c # replace
|
||||
self.model = model.model
|
||||
self.stride = model.stride
|
||||
self.save = []
|
||||
self.nc = nc
|
||||
|
||||
def _from_yaml(self, cfg):
|
||||
# Create a YOLOv5 classification model from a *.yaml file
|
||||
self.model = None
|
||||
|
||||
|
||||
def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
@@ -321,7 +344,7 @@ if __name__ == '__main__':
|
||||
|
||||
# Options
|
||||
if opt.line_profile: # profile layer by layer
|
||||
_ = model(im, profile=True)
|
||||
model(im, profile=True)
|
||||
|
||||
elif opt.profile: # profile forward-backward
|
||||
results = profile(input=im, ops=[model], n=3)
|
||||
|
||||
Reference in new issue
Block a user