diff --git a/.github/ISSUE_TEMPLATE/feature-request.md b/.github/ISSUE_TEMPLATE/feature-request.md index 1fdf990..994f506 100644 --- a/.github/ISSUE_TEMPLATE/feature-request.md +++ b/.github/ISSUE_TEMPLATE/feature-request.md @@ -13,7 +13,7 @@ assignees: '' ## Motivation - ## Pitch diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml index 6d16038..8ebfdec 100644 --- a/.github/workflows/ci-testing.yml +++ b/.github/workflows/ci-testing.yml @@ -83,7 +83,7 @@ jobs: # Python python - <> $GITHUB_ENV + - uses: actions/cache@v2 + with: + path: ~/.cache/pre-commit + key: pre-commit|${{ env.PY }}|${{ hashFiles('.pre-commit-config.yaml') }} + + - uses: pre-commit/action@v2.0.3 + # this action also provides an additional behaviour when used in private repositories + # when configured with a github token, the action will push back fixes to the pull request branch + with: + token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml index 2305ea0..67f51f0 100644 --- a/.github/workflows/codeql-analysis.yml +++ b/.github/workflows/codeql-analysis.yml @@ -1,4 +1,4 @@ -# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities. +# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities. # https://github.com/github/codeql-action name: "CodeQL" diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml index a00ee8d..0daf951 100644 --- a/.github/workflows/greetings.yml +++ b/.github/workflows/greetings.yml @@ -57,4 +57,3 @@ jobs: CI CPU testing If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. - diff --git a/.gitignore b/.gitignore index 375b718..5f8cab5 100755 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,7 @@ *.data *.json *.cfg +!setup.cfg !cfg/yolov3*.cfg storage.googleapis.com diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..2eb78aa --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,67 @@ +# Define hooks for code formations +# Will be applied on any updated commit files if a user has installed and linked commit hook + +default_language_version: + python: python3.8 + +# Define bot property if installed via https://github.com/marketplace/pre-commit-ci +ci: + autofix_prs: true + autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' + autoupdate_schedule: quarterly + # submodules: true + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-case-conflict + - id: check-yaml + - id: check-toml + - id: pretty-format-json + - id: check-docstring-first + + - repo: https://github.com/asottile/pyupgrade + rev: v2.23.1 + hooks: + - id: pyupgrade + args: [--py36-plus] + name: Upgrade code + + # TODO + #- repo: https://github.com/PyCQA/isort + # rev: 5.9.3 + # hooks: + # - id: isort + # name: imports + + # TODO + #- repo: https://github.com/pre-commit/mirrors-yapf + # rev: v0.31.0 + # hooks: + # - id: yapf + # name: formatting + + # TODO + #- repo: https://github.com/executablebooks/mdformat + # rev: 0.7.7 + # hooks: + # - id: mdformat + # additional_dependencies: + # - mdformat-gfm + # - mdformat-black + # - mdformat_frontmatter + + # TODO + #- repo: https://github.com/asottile/yesqa + # rev: v1.2.3 + # hooks: + # - id: yesqa + + - repo: https://github.com/PyCQA/flake8 + rev: 3.9.2 + hooks: + - id: flake8 + name: PEP8 diff --git a/LICENSE b/LICENSE index 9e419e0..92b370f 100644 --- a/LICENSE +++ b/LICENSE @@ -671,4 +671,4 @@ into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read -. \ No newline at end of file +. diff --git a/README.md b/README.md index 0d474cb..d3fd7e9 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ YOLOv5 🚀 is a family of object detection architectures and models pretrained open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.

