lrFinder_way_more_doc

Author: SubhadityaMukherjee

README.md

@@ -11,6 +11,7 @@
 - Check features.md for a full list
 
 ## Documentation
+- Okay so writing examples will take forever, so please check function documentation and the demos folder for most of what you need
 - Check the syntax folder for documentation
 - Check demos folder
 - [Function documentation](https://subhadityamukherjee.github.io/sprintdl/)

demos/.ipynb_checkpoints/ArtClassification-checkpoint.py

@@ -0,0 +1,174 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.10.1
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+
+# +
+# %load_ext autoreload
+# %autoreload 2
+
+# %matplotlib inline
+
+import os
+
+os.environ["TORCH_HOME"] = "/media/hdd/Datasets/"
+import sys
+
+sys.path.append("../")
+# -
+
+from sprintdl.main import *
+from sprintdl.nets import *
+
+device = torch.device("cuda", 0)
+import math
+
+import torch
+from torch.nn import init
+
+# # Define required
+
+# +
+fpath = Path("/media/hdd/Datasets/ArtClass/")
+
+tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_float_tensor]
+bs = 256
+# -
+
+# # Actual process
+
+il = ImageList.from_files(fpath, tfms=tfms)
+
+il
+
+tm = Path(
+    "/media/hdd/Datasets/ArtClass/Unpopular/mimang.art/69030963_140928767119437_3621699865915593113_n.jpg"
+)
+
+str(tm).split("/")[-3]
+
+sd = SplitData.split_by_func(il, partial(random_splitter, p_valid=0.2))
+ll = label_by_func(sd, lambda x: str(x).split("/")[-3], proc_y=CategoryProcessor())
+
+n_classes = len(set(ll.train.y.items))
+
+data = ll.to_databunch(bs, c_in=3, c_out=2)
+
+show_batch(data, 4)
+
+# +
+lr = 0.001
+pct_start = 0.5
+phases = create_phases(pct_start)
+sched_lr = combine_scheds(phases, cos_1cycle_anneal(lr / 10.0, lr, lr / 1e5))
+sched_mom = combine_scheds(phases, cos_1cycle_anneal(0.95, 0.85, 0.95))
+
+cbfs = [
+    partial(AvgStatsCallback, accuracy),
+    partial(ParamScheduler, "lr", sched_lr),
+    partial(ParamScheduler, "mom", sched_mom),
+    partial(BatchTransformXCallback, norm_imagenette),
+    ProgressCallback,
+    Recorder,
+    #     MixUp,
+    partial(CudaCallback, device),
+]
+
+loss_func = LabelSmoothingCrossEntropy()
+# arch = partial(xresnet34, n_classes)
+arch = get_vision_model("resnet34", n_classes=n_classes, pretrained=True)
+
+# opt_func = partial(sgd_mom_opt, wd=0.01)
+opt_func = adam_opt(mom=0.9, mom_sqr=0.99, eps=1e-6, wd=1e-2)
+# opt_func = lamb
+# -
+
+# # Training
+
+clear_memory()
+
+# learn = get_learner(nfs, data, lr, conv_layer, cb_funcs=cbfs)
+learn = Learner(arch, data, loss_func, lr=lr, cb_funcs=cbfs, opt_func=opt_func)
+
+# +
+# model_summary(learn, data)
+# -
+
+learn.fit(1)
+
+save_model(learn, "m1", fpath)
+
+# +
+temp = Path(
+    "/media/hdd/Datasets/ArtClass/Popular/artgerm/10004370_1657536534486515_1883801324_n.jpg"
+)
+
+get_class_pred(temp, learn, ll, 128)
+# -
+
+temp = Path("/home/eragon/Downloads/Telegram Desktop/IMG_1800.PNG")
+
+get_class_pred(temp, learn, ll, 128)
+
+temp = Path("/home/eragon/Downloads/Telegram Desktop/IMG_20210106_180731.jpg")
+
+get_class_pred(temp, learn, ll, 128)
+
+# # Digging in
+
+# +
+# classification_report(learn, n_classes, device)
+# -
+
+learn.recorder.plot_lr()
+
+learn.recorder.plot_loss()
+
+# # Model vis
+
+run_with_act_vis(1, learn)
+
+# # Multiple runs with model saving
+
+dict_runner = {
+    "xres18": [
+        1,
+        partial(xresnet18, c_out=n_classes)(),
+        data,
+        loss_func,
+        0.001,
+        cbfs,
+        opt_func,
+    ],
+    "xres34": [
+        1,
+        partial(xresnet34, c_out=n_classes)(),
+        data,
+        loss_func,
+        0.001,
+        cbfs,
+        opt_func,
+    ],
+    "xres50": [
+        1,
+        partial(xresnet50, c_out=n_classes)(),
+        data,
+        loss_func,
+        0.001,
+        cbfs,
+        opt_func,
+    ],
+}
+
+learn = Learner(arch(), data, loss_func, lr=lr, cb_funcs=cbfs, opt_func=opt_func)
+
+multiple_runner(dict_runner, fpath)