- @@ -109,7 +109,7 @@ the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and ```bash $ python detect.py --source 0 # webcam - file.jpg # image + file.jpg # image file.mp4 # video path/ # directory path/*.jpg # glob @@ -136,7 +136,7 @@ $ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size - +
Tutorials @@ -178,7 +178,7 @@ Get started in seconds with our verified environments. Click each icon below for - + ##
Integrations
@@ -239,7 +239,7 @@ We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competi |[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |16.8 |12.6 |[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0 |[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.8 |111.4 -|[YOLOv5x6][assets]
+ [TTA][TTA]|1280
1536 |54.7
**55.4** |**72.4**
72.3 |3136
- |26.2
- |19.4
- |140.7
- |209.8
- +|[YOLOv5x6][assets]
+ [TTA][TTA]|1280
1536 |54.7
**55.4** |**72.4**
72.3 |3136
- |26.2
- |19.4
- |140.7
- |209.8
-
Table Notes (click to expand) diff --git a/data/Objects365.yaml b/data/Objects365.yaml index 97a424f..b10c28e 100644 --- a/data/Objects365.yaml +++ b/data/Objects365.yaml @@ -62,21 +62,21 @@ names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Gla download: | from pycocotools.coco import COCO from tqdm import tqdm - + from utils.general import Path, download, np, xyxy2xywhn - + # Make Directories dir = Path(yaml['path']) # dataset root dir for p in 'images', 'labels': (dir / p).mkdir(parents=True, exist_ok=True) for q in 'train', 'val': (dir / p / q).mkdir(parents=True, exist_ok=True) - + # Train, Val Splits for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: print(f"Processing {split} in {patches} patches ...") images, labels = dir / 'images' / split, dir / 'labels' / split - + # Download url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" if split == 'train': @@ -86,11 +86,11 @@ download: | download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) - + # Move for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): f.rename(images / f.name) # move to /images/{split} - + # Labels coco = COCO(dir / f'zhiyuan_objv2_{split}.json') names = [x["name"] for x in coco.loadCats(coco.getCatIds())] diff --git a/data/coco128.yaml b/data/coco128.yaml index 70cf52c..b1dfb00 100644 --- a/data/coco128.yaml +++ b/data/coco128.yaml @@ -27,4 +27,4 @@ names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 't # Download script/URL (optional) -download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip \ No newline at end of file +download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip diff --git a/data/hyps/hyp.scratch-high.yaml b/data/hyps/hyp.scratch-high.yaml index 519c826..5a586cc 100644 --- a/data/hyps/hyp.scratch-high.yaml +++ b/data/hyps/hyp.scratch-high.yaml @@ -31,4 +31,4 @@ flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 1.0 # image mosaic (probability) mixup: 0.1 # image mixup (probability) -copy_paste: 0.1 # segment copy-paste (probability) \ No newline at end of file +copy_paste: 0.1 # segment copy-paste (probability) diff --git a/data/hyps/hyp.scratch-low.yaml b/data/hyps/hyp.scratch-low.yaml index b093a95..b9ef1d5 100644 --- a/data/hyps/hyp.scratch-low.yaml +++ b/data/hyps/hyp.scratch-low.yaml @@ -31,4 +31,4 @@ flipud: 0.0 # image flip up-down (probability) fliplr: 0.5 # image flip left-right (probability) mosaic: 1.0 # image mosaic (probability) mixup: 0.0 # image mixup (probability) -copy_paste: 0.0 # segment copy-paste (probability) \ No newline at end of file +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/models/common.py b/models/common.py index 5da3569..d0fb0e8 100644 --- a/models/common.py +++ b/models/common.py @@ -79,7 +79,7 @@ class TransformerBlock(nn.Module): if c1 != c2: self.conv = Conv(c1, c2) self.linear = nn.Linear(c2, c2) # learnable position embedding - self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) + self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) self.c2 = c2 def forward(self, x): @@ -114,7 +114,7 @@ class BottleneckCSP(nn.Module): self.cv4 = Conv(2 * c_, c2, 1, 1) self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) self.act = nn.LeakyReLU(0.1, inplace=True) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) @@ -130,7 +130,7 @@ class C3(nn.Module): self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) def forward(self, x): @@ -158,7 +158,7 @@ class C3Ghost(C3): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*[GhostBottleneck(c_, c_) for _ in range(n)]) + self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) class SPP(nn.Module): @@ -362,7 +362,7 @@ class Detections: def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super().__init__() d = pred[0].device # device - gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations + gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1., 1.], device=d) for im in imgs] # normalizations self.imgs = imgs # list of images as numpy arrays self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) self.names = names # class names diff --git a/models/experimental.py b/models/experimental.py index edccc96..adb86c8 100644 --- a/models/experimental.py +++ b/models/experimental.py @@ -97,7 +97,6 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True): else: model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse - # Compatibility updates for m in model.modules(): if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: diff --git a/models/hub/yolov5-bifpn.yaml b/models/hub/yolov5-bifpn.yaml index 119aebb..2f2c82c 100644 --- a/models/hub/yolov5-bifpn.yaml +++ b/models/hub/yolov5-bifpn.yaml @@ -18,7 +18,7 @@ backbone: [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 9, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]] + [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, SPP, [1024, [5, 9, 13]]], [-1, 3, C3, [1024, False]], # 9 diff --git a/models/tf.py b/models/tf.py index 1c6da43..5599ff5 100644 --- a/models/tf.py +++ b/models/tf.py @@ -40,7 +40,7 @@ LOGGER = logging.getLogger(__name__) class TFBN(keras.layers.Layer): # TensorFlow BatchNormalization wrapper def __init__(self, w=None): - super(TFBN, self).__init__() + super().__init__() self.bn = keras.layers.BatchNormalization( beta_initializer=keras.initializers.Constant(w.bias.numpy()), gamma_initializer=keras.initializers.Constant(w.weight.numpy()), @@ -54,7 +54,7 @@ class TFBN(keras.layers.Layer): class TFPad(keras.layers.Layer): def __init__(self, pad): - super(TFPad, self).__init__() + super().__init__() self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) def call(self, inputs): @@ -65,7 +65,7 @@ class TFConv(keras.layers.Layer): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, weights, kernel, stride, padding, groups - super(TFConv, self).__init__() + super().__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert isinstance(k, int), "Convolution with multiple kernels are not allowed." # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) @@ -96,7 +96,7 @@ class TFFocus(keras.layers.Layer): # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): # ch_in, ch_out, kernel, stride, padding, groups - super(TFFocus, self).__init__() + super().__init__() self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) @@ -110,7 +110,7 @@ class TFFocus(keras.layers.Layer): class TFBottleneck(keras.layers.Layer): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion - super(TFBottleneck, self).__init__() + super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) @@ -123,7 +123,7 @@ class TFBottleneck(keras.layers.Layer): class TFConv2d(keras.layers.Layer): # Substitution for PyTorch nn.Conv2D def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): - super(TFConv2d, self).__init__() + super().__init__() assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" self.conv = keras.layers.Conv2D( c2, k, s, 'VALID', use_bias=bias, @@ -138,7 +138,7 @@ class TFBottleneckCSP(keras.layers.Layer): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion - super(TFBottleneckCSP, self).__init__() + super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) @@ -158,7 +158,7 @@ class TFC3(keras.layers.Layer): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, number, shortcut, groups, expansion - super(TFC3, self).__init__() + super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) @@ -172,7 +172,7 @@ class TFC3(keras.layers.Layer): class TFSPP(keras.layers.Layer): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13), w=None): - super(TFSPP, self).__init__() + super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) @@ -186,7 +186,7 @@ class TFSPP(keras.layers.Layer): class TFSPPF(keras.layers.Layer): # Spatial pyramid pooling-Fast layer def __init__(self, c1, c2, k=5, w=None): - super(TFSPPF, self).__init__() + super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) @@ -201,7 +201,7 @@ class TFSPPF(keras.layers.Layer): class TFDetect(keras.layers.Layer): def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer - super(TFDetect, self).__init__() + super().__init__() self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor @@ -249,7 +249,7 @@ class TFDetect(keras.layers.Layer): class TFUpsample(keras.layers.Layer): def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' - super(TFUpsample, self).__init__() + super().__init__() assert scale_factor == 2, "scale_factor must be 2" self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode) # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) @@ -263,7 +263,7 @@ class TFUpsample(keras.layers.Layer): class TFConcat(keras.layers.Layer): def __init__(self, dimension=1, w=None): - super(TFConcat, self).__init__() + super().__init__() assert dimension == 1, "convert only NCHW to NHWC concat" self.d = 3 @@ -272,7 +272,7 @@ class TFConcat(keras.layers.Layer): def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) - LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) @@ -299,7 +299,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: - c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) + c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) elif m is Detect: args.append([ch[x + 1] for x in f]) if isinstance(args[1], int): # number of anchors @@ -312,11 +312,11 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ else tf_m(*args, w=model.model[i]) # module - torch_m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type - np = sum([x.numel() for x in torch_m_.parameters()]) # number params + np = sum(x.numel() for x in torch_m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + LOGGER.info(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) ch.append(c2) @@ -325,7 +325,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) class TFModel: def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes - super(TFModel, self).__init__() + super().__init__() if isinstance(cfg, dict): self.yaml = cfg # model dict else: # is *.yaml @@ -336,7 +336,7 @@ class TFModel: # Define model if nc and nc != self.yaml['nc']: - print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc)) + print(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") self.yaml['nc'] = nc # override yaml value self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) diff --git a/models/yolo.py b/models/yolo.py index 497a0e9..0fa2db9 100644 --- a/models/yolo.py +++ b/models/yolo.py @@ -247,7 +247,7 @@ class Model(nn.Module): def parse_model(d, ch): # model_dict, input_channels(3) - LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors no = na * (nc + 5) # number of outputs = anchors * (classes + 5) @@ -275,7 +275,7 @@ def parse_model(d, ch): # model_dict, input_channels(3) elif m is nn.BatchNorm2d: args = [ch[f]] elif m is Concat: - c2 = sum([ch[x] for x in f]) + c2 = sum(ch[x] for x in f) elif m is Detect: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors @@ -287,11 +287,11 @@ def parse_model(d, ch): # model_dict, input_channels(3) else: c2 = ch[f] - m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module t = str(m)[8:-2].replace('__main__.', '') # module type - np = sum([x.numel() for x in m_.parameters()]) # number params + np = sum(x.numel() for x in m_.parameters()) # number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n_, np, t, args)) # print + LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist layers.append(m_) if i == 0: diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..7d25200 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,45 @@ +# Project-wide configuration file, can be used for package metadata and other toll configurations +# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments + +[metadata] +license_file = LICENSE +description-file = README.md + + +[tool:pytest] +norecursedirs = + .git + dist + build +addopts = + --doctest-modules + --durations=25 + --color=yes + + +[flake8] +max-line-length = 120 +exclude = .tox,*.egg,build,temp +select = E,W,F +doctests = True +verbose = 2 +# https://pep8.readthedocs.io/en/latest/intro.html#error-codes +format = pylint +# see: https://www.flake8rules.com/ +ignore = + E731 # Do not assign a lambda expression, use a def + F405 + E402 + F841 + E741 + F821 + E722 + F401 + W504 + E127 + W504 + E231 + E501 + F403 + E302 + F541 diff --git a/tutorial.ipynb b/tutorial.ipynb index 47c4425..115d767 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -1014,4 +1014,4 @@ "outputs": [] } ] -} \ No newline at end of file +} diff --git a/utils/datasets.py b/utils/datasets.py index 3997a5d..fce005b 100755 --- a/utils/datasets.py +++ b/utils/datasets.py @@ -140,7 +140,7 @@ class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): yield next(self.iterator) -class _RepeatSampler(object): +class _RepeatSampler: """ Sampler that repeats forever Args: @@ -287,7 +287,7 @@ class LoadStreams: self.stride = stride if os.path.isfile(sources): - with open(sources, 'r') as f: + with open(sources) as f: sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] else: sources = [sources] @@ -398,14 +398,14 @@ class LoadImagesAndLabels(Dataset): f += glob.glob(str(p / '**' / '*.*'), recursive=True) # f = list(p.rglob('*.*')) # pathlib elif p.is_file(): # file - with open(p, 'r') as t: + with open(p) as t: t = t.read().strip().splitlines() parent = str(p.parent) + os.sep f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) else: raise Exception(f'{prefix}{p} does not exist') - self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS]) + self.img_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib assert self.img_files, f'{prefix}No images found' except Exception as e: @@ -681,7 +681,7 @@ def load_mosaic(self, index): # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic labels4, segments4 = [], [] s = self.img_size - yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices random.shuffle(indices) for i, index in enumerate(indices): @@ -767,7 +767,7 @@ def load_mosaic9(self, index): c = s - w, s + h0 - hp - h, s, s + h0 - hp padx, pady = c[:2] - x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords + x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords # Labels labels, segments = self.labels[index].copy(), self.segments[index].copy() @@ -782,7 +782,7 @@ def load_mosaic9(self, index): hp, wp = h, w # height, width previous # Offset - yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y + yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] # Concat/clip labels @@ -838,7 +838,7 @@ def extract_boxes(path='../datasets/coco128'): # from utils.datasets import *; # labels lb_file = Path(img2label_paths([str(im_file)])[0]) if Path(lb_file).exists(): - with open(lb_file, 'r') as f: + with open(lb_file) as f: lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels for j, x in enumerate(lb): @@ -866,7 +866,7 @@ def autosplit(path='../datasets/coco128/images', weights=(0.9, 0.1, 0.0), annota annotated_only: Only use images with an annotated txt file """ path = Path(path) # images dir - files = sorted([x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS]) # image files only + files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only n = len(files) # number of files random.seed(0) # for reproducibility indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split @@ -902,7 +902,7 @@ def verify_image_label(args): # verify labels if os.path.isfile(lb_file): nf = 1 # label found - with open(lb_file, 'r') as f: + with open(lb_file) as f: l = [x.split() for x in f.read().strip().splitlines() if len(x)] if any([len(x) > 8 for x in l]): # is segment classes = np.array([x[0] for x in l], dtype=np.float32) @@ -944,7 +944,7 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profil def round_labels(labels): # Update labels to integer class and 6 decimal place floats - return [[int(c), *[round(x, 4) for x in points]] for c, *points in labels] + return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] def unzip(path): # Unzip data.zip TODO: CONSTRAINT: path/to/abc.zip MUST unzip to 'path/to/abc/' @@ -1019,7 +1019,7 @@ def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profil with open(file, 'w') as f: json.dump(stats, f) # save stats *.json t2 = time.time() - with open(file, 'r') as f: + with open(file) as f: x = json.load(f) # load hyps dict print(f'stats.json times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write') diff --git a/utils/general.py b/utils/general.py index 02bc741..f229089 100755 --- a/utils/general.py +++ b/utils/general.py @@ -136,7 +136,7 @@ def is_writeable(dir, test=False): pass file.unlink() # remove file return True - except IOError: + except OSError: return False else: # method 2 return os.access(dir, os.R_OK) # possible issues on Windows @@ -355,7 +355,7 @@ def check_dataset(data, autodownload=True): assert 'nc' in data, "Dataset 'nc' key missing." if 'names' not in data: data['names'] = [f'class{i}' for i in range(data['nc'])] # assign class names if missing - train, val, test, s = [data.get(x) for x in ('train', 'val', 'test', 'download')] + train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) if val: val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path if not all(x.exists() for x in val): diff --git a/utils/google_app_engine/app.yaml b/utils/google_app_engine/app.yaml index ac29d10..5056b7c 100644 --- a/utils/google_app_engine/app.yaml +++ b/utils/google_app_engine/app.yaml @@ -11,4 +11,4 @@ manual_scaling: resources: cpu: 1 memory_gb: 4 - disk_size_gb: 20 \ No newline at end of file + disk_size_gb: 20 diff --git a/utils/loggers/__init__.py b/utils/loggers/__init__.py index 0b457df..ae2d98b 100644 --- a/utils/loggers/__init__.py +++ b/utils/loggers/__init__.py @@ -135,7 +135,7 @@ class Loggers(): # Callback runs on training end if plots: plot_results(file=self.save_dir / 'results.csv') # save results.png - files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]] + files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter if self.tb: diff --git a/utils/loggers/wandb/README.md b/utils/loggers/wandb/README.md index dd7dc1e..d787fb7 100644 --- a/utils/loggers/wandb/README.md +++ b/utils/loggers/wandb/README.md @@ -61,10 +61,10 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
Usage Code $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. - + ![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
- +

2: Train and Log Evaluation simultaneousy

This is an extension of the previous section, but it'll also training after uploading the dataset. This also evaluation Table Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets, @@ -72,31 +72,31 @@ You can leverage W&B artifacts and Tables integration to easily visualize and ma
Usage Code $ python utils/logger/wandb/log_dataset.py --data .. --upload_data - + ![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
- +

3: Train using dataset artifact

- When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that + When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that can be used to train a model directly from the dataset artifact. This also logs evaluation
Usage Code $ python utils/logger/wandb/log_dataset.py --data {data}_wandb.yaml - + ![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
- +

4: Save model checkpoints as artifacts

- To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval. + To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval. You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
Usage Code $ python train.py --save_period 1 - + ![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
- +

5: Resume runs from checkpoint artifacts.

@@ -105,28 +105,28 @@ Any run can be resumed using artifacts if the --resume argument sta
Usage Code $ python train.py --resume wandb-artifact://{run_path} - + ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
- +

6: Resume runs from dataset artifact & checkpoint artifacts.

Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device - The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot --upload_dataset or + The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot --upload_dataset or train from _wandb.yaml file and set --save_period
Usage Code $ python train.py --resume wandb-artifact://{run_path} - + ![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
- +

Reports

W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)). - + Weights & Biases Reports diff --git a/utils/loggers/wandb/sweep.yaml b/utils/loggers/wandb/sweep.yaml index c3727de..c7790d7 100644 --- a/utils/loggers/wandb/sweep.yaml +++ b/utils/loggers/wandb/sweep.yaml @@ -1,17 +1,17 @@ # Hyperparameters for training -# To set range- +# To set range- # Provide min and max values as: # parameter: -# +# # min: scalar # max: scalar # OR # # Set a specific list of search space- -# parameter: +# parameter: # values: [scalar1, scalar2, scalar3...] -# -# You can use grid, bayesian and hyperopt search strategy +# +# You can use grid, bayesian and hyperopt search strategy # For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration program: utils/loggers/wandb/sweep.py diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py index 7fb76b0..8546ec6 100644 --- a/utils/loggers/wandb/wandb_utils.py +++ b/utils/loggers/wandb/wandb_utils.py @@ -5,6 +5,7 @@ import os import sys from contextlib import contextmanager from pathlib import Path +from typing import Dict import pkg_resources as pkg import yaml @@ -25,7 +26,7 @@ try: assert hasattr(wandb, '__version__') # verify package import not local dir except (ImportError, AssertionError): wandb = None - + RANK = int(os.getenv('RANK', -1)) WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' @@ -127,7 +128,7 @@ class WandbLogger(): arguments: opt (namespace) -- Commandline arguments for this run run_id (str) -- Run ID of W&B run to be resumed - job_type (str) -- To set the job_type for this run + job_type (str) -- To set the job_type for this run """ # Pre-training routine -- @@ -142,7 +143,8 @@ class WandbLogger(): self.max_imgs_to_log = 16 self.wandb_artifact_data_dict = None self.data_dict = None - # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call + # It's more elegant to stick to 1 wandb.init call, + # but useful config data is overwritten in the WandbLogger's wandb.init call if isinstance(opt.resume, str): # checks resume from artifact if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): entity, project, run_id, model_artifact_name = get_run_info(opt.resume) @@ -212,7 +214,7 @@ class WandbLogger(): Setup the necessary processes for training YOLO models: - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded - - Setup log_dict, initialize bbox_interval + - Setup log_dict, initialize bbox_interval arguments: opt (namespace) -- commandline arguments for this run @@ -301,7 +303,7 @@ class WandbLogger(): path (Path) -- Path of directory containing the checkpoints opt (namespace) -- Command line arguments for this run epoch (int) -- Current epoch number - fitness_score (float) -- fitness score for current epoch + fitness_score (float) -- fitness score for current epoch best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. """ model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ @@ -325,7 +327,7 @@ class WandbLogger(): data_file (str) -- the .yaml file with information about the dataset like - path, classes etc. single_class (boolean) -- train multi-class data as single-class project (str) -- project name. Used to construct the artifact path - overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new + overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new file with _wandb postfix. Eg -> data_wandb.yaml returns: @@ -371,14 +373,14 @@ class WandbLogger(): for i, data in enumerate(tqdm(self.val_table.data)): self.val_table_path_map[data[3]] = data[0] - def create_dataset_table(self, dataset, class_to_id, name='dataset'): + def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int,str], name: str = 'dataset'): """ Create and return W&B artifact containing W&B Table of the dataset. arguments: - dataset (LoadImagesAndLabels) -- instance of LoadImagesAndLabels class used to iterate over the data to build Table - class_to_id (dict(int, str)) -- hash map that maps class ids to labels - name (str) -- name of the artifact + dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table + class_to_id -- hash map that maps class ids to labels + name -- name of the artifact returns: dataset artifact to be logged or used @@ -419,7 +421,7 @@ class WandbLogger(): arguments: predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class] - path (str): local path of the current evaluation image + path (str): local path of the current evaluation image names (dict(int, str)): hash map that maps class ids to labels """ class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) @@ -430,7 +432,7 @@ class WandbLogger(): box_data.append( {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": int(cls), - "box_caption": "%s %.3f" % (names[cls], conf), + "box_caption": f"{names[cls]} {conf:.3f}", "scores": {"class_score": conf}, "domain": "pixel"}) total_conf += conf @@ -450,7 +452,7 @@ class WandbLogger(): arguments: pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class] - path (str): local path of the current evaluation image + path (str): local path of the current evaluation image """ if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact self.log_training_progress(predn, path, names) @@ -459,7 +461,7 @@ class WandbLogger(): if self.current_epoch % self.bbox_interval == 0: box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, "class_id": int(cls), - "box_caption": "%s %.3f" % (names[cls], conf), + "box_caption": f"{names[cls]} {conf:.3f}", "scores": {"class_score": conf}, "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space diff --git a/utils/loss.py b/utils/loss.py index fac432d..e8ce42a 100644 --- a/utils/loss.py +++ b/utils/loss.py @@ -18,7 +18,7 @@ def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#iss class BCEBlurWithLogitsLoss(nn.Module): # BCEwithLogitLoss() with reduced missing label effects. def __init__(self, alpha=0.05): - super(BCEBlurWithLogitsLoss, self).__init__() + super().__init__() self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() self.alpha = alpha @@ -35,7 +35,7 @@ class BCEBlurWithLogitsLoss(nn.Module): class FocalLoss(nn.Module): # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(FocalLoss, self).__init__() + super().__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha @@ -65,7 +65,7 @@ class FocalLoss(nn.Module): class QFocalLoss(nn.Module): # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(QFocalLoss, self).__init__() + super().__init__() self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() self.gamma = gamma self.alpha = alpha diff --git a/utils/plots.py b/utils/plots.py index 00b8f88..00cda6d 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -250,7 +250,7 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) ax = ax.ravel() for i in range(4): - ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) + ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') ax[i].legend() ax[i].set_title(s[i]) plt.savefig('targets.jpg', dpi=200) @@ -363,7 +363,7 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=''): else: a.remove() except Exception as e: - print('Warning: Plotting error for %s; %s' % (f, e)) + print(f'Warning: Plotting error for {f}; {e}') ax[1].legend() plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) @@ -384,10 +384,10 @@ def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plt.subplot(6, 5, i + 1) plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none') plt.plot(mu, f.max(), 'k+', markersize=15) - plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters + plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters if i % 5 != 0: plt.yticks([]) - print('%15s: %.3g' % (k, mu)) + print(f'{k:>15}: {mu:.3g}') f = evolve_csv.with_suffix('.png') # filename plt.savefig(f, dpi=200) plt.close() diff --git a/utils/torch_utils.py b/utils/torch_utils.py index 6f52f9a..e6d8ebd 100644 --- a/utils/torch_utils.py +++ b/utils/torch_utils.py @@ -123,7 +123,7 @@ def profile(input, ops, n=10, device=None): y = m(x) t[1] = time_sync() try: - _ = (sum([yi.sum() for yi in y]) if isinstance(y, list) else y).sum().backward() + _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() t[2] = time_sync() except Exception as e: # no backward method # print(e) # for debug @@ -223,7 +223,7 @@ def model_info(model, verbose=False, img_size=640): n_p = sum(x.numel() for x in model.parameters()) # number parameters n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients if verbose: - print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") for i, (name, p) in enumerate(model.named_parameters()): name = name.replace('module_list.', '') print('%5g %40s %9s %12g %20s %10.3g %10.3g' % @@ -270,7 +270,7 @@ def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) s = (int(h * ratio), int(w * ratio)) # new size img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize if not same_shape: # pad/crop img - h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] + h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